# document

id: 2e5a3b8a3e13e19aa07bd32d71a99832
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 5430
content:
"in data acquisition and not paucity of data (GCOS, 2015). This is \r\nbecause GPCC is the result of a  single time scale, single Essential \r\nClimate Variable (ECV) and single data collection centre. There is \r\nno similar drop-off of the rainfall reports in the Global Historical \r\nClimatology Network Daily database (GHCNd, Menne et al., 2012) \r\nor the Integrated Surface Database (ISD) at the sub-daily time scale."


id: 9ecaab4de8c5246481998dd3fd673dfd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25160
content:
"homogenization stems from the homogenization of temperature \r\nobservations from dense networks. Recent studies suggest that \r\nour ability to remove biases quickly diminishes for sparse networks \r\n(Gubler et al., 2017; Lindau and Venema, 2018a). This affects early \r\ninstrumental data and observations that are not strongly correlated \r\nbetween stations, such as wind and humidity (Chimani et al., 2018)."


id: 84e9085a6ca97622b5045f01c8e4000d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20518
content:
"It is virtually certain that statistical homogenization methods reduce the \r\nuncertainties of long-term estimates. Considering a decomposition of \r\nthe long-term warming error into a bias and a noise uncertainty around \r\nthe bias, the (trend) bias especially will be reduced, but also most of \r\nthe noise uncertainty. This conclusion is based on our understanding \r\nof the causes of inhomogeneities and their statistical nature combined \r\nwith the design principles of statistical homogenization methods, as \r\nwell as on analytical (Lindau and Venema, 2018b), numerical (Venema \r\net  al., 2012; Williams et  al., 2012) and empirical validation studies \r\n(Hausfather et al., 2016; Gubler et al., 2017; Killick et al., 2020)."


id: d20b20537c64e806b43cf0ab49ccab79
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 34307
content:
"The above section is about the homogenization of land stations. \r\nSatellite data has its own issues and methods for homogenization \r\n(Brinckmann et al., 2013; Huang et al., 2015; Brogniez et al., 2016). \r\nThe homogenization of radiosonde data and land station data use \r\nsimilar methods (Haimberger et al., 2012; Jovanovic et al., 2017)."


id: ae09a84d6c76e541e2ce2a598a653ef5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27860
content:
"Data scarcity arises largely due to the lack of maintenance of \r\nobserving stations, inaccessibility of the data held in national \r\nnetworks, and uneven spatial distribution of stations that lead to \r\na low density in many regions. This is particularly problematic when \r\ntrying to assess regional climate change, for which a  high density \r\nof observational data is desirable. Although in several regions \r\nnumerous stations provide (monthly) data covering more than \r\n100 years for both temperature and precipitation (GCOS, 2015), large \r\nareas of the world remain sparsely covered. The post-1990 decline in \r\nthe total number of stations contributing to the Global Precipitation \r\nClimatology Centre (GPCC) monthly product may be related to delays "


id: 11319d6f0624ed76b494e165f006dacf
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 220
content:
"Data scarcity arising from changing coverage in observation station \r\nnetworks results in substantial problems for climate monitoring \r\n(e.g.,  trend analysis of extreme events requires high temporal and \r\nspatial resolutions) or model evaluation (Section 10.3.3.1). It is virtually \r\ncertain that the scarcity and decline of observational availability in "


id: ab08289f7525151dcdb4b30e836c6678
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27314
content:
"some regions (but not necessarily globally), increase the uncertainty of \r\nthe long-term global temperature and precipitation estimates. As an \r\nexample, Lin and Huybers (2019) found that changes in the number \r\nof rain gauges after 1975 resulted in spurious trends in extremes of \r\nIndian rainfall in a 0.25deg gridded dataset spanning the 20th century. \r\nIn fact, the number of stations used to construct the gridded dataset \r\ndropped by half after 1990, leading to inhomogeneity and spurious \r\ntrends (Section 10.6.3). Over the southern part of the Mediterranean, \r\nwhich is an area sparsely covered by meteorological stations, data \r\nscarcity can lead to large uncertainties in the different gridded datasets \r\nand strongly affect model evaluation (Section  10.6.4). Satellite \r\nobservations can compensate the ground-based precipitation radar \r\ndata sparsity to prevent an oversight of significant climate change \r\nsignals (Yokoyama et al., 2019)."


id: 7202d78eaa403bed9ca0a887118f3141
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17303
content:
"There are techniques for estimating and reconstructing missing \r\ndata. The methods depend on the variable of interest, the temporal \r\nresolution (e.g., daily or monthly), and the type of climate (wet or dry), \r\namong others. There has been very little evaluation of the performance \r\nof classical and data mining methods (e.g., Sattari et al., 2017). The \r\nclassical methods include the arithmetic mean, inverse distance \r\nweighting method, multiple regression analysis, multiple imputation, \r\nand single best estimator, while the data-mining methods include \r\nmultilayer perceptron artificial neural network, support vector machine, \r\nadaptive neuro-fuzzy inference system, gene expression programming \r\nmethod, and K-nearest neighbour. Crowd-sourced data (individuals \r\ncontribute their own data points to create a dataset for others to use) \r\ncould play a role in minimizing data scarcity (Section 10.2.4)."


id: 3a821289751960a929ebc7ed68f034e1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7498
content:
"Derived gridded datasets require merging data from different sources of \r\nobservations and/or reanalysis data on a regular grid (Section 10.2.1.2; \r\ne.g., Xie and Arkin, 1997). However, in situ observations are distributed \r\nirregularly, especially over sparsely populated areas. This leads to \r\nan interpolation challenge. Gridded products of climate variables, \r\nincluding temperature and precipitation, are strongly affected (high \r\nconfidence) by the interpolation method over complex orography and \r\ndata scarce regions (Hofstra et al., 2008; Herrera et al., 2016)."


id: 6dba3e41437fe85431abb19908c23fee
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16566
content:
"There are two main approaches to produce gridded datasets: (i) based \r\non in situ observations only, and (ii) combining in situ observations \r\nwith remote-sensing data and/or reanalysis data. The first approach \r\nhas been widely employed in regions with high station density using \r\ninterpolation techniques, such as inverse-distance weighting, optimal \r\ninterpolation, and kriging (Chen et al., 2008; Haylock et al., 2008; Frei, \r\n2014; Isotta et al., 2014; Masson and Frei, 2014; Hiebl and Frei, 2016; \r\nNguyen-Xuan et  al., 2016). The second approach has been mainly \r\napplied in data-sparse regions with low station density, using simple \r\nbias adjustment, quantile mapping, and kriging techniques with in \r\nsitu observations, remote-sensing and reanalysis data (Cheema and \r\nBastiaanssen, 2012; Erdin et al., 2012; Dinku et al., 2014; Abera et al., \r\n2016; Krahenmann et al., 2018)."


id: 536aa98959bc6d1c66d8a75cf5b1f1fd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 11950
content:
"direction and temperature as the minimum standard ancillary data \r\nfor solid precipitation monitoring (Nitu et al., 2018)."


id: a1b02d3eb5f99476d774624b63492121
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25667
content:
"All the limitations and challenges of observational data discussed \r\nin Section 10.2.2 also apply to its use for post-processing of climate \r\nmodel data. High quality and long observational data series are \r\nparticularly relevant to quantify uncertainties. Different reanalyses \r\npresent significant discrepancies when used as key predictor variables \r\nat the daily scale and may even affect the downscaled climate change \r\nsignal (Brands et  al., 2012; Dayon et  al., 2015; Manzanas et  al., \r\n2015; Horton and Bronnimann, 2019). There is high confidence that \r\nreanalysis uncertainties limit the quality of statistical downscaling in \r\nsome regions, although no assessment has been made for the most \r\nrecent reanalysis products."


id: 8f776313a927d82ac630a0fa4a2dc5c4
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 22451
content:
"An important issue for bias adjustment is the correct representation \r\nof the required spatial scale. Ideally, bias adjustment is calibrated \r\nagainst area-averaged data of the same spatial scale as the climate \r\nmodel output. Hence, high-quality observed gridded datasets with an \r\neffective resolution close to the nominal model resolution are required. \r\nDriven by the need to also generate regional-scale information in \r\nstation-sparse regions, researchers have considered derived datasets \r\nthat blend in situ and remote-sensing data to produce high-resolution \r\nobservations to be used as predictands (Sections 10.2.1.2 and 10.2.2.4; \r\nHaiden et al., 2011; Wilby and Yu, 2013)."


id: 44b7bed2b8938d356d5eb7fd5792ca6a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9319
content:
"data to reconstruct past climate changes for analysis of a  specific \r\nprocess or case (Widmann et  al., 2010). Recently, assimilation of \r\nmultiple series from various data sources, including tree rings, ice \r\ncores, lake cores, corals, and bivalves, has allowed production of \r\nreconstructions that can be widely shared and applied to multiple \r\npurposes, as with modern reanalyses (Hakim et  al., 2016; Franke \r\net al., 2017; Steiger et al., 2018; Tardif et al., 2019). Most of these \r\npaleo-reanalyses are global but there are products using regional \r\nmodels or targeted at specific regions such as Europe, East Africa and \r\nthe Indian Ocean (Fallah et al., 2018; Klein and Goosse, 2018)."


id: e75bc8d1909251786985f408a1baebf8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38001
content:
"Delobbe, 2016; Humphrey et al., 2017; Yang and Ng, 2019). While \r\nthey are far less reliable and accurate than professional observations, \r\ncrowdsourced data are abundantly available and can give spatial \r\nrepresentations at very high resolution. This technological trend \r\ncould prove very useful (high confidence), and the regional climate \r\ncommunity is making efforts to understand the extent to which these \r\ndata sources can be exploited, at least as a complement to traditional \r\ndatasets (Overeem et al., 2013; Meier et al., 2017; Uijlenhoet et al., \r\n2018; de Vos et al., 2019; Langendijk et al., 2019b)."


id: b720cd9d08da38b9f221e6b0fe5daf72
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 29430
content:
"Much of the information available on future regional climate arises \r\nfrom studies based on climate model simulations (Chapters 3, 4 \r\nand 8). In this section, different types of models (Section  10.3.1) \r\nand model experiments (Section  10.3.2) for generating regional \r\nclimate information are discussed, followed by an assessment of \r\nthe performance, added value, and fitness-for-purpose of different \r\nmodel types (Section 10.3.3). The focus is on the representation of \r\nlarge- to local-scale phenomena and processes relevant for regional \r\nclimate. Finally, uncertainties of regional climate projections and \r\nmethodologies to manage these are assessed (Section 10.3.4)."


id: f0d94700b21fe3c20ca75032d035e38
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39739
content:
"Regional climate change information may be derived from a hierarchy \r\nof different model types covering a  wide range of spatial scales \r\nand processes (Figure  10.5). The application of any model relies \r\non assumptions, depending on the specific model as well as the \r\napplication. Table 10.1 gives an overview of the generic assumptions \r\nof the different model types discussed here for generating regional \r\nclimate information. The violation of these assumptions will affect \r\nthe model performance, which is discussed in Section 10.3.3."


id: 443925e6277dab76d1b81554b56d8344
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9240
content:
"An encouraging development for understanding climate variations \r\nover the past 250 years or so at the global and regional scale lies \r\nin the field of data rescue, in which hitherto hidden archives of \r\nmeteorological data are brought to the forefront (Sections 1.5.1.1 \r\nand 2.5). Surface observations from data rescue projects may then \r\nbe assimilated to derive long-term high-resolution gridded surface \r\nregional reanalysis (Devers et al., 2020). Global extended reanalyses \r\nsuch as 20CR (Compo et al., 2011), ERA-20C (Poli et al., 2016a, b) \r\nor CERA-20C (Laloyaux et  al., 2018) may be further downscaled \r\nto quantify the variability of past climate at the regional scale \r\n(Caillouet et al., 2016, 2019)."


id: c1e0bc69bcc9f90f7a486d49646e5cef
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 31409
content:
"One of the main scientific challenges related to high-resolution \r\nregional climate modelling is dealing with the representation of \r\nfine-scale processes (e.g., Yano et al., 2018) in observational datasets. \r\nAdditionally, reliable observation networks following WMO standards \r\nhave a  very sparse geographical representation. Hence, regional \r\nclimate models have started to use high-resolution data combined \r\nwith crowdsourced observations (Zheng et al., 2018). Recent efforts \r\nhave led to the production of homogeneously processed long-term \r\ndatasets for regional climate model evaluation (Goudenhoofdt and "


id: 5dbd78d1028118b9ab6ed73cc5eba81b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13782
content:
"Table 10.1 | Assumptions underlying different model types in simulating regional climate and climate change. Violating these assumptions will affect model \r\nperformance (see links to different subsections for details). All assumptions regarding future climate are in addition to those regarding present climate and predicated on \r\nthe driving global model simulating a plausible global climate sensitivity (Section 1.3.5, Chapters 4 and 7). The assumptions listed for future climate applications of perfect prognosis \r\nstatistical downscaling and bias adjustment are often called the 'stationarity assumption'. Numbers in curly brackets refer to chapters and sections assessing these assumptions."


id: 495fb00e5968389c5be0a9f6cc1d6336
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10127
content:
"State-of-the-art global models are generally used to derive climate \r\ninformation at continental to global scales both for past and future \r\nclimates (e.g., Chapters 3 and 4). The nominal horizontal resolution \r\nin CMIP5 global models is typically 100-200 km. The effective \r\nresolution, for which the shape of the kinetic energy spectrum is \r\nsimulated correctly, is about three to five times larger (Klaver et al., \r\n2020), and a similar relationship also applies to RCMs (Skamarock, \r\n2004). This strongly limits their ability to resolve local details. Since "


id: eb1aed90b58fc55e6bd86389deb5217a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38716
content:
"Model-based regional climate projections are all based upon \r\nsome type of global model, including state-of-the-art Earth system \r\nmodels (ESMs), coupled atmosphere-ocean general circulation models \r\n(GCMs) or atmosphere-only general circulation models (AGCMs) \r\n(see Section 1.5.3.1). They are collectively referred to as global models."


id: b47246eba2fd2551babe88a3a441534
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 28986
content:
"AR5 the progress in reducing biases and providing more credible \r\nregional projections by global models has been moderate in spite \r\nof the more realistic representation of a number of processes and \r\nthe increase in resolution of some models. For AR6, several of the \r\nnew CMIP6 (Eyring et  al., 2016a) model intercomparison projects \r\n(MIPs) address some of these limitations. The list of MIPs is provided \r\nin Chapter 1 (Table 1.3). High-Resolution MIP (HighResMIP; Haarsma \r\net al., 2016) and Global Monsoons MIP (GMMIP; Zhou et al., 2016) \r\nspecifically address the regional climate challenge using global \r\nmodels. HighResMIP focuses on producing global climate projections \r\nat a horizontal resolution of around 50 km grid spacing or finer while \r\nGMMIP aims at better understanding and predicting the monsoons."


id: 60e7778b349d0f5939ba81e6c342a9e6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14345
content:
"An alternative to increasing resolution everywhere is offered by \r\nvariable resolution global models, that is, with regionally finer \r\nresolution. They have been developed since the 1970s (Li, 1999), \r\nresulting in a first coordinated effort (SGMIP) by Fox-Rabinovitz et al. \r\n(2006, 2008). They are expected to offer the finest resolution possible \r\nin the region of interest, while still resolving the climate processes at \r\nthe global scale (although at lower resolution). An overview of recent \r\ndevelopments is in McGregor (2015). This is a rapidly developing field \r\n(Krinner et al., 2014; Ferguson et al., 2016; Huang et al., 2016) that \r\nwill possibly contribute to improved future regional projections."


id: ccb1162ed7be4d280bfeddf26f39d768
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 33339
content:
"The Coordinated Regional Climate Downscaling Experiment \r\n(CORDEX) initiative (Giorgi et al., 2009; Giorgi and Gutowski, 2015; "


id: 6bac28ad57b2c35760112a7d5036dd6e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16250
content:
"Perfect-prognosis models are statistical models calibrated between \r\nobservation-based large-scale predictors (e.g., from reanalysis) and \r\nobserved local-scale predictands (Maraun and Widmann, 2018b). \r\nRegional climate projections are then generated by replacing the \r\nquasi-observed predictors by those from climate model (typically \r\nglobal model) projections. Predictor patterns that are common to \r\nobservations and climate model data can be defined by common \r\nempirical orthogonal functions (Benestad, 2011). The perfect prognosis \r\napproach can either be used to generate daily (or even sub-daily) \r\ntime series, or local weather statistics (e.g., Benestad et al., 2018)."


id: 5d1260718efeedead3565286e82f25b8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13642
content:
"Regression-like models (Maraun and Widmann, 2018b) rely on a transfer \r\nfunction linking an observed local statistic (such as the temperature at \r\na given day) to some set of large-scale predictors. Recent developments \r\ninclude stochastic regression models to explicitly  simulate local \r\nvariability (San-Martin et al., 2017; those explicitly modelling temporal \r\ndependence are assessed in Section 10.3.1.3.4). The use of machine \r\nlearning techniques has been reinvigorated, including genetic \r\nprogramming to construct a  data-driven model structure (Zerenner \r\net al., 2016) and deep and convolutional neural networks (Reichstein \r\net al., 2019; Bano-Medina et al., 2020)."


id: 282570751afe585b2700cf9860ed56fc
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4261
content:
"In the delta change approach, selected observations are modified \r\naccording to corresponding changes derived from dynamical model \r\nsimulations. Traditionally, only long-term means have been adjusted, \r\nbut recently approaches to modify temporal dependence (Webber \r\net  al., 2018) have been developed, as well as quantile mapping \r\napproaches that individually adjust quantiles of the observed \r\ndistribution (Willems and Vrac, 2011). By construction, the approach \r\ncannot modify the spatial and temporal dependence structure of the \r\ninput observations (Maraun, 2016)."


id: c7e1884f325f3ca09cbc49a7242ee706
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32475
content:
"Analogue methods (Martin et  al., 1996; Maraun and Widmann, \r\n2018b) compare a simulated large-scale atmospheric field with an \r\narchive of observations and select, using some distance metric, the \r\nclosest observed field in the archive. The downscaled atmospheric \r\nfield is then chosen as the local atmospheric field observed on the \r\ninstant the analogue occurred. New analogue methods have been \r\ndeveloped to simulate unobserved values including a  rescaling of \r\nthe analogue (Pierce et  al., 2014) or by combining analogues and \r\nregression models (Chardon et al., 2018)."


id: 357f0f8eb8d289954f0e903d4e10c60f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 6661
content:
"Weather generators are statistical models that simulate weather time \r\nseries of arbitrary length. They are calibrated to represent observed \r\nweather statistics, in particular daily or even sub-daily variability. One \r\nvariant of these models are advanced stochastic perfect-prognosis \r\nmethods, conditioned on large-scale atmospheric predictors on a daily \r\nbasis, for instance multisite generalized linear models (Chandler, \r\n2020). Another widely used variant is change-factor weather \r\ngenerators: the weather generator parameters are calibrated against \r\npresent and future climate model simulations, and the climate change \r\nsignals are then applied to the parameters calibrated to observations. \r\nRecent research has mainly focussed on multi-site Richardson type \r\n(Markov-chain) weather generators (Keller et  al., 2015; Dubrovsky \r\net  al., 2019), some explicitly modelling extremes and their spatial \r\ndependence (Evin et al., 2018)."


id: a833c59e3ca0fdff46fc8e4bbd73acdf
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 26809
content:
"Bias adjustment is a  statistical post-processing technique used to \r\npragmatically reduce the mismatch between the statistics of climate \r\nmodel output and observations. The approach estimates the bias or \r\nrelative error between a chosen simulated statistical property (such \r\nas the long-term mean or specific quantiles of the climatological \r\ndistribution) and that observed over a  calibration period; the \r\nsimulated statistic is then adjusted taking into account the simulated \r\ndeviation. Bias adjustment methods are regularly applied on a spatial \r\nscale similar to that of the simulation being adjusted, but they are \r\noften used as a simple statistical downscaling method by calibrating \r\nthem between coarse resolution (e.g., global) model output and finer \r\nobservations (Maraun and Widmann, 2018b)."


id: 937bec9015c431bcdb94c484fa6f3978
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23152
content:
"Typical implementations of bias adjustment are (i) additive \r\nadjustments, where the model data is adjusted by adding a constant, \r\n(ii) rescaling, where the model data is adjusted by a  factor, and \r\n(iii) more flexible quantile mapping approaches that adjust different \r\nranges of a  distribution individually. Hempel et  al. (2013), Pierce \r\net  al. (2015), Switanek et  al. (2017), and Lange (2019) developed \r\nvariants of quantile mapping that preserve trends in the mean or \r\neven further distributional statistics. Multivariate bias adjustment "


id: 5b57300f7cdc0ad8dfd6945598accfee
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13313
content:
"A wide variety of approaches has been proposed to combine the \r\nadvantages of different statistical approaches. For instance, to \r\novercome the scale mismatch between climate model output and \r\nobservations, bias adjustment has been combined with stochastic \r\ndownscaling (Volosciuk et  al., 2017; Lange, 2019) or rescaled \r\nanalogues (Pierce et al., 2014). Other approaches known as emulators \r\nhave been developed to emulate an RCM using a statistical model "


id: 9d61097b6f24349b6d7eafea38707b10
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24921
content:
"and also applied to a range of driving global models (Deque et al., \r\n2012; Haas and Pinto, 2012; Walton et  al., 2015, 2017; Beusch \r\net al., 2020; Erlandsen et al., 2020)."


id: 4b577674ab7d5d2d108a7904a384b803
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10504
content:
"PGW experiments, boundary conditions for the downscaling are taken \r\nfrom reanalysis data, but modified according to the thermodynamic \r\nsignals of climate change. The boundary conditions thus represent \r\nthe sequence of observed weather, but with adjusted temperatures, \r\nhumidity and atmospheric stability. Recent applications of PGW \r\nexperiments include assessments of climate change in Japan (Adachi \r\net  al., 2012; Kawase et  al., 2012, 2013), the Los Angeles area \r\n(Walton et al., 2015), Hawaii (C. Zhang et al., 2016), and the Alps \r\n(Keller et  al., 2018). Recently, PGW studies have been generalized \r\nto modify global model simulations with the objective of separating \r\nthe drivers of regional climate change, such as the Mediterranean \r\namplification (e.g., Brogli et al., 2019b; Section 10.3.2.3)."


id: 8f8ff4cd51344102c9d90ef778c52931
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 22468
content:
"The most commonly used model experiments to generate regional \r\nclimate information are transient simulations. Alternative experiment \r\ntypes serve specific purposes. The role of these experiment types for \r\ngenerating regional climate information is assessed in this subsection."


id: ae718a6f6274563a121f5fb892045c7d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27923
content:
"Transient simulations intend to represent the evolving climate \r\nstate of the Earth system (Chapter  4). They are typically based on \r\ncoupled global model simulations, such as those in the Diagnostic, \r\nEvaluation and Characterization of Klima (DECK) and ScenarioMIP \r\npart of CMIP6 covering the period 1850-2100 (Eyring et al., 2016a), \r\nand HighResMIP (1950-2050; Haarsma et al., 2016). Global transient \r\nclimate simulations may be further downscaled by either dynamical or \r\nstatistical downscaling. Currently available CORDEX RCM simulations \r\n(1950-2100) are based on CMIP5 (Gutowski Jr. et al., 2016)."


id: 9449a77aa972c890659fc796fe615a8c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23295
content:
"Sensitivity studies are used to identify the impact of a specific forcing, \r\ndriver or process on regional climate phenomena and changes and \r\nimprove the process understanding. The influence of a single external \r\nforcing can be assessed with transient historical simulations within \r\ntwo different frameworks (Bindoff et al., 2013; Gillett et al., 2016). The \r\nfirst entails simulations taking prescribed (often observed) changes \r\nonly in the external forcing of interest, the others being fixed at \r\na constant value (often pre-industrial). The second framework is based \r\non simulations in which all external forcings are applied other than \r\nthe one of interest. Both approaches may not give the same results \r\nsince the climate response to a  range of forcings is not necessarily \r\nequal to the sum of climate responses to individual forcings (Ming and \r\nRamaswamy, 2011; Jones et al., 2013; Schaller et al., 2013; Shiogama \r\net al., 2013; Marvel et al., 2015; Deng et al., 2020)."


id: c1ffe11981da4e08c69ba91687ac5e62
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31428
content:
"Results from downscaling experiments often suffer from large-scale \r\ncirculation biases in the driving global models such as misplaced storm \r\ntracks (Section 10.3.3.4), while changes in atmospheric circulation \r\nare often uncertain owing to both climate response uncertainty \r\n(Section 10.3.4.2) and internal variability (Section 10.3.4.3). In a given \r\napplication, if one can assume that changes in the regional climate \r\nare dominated by thermodynamic rather than by circulation changes, \r\nso-called pseudo-global warming (PGW) experiments (Schar et  al., \r\n1996) may be helpful in mitigating the effects of circulation biases, \r\nand to fix the large-scale circulation to present climate. In classical "


id: 37ca929a2c7e900defc60851f5d41284
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7024
content:
"To study the influence of internal variability, new approaches such \r\nas partial coupling simulations are now routinely used since AR5. \r\nThese are coupled ocean-atmosphere simulations in which the \r\ninteraction between atmosphere and ocean is only one-way over \r\na specified ocean basin or sub-basin and two-way everywhere else. \r\nDifferent implementations have been used such as SST anomaly \r\nNewtonian relaxation at the air-sea interface or prescription of \r\nwind-stress anomalies from reanalysis (Kosaka and Xie, 2013, 2016; \r\nEngland et  al., 2014; McGregor et  al., 2014; Douville et  al., 2015; \r\nDeser et al., 2017a). Such simulations have been applied to identify "


id: 5fc636601ba98be14b54d978397a0e9c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14149
content:
"Zubler et al., 2011; Nabat et al., 2014; Gutierrez et al., 2018; Druge et al., \r\n2019; Bozkurt et al., 2020) and the added value of downscaling compared \r\nto the reanalysis fields (Section 10.3.3.2). Although the reanalysis model \r\nitself can introduce biases especially for non-assimilated variables (such \r\nas precipitation) it is assumed that in such a  setting, discrepancies \r\nbetween the modelled and observed climate arise mostly from errors \r\nin the downscaling method (Laprise et  al., 2013) or internal climate \r\nvariability generated by the downscaling method (Bohnisch et  al., \r\n2020; Ehmele et  al., 2020). Since AR5, reanalysis-driven RCMs have \r\nbeen extensively evaluated for many regions, especially in the CORDEX \r\nframework (see region specific examples in the Atlas)."


id: bea615009e9c4a6a8c06e946f168b1b1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 30801
content:
"the regional impacts of the Pacific Decadal Variability (PDV) and \r\nAtlantic Multi-decadal Variability (AMV) (Kosaka and Xie, 2013; \r\nWatanabe et  al., 2014; Delworth et  al., 2015; Boer et  al., 2016; \r\nRuprich-Robert et al., 2017, 2018)."


id: f4c01c26eaf0489aed216ac8ff04c2b2
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 40432
content:
"Nudging experiments have been used to identify the relative roles of \r\ndynamic and thermodynamic processes in climate model biases and \r\nspecific extreme events (Wehrli et al., 2018, 2019). Another related \r\nframework is used to evaluate the impact land conditions have on \r\na climate phenomenon in a pair of experiments with one simulation \r\nserving as control run, and a perturbed simulation with prescribed \r\nland conditions (i.e., soil moisture, leaf area index, or surface albedo) \r\ncharacterizing a specific state of the land surface (i.e., afforestation \r\nor deforestation). The difference between the perturbed and control \r\nsimulations enables a robust assessment of the possible impact of \r\nland conditions on events like droughts and heatwaves (Seneviratne \r\net al., 2013; Stegehuis et al., 2015; Hauser et al., 2016, 2017; van den \r\nHurk et al., 2016; Vogel et al., 2017; Rasmijn et al., 2018; Strandberg \r\nand Kjellstrom, 2019)."


id: 59930acf7d00024c4b84d13ca6e876c9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12996
content:
"Over Europe, the VALUE initiative assessed statistical downscaling \r\nfor marginal, temporal, and spatial aspects of temperature and \r\nprecipitation including extremes, and performed a  process-based \r\nevaluation of specific climatic phenomena (Gutierrez et  al., 2019; \r\nMaraun et al., 2019a). Alternatively, statistical downscaling can be \r\nevaluated in so-called perfect model or pseudo-reality simulations \r\n(Charles et  al., 1999), where a  high-resolution climate model \r\nsimulation is used as a proxy for a hypothetical present and future \r\nrealities. A statistical downscaling model is first calibrated with this \r\npseudo present-day climate and, subsequently, assessed whether it \r\ncorrectly reproduces the pseudo-future conditions (Dixon et al., 2016)."


id: da2544aee95bd2d840cae0d21b2778b5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35674
content:
"RCM sensitivity simulations have been used in a  similar way to \r\nassess the contribution of external forcings and large-scale drivers \r\nto projected regional climate change (Nabat et  al., 2014; Brogli \r\net al., 2019a, b) and the influence of selected drivers on observed \r\nextreme events (Meredith et  al., 2015b; J. Wang et  al., 2017; \r\nArdilouze et al., 2019)."


id: c7980b0e8444577009b1ae0641413b75
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32425
content:
"In summary, there is robust evidence that sensitivity experiments are \r\nkey to assessing the influence of different forcings and drivers on \r\nregional climate change."


id: 77a88190504bf8eecee8f4b8875f8a6b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18256
content:
"Assessing model performance is a prerequisite for building confidence in \r\nregional climate projections. This subsection assesses the performance \r\nof different model types at simulating regional climate and climate \r\nchange. The subsection builds on the assessment of global model \r\nperformance in Chapter 3, and complements the model assessment in \r\nChapter 8, which focuses on the water cycle, and the Atlas."


id: d1a3daa261f23f022f1d04d791a84f46
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 34233
content:
"Experiments driven by quasi-perfect boundary conditions or predictors \r\n(observations or reanalysis) can be useful to evaluate downscaling \r\nperformance (Frei et  al., 2003; Laprise et  al., 2013), including \r\nthe simulation of observed past trends (Lorenz and Jacob, 2010; "


id: 48946f0636c63cc43f7c04b9e054692d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9985
content:
"Figure 10.6 shows the mean summer temperature and precipitation \r\nbiases of several state-of-the-art climate model ensembles for \r\nthe western Mediterranean. It additionally illustrates the role of "


id: abf802831cbf806aa66847013c3f4471
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27496
content:
"observational uncertainty for model evaluation (Section 10.2), where \r\nobservations display differences that can be substantial. Model \r\nperformance varies strongly from model to model, but also between \r\nensembles. These biases are an expression of model error that leads \r\nto misrepresented phenomena and processes, and thus limit the \r\nconfidence in future projections of regional climate. The focus of this \r\nsubsection is therefore to evaluate the representation of relevant \r\nregional-scale phenomena for representing regional climate."


id: 2ab78a9f97e6caa28f10da064b05d746
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4740
content:
"usually synoptic-scale weather patterns. The regime-based approach \r\nhas been used with both global models (e.g.,  Barton et  al., 2012; \r\nCatto et al., 2015; Taylor et al., 2019) and RCMs (Endris et al., 2016; \r\nBukovsky et al., 2017; Whan and Zwiers, 2017; Pinto et al., 2018), but \r\nalso with perfect prognosis and bias adjustment methods (Marteau \r\net al., 2015; Addor et al., 2016; Beranova and Kysely, 2016; Soares and \r\nCardoso, 2018; Soares et al., 2019b)."


id: d715e9a1aa40d7e845824cb3ad28b17f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35148
content:
"There is high confidence that to assess whether a  climate model \r\nrealistically simulates required aspects of present-day regional \r\nclimate, and to increase confidence of future projections of these \r\naspects, evaluation needs to be based on diagnostics taking into \r\naccount multiple variables and process understanding."


id: b55dde96b97d8fd3fb42c708f727df32
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 29141
content:
"New diagnostics for multivariate dependencies are needed to \r\ncharacterize compound events (Section  11.8; Hobaek Haff et  al., \r\n2015; Wahl et al., 2015; Sippel et al., 2016, 2017; Tencer et al., 2016; \r\nBevacqua et al., 2017; Careto et al., 2018; Zscheischler et al., 2018). \r\nHowever, their success depends on the availability of adequate \r\nobservational data (Section  10.2.2). Multivariate dependencies \r\ndiscovered in compound events can also be used for designing and \r\nevaluating multivariate bias adjustment and statistical downscaling. \r\nProcess-based diagnostics are useful for identifying the cause of model \r\nerrors, although it is not always possible to associate a  systematic \r\nerror with a  specific cause (Eyring et  al., 2019). AR5 discussed \r\ntwo approaches of process-based evaluation: (i) the isolation of \r\nphysical components or parametrizations by dedicated experiments \r\n(Section 10.3.2.4) and (ii) diagnostics conditioned on relevant regimes, "


id: 5a17442975f56c86c7fb9c0e33411a29
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13098
content:
"Obtaining regional information from global simulations may involve \r\na  range of different methods (Section  10.3.1). An approach with \r\nhigher complexity or resolution is useful if it adds further, useful \r\ninformation to that of a reference model. Section 10.5 discusses the \r\nset of considerations that determine if the information is useful. This \r\nfurther useful information is often referred to as added value and \r\nis a function of variables, processes, and the temporal and spatial \r\nscales targeted taking into account the needs of specific users \r\n(Di Luca et al., 2012; Ekstrom et al., 2015; Giorgi and Gutowski, 2015; \r\nTorma et  al., 2015; Rummukainen, 2016; Falco et  al., 2019). There \r\nis no common definition of added value, but here it is considered \r\na  characteristic that arises when one methodology gives further \r\nvalue to what another methodology yields."


id: feda394cda1226acb044996874a97470
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 42235
content:
"a specific regional climate and its change can be highly uncertain \r\n(Hall, 2014). RCMs have difficulties improving especially large-scale \r\ncirculation biases, although some successful examples exist. But \r\ndue to their enhanced representation of complex topography and \r\ncoastlines, RCMs may add value to simulating the regional expression \r\nof teleconnections. Bias adjustment cannot mitigate fundamental \r\nmisrepresentations of the large-scale atmospheric circulation \r\n(Maraun et  al., 2017, Cross-Chapter Box  10.2). This subsection \r\nillustrates the relevance of large-scale circulation biases for regional \r\nclimate assessments with selected examples from the mid- to high \r\nlatitudes and tropics."


id: 1fe94b6e3459ff6493769ddc45f1b316
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 2825
content:
"10.3.3.3.1 Mid- to high-latitude atmospheric variability \r\nphenomena: Blocking and extratropical cyclones"


id: f0daddecd6931c30f453d223ddb29206
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39740
content:
"A first step in determining added value in downscaling is to analyse \r\nwhether the downscaling procedure gives detail on spatial or \r\ntemporal scales not well-resolved by a global model, thus potentially \r\nrepresenting climatic features missing in the GCM. This added detail, \r\nreferred to as potential added value (PAV; Di Luca et al., 2012), is \r\ninsufficient for demonstrating added value in downscaling (Takayabu \r\net al., 2016), but lack of PAV indicates that the downscaling method \r\nlacks usefulness. Added value is not guaranteed simply by producing \r\nmodel output at finer resolution. It depends on several factors, such \r\nas the simulation setup and the specific climatic variables analysed \r\n(Di Luca et al., 2012; Hong and Kanamitsu, 2014; Xue et al., 2014). \r\nA variety of performance measures are needed to assess added value \r\n(Section  10.3.3.1; Di Luca et  al., 2016; Wilks, 2016; Ivanov et  al., \r\n2017, 2018; Soares and Cardoso, 2018)."


id: f6aa65adc5a620416bf4be724f09cc3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 40769
content:
"A further challenge, especially at increasingly higher resolutions, is \r\nthat adequate observational data may not be available to assess \r\nadded value (Section 10.2, e.g., Di Luca et al., 2016; Zittis et al., 2017; \r\nBozkurt et  al., 2019). This implies a  need for additional efforts to \r\nobtain, catalogue and quality-control higher resolution observational \r\n(or observation-based) datasets (Thorne et al., 2017; Section 10.2). \r\nUnivariate demonstration of added value is necessary, but may be \r\ninsufficient, as better agreement with observations in the downscaled \r\nvariable may be a consequence of compensating errors that are not \r\nguaranteed to compensate similarly as climate changes. Multivariate \r\nanalysis of added value is better able to demonstrate physical \r\nconsistency between observed and simulated behaviour (Prein et al., \r\n2013a; Meredith et al., 2015a; Reboita et al., 2018)."


id: dae87709a8d07a57124680b0f22ac5a7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35801
content:
"10.3.3.3 Performance at Simulating Large-scale Phenomena \r\nand Teleconnections Relevant for Regional Climate"


id: c21f8ede7e117bb457345d2749bf8067
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31450
content:
"Regional climate is often controlled by large-scale weather phenomena, \r\nmodes of variability and teleconnections (e.g., Sections 2.3 and 2.4, \r\nAnnex IV). In particular, extreme events are often caused by specific, \r\nin some cases persistent, circulation patterns (Sections 11.3-11.7). \r\nIt is therefore important for climate models to reasonably represent \r\nnot only continental, but also regional climate and its variability for \r\nsuch extremes. As explained in Section  3.3.3, standard resolution \r\nglobal models can suffer biases in the location, occurrence frequency \r\nor intensity of large-scale phenomena, such that statements about "


id: 9d5ed50d3fd6e5872a3e008f201d5c77
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24919
content:
"RCMs have a  very limited ability to reduce large-scale circulation \r\nerrors of the driving GCM (Hall, 2014). In a study of five ERA-Interim\u0002driven RCMs, Jury et al. (2018) showed that RCMs typically simulate \r\nfewer blocking events over Europe than are present in the driving \r\ndata, irrespective of the RCM horizontal resolution. Based on a simple \r\nblocking bias-decomposition method, they suggest that blocking \r\nfrequency biases can contribute to the RCM mean surface biases. \r\nOver some large domains, reanalysis-driven RCMs can significantly \r\nimprove the representation of storm characteristics compared to the "


id: 7d1837474b9c9aacc733b688d0cbe961
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19193
content:
"The robust quantification of the influence of atmospheric circulation \r\nerrors on regional climate remains a challenge as many parametrized \r\nprocesses such as cloud radiative effects and soil moisture or snow \r\nfeedbacks can also contribute and interact with the circulation errors. \r\nAtmospheric nudging experiments where the simulated circulation is \r\nconstrained to be close to that observed have been used to separate \r\nthe circulation effect from other contributions to regional climate \r\nbiases (Wehrli et al., 2018). The nudging approach requires detailed \r\nand careful implementation in order to limit detrimental effects due \r\nto the added tendency term in the model equations (Zhang et al., \r\n2014; Lin et al., 2016). Based on single-model experiments, Wehrli \r\net  al. (2018) show that the circulation induced biases are often \r\nnot the main contributors to mean and extreme temperature and \r\nprecipitation biases for many regions and seasons."


id: 2d3896f5aae60412bd72288b6544554c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 5243
content:
"There is high confidence that atmospheric circulation biases can \r\ndeteriorate the model representation of regional land surface climate. \r\nAssessing the relative contributions of atmospheric circulation and \r\nother sources of bias remains a challenge due to the strong coupling \r\nbetween the atmosphere and other components of the climate \r\nsystem, including the land surface."


id: cf2c89aea7c503738a455437ec989fd7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 33796
content:
"Model performance in simulating ENSO characteristics, including \r\nENSO spatial pattern, frequency, asymmetry between warm and cold \r\nevents, and diversity, is assessed in Chapter  3 (Section  3.7.3). The \r\nability of the recent generation of GCMs and RCMs to adequately \r\nsimulate ENSO-related teleconnections is reviewed here along with \r\nrelevant methodological issues (see also Annex IV2.3.2, Figure 3.38 \r\nand Section 3.7.3)."


id: 55eaf6e23d9ae272c7d7c8a3ad89d46d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12389
content:
"Langenbrunner and Neelin (2013) show that there is little \r\nimprovement in CMIP5 relative to CMIP3 in amplitude and spatial \r\npatterns of the ENSO influence on boreal winter precipitation \r\n(spatial  pattern correlations against observations are typically less \r\nthan 0.5). However, the CMIP5 ensemble accurately represents \r\nthe amplitude of the precipitation response in regions where \r\nobserved teleconnections are strong. Garcia-Villada et  al. (2020) \r\nfound a decline in performance of the representation of simulated "


id: 2179a296a61a57dff5bed4cb73e4891f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3097
content:
"Biases in the representation of large-scale atmospheric circulation can \r\nresult in biased representation of regional climate. While, in principle, "


id: 975c6799224ce19b1e940270cb651b21
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23835
content:
"Regional climate is shaped by a wide range of weather phenomena \r\noccurring at scales from about 2000 km to 2 km (Figure 10.3). These \r\nmodulate the influence of large-scale atmospheric phenomena and \r\ncreate the characteristic and potentially severe weather conditions. \r\nThe climate in different regions will be affected by different mesoscale \r\nphenomena, of which several may be relevant. A skilful representation \r\nof these phenomena is a  necessary condition for providing credible \r\nand relevant climate information for a given region and application. \r\nTherefore, it is important to understand the strengths and weaknesses \r\nof different model types in simulating these phenomena. The \r\nperformance of different dynamical climate model types to simulate \r\na selection of relevant mesoscale weather phenomena is assessed here."


id: 3987cbf000114772cf1ec15766cbdbb9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7334
content:
"In RCMs, the effects of tropical large-scale modes and \r\nteleconnections are inherited through the boundary conditions \r\nand influenced by the size of the numerical domain. Done et  al. \r\n(2015) and Erfanian and Wang (2018) claim that large domains that \r\ninclude source oceanic regions are required to capture the remote \r\ninfluence of teleconnections, although, without spectral nudging, \r\nthis can lead to biased synoptic-scale patterns (Prein et al., 2019). \r\nRCMs generally reproduce the regional precipitation responses \r\nto ENSO, and can sometimes even improve the representation of \r\nthese teleconnections compared to the driving reanalysis (Endris \r\net  al., 2013; Fita et  al., 2017), but the overall performance may \r\ndepend both on the driving reanalysis or GCM (Endris et al., 2016; \r\nChandrasa and Montenegro, 2020) and on the chosen RCMs (Whan \r\nand Zwiers, 2017)."


id: c339158c2d371a8f4c8b68a100ed5418
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31619
content:
"New studies since AR5 have shown that model performance \r\nassessment regarding ENSO teleconnections remains a  difficult \r\nchallenge due to the different types of ENSO and model errors in \r\nENSO spatial patterns, as well as the strong influence of atmospheric \r\ninternal variability at mid- to high latitudes (Coats et al., 2013; Polade \r\net  al., 2013; Capotondi et  al., 2015; Deser et  al., 2017c; Tedeschi \r\nand Collins, 2017; Garcia-Villada et  al., 2020). Another difficulty \r\ncomes from the non-stationary aspects of teleconnections in both \r\nobservations and models, raising methodological questions on how \r\nbest to compare a given model with another model or observations \r\n(Herein et al., 2017; Perry et al., 2017; O'Reilly, 2018; O'Reilly et al., \r\n2019; Abram et al., 2020)."


id: 777de1dde7931bab0d623d1040671faa
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18226
content:
"There is robust evidence that an accurate representation of both \r\natmospheric circulation and sea surface temperature (SST) variability \r\nare key factors for the realistic representation of ENSO teleconnections \r\nin climate models. A  robust and thorough evaluation of model \r\nperformance regarding ENSO teleconnections is a challenging task \r\nwith many methodological issues related to asymmetry between the \r\nwarm and cold phases, non-stationarity and time-varying interaction \r\nbetween the Pacific and other ocean basins, signal-to-noise issues \r\nin the mid-latitudes and observational uncertainties, particularly for \r\nprecipitation (Section 10.2.2.3)."


id: c39241246a92b05a5c74ca8327c93a2f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31686
content:
"4 km resolution showed good skill in simulating the diurnal cycle \r\nof temperature and wind on days of weak synoptic forcing in the \r\nRocky Mountains (Letcher and Minder, 2017) as well as in simulating \r\nthe mountain-plain wind circulation over the Tianshan mountains in \r\ncentral Asia (Cai et al., 2019), while in the Alps, a 1 km resolution has \r\nbeen required (Zangl, 2004)."


id: 26b5b0b54e76958e93a65d0af659ced7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3966
content:
"Fohn winds are synoptically-driven winds across a mountain range \r\nthat are warm and dry due to adiabatic warming in the downwind \r\nside. In an RCM study for the Japanese Alps, Ishizaki and Takayabu \r\n(2009) found that at least 10 km resolution was required to \r\nrealistically simulate the basic characteristics of Fohn events."


id: e3876dd159a2a44bb1395a75f150785b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 37322
content:
"There is high confidence that climate models with resolutions \r\nof around 10 km or finer are necessary for realistically simulating \r\nmountain wind systems such as slope and valley winds and the \r\nchannelling of winds in valleys."


id: eef80cd0afe2bad7c1d5c9309fa7d4b9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39412
content:
"Simulating coastal climates and the influence of big lakes are \r\na modelling challenge, due to the complex coastlines, the different \r\nheat capacities of land and water, the resulting wind system, and \r\ndifferential evaporation. The AR5 concluded that RCMs can add value \r\nto the simulation of coastal climates."


id: 9266a54c6b00a1c41dc5d28dd7af6e6c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 22977
content:
"Initial studies with convection-permitting global models suggests \r\nthat improvements in representing convection, as described for \r\nRCMs above, have a positive impact on the tropical and extratropical \r\natmospheric circulation and, thus, regional climate (Satoh et  al., \r\n2019; Stevens et al., 2019; see also Section 8.5.1.2 and Chapter 7). \r\nComputational constraints currently limit these simulations to \r\na length of few months only, such that they cannot yet be used for \r\nroutine climate change studies."


id: 19c4913c02b1015f06415f499d918dab
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 1730
content:
"Summer coastal low-level jets off the mid-latitude western continental \r\ncoasts are forced by the semi-permanent subtropical anticyclones, \r\ninland thermal lows, strong across-shore temperature contrasts in \r\nupwelling regions, and high coastal topography. They are important \r\nfactors in shaping regional climate by, for instance, preventing \r\nonshore advection of humidity and thereby causing aridity in the \r\nIberian Peninsula (Soares et al., 2014), or by transporting moisture \r\ntowards precipitating regions as in the North American monsoon \r\n(Bukovsky et al., 2013)."


id: b8d85a7f7b26d209f0626b2e5d513359
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 29725
content:
"Mountain slope and valley winds are localized thermally generated \r\ndiurnal circulations that have a strong influence on temperature and \r\nprecipitation patterns in mountain regions. During the day, heating \r\nof mountain slopes induces upslope winds; during the night this \r\ncirculation reverses. This phenomenon is not realistically represented \r\nby global models and coarse-resolution RCMs. RCM simulations at "


id: b09704ae767f90e06d4cf197a56065ec
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 28299
content:
"Reanalyses and most global models do not well resolve the details \r\nof coastal low-level jets (Bukovsky et al., 2013; Soares et al., 2014), \r\nbut they are still able to represent annual and diurnal cycles and \r\ninterannual variability (Cardoso et  al., 2016; Lima et  al., 2019). \r\nBukovsky et al. (2013) found RCM simulations at a 50 km resolution "


id: 50ee45bfd35002976573108a5b52c14b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 11520
content:
"to improve the representation of the coastal low-level jet in the Gulf \r\nof California and the associated precipitation pattern compared to \r\nthe driving global models. Lucas-Picher et  al. (2017) find indirect \r\nevidence via precipitation patterns that 12 km simulations further \r\nimprove the representation. Soares et al. (2014) demonstrated that \r\nan 8 km resolution RCM simulated a  realistic three-dimensional \r\nstructure of the Iberian coastal low-level jet, and the surface winds \r\ncompare well with observations. Lucas-Picher et al. (2017) showed \r\nthat a  0.44deg resolution RCM underestimated winds along the \r\nCanadian east coast, whereas a 0.11deg resolution version simulated \r\nmore realistic 10 metre wind speed. Also, the Etesian winds in the \r\nAegean Sea were realistically simulated by 12 km resolution RCMs \r\n(Dafka et al., 2018)."


id: c550652cff281ed4c113c92d030430b0
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32021
content:
"in an RCM to realistically represent regional temperatures (Samuelsson \r\net al., 2010; Deng et al., 2013; Mallard et al., 2014; Thiery et al., 2015; \r\nPietikainen et al., 2018), as well as remote effects (Spero et al., 2016). \r\nThe most common approach in RCMs is the two-layer lake model, \r\nincluding a  lake-ice model, with parametrized vertical temperature \r\nprofiles (Mironov et al., 2010; Golosov et al., 2018). For the Caspian \r\nSea, it is found that a three-dimensional ocean model simulated the \r\nSST fields better than a one-dimensional lake model when coupled to \r\nthe same RCM (Turuncoglu et al., 2013)."


id: 9c7649d58c386fcc7fa0a9731f1c29df
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24779
content:
"There is medium evidence and high agreement that it is important to \r\ninclude interactive lake models in RCMs to improve the simulation \r\nof regional temperature, in particular in seasonally ice-covered areas \r\nwith large fractions of lakes. There is medium evidence of the local \r\ninfluence of lakes on snow and rainfall as well as the importance of \r\nincluding lakes in regional climate simulations."


id: 5e8a9bcefc6c9937477a9ad5b7124b65
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13923
content:
"Big lakes modify the downwind climate. In particular during winter \r\nthey are relatively warm compared to the surrounding land, provide \r\nmoisture, destabilize the passing air column and produce convective \r\nsystems. The increase in friction when moving air reaches land causes \r\nconvergence and uplift, and may trigger precipitation. Gula and \r\nPeltier (2012) found that a state-of-the-art GCM does not realistically \r\nsimulate these effects over the North American Great Lakes, but \r\na 10 km RCM better represents them and thereby simulates realistic \r\ndownwind precipitation patterns, in particular enhanced snowfall \r\nduring the winter season. Similar results were found by Wright \r\net  al. (2013), Notaro et  al. (2015) and Lucas-Picher et  al. (2017). \r\nIn a  convection permitting simulation of the Lake Victoria region, \r\na  too strong nocturnal land breeze resulted in unrealistically high \r\nprecipitation (Finney et al., 2019)."


id: 6b921caaa7d55ad61fdcd609dad100e5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16229
content:
"There is high confidence that climate models with sufficiently high \r\nresolution are necessary for realistically simulating lake and coastal \r\nweather including coastal low-level jets, lake and sea breezes, as well \r\nas lake effects on rainfall and snow."


id: 75c7de44af5f6907a71688d518217109
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17953
content:
"Both SRCCL (Jia et al., 2019) and SROCC (Hock et al., 2019) highlight \r\nthe weaknesses of climate models at simulating atmosphere-surface \r\nfeedbacks. The performance at simulating some of these feedbacks \r\nis assessed below (climate feedbacks in urban areas are discussed in \r\nBox 10.3)."


id: 12ecc39f839293a4fb3c75bc00699b40
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 535
content:
"In regions like Fenno-Scandinavia or central-eastern Canada, very large \r\nfractions of land are covered by small and medium sized lakes. Other \r\nregions have fewer but larger lakes, such as central-eastern Africa, \r\nthe eastern border between the USA and Canada, and central Asia. In \r\nthese regions it has been considered essential to include a lake model "


id: fbf321df42efba7084ce0a06f48a78d1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41687
content:
"The influence of ice-sheet mass balance on regional climate, explored \r\nwith global and regional models by (Noel et al., 2018; Fettweis et al., \r\n2020), is discussed in Section 9.4."


id: dfcad4ceea0c2f43559516112402f8e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 36618
content:
"Volcanoes also provide forcings with a  marked regional impact \r\n(Cross-Chapter Box  4.1). This implies that models are expected to \r\ncapture these effects (Bethke et al., 2017). Both proxy analyses and \r\nsimulations have demonstrated reduced Asian monsoon precipitation \r\nafter tropical and Northern Hemisphere (NH) volcanic eruptions due \r\nto reduced humidity and divergent circulation (Man and Zhou, 2014; \r\nZhuo et al., 2014; F. Liu et al., 2016; Stevenson et al., 2016). Global \r\nmodel experiments (Zanchettin et al., 2013; Ortega et al., 2015; Sjolte \r\net al., 2018; Michel et al., 2020) have suggested that tropical volcanic "


id: c02505fe20afc063ce9144afcc2b3cbd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31101
content:
"daytime temperature extremes, few compare modelled results with \r\nobservations. Global model studies have found improvements in \r\nsimulated surface temperature when including irrigation (Thiery \r\net  al., 2017), in particular in areas where the model used has \r\na  strong land-atmosphere coupling (Chen and Dirmeyer, 2019). \r\nAn RCM study over the North China Plain showed that the inclusion \r\nof irrigation led to a better representation of the observed nighttime \r\nwarming (Chen and Jeong, 2018)."


id: 48a80a671a64d8356281a8dac3982882
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10003
content:
"There is medium confidence that representing irrigation is important \r\nfor a realistic simulation of South Asian monsoon precipitation. There \r\nis limited evidence that including irrigation in climate models improves \r\nthe simulation of maximum and minimum daily temperatures as well \r\nas precipitation for other regions."


id: 610b905739a1f243327765e49f400a2
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14367
content:
"Regional land-radiation management, including modifying the \r\nalbedo through, for instance, no-tillage practices, has been suggested \r\nas a  measure to decrease regional maximum daily temperatures \r\n(see  review in Seneviratne et  al., 2018), but although modelled \r\nresults and theoretical understanding are coherent, few studies \r\nhave verified the results with observations. Hirsch et al. (2018) is an \r\nexception, showing that implementing minimal tillage, crop residue \r\nmanagement and crop rotation in a global model over regions where \r\nit is practiced, improves the simulation of surface heat fluxes."


id: 2ea1001ff3133878e4f47c64e7e3978f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 5481
content:
"The performance of statistical downscaling models, bias adjustment \r\nand weather generators is determined by the chosen model structure \r\n(e.g.,  to represent variability and extremes or spatial dependence) \r\nand, if applicable, the predictors selected (Maraun et  al., 2019a). \r\nThe VALUE initiative has assessed a  range of such methods in \r\na perfect-predictor experiment where the predictors are taken from \r\nreanalysis data (Maraun et al., 2015, 2019a; Gutierrez et al., 2019). \r\nTable 10.2 shows an overview comprising performance results from \r\nVALUE and other studies. These results isolate the performance of the \r\nstatistical method in the present climate. The overall performance in \r\na climate change application also depends on the performance of the \r\ndriving climate model (Sections 10.3.3.3-10.3.3.6) and the fitness of \r\nboth the driving model and the statistical method for projecting the \r\nclimatic aspects of interest (Section 10.3.3.9)."


id: 6566a5f4ad60b9e9052d4c0cb6d82b40
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15109
content:
"The inclusion of irrigation in global and regional models over the \r\nSouth Asian monsoon region (Section  10.6.3) has been found to \r\nbe important to represent the monsoon circulation and rainfall \r\ncorrectly (Lucas-Picher et al., 2011; Guimberteau et al., 2012; Shukla \r\net al., 2014; Tuinenburg et al., 2014; Cook et al., 2015a; Devanand \r\net al., 2019). Similarly, the inclusion of irrigation over northern India \r\nand western Pakistan could be important for the correct simulation \r\nof precipitation over the Upper Indus Basin in northern Pakistan \r\n(Saeed et  al., 2013). Irrigation in the East African Sahel inhibits \r\nrainfall over the irrigated region and instead enhances rainfall to the \r\neast, coherent with both observations and theoretical understanding \r\nof the local circulation anomalies induced by the lower surface \r\nair temperatures over the irrigated region (Alter et  al., 2015). \r\nAlthough several studies show how modelled irrigation reduces "


id: ee61cb96116d1fe11856fdc47b80e018
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39312
content:
"Perfect prognosis methods can perform well when the synoptic \r\nforcing (i.e., the explanatory power of large-scale predictors) is strong \r\n(Schoof, 2013). Using this approach, downscaling of precipitation is \r\nparticularly skilful in the presence of strong orographic forcing. The \r\nrepresentation of daily variability and extremes requires analogue \r\nmethods or stochastic regression models, although the former \r\ntypically do not extrapolate to unobserved values (Gutierrez et al., \r\n2019; Hertig et al., 2019). Temporal precipitation variability is well\u0002represented by analogue methods and stochastic regression, but \r\nanalogue methods typically underestimate temporal dependence \r\nof temperature (Maraun et al., 2019b). Spatial dependence of both \r\ntemperature and precipitation is only well-represented by analogue "


id: 8b1db4833abbdcd3a6ed1d6a01474684
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 21638
content:
"Table 10.2 | Performance of different statistical method types in representing local weather at daily resolution. Individual state-of-the-art implementations \r\nmay perform better. '+': should work reasonably well based on empirical evidence and/or expert judgement; 'o': problems may arise depending on the specific context; '-': weak \r\nperformance either by construction or inferred from empirical evidence; '?': not studied. The categorisation assumes that predictors are provided by a well-performing dynamical \r\nmodel. Statements about extremes refer to moderate events occurring at least once every 20 years. Adopted and extended from Maraun and Widmann (2018b)."


id: 77cc83981fb3f96377508c7992fc5716
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18287
content:
"References: (1) Casanueva et al. (2020); (2) Dubrovsky et al. (2019); (3) Evin et al. (2018); (4) Frost et al. (2011); (5) Gutierrez et al. (2013); (6) Gutierrez et al. (2019); \r\n(7) Gutmann et al. (2014); (8) Hertig et al. (2019); (9) Hu et al. (2013a); (10) Huth et al. (2015); (11) Keller et al. (2015); (12) Maraun et al. (2019b); (13) San-Martin et al. (2017); \r\n(14) Widmann et al. (2019)."


id: e8c7969865ca6bf90bb6af4320409671
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38304
content:
"methods, for which analogues are defined jointly across locations, \r\nand by stochastic regression methods explicitly representing spatial \r\ndependence (Widmann et al., 2019). Overall, there is high confidence\r\nthat analogue methods and stochastic regression are able to \r\nrepresent many aspects of daily temperature and variability, but \r\nthe analogue method is inherently limited in representing climate \r\nchange (Gutierrez et al., 2013)."


id: 2b025f7529f37061ec2e0cb133467774
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4800
content:
"This subsection assesses the performance of bias adjustment in \r\na perfect predictor context. In practice, climate model imperfections \r\nmay cause substantial additional issues in the application of \r\nbias adjustment. These are assessed separately in Cross-Chapter \r\nBox 10.2."


id: 38149a12efaafd0e2273764d5cae5533
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7068
content:
"underestimate precipitation trends (CMIP5) (Sections 3.3.1 and 3.3.2). \r\nFor regional trends, AR5 concluded that the CMIP5 ensemble cannot \r\nbe taken as a  reliable representation of reality and that the true \r\nuncertainty can be larger than the simulated model spread (Kirtman \r\net  al., 2014). Case studies of regional trend simulations by global \r\nmodels can be found in Sections 10.4.1 and 10.6, and region-by\u0002region assessments in the Atlas. A  key limitation for assessing the \r\nrepresentation of regional observed trends by single transient \r\nsimulations of global models (or downscaled versions thereof) is the \r\nstrong amplitude of internal variability compared to the forced signal \r\nat the regional scale (Section 10.3.4.3). Even on multi-decadal time \r\nscales, an agreement between observed and individual simulated \r\ntrends would be expected to occur only by chance (Laprise, 2014)."


id: 27502bdfb67215e51b31b1219135e102
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4087
content:
"In the context of downscaling, the ability of downscaling methods to \r\nreproduce observed trends when driven with boundary conditions or \r\npredictors taken from reanalysis data (which reproduce the observed \r\ninternal variability on long time scales) can be assessed. For temperature \r\nin the continental USA, reanalysis-driven RCMs skilfully simulated recent \r\nspring and winter trends, but did not reproduce summer and autumn \r\ntrends, (Bukovsky, 2012). Over Central America, observed warming \r\ntrends were reproduced (Cavazos et al., 2020). In contrast, a reanalysis\u0002driven coupled atmosphere-ocean RCM covering the Mediterranean \r\ncould not reproduce the observed SST trend (Sevault et al., 2014)."


id: 421a31ec0ea79f8da249505a231d489c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 8823
content:
"Several studies show improved simulations of present-day impacts, \r\nwhen the impact model is fed with bias-adjusted climate model \r\noutput, including the assessment of river discharge (Rojas et al., 2011; \r\nMuerth et al., 2013; Montroull et al., 2018), forest fires (Migliavacca \r\net al., 2013), crop production (Ruiz-Ramos et al., 2016), and regional \r\nocean modelling (Macias et al., 2018)."


id: 928757391283fe1082e2d5c24aee5e6e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23002
content:
"Similar studies have been carried out for statistical downscaling \r\nand bias adjustment using predictors from reanalyses (or in case of \r\nbias adjustment, dynamically downscaled reanalyses). For a  range \r\nof different perfect prognosis methods, Huth et  al. (2015) found \r\nthat simulated temperature trends were too strong for winter and \r\ntoo weak for summer. The performance was similar for the different \r\nmethods, indicating the importance of choosing informative predictors. \r\nSimilarly, Maraun et al. (2019b) found that the performance of perfect \r\nprognosis methods depends mostly on the predictor and domain \r\nchoice (for instance, temperature trends were only captured by those \r\nmethods including surface temperature as predictor). Bias adjustment \r\nmethods reproduced the trends of the driving reanalysis, apart from \r\nquantile mapping methods, which deteriorated these trends."


id: 31ac5986c0af36437a18d84eaac0aec0
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 5983
content:
"There is high confidence that bias adjustment can improve the \r\nmarginal distribution of simulated climate variables, if applied to \r\na climate model that adequately represents the processes relevant \r\nfor a given application (Cross-Chapter Box 10.2)."


id: 9e455933cc1bb0e33a2acb1b6fefbc9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25074
content:
"This section assesses how well climate models perform at realistically \r\nsimulating historical regional climatic trends. Current global model \r\nensembles reproduce global to continental-scale surface temperature \r\ntrends at multi-decadal to centennial time scales (CMIP5, CMIP6), but "


id: 90c41062e26cd20322f55a2e866ac560
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 22677
content:
"Overall, there is high confidence that including all relevant forcings is \r\na prerequisite for reproducing historical trends."


id: 9e6ff45b110ff4bcd935d69220b2aab0
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25097
content:
"Including additional components, feedbacks and drivers can \r\nsubstantially modify the simulated future climate. For example, \r\nKjellstrom et  al. (2005) and Somot et  al. (2008) have shown that \r\na  regional ESM can significantly modify the SST response to \r\nclimate change of its driving global model with implications for \r\nthe climate change signal over both the sea and land. In particular, \r\ncoupled ocean-atmosphere RCMs may increase the credibility of \r\nprojections in regions of strong air-sea coupling such as the East \r\nAsia-western North Pacific domain (Zou and Zhou, 2016b, 2017). \r\nRecent studies demonstrate the importance of including regional \r\npatterns of evolving aerosols in RCMs for simulating regional climate \r\nchange (Boe et al., 2020a; Gutierrez et al., 2020). RCMs not including \r\nthe plant physiological response to increasing CO2 concentrations \r\nhave been shown to substantially underestimate projected increases "


id: 1f07a5faffb07f9d54732afdc47dd4e7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 2662
content:
"The evaluation of model performance at historical variability and long\u0002term changes provides further relevant information (Flato et al., 2014). \r\nTrend evaluation may provide very useful insight, but has limitations, \r\nin particular at the regional scale, mainly due to multi-decadal internal \r\nclimate variability (Section  10.3.3.8), observational uncertainty \r\n(in  both driving reanalysis and local trends; Section  10.2), and the \r\nfact that often not all regional forcings are known, and that past \r\ntrends  may be driven by forcings other than those driving future \r\ntrends (Sections 10.4.1 and 10.6.3)."


id: 7c5cda4327931a579df44518313b5267
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19082
content:
"in extreme temperatures across Europe compared to global models \r\nthat explicitly model this effect (Schwingshackl et al., 2019)."


id: ca6400135bc1664d278797f5efb34558
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32948
content:
"Overall, there is high confidence that an assessment of model fitness \r\nfor projections applying process-based evaluation, process-based \r\nplausibility checks of projections and a comparison of different model \r\ntypes, increases the confidence in climate projections. There is high \r\nconfidence that increasing model resolution, dynamical downscaling, \r\nstatistical downscaling with well-simulated predictors controlling \r\nregional climate change, and adding relevant model components can \r\nincrease the fitness for projecting some aspects of regional climate \r\nwhen accompanied by a process-understanding analysis."


id: 2e97adb53f18d8e75453f88b690dc780
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 5476
content:
"A difference between the climate changes simulated by two models \r\ndoes not automatically imply the more complex or higher resolution \r\nmodel is superior (e.g.,  Dosio et  al., 2019). Studies comparing \r\nconvection-permitting RCM simulations to simulations of climate \r\nmodels with parametrized convection find, depending on the \r\nconsidered models, regions and seasons, either similar or qualitatively \r\ndifferent projected changes in short duration extreme precipitation \r\n(Chan et  al., 2014a, b, 2020; Ban et  al., 2015; Tabari et  al., 2016; \r\nFosser et al., 2017; Kendon et al., 2017, 2019; Vanden Broucke et al., \r\n2018). Process studies provide evidence that convection-permitting \r\nsimulations better represent crucial local and mesoscale features of \r\nconvective storms and thus simulate more plausible changes (Meredith \r\net al., 2015a; Prein et al., 2017; Fitzpatrick et al., 2020), but further \r\nresearch is required to confirm and reconcile the different findings."


id: 84e2fffa967d83119a0d20d5c904c046
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20515
content:
"Studies assessing the fitness of statistical approaches for regional \r\nclimate projections are still very limited in number. For statistical \r\ndownscaling, a key issue is to include predictors that control long-term \r\nchanges in regional climate. Models differing only in the choice \r\nof predictors may perform similarly in the present climate, but may \r\nproject opposite precipitation changes (Fu et  al., 2018; Manzanas \r\net al., 2020). In addition to trend-evaluation studies (Section 10.3.3.8), \r\nperfect-model experiments (Section 10.3.2.5) have been used to assess \r\nwhether a  given model structure with a  chosen set of predictors is \r\ncapable of reproducing the simulated future climates (Gutierrez et al., \r\n2013; Raty et al., 2014; Dayon et al., 2015; Dixon et al., 2016; San-Martin \r\net al., 2017). Importantly, it is found that standard analogue methods \r\ninherently underestimate future warming trends because of missing \r\nanalogues for a warmer climate (Gutierrez et al., 2013). "


id: 42f9cca273074cb0e8bd1ab6527b47ae
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 8985
content:
"The special class of high-resolution global models (Sections 1.5.3.1 \r\nand 10.3.3.1, Chapter  3; Haarsma et  al., 2016; Prodhomme et  al., \r\n2016) is expected to improve some of the regional processes \r\nthat are not appropriately represented in standard global models \r\n(Roberts et  al., 2018). There is general consensus that increasing \r\nglobal model resolution improves some long-standing biases \r\n(Chapter  3, Section  10.3.3.3, and Figures 10.6 and 10.7; Demory \r\net al., 2014, 2020; Schiemann et al., 2014; Dawson and Palmer, 2015; "


id: 7868a219cf5c31ed170a42bd8635f091
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18390
content:
"In any case, the fitness of regional climate projections based on \r\ndynamical downscaling or statistical approaches depends on the fitness \r\nof the driving models in projecting boundary conditions, predictors and \r\nchange factors (Hall, 2014; Maraun and Widmann, 2018b)."


id: e7d3e05913ec7302a6a4a9cbecf47718
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38093
content:
"Despite their known errors that affect model performance, there \r\nis high confidence that global models provide useful information \r\nfor the production of regional climate information. There is robust \r\nevidence and high agreement that the increase of global model \r\nresolution helps in reducing the biases limiting performance at the \r\nregional scale, although resolution per se does not automatically \r\nsolve all performance limitations shown by global models. There is \r\nrobust evidence that stochastic parametrizations can help to improve \r\nsome aspects of the global model performance that are relevant to \r\nregional climate information."


id: 6cfcfdaa88c75b5cbe25d709302ef692
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16456
content:
"Global models tend to have difficulties in simulating climate over \r\nregions where unresolved local scale processes, feedbacks and \r\nnon-linear scale interactions result in a  degradation of the model \r\nperformance compared to models with higher resolution. In this case, \r\nRCMs and variable resolution global models can resolve part of these \r\nprocesses in the regions of interest at an acceptable computational \r\ncost (Rummukainen, 2016; Giorgi, 2019; Gutowski Jr. et al., 2020)."


id: 1c6eb7226f13b40399563ecb3a40211c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 2191
content:
"Overall, statistical downscaling methods with carefully chosen \r\npredictors and an appropriate model structure for a given application \r\nrealistically represent many statistical aspects of present-day daily \r\ntemperature and precipitation (high confidence, Section  10.3.3.7). \r\nBias adjustment has proven beneficial as an interface between \r\nclimate model projections and impact modelling in many different \r\ncontexts (high confidence) (Section  10.3.3.7). Weather generators \r\nrealistically simulate many statistical aspects of present-day daily \r\ntemperature and precipitation (high confidence) (Section 10.3.3.7). \r\nThe performance of these approaches and their fitness for future \r\nprojections also depends on predictors and change factors taken from \r\nthe driving dynamical models (high confidence) (Section 10.3.3.9)."


id: 50ee00d31dfaecebf281f242f2fed762
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 11519
content:
"The assessment of RCM performance needs to focus not only on \r\nmean climatology (Atlas), but also trends (Section  10.3.3.8) and \r\nextremes (Chapter 11), and the RCM's ability at correctly reproducing \r\nrelevant processes, forcings and feedbacks including aerosols, plant \r\nresponses to increasing CO2, and so on, (Schwingshackl et al., 2019; \r\nBoe et al., 2020a; Sections 11.2. and 10.3.3.3 to 10.3.3.8) to be fit for \r\nfuture projections (Section 10.3.3.9)."


id: 271c6da33dd023f013280879be360c7b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4044
content:
"When RCMs are driven by global models, part of the uncertainty \r\nin the RCM simulation is introduced by the global model biases \r\n(Kjellstrom et  al., 2018; Sorland et  al., 2018; Christensen and \r\nKjellstrom, 2020). As RCMs are typically not able to mitigate global \r\nmodel biases in large-scale dynamical processes, if such biases \r\nare substantial, and if the corresponding large-scale processes are \r\nimportant drivers of regional climate, downscaling is questionable \r\n(Section  10.3.3.3). However, when global models have weak \r\ncirculation biases and regional climate change is controlled mainly \r\nby regional-scale processes and feedbacks, dynamical downscaling \r\nhas the potential to add substantial value to global model \r\nsimulations (Section 10.3.3.4 and Atlas; Hall, 2014; Rummukainen, \r\n2016; Giorgi, 2019; Schwingshackl et al., 2019; Boe et al., 2020a; \r\nLloyd et al., 2021)."


id: 86a827f37ec6622fb701c3c75fcbe0dc
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20854
content:
"Regional climate projections are affected by three main sources \r\nof uncertainty (Sections 10.2.2, 1.4.3 and 4.2.5): unknown future \r\nexternal forcings, imperfect knowledge and implementation of the \r\nresponse of the climate system to external forcings, and internal \r\nvariability (Lehner et al., 2020). In a regional downscaling context, \r\nuncertainties arise in every step of the modelling chain. Here the \r\npropagation of uncertainties (Section 10.3.4.1), the management of "


id: c5d7dfa90b85fe20e06ec415bf01a77d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32113
content:
"uncertainties (Section  10.3.4.2), the role of the internal variability \r\nfor regional projections (Section 10.3.4.3), and the design and use \r\nof ensembles to account for uncertainties (Section 10.3.4.4) will be \r\nassessed. Observational uncertainty, in particular for the calibration of \r\nstatistical downscaling methods (Section 10.2.3.1), also contributes \r\nto projection uncertainty."


id: 70210490c081b5615c8aca73aa2efc3f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16978
content:
"some regional future climate changes are not well-characterized by \r\nmulti-model mean and spread."


id: 6f7ade5a0b04f57caa5ae679fadf82ff
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16879
content:
"These physical climate storylines help to physically explain contradicting \r\nregional projections and thus make the conveyed information a better \r\nrepresentation of the true uncertainty (Hewitson et al., 2014a). \r\nAdditionally, the attribution of regional uncertainties to drivers may in \r\nprinciple help reduce uncertainty in the case where some storylines can \r\nbe ruled out because the projected changes in the driving processes \r\nappear to be physically implausible (Zappa and Shepherd, 2017). There \r\nis thus high confidence that storylines attributing uncertainties in \r\nregional projections to uncertainties in changes of remote drivers aid \r\nthe interpretation of uncertainties in climate projections."


id: c0ddbf04aa4815795bb6a3b55c63a8dc
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31229
content:
"Uncertainty propagation often increases the spread in regional climate \r\nprojections when comparing global model and downscaled results, \r\nwhich has been used as an argument against top-down approaches \r\nto climate information (Prudhomme et al., 2010). Increased spread \r\nin the modelling chain may also arise from a more comprehensive \r\nrepresentation of previously unknown or underrepresented \r\nuncertainties (Maraun and Widmann, 2018b). The increased spread \r\nin this case goes together with a better representation of processes \r\nand thus an increased model fitness-for-purpose (Section 10.3.3.9)."


id: 1e8bfeaf768b2b418d506923c693fc33
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 2578
content:
"Another approach that has continued to develop for characterising \r\nand reducing projection uncertainties is the use of emergent \r\nconstraints (Chapters 1, 4, 5 and 7; Hall et  al., 2019). The idea is \r\nto link the spread in climate model projections via regression to \r\nthe spread in present climate model biases for relevant driving \r\nprocesses. Models with lower biases are assigned higher weight in \r\nthe projections, which in turn reduces the spread of the projections \r\nin a physical way and may additionally reduce projection uncertainty. \r\nFor instance, Simpson et  al. (2016) have reduced the spread in \r\nprojections of North American winter hydroclimate by linking this \r\nspread to model biases in the representation of relevant stationary \r\nwave patterns. Other examples of using emergent constraints in \r\na regional context are Brown et al. (2016), G. Li et al. (2017), Giannini \r\nand Kaplan (2019), Ose (2019) and Zhou et al. (2019)."


id: 3de14ac74c3b743f60a5b24d2e37e7f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 8052
content:
"A regional climate projection based on a  single simulation from \r\na single global model or driving a single RCM alone will inevitably \r\nbe affected by not considering the internal variability (Figure 10.10). \r\nThis is mainly due to the dominant influence of the chaotic atmospheric "


id: 2828cdd43a487c8a25788c116510f86b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4265
content:
"external forcing estimates (Chapters 2, 6 and 7) and the limitations \r\nof assumptions underlying the statistical methods used to derive \r\nobservational large ensembles (McKinnon et al., 2017; McKinnon and \r\nDeser, 2018; Castruccio et al., 2019). Calibration methods inspired by "


id: be4d6fb7767c9d288fbda407d5ee9201
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 30748
content:
"circulation on regional climate variability, in particular at mid- to high \r\nlatitudes. Internal variability is an irreducible source of uncertainty \r\nfor mid- to long-term projections with an amplitude that typically \r\ndecreases with increasing spatial scale and lead time (Sections 1.4.3 \r\nand 4.2.1). However, regional-scale studies show that both large\u0002and local-scale internal variability together can still represent \r\na substantial fraction of the total uncertainty related to hydrological \r\ncycle variables, even at the end of the 21st century (Lafaysse et al., \r\n2014; Vidal et al., 2016; Aalbers et al., 2018; Gu et al., 2018)."


id: 5477225b78cfc0ad7213636602d09b1e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12130
content:
"The appropriate ensemble size for a robust use of SMILEs depends on \r\nthe model and physical variable being investigated, the spatial and \r\ntime aggregation being performed, the magnitude of the acceptable \r\nerror and the type of questions one seeks to answer (Deser et al., 2012, \r\n2017b; Kang et al., 2013; Wettstein and Deser, 2014; Dai and Bloecker, \r\n2019; Maher et al., 2019). It is noteworthy that the recent development \r\nof ensembles with a very large ensemble size (greater than 100) have \r\nled to new insights and methodologies to robustly assess the required \r\nensemble size for questions such as the estimation of the forced response \r\nto external forcing or a forced change in modes of internal variability, \r\nsuch as ENSO, and its associated teleconnections (Herein et al., 2017; \r\nMaher et al., 2018; Haszpra et al., 2020; Milinski et al., 2020)."


id: a4ff727937ae88709d581f4b62f66782
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 26250
content:
"The use of SMILEs assumes that they have a realistic representation \r\nof internal variability and its evolution under anthropogenic climate \r\nchange (Eade et  al., 2014; McKinnon et  al., 2017; McKinnon and \r\nDeser, 2018; Chen and Brissette, 2019). Assessing the realism of \r\nsimulated internal variability for past and current climates remains \r\nan active research field with a  number of issues such as the \r\nshortness and uncertainties of the observed record, in particular in \r\ndata-scarce regions (Section  10.2.2.3), the signal-to-noise paradox \r\n(Section 4.4.3.1; Scaife and Smith, 2018), uncertainty in past observed "


id: f45714b49c07a91844aef3bea4174477
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 40363
content:
"weather and seasonal forecasts can be used to improve the reliability \r\nof regional-scale climate projections from large ensembles (Brunner \r\net al., 2019; O'Reilly et al., 2020). Interestingly, reliability is improved \r\nwhen the calibration is performed separately for the dynamical and \r\nresidual components of the ensemble resulting from dynamical \r\nadjustment (Section 10.4.1; O'Reilly et al., 2020)."


id: c920778e7ebbc0f9ccea38d9a6418bd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32709
content:
"Importantly, accurately partitioning uncertainty in regional climate \r\nprojections can provide an incentive for immediate action, accepting \r\na  large range of possible outcomes due to internal variability, \r\nwhile confounding model uncertainty with internal variability may \r\nbe understood as a lack of knowledge and lead to delayed action \r\nin adaptation decision-making (Section  10.5.3; Maraun, 2013b; \r\nMankin et al., 2020)."


id: 57748b08af1ca49e6211647b7c7484e1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12650
content:
"There is high confidence that the availability of SMILEs allows a robust \r\nassessment of the relative contributions of model uncertainty and \r\ninternal variability in regional-scale projection uncertainty. There is \r\nhigh confidence that the use of SMILEs with appropriate ensemble \r\nsize leads to an improved estimate of regional-scale forced response \r\nto an external forcing as well as of the full spectrum of possible \r\nchanges associated with internal variability. There is high confidence \r\nthat these improved estimates are beneficial for characterizing the \r\nfull distribution of outcomes that is a  key ingredient of climate \r\ninformation for robust decision-making and risk-analysis frameworks."


id: 55c0ecf44ddaaa3cf86ae26bddeb3a24
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12359
content:
"10.3.4.4 Designing and Using Ensembles for Regional Climate \r\nChange Assessments to Take Uncertainty Into Account"


id: 1da17a4de2da92c5a93bba4f7b535b32
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 2396
content:
"An important consideration is which set of global models should be \r\nused for global model-RCM combinations. If adequate resources \r\nexist, then large numbers of global model-RCM combinations are \r\npossible (Deque et  al., 2012; Coppola et  al., 2021; Vautard et  al., \r\n2021). However, coordinated downscaling programmes can be limited \r\nby the human and computational resources available, for producing \r\nensembles of downscaled output, which limits the number of feasible \r\nglobal model-RCM combinations. With this limitation in mind, a small \r\nset of GCMs may be chosen that span the range of equilibrium climate \r\nsensitivity in available global models (e.g., Mearns et al., 2012, 2013; \r\nInatsu et al., 2015), though this range may be inconsistent with the "


id: e7fc43dbdb26fffa4745ec6a1c239b4e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38126
content:
"There is high confidence that ensembles for regional climate projections \r\nshould be selected such that models unrealistically simulating processes \r\nrelevant for a given application are discarded, but at the same time, the \r\nchosen ensemble spans an appropriate range of projection uncertainties."


id: 2509bdc85732289cd6375f72df67b60e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3712
content:
"Bias adjustment is an approach to post-process climate model output and has become widely used in climate hazard and impact studies \r\n(Gangopadhyay et  al., 2011; Hagemann et  al., 2013; Warszawski et  al., 2014) and national assessment reports (Cayan et  al., 2013; \r\nGeorgakakos et al., 2014). Despite its wide use, bias adjustment was not assessed in AR5 (Flato et al., 2014). Several problems have been \r\nidentified that may arise from an uncritical use of bias adjustment, and that may result in misleading impact assessments. The rationale of \r\nthis Cross-Chapter Box is to provide an overview of the use of bias adjustment in this Report, and to assess key limitations of the approach."


id: a9913a8f5b9cb21d5d3bc0ed613e8ca6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27054
content:
"Bias-adjusted climate model output is used extensively throughout this Report. Several results from Chapter 8, and many of the \r\nclimatic impact-drivers in Chapter 12 (Section 12.2) are based on bias adjustment. The Atlas presents many results both as raw and \r\nbias-adjusted data (Atlas.1.4.5). The application of bias adjustment in the WGI report was informed by the assessment in Chapter 10 \r\nand this Cross-Chapter Box. Finally, bias adjustment is crucial for many studies assessed in the WGII report. An overview of bias \r\nadjustment can be found in Section 10.3.1.3, a general performance assessment of individual method classes in Section 10.3.3.7. \r\nThe fitness of bias adjustment for climate change applications is assessed in Section 10.3.3.9."


id: ef56e63096db7931593dd3764d78106d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39475
content:
"An argument made for the use of bias adjustment is the fact that impact models are commonly very sensitive, often non-linearly, to \r\nthe input climatic variables and their biases, in particular when threshold-based climate indices are required (Dosio, 2016). There are, \r\nhowever, cases where bias adjustment may not be necessary or useful, such as: when only qualitative statements are required; when \r\nonly changes in mean climate are considered (instead of absolute values); when percentile-based indices are used."


id: d4e3806b1a6e491172652d083f822f75
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 34770
content:
"There is medium confidence that the selection of climate models with low biases in the frequency, persistence and location of \r\nlarge-scale atmospheric circulation can reduce negative impacts of bias adjustment."


id: 7cb27c19bbdc5d1e3e272fdb635857a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19134
content:
"Using bias adjustment for statistical downscaling\r\nBias adjustment is often used to downscale climate model results from grid box data to finer resolution or point scale. It is sometimes \r\neven directly applied to coarse-resolution global model output to avoid an intermediate dynamical downscaling step (Johnson and\r\nSharma, 2012; Stoner et al., 2013). But bias adjustment does not add any information about the processes acting on unresolved scales \r\nand is therefore by construction not capable of bridging substantial scale gaps (Maraun, 2013a; Maraun et al., 2017). Using bias"


id: 107ad8c2160fb1a2acbcccc3693f0524
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 85
content:
"Overall, there is high confidence that the use of bias adjustment for statistical downscaling, in particular to downscale coarse resolution \r\nglobal models, has severe limitations."


id: 60a1502ec7ef4763cc1ef926d60bff2
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14298
content:
"Bias adjustment in the presence of observational uncertainty and internal variability\r\nObservational uncertainties and internal variability introduce uncertainty in the estimation of biases and thus in the calibration of \r\nbias-adjustment methods. Dobor and Hlasny (2019) found a considerable influence of the choice of the observational dataset and \r\ncalibration period on the adjustment for some regions. RCM biases are typically larger than observational uncertainties, but in some \r\nregions, and in particular for wet-day frequencies, spatial patterns and the intensity distribution of daily precipitation, the situation \r\nmay reverse (Kotlarski et al., 2019). Switanek et al. (2017) found a strong influence of internal variability and thus of the choice of \r\ncalibration period on the calibration of quantile mapping and on the modification of the climate change signal."


id: 71f7b97f769482bc6d5e831547cebedd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17301
content:
"Bias adjustment is typically evaluated using cross-validation, that is, by calibrating the adjustment function to one period of the observational \r\nrecord, and by evaluating it on a different one. Maraun et al. (2017) and Maraun and Widmann (2018a) demonstrated that, in the presence \r\nof multi-decadal internal variability, cross-validation may lead to a rejection of a valid bias adjustment or even lead to a positive evaluation \r\nof an invalid adjustment. The authors therefore argued that, in the presence of substantial internal variability, the evaluation of bias \r\nadjustment requires to consider aspects that have not been adjusted, such as temporal, spatial, or multivariable dependence."


id: c28333ee754fccc3568e808ef3d6ba89
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31509
content:
"There is high confidence that observational uncertainty and internal variability adversely affect bias adjustment and introduce \r\nuncertainties in bias-adjusted future projections."


id: ba6b29efa87720be0c7c47dca1d73715
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 30017
content:
"In this chapter, regional-scale attribution is defined as the process \r\nof evaluating the relative contributions of multiple causal factors \r\n(or drivers) to regional climate change (Cross-Working Group Box: \r\nAttribution in Chapter 1; Rosenzweig and Neofotis, 2013; Shepherd, \r\n2019). Attribution at regional scale builds upon the usual definition of \r\nattribution used in the AR5 (Cross-Working Group Box: Attribution in \r\nChapter 1; Hegerl et al., 2010). However, in contrast with global-scale \r\nattribution methods where internal variability might be considered as \r\na noise problem (Section 3.2), the preliminary detection step is not \r\nalways required to perform regional-scale attribution since causal "


id: fff9f126d2847c79c5bd86170afc8c6a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 42428
content:
"This section focuses on the assessment of the methodologies used to \r\nidentify the physical causes of past and future regional climate change \r\nin the context of the ongoing anthropogenic influence on the global \r\nclimate. The main foci are the attribution of past regional-scale changes \r\n(Sections 10.4.1-2) and the robustness and future emergence of the \r\nregional-scale response to anthropogenic forcing (Section 10.4.3)."


id: 26c3612585a6742945ebc20ffc9a22f7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3973
content:
"factors of regional climate change may also include internal modes of \r\nvariability in addition to external natural and anthropogenic forcing. \r\nImportantly, regional-scale (or process-based) attribution also seeks \r\nto determine the physical processes and uncertainties involved in the \r\ncausal factor's influence (Cross-Working Group Box: Attribution in \r\nChapter 1)."


id: 8ee31d31106c71f2f0921950b4d5432
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 22324
content:
"anthropogenic-induced warming while the aerosol forcing leads to \r\na cooling offsetting a fraction of the GHG change (C. Li et al., 2016, \r\n2017). While the influence of external natural forcing can often be \r\ndetected as well, its contribution to observed changes is usually much \r\nsmaller (C. Li et al., 2017; Wan et al., 2019). Temperature detection \r\nresults are found to be robust to the use of different observational \r\ndatasets and detection methodologies (Dileepkumar et al., 2018)."


id: e849233aa16caf04f5cc5c266e6c8c14
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38200
content:
"Section  10.4.1 describes regional-scale attribution methodologies \r\nand assesses their application to regional changes of temperature \r\nand precipitation. Section 10.4.2 presents three illustrative attribution \r\nexamples that illustrate a number of specific regional-scale challenges \r\nand methodological aspects. Section 10.4.3 focuses on methodologies \r\nused to assess the robustness and emergence of the regional climate \r\nresponse to anthropogenic forcing. A  basic description of future \r\nregional climate change for all regions considered in the report \r\n(as defined in Section 1.4.5) appears in the Atlas."


id: f9e837c1d652f87e643a2e8351bcc254
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41331
content:
"It is noteworthy that these methods require a very significant reduction \r\nof spatial and temporal dimensions in order to reliably estimate the \r\nco-variance matrix of internal variability (an entire region is thus often \r\nconsidered as being only one or a few spatial points that represent \r\nthe spatial average of the whole region or a few sub-regions; time \r\nsamples are often 5- or 10-year averages). Finally, model bias is rarely \r\nconsidered in statistical models used in detection and attribution \r\nregional studies, while it has been shown to have a strong impact \r\non the stability of detection results and their associated confidence \r\nintervals when increasing the spatial dimension (Ribes and Terray, \r\n2013). New statistical methods are emerging to provide some \r\nalternative to standard optimal fingerprinting but they have not yet \r\nbeen evaluated and applied at regional scales (Section 3.2.2)."


id: 550c407bcd1feb928a8139891df6b818
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12236
content:
"Optimal fingerprint regression-based methods have been applied to \r\ndetection and attribution of mean temperature anthropogenic signal \r\nin several regions of the world such as Canada, India, central Asia, \r\nnorthern and western China, Australia, and North Africa (Xu et al., \r\n2015; C. Li et al., 2017; Dileepkumar et al., 2018; Y. Wang et al., 2018; \r\nPeng et al., 2019; Wan et al., 2019). The influence of anthropogenic \r\nforcing, and in particular that of greenhouse gases (GHGs), is \r\nrobustly detected in annual and seasonal mean temperatures for \r\nall considered regions. Most of the observed regional temperature \r\nchanges since the mid-twentieth century can only be explained by \r\nexternal forcings, with anthropogenic influence being the dominant \r\nfactor. GHG increase is found to be the primary factor of the "


id: 95996ebdd7679c620390166c6b0a2bb5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23517
content:
"10.4.1.2 Other Spatiotemporal Statistical Methods for Isolating \r\nRegional Climate Responses to External Forcing"


id: afce4db3137c56674acb6602c40647f1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 28167
content:
"The primary objective of any attribution method is to optimally \r\nseparate the influences of external forcing and internal variability \r\non a global or regional climate record. In a multi-model ensemble \r\ncontext, the estimation of the externally-forced climate response has \r\nbeen typically performed by ensemble averaging of linear trends or "


id: ada3b8dbca4834d0946912c713fc5ee3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27789
content:
"regional domain spatial average, thus not taking into account the \r\navailable and complete space and time co-variance information. \r\nSince AR5, methods using spatiotemporal information have been \r\nfurther developed and used to improve the separation between \r\nexternal and internal drivers in multiple or single historical climate \r\nrealizations performed by a given global model."


id: 590dba02cbdadd26f5cc13d9d483ad05
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12910
content:
"The ensemble empirical mode decomposition method (Wu and \r\nHuang, 2009; Wilcox et  al., 2013; Ji et  al., 2014; Qian and Zhou, \r\n2014) decomposes data, such as time series of historical temperature \r\nand precipitation, into independent oscillatory modes of decreasing \r\nfrequency. The last step of the method leaves behind a  smooth \r\nand low-frequency residual time series. Typically, the non-linear \r\nanthropogenic trend (e.g.,  of 20th-century temperature) can be \r\nreconstructed by summing the long-term mean, the residual, and \r\neventually the lowest-frequency mode to account for a multi-decadal \r\nforced signal, for instance associated with anthropogenic aerosol \r\nforcing. The ensemble empirical mode decomposition method is an \r\nexample of a data-driven, non-parametric approach that can be used \r\nto directly provide an estimate of the forced response without the \r\nneed for model data (Qian, 2016)."


id: 2f95c57d15075ec067a3b265534069fa
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 5635
content:
"Dynamical adjustment methods have been used by, for instance, \r\nDeser et al. (2016), Saffioti et al. (2016), O'Reilly et al. (2017), Gong \r\net al. (2019), and R. Guo et al. (2019). Deser et al. (2016) focused on \r\nthe causes of observed and simulated multi-decadal trends in North \r\nAmerican temperature. They demonstrated that the main advantage \r\nof this technique is to narrow the spread of temperature trends \r\nfound by the model ensemble and to bring the dynamically-adjusted \r\nobservational trend much closer to the forced response estimated \r\nby the model ensemble mean. Similar results were obtained by \r\nSaffioti et al. (2016) regarding recent observed winter temperature "


id: 4412785715ae52b1122f304f6a9e0cdf
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9212
content:
"The univariate detection method does not use spatial pattern \r\ninformation, but compares observed trends in gridded datasets \r\nwith distributions of trends from ensembles of simulations during \r\nthe historical period (Knutson et al., 2013; Knutson and Zeng, 2018). \r\nThe trends arising from simulations constrained by natural forcing-only "


id: b6a8eaca910eb6ddbadca3d919f8e887
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 29338
content:
"simulate internal variability modes that are known to be important \r\ndrivers of regional climate change (Sections 3.7 and 10.3.3.3 \r\nand Annexes IV.2 and IV.3). Models are likely to have different \r\nperformance in different regions and therefore their evaluation \r\nneeds to be performed in terms of key physical processes and \r\nmechanisms relevant to the climate of the region under consideration \r\n(Section 10.3.3)."


id: 8d621d30cd993fe3cfa97e7504f1e60b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 22057
content:
"To conclude, there is very high confidence (robust evidence and high \r\nagreement) that the use of diverse and independent attribution \r\nmethods, multiple model ensemble types and observed datasets \r\nstrengthens the robustness of results of regional-scale attribution \r\nstudies. Since AR5, multiple SMILEs have provided an adequate \r\ntestbed for new attribution methodologies aimed at separating \r\nforced signals from internal variability in observational records as \r\nwell as small-size single-model ensembles."


id: bfced59f72e7a7ee166e46e781082027
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31026
content:
"This section focuses on three illustrative examples that span \r\ndifferent regions, time scales, and attribution methods, without \r\naiming at being comprehensive. These examples illustrate attribution \r\nstatements that are based upon multiple lines of evidence, combining \r\nmultiple observational datasets, different generations and types of \r\nmodels, process understanding and assessment of various sources \r\nof uncertainty. Detection and attribution assessments for all AR6 \r\nregions and specific variables can be found in the Atlas."


id: dc072a990f372a5791346384acfc3e5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35988
content:
"New methods aiming to remove underlying model biases before \r\nperforming detection and attribution, for instance related to \r\nprecipitation changes, are emerging based on image transformation \r\ntechniques such as warping (Levy et al., 2014a). By correcting location \r\nand seasonal precipitation biases in CMIP5 models, Levy et al. (2014b) \r\nshowed that the agreement between observed and fingerprint \r\npatterns can be improved, further enhancing the ability to attribute \r\nobserved precipitation changes to external forcings. The improvement \r\nmainly relies on the assumption that precipitation changes are tied to \r\nthe underlying climatology, which has been shown to be a reasonable \r\nassumption in regions of the world where intensification of the \r\nhydrological cycle is expected (Held and Soden, 2006)."


id: e1268b719c36b0be6f0521665103776a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 36878
content:
"The role of SST forcing in the rainfall decline is assessed first. Competing \r\nmechanisms from equatorial Atlantic SSTs and inter-hemispheric \r\nSST gradients regulate decadal variability in the Sahel (Nicholson, \r\n2013), alternatively explained by tropical warming leading to "


id: c97bb26826a218ac6b3aaa49592c4f58
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32769
content:
"Importantly, evidence that the models employed in regional-scale \r\nattribution are fit for purpose is essential in order to estimate the \r\ndegree of confidence in the attribution results (Section 10.3.3). For \r\nexample, models need to be evaluated and assessed in their ability to "


id: 83e918316df9f1a5642bfa788359ada2
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20356
content:
"which they are not, suggests that the underlying ocean surface might \r\nbe essential in driving the decline (Figure 10.11e)."


id: bbaba184e98ea927eda624635aab0366
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 30242
content:
"period in order to sample the uncertainty in CMIP5 aerosol radiative \r\nforcing, Shonk et al. (2020) found differences of 0.5 mm day-1 for Gulf \r\nof Guinea rainfall between strong and weak aerosol experiments \r\nas illustrated in Figure 10.11c, although the drying appears further \r\nsouth than observed due to model bias."


id: 75229e3544629df62f3c4abd0c9d3c40
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17844
content:
"mechanisms governing such a change have been shown to be largely \r\ndynamical in nature (D'Agostino et  al., 2019), suggesting that the \r\nmid-Holocene cannot be used to inform the credibility of changes \r\ndue to greenhouse warming."


id: cfd9000cffb04735f35081378c183981
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 33926
content:
"There is very high confidence (robust evidence and high agreement) \r\nthat patterns of 20th-century ocean and land surface temperature \r\nvariability have caused the Sahel drought and subsequent recovery \r\nby adjusting meridional gradients. There is high confidence (robust \r\nevidence and medium agreement) that the changing temperature \r\ngradients that perturb the West African monsoon and Sahel rainfall \r\nare themselves driven by anthropogenic emissions: warming by \r\nGHG emissions was initially restricted to the tropics but suppressed \r\nin the North Atlantic due to nearby emissions of sulphate aerosols, \r\nleading to a  reduction in rainfall. The North Atlantic subsequently \r\nwarmed following the reduction of aerosol emissions, leading to \r\nrainfall recovery."


id: d8db2c23655f433117e9ad3cf6b95e43
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35443
content:
"The influence of SST anomalies on south-eastern South America \r\nprecipitation have been studied extensively on interannual to \r\nmulti-decadal time scales (Paegle and Mo, 2002). The positive phase \r\nof El Nino-Southern Oscillation (ENSO; Annex IV.2.3) is related \r\nto stronger mean and extreme rainfall over south-eastern South \r\nAmerica (Ropelewski and Halpert, 1987; Grimm and Tedeschi, 2009; \r\nRobledo et al., 2016). The ENSO influence may be modulated by the \r\nPDV (Kayano and Andreoli, 2007; Fernandes and Rodrigues, 2018) \r\nand the AMV (Kayano and Capistrano, 2014). PDV and AMV also \r\ninfluence the south-eastern South American climate independently \r\nof ENSO (Barreiro et al., 2014; Grimm and Saboia, 2015; Robledo \r\net  al., 2020). While Pacific SSTs dominate the overall influence of \r\noceanic variability in the region, the Atlantic variability seems \r\nto dominate on multi-decadal time scales and has been proposed \r\nas a  driver for the long-term positive trend (Seager et  al., 2010; "


id: b84d570d9a98168bd94e6c72e0a9fbd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 29637
content:
"Barreiro et al., 2014). Based on experiments designed to test how \r\nsouth-eastern South America precipitation is modulated by tropical \r\nAtlantic SSTs, Seager et  al. (2010) showed that cold anomalies \r\nin the tropical Atlantic favour wetter conditions by inducing an \r\nupper-tropospheric flow towards the equator, which, via advection \r\nof vorticity, leads to ascending motion over south-eastern South \r\nAmerica (Figure  10.12a). Monerie et  al. (2019) supported this \r\nargument showing a negative relationship between south-eastern \r\nSouth America precipitation and the AMV index (Huang et  al., \r\n2015) using an AGCM coupled to an ocean mixed-layer model with \r\nnudged SSTs."


id: 3c151a085e28efa03f686ed80b4cb127
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7761
content:
"There is high confidence that South-Eastern South America summer \r\nprecipitation has increased since the beginning of the 20th century. \r\nSince AR5, science has advanced in the identification of the drivers of \r\nthe precipitation increase in South-Eastern South America since 1950, \r\nincluding GHG through various mechanisms, stratospheric ozone \r\ndepletion and Pacific and Atlantic variability. There is high confidence\r\nthat anthropogenic forcing has contributed to the South-Eastern \r\nSouth America summer precipitation increase since 1950, but very \r\nlow confidence on the relative contribution of each driver to the \r\nprecipitation increase."


id: 73ef931ac8ae81dab22bae295b362a09
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17643
content:
"the south-west (Delworth et al., 2015; Seager and Ting, 2017). The \r\nmulti-year drought of 2012-2016 has been linked to the multi-year \r\npersistence of anomalously high atmospheric pressure over the \r\nnorth-eastern Pacific Ocean, which deflected the Pacific storm track \r\nnorthward and suppressed regional precipitation during California's \r\nrainy season (Swain et al., 2017). Going into more detail, Prein et al. \r\n(2016a) used an assessment of changing occurrence of weather \r\nregimes to judge that changes in the frequency of certain regimes \r\nduring 1979-2014 have led to a decline in precipitation by about \r\n25%, chiefly related to the prevalence of anticyclonic circulation \r\npatterns in  the north-east Pacific. Finally, the moderate model \r\nperformance in representing Pacific SST decadal variability and \r\nits remote influence (Section  3.7.6) as well as its change under \r\nwarming may affect attribution results of observed and future \r\nprecipitation changes (Seager et al., 2019)."


id: 5a5464f51ecf58f9d21508f300bc0425
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13145
content:
"Importantly, as the AR6 assessment views the PDV as being mostly \r\ndriven by internal variability (Section 3.7.6), the lines of evidence cited \r\nabove suggest that the contribution of natural and anthropogenic \r\nforcings to the precipitation decline has a  small amplitude. Unlike \r\nthe precipitation deficit, the accompanying south-western North \r\nAmerica warming is driven primarily by anthropogenic forcing from \r\nGHGs rather than atmospheric circulation variability and may help to \r\nenhance the drought through increased evapotranspiration (Knutson \r\net  al., 2013; Diffenbaugh et  al., 2015; Williams et  al., 2015, 2020; \r\nLehner et al., 2018, 2020)."


id: 12cb9b8e238614043ec583b6c58a12c3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 512
content:
"A second possible causal factor is the role for ocean-forced or \r\ninternal atmospheric circulation change. Analysis of observed \r\nand CMIP5-simulated precipitation indicates that the drought \r\nprevalence since 1980 is linked to natural, internal variability in the \r\nclimate system (Knutson and Zeng, 2018). Based on observations \r\nand ensembles of SST-driven atmospheric simulations, Seager \r\nand Hoerling (2014) suggested that robust tropical Pacific and \r\ntropical North Atlantic forcing drove an important fraction of \r\nannual mean precipitation and soil moisture changes and that \r\nearly 21st  century multi-year droughts could be attributed to \r\nnatural decadal swings in tropical Pacific and North Atlantic SSTs. \r\nA cold state of the tropical Pacific would lead by well-established \r\natmospheric teleconnections to anomalous high pressure across \r\nthe North Pacific and southern North America, favouring a weaker \r\njet stream and a  diversion of the Pacific storm track away from "


id: 65460385dc2e25066836abb2a0b71137
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15095
content:
"To conclude, there is high confidence (robust evidence and medium \r\nagreement) that most (>50%) of the anomalous atmospheric \r\ncirculation that caused the south-western North America negative \r\nprecipitation trend can be attributed to teleconnections arising from \r\ntropical Pacific SST variations related to PDV. There is high confidence\r\n(robust evidence and medium agreement) that anthropogenic \r\nforcing has made a  substantial contribution (about 50%) to the \r\nsouth-western North America warming since 1980."


id: b6b028f7a49121649d686b0c9d9f719
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 29343
content:
"change has not yet fully occurred for many land regions \r\n(Section 10.4.3). Although the contribution of anthropogenic forcing \r\nto long-term regional precipitation change has been detected in \r\nsome regions, a robust quantification of the contributions of different \r\ndrivers remains elusive. The delayed emergence of the anthropogenic \r\nprecipitation fingerprint with respect to temperature is likely due \r\nto the opposing sign of the fast and slow land precipitation forced "


id: 3452894587a638a9ad7e7a5a78efd59c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 6445
content:
"The robustness of regional-scale attribution differs strongly between \r\ntemperature and precipitation changes. While the influence of \r\nanthropogenic forcing on regional temperature long-term change \r\nhas been detected and attributed in almost all land regions, a robust \r\ndetection and attribution of human influence on regional precipitation "


id: 17e26de85550ba735eb157a95d287342
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 1405
content:
"responses and time-dependent SST change patterns (Sections 8.2.1 \r\nand Section 10.4.3), stronger internal variability (Section 10.3.4.3) as \r\nwell as larger observational uncertainty (Section 10.2) and impact of \r\nmodel biases. The contribution of internal variability to the observed \r\nchanges can also be very sensitive to the period length and level \r\nof spatial aggregation for the region under scrutiny (Section 4.4.1 \r\nand Cross-Chapter Box 3.1; Kumar et al., 2016). Finally, even in the \r\ncase of temperature changes at multi-decadal time scale, internal \r\nvariability can still be a substantial driver of regional changes due to \r\ncancellation between different external forcings (Nath et al., 2018)."


id: 4cda22f5f5b568bdf23748f1ae1140fb
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10792
content:
"To conclude, it is virtually certain (robust evidence and high agreement) \r\nthat anthropogenic forcing has been a major driver of temperature \r\nchange since 1950 in many sub-continental regions of the world. \r\nThere is high confidence (robust evidence and medium agreement) \r\nthat anthropogenic forcing has contributed to multi-decadal mean \r\nprecipitation changes in several regions, for example western Africa, \r\nsouth-east South America, south-western Australia, northern central \r\nEurasia, and South and East Asia. However, at regional scale, the role \r\nof internal variability is stronger while uncertainties in observations, \r\nmodels and external forcing are all larger than at the global scale, \r\nprecluding a  robust assessment of the magnitude of the relative \r\ncontributions of greenhouse gases, including stratospheric ozone, \r\nand different aerosol species."


id: ac922d457797dc6d7299dd747bc24960
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27602
content:
"Regional climate projections are one key element of the multiple lines \r\nof evidence that are used for climate risk assessments as well as for \r\nadaptation and policy decisions at regional scales (Sections 10.3.3.9 \r\nand 10.5). Regional climate projections can be separated into \r\ntwo components: the regional-scale forced response or regional-scale \r\nclimate sensitivity when normalized by the global mean temperature \r\nchange (Seneviratne and Hauser, 2020) and the climate internal \r\nvariability characterizing the future period or global warming level \r\nunder scrutiny. This section assesses a few methodological aspects \r\nrelated to robustness and emergence properties of the regional\u0002scale forced response as well as the possible influence of internal \r\nvariability on the emergence of the anthropogenic signal."


id: 735bf6db8b751cd135dd108ac9430219
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17546
content:
"Figure  10.14 illustrates a  similar comparison based on the CMIP6 \r\nmulti-model ensemble forced with the scenario SSP5-8.5 and \r\napplied to two large-scale continental areas. The forced response \r\nto anthropogenic forcing is simply taken as the CMIP6 multi-model \r\nmean of future regional climate change relative to the 1850-1900 \r\nreference period. Robustness of the forced response is based on \r\nboth significance of the change and model agreement about the \r\nsign (direction) of change (Cross-Chapter Box Atlas.1; Figure 10.14). \r\nCaution has to be exercised against a  too literal interpretation of \r\nlack of robust change given that significance and sign agreement \r\ncan be sensitive to spatial and temporal aggregation (Cross-Chapter \r\nBox Atlas.1, Figure  2) and lack of a  robust change does not \r\nnecessarily translate to lack of regional-scale climate change impacts \r\n(McSweeney and Jones, 2013; Hibino and Takayabu, 2016)."


id: fdc044ac83758d2b800102fbbca930d5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 42004
content:
"Standard methodologies to derive the regional forced response include \r\npattern-scaling and the time-shift or epoch approach (Section 4.2.4; \r\nTebaldi and Arblaster, 2014; Vautard et al., 2014; Herger et al., 2015; \r\nTebaldi and Knutti, 2018; Christensen et  al., 2019). Pattern-scaling \r\nassumes that the spatial patterns of regional change, often based on \r\na time-averaged 20- or 30-year period at the end of the 21st century, \r\nare roughly constant in time, and simply scale linearly with global \r\nmean warming. The time-shift approach defines a target in terms of \r\nglobal warming level (GWL) and locates the time segment, usually \r\n20 or 30 years, in historical or scenario simulations in which global \r\nmean warming matches the required GWL (Section  10.1.2 and \r\nCross-Chapter Box  11.1). Physical consistency between multiple "


id: 7f2d32fd62d23bc071cd2127daeab3d6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19529
content:
"If projected regional mean temperature (Figure  10.14a) and \r\nprecipitation (Figure 10.14b) changes were to scale linearly with global \r\nmean warming, the adjusted spatial patterns would be congruent "


id: 82d5638f67c09afc70a84fddeec36eab
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20144
content:
"spatial pattern amplitude with increasing GWL suggests that the \r\ninitial transient regional response overshoots the long-term change \r\nin regions such as northern Africa for summer temperature and south\u0002western South America for precipitation (Zappa et al., 2020). In the \r\nlatter region, long simulations with stabilized GHG concentrations \r\neven suggest a  change of sign when near-equilibrium is reached \r\n(Sniderman et  al., 2019). The reverse behaviour, increasing pattern \r\namplitude with increasing GWL, is seen for summer temperature in \r\nsouthern and eastern Europe and for precipitation in south-western \r\nNorth America (Sniderman et al., 2019; Zappa et al., 2020), suggesting \r\nthat, in these regions, the initial transient response is lagging global \r\nmean warming and final regional climate change will be reached once \r\nGHG concentrations are stabilized."


id: ab1f1d9b7c0a067c622ae988d736a81c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27332
content:
"with each other at different GWLs. While pattern scaling seems to \r\nbe a reasonable first-order approximation for both temperature and \r\nprecipitation changes in tropical and high latitude regions (high pattern \r\ncorrelation values), there are a number of regions exhibiting substantial \r\namplitude differences at different GWLs (northern Africa and Middle \r\nEast, southern and eastern Europe for temperature; south-western \r\nNorth America, Chile and north-eastern Brazil for precipitation). These \r\ndifferences hint at the possible influence of non-linear mechanisms \r\n(Good et  al., 2015), including soil-moisture feedbacks (Seneviratne \r\net al., 2010; Vogel et al., 2017), a time-dependent balance between the \r\ndifferent contributions of fast and slow response to greenhouse gas \r\nforcing as well as changing SST response patterns (Long et al., 2014; \r\nGood et al., 2016; Ceppi et al., 2018; Zappa et al., 2020). Decreasing "


id: f47e467b15815104ee8cfa28c63e363f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 40391
content:
"period (Section 1.4.1). The reference period can be the pre-industrial, \r\nthe very recent past or even a  time-evolving baseline, depending \r\non both the framing and assumption that adaptation to the current \r\nclimate has already occurred (King et al., 2015; Zhang and Delworth, \r\n2018; Brouillet and Joussaume, 2020). These choices will then \r\ndetermine the type of simulations and periods that will be used to \r\nconstruct the noise distribution. Finally, the permanence of future \r\nemergence cannot be taken for granted when emergence occurs in \r\nthe late-21st century based on simulations ending in 2100 (Hawkins \r\net al., 2014; King et al., 2015; Lehner et al., 2017a)."


id: cd1757c6f64f85934c3ae95f62deab25
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 33420
content:
"There is high confidence that the time-evolving contribution of \r\ndifferent mechanisms operating at different time scales can modify \r\nthe amplitude of the regional-scale response of temperature, and \r\nboth the amplitude and sign of the regional-scale response of \r\nprecipitation, to anthropogenic forcing. These mechanisms include \r\nnon-linear temperature, precipitation and soil-moisture feedbacks, \r\nand slow and fast response of SST patterns and atmospheric \r\ncirculation changes to increasing GHGs."


id: 96a5bf1ea5ea818a1f899f3718c46331
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23709
content:
"This section provides an assessment of the different approaches \r\nused in emergence studies as well as sensitivities to methodological \r\nchoices. The section then focuses on the possible influence of \r\ninternal variability on future emergence of the simulated mean \r\nprecipitation anthropogenic signal at regional scales with some \r\nillustrative examples."


id: 940d6efbd8000fa1baf686ff64050d8c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23252
content:
"In climate science, emergence or distinguishability of a signal refers to \r\nthe appearance of a persistent change in the probability distribution \r\nand/or temporal properties of a  climate variable compared with \r\nthat of a  reference period (Section  1.4.2; Giorgi and Bi, 2009; \r\nMahlstein et al., 2011, 2012; Hawkins and Sutton, 2012). Similar to \r\nanthropogenic climate change detection (Cross-Working Group Box: \r\nAttribution in Chapter 1), signal emergence can be detected, at least \r\ninitially, without identifying the physical causes of the emergence \r\n(Section  1.4.2). In the context of human influence on climate, the \r\nobjective of emergence studies is the search for the appearance of \r\na signal characterizing an anthropogenically-forced change relatively \r\nto the climate variability of a reference period, defined as the noise."


id: 7c05248815557e508c635271b9b9db5c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19025
content:
"Metrics can vary from a  simple signal-to-noise ratio to statistical \r\ndistributional tests (King et al., 2015; Gaetani et al., 2020) and give \r\nmedian estimates and uncertainty bounds for the date (or time of \r\nemergence) corresponding to the exceedance of specific thresholds by \r\nthe emergence metric. Reconciling future emergence results among \r\ndifferent studies is challenging due to their many methodological \r\ndifferences including the choice of the reference period, the selected \r\nclimate models and scenario, the precise definition of signal and noise \r\nand the choice of different signal-to-noise thresholds to characterize \r\nrobust emergence. Contrasting with binary yes/no statements, \r\nemergence can also be viewed as a continuous process characterized \r\nby an amplitude or level, for example the value of the signal-to-noise \r\nratio, that is a function of time or global warming level."


id: a66991f197b2dc59b676a2bfaf9b2fca
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 26498
content:
"Emergence is also sensitive to the noise characteristics: assuming \r\na  common signal definition, larger signal-to-noise values and \r\nearlier emergence will arise if the noise is based on decadal mean \r\nvariability rather than interannual variability (Kusunoki et al., 2020). \r\nDepending on the framing question, the noise can include or omit \r\nexternal natural forcing such as volcanic and solar forcing (Zhang \r\nand Delworth, 2018; Silvy et  al., 2020). Furthermore, emergence \r\nresults are very sensitive to the choice and length of the reference "


id: 9605c8b9c67fc28e7f0ecee68e5b1f73
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23591
content:
"Since AR5, the development and production of SMILEs (Sections 4.2.5 \r\nand 10.3.4.3) has allowed the assessment of the influence of internal \r\nvariability on anthropogenic signal emergence. The influence of \r\ninternal variability, and specifically of the unforced atmospheric \r\ncirculation, on temperature signal emergence can delay or advance \r\nthe time of emergence by a decade or two in mid- to high-latitude \r\nregions (Lehner et al., 2017a; Koenigk et al., 2020). Internal variability \r\ncan also result in small or decreasing decadal to multi-decadal "


id: 6537f5f15410ae5d8551bd41f6506ab7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15079
content:
"heatwave frequency trends under the historical anthropogenic forcing \r\nover most regions, thereby delaying emergence of unprecedented \r\nheatwave frequency trends relative to the pre-industrial trend \r\ndistribution (Sections 11.2-11.3; Perkins-Kirkpatrick et al., 2017)."


id: 65d7b145aa9c70d8ce9ca0371d948098
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15178
content:
"(Sarojini et al., 2016), to a much delayed emergence of the forced \r\nprecipitation response in observations (Hawkins et al., 2020). Based on \r\nthe CMIP6 multi-model ensemble forced with the scenario SSP5-8.5, \r\nwe assess the future emergence of mean precipitation forced change \r\nas a  function of GWLs for all AR6 land regions (Figure  10.15a). \r\nThe methodology is a  straightforward adaptation of the standard \r\napproach (Hawkins and Sutton, 2012). While the standard method \r\nis only based on the signal-to-noise ratio exceedance of a specified \r\nthreshold (taken as one), the approach used here assumes that \r\ngrid-point emergence occurs when the forced change is considered \r\nrobust following the AR6 WGI definition of robustness for projected "


id: 7e19768ca6af29ed5f932bcc0a09bdc6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19356
content:
"Regional precipitation future changes are much more impacted by \r\ninternal variability than their temperature counterpart (Monerie \r\net al., 2017b; Dai and Bloecker, 2019; Singh and AchutaRao, 2019; \r\nvon Trentini et  al., 2019; Koenigk et  al., 2020). Relative to mean \r\ntemperature changes, this larger influence of internal variability \r\non mean precipitation changes contributes, among other factors "


id: 297706a1be43afa05161919e2ec80ce5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4512
content:
"changes (Cross-Chapter Box Atlas.1). At a GWL of 1degC, emergence \r\nonly occurs in high-latitude regions (Wan et al., 2015; R. Guo et al., \r\n2019), albeit with only small (less than 30%) area fraction with \r\nrobust change. Robust changes in tropical and subtropical regions \r\nonly appear from GWLs of 1.5degC, for example in south-western South \r\nAmerica (Boisier et al., 2016), western Africa (Hawkins et al., 2020; \r\nSection 10.4.2.1) and southern Australia (Delworth and Zeng, 2014). \r\nSubstantial (taken here simply as area fraction greater than 50%) \r\nemergence only occurs in some tropical, subtropical and mid-latitude \r\nregions when high GWLs (3degC-4degC) are reached. Importantly, even \r\nat these high GWL values, there are still a  large number of these \r\nregions with robust changes covering less than 50% of their area. \r\nIn contrast, most high-latitude regions have an area fraction with \r\nrobust changes greater than 80% at GWLs of 3degC and above."


id: 16bead6863a512392af471512b4da008
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 1214
content:
"There is high confidence that consistency in definitions of signal and \r\nnoise, choice of the reference period and signal-to-noise threshold, is \r\nimportant to robustly assess the future emergence of anthropogenic \r\nsignals across different types or generations of models, as well \r\nas comparing past emergence results between observations and \r\nmodels. There is high confidence that internal variability can delay the \r\nemergence of the regional-scale mean precipitation anthropogenic \r\nsignal in many regions, mainly located in the tropics, subtropics and \r\nmid-latitudes. An accurate estimation of the delay in regional-scale \r\nemergence caused by internal variability remains challenging due to \r\nglobal model biases in their representation of internal variability as "


id: 89681dcf5ee66572097a92e8832c64c2
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 21352
content:
"Regional climate information may be constructed from a  diverse \r\nrange of sources, each depending on different assumptions and \r\naffected by different methodological limitations (Sections 10.2, 10.3 \r\nand 10.4). The construction of information may lead to products for \r\ndirect adoption by users, or intermediate products for further analysis \r\nby users and climate services agencies in collaboration with climate \r\nscientists. Widely used sources include:"


id: da98487b79c016aecb9f48151ebce201
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
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vector_id: 35753
content:
"of both local and national government legislation that can involve \r\na range of different constituencies and decision makers (Scott et al., \r\n2018; Savelli et al., 2021)."


id: 4b1d60bc6fbdc251f8542b721675ee95
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10461
content:
"Context plays a role in determining the risks that may affect human \r\nsystems and ecosystems and consequently the climate information \r\nneeds. The context may also limit access to such information. Hence, \r\nthe context imposes inherent constraints on how climate information \r\ncan be constructed and optimally aligned with its intended \r\napplication. Although contexts are unlimited in variety, some key \r\ncontextual elements include:"


id: 275c089653b25d3976ffec0e397d0680
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4102
content:
"Users of climate information may face the so-called practitioner's \r\ndilemma: a  plethora of different and potentially contrasting \r\nsources (Figure  10.16) may be available without a  comprehensive \r\nand user-relevant evaluation, and these datasets may also lack \r\na transparent and easily understandable explanation of underlying \r\nassumptions, strengths and limitations (Barsugli et  al., 2013; \r\nHewitson et  al., 2017). Often, the choice of information source is \r\ntherefore not determined by what is most relevant and informative \r\nfor the question at hand, but rather by practical constraints such as \r\naccessibility and ease of use and may be limited to the availability of \r\njust one source in extreme cases (Rossler et al., 2019a)."


id: e6fed73e64963e7a7f6fea0d419c2396
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 37934
content:
"These and other contextual elements can frame subsequent \r\ndecisions about the construction of regional climate information for \r\napplications. For example, an engineer typically seeks quantitative \r\ninformation, while the policy community may be more responsive to \r\nstorylines and how information is positioned within a causal network \r\ndescribing regional climate risk (Section 1.4.4 and Box 10.2). Multiple \r\ncontexts can coexist and potentially result in competing approaches \r\n(for example, when urban governance contends with regional \r\nwater-resource management in the same area)."


id: e0f2e2a8cd7931b8843043dc497fdb8e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 36840
content:
"Without considering the specific context, the distillation of climate \r\ninformation relevant to users may poorly serve the goal of informing \r\nadaptation and policy (Cash et al., 2003; Lemos et al., 2012; Baztan \r\net al., 2017). Section 10.1.4 identifies three implicit framing issues \r\nof constructing and delivering user-relevant climate information: \r\npractical issues arising from the climate information sources, issues \r\nwith including the context in constructing the information, and \r\ndifficulties presented by complex networks of practitioners. The social \r\ncontext strongly influences decisions about constructing information \r\nand requires a  nuanced and holistic approach to recognize the \r\ncomplexity of a  coupled social and physical system (Daron et  al., \r\n2014). For example, urban water managers must recognize the \r\ndependency of the city on different water resources and the interplay "


id: 98437d98c1b1c56f55b7fc18ae6ef1dd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24000
content:
"Developing climate information relevant to user needs can be \r\ninfluenced by the explicit and implicit values of all parties: those \r\nconstructing the information, those communicating the information, \r\nthose receiving the information, and, critically, those who construct \r\nthe problem statement being addressed. A  discussion of how "


id: daa99361fbd87d2d9ef5638f2fb8f201
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35764
content:
"values in the scientific community shape climate research appears \r\nin Section 1.2.3.2. The influence of values need not be a source of \r\nbias or distortion; it is sometimes appropriate and beneficial: critical \r\nscrutiny from a diverse range of value-governing perspectives may \r\nuncover and challenge biases and omissions in the information that \r\nmight otherwise go unrecognized (Longino, 2004). Dialogue among \r\nall parties in a culturally, socially, and economically heterogeneous \r\nsociety is therefore important for recognizing and reconciling value \r\ndifferences to best yield information that is salient, relevant and \r\navoids ambiguity, most notably when informing the complexity of \r\nrisks and resilience for human systems and ecosystems in developing \r\nnations (e.g., Baztan et al., 2017)."


id: 6659be0b26751eace8c846219a40aa69
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15270
content:
"A substantial body of evidence shows that the receptivity of \r\nindividuals to climate information is strongly conditioned by \r\nmotivated reasoning (Hart and Nisbet, 2012; Kahan, 2012, 2013), \r\nwherein a  person's reception of climate information is influenced \r\nby the values of the community with which the person identifies. \r\nAdherence to a  community's values forms part of an individual's \r\nsocial identity (Hart and Nisbet, 2012). Individuals thus frame their \r\nanalysis and understanding of climate information in the context \r\nof cultural values espoused by their community (Hart and Nisbet, \r\n2012; Kahan, 2012, 2013; Campbell and Kay, 2014; Bessette et al., \r\n2017; Tschakert et al., 2017; Vezer et al., 2018). Successful framing of \r\nclimate information products thus seeks to identify common ground \r\nwith users, taking account of their values and interests."


id: 33416879807646c2ce34794ac5c998f8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 6270
content:
"Thus, a  challenge with constructing climate information for users, \r\nespecially about impactful change, is that producing the information \r\nmay need to involve people with a  variety of backgrounds, who \r\nhave different sets of experiences, capabilities, and values. The \r\ninformation thus would need to accommodate and be relevant to \r\na range of different ways of viewing the problem (Sarewitz, 2004; \r\nRosenzweig and Neofotis, 2013; Gorddard et  al., 2016). Failure to \r\nrecognize the variety of people using the climate information can \r\nmake it ineffective, even if the source data on which it is based is of \r\nthe highest quality, and may create a danger of maladaptation."


id: 58b6b1a2a66fb3d3959a24ff508b0474
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12849
content:
"Given the relevance of both context and values, the effectiveness of \r\nclimate information can increase if developed in partnership with the \r\ntarget communities (Figure  10.17; Tschakert et  al., 2016). Such an \r\napproach can inspire trust among all parties and at the same time \r\npromote a  co-production process (Cash et  al., 2003). Recipients \r\nof information have the greatest trust when the communicator is \r\nperceived as understanding their context and sharing their values \r\nand identity (Corner et  al., 2014). As a  consequence, developing \r\nmental models informed by user values can help with understanding \r\ncomplex climate models and their outcomes (Bessette et al., 2017)."


id: c53ee0fd3e6dad219688643cdb2c7dde
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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vector_id: 32006
content:
"Climate processes occur on a range of spatial and temporal scales, from \r\nglobal to local, from centuries and longer to days or less (Section 10.1.2 \r\nand Figure 10.3). Similarly, decisions by stakeholders cover a range of "


id: 1ee4b657a172d05fc05a9d9d1d429f46
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content:
"spatial and temporal scales that can vary with the size of their region \r\nof interest and scope of activity. However, the link between decision \r\nscales and the spatial and temporal resolution of climate and related \r\nnon-climatic, natural-system information is not straightforward, and \r\nfailure to recognize mismatches between the two can undermine the \r\neffectiveness and relevance of the information (Cumming et al., 2006; \r\nSayles, 2018)."


id: 7858c782c923cc19fc27fc3d158ccd1d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 18383
content:
"Data, either from observations or models, is in general not inherently \r\ninformation, but may contain relevant information if interpreted \r\nappropriately (Hewitson et  al., 2017). The same applies to other \r\nsources of climate information. Relevance is controlled by the given \r\nuser context (Section 10.5.2.1) and relates to the required temporal \r\nand spatial scales (Section 10.5.2.3), the characteristics of required \r\nvariables (often referred to as indicators), and the meteorological \r\nand climatic phenomena driving these variables (Section 10.1.3). For \r\nexample, if climate information for driving impact models is sought \r\n(e.g., McSweeney et al., 2015), the impact modelling analysis in the \r\ntarget region is the specific user context."


id: 64f928b2112a1e2db125269c3717bb17
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15037
content:
"Climate risk assessment considers all plausible outcomes (Weaver \r\net al., 2017; Marchau et al., 2019; Sutton, 2019). Thus, a key element \r\nof information construction is the exploration and reconciliation of \r\ndifferent sources of information (Barsugli et al., 2013; Hewitson et \r\nal., 2014b; Maraun and Widmann, 2018b) and involves mainly two \r\nissues: first, assessing the fitness of different sources in the given \r\ncontext and thereby potentially omitting (or down-weighting) \r\nselected sources (Sections 10.3.3), and, second, integrating different \r\nsources into a broader picture within a context (Sections 10.3.4). "


id: 6e45d7b573c383956e85759231f42323
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16668
content:
"Distilling climate information for a  specific purpose benefits from \r\na co-production process that includes non-climate-scientists in the \r\nresearch design, analysis and the exploration and interpretation of \r\nthe  results to best place it in context of the intended application \r\n(Collins and Ison, 2009; Berkhout et  al., 2013; Wildschut, 2017; \r\nBhave et al., 2018; Dessai et al., 2018). Consideration of the specific \r\ncontexts of information requirements by the provider as well as \r\nincluding the user values in connecting the science with users is \r\nincreasingly recognized as paramount to construct information "


id: 9e5819198aee71944da7d701a948ef2
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 25087
content:
"(Hewitson et  al., 2017). Context-specific collaboration, especially if \r\norganized in workshop, enables a close transdisciplinary co-exploration \r\nof the results as in the form of climate risk narratives (Jack et al., 2020, \r\nBox  10.2). Such approaches explicitly account for the user context, \r\nvalues and non-climatic stressors (Steynor and Pasquini, 2019)."


id: 8194a37e04ec33427038c441997587ae
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19926
content:
"These approaches often can be used in combination to increase \r\nconfidence in conclusions drawn (Hewitson et al., 2017)."


id: 95fccad9702ea260e9e1515bd1b54106
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23581
content:
"As implied by Section  10.5.2, meeting the needs of users can be \r\na substantial challenge for climate scientists if they misunderstand \r\nor have limited understanding of user needs and context (Porter \r\nand Dessai, 2017). Several barriers in user communities can trigger \r\nand sustain this challenge. This can include an institutional aversion to \r\nincorporating new tools into decision-making (Callahan et al., 1999). \r\nCoincident with this factor, there may be limited staff capacity, lack \r\nof management support and lack of a mandate to plan for climate \r\nchange (Lee and Whitely Binder, 2010)."


id: 425243bf30610c935c604dd2411f0b75
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
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content:
"Following from those challenges, constructing and communicating \r\nregional climate information often occurs under the overarching \r\nassumption that uncertainty is a  problem and reducing uncertainty \r\nis the priority (Eisenack et al., 2014; J. Otto et al., 2016). This is both \r\na psychological (Morton et al., 2011) as well as a pragmatic barrier in \r\ncases where uncertainty appears to limit the ability to make decisions \r\n(Mukheibir and Ziervogel, 2007). However, where in-depth engagements \r\nwith decision contexts are undertaken, these initial barriers are often \r\ndismantled to reveal a more complex, nuanced and potentially more \r\nproductive intersection with climate information producers that can \r\nefficiently handle uncertainty (e.g., Rice et al., 2009; Lemos et al., 2012; \r\nMoss, 2016). Specifically, disclosure of all uncertainties in the climate "


id: 37fda5f60479faf348ee7fc3a8f62f35
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7057
content:
"Climate information products may often lack explanations of their \r\npotential use and misuse (Street, 2016; Lamb, 2017; Chimani et al., \r\n2020). This is particularly important if the information is provided \r\nas a  generic, publicly accessible product without a  specific context "


id: f995f6bb70fa490b01114806be4fba0f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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content:
"necessary to consider all physically plausible outcomes from multiple \r\nlines of evidence, especially in the case when they are contrasting \r\nsuch as in the examples of Cross-Chapter Box 10.1 and Section 10.6.2."


id: 49b573f1764de3f6acc4b170348bc16f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10199
content:
"information, transparency about the sources of these uncertainties, \r\nand tailoring the uncertainty information to specific decision \r\nframeworks have the potential for reducing problems of distilling and \r\ncommunicating uncertain climate information (J. Otto et al., 2016)."


id: fe9cfbea051e2b64de7acede799610d8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 42173
content:
"The preceding subsections assess research on the distillation of \r\nclimate information, which is directly relevant for the development \r\nof climate services. Distillation, when implemented appropriately and \r\ninterpreted with all due caveats, leads to credible climate information \r\nwith a broader foundation of evidence to be used in climate services \r\npractice according to the recommendations of the Global Framework \r\nfor Climate Services (Hewitt et al., 2012). As stated in Chapter 12, \r\nclimate services set new scientific challenges to research. Examples \r\nof some of the challenges have been given in Chapters 1 and 12, \r\nwhich are complemented by the barriers to the distillation assessed \r\nin Section 10.5.3.3."


id: 4c71e1686fa329c9d2c773183757f482
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10709
content:
"Drawing upon multiple lines of evidence in the construction of \r\nclimate information increases the fitness of this information and \r\ncreates a  stronger foundation (high confidence). The lines of \r\nevidence can include multiple observational datasets, ensembles \r\nof different model types, process understanding, expert judgement, \r\nand indigenous knowledge, among others. Attribution studies, the \r\ncharacterization of possible outcomes associated with internal \r\nvariability and a comprehensive assessment of observational, model \r\nand forcing uncertainties and possible contradictions using different \r\nanalysis methods are important elements of distillation. To make the \r\nmost appropriate decisions and responses to changing climate it is "


id: d60204544f3b605e70410d5b954e9c9a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 34969
content:
"Communicating the full extent of available information on future climate for a region, including an uncertainty quantification, can \r\nact as a barrier to the uptake and use of such information (Lemos et al., 2012; Daron et al., 2018). To address the need to simplify \r\nand increase the relevance of information for specific contexts, recent studies have adopted storyline and narrative approaches \r\n(Section 1.4.4.2; Hazeleger et al., 2015; Shepherd et al., 2018). As such, these approaches are an important tool for the climate \r\ninformation distillation (Section  10.5.3). Here we assess these in a  regional climate information context, namely for exploring \r\nuncertainties, embedding climate information into a given user context, and communicating climate change information."


id: e3b8eb08301932a486e64d9b1f612a6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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content:
"or based on expert judgment of plausible changes to observed events (Pisaric et al., 2011; Dessai et al., 2018). They can be used for \r\nattributing events to different causal factors (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; Trenberth et al., 2015; \r\nShepherd, 2016a; Section 11.2.4) as well as for exploring the unfolding of events in future climates."


id: 8b6f07fb412707cd69ac353864efafc5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 21690
content:
"Physical climate storylines are complementary to probabilistic or unconditional risk-based approaches, and are particularly suitable to \r\nexplore low-likelihood changes or events, which are often associated with the highest impacts (Shepherd et al., 2018; Sillmann et al., \r\n2020; Section 4.8). They also facilitate providing local context to large-scale trends and changes, by conditioning the projections on \r\nlocally relevant circumstances (Hazeleger et al., 2015). Storylines are also developed based on expert elicitation and include plausible \r\nchanges beyond those simulated by existing model projections in order to explore deep uncertainties (Dessai et al., 2018)."


id: 9fec24f8cbb98e011590e33876cf41b4
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 25346
content:
"Storylines can be combined with impact modelling (Strasser et al., 2019; Hegdahl et al., 2020) and can be embedded in a user's \r\nrisk landscape (Shepherd, 2019; Box 10.2, Figure 1). In particular, this holds for event storylines, where confounding factors such as \r\nregional characteristics like land-use changes and non-climatic drivers of the event are an element of the storyline (Pisaric et al., 2011; \r\nDessai et al., 2018; Lloyd and Shepherd, 2020; Sillmann et al., 2021). In a co-production process, multidisciplinary expert knowledge \r\nas well as the values and interests of the intended audiences and stakeholders can be explicitly considered (Kok et al., 2014; Bhave \r\net al., 2018; Dessai et al., 2018; Scott et al., 2018; Hegdahl et al., 2020)."


id: 71e3cf8d36163f9e20345a2554d66495
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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content:
"Contributors: Claudine Dereczynski (Brazil), Melissa I. Gomis (France/Switzerland), Richard G. Jones (United Kingdom), Roshanka Ranasinghe \r\n(The Netherlands/Sri Lanka, Australia), Alex C. Ruane (The United States of America), Sonia I. Seneviratne (Switzerland), Anna A. Sorensson \r\n(Argentina), Bart van den Hurk (The Netherlands), Robert Vautard (France), Sergio M. Vicente-Serrano (Spain)"


id: 410aff5ab7e95ce73eeafa7ac01d1a37
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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content:
"This Cross-Chapter Box illustrates how assessments of past, present and future regional climate changes (e.g., change in an extreme \r\nevent index or climatic impact-driver, CID) are derived in the WGI report. Robust assessments can be derived when changes are \r\nsupported by multiple lines of evidence."


id: ef1a59275104a7013f4e62d4106832d1
created_at: 2023-04-19 13:21:39
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content:
"The assessment is derived following the IPCC uncertainty guidance through a distillation process of multiple lines of evidence on \r\nobserved trends, attribution of trends or events, climate model projections and physical understanding, covered in several chapters \r\nof the WGI Report."


id: 4ebec607b135fed0908eb9ca20443cba
created_at: 2023-04-19 13:21:39
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"In particular, this Cross-Chapter Box explains the methodology used to derive the regional assessments summarized in the Technical \r\nSummary (TS) table that are, in turn, used as a basis for the synthesis assessment in the Summary for Policymakers (SPM)."


id: bdd98f018059014331127a111f80339d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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content:
"The process consists of three discrete steps, listed below and schematically illustrated in Cross-Chapter Box 10.3, Figure 1:"


id: 850d4947a7e0b2c09af6a4395f5b6907
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20552
content:
"1. Collection and assessment of the fitness-for-purpose of available information\r\nAny specific climate change that is regionally relevant is assessed looking at lines of evidence, potentially across multiple indices. For \r\nexample, several definitions of 'drought' exist that refer to a variety of the underlying processes, temporal and spatial scales, as well as \r\nsectoral applications and associated impacts (Sections 11.6 and 12.3). Such diverse definitions need to be gathered from the relevant \r\nliterature, compared, and individually assessed if appropriate."


id: 9031f29ff3f44f584b45a06dae5b2b3d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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content:
"2. Assessment of confidence of the multiple lines of evidence\r\nOnce the relevant information has been collated for a given regional change, an assessment of the confidence is first made for each \r\nline of evidence separately. The assessment of confidence is the result of expert judgment drawing around a set of questions such as:"


id: 1591331df56c801c81d958597e9cc673
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
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content:
"3. Distillation of regional information and synthesis of the independent assessments\r\nTo ensure transparency, a traceback matrix is constructed (refer to 10.SM) that, for each region and index, identifies where in the \r\nchapters the relevant information can be found, together with a summary of the relevant information in the Technical Summary."


id: 8a539d125083b245fddafd19f88f1059
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updated_at: 2023-04-19 13:21:39
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content:
"Based on assessments mainly in Chapters 8, 9 11, 12 and Atlas, the table in Technical Summary (TS.4.3.1) collates, by means of colours \r\nand symbols, the assessment of the confidence in past trend, attribution and direction of future change. This distillation process is \r\nillustrated below with two examples: (i) a relatively simple case for the assessment of extreme heat over South-Eastern South America, \r\nwhere most of the lines of evidence agree, and (ii) ecological, agricultural and hydrological drought in the Mediterranean, which is \r\nmore complex due to the different definitions of 'drought' and the sometimes conflicting information arising from different lines \r\nof evidence and the example shown here is preceded by the decision to focus on these types of drought rather than, for example, \r\nmeteorological drought."


id: 4cc043b76e281e4e0c46cb74f00c77ec
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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content:
"Mean temperature and extreme maximum and minimum temperatures have shown an increasing trend (high confidence). An increase \r\nin the intensity and in the frequency of heatwave events between 1961 and 2014 is also observed. However, there is medium \r\nconfidence that warm extremes have decreased in the last decades over the central region of SES during austral summer (Section 11.9 \r\nand Atlas.7.2.2)."


id: eddafc0556b749ac40f962f49e419ccc
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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content:
"Based on trend detection and attribution studies of maximum and minimum temperatures and event attribution of heatwaves in \r\nthe region, there is high confidence in a human contribution to the observed increase in the intensity and frequency of hot extremes \r\n(Section 11.9)."


id: 7281b5d95cb8ef97497368acf89d55fe
created_at: 2023-04-19 13:21:39
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content:
"The increasing heat stress over summer in much of SES has been attributed to human influence on the climate system (Section 12.4.4.1)"


id: 4c8137b0dfb5806bdeee04c8d0d960aa
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updated_at: 2023-04-19 13:21:39
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"There is high confidence that by the end of century most regions in South America will undergo extreme heat stress conditions much \r\nmore often than in the recent past, with about 50-100 more days per year under SSP1-2.6 and more than 200 additional days per year \r\nunder SSP5-8.5 (high confidence) (Section 12.4.4.1)."


id: 2c1ed1cac5c7a938bd757fa6d7b9c0a2
created_at: 2023-04-19 13:21:39
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content:
"Based on different lines of evidence (GCMs, RCMs) an increase in the intensity and frequency of hot extremes is extremely likely for \r\nSES at all assessed warming levels (compared with pre-industrial) (Section 11.9)."


id: d9f92030ff5bda8e84488a1f1d4ad763
created_at: 2023-04-19 13:21:39
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content:
"Synthesized assessment in the Technical Summary from multiple lines of evidence\r\nThere is high confidence that extreme temperatures have increased over SES over the last decades and that human influence likely\r\ncontributed to the observed changes in extreme temperatures. An increase in the frequency and intensity of heatwave events has been \r\nobserved. Most land regions will frequently undergo extreme heat stress conditions by the end of the 21st century, with an increase \r\nin the frequency of heatwaves and heat stress conditions (Technical Summary TS.4.3.2)."


id: 5b003a9be3aeedf25f1c0b7d99ea077c
created_at: 2023-04-19 13:21:39
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content:
"Hydrological modelling suggests that the recent decline in soil moisture in the Mediterranean is unprecedented in the last 250 years. \r\nPaleoclimate evidence extends this view, additionally indicating that dryness in the Mediterranean is approaching an extreme \r\ncondition compared to the last millennium (Section 8.3.1.6)."


id: d771250702b9317d1685316b48bd1ee9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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"There is an increase in probability and intensity of agricultural and ecological droughts (medium confidence) and there is an increase \r\nin frequency and severity of hydrological droughts (high confidence) (Section 11.9)."


id: 7d150114c3c48da923b45f17fe1a30a1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19190
content:
"Global warming has contributed to drying in dry summer climates including the Mediterranean (high confidence). Records of soil \r\nmoisture indicate that higher temperatures and increased atmospheric demand have played a strong role in driving Mediterranean \r\naridity. Multiple lines of evidence suggest that anthropogenic forcings are causing increased aridity and drought severity in the \r\nMediterranean region (high confidence) (Section 8.3.1.6)."


id: 3600851805e76a106ad8ed899d3eb59d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
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content:
"An increasing trend towards agricultural and ecological droughts has been attributed to human-induced climate change in the \r\nMediterranean (medium confidence). Model-based assessment shows with medium confidence a human fingerprint on increased \r\nhydrological drought, related to rising temperature and atmospheric demand, and frequency and intensity of recent drought events. \r\nThere is medium confidence that change in land-use and terrestrial water management contribute to trends in hydrological drought \r\n(Section 11.9)."


id: 9b4bf38dbb97bf45e33ef36561ca081
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24569
content:
"There is high confidence that drought severity and intensity will increase in the Mediterranean. Increased evapotranspiration due \r\nto growing atmospheric water demand will decrease soil moisture (high confidence). The seasonality of runoff and streamflow (the \r\nannual difference between the wettest and driest months of the year) is expected to increase with global warming (high confidence). \r\nAnnual runoff is very likely to decrease. Under middle or high-emissions scenarios, the likelihood of extreme droughts increases by \r\n200-300% in the Mediterranean. The paleoclimate record provides context for these future expected changes: climate change will shift \r\nsoil moisture outside the range of observed and reconstructed values spanning the last millennium (high confidence) (Sections 8.4.1.5 \r\nand 8.4.1.6)."


id: 408609f639b081b729a959d7677a129b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 8540
content:
"There is medium confidence in the increase of agricultural and ecological drought at +1.5degC, high confidence at +2degC and very likely\r\nat +4degC, with large decreases in soil water availability during drought events and increase in drought magnitude. There is medium \r\nconfidence in the increase in hydrological drought at +1.5degC, high confidence at +2degC and very likely at +4degC with very strong decrease \r\n(40-60%) of total runoff in the spring-summer half-year and a 50-60% increase in frequency of days under low flow (Section 11.9)."


id: 3be56c882ce605206d95fb7288cfce40
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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vector_id: 7733
content:
"There is high confidence that agricultural, ecological and hydrological droughts will increase in the Mediterranean region by mid- and \r\nend-of-century under all RCPs (except RCP2.6/SSP1-2.6), or for GWLs equal to or higher than 2degC (Section 12.4.5.2)."


id: 5fd9791394761f11322587f38317fbb
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 14171
content:
"Synthesized assessment in the Technical Summary from multiple lines of evidence\r\nThere is high confidence that hydrological droughts have increased in the Mediterranean since the 1960s related to rising temperature \r\nand atmospheric demand, and medium confidence of a human fingerprint on this increase. There is medium confidence in the increase \r\nof ecological and agricultural droughts and in their attribution to human-induced climate change. There is high confidence of an \r\nincrease in ecological, agricultural and hydrological droughts for warming levels exceeding 2degC, and medium confidence of an increase \r\nfor lower warming levels (Technical Summary TS4.3.2)."


id: 2d4b4bf6bdccfc0cc7fc0b675d3cfa17
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
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content:
"because most of the components for constructing regional climate \r\ninformation outlined in Chapter 10 are directly relevant to each case."


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vector_id: 30993
content:
"Following this structure, construction of the regional climate \r\ninformation presented in these examples depends on an assessment \r\nof observational uncertainty relative to the magnitude of a climate \r\nchange signal (Section 10.2), the evaluations of model performance \r\nto judge the fitness-for-purpose of a  given model (Section  10.3), \r\nand expert judgement.  These factors contribute to attribution of \r\nhistorical climate change signals (Section  10.4), recognizing that \r\nattribution must account for the interplay between externally forced "


id: 55bfa77ea5e46a9c4c94356d1a53dcdf
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12357
content:
"signals and unforced internal variability. This interplay is explored \r\nusing multiple model ensembles, including, when appropriate and \r\nfeasible, single-model initial-condition large ensembles (SMILEs). The \r\nmultiple lines of evidence for the climate information may conflict, \r\nthus requiring distillation of the evidence (Section  10.5) to arrive \r\nat climate-change statements. When moving from global climate \r\ninformation to climate information at the regional scale, following the \r\nstructure above provides a basis for arriving at relevant and credible \r\nclimate information. The comprehensive examples of distilling climate \r\ninformation thus show the value of working with multiple lines of \r\nevidence to develop robust climate change information for a region."


id: fe5b61e5a0547384f5f33d0666bbcf86
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 42124
content:
"In addition to the three comprehensive examples, this section \r\ncontains two additional examples analysing multiple sources of \r\nregional climate information. Box  10.3 on urban climate assesses \r\ninformation that provides a  foundation for understanding climatic \r\nbehaviour in urban areas and its projected change. Cross-Chapter \r\nBox 10.4 on climate change over the Hindu Kush Himalaya assembles \r\ninformation rooted in several chapters and previous assessment \r\nreports to assess understanding of several climate elements \r\n(temperature, precipitation, snow and glaciers, and extreme events) \r\nfor the region and their projected changes."


id: 35960d165aecf8fc463b85057a86be89
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 6683
content:
"As these examples will show, the distillation process of regional climate \r\ninformation from multiple lines of evidence can vary substantially \r\nfrom one case to another. Confidence in the distilled regional climate \r\ninformation is enhanced when there is agreement across multiple lines \r\nof evidence, but the outcome of distilling regional climate information \r\ncan be limited by inconsistent or contradictory sources."


id: c1ce4817ff0c4e04933b6e072efb9509
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31397
content:
"An evaluation of the relative role of rainfall and temperature signal \r\nin the 2015-2017 hydrological drought gives a  strong indication \r\nthat lack of rainfall was the primary driver (Otto et al., 2018) leading \r\nto the 2018 water crisis. Thus, the remainder of this section focuses \r\non rainfall. Section 11.6 offers a discussion of African drought over \r\nbroader areas, including mechanisms relevant to them."


id: fdbb369a2ba7576960d4a212082393d7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 42001
content:
"Cape Town is located at the south-western tip of Africa, within an \r\napproximately 100 km x 300 km region that receives 80% of its \r\nrainfall during the austral winter (March to October), with the largest \r\nportion in June to August. In the vicinity of Cape Town, rainfall is \r\nstrongly heterogeneous, ranging from about 300 mm/year on coastal \r\nplains to >2,000 mm/year in mountain ranges. The Cape Town water \r\nsupply relies on surface water reservoirs located in a  few small \r\nmountain catchments (about 800 km2 in total). The Cape Town region \r\nreceives 85% of its rainfall from a series of cold fronts forming within \r\nmid-latitude cyclones. The remainder is brought in by infrequent \r\ncut-off lows that occur throughout the year (Favre et al., 2013). This \r\ncreates a very strong water resource dependency on a single rainfall \r\ndelivery mechanism that may be strongly affected by anthropogenic \r\nclimate change (Chapter 4 and Section 10.6.2.6)."


id: 68d8bcf64d313677bd2c3038ac8b1f12
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15712
content:
"The 2015-2017 drought had strong low-rainfall anomalies in shoulder \r\nseasons (March to May and September to November, though weaker \r\nin the latter), and average rainfall in June and July (Sousa et al., 2018a; \r\nMahlalela et al., 2019). The anomaly resulted from fewer rainfall events \r\nand lower average intensity of events. The anomaly was strongest in \r\nthe mountainous region where the water supply system's catchments \r\nare located (Wolski et al., 2021)."


id: ecd3405843192314643ecb4fd7e705e4
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39033
content:
"2015-2017 drought to the hemispheric processes expressed by the \r\nSAM (Sousa et al., 2018a; Burls et al., 2019; Mahlalela et al., 2019) \r\nfocused almost exclusively on the post-1979 period, when global \r\nreanalyses are available. Detailed understanding of the drivers of \r\nprevious (1930s and 1970s) Cape Town region droughts and the \r\nrole of hemispheric processes expressed by the SAM in the pre-1979 \r\nperiod is missing."


id: 857a2d42c3b8901d53c7561ca7cc06da
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20621
content:
"Although the 2015-2017 drought was unprecedented in the historical \r\nrecord, the Cape Town region has experienced other droughts of \r\nsubstantial magnitude, notably in the 1930s, 1970s and more recently \r\nin 2000-2003. Long-term (>90 years) rainfall trends are mixed in sign, \r\nlocation-dependent, and weak (Kruger and Nxumalo, 2017; Wolski \r\net al., 2021); mid-term (about 50 years) trends are similarly mixed in \r\nsign (MacKellar et al., 2014). In the south-western part of the region, \r\nrainfall is mostly decreasing in the post 1981 period, particularly in \r\nDecember-January-February and March-April-May, although there \r\nis no trend or a weak wetting in June-July-August (Sousa et al., 2018a; \r\nWolski et al., 2021). Rainfall trends of similar magnitude and duration \r\nto the post-1981 trend accompanied previous strong droughts in the \r\nregion (Wolski et al., 2021)."


id: f20addb4fa4d72db617370b4773407f7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39976
content:
"South Africa and the Cape Town region have good instrumental \r\nweather data. Records start in the late 1800s, with in excess of \r\n10 gauges reporting since the 1920s, expanding to about 80 gauges \r\nin the 1980s, but the number of stations has declined since. The \r\nmountains have only a  few stations, which receive more than \r\n1000 mm per year. In view of the strong heterogeneity of rainfall, \r\nchanges in the number of stations contributing to datasets such as \r\nClimatic Research Unit (CRU) and Global Precipitation Climatology \r\nProject results in a lack of consistency between them, which limits \r\ntheir reliability in the region (Section 10.2; Wolski et al., 2021)."


id: 1c59328b8c94ad8f0e6938b0559f0638
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 2175
content:
"Rainfall in the Cape Town region also responds to SST anomalies in \r\nthe south-east Atlantic, including the Agulhas Current retroflection \r\nregion, which may drive intensification of low-pressure systems, \r\nleading to the trailing front strengthening as it makes landfall over \r\nthe Cape Town region (Reason and Jagadheesha, 2005). There are \r\nalso linkages at the seasonal time scale between the Cape Town \r\nregional rainfall and Antarctic sea ice (Blamey and Reason, 2007)."


id: 876dd9bef228319948cf55e232181539
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20983
content:
"In addition to mid-latitude controls, subtropical processes also play \r\na role in the Cape Town region's rainfall variability. The 10degS-30degS \r\nregion of the subtropical Atlantic, parts of the South American \r\ncontinent and even parts of the African continent north of Cape Town \r\nare sources of moisture for atmospheric river events contributing \r\nto frontal rainfall (Blamey et  al., 2018; Ramos et  al., 2019), with \r\nimplications for the 2015-2017 drought (Sousa et  al., 2018a). \r\nAlso, the second major rainfall contributing system, cut-off-lows, is \r\nconditional on moisture supply from the subtropics (Abba Omar and \r\nAbiodun, 2020)."


id: c6922823fa10e1bd2ee6d2166f2088bd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32239
content:
"Although El Nino-Southern Oscillation (ENSO) influences climate in \r\nsouthern Africa, any relationship between ENSO and Cape Town's \r\nrainfall is weak and inconsistent, showing the strongest impact in \r\nMay to June (Philippon et al., 2012). ENSO, however, does influence \r\nlarge-scale processes and phenomena relevant to the drought, \r\nthough the relationship between ENSO and the SAM is complex, with \r\neach ENSO event influencing the SAM differently in different seasons "


id: fc9fd799358ee92f7e379477129731b6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41812
content:
"In the longer-term, Cape Town regional rainfall is characterized by \r\na multi-decadal scale quasi-periodicity (Figure 10.18; Dieppois et al., \r\n2019; Wolski et al., 2021), with the 2015-2017 drought and previous \r\nstrong droughts (1930s and 1970s) occurring during the rainfall's \r\nperiodic low phases. However, the studies linking the Cape Town "


id: 294f8482e6366b4a1f1f81cf633c6d1c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4487
content:
"(Ding et al., 2012). Similarly, ENSO affects meridional circulation and \r\nthus the subtropical anticyclone as well as the polar and subtropical \r\njets (Seager et al., 2019), but only modifying, not controlling, their \r\nrole in Cape Town's rainfall."


id: abe32d8b1ca4d14b64ac10fbdd8ebc57
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27481
content:
"There is also a  substantial increase in the frequency of conditions \r\nsupporting atmospheric rivers and water vapour transport towards \r\nthe south-west coast of southern Africa in the projected climate \r\n(Espinoza et  al., 2018). This behaviour has strong implications for \r\nthe region, as most topographically high locations receive rainfall \r\nfrom persistent atmospheric rivers (Blamey et al., 2018). A thorough \r\nunderstanding of the role of atmospheric rivers in the Cape Town \r\nregion under a changing climate is missing."


id: 44df4e49ec0ff8a49ef50d9731320438
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9344
content:
"Global models capture the overall behaviour of the observed main \r\nhemispherical processes, such as the expansion of the tropics, \r\na positive trend in SAM and the poleward shift of the westerly jet. \r\nHowever, they fail to capture details of their observed climatology \r\nand variability (Simpson and Polvani, 2016), and the magnitudes of \r\nsimulated trends vary, though the models typically underestimate \r\nobserved trends in these processes (Purich et al., 2013; Staten et al., \r\n2018). In general, CMIP5 models do capture the SAM-regional \r\nrainfall association, although not consistently across all seasons \r\n(Purich et al., 2013; E.-P. Lim et al., 2016)."


id: 5906959b929edd2c33f89245c1bd2182
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12903
content:
"There is a  consistency in rainfall projections with the projections of \r\nrainfall drivers and with the general understanding of the influence \r\nof global warming on the circulation dynamics and rainfall patterns \r\nin the region. Thus, the expansion of the South Atlantic high-pressure "


id: bd6aae19c8bec62fbe8ec606804a03f5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 30578
content:
"Global models show strong consistency in a drying signal for the Cape \r\nTown region, with the reduction in total annual rainfall of up to 20% \r\nby the end of the 21st century in CMIP5 RCP8.5 and CMIP6 SSP5-8.5 "


id: 4db8d531be7528b798a11edf00fef3d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10948
content:
"system, related to widespread warming of the tropics and poleward \r\nshift of the subsiding limb of the Hadley cell, is associated with the \r\nsouthward displacement of the subtropical jet, and southward \r\nmigration of mid-latitude westerlies and storm tracks, in addition to \r\nchanges in the SAM (Section 10.6.2.4). These effects are also relatively \r\nconsistent with recent (post-1980s) declines in rainfall in the Cape Town \r\nregion. The storyline of an extended drought is thus a set of events that \r\ncan yield reduced rainfall in the Cape Town region: a poleward shift \r\nof the downward branch of the Hadley cell that produces a sustained \r\nsouthward shift in mid-latitude westerlies and storm tracks. The \r\nbehaviour is potentially reinforced by changes in the SAM."


id: f0f5c737fe919eb4a122fd3f3703f62d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39760
content:
"There is high agreement among observational data and reanalyses \r\nthat the recent (post-1979) downward trend in the Cape Town \r\nregion's rainfall leading to the 2015-2017 drought is related to \r\nthe hemispheric processes of poleward shift in the westerlies and \r\nexpansion of the Hadley circulation. However, there is less support \r\nfor the precipitation-circulation relationship in historical CMIP5 \r\nand CMIP6 simulations. As a  consequence, there is only medium \r\nconfidence that these process changes produced the 2015-2017 \r\ndrought leading to the 2018 water crisis."


id: 2dff48a5b0d8848b3e2572fc31af0b53
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 5369
content:
"Local geography gives rise to distinct differences in societal experience \r\nof the summer monsoon. The south-westerly monsoon winds are \r\nincident upon the Western Ghats mountains on the west coast, leading \r\nto orographic enhancement and heavy rains (Shige et al., 2017), which \r\nsupply rivers with water for much of the southern peninsula, often the \r\nsubject of inter-regional water disputes. The northern plains contain \r\nthe Ganges river and also India's most intensive agriculture, both rainfed \r\nand irrigated. Synoptic systems known as monsoon depressions cross \r\nthe northern east coast, supplying much of the rain in central India \r\n(Hunt and Fletcher, 2019). Further north, the eastern Himalayas  are \r\ndominated by the summer monsoon, while the western Himalayas \r\nreceive most rainfall from western disturbances during winter (Palazzi \r\net al., 2013). Meanwhile, south-eastern India sits under a rain shadow \r\n(the only region to receive more rainfall during the winter monsoon)."


id: a391abf167e294e6f1806b057722d505
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 25984
content:
"For the water-resource planner who has to deal with potential \r\ndrought like the 2015-2017 event, several lines of evidence \r\nindicate future drying: the projected precipitation by global models \r\nand RCMs of different spatial resolutions, and the observed and \r\nprojected changes of circulation patterns consistent with drier \r\nconditions, the paleoclimatic evidence confirming a millennial-scale \r\ncirculation-rainfall link. However, the distillation is limited by a lack \r\nof information about whether or not a relationship between Cape \r\nTown precipitation and large-scale circulation processes adequately \r\nexplains droughts in the twentieth century prior to 1979."


id: c47e7a191371c4984d25fcb36f661eab
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31873
content:
"India has one of the oldest rain-gauge networks in the world, leading \r\nto the production of numerous observational products (reviewed \r\nin Khouider et  al., 2020). Gridded gauge-based products dating \r\nback to the 19th  century reveal pronounced decadal variability \r\n(Sontakke et al., 2008). Trends for India over the whole 20th century \r\nare inconclusive (Knutson and Zeng, 2018), although declining over \r\ncentral and northern areas (Roxy et al., 2015). Assessment of multiple \r\nobservational datasets covering the Indian summer monsoon reveals \r\nsignificant declining rainfall over the second half of the 20th century \r\n(Section 8.3.2.4.1 and Figure 10.19c,d). A subsequent recovery has \r\nbeen noted since the early 2000s (Jin and Wang, 2017)."


id: 6e7c543f895012c3917fdf24f926c7aa
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16702
content:
"Thus, although a clear association appears in observations from 1979 \r\nonward between increasing GHG concentrations, drying in the Cape \r\nTown region and behaviour of a key circulation process, the SAM, \r\nfurther analysis suggests caution. Not all global models show \r\nthe historical post-1979 association among these factors, and when \r\nthe observational record is extended back further to times when the \r\nanthropogenic greenhouse forcing was weaker, there is no strong \r\nassociation between the SAM and Cape Town drought. Thus, there is \r\nonly medium confidence in the expectation of a future drier climate \r\nfor Cape Town."


id: c84e464c2084afb194d69f2502218bac
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32547
content:
"Observational products containing critical inhomogeneities in gauge \r\ndistribution and reporting over time are acknowledged as suitable for \r\nmesoscale analysis (Rajeevan and Bhate, 2009), while use for climate \r\ntrends requires consistent reporting over time from quality-controlled \r\ngauges (e.g., about 2000 gauges since the 1950s in Rajeevan et al., \r\n2006). A  newer 0.25deg-gridded product covering 1901 onwards \r\n(Pai et  al., 2014, 2015), based on Shepard's interpolation method \r\nfor irregularly-spaced stations (Shepard, 1968), shows increased \r\nintensity of daily rainfall and extremes over some regions, especially \r\nin the late-20th century. However, changes to the inputted gauges "


id: 4018c20343d6f9b3eef60b088be11792
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 8463
content:
"The Indian summer monsoon provides 80% of the country's annual \r\nrainfall from June to September, supplying the majority of water for \r\nagriculture, industry, drinking and sanitation to over a billion people. \r\nAny variations in the monsoon on time scales from days to decades can \r\nhave large impacts (Challinor et al., 2006; Gadgil and Gadgil, 2006). "


id: 30c60ca9e1906df89ddf21ecaa8cc267
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 5838
content:
"may have introduced an artificial jump in extreme rainfall since 1975 \r\nover central India (Section  10.2.2.3; Lin and Huybers, 2019). They \r\nsuggest that this method may have masked declines in mean rainfall \r\nand highlight the need for availability of raw gauge data to allow \r\ntransparent assessments. Khouider et  al. (2020) have successfully \r\ntested a  probabilistic interpolation method for India to overcome \r\nproblems inherent in algorithms based on inverse-distance weighting \r\nwhen applied to data-sparse regions. An example snapshot of the \r\nuneven distribution of rain gauges in a  common observational \r\nproduct is shown in Figure 10.19a."


id: e898c0e189397e1761efc14675dc247a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38261
content:
"is not in line with expectations arising from thermodynamic \r\nconstraints on the water cycle in a  warming world (Section  8.2.2) \r\nand has been regarded as a puzzle (Goswami et al., 2006). Assessing \r\nthe attribution of 20th-century changes to Indian rainfall is the \r\nsubject of coordinated modelling under the Global Monsoon MIP \r\n(GMMIP; Zhou et al., 2016), but is complicated by long-standing dry \r\nbiases in coupled CMIP3, CMIP5 (Sperber et al., 2013) and CMIP6 \r\n(Figure  10.19b) global models. These dry biases are connected to \r\na lower tropospheric circulation that is too weak (Sperber et al., 2013) \r\nand wet biases in the equatorial Indian Ocean (Bollasina and Ming, \r\n2013). Section  8.3.2.4.1 finds high confidence that anthropogenic \r\naerosol emissions have dominated the observed declining trends \r\nof countrywide Indian summer monsoon rainfall, consistent with \r\nfindings at the global-monsoon scale (Section 3.3.3.2)."


id: 2ab33028ba9d30f8936c01676752c3d0
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4736
content:
"The uncertainty among local and international observational products \r\nfor India can pose challenges when evaluating climate models \r\n(as in Section 10.2.2.6; Prakash et al., 2015). For the seasonal mean \r\nsummer monsoon rainfall, Collins et al. (2013a) found large biases \r\nseparating many CMIP5 models from the available observational \r\nproducts. However, for seasonal mean variability, the spread across \r\nobservational products was larger than across the CMIP5 ensemble."


id: 63e5ffcad6eca78dc847a334ce337a4
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14863
content:
"There is some evidence that declining Indian monsoon rainfall \r\nis due to regional SST warming patterns, themselves arising due \r\nto radiative forcing from GHG (e.g.,  in the Indian Ocean, Guemas \r\net al., 2013). Roxy et al. (2015) artificially raised SST in a GCM in \r\nthe equatorial Indian Ocean (the region of strongest observed SST \r\nwarming), leading to a weakened monsoon. Annamalai et al. (2013) \r\nused a  GCM to suggest instead that preferential warming of the \r\nwestern North Pacific may force a Rossby-wave response to its west \r\nthat weakens the monsoon through dry advection and subsidence. \r\nThese hypotheses are not borne out in GHG-forced future projections \r\n(Section 10.6.3.6)."


id: 4b8b7d98e03524d8d372b58b9f01c15a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 10550
content:
"A small anthropogenic contribution may be expected from local \r\nland-use/land-cover changes and land management. India is the \r\nworld's most irrigated region with around 0.5  mm/day in places, \r\nalthough peaking higher in summer (Cook et al., 2015b; McDermid \r\net  al., 2017). Including irrigation in GCMs and RCMs slows the \r\nmonsoon circulation and diminishes rainfall (Lucas-Picher et al., 2011; \r\nGuimberteau et al., 2012; Shukla et al., 2014; Tuinenburg et al., 2014; \r\nCook et al., 2015b) due to reduced surface temperature (Thiery et al., \r\n2017), reducing the monsoon wind and moisture fluxes towards "


id: 835a611dab531504bbe79c3128549caf
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20242
content:
"The interplay of these external and internal drivers is key to \r\nunderstanding past and future monsoon change."


id: 39a6d11922f41e58cbbddf52e89990ea
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7355
content:
"The robust decline of Indian summer monsoon rainfall averaged \r\nover India in the second half of the 20th century (Section 10.6.3.3) "


id: f3bd301e5d649f5396ac546c9e5aadc4
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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vector_id: 40261
content:
"India (Mathur and AchutaRao, 2020). However, implementation \r\nmethodologies for irrigation in climate models are simplified and \r\noften do not account for spatial heterogeneity while overestimating \r\ndemand and supply (Section  10.3.3.6; Nazemi and Wheater, 2015; \r\nPokhrel et al., 2016). Changing forest cover to agricultural land in \r\nan RCM (Paul et al., 2016) finds weakened summer monsoon rainfall \r\nespecially in central and eastern India, due to decreased local \r\nevapotranspiration. Decreased evapotranspiration from a  warmer \r\nsurface since the 1950s in the CMIP5 ensemble may also feedback \r\non the supply of moisture (Ramarao et al., 2015). Based on an AGCM \r\nstudy and literature review, Krishnan et al. (2016) support the role \r\nof land-use/land-cover change in adding to the effects of aerosol \r\nin weakening the monsoon, in addition to dynamic effects on the \r\ncirculation caused by rapid warming of the Indian Ocean."


id: c47281bdc5f9fa4c50617d04781151f1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31862
content:
"In addition to anthropogenic forcing, there is evidence that internal \r\nvariability in the Pacific is a significant driver. Huang et al. (2020b) \r\ncompared a  large perturbed-physics ensemble (HadCM3C) with \r\na SMILE for the historical period. Ensemble members replicating the \r\nnegative Indian rainfall trend were accompanied by a strong phase \r\nchange in the PDV from negative to positive, consistent with SST \r\nobservations. Jin and Wang (2017) have demonstrated increasing \r\nIndian monsoon rainfall since 2002 in a variety of observed datasets, \r\nsuggesting the increase is due either to a  change in dominance \r\nof a  particular forcing (for example from aerosol to GHG) or to \r\na change in phase of internal variability such as the PDV. Huang et al. \r\n(2020b) partially attribute the rainfall recovery to a  phase change \r\nin the PDV, supported by a SMILE study combined with reanalyses \r\n(Ha et al., 2020)."


id: 990a535537373405a19a73b9e46f9d0d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24142
content:
"In long-term projections, robust signals consist of a  weakened \r\nupper-tropospheric meridional temperature gradient, either due to \r\nupper-level heating over the tropical Pacific (Sooraj et al., 2015) or \r\nIndian oceans (Sabeerali and Ajayamohan, 2018) in CMIP5, and \r\nincreased seasonal mean rainfall, including in CMIP6 (Almazroui \r\net  al., 2020b; B. Wang et  al., 2021). The weakened temperature \r\ngradient combines with increased atmospheric stability to weaken \r\nthe monsoon overturning circulation, with some findings showing \r\nnorthward movement of the lower-tropospheric monsoon winds \r\nin response to a stronger land-sea temperature contrast in CMIP3 \r\nand CMIP5 (Sandeep and Ajayamohan, 2015; Endo et al., 2018). The \r\nnorthward shift was also found in the genesis of synoptic systems \r\n(monsoon depressions) in a  single high-resolution AGCM forced \r\nby an ensemble of SSTs derived from four GCMs under the RCP8.5 \r\nscenario (Sandeep et al., 2018)."


id: a44bf1366f09b9d0933af5ef6d30d890
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 26100
content:
"The drying trend of Indian summer monsoon rainfall since the \r\nmid-20th century can be attributed with high confidence to aerosol \r\nas the dominant anthropogenic forcing with a further contribution \r\nfrom internal variability, supported by the review of B. Wang et al. \r\n(2021) including CMIP6 results. Understanding the interplay \r\nbetween anthropogenic and internal drivers will be important for \r\nunderstanding future change."


id: 8a36c621fd54320b95d3d5feab34e977
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 21496
content:
"Projections can also be expressed in terms of global-mean warming \r\nlevels (GWLs) rather than time horizons (Cross-Chapter Box  11.1). \r\nAdvancing on SR1.5, amplification of mean and extreme monsoon \r\nrainfall at 2.0degC compared to 1.5degC has been found both by an \r\nAGCM forced by future SST patterns (Chevuturi et al., 2018) and by \r\nusing time slices in CMIP5 GCMs (Yaduvanshi et al., 2019; J. Zhang \r\net al., 2020). These findings are consistent with the general scaling \r\nof Indian monsoon precipitation per degree of warming in CMIP5 \r\n(Zhang et  al., 2019) and CMIP6 (B. Wang et  al., 2021). Increasing \r\nGWLs also lead to emergence of the anthropogenic signal over larger \r\nproportions of the South Asian region (Figure 10.15a)."


id: 3fb559a2dc61954e588d4f3dce6fdbc8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 8398
content:
"The AR5 (Christensen et  al., 2013) concluded that Indian summer \r\nmonsoon rainfall will strengthen under all RCP future climate \r\nscenarios, while the circulation will weaken (medium confidence). \r\nSR1.5 (Hoegh-Guldberg et al., 2018) found only low confidence in \r\nprojections of monsoon change at 1.5degC and 2degC, or any difference \r\nbetween them. The AR6 assessment of Chapter 8 (Section 8.4.2.4.1) \r\nfinds more precipitation in future projections (also depicted in \r\nFigure  10.19c,d,e), supported by reviews of CMIP3, CMIP5 and \r\nCMIP6 models (Turner and Annamalai, 2012; Kitoh, 2017; Z. Chen \r\net al., 2020; B. Wang et al., 2021)."


id: 381a849edafc839f4ae973bf105c068
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7072
content:
"Decomposition of the increased rainfall signal showed that while the \r\ndynamic component led to a drying tendency, this was overcome by \r\nthe thermodynamic contribution (Sooraj et al., 2015; Z. Chen et al., \r\n2020). Alternative decomposition experiments using AGCMs and \r\ntheir coupled counterparts found increases in the lower-tropospheric \r\ntemperature gradient and monsoon rainfall to be dominated by the \r\nfast radiative response to GHG increase rather than SST changes \r\n(Li and Ting, 2017; Endo et al., 2018). The response to SST forcing \r\nfeatured a large model spread, particularly arising from the dynamic "


id: 888cce90e9131c0edb602bca0761658d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 21188
content:
"Given the assessment for a  future wetter monsoon dominated by \r\nGHG emissions and attribution of the late-20th  century decline to \r\naerosol (Sections 8.3.2.4.1 and 10.6.3.5), the change between \r\ndominant forcings will lead, at some point, to a positive trend. For \r\nexample, RCP4.5 experiments in an AGCM forced by coupled model\u0002derived future SSTs showed continuation of 20th-century drying, "


id: c986c8d8a54499941bf6dc8517bc4114
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32780
content:
"component (Li and Ting, 2017). Chen and Zhou (2015) found that \r\nthe Indo-Pacific SST warming pattern dominated the uncertainty in \r\nIndian monsoon rainfall change. Finally, in assessing the relative \r\nimpact of CO2 radiative forcing and plant physiological changes in \r\nquadrupled CO2 experiments in four Earth system models, Cui et al. \r\n(2020) showed little impact of plant physiology on annual rainfall for \r\nthe Indian region."


id: 9eed1b3fa995da90731e3562d5b76b9b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25188
content:
"In summary, long-term future scenarios dominated by GHG increases \r\n(such as the RCPs) suggest increases in Indian summer monsoon \r\nrainfall (high confidence), dominated by thermodynamic mechanisms \r\nleading to increases in the available moisture. In the near-term, \r\nthere is high confidence (medium agreement, robust evidence) that \r\nincreased rainfall trends due to GHGs could be overcome by aerosol \r\nforcing or internal variability."


id: baafdf20d0f6f4fafd1c8f6ef8e6425e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 30063
content:
"Coordinated monsoon-relevant dynamical downscaling efforts \r\nsuch as CORDEX South Asia (Gutowski Jr. et  al., 2016; Choudhary \r\net al., 2018) are relevant to the Indian summer monsoon, first with \r\nassessment of their added value (Section 10.3.3.2 and Atlas.5.3.3). \r\nSingh et  al. (2017) compared nine CORDEX-South Asia RCMs \r\nagainst their driving CMIP5 GCMs, for present-day rainfall patterns \r\nand processes related to intra-seasonal variability. They found no "


id: a8a0b483c386a1de44078bbb10e7264e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 26876
content:
"increased rainfall, the downscaled ensemble depicted both increasing \r\nand decreasing trends, indicating uncertainty. However, key physical \r\nprocesses operating at below-GCM scale cannot be resolved nor \r\ncalibrated for, such as aspects of the flow around topography. \r\nThis is notably an issue given the resolution disparity between the \r\ndriving global models and output, and the regional challenges in \r\nobservational data used for calibration (Section 10.6.3.3)."


id: fbbabc2b4d9d83eff50d04ab38c8a13d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41658
content:
"Above, we presented assessments from observational and model \r\nattribution studies of the historical period, followed by future climate \r\nprojections in global and regional models, and storylines approaches \r\nincluding low-likelihood high impact events. Miscellaneous lines of \r\nevidence are considered here."


id: be72546aff3cf9e390fc866d70e976b3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 30771
content:
"There are mixed messages as to whether downscaling adds value to \r\nclimate projections of the Indian summer monsoon; however, there is \r\nhigh confidence in projections of precipitation changes in orographic \r\nregions given the consistent improved representation in these regions \r\namong several dynamical downscaling studies."


id: 57098c133dcd5edc902cb2ef9d5eb028
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12567
content:
"Our assessment could also be informed by attempting to constrain \r\nfuture projections of the Indian summer monsoon using paleoclimate \r\nevidence. In modelling work of the mid-Holocene (D'Agostino et al., \r\n2019), the increased obliquity (axial tilt) and altered orbital precession \r\nlead to an enhanced monsoon with a stronger dynamic component \r\n(strengthening the mean monsoon overturning) controlling the \r\nincrease in monsoon rainfall. In future climates however, the dynamic \r\ncontribution decreases (Section  10.6.3.6), yet the increased \r\nthermodynamic component (greater moisture availability) overcomes \r\nthis to cause a wetter monsoon. Monsoon changes under different \r\nepochs may not be governed by the same mechanisms (D'Agostino \r\net  al., 2019; Hill, 2019), making the mid-Holocene, in particular, \r\nunsuitable as a period to compare with."


id: d52950162dfeea8eae6a1f6ee6c26276
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 34825
content:
"Formal storyline approaches (see Box  10.2) have been used \r\ninfrequently for the Indian summer monsoon, representing \r\na  knowledge gap. In an expert-elicitation approach (Dessai et  al., \r\n2018), physically plausible futures substantiated by climate processes \r\nwere constructed, focusing on a river basin in southern India. Possible \r\noutcomes were framed based on changes in two drivers: availability \r\nof moisture from the Arabian Sea and strength of the low-level flow. \r\nThe narratives identified were able to explain 70% of the variance in \r\nmonsoon rainfall over 1979-2013, the implication being that climate \r\nuncertainties could be easily communicated to stakeholders in the \r\ncontext of present-day variability."


id: e8f1f94928f9af4897c9c30bcad8e722
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38334
content:
"Finally, the recent national climate-change assessment for India \r\n(Krishnan et  al., 2020) has distilled multiple lines of evidence to \r\nshow declining summer monsoon rainfall over the second half \r\nof the 20th  century, attributable to emissions of anthropogenic \r\naerosols, while future projections informed by CMIP5 modelling and \r\ndominated by GHG forcing show increased mean rainfall by the end \r\nof the 21st century."


id: 7b48cf8ddd59b66b562da711ebace325
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18893
content:
"The few studies on low-likelihood high-impact scenarios, often in \r\nsingle models, together with findings in SR1.5 (Hoegh-Guldberg et al., \r\n2018), noting the small radiative forcing in 1.5degC or 2degC scenarios, or \r\nthe absence of large aerosol emissions at the end of the 21st century \r\nin RCPs, give us low confidence in abrupt changes to the monsoon \r\non this time scale."


id: 759506e1495892d301b987a28660c304
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17917
content:
"Consequently, basin-wide, homogeneous, quality controlled \r\nobservational datasets are lacking, especially before the advent \r\nof substantial satellite observations in the 1970s. Observational \r\nuncertainties exist also for those regions that are covered by high \r\nquality networks such as European Climate Assessment & Dataset \r\n(ECA&D; Flaounas et al., 2012)."


id: 1a9bec6125c518bdbe9f7ea93a8d28d3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 1883
content:
"The Mediterranean region is loosely denoted as the region that \r\nsurrounds the Mediterranean Sea, and it is characterized by complex \r\norography and strong land-sea contrasts. The region contains a dense \r\nand growing human population, with large regional differences: \r\nwhereas the population of the European Mediterranean countries has \r\nbeen relatively stable or even declining during the past decades, the \r\npopulation of countries in Mediterranean areas of the Middle East and \r\nNorth Africa has quadrupled between 1960 and 2015, and the degree \r\nof urbanization has risen from 35 to 64% during the same period \r\n(Cramer et al., 2018) and during the more recent period 2000-2020 \r\nthe urban expansion rate has exceeded 5% (Kuang et al., 2021)."


id: 9b8f9bbd75cb0c43f3d4c7c68a1a84b9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24621
content:
"Large differences of up to 7degC between the CRU and UDEL datasets \r\nhave been found especially over mountainous areas, such as the Atlas \r\nin Morocco (Zittis and Hadjinicolaou, 2017; Strobach and Bel, 2019). \r\nBucchignani et al. (2016a, b) compared three different datasets (CRU, \r\nUDEL, and MERRA) with the available ground observations and found \r\nthat although the geographical distribution of the bias is qualitatively \r\nsimilar for the three datasets, differences exist, with the absolute \r\nbias being generally lower in Modern-Era Retrospective Analysis \r\nfor Research and Applications (MERRA) especially over North Africa \r\nduring the summer and winter season. There is high confidence that \r\nthe sparse monitoring network in parts of the Mediterranean region \r\nstrongly increases the uncertainty across different gridded datasets \r\n(Section 10.2.2.3, Figure 10.20b,c)."


id: 5b49da0eaf7f50e2790b6fa38d2e69e5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13307
content:
"The Mediterranean region has experienced significant climate variability \r\nover recent decades and has been affected in particular by severe \r\nheatwaves and droughts (Sections 8.3, 11.3, 11.6 and 12.4; Hoegh\u0002Guldberg et al., 2018). Increasing summer temperatures will enhance \r\nthe frequency and intensity of such extreme events and will cause \r\nadditional environmental and socio-economic pressure on the region."


id: 1250f7bd76721f3265353ee254b52d3e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 424
content:
"The attribution of observed Mediterranean summer warming to \r\nabove drivers and implications for future projections will be discussed \r\nin Sections 10.6.4.5 and 10.6.4.6."


id: c16d16fa05afa28a9f02bad01772449f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31334
content:
"Observational datasets show large agreement on the historical \r\n(1960-2014) temperature evolution at basin-wide scale (Figure 10.20e), \r\nwith an enhanced warming since the 1990s, and the early decades \r\nof the 21st century being on average approximately more than 1degC \r\nwarmer than late 19th  century levels (van der Schrier et  al., 2013; \r\nCramer et  al., 2018; Lionello and Scarascia, 2018; Figure  10.20e). "


id: 9e0260bdf22ade92b3dab4fa643a91dd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25035
content:
"The Mediterranean region spans a  wide variety of countries \r\nand economies. This has led to large differences in the existence and \r\navailability of observational records, with the southern part of the area \r\nbeing sparsely covered by meteorological stations (Figure 10.20b). "


id: 89b32d7fc99619fb3693d358d9372ce2
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 21400
content:
"Over recent decades, the surface air temperature of the Mediterranean \r\nincluding the Mediterranean Sea has warmed by around 0.4degC per \r\ndecade (Macias et al., 2013). Observed trends over land show large \r\ngeographical heterogeneity (Figure  10.20d) and notable differences \r\nexist amongst different datasets at grid point scale (Figure  10.20c; \r\nQasmi et al., 2021)."


id: af206d1e474796856c752106a0abf807
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 28056
content:
"Boers et al. (2017) who attribute it to the direct aerosol effect. Using \r\nmodel sensitivity experiments, Nabat et al. (2014) also associated the \r\nincrease in Mediterranean SST since 1980-2012 with the decrease in \r\naerosol concentrations (Atlas.8.2, Atlas.8.3 and Atlas.8.5)."


id: 80c0eb2b63332d72844de3d74f7a38
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19785
content:
"Several mechanisms have been proposed for the enhanced \r\nMediterranean warming, although their relative importance and the \r\npossible interplay between them are not fully understood. Circulation \r\nchanges might have contributed to this enhanced warming \r\n(Figure  10.20a). Sutton and Dong (2012) argued that the AMV \r\ninduced a shift around the 1990s towards warmer southern European \r\nsummers. This mechanism is associated with a  linear baroclinic \r\natmospheric response to the AMV-related surface heat flux. Also \r\nO'Reilly et al. (2017) related warm summer decades to the AMV, but \r\nthe connection was shown to be mainly thermodynamic. Qasmi et al. \r\n(2021) estimate an increase in Mediterranean summer temperature of \r\n0.2degC-0.8degC during a positive AMV."


id: 8a0fb631b8fc7a59e71611241761064e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 21462
content:
"Increased warming over land compared to the sea is expected due to \r\nthe lapse-rate changes associated with tropospheric moisture contrasts \r\n(Kroner et al., 2017; Byrne and O'Gorman, 2018; Brogli et al., 2019b; \r\nFigure  10.20a). Enhanced land-sea temperature contrast leads to \r\nrelative humidity and soil moisture feedbacks (Rowell and Jones, 2006), \r\nthe latter also depending on weather regimes (Quesada et al., 2012). \r\nThe globally enhanced land-sea contrast in near surface temperature \r\nis also a robust result in CMIP5 and CMIP6 models (Section 4.5.1.1)."


id: c785da6ab3627b87ef863f56393fcf4e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32413
content:
"Due to its semi-arid climate, strong atmosphere-land coupling has \r\ncontributed to the larger increase of mean summer temperature \r\ncompared to the increase of annual mean temperature (Seneviratne \r\net al., 2006). In particular, during drought spells, limits to evaporation \r\ndue to low soil moisture provide a positive feedback and enhances the \r\nintensity of heatwaves (Lorenz et al., 2016; Box 11.1). By comparing \r\nreanalysis-driven RCM simulations with observations, Knist et  al. \r\n(2017) found that RCMs are able to reproduce soil moisture interannual \r\nvariability, spatial patterns, and annual cycles of surface fluxes over \r\nthe period 1990-2008, revealing a strong land-atmosphere coupling \r\nespecially in southern Europe in summer. In addition cloud feedbacks \r\ncan modulate the Mediterranean summer temperature (Mariotti and \r\nDell'Aquila, 2012)."


id: d8b4dd86f0c90411df8d1fa747f15924
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35419
content:
"The observed trends over 1901-2010 are outside the range of internal \r\nvariability shown in CMIP5 pre-industrial control experiments and \r\nconsistent with, or greater than those simulated by experiments \r\nincluding both anthropogenic and natural forcings (Knutson et al., \r\n2013) and therefore partly attributable to anthropogenic forcing. \r\nThe decrease of anthropogenic aerosols over Europe including the \r\nMediterranean resulting from European de-industrialisation and \r\nair pollution policies (Turnock et  al., 2016) has been highlighted \r\nas an important contributor to the observed warming (Ruckstuhl \r\net al., 2008; Philipona et al., 2009; de Laat and Crok, 2013; Nabat \r\net al., 2014; Besselaar et al., 2015; Dong et al., 2017; Boe et al., 2020a). \r\nPfeifroth et al. (2018) argue that this brightening is mainly due to \r\ncloud changes caused by the indirect aerosol effect with a minor role \r\nfor the direct aerosol effect, in contrast to Nabat et al. (2014) and "


id: fc31e361a6a3a8559d185f3e3dd3e22c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41736
content:
"can contribute to the enhanced warming by land surface feedbacks \r\n(Whan et al., 2015; Lorenz et al., 2016; Russo et al., 2019). A negative \r\nfeedback to this dryness-induced warming might be provided by an \r\nenhanced moisture transport into the dry area associated with the \r\ndynamical response of the atmosphere (Zhou et al., 2021). Due to \r\nthe arid climate, no positive soil moisture-temperature feedback is \r\nfound over the North African regions of the Mediterranean, where \r\nthe surface energy budget is mostly governed by radiative cooling \r\n(Lelieveld et al., 2016), implying that soil moisture feedbacks are not \r\ncontributing to enhanced warming over those regions."


id: 94b6e6f1b7b3aa7eb805c1386544f2bb
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 23370
content:
"As noted in Section 10.6.4.4, the Mediterranean summer climate is \r\naffected by large-scale circulation patterns, of which the summer \r\nNAO is the most important (Folland et al., 2009; Blade et al., 2012). \r\nBarcikowska et  al. (2020) highlight the importance of correctly \r\nsimulating the summer NAO impact on the Mediterranean climate, \r\nas it partly offsets the anthropogenic warming signal in the western \r\nand central Mediterranean."


id: 1f1d4b0f5543d9d11d9376a32f5832f9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 2682
content:
"Climate models project a reduction in precipitation in all seasons, and \r\na northward and eastward expansion of the Mediterranean climate, \r\nwith the affected areas becoming more arid with an increased \r\nsummer drying (Atlas.8.5; Alessandri et al., 2015; Mariotti et al., 2015; \r\nRajczak and Schar, 2017; Waha et  al., 2017; Barredo et  al., 2018; \r\nLionello and Scarascia, 2018; Spinoni et al., 2018, 2020). The drying "


id: 5ee9d01e7b29894d1edeb292b246bf5d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 13994
content:
"Over the Mediterranean region, daily maximum temperature is \r\nprojected to increase more than the daily minimum. Consequently, \r\nthe difference between daytime maxima and nighttime minima is "


id: 1dbdfafa3b25eefc7f3cac28101c8268
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 2417
content:
"has been addressed in many studies since AR5. Recent examples \r\nof dynamical downscaling are EURO-CORDEX (Jacob et  al., 2014) \r\nand Med-CORDEX (Ruti et al., 2016; Somot et al., 2018), but earlier \r\nactivities have included ENSEMBLES (Deque et al., 2012; Fernandez \r\net  al., 2019), PRUDENCE (Christensen et  al., 2002), CIRCE (Gualdi \r\net al., 2013) and ESCENA (Jimenez-Guerrero et al., 2013)."


id: 2c7581dfc54be00f2387f50795a8be10
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 5087
content:
"expected to increase, particularly in summer (Lionello and Scarascia, \r\n2018). Temperature extremes will be affected as well, with a dramatic \r\nincrease in the number of warm days and reduction of cold nights \r\n(Section  11.9; Lionello and Scarascia, 2020). The Mediterranean \r\nsummer warming will also increase the frequency and intensity of \r\nheatwaves (Section 11.9)."


id: be116198179b09dfb9bd79eed24a882e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 30707
content:
"From an analysis of CORDEX results, studies showed that southern \r\nEurope is projected to face a  robust non-linear increase in \r\ntemperature larger than the global mean (Zittis et al., 2019), EURO\u0002CORDEX projections, that are driven by CMIP5 global models, \r\nproject a less pronounced warming than that of CMIP6 (Coppola \r\net al., 2021; see Figure 10.21c). The non-linear increase is especially \r\nevident for both hot and cold extremes (Section 11.9; Maule et al., \r\n2017; Jacob et al., 2018; Kjellstrom et al., 2018). In particular, Dosio \r\nand Fischer (2018) showed that in many places in southern Europe \r\nand the Mediterranean, the increase in the number of nights "


id: 1ed724237123066d893537d5895e1ce0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 2628
content:
"To unravel the complex interactions and feedbacks over the region on \r\na range of spatial and temporal scales, regional downscaling projects \r\nare being developed to provide more comprehensive and detailed \r\ninformation on the future of the Mediterranean. The importance \r\nof regional downscaling for investigating the subregional details \r\ncaused by the complex morphology of the Mediterranean region \r\nis a well-known issue in the literature (Planton et al., 2012), which "


id: 393fe194d518e2ee3687d6b501a1dc54
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 7284
content:
"with temperature above 20degC is more than 60% larger under \r\n2degC warming compared to 1.5degC. Over the region, the projected \r\ntemperature increase, including a  higher probability of severe \r\nheatwaves (Russo et al., 2015), is accompanied by a reduction in \r\nprecipitation (Jacob et  al., 2014; Dosio, 2016; Rajczak and Schar, \r\n2017), resulting in projected increases of drought frequency and \r\nseverity (Spinoni et al., 2018, 2020; Raymond et al., 2019). Also, the \r\nfrequency and severity of marine heatwaves of the Mediterranean \r\nSea are projected to increase (Darmaraki et  al., 2019; see \r\nSection 12.4 and Atlas.8.4)."


id: 8e911c27dd685b024054bf0da4299cc0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 22262
content:
"Only a  limited number of RCM simulations for the MENA domain \r\nare currently available. For the southern and eastern Mediterranean, \r\nthey project a mean warming ranging from 3degC for RCP4.5 to 9degC \r\nfor RCP8.5 at the end of this century compared to its beginning \r\n(Bucchignani et  al., 2018; Ozturk et  al., 2018). The frequency and \r\nduration of heatwaves and annual number of extremely hot days \r\n(i.e., those with maximum temperature >50degC) in the southern \r\nMediterranean will increase substantially. For 2070-2099 with \r\nrespect to 1971-2000 the latter might even reach 70 days for \r\nRCP8.5 (Lelieveld et al., 2016; Almazroui, 2019; Driouech et al., 2020; \r\nVarela et al., 2020)."


id: 915790c1c9cff4bd11ef9b9e7e414aa3
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 22774
content:
"Low-likelihood high-impact events might affect future Mediterranean \r\nclimate. An example of such an event is the collapse of the AMOC \r\n(Weijer et al., 2019), that would bring widespread cooling over the \r\nNorthern Hemisphere. For the Mediterranean this is estimated to be \r\na few degrees Celsius during summer in the case of a total collapse \r\n(Jackson et al., 2015)."


id: 14c2bfe6aef70c4eb8cc403aefc4ef95
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 880
content:
"There is very high confidence (high agreement, robust evidence) that \r\nthe Mediterranean region has experienced a  summer temperature \r\nincrease in recent decades that is faster than the increase for \r\nthe Northern Hemisphere summer mean. There is also very high \r\nconfidence (high agreement, robust evidence) that the projected \r\nMediterranean summer temperature increase will be larger than the \r\nglobal warming level, with an increase in the frequency and intensity \r\nof heatwaves."


id: 578c219a4ac7e16a8cc1dc0ba8bfceba
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 12668
content:
"Statistical downscaling studies for the Mediterranean confirm the \r\nresults from global model and RCM studies, with large agreement \r\namong future projections showing lower rates of warming in winter \r\nand spring, and, in most cases, higher ones in summer and autumn \r\n(Jacobeit et al., 2014)."


id: 3450c35994590de7156448a30d239868
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 6442
content:
"The atmospheric circulation is influenced by large-scale, often \r\nslowly varying components of the climate system, such as ocean, \r\nsea ice and soil moisture. Historical and future changes of the "


id: 425923125a6c5c3dc108c0cb575e3246
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 8871
content:
"Urban areas have special interactions with the climate system that produce heat islands. This box presents information about these \r\nprocesses, how they are parametrized in climate modules, and on the role of urban monitoring networks. A discussion on the observed \r\nclimate trends and climate change projections for urban areas follows."


id: 54f4206b48e1d417574726e3ed7901b0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 12218
content:
"There is medium evidence but high agreement (Parker, 2010; Zhang et al., 2013; H. Chen et al., 2016) that the global annual mean \r\nsurface air temperature response to urbanization is negligible. There is very high confidence that the different observed warming trend \r\nin cities as compared to their surroundings can partly be attributed to urbanization (Box 10.3, Figure 1; Park et al., 2017)."


id: dd28519b760da8f322290b2842d1adc2
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 36174
content:
"There is very high confidence (robust evidence and high agreement) that the annual mean minimum temperature is more affected by \r\nurbanization than the maximum temperature (Ezber et al., 2007; Fujibe, 2009; Hamdi, 2010; Elagib, 2011; Camilloni and Barrucand, \r\n2012; Hausfather et al., 2013; Robaa, 2013; Argueso et al., 2014; Alghamdi and Moore, 2015; Alizadeh-Choobari et al., 2016; Sachindra \r\net al., 2016; Liao et al., 2017; Lokoshchenko, 2017; J. Wang et al., 2017; Arsiso et al., 2018). Beside temperature, urbanization can \r\ninduce an urban dryness island, which refers to lower relative humidity in cities than in nearby rural locations (Lokoshchenko, 2017; \r\nBian et al., 2020) and the urban wind island, where slower wind speeds are observed in cities (Wu et al., 2017; Bader et al., 2018; Peng \r\net al., 2018). There is medium confidence (medium evidence and medium agreement) (Schlunzen et al., 2010; Ganeshan et al., 2013; "


id: adae23c69bf28dcf5479fa91e92cc1a7
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 27798
content:
"Ganeshan and Murtugudde, 2015; Haberlie et al., 2015; Daniels et al., 2016; Liang and Ding, 2017; McLeod et al., 2017; Y. Li et al., 2020b) \r\nthat cities induce increases in mean and extreme precipitation over and downwind of the city especially in the afternoon and early evening."


id: 3d46bce84e686269b556b002a4229277
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 7961
content:
"Estimates of the urban heat island under further climate change are very uncertain because studies using different methods report \r\ncontrasting results. However, there is very high confidence (robust evidence and high agreement) that the projected change of the urban \r\nheat island under climate change conditions is one order of magnitude less than the projected warming in both urban and rural areas under \r\nsimulation constraints of no urban growth (McCarthy et al., 2010, 2012; Oleson et al., 2011; Fruh et al., 2011; Adachi et al., 2012; Kusaka et \r\nal., 2012a; Oleson, 2012; Hamdi et al., 2014; Sachindra et al., 2016; Hatchett et al., 2016; Arsiso et al., 2018; Hoffmann et al., 2018)."


id: 98052459b3f6f81821d04ab0f5aa4997
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 23961
content:
"Combining climate change conditions together with urban growth scenarios, there is very high confidence (robust evidence and \r\nhigh agreement) that future urbanization will amplify the projected air temperature warming irrespective of the background climate \r\n(Georgescu et al., 2013; Argueso et al., 2014; Mahmood et al., 2014; Doan et al., 2016; Kim et al., 2016; Kusaka et al., 2016; Grossman\u0002Clarke et al., 2017; Kaplan et al., 2017; X. Li et al., 2018). Urbanization will have a strong influence on minimum temperatures that \r\ncould be locally comparable in magnitude to the global GHG-induced warming (Berckmans et al., 2019). There is very high confidence\r\n(robust evidence and high agreement) for the combination of future urban development and more frequent occurrence of extreme \r\nclimatic events, such as heatwaves (Hamdi et al., 2016; Bader et al., 2018; He et al., 2021)."


id: 23e8a2b812896dc0debbf555b064fb2a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 3503
content:
"The choice of urban planning scenarios and RCM projections shows a large sensitivity during nighttime, up to 0.6degC (Kusaka et al., 2016). \r\nThe sensitivity is significantly less than the uncertainties arising from global emissions scenarios or global model projections. However, \r\nthere is a large difference between RCM simulations with and without urban land use, indicating that this impact is comparable to \r\nthe uncertainties related to the use of different global model projections (Hamdi et al., 2014; Kusaka et al., 2016; Daniel et al., 2019). \r\nTherefore, impact assessments and adaptation plans for urban areas require high spatial resolution climate projections along with \r\nmodels that represent urban processes, ensemble dynamical and statistical downscaling, and local-impact models (Masson et al., \r\n2014; Baklanov et al., 2018, 2020; Duchene et al., 2020; Schoetter et al., 2020; Le Roy et al., 2021; Zhao et al., 2021)."


id: a5ea9911d56eeec57559ce8dcace215a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 26416
content:
"Contributors: Bhupesh Adhikary (Nepal), Muhammad Adnan (Pakistan), Muhammad Amjad (Pakistan), Subimal Ghosh (India), \r\nRafiq Hamdi (Belgium), Akm Saiful Islam (Bangladesh), Richard G. Jones (United Kingdom), Martin Jury (Austria), Asif Khan (Pakistan), \r\nAkio Kitoh (Japan), Krishnan Raghavan (India), Lucas Ruiz (Argentina), Laurent Terray (France)"


id: f5f7459e0201b9080c6b6f334e05ac3e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 40629
content:
"The Hindu Kush Himalaya (HKH) constitutes the largest glacierized region outside the poles and provides the headwaters for several \r\nmajor rivers (Sharma et al., 2019). Since the 1960s, the HKH has experienced significant trends in the mean and extremes of temperature \r\nand precipitation, accompanied by glacier mass loss and retreat, snowmelt and permafrost degradation (Yao et al., 2012a, b; Azam \r\net al., 2018; Bolch et al., 2019; Krishnan et al., 2019a, b; Chug et al., 2020; Sabin et al., 2020). Observational uncertainty and lack \r\nof consistent, high-quality datasets hamper reliable assessments of climate change and model evaluation over several mountain \r\nareas, including the HKH (Section 10.2.2). This box assesses observed and projected climate change in the extended HKH (outline in \r\nCross-Chapter Box 10.4, Figure 1a), in which we include the Tibetan Plateau (TP) and Pamir mountains."


id: 841fb081f4ac22e09219d07f9dbaf288
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 20404
content:
"Little evidence was presented in the AR5 (IPCC, 2014) other than increased minimum and maximum temperature trends in the western \r\nHimalaya (Hartmann et al., 2013). The SROCC assessed that HKH (named High Mountain Asia) surface-air temperature has warmed \r\nmore rapidly than the global mean over recent decades (high confidence). Annual mean HKH surface air temperature increased \r\nsignificantly (about 0.1degC per decade) over 1901-2014 (Ren et al., 2017), although Cross-Chapter Box 10.4, Figure 1d shows an \r\nobservational range of 0.20degC-0.25degC per decade over 1961-2014. There is a rising trend of extreme warm events and fewer extreme \r\ncold events over 1961-2015 (Krishnan et al., 2019b; Wester et al., 2019). However, summer cooling over the Karakoram (western \r\nHKH) was reported for 1960-2010 (Forsythe et al., 2017). A key relevant process is elevation-dependent warming (EDW; reviewed in"


id: d9d09d633023c6e2f7138c691f214864
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 35620
content:
"The assessments in this chapter are based on a  rapidly growing \r\nbody of evidence from the peer-reviewed literature, most of which \r\nwas not previously considered by IPCC reports. Several challenges \r\nin the construction of regional climate change information have \r\nbeen identified:"


id: 66a7af84543490297a3bc8a442ade175
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 15317
content:
"Addressing these challenges could facilitate the assessment of both \r\nsources and methodologies that lead to an increased fitness and \r\nusefulness of regional climate information for a wide range of purposes."


id: affba9cec4512108d7ae6132d910c768
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 28203
content:
"We acknowledge the E-OBS dataset and the data providers in the \r\nECA&D project (https://www.ecad.eu) for their help and the Japan \r\nAerospace Exploration Agency (JAXA) for delivering the  GSMaP \r\n(Global Satellite Mapping of Precipitation) data to us. The invaluable \r\ncontributions from Lisa van Aardenne (South Africa), Peng Cai \r\n(China), Joseph Ching (China), Huili He (China), Kenshi  Hibino \r\n(Japan), Yukiko  Imada (Japan), Nazrul Islam (Saudi Arabia), \r\nIsadora Christel Jimenez (Spain) and Misako Kachi (Japan) are also \r\ngreatly acknowledged. We acknowledge the World Climate Research \r\nProgramme for coordinating the modelling intercomparison projects \r\nCMIP and CORDEX and thank the climate modelling groups for \r\nproducing and making available their model output."


id: adcf021686019e464f1c8041f399ee79
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 27823
content:
"The world is physically and culturally diverse, and the challenges posed by climate change vary by region and \r\nlocation. Because climate change affects so many aspects of people's daily work and living, climate change \r\ninformation can help with decision-making, but only when the information is relevant for the people involved in \r\nmaking those decisions. Users of climate information may be highly diverse, ranging from professionals in areas \r\nsuch as human health, agriculture or water management to a broader community that experiences the impacts \r\nof changing climate. Providing information that supports response actions thus requires engaging all relevant \r\nstakeholders, their knowledge and their experiences, formulating appropriate information, and developing \r\na mutual understanding of the usefulness and limitations of the information."


id: f225a57943f32e0c41fdbc16c95e02ae
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 39997
content:
"Forging partnerships among all involved with producing, exploring and distilling climate data into climate \r\ninformation is at the centre of creating stakeholder-relevant information. These partnerships can occur through \r\ndirect interaction between climate scientists and stakeholders as well as through organizations that have \r\nemerged to facilitate this process, such as climate services, national and regional climate forums, and consulting \r\nfirms providing specialized climate information. These so-called 'boundary organizations' can serve the varied \r\nneeds of all who would fold climate information into their decision processes. All of these partnerships are vital "


id: 9154aa8fc123cb4fce00ba48df02f069
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 22772
content:
"for arriving at climate information that responds to physical and cultural diversity and to challenges posed by \r\nclimate change that can vary region-by-region around the world."


id: 6b805510f365079eaf0f125dff059ccf
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 16217
content:
"In decision-making, climate information is more useful if the physical and cultural diversity across the world is considered."


id: d4a006790337819e12c1948cb89acdaf
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 34727
content:
"FAQ 10.1, Figure 1 | Climate information for decision makers is more useful if the physical and cultural diversity across the world is \r\nconsidered. The figure illustrates schematically the broad range of knowledge that must be blended with the diversity of users to distil information that will \r\nhave relevance and credibility. This blending or distillation should engage the values and knowledge of both the stakeholders and the scientists. The bottom \r\nrow contains examples of stakeholders' interests and is not all-inclusive. As part of the distillation, the outcomes can advance the United Nations' Sustainable \r\nDevelopment Goals, covered in part by these examples."


id: ca91b07864997e8d8b355b0851fe2f33
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 32981
content:
"Urban areas experience air temperatures that can be several degrees Celsius warmer than surrounding areas, \r\nespecially during the night. This 'urban heat island' effect results from several factors, including reduced \r\nventilation and heat trapping due to the close proximity of tall buildings, heat generated directly from human \r\nactivities, the heat-absorbing properties of concrete and other urban building materials, and the limited amount \r\nof vegetation. Continuing urbanization and increasingly severe heatwaves under climate change will further \r\namplify this effect in the future."


id: a318ee23535a48b0ff9581bb16201551
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 25898
content:
"Today, cities are home to 55% of the world's population. This number is increasing, and every year cities welcome \r\n67 million new residents, 90% of whom are moving to cities in developing countries. By 2030, almost 60% of \r\nthe world's population is expected to live in urban areas. Cities and their inhabitants are highly vulnerable to \r\nweather and climate extremes, particularly heatwaves, because urban areas already are local hotspots. Cities are \r\ngenerally warmer - up to several degrees Celsius at night - than their surroundings. This warming effect, called \r\nthe urban heat island, occurs because cities both receive and retain more heat than the surrounding countryside \r\nareas and because natural cooling processes are weakened in cities compared to rural areas."


id: dd4f6ba6866b0ce3c0167c0e15648821
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 36196
content:
"Three main factors contribute to amplify the warming of urban areas (orange bars in FAQ 10.2, Figure 1). The \r\nstrongest contribution comes from urban geometry, which depends on the number of buildings, their size and \r\ntheir proximity. Tall buildings close to each other absorb and store heat and also reduce natural ventilation. \r\nHuman activities, which are very concentrated in cities, also directly warm the atmosphere locally, due to heat \r\nreleased from domestic and industrial heating or cooling systems, running engines, and other sources. Finally, \r\nurban warming also results directly from the heat-retaining properties of the materials that make up cities, \r\nincluding concrete buildings, asphalt roadways, and dark rooftops. These materials are very good at absorbing \r\nand retaining heat, and then re-emitting that heat at night."


id: 49f533c513e0532d3a50b57ea572c13c
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 10256
content:
"The urban heat island effect is further amplified in cities that lack vegetation and water bodies, both of which can \r\nstrongly contribute to local cooling (green bars in FAQ 10.2, Figure 1). This means that when enough vegetation \r\nand water are included in the urban fabric, they can counterbalance the urban heat island effect, to the point of \r\neven cancelling out the urban heat island effect in some neighbourhoods."


id: 4026b2f62d8f011c802d28c79adb2c69
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 8476
content:
"The urban heat island phenomenon is well-known and understood. For instance, temperature measurements \r\nfrom thermometers located in cities are corrected for this effect when global warming trends are calculated. \r\nNevertheless, observations, including long-term measurements of the urban heat island effect are currently too \r\nlimited to allow a full understanding of how the urban heat island varies across the world and across different \r\ntypes of cities and climatic zones, or how this effect will evolve in the future."


id: fd713a655131c694c58cc7018b48a38b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 41942
content:
"As a result, it is hard to assess how climate change will affect the urban heat island effect, and various studies \r\ndisagree. Two things are, however, very clear. First, future urbanization will expand the urban heat island areas, \r\nthereby amplifying future warming in many places all over the world. In some places, the nighttime warming \r\nfrom the urban heat island effect could even be on the same order of magnitude as the warming expected from \r\nhuman-induced climate change. Second, more intense, longer and more frequent heatwaves caused by climate \r\nchange will more strongly impact cities and their inhabitants, because the extra warming from the urban heat \r\nisland effect will exacerbate the impacts of climate change."


id: 70b5e81b3f7f6aa6ff0275ba2d7e4d63
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 17086
content:
"In summary, cities are currently local hotspots because their structure, material and activities trap and release \r\nheat and reduce natural cooling processes. In the future, climate change will, on average, have a limited effect \r\non the magnitude of the urban heat island itself, but ongoing urbanization together with more frequent, longer \r\nand warmer heatwaves will make cities more exposed to global warming."


id: 859ba1960a50b16532040b7d0428e439
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 20646
content:
"Cities are usually warmer than their surrounding areas due to factors that trap and release heat\r\nand a lack of natural cooling influences, such as water and vegetation."


id: a8ddfe49293af70bc551d16daa047bc6
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 26918
content:
"FAQ 10.2, Figure 1 | Efficiency of the various factors at warming up or cooling down neighbourhoods of urban areas. Overall, cities tend to be \r\nwarmer than their surroundings. This is called the 'urban heat island' effect. The hatched areas on the bars show how the strength of the warming or cooling \r\neffects of each factor varies depending on the local climate. For example, vegetation has a stronger cooling effect in temperate and warm climates. Further \r\ndetails on data sources are available in the chapter data table (Table 10.SM.11)."


id: 2882c4bd37adaed0dc71a80b2f0cf482
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 4334
content:
"Zscheischler, J. et al., 2018: Future climate risk from compound events. Nature \r\nClimate Change, 8(6), 469-477, doi:10.1038/s41558-018-0156-3."


id: 3b2bf769cf42a7597131097415f96f89
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 7595
content:
"Muhammad Adnan (Pakistan), Wafae Badi (Morocco), Claudine Dereczynski (Brazil), \r\nAlejandro  Di  Luca (Australia/Canada/Argentina), Subimal Ghosh (India), Iskhaq Iskandar \r\n(Indonesia), James Kossin (United States of America), Sophie Lewis (Australia), Friederike Otto \r\n(United Kingdom/Germany), Izidine Pinto (South Africa/Mozambique), Masaki Satoh (Japan), \r\nSergio M. Vicente-Serrano (Spain), Michael Wehner (United States of America), Botao Zhou (China)"


id: c35397fb5595230807c4cfc9d60b30fa
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 31637
content:
"Qiaohong Sun (Canada/China), Ying Sun (China), Mouhamadou Bamba Sylla (Rwanda/Senegal), \r\nClaudia Tebaldi (United States of America), Laurent Terray (France), Wim Thiery (Belgium), \r\nJessica  Tierney (United States of America), Maarten K. van Aalst (The Netherlands), \r\nBart van den Hurk (The Netherlands), Robert Vautard (France), Wen Wang (China), Seth Westra \r\n(Australia), Jakob Zscheischler (Germany) "


id: 1e6d02b41f3fc460d361302243551d0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 2553
content:
"Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, \r\nF.  Otto, I.  Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme \r\nEvents in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group \r\nI  to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, \r\nA. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, \r\nJ.B.R.  Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 1513-1766, doi:10.1017/9781009157896.013."


id: c01e7a9c29f26e49b80906d00329a1e1
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 31091
content:
"global warming. Projected percentage changes in frequency are \r\nhigher for the rarer extreme events (high confidence). {11.1, 11.2, \r\n11.3, 11.4, 11.6, 11.9, Cross-Chapter Box 11.1}"


id: 2039f96eb1470b10cede0ef9bd0b276f
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 2885
content:
"It is an established fact that human-induced greenhouse gas \r\nemissions have led to an increased frequency and/or intensity \r\nof some weather and climate extremes since pre-industrial \r\ntime, in particular for temperature extremes. Evidence of \r\nobserved changes in extremes and their attribution to human \r\ninfluence (including greenhouse gas and aerosol emissions and land\u0002use changes) has strengthened since AR5, in particular for extreme \r\nprecipitation, droughts, tropical cyclones and compound extremes \r\n(including dry/hot events and fire weather). Some recent hot extreme \r\nevents would have been extremely unlikely to occur without human \r\ninfluence on the climate system. {11.2, 11.3, 11.4, 11.6, 11.7, 11.8}"


id: bd619706b1bc9a4106ece3483d07a417
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 30569
content:
"The frequency and intensity of hot extremes (including \r\nheatwaves) have increased, and those of cold extremes have \r\ndecreased on the global scale since 1950 (virtually certain). \r\nThis also applies at regional scale, with more than 80% of AR6 \r\nregions1 showing similar changes assessed to be at least likely.\r\nIn a few regions, limited evidence (data or literature) prevents the \r\nreliable estimation of trends. {11.3, 11.9}"


id: 8078cea2eba4f23f88d17f20a124e19b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 19732
content:
"Regional changes in the intensity and frequency of climate \r\nextremes generally scale with global warming. New evidence \r\nstrengthens the conclusion from the IPCC Special Report on \r\nGlobal Warming of 1.5degC (SR1.5) that even relatively small \r\nincremental increases in global warming (+0.5degC) cause \r\nstatistically significant changes in extremes on the global scale \r\nand for large regions (high confidence). In particular, this is the \r\ncase for temperature extremes (very likely), the intensification \r\nof heavy precipitation (high confidence) including that \r\nassociated with tropical cyclones (medium confidence), and \r\nthe worsening of droughts in some regions (high confidence). \r\nThe occurrence of extreme events unprecedented in the observed \r\nrecord will rise with increasing global warming, even at 1.5degC of "


id: 2faa51f4cf6b0b6136a444b865e0b40b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 5646
content:
"Human-induced greenhouse gas forcing is the main driver \r\nof the observed changes in hot and cold extremes on the \r\nglobal scale (virtually certain) and on most continents (very \r\nlikely). The effect of enhanced greenhouse gas concentrations on \r\nextreme temperatures is moderated or amplified at the regional \r\nscale by regional processes such as soil moisture or snow/ice-albedo \r\nfeedbacks, by regional forcing from land-use and land-cover changes, \r\nor aerosol concentrations, and decadal and multi-decadal natural \r\nvariability. Changes in anthropogenic aerosol concentrations have \r\nlikely affected trends in hot extremes in some regions. Irrigation and \r\ncrop expansion have attenuated increases in summer hot extremes \r\nin some regions, such as the Midwestern USA (medium confidence). "


id: c7b170e3f2edd33192661d8eb14ace76
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 32441
content:
"Urbanization has likely exacerbated changes in temperature extremes \r\nin cities, in particular for nighttime extremes. {11.1, 11.2, 11.3}"


id: 9b46478ba2aec7b5d4e5dabee70b902e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 24564
content:
"heavy precipitation will follow the rate of increase in the maximum \r\namount of moisture that the atmosphere can hold as it warms (high \r\nconfidence), of about 7% per 1degC of global warming. The increase in \r\nthe frequency of heavy precipitation events will be non-linear with \r\nmore warming and will be higher for rarer events (high confidence), \r\nwith a likely doubling and tripling in the frequency of 10-year and \r\n50-year events, respectively, compared to the recent past at 4degC of \r\nglobal warming. Increases in the intensity of extreme precipitation \r\nat regional scales will vary, depending on the amount of regional \r\nwarming, changes in atmospheric circulation and storm dynamics \r\n(high confidence). {11.4, Box 11.1}"


id: 5ea4579200ed2877e78d28cd54f3d737
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 13939
content:
"The projected increase in the intensity of extreme precipitation \r\ntranslates to an increase in the frequency and magnitude \r\nof pluvial floods  - surface water and flash floods  - (high \r\nconfidence), as pluvial flooding results from precipitation \r\nintensity exceeding the capacity of natural and artificial \r\ndrainage systems. {11.4}"


id: 5696e4644edb66a20951fbe349d4061a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 12488
content:
"Significant trends in peak streamflow have been observed \r\nin some regions over the past decades (high confidence). \r\nThe  seasonality of river floods has changed in cold regions where \r\nsnow-melt is involved, with an earlier occurrence of peak streamflow \r\n(high confidence). {11.5}"


id: 570c0c6979b1b711ac7e68de47f1d52a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 12569
content:
"Global hydrological models project a larger fraction of land \r\nareas to be affected by an increase in river floods than by \r\na  decrease in river floods (medium confidence). Regional \r\nchanges in river floods are more uncertain than changes in pluvial \r\nfloods because complex hydrological processes and forcings, \r\nincluding land cover change and human water management, are \r\ninvolved. {11.5}"


id: 77f15526ed3617d11fc652eed013ab4a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 18304
content:
"The frequency and intensity of heavy precipitation events have \r\nlikely increased at the global scale over a  majority of land \r\nregions with good observational coverage. Heavy precipitation \r\nhas likely increased on the continental scale over three \r\ncontinents: North America, Europe, and Asia. Regional increases \r\nin the frequency and/or intensity of heavy precipitation have been \r\nobserved with at least medium confidence for nearly half of AR6 \r\nregions, including WSAF, ESAF, WSB, SAS, ESB, RFE, WCA, ECA, TIB, \r\nEAS, SEA, NAU, NEU, EEU, GIC, WCE, SES, CNA, and ENA. {11.4, 11.9}"


id: 917fed402985e2b6f89d11c2e8f85d86
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 22812
content:
"Different drought types exist, and they are associated with \r\ndifferent impacts and respond differently to increasing \r\ngreenhouse gas concentrations. Precipitation deficits and changes \r\nin evapotranspiration govern net water availability. A lack of sufficient \r\nsoil moisture, sometimes amplified by increased atmospheric \r\nevaporative demand, results in agricultural and ecological drought. \r\nLack of runoff and surface water result in hydrological drought. {11.6}"


id: 4e48adea31a817c2c507b2cbba1e687a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 11044
content:
"Human influence, in particular greenhouse gas emissions, \r\nis likely the main driver of the observed global-scale \r\nintensification of heavy precipitation over land regions. It is \r\nlikely that human-induced climate change has contributed to the \r\nobserved intensification of heavy precipitation at the continental \r\nscale in North America, Europe and Asia. Evidence of a  human \r\ninfluence on heavy precipitation has emerged in some regions (high \r\nconfidence). {11.4, 11.9, Table 11.1}"


id: 2e094d77699c18ac98474d62daefa6bb
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 5374
content:
"Human-induced climate change has contributed to increases \r\nin agricultural and ecological droughts in some regions due to \r\nevapotranspiration increases (medium confidence). Increases \r\nin evapotranspiration have been driven by increases in atmospheric \r\nevaporative demand induced by increased temperature, decreased \r\nrelative humidity and increased net radiation (high confidence). \r\nTrends in precipitation are not a main driver in affecting global-scale \r\ntrends in drought (medium confidence), but have induced increases in \r\nmeteorological droughts in a few AR6 regions (NES: high confidence; \r\nWAF, CAF, ESAF, SAM, SWS, SSA, SAS: medium confidence). Increasing "


id: 52de86a28ddf3488db0fe5656270064d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 11848
content:
"Heavy precipitation will generally become more frequent and \r\nmore intense with additional global warming. At a global \r\nwarming level of 4degC relative to the pre-industrial level, very \r\nrare (e.g., one in 10 or more years) heavy precipitation events \r\nwould become more frequent and more intense than in the \r\nrecent past, on the global scale (virtually certain) and in all \r\ncontinents and AR6 regions. The increase in frequency and \r\nintensity is extremely likely for most continents and very likely\r\nfor most AR6 regions. At the global scale, the intensification of "


id: 8cd27c7680af5e146589888f96a754a2
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 21961
content:
"of mean water vapour increase over oceans (about 7% per 1degC of \r\nwarming)  and in some cases exceeding this rate  due to increased \r\nlow-level moisture convergence caused by increases in TC wind \r\nintensity (medium confidence).  {11.7, 11.4, Box 11.1}"


id: feb8fbf8f5b1358ed349b4bc855b1187
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 42202
content:
"trends in agricultural and ecological droughts have been observed \r\non all continents (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, \r\nWCE, WNA, NES: medium confidence), but decreases only in one AR6 \r\nregion (NAU: medium confidence). Increasing trends in hydrological \r\ndroughts have been observed in a  few AR6 regions (MED: high \r\nconfidence; WAF, EAS, SAU: medium confidence). Regional\u0002scale attribution shows that human-induced climate change has \r\ncontributed to increased agricultural and ecological droughts (MED, \r\nWNA), and increased hydrological drought (MED) in some regions \r\n(medium confidence). {11.6, 11.9}"


id: baaf26feec2d330e745d42f0713157da
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 30062
content:
"It is likely that the global proportion of Category 3-5 tropical \r\ncyclone instances2 has increased over the past four decades. \r\nThe average location where TCs reach their peak wind intensity \r\nhas very likely migrated poleward in the western North Pacific \r\nOcean since the 1940s, and TC translation speed has likely slowed \r\nover the conterminous USA since 1900. Evidence of similar trends \r\nin other regions is not robust. The global frequency of TC rapid \r\nintensification events has likely increased over the past four decades. \r\nNone of these changes can be explained by natural variability alone \r\n(medium confidence). "


id: ba8a99e6bf7d28edaa262d602cf73131
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 30033
content:
"The proportion of intense TCs, average peak TC wind speeds, \r\nand peak wind speeds of the most intense TCs will increase \r\non the global scale with increasing global warming (high \r\nconfidence). The total global frequency of TC formation will \r\ndecrease or remain unchanged with increasing global warming \r\n(medium confidence). {11.7.1} "


id: 4ad4bb2406d0a510bd18b7c3fd897e0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 10407
content:
"There is low confidence in past changes of maximum \r\nwind speeds and other measures of dynamical intensity of \r\nextratropical cyclones. Future wind speed changes are expected \r\nto be small, although poleward shifts in the storm tracks \r\ncould lead to substantial changes in extreme wind speeds in \r\nsome regions (medium confidence). There is low confidence in \r\npast trends in characteristics of severe convective storms, such as \r\nhail and severe winds, beyond an increase in precipitation rates. The \r\nfrequency of spring severe convective storms is projected to increase \r\nin the USA, leading to a lengthening of the severe convective storm \r\nseason (medium confidence); evidence in other regions is limited. \r\n{11.7.2, 11.7.3}."


id: 72da79220906c0b60d7f03b949318d0c
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 17456
content:
"The probability of compound events has likely increased in \r\nthe past due to human-induced climate change and will likely \r\ncontinue to increase with further global warming. Concurrent \r\nheatwaves and droughts have become more frequent, and this \r\ntrend will continue with higher global warming (high confidence). \r\nFire weather conditions (compound hot, dry and windy events) \r\nhave become more probable in some regions (medium confidence) \r\nand there is high confidence that they will become more frequent \r\nin some regions at higher levels of global warming. The probability \r\nof compound flooding (storm surge, extreme rainfall and/or river \r\nflow) has increased in some locations (medium confidence), and will \r\ncontinue to increase due to sea level rise and increases in heavy \r\nprecipitation, including changes in precipitation intensity associated \r\nwith tropical cyclones (high confidence). The land area affected by \r\nconcurrent extremes has increased (high confidence). Concurrent "


id: c97ed1692688ecbb73ad9942be75952a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 32775
content:
"The average and maximum rain rates associated with tropical \r\ncyclones (TCs), extratropical cyclones and atmospheric rivers \r\nacross the globe, and severe convective storms in some \r\nregions, increase  in a  warming world (high confidence). \r\nAvailable event attribution studies of observed strong TCs provide \r\nmedium confidence for a human contribution to extreme TC rainfall. \r\nPeak TC rain rates increase with local warming at least at the rate "


id: 85e4aef52d3e3cc3e50cff40eade5246
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 20700
content:
"extreme events at different locations, but possibly affecting similar \r\nsectors (e.g.,  critical crop-producing areas for global food supply) \r\nin different regions, will become more frequent with increasing \r\nglobal warming, in particular above 2degC of global warming (high \r\nconfidence). {11.8, Box 11.2, Box 11.4}. "


id: 5e685b6ee5da5824fbccdfb9d5fbda6b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 13900
content:
"The future occurrence of low-likelihood, high-impact \r\nevents linked to climate extremes is generally associated \r\nwith low confidence, but cannot be excluded, especially at \r\nglobal warming levels above 4degC. Compound events, including \r\nconcurrent extremes, are a factor increasing the probability of low\u0002likelihood, high-impact events (high confidence). With increasing \r\nglobal warming, some compound events with low likelihood in \r\npast and current climates will become more frequent, and there is \r\na higher chance of occurrence of historically unprecedented events \r\nand surprises (high confidence). However, even extreme events that \r\ndo not have a particularly low probability in the present climate (at \r\nmore than 1degC of global warming) can be perceived as surprises \r\nbecause of the pace of global warming (high confidence). {Box 11.2}"


id: cb1ee04f2c4ac8ce0153b9953c9f9818
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 33074
content:
"This chapter provides assessments of changes in weather and climate \r\nextremes (collectively referred to as extremes) framed in terms of \r\nthe relevance to the Working Group II (WGII) assessment. It assesses \r\nobserved changes in extremes, their attribution to causes, and future \r\nprojections, at three global warming levels: 1.5degC, 2degC, and 4degC. This \r\nchapter is also one of the four 'regional chapters' of the WGI Report \r\n(along with Chapters 10 and 12 and the Atlas). Consequently, while \r\nit encompasses assessments of changes in extremes at global and \r\ncontinental scales to provide a large-scale context, it also addresses \r\nchanges in extremes at regional scales. "


id: 94a1ff238c3f40373409616867b6e3f
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 23354
content:
"Extremes are climatic impact-drivers (Annex VII: Glossary, see \r\nChapter  12 for a  comprehensive assessment). The IPCC risk \r\nframework (Chapter  1) articulates clearly that the exposure and \r\nvulnerability to climatic impact-drivers, such as extremes, modulate \r\nthe risk of adverse impacts of these drivers, and that adaptation \r\nwhich reduces exposure and vulnerability will increase resilience, \r\nresulting in a reduction in impacts. Nonetheless, changes in extremes \r\nlead to changes in impacts as a direct consequence of changes in \r\ntheir magnitude and frequency, and also through their influence on \r\nexposure and resilience. "


id: cf01f017d7618d25855633f1e0b34858
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 33762
content:
"This chapter is structured as follows (Figure  11.1): This section \r\n(11.1) provides the general framing and introduction to the chapter, \r\nhighlighting key aspects that underlie the confidence and uncertainty \r\nin the assessment of changes in extremes, and introducing some main \r\nelements of the chapter. To provide readers with a quick overview \r\nof past and future changes in extremes, a synthesis of global-scale \r\nassessments for different types of extremes is included at the end \r\nof this section (Tables 11.1 and 11.2). Section  11.2 introduces \r\nmethodological aspects of research on climate extremes. Sections \r\n11.3 to 11.7 assess past changes and their attribution to causes, \r\nand projected future changes in extremes, for different types of \r\nextremes, including temperature extremes, heavy precipitation and "


id: 23790c92b5d456c66c28198f57d235b2
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 3436
content:
"pluvial floods, river floods, droughts, and storms, in separate sections. \r\nSection 11.8 addresses compound events. Section 11.9 summarizes \r\nregional assessments of changes in temperature extremes, in \r\nprecipitation extremes and in droughts by continents in tables. The \r\nchapter also includes several boxes and FAQs on more specific topics."


id: 338df08a88e5a9083b63a2f4f95de8cf
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 6321
content:
"often based on extremes derived from daily values. Studies of events \r\non longer time scales for temperature or precipitation, or on sub\u0002daily extremes, are scarcer, which generally limits the assessment for \r\nsuch events. Nevertheless, extremes on time scales different from \r\ndaily are assessed for temperature extremes and heavy precipitation, \r\nwhen possible (Sections 11.3 and 11.4). Droughts and tropical \r\ncyclones are treated as phenomena in general in the assessment, \r\nnot limited by their extreme forms, because these phenomena are \r\nrelevant to impacts (Sections 11.6 and 11.7). Both precipitation and \r\nwind extremes associated with storms are considered. "


id: 1b01e29b50ccb618010b51ccd7bde6e0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 1949
content:
"Building on the SREX report and AR5, this Report defines an extreme \r\nweather event as 'an event that is rare at a  particular place and \r\ntime of year', and an extreme climate event as 'a pattern of extreme \r\nweather that persists for some time, such as a season' (see Glossary). \r\nThe definitions of 'rare' are wide ranging, depending on applications. \r\nSome studies consider an event as an extreme if it is unprecedented; \r\nother studies consider events that occur several times a  year as \r\nmoderate extreme events. Rarity of an event with a fixed magnitude \r\nalso changes under human-induced climate change, making events \r\nthat are unprecedented so far rather probable under present \r\nconditions, but unique in the observational record - and thus often \r\nconsidered as 'surprises' (see Box 11.2). "


id: 19561952ba2aa7f00d8380fc29687f66
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 1673
content:
"Multiple concomitant extremes can lead to stronger impacts than \r\nthose resulting from the same extremes had they happened in \r\nisolation. For this reason, the occurrence of multiple extremes that \r\nare multivariate and/or concurrent and/or happening in succession, \r\nalso called 'compound events' (SREX Chapter 3), are assessed in this \r\nchapter based on emerging literature on this topic (Section  11.8). \r\nBox 11.2 also provides an assessment on low-likelihood, high-impact \r\nscenarios associated with extremes."


id: 9b74aa9fb5e7de58e7ce83924039212b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 24603
content:
"Various approaches are used to define extremes. These are generally \r\nbased on the determination of relative (e.g.,  90th percentile) or \r\nabsolute (e.g., 35degC for a hot day) thresholds above which conditions \r\nare considered extremes. Changes in extremes can be examined from \r\ntwo perspectives, either focusing on changes in frequency of given \r\nextremes, or on changes in their intensity. These considerations in the \r\ndefinition of extremes are further addressed in Section 11.2.1."


id: a118447eed6aa8c280b03af56fe61f5d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 25565
content:
"The types of extremes assessed in this chapter include temperature \r\nextremes, heavy precipitation and pluvial floods, river floods, droughts, \r\nand storms. The drought assessment addresses meteorological \r\ndroughts, agricultural and ecological droughts, and hydrological \r\ndroughts (see Glossary). The storms assessment addresses tropical \r\ncyclones, extratropical cyclones, and severe convective storms. \r\nThis chapter also assesses changes in compound events  - that \r\nis, multivariate or concurrent extreme events  - because of their \r\nrelevance to impacts as well as the emergence of new literature on \r\nthe subject. Most of the considered extremes were also assessed in \r\nSREX and AR5. Compound events were not assessed in depth in past \r\nIPCC reports (SREX Chapter 3; Section 11.8 of this Report). Marine\u0002related extremes such as marine heatwaves and extreme sea level, \r\nare assessed in Section 9.6.4 and Box 9.2 of this Report. "


id: fcfe9e869bce66b0034ba28ebf4706f5
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 41870
content:
"A global-scale synthesis of this chapter's assessments is provided in \r\nSection 11.1.7. In particular, Tables 11.1 and 11.2 provide a synthesis \r\nfor observed and attributed changes, and projected changes in \r\nextremes, respectively, at different global warming levels (1.5degC, \r\n2degC, and 4degC). Tables on regional-scale assessments for changes \r\nin temperature extremes, heavy precipitation and droughts, are \r\nprovided in Section 11.9."


id: 66b0ca62f8c7ecef5b9bdaf248763dd8
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 15328
content:
"Extremes and related phenomena are of various spatial and \r\ntemporal scales. Tornadoes have a  spatial scale as small as less \r\nthan 100 metres and a temporal scale as short as a few minutes. \r\nIn contrast, a  drought can last for multiple years, affecting vast \r\nregions. The level of complexity of the involved processes differs from \r\none type of extreme to another, affecting our capability to detect, \r\nattribute and project changes in weather and climate extremes. \r\nTemperature and precipitation extremes studied in the literature are "


id: 7bc48d04695fbdd9daec443dd99f12e1
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 18985
content:
"The SREX, AR5, and SR1.5 assessed that there is evidence from \r\nobservations that some extremes have changed since the mid\u000220th century, that some of the changes are a result of anthropogenic \r\ninfluences, and that some observed changes are projected to continue \r\ninto the future. Additionally, other changes are projected to emerge \r\nfrom natural climate variability under enhanced global warming \r\n(SREX Chapter 3; AR5 Chapter 10). "


id: b429c8df3603a6e6ecf7257f5345dc6b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 28932
content:
"example, associated with changes in land cover and land use or \r\naerosol emissions, can have non-local or some (non-homogeneous) \r\nglobal-scale effects. In general, there is high confidence in changes in \r\nextremes due to global-scale thermodynamic processes (i.e., global \r\nwarming, mean moistening of the air) as the processes are well \r\nunderstood, while the confidence in those related to dynamic \r\nprocesses or regional and local forcing, including regional and local \r\nthermodynamic processes, is much lower due to multiple factors (see \r\nthe following subsection and Box 11.1). "


id: 6b62d89e71d56f479b512334aa7026d9
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 16194
content:
"Changes in annual maximum one-day precipitation (Rx1day) are \r\nproportional to mean global surface temperature changes, at about \r\n7% increase per 1degC of warming, that is, following the Clausius-\r\nClapeyron relation (Box  11.1), both in observations (Westra et al., \r\n2013) and in future projections (Kharin et al., 2013) at the global \r\nscale. Extreme short-duration precipitation in North America also \r\nscales with global surface temperature (Prein et al., 2016b; C. Li et al., \r\n2019a). At the local and regional scales, changes in extremes are also \r\nstrongly modulated and controlled by regional forcings and feedback \r\nmechanisms (Section  11.1.6), whereby some regional forcings, for "


id: 503d4ef2548ba7c2d98794a03c8ba21d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 11399
content:
"Figure 11.2 | Time series of observed temperature anomalies for global average annual mean temperature (black), land average annual mean temperature \r\n(green), land average annual hottest daily maximum temperature (TXx, purple), and land average annual coldest daily minimum temperature (TNn, blue).\r\nGlobal and land mean temperature anomalies are relative to their 1850-1900 means and are based on the multi-product mean annual time series assessed in Section 2.3.1.1.3 \r\n(see text for references). TXx and TNn anomalies are relative to their respective 1961-1990 means and are based on the HadEX3 dataset (Dunn et al., 2020) using values for \r\ngrid boxes with at least 90% temporal completeness over 1961-2018. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9)."


id: 97646620f3a2aaa2328c9273cbd0a809
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 23844
content:
"Changes in weather and climate extremes are determined by local exchanges in heat, moisture, and other related quantities \r\n(thermodynamic changes) and those associated with atmospheric and oceanic motions (dynamic changes). While thermodynamic and \r\ndynamic processes are interconnected, considering them separately helps to disentangle the roles of different processes contributing \r\nto changes in climate extremes (e.g., Shepherd, 2014). "


id: d0d3cd451c5247cb017908aba07a5060
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 34089
content:
"Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations and model simulations \r\nshow that temperature increases, including extreme temperatures, are amplified in areas where seasonal snow cover is reduced due \r\nto decreases in surface albedo (see Section 11.3.1). In some mid-latitude areas, temperature increases are amplified by the higher \r\natmospheric evaporative demand (Fu and Feng, 2014; Vicente-Serrano et al., 2020a) that results in a drying of soils in some regions \r\n(Section 11.6), leading to increased sensible heat fluxes (soil-moisture-temperature feedback, see Sections 11.1.6 and 11.3.1 for \r\nmore background). Other thermodynamic feedback processes include changes in the water-use efficiency of plants under enhanced \r\natmospheric carbon dioxide (CO2) concentrations that can reduce the overall transpiration, and thus also enhance temperature in \r\nprojections (Sections 8.2.3.3, 11.1.6, 11.3 and 11.6)."


id: 706068dde7a0a95670cc826126be15f9
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 17028
content:
"Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the characteristics of weather \r\npatterns (e.g.,  Suarez-Gutierrez et al., 2020a). For example, a  robust thermodynamic effect of polar amplification is a  weakened \r\nnorth-south temperature gradient, which amplifies the warming of cold extremes in the Northern Hemisphere mid- and high latitudes \r\nbecause of the reduction of cold air advection (Holmes et al., 2015; Schneider et al., 2015; Gross et al., 2020). Much less robust is the \r\ndynamic effect of polar amplification (Section 7.4.4.1) and the reduced low-altitude meridional temperature gradient that has been \r\nlinked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent increases in temperature extremes \r\n(Cross-Chapter Box 10.1; Francis and Vavrus, 2012; Coumou et al., 2015, 2018; Mann et al., 2017). "


id: a2af3a00f62e786afe5213238489f799
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 25839
content:
"Dynamic contributions can occur in response to changes in the vertical and horizontal distribution of temperature (thermodynamics) \r\nand can affect the frequency and intensity of synoptic and subsynoptic phenomena, including tropical cyclones, extratropical cyclones, \r\nfronts, mesoscale-convective systems and thunderstorms. For example, the poleward shift and strengthening of the Southern \r\nHemisphere mid-latitude storm tracks (Section 4.5.1) can modify the frequency or intensity of extreme precipitation. However, the \r\nprecise way in which dynamic changes will affect precipitation extremes is unclear due to several competing effects (Shaw et al., 2016; \r\nAllan et al., 2020)."


id: a8394dae5dd460e866e9d3246d57c60e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 26814
content:
"Droughts are also affected by thermodynamic and dynamic processes (Sections 8.2.3.3 and 11.6). Thermodynamic processes affect \r\ndroughts by increasing atmospheric evaporative demand (Martin, 2018; Gebremeskel Haile et al., 2020; Vicente-Serrano et al., \r\n2020a) through changes in air temperature, radiation, wind speed, and relative humidity. Dynamic processes affect droughts through \r\nchanges in the occurrence, duration and intensity of weather anomalies, which are related to precipitation and the amount of sunlight \r\n(Section 11.6). While atmospheric evaporative demand increases with warming, regional changes in aridity are affected by increasing \r\nland-ocean warming contrast, vegetation feedbacks and responses to rising CO2 concentrations, and dynamic shifts in the location of \r\nthe wet and dry parts of the atmospheric circulation in response to climate change, as well as internal variability (Byrne and O'Gorman, \r\n2015; Kumar et al., 2015; Allan et al., 2020)."


id: d87c69dc2c0a6c9479658655b74f4184
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 35382
content:
"In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. \r\nAnthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall \r\nincreases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify \r\nheatwaves, droughts and heavy precipitation events when they occur (high confidence). Dynamic processes are often indirect responses \r\nto thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is low \r\nconfidence in how dynamic changes affect the location and magnitude of extreme events in a warming climate."


id: 6b0871cd370473492e3529677662653
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 16126
content:
"spatial distribution of these patterns, which contributes to uncertainty \r\nin projected responses of extremes, especially in the near term. "


id: 9c87c9e6dc7b39815b58383875b280a7
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 24788
content:
"At the local and regional scales, changes in extremes are strongly \r\nmodulated by local and regional feedbacks (SRCCL, Jia et al., 2019; \r\nSeneviratne et al., 2013; Miralles et al., 2014a; Lorenz et al., 2016; \r\nVogel et al., 2017), changes in large-scale circulation patterns \r\n(Section 11.1.5), and regional forcings such as changes in land use \r\nor aerosol concentrations (Chapters 3  and 7; Findell et al., 2017; \r\nHirsch et al., 2017, 2018; Thiery et al., 2017; Z. Wang et al., 2017b). \r\nIn some cases, such responses may also include non-local effects \r\n(e.g., de Vrese et al., 2016; Persad and Caldeira, 2018; Miralles et al., \r\n2019; Schumacher et al., 2019). Regional-scale forcing and feedbacks \r\noften affect temperature distributions asymmetrically, with generally \r\nhigher effects for the hottest percentiles (Section 11.3). "


id: 4becaba8b42a01d2b267743903f9eed
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 10621
content:
"In summary, large-scale atmospheric circulation patterns are \r\nimportant drivers for local and regional extremes. There is overall low \r\nconfidence about future changes in the magnitude, frequency, and "


id: 381dd85f41a2f232d5d41b1f22a36d1b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 7074
content:
"2014). However, these feedbacks play an important role in projected \r\nchanges in high-latitude warming (Hall and Qu, 2006), and, in \r\nparticular, in changes in cold extremes in these regions (Section 11.3)."


id: cc80ecfb82ccaf803f5b7f0db7152ad4
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 33302
content:
"Finally, extreme events may also regionally amplify one another. \r\nFor example, this is the case for heatwaves and droughts, with high \r\ntemperatures and stronger radiative forcing leading to drying tendencies \r\non land due to increased evapotranspiration (Section 11.6), and drier \r\nsoils then inducing decreased evapotranspiration and higher sensible \r\nheat flux and hot temperatures (Box 11.1, Section 11.8; Seneviratne \r\net al., 2013; Miralles et al., 2014a; Vogel et al., 2017; Zscheischler and \r\nSeneviratne, 2017; S. Zhou et al., 2019; Kong et al., 2020). "


id: 531529c79ea22908211f46c84252d54b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 11887
content:
"In summary, regional forcings and feedbacks  - in particular those \r\nassociated with land use and aerosol forcings - and soil-moisture-\r\ntemperature, soil moisture-precipitation, and snow/ice-albedo-\r\ntemperature feedbacks, play an important role in modulating \r\nregional changes in extremes. These can also lead to a  higher \r\nwarming of extreme temperatures compared to mean temperature \r\n(high confidence), and possibly cooling in some regions (medium \r\nconfidence). However, there is only medium confidence in the \r\nrepresentation of the associated processes in state-of-the-art ESMs."


id: 5a6c730c020c69b5b243f6e0913286fe
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 13165
content:
"Tables 11.1 and 11.2 provide a synthesis for observed and attributed \r\nchanges in extremes, and projected changes in extremes, respectively, \r\nat different levels of global warming. This synthesis assessment \r\nfocuses on the assessed range of observed and projected changes. \r\nIn this chapter, the assessed likely range in a  projection typically "


id: ac81c48df8737e32da060fcc0d335a4a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 27589
content:
"Figure 11.5 | Confidence and likelihood of past changes and projected future changes at 2degC of global warming on the global scale. The information in this \r\nfigure is based on Tables 11.1 and 11.2. "


id: 66658ea7fc5a849a46957323f3811862
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 15278
content:
"134\r\nN\r\n0\r\n153045\r\nW. North-A\r\n0\r\n153045\r\nN. C\r\n2.0 3.0 \r\ndegC\r\nobal land\r\near eventear event\r\ned changes in th\r\nperatures that we\r\ne AR6 regions. Fo\r\nrtainty range. The \r\ndifferent Shared S"


id: e645d21ef72ccaf83f31a5256699a035
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 37816
content:
"02468\r\nW. No\r\n1.5 2.0 3.0 \r\ndegC\r\nGlobal land\r\n0-year event0-year event\r\nojected change\r\nximum daily precip\r\n global land area \r\n extend to the 90\r\nroject Phase 6 (CM"


id: c420991eb0c06144a43c421a73c0d9f3
created_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 31791
content:
"corresponds to the 90% range of the multi-model ensemble spread \r\nto take into account other sources of uncertainty, unless stated \r\notherwise. Some low-likelihood, high-impact scenarios that can be of \r\nhigh relevance are addressed in Box 11.2."


id: f63046cedc7c10e496980181a30c0798
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 40673
content:
"Building on the assessments from Tables 11.1 and 11.2, Figure 11.5 \r\nprovides a synthesis on the level of confidence in the attribution and \r\nprojection of changes in extremes. In the case where the signal in \r\nthe observations is still relatively weak but the physical processes \r\nunderlying the changes in extremes in response to human forcing \r\nare well understood, confidence in the projections would be higher \r\nthan in the attribution because of strengthening in the signal with \r\nwarming. But, when the observed signal is already strong and when \r\nobservational evidence is consistent with model simulated responses, "


id: 178a2283b29f6ccac86735764d1325b0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 1348
content:
"Table 11.1 | Synthesis table on observed changes in extremes and contribution by human influence. Note that observed changes in marine extremes are asses\r\nn Cross-Chapter Box 9.1. "


id: 6d071e4581f1aec711285baba899dcf1
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 16462
content:
"Further synthesis for regional assessments are provided in \r\nFigure  11.4 (event attribution), Figure  11.6 (projected change \r\nin hot temperature extremes) and Figure 11.7 (projected changes in \r\nprecipitation extremes). A  synthesis on regional assessments for \r\nobserved, attributed and projected changes in extremes is provided \r\nin Section 11.9 for all AR6 reference regions (see Section 1.4.5 and \r\nFigures 1.18 and Atlas.2 for definitions of AR6 regions)."


id: 63d36c1bcc728e53d15c77eef611249b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 14853
content:
"confidence in the projection may be lower than that in attribution if \r\ncertain physical processes could be expected to behave differently \r\nin a  much warmer world and under much higher greenhouse gas \r\nforcing, and in particular if such a behaviour is poorly understood."


id: 150e40956180e60dee1e5b53d7d96f57
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 919
content:
"Table 11.2 | Synthesis table on projected changes in extremes. Note that projected changes in marine extremes are assessed in Chapter 9 and Cross-Chapter Box 9.1 \r\n(marine heatwaves). Assessments are provided compared to pre-industrial conditions."


id: f83efb5e02468eb68832ac98d1e053d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 41049
content:
"In many parts of the world, observational data are limited to 50-60 years. This means that the chance to observe an extreme event \r\nat a particular location that occurs once in several hundred or more years is small. Thus, when a very extreme event occurs, it becomes \r\na  surprise to many (Bao et al., 2017; McCollum et al., 2020), and very rare events are often associated with high impacts (van \r\nOldenborgh et al., 2017; Philip et al., 2018b; Tozer et al., 2020). Attributing and projecting very rare events in a particular location by \r\nassessing their likelihood of occurrence within the same larger region and climate thus provides another way to make quantitative \r\nassessments regarding events that are extremely rare locally. Some examples of such events include: "


id: 19229525cd934d59128658684d9fb6f5
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 1638
content:
"Another factor highlighted in Section 11.8 and Box 11.4 making events high-impact and difficult to anticipate is that several locations \r\nunder moderate warming levels could be affected simultaneously, or very repeatedly by different types of extremes (Mora et al., 2018; \r\nGaupp et al., 2019; Vogel et al., 2019). Box 11.4 shows that concurrent events at different locations, which can lead to major impacts \r\nacross the world, can also result from the combination of anomalous circulation or natural variability (e.g., El Nino-Southern Oscillation) "


id: 9e1ef2ed8da709ed4323c155237cadf2
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 25052
content:
"Box 11.2, Table 1 | Examples of changes in low-likelihood, high-impact extreme conditions (single extremes, compound events) at different \r\nglobal warming levels."


id: 7000e54fc1bd6f98074735c2ee44d6af
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 16960
content:
"In summary, the future occurrence of LLHI events linked to climate extremes is generally associated with low confidence, but cannot \r\nbe excluded, especially at global warming levels above 4degC. Compound events, including concurrent extremes, are a factor increasing \r\nthe probability of LLHI events (high confidence). With increasing global warming, some compound events with low likelihood in past \r\nand current climate will become more frequent, and there is a higher chance of historically unprecedented events and surprises (high \r\nconfidence). However, even extreme events that do not have a particularly low probability in the present climate (at more than 1degC of \r\nglobal warming) can be perceived as surprises because of the pace of global warming (high confidence)."


id: 1d27a28e0f65c7a95756fb73c1f7624f
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index: climate_gpt_v2
vector_id: 2303
content:
"of the year, possibly affected by different types of processes and \r\nmechanisms, and possibly also associated with different impacts. "


id: 34b20846d34db358458eab171d0f3119
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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vector_id: 6511
content:
"This section provides an assessment of observational data and \r\nmethods used in the analysis and attribution of climate change \r\nspecific to weather and climate extremes. It also introduces some \r\nconcepts used in presenting future projections of extremes. Later \r\nsections (Sections 11.3-11.8) also provide additional assessments \r\non relevant observational datasets and model validation specific to \r\nthe type of extremes to be assessed. General background on climate \r\nmodelling is provided in Chapters 4 and 10."


id: d9973acd47924063659dc038614c06e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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vector_id: 35581
content:
"rarely integrated in the assessment of projected changes in extremes. \r\nFraming is considered when forming the assessments of this Chapter, \r\nincluding how extremes are defined and how the questions are asked \r\nin the literature."


id: ad3f801045beca672c67ff1a03288eec
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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vector_id: 27719
content:
"to represent areal means (Chen and Knutson, 2008; Gervais et al., \r\n2014; Avila et al., 2015; Di Luca et al., 2020b). In regions with very \r\nlimited station density (e.g., Africa), the gridded values are closer to \r\npoint estimates of extremes. The difference in spatial scales among \r\nobservational data products and model simulations needs to be \r\ncarefully accounted for when interpreting the comparison among \r\ndifferent data products. For example, the average annual maximum \r\ndaily maximum temperature (TXx) over land computed from the \r\noriginal ERA-Interim reanalysis (at 0.75deg resolution) is about 0.4degC \r\nwarmer than that computed when the ERA-Interim dataset is \r\nupscaled to the resolution of 2.5deg x 3.75deg (Di Luca et al., 2020)."


id: 9f76d4cbb2d236c4e36a22bb6ba18615
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 25272
content:
"Studies of past and future changes in weather and climate extremes, \r\nand in the mean state of the climate, use the same original sources \r\nof weather and climate observations, including in situ observations, \r\nremotely sensed data, and derived data products such as reanalyses. \r\nSections 2.3 and 10.2 assess various aspects of these data sources \r\nand data products from the perspective of their general use, and in \r\nthe analysis of changes in the mean state of the climate in particular. \r\nBuilding on these previous chapters, this subsection highlights \r\nparticular aspects that are related to extremes and are most relevant \r\nto the assessment of this Chapter. The SREX (Chapter 3, Seneviratne \r\net al., 2012) and AR5 (Chapter 2, Hartmann et al., 2013) addressed \r\ncritical issues regarding the quality and availability of observed data \r\nand their relevance for the assessment of changes in extremes. "


id: cd24e800afa24bff9ebeff5979a5ee6d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 33428
content:
"Extreme indices computed from various reanalysis data products have \r\nbeen used in some studies, but reanalysis extreme statistics have not \r\nbeen rigorously compared to observations (Donat et al., 2016a). "


id: 2855aefbbc492f7a9a74c03155f6da75
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 4296
content:
"Satellite remote sensing data have been used to provide information \r\nabout precipitation extremes because several products provide \r\ndata at sub-daily resolution for precipitation, for example, Tropical \r\nRainfall Measuring Mission (TRMM; Maggioni et al., 2016) and \r\nclouds, for example, Himawari (Bessho et al., 2016; Chen et al., \r\n2019). However, satellites do not observe the primary atmospheric \r\nstate variables directly and polar orbiting satellites do not observe \r\nany given place at all times. Hence, their utility as a substitute for \r\nhigh-frequency (i.e., daily) ground-based observations is limited. For \r\ninstance, Timmermans et al. (2019) found little relationship between \r\nthe timing of extreme daily and five-day precipitation in satellite and \r\ngridded station data products over the USA. "


id: 6310f1b6af6d467fb524144ed12e0f7f
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 14719
content:
"In some cases, such as for the HadEX3 dataset (Dunn et al., 2020), \r\nindices of extremes are computed using time series directly derived \r\nfrom stations first, and are then gridded over the space. As the indices \r\nare computed at the station level, the gridded data products represent \r\npoint estimates of the indices averaged over the spatial scale of the \r\ngrid box. In other instances, daily values of station observations are \r\nfirst gridded (e.g.,  Contractor et al., 2020a), and the interpolated \r\nvalues can then be used to compute various indices. Depending on the \r\nstation density, values for extremes computed from data gridded this \r\nway represent extremes of spatial scales anywhere from the size of \r\nthe grid box to a point. In regions with high station density (e.g., North \r\nAmerica, Europe), the gridded values are closer to extremes of area \r\nmeans and are thus more appropriate for comparisons with extremes \r\nestimated from climate model output, which is often considered "


id: ee5d00e7fa6511afd1d0aa85385ed470
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
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content:
"Examining extremes in pre-instrumental information can help to put events occurring in the instrumental record (referred to as \r\n'observed') in a longer-term context. This box focuses on extremes in the Common Era (CE, the last 2000 years), because there is \r\ngenerally higher confidence in pre-instrumental information gathered from the more recent archives from the Common Era than from\r\nearlier evidence. It addresses evidence of extreme events in paleoreconstructions, documentary evidence (such as grape harvest data, \r\nreligious documents, newspapers, and logbooks) and model-based analyses, and whether observed extremes have or have not been \r\nexceeded in the Common Era. This box provides overviews of: (i) AR5 assessments; (ii) types of evidence assessed here; evidence of: \r\n(iii) droughts; (iv) temperature extremes; (v) paleofloods; and (vi) paleotempests; and (vii) a summary. "


id: 7c7fffb1120a6fcbe751e9e989121ebe
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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content:
"Chapter 5 of AR5 (Masson-Delmotte et al., 2013) concluded with high confidence that droughts of greater magnitude and of longer \r\nduration than those observed in the instrumental period occurred in many regions during the preceding millennium. There was high \r\nconfidence in evidence that floods during the past five centuries in northern and Central Europe, the western Mediterranean region, \r\nand eastern Asia were of a greater magnitude than those observed instrumentally, and medium confidence in evidence that floods \r\nin the Near East, India and Central North America were comparable to modern observed floods. While AR5 assessed 20th century \r\nsummer temperatures compared to those reconstructed in the Common Era, it did not assess shorter duration temperature extremes."


id: 466567e32959a73a11497acb92c1306b
created_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 9605
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"Overall, the most complete pre-instrumental evidence of extremes occurs for long-duration, large spatial-scale extremes, such as for \r\nmulti-year meteorological droughts or seasonal- and regional-scale temperature extremes. Additionally, more precise insights into \r\nrecent extremes emerge where multiple studies have been undertaken, compared to the confidence in extremes reported at single sites \r\nor in single studies, which may not necessarily be representative of large-scale changes, or for reconstructions that synthesize multiple \r\nproxies over large areas (e.g., drought atlases). Multiproxy synthesis products combine paleoclimate temperature reconstructions \r\nand cover sub-continental- to hemispheric-scale regions to provide continuous records of the Common Era (e.g., Ahmed et al., 2013; \r\nNeukom et al., 2014 for temperature)."


id: 6c1ad4cf59c5543fb30694dfa3614038
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 16319
content:
"depending on the length of the reconstruction and the seasonal perspective provided (see Cook et al., 2016c; Freund et al., 2017 for \r\nAustralia). There can also be differing conclusions for the severity, or even the occurrence, of specific individual pre-instrumental \r\ndroughts when different evidence is compared (e.g., Wetter et al., 2014; Buntgen et al., 2015). "


id: 9cf2fddf9ed94167fb216da0f4e90076
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 24848
content:
"There is medium confidence that the magnitudes of large-scale, seasonal-scale extreme high temperatures in observed records exceed \r\nthose reconstructed over the Common Era in some locations, such as Central Europe. In one example, multiple studies have examined \r\nthe unusualness of present-day European summer temperature records in a long-term context, particularly in comparison to the \r\nexceptionally warm year of 1540 CE in Central Europe. Several studies indicate that recent extreme summers (2003 and 2010) in \r\nEurope have been unusually warm in the context of the last 500 years (Barriopedro et al., 2011; Wetter and Pfister, 2013; Wetter et al., \r\n2014; Orth et al., 2016b), or longer (Luterbacher et al., 2016). Others studies show that summer temperatures in Central Europe in \r\n1540 were warmer than the present-day (1966-2015) mean, but note that it is difficult to assess whether or not the 1540 summer \r\nwas warmer than observed record extreme temperatures (Orth et al., 2016b). "


id: 7b1fe4ca595a93d78cfb3a90c50b1311
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 18859
content:
"There is medium confidence that periods of both more and less tropical cyclone activity (frequency or intensity) than observed \r\noccurred over the Common Era in many regions. Paleotempest studies cover a limited number of locations that are predominantly \r\ncoastal, and hence provide information on specific locations that cannot be extrapolated basin-wide (see Muller et al., 2017). In \r\nsome locations, such as the Gulf of Mexico and the New England, USA, coast, similarly intense storms to those observed recently \r\nhave occurred multiple times over centennial timescales (Donnelly et al., 2001; Bregy et al., 2018). Further research focused on the \r\nfrequency of tropical storm activity. Extreme storms occurred considerably more frequently in particular periods of the Common Era, \r\ncompared to the instrumental period in north-east Queensland, Australia (Nott et al., 2009; Haig et al., 2014), and the Gulf Coast \r\n(e.g., Brandon et al., 2013; Lin et al., 2014). "


id: 9b89f8364bdac490ffb96616900b423c
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 24616
content:
"The probability of finding an unprecedented extreme event increases with a longer length of past record-keeping, in the absence \r\nof longer-term trends. Thus, as a record is extended to the past based on paleoreconstruction, there is a higher chance of very rare \r\nextreme events having occurred at some time prior to instrumental records. Such an occurrence is not, in itself, evidence of a change, \r\nor lack of a change, in the magnitude or the likelihood of extremes in the past or in the instrumental period at regional and local scales. \r\nYet, the systematic collection of paleoclimate records over wide areas may provide evidence of changes in extremes. In one study, \r\nextended evidence of the last millennium from observational data and paleoclimate reconstructions using tree rings indicates that \r\nhuman activities affected the worldwide occurrence of droughts as early as the beginning of the 20th century (Marvel et al., 2019). "


id: db688d9de72e41da498bc8c1a4372d0d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 35874
content:
"In summary, there is low confidence in overall changes in extremes derived from paleo-archives. There is high confidence that long\u0002duration and severe drought events occurred at many locations during the last 2000 years. There is also high confidence that high\u0002magnitude flood events occurred at some locations during the last 2000 years, but overall changes in infrastructure and human water \r\nmanagement make the comparison with present-day records difficult. But these isolated paleo-drought and paleo-flood events are \r\nnot evidence of a change, or lack of a change, in the magnitude or the likelihood of relevant extremes. "


id: 798abff0465e0207129157203248537b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 18587
content:
"Apart from the detection and attribution of trends in extremes, new \r\napproaches have been developed to answer the question of whether, \r\nand to what extent, external drivers have altered the probability and \r\nintensity of an individual extreme event (NASEM, 2016). In AR5, there \r\nwas an emerging consensus that the role of external drivers of climate \r\nchange in specific extreme weather events could be estimated and \r\nquantified in principle, but related assessments were still confined \r\nto particular case studies, often using a single model, and typically \r\nfocusing on high-impact events with a clear attributable signal. "


id: da9c14822edfb6678e11cd78b0fef108
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 35756
content:
"The reliability of the representation of the event in question in the \r\nclimate models used in a  study is essential (Angelil et al., 2016; \r\nHerger et al., 2018). Extreme events characterized by atmospheric \r\ndynamics that stretch the capabilities of current-generation models \r\n(Section 10.3.3.4; Shepherd, 2014; Woollings et al., 2018) limit the \r\napplicability of the probability-based approach of event attribution. "


id: f6efe2d034f000b6e52216f6fc1748c
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 40813
content:
"These encompass a scenario compatible with the lowest limit of the \r\nParis Agreement (+1.5degC), a scenario slightly overshooting the aims \r\nof the Paris Agreement (+2degC), and a 'worst-case' scenario with no \r\nmitigation (+4degC). Cross-Chapter Box  11.1 provides a  background \r\non the GWL sampling approach used in AR6, for the computation \r\nof GWL projections from climate models contributing to Phase 6 of \r\nthe Coupled Model Intercomparison Project (CMIP6) as well as for \r\nthe mapping of existing scenario-based literature for CMIP6 and the \r\nCMIP Phase 5 (CMIP5) to assessments as function of GWLs (see also \r\nSection 11.9. and Table 11.3 for an example)."


id: fe59cd2a6f3131d4abb4f8a853ab83ed
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 42121
content:
"In the regional tables provided in Section  11.9, the different lines \r\nof evidence from event attribution studies and trend attributions \r\nare assessed alongside one another to provide an assessment \r\nof the human contribution to observed changes in extremes in \r\nall AR6 regions. "


id: 866c3a33ce4b65e46f602379afa7281b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
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content:
"The SR1.5 (Chapter  3) assessed different climate responses at \r\n+1.5degC of global warming, including transient climate responses, \r\nshort-term stabilization responses, and long-term equilibrium \r\nstabilization responses, and their implications for future projections \r\nof different extremes. Indeed, the temporal dimension  - that \r\nis, when the given GWL occurs  - also matters for projections, in \r\nparticular beyond the 21st century, and for some climate variables \r\nrelated to components of the climate system associated with large \r\ninertia (e.g., sea level rise and associated extremes). Nonetheless, \r\nfor assessments focused on conditions within the next decades, \r\nand for the main extremes considered in this chapter, derived \r\nprojections are relatively insensitive to details of climate scenarios \r\nand can be well-estimated based on transient simulations (Cross\u0002Chapter Box 11.1; see also SR1.5)."


id: 69ffd5ab4fedf705deefab4d3a5b05c9
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 15914
content:
"The assessment of projections of future changes in extremes as \r\nfunction of GWL has an advantage in separating uncertainty \r\nassociated with the global warming response (see Chapter 4) from \r\nthe uncertainty resulting from the regional climate response as \r\na function of GWLs (Seneviratne and Hauser, 2020). If the interest is \r\nin the projection of regional changes at certain GWLs, such as those \r\ndefined by the Paris Agreement, projections based on time periods \r\nand emissions scenarios have unnecessarily larger uncertainty due \r\nto differences in model global transient climate responses. To take \r\nadvantage of this feature and to provide easy comparison with \r\nSR1.5, assessments of projected changes in this chapter are largely \r\nprovided in relation to future GWLs, with a  focus on changes at \r\n+1.5degC, +2degC, and +4degC of global warming above pre-industrial \r\nlevels (e.g., Tables 11.1 and 11.2 and regional tables in Section 11.9). "


id: 455cee656bfc64c0ded3fe290e2e0578
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
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content:
"An important question is the identification of the GWL at which \r\na  given change in a  climate extreme can begin to emerge from \r\nclimate noise. Figure 11.8 displays analyses of the GWLs at which \r\nemergence in hot extremes - annual maximum daily temperature \r\nrepresented by TXx and heavy precipitation represented by Rx1day \r\nis identified in AR6 regions for the whole CMIP5 and CMIP6 \r\nensembles. Overall, signals for extremes emerge very early for \r\nTXx, already below 0.2degC in many regions (Figure 11.8a,b), and at \r\naround 0.5degC in most regions. This is consistent with conclusions \r\nfrom the SR1.5 Chapter 3 for less-rare temperature extremes (TXx \r\non the yearly time scale), which shows that a difference as small as "


id: 8da833bf602a7c82498bc86f25430695
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 22113
content:
"temperature relative to the 1850-1900 period (see Cross-Chapter \r\nBox 2.3), are used in SR1.5 and in the assessment of Reasons for \r\nConcerns in the WGII reports (see also Cross-Chapter Box 12.1). \r\nCross-Chapter Box 11.1, Figure 1 illustrates how the assessment \r\nof the climate response at GWLs relates to the uncertainty in \r\nscenarios regarding the timing of the respective GWLs, as well as \r\nto the uncertainty in the associated regional climate responses, \r\nincluding extremes and other climatic impact-drivers (CIDs). \r\nFor many (but not all) climate variables and CIDs, the response \r\npattern for a given GWL is consistent across different scenarios \r\n(Chapters 1, 4, 9, 11 and Atlas). GWLs are defined as long-term \r\nmeans (e.g.,  20-year averages) compared to the pre-industrial \r\nperiod, are commonly used in the literature, and were also \r\nunderlying main assessments of SR1.5 (Chapter 3). "


id: 3f1b82cc6cf332034c36bbeb199950ef
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
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content:
"Numerous studies have compared the regional response to \r\nanthropogenic forcing at GWLs in annual and seasonal mean \r\nvalues and extremes of different climate and impact variables \r\nacross different multi-model ensembles and/or different \r\nscenarios (e.g., Frieler et al., 2012; Schewe et al., 2014; Herger \r\net al., 2015; Schleussner et al., 2016; Seneviratne et al., 2016; "


id: 347cecee5fc249a4692985f76f3d6bd
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 6470
content:
"In contrast to linear pattern scaling (Mitchell, 2003; Collins et al., 2013), the use of GWLs as a dimension of integration does not \r\nrequire linearity in the response of a climate variable. It is therefore useful even for metrics that do not show a linear response, such \r\nas the frequency of heat extremes over land and oceans (Fischer and Knutti, 2015; Frolicher et al., 2018; Kharin et al., 2018; Perkins\u0002Kirkpatrick and Gibson, 2017) if the relationship of the variable of interest to the GWL is scenario independent. The latter means that \r\nthe response is independent of the pathway and relative contribution of various radiative forcings. For some more complex indices like \r\nwarm-spell duration, or for regions with strong aerosol changes, discrepancies can be larger (Z. Wang et al., 2017b; King et al., 2018; \r\nTebaldi et al., 2020). (See also the subsection below on GWLs vs scenarios for further caveats.) "


id: 32b160f66d38cb5993f5fbff9993884b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 6185
content:
"Mapping between GWL- and scenario-based responses in model analyses \r\nTo map scenario-based climate projections into changes at specific GWLs, first, all individual Earth system model (ESM) simulations \r\nthat reach a certain GWL are identified. Second, the climate response patterns at the respective GWL are calculated using an approach \r\ntermed here 'GWL-sampling' - sometimes also referred to as epoch analysis, time shift, or time sampling approach - taking into \r\naccount all models and scenarios (Cross-Chapter Box, Figure 3). Note that the range of years when a given GWL is reached in the\r\nCMIP6 ensemble is different from the AR6 assessed range of projected global surface temperature (Section 4.3.4; Table 4.5). The latter \r\nfurther takes into account different lines of evidence, including the assessed observed warming between pre-industrial and present "


id: e76c24ec94327d92aa7c043819c6fdc3
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 38014
content:
"day, information from observational constraints on CMIP6, and emulators using the assessed transient climate response (TCR) \r\nand equilibrium climate sensitivity (ECS) ranges (Section 4.3.4). Hence the Chapter 4 assessed range (Table 4.5) is the reference to \r\ndetermine when a given GWL is likely reached under given scenarios, while the mapping between scenarios/time frames and GWLs is \r\nused to assess the respective regional responses happening at these time frames (which also allows accounting for the global surface \r\ntemperature assessment, rather than using scenarios analyses directly from CMIP6 output). "


id: 998527515e6cf992cf91d81707fc7379
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 24237
content:
"A large fraction of the literature considers scenario-based analyses for given time slices. When GWL-based information is required \r\ninstead, an approximated mapping of the multi-model mean can be derived based on the known GWL in the given experiments for \r\na particular time period. As a rough approximation, CMIP6 multi-model mean projections for the near-term (2021-2040) correspond \r\nto changes at about 1.5degC, and projections for the high-end scenario (SSP5-8.5) for the long-term (2081-2100) correspond to about \r\n4degC-5degC of global warming (see Table 4.2 for changes in the CMIP6 ensemble and the Chapter 11 Supplementary Material (11.SM.1) \r\nand Hauser (2021) for details on other time periods and CMIP5). These approximated changes are used for some of the GWL-based \r\nassessments provided in the Chapter 11 regional tables (Section 11.9 and Table 11.3) when literature based on scenario projections is \r\nused to assess estimated changes at given GWLs."


id: 138a8721b327519f2c73f0b996563a39
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 649
content:
"The use of scenarios remains a key element to inform mitigation decisions (Cross-Chapter Box 1.4), to assess which emissions pathways \r\nare consistent with a certain GWL (Cross-Chapter Box 1.4, Figure 1), to estimate when certain GWLs are reached (Section 4.3.4), and \r\nto assess for which variables it is meaningful to use GWLs as a dimension of integration. The use of scenarios is also essential for \r\nvariables whose climate response strongly depends on the contribution of radiative forcing (e.g., aerosols) or land-use and land \r\nmanagement changes, are time and warming rate dependent (e.g., sea level rise), or differ between transient and quasi-equilibrium \r\nstates. Furthermore, the use of concentration or emission-driven scenario simulations is required if regional climate assessments need \r\nto account for the uncertainty in GSAT changes or climate-carbon feedbacks."


id: 9099de21bee7a52ba787d29f32fc0794
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 22648
content:
"Forcing dependence of the GWL response is found for global mean precipitation (Section 8.4.3), but less for regional patterns of mean \r\nprecipitation changes (Cross-Chapter Box 11.1, Figure 2). Limited dependence is found for extremes, as highlighted above. In the \r\ncryosphere, elements that are quick to respond to warming like sea ice area, permafrost and snow, show little scenario dependence \r\n(Sections 9.3.1.1, 9.5.2.3 and 9.5.3.3), whereas slow-responding variables such as ice volumes of glaciers and ice sheets respond with \r\na substantial delay and, due to their inertia, the response depends on when a certain GWL is reached. This also applies to some extent \r\nfor sea level rise where, for example, the contributions of melting glaciers and ice sheets depend on the pathway followed to reach \r\na given GWL (Section 9.6.3.4). "


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content:
"In addition to the lagged effect, the climate response at a given GWL may differ before and after a period of overshoot, for example \r\nin the Atlantic Meridional Overturning Circulation (e.g., Palter et al. 2018). Finally, as assessed in IPCC SR1.5, there is a difference in \r\nthe response even for temperature-related variables if a GWL is reached in a rapidly warming transient state or in an equilibrium state \r\nwhen the land-sea warming contrast is less pronounced (e.g., King et al. 2020). However, in this Report, GWLs are used in the context \r\nof projections for the 21st century when the climate response is mostly not in equilibrium and where projections for many variables \r\nare less dependent on the pathway than for projections beyond 2100 (Section 9.6.3.4)."


id: 40bf0010c397cd803c9a247d241c9a6b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
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content:
"GWL-based projections can inform society and policymakers on how climate would change under GWLs consistent with the aims \r\nof the Paris Agreement (stabilization at 1.5degC/well below 2degC), as well as on the consequences of missing these aims and reaching \r\nGWLs of 3degC or 4degC by the end of the century. The AR6 assessment shows that every bit of global warming matters and that changes \r\nin global warming of 0.5degC lead to statistically significant changes in mean climate and climate extremes on global scale and for \r\nlarge regions (Sections 4.6.2, 11.2.4, 11.3, 11.4, 11.6 and 11.9, Figures 11.8 and 11.9, Atlas and Interactive Atlas), as also assessed \r\nin IPCC SR1.5. "


id: c60d7aebd16fd15032b71631a7742706
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 32155
content:
"The SREX (IPCC, 2012) and AR5 (IPCC, 2014) concluded that \r\ngreenhouse gas forcing is the dominant factor for the increases in the \r\nintensity, frequency, and duration of warm extremes and the decrease \r\nin those of cold extremes. This general global-scale warming is \r\nmodulated by large-scale atmospheric circulation patterns, as well as \r\nby feedbacks such as soil moisture-evapotranspiration-temperature \r\nand snow/ice-albedo-temperature feedbacks, and local forcings \r\nsuch as land-use change or changes in aerosol concentrations at the "


id: 9181e08f952ad7251122b10fcaf636dd
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 22817
content:
"This section assesses changes in temperature extremes at global, \r\ncontinental and regional scales. The main focus is on the changes in the \r\nmagnitude and frequency of moderate extreme temperatures (those \r\nthat occur several times a  year) to very extreme temperatures \r\n(those that occur once in 10 or more years) of time scales from a day \r\nto a season, though there is a strong emphasis on the daily scale \r\nwhere literature is most concentrated. "


id: 3bcded1df0396d1293f6e822d408848
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 7710
content:
"regional and local scales (Sections 11.1.5 and 11.1.6, and Box 11.1). \r\nTherefore, changes in temperature extremes at regional and local \r\nscales can have heterogeneous spatial distributions. Changes in \r\nthe magnitudes (or intensities) of extreme temperatures are often \r\nlarger than changes in global surface temperature, because of larger \r\nwarming on land than on the ocean surface (Section 2.3.1.1), and \r\nbecause of feedbacks, though they are of similar magnitude to \r\nchanges in the local mean temperature (Figure 11.2). "


id: 647a01708c48f51ec48e208000e4916c
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 14953
content:
"Land-atmosphere feedbacks strongly modulate regional- and \r\nlocal-scale changes in temperature extremes (high confidence) \r\n(Section 11.1.6; Seneviratne et al., 2013; Lemordant et al., 2016; Donat \r\net al., 2017; Sillmann et al., 2017b; Hirsch et al., 2019). This effect is \r\nparticularly notable in mid-latitude regions where the drying of soil \r\nmoisture amplifies high temperatures, especially through increases \r\nin sensible heat flux (Whan et al., 2015; Douville et al., 2016; Vogel \r\net al., 2017). Land-atmosphere feedbacks amplifying temperature \r\nextremes also include boundary-layer feedbacks and effects on \r\natmospheric circulation (Miralles et al., 2014a; Schumacher et al., "


id: 63f571c365f381bed958a06a8a2c2809
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 14875
content:
"feedbacks, and local and regional forcings such as land-use and land\u0002cover changes, or aerosol concentrations, and decadal and multi\u0002decadal natural variability. This leads to heterogeneity in regional \r\nchanges and their associated uncertainties (high confidence). \r\nChanges in anthropogenic aerosol concentrations have likely affected \r\ntrends in hot extremes in some regions. Irrigation and crop expansion \r\nhave attenuated increases in summer hot extremes in some regions, \r\nsuch as the Midwestern USA (medium confidence). Urbanization has \r\nlikely exacerbated the effects of global warming in cities, in particular \r\nfor nighttime temperature extremes."


id: b52003e98b2405b48fd3a9c9b9e733fc
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 29101
content:
"The SREX (IPCC, 2012) reported a very likely decrease in the number \r\nof cold days and nights and increase in the number of warm days \r\nand nights at the global scale. Confidence in trends was assessed as \r\nregionally variable (low to medium confidence) due to either a lack \r\nof observations or varying signals in sub-regions. "


id: 8199a78b457b063b18f268ac2e435df4
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 19930
content:
"On the global scale, evidence of an increase in the number of \r\nwarm days and nights and a decrease in the number of cold days \r\nand nights, and an increase in the coldest and hottest extreme \r\ntemperatures is very robust and consistent among all variables. \r\nFigure 11.2 displays time series of globally averaged TXx and TNn on \r\nland. Warming of land mean TXx is similar to the mean temperature \r\nwarming on land, which is about 45% higher than global warming \r\n(Section 2.3.1). Warming of land mean TNn is even higher, with about \r\n3degC of warming since 1960 (Figure 11.2). Figure 11.9 shows maps \r\nof linear trends over 1960-2018 in TXx, TNn, and frequency of warm \r\ndays (TX90p). The maps for TXx and TNn show trends consistent \r\nwith overall warming in most regions, with a  particularly high "


id: 13fd990ec0fbced0ca08104b9f46b4be
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 733
content:
"In Asia (Table 11.7), there is very robust evidence for a  very likely\r\nincrease in the intensity and frequency of hot extremes and decrease \r\nin the intensity and frequency of cold extremes in recent decades. \r\nThis is clear in global studies (e.g.,  Alexander, 2016; Dunn et al., "


id: 4b5327831ea28804aa8159c1ee0ad189
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 10500
content:
"extremes have been reported for the period 1951-2011 (Whan et al., \r\n2014; McGree et al., 2019). "


id: 82e0f9ec05fcbdda83736ce5ce3728f9
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 20156
content:
"In Europe (Table  11.16), there is very robust evidence for a  very \r\nlikely increase in maximum temperatures and the frequency of \r\nheatwaves. The increase in the magnitude and frequency of high \r\nmaximum temperatures has been observed consistently across \r\nregions, including in central Europe (Twardosz and Kossowska-Cezak, \r\n2013; Christidis et al., 2015; Lorenz et al., 2019) and southern Europe \r\n(Croitoru and Piticar, 2013; El Kenawy et al., 2013; Christidis et al., \r\n2015; Nastos and Kapsomenakis, 2015; Fioravanti et al., 2016; Ruml \r\net al., 2017). In Northern Europe, a strong increase in extreme winter \r\nwarming events has been observed (Matthes et al., 2015; Vikhamar\u0002Schuler et al., 2016). Temperature observations for winter cold spells \r\nshow a  long-term decreasing frequency in Europe (Brunner et al., \r\n2018; van Oldenborgh et al., 2019), and typical cold spells, such \r\nas that observed during the 2009-2010 winter, had an occurrence "


id: 67d46b43ee817429380b3e574e5df759
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 15540
content:
"probability two times smaller currently than if climate change had \r\nnot occurred (Christiansen et al., 2018)."


id: ea8c3a0eec9ede9a806619f15e703abe
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 38625
content:
"of England (M. Sanderson et al., 2017). In Central and South America, \r\nthere are increases in the frequency of heatwaves (Barros et al., \r\n2015; Bitencourt et al., 2016; Ceccherini et al., 2016; Piticar, 2018), \r\nalthough decreases in Excess Heat Factor (EHF), which is a metric for \r\nheatwave intensity, are observed in South America in data derived \r\nfrom HadGHCND (Cavanaugh and Shen, 2015). "


id: f9825bf23e7888f44218495ff8bc3e73
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 41265
content:
"Extreme heat events have increased around the Arctic since 1979, \r\nparticularly over Arctic North America and Greenland (Matthes \r\net al., 2015; Dobricic et al., 2020), which is consistent with summer \r\nmelt (Section 9.4.1). Observations north of 60@N show increases in \r\nwinter warm days and nights over 1979-2015, while cold days and \r\nnights declined (Sui et al., 2017). Extreme heat days are particularly \r\nstrong in winter, with observations showing the warmest mid-winter \r\ntemperatures at the North Pole rising at twice the rate of mean \r\ntemperature (Moore, 2016), as well as increases in Arctic winter \r\nwarm days (Vikhamar-Schuler et al., 2016; Graham et al., 2017). \r\nArctic annual minimum temperatures have increased at about three \r\ntimes the rate of global surface temperature since the 1960s (Figures \r\n11.2 and 11.9), consistent with the observed mean cold season \r\n(October-May) warming of 3.1degC in the region (Atlas 11.2)."


id: e1359846b5edac8c7c74bfe95ae9c6a8
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 36890
content:
"Initial analyses of CMIP6 simulations (H. Chen et al., 2020; Di Luca \r\net al., 2020a; Kim et al., 2020; Thorarinsdottir et al., 2020; Wehner \r\net al., 2020; Li et al., 2021) indicate that the CMIP6 models perform \r\nsimilarly to the CMIP5 models regarding biases in hot and cold \r\nextremes. In general, CMIP5 and CMIP6 historical simulations are \r\nsimilar in their performance in simulating the observed climatology \r\nof extreme temperatures (high confidence). The general warm bias \r\nin hot extremes and cold bias in cold extremes reported for CMIP5 \r\nmodels (Kharin et al., 2013; Sillmann et al., 2013a) remain in CMIP6 \r\nmodels (Di Luca et al., 2020a). However, there is some evidence that "


id: c4229b0519ee5a301e7d36fe4ba7385d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 31794
content:
"in the duration of the events does not impact on the reliability of the \r\nmodels' positive trends (Freychet et al., 2018). "


id: 474b32d42c601f19906909845f591a7e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 9748
content:
"Atmospheric Model Intercomparison Project (AMIP) simulations \r\nare often used in event attribution studies to assess the influence \r\nof global warming on observed temperature-related extremes. These \r\nsimulations typically capture the observed trends in temperature \r\nextremes, though some regional features, such as the lack of warming \r\nin daytime warm temperature extremes over South America and \r\nparts of North America, are not reproduced in the model simulations \r\n(Dittus et al., 2018), possibly due to internal variability, deficiencies \r\nin local surface processes, or forcings that are not represented in \r\nthe sea surface temperatures (SSTs). Additionally, the AMIP models \r\nassessed tend to produce overly persistent heatwave events. This bias "


id: 3fec79754a6c0b22184482b779de5c45
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 8439
content:
"extremes shown in both RCMs (Whan and Zwiers, 2016). The most \r\nsignificant biases are found in TXx and TNn, with fewer differences \r\nin the simulation of annual minimum daily maximum temperature \r\n(TXn) and annual maximum daily minimum temperature (TNx) \r\nin Central and Western North America. Over Central and South \r\nAmerica, maximum temperatures from the Eta RCM are generally \r\nunderestimated, although hot days, warm nights, and heatwaves are \r\nincreasing in the period 1961-1990, in agreement with observations \r\n(Chou et al., 2014b; Tencer et al., 2016; Bozkurt et al., 2019)."


id: cbbe23d725005476b72b7ad3d9a3d68e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 33171
content:
"In summary, there is high confidence that climate models can \r\nreproduce the mean state and overall warming of temperature \r\nextremes observed globally and in most regions, although the \r\nmagnitude of the trends may differ. The ability of models to capture \r\nobserved trends in temperature-related extremes depends on the \r\nmetric evaluated, the way indices are calculated, and the time \r\nperiods and spatial scales considered. Regional climate models add \r\nvalue in simulating temperature-related extremes over GCMs in some \r\nregions. Some land forcings on temperature extremes are not well\u0002captured (effects of deforestation) or generally not representated \r\n(irrigation) in ESMs."


id: 7b9ab7c54966a5f14b63b00d04ff99fa
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 18955
content:
"The SREX (IPCC, 2012) assessed that it is likely anthropogenic \r\ninfluences have led to the warming of extreme daily minimum and \r\nmaximum temperatures at the global scale. The AR5 concluded that \r\nhuman influence has very likely contributed to the observed changes \r\nin the intensity and frequency of daily temperature extremes on the \r\nglobal scale in the second half of the 20th  century (IPCC, 2014). \r\nWith regard to individual, or regionally or locally specific events, AR5 \r\nconcluded that it is likely human influence has substantially increased \r\nthe probability of occurrence of heatwaves in some locations."


id: 683b630380e16ca4ab00c96fa4babb85
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 15605
content:
"A significant advance since AR5 has been a large number of studies \r\nfocusing on extreme temperature events at monthly and seasonal \r\nscales, using various extreme event attribution methods. Diffenbaugh \r\net al. (2017) found that anthropogenic warming has increased the "


id: 7f3b4141e3ca097e6278dd36ff516b89
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 19537
content:
"the attributable signal (Fischer and Knutti, 2015; Kirchmeier-Young \r\net al., 2019). Large-scale, longer-duration events tend to have notably \r\nlarger attributable risk ratios (Angelil et al., 2014, 2018; Uhe et al., \r\n2016; Harrington, 2017; Kirchmeier-Young et al., 2019), as natural \r\nvariability is smaller. While uncertainty in the best estimates of the \r\nrisk ratios may be large, their lower bounds can be quite insensitive to \r\nuncertainties in observations or model descriptions, thus increasing \r\nconfidence in conservative attribution statements (Jeon et al., 2016)."


id: 2eb46e0ce8a26ad7afb4ec3fe1939db2
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 5494
content:
"Studies that focused on the attributable signal in observed cold extreme \r\nevents show human influence reducing the probability of those events. \r\nIndividual attribution studies on the extremely cold winter of 2011 in \r\nEurope (Peterson et al., 2012), in the eastern USA during 2014 and \r\n2015 (Trenary et al., 2015, 2016; Wolter et al., 2015; Bellprat et al., \r\n2016), in the cold spring of 2013 in the United Kingdom (Christidis \r\net al., 2014), and of 2016 in eastern China (Qian et al., 2018; Y. Sun \r\net al., 2018b) all showed a reduced probability due to human influence \r\non the climate. An exception is the study of Grose et al. (2018), which \r\nfound an increase in the probability of the severe western Australian \r\nfrost of 2016 due to anthropogenically-driven changes in circulation \r\npatterns that drive cold outbreaks and frost probability. "


id: 375503b1961c8781474a46315af47dea
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 6950
content:
"The AR5 (Chapter12, Collins et al., 2013) concluded that it is virtually \r\ncertain there will be more frequent hot extremes and fewer cold \r\nextremes at the global scale and over most land areas in a future "


id: 596d37c275bd15b8ce3f4bd1ebf02807
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 12968
content:
"Different event attribution studies can produce a  wide range of \r\nchanges in the probability of event occurrence because of different \r\nframing. The temperature event definition itself plays a crucial role in "


id: b2c6a18fd1cbe863887c85f1ed64328
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 28693
content:
"warmer climate, and it is very likely that heatwaves will occur \r\nwith a higher frequency and longer duration.The SR1.5 (Chapter 3, \r\nHoegh-Guldberg et al., 2018) assessment on projected changes in \r\nhot extremes at 1.5degC and 2degC global warming is consistent with the \r\nAR5 assessment, concluding that it is very likely a global warming \r\nof 2degC, when compared with a 1.5degC warming, would lead to more \r\nfrequent and more intense hot extremes on land, as well as to longer \r\nwarm spells, affecting many densely inhabited regions. The SR1.5 \r\nalso assessed it is very likely that the strongest increases in the \r\nfrequency of hot extremes are projected for the rarest events, while \r\ncold extremes will become less intense and less frequent, and cold \r\nspells will be shorter. "


id: aea4f12041a145a33d184fbe89f400cc
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 27956
content:
"extremes will occur, even at the 1.5degC GWL (Figure 11.11). At this \r\nlevel, widespread significant changes at the grid-box level occur for \r\ndifferent temperature indices (Aerenson et al., 2018). In agreement \r\nwith CMIP5 projections, CMIP6 simulations show that a  0.5degC \r\nincrement in global warming will significantly increase the intensity \r\nand frequency of hot extremes, and decrease the intensity and \r\nfrequency of cold extremes on the global scale (Figures 11.6, 11.8 \r\nand 11.12). It takes less than half of a degree for the changes in TXx \r\nto emerge above the level of natural variability (Figure 11.8) and the \r\n66% ranges of the land medians of the 10-year or 50-year TXx events \r\ndo not overlap between 1.0degC and 1.5degC in the CMIP6 multi-model \r\nensemble simulations (Figure 11.6, Li et al., 2021). "


id: a030280adf86d4b2f5096ab9fa6656dc
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 25398
content:
"Projected warming is larger for TNn and exhibits strong equator-to\u0002pole amplification, similar to the warming of boreal winter mean \r\ntemperatures. The warming of TXx is more uniform over land and \r\ndoes not exhibit this behaviour (Figure  11.11). The warming of \r\ntemperature extremes on global and regional scales tends to scale \r\nlinearly with global warming (Section 11.1.4; Fischer et al., 2014; \r\nSeneviratne et al., 2016; Wartenburger et al., 2017; Li et al., 2021; \r\nsee also SR1.5, Chapter 3). In the mid-latitudes, the rate of warming \r\nof hot extremes can be as large as twice the rate of global \r\nwarming (Figure 11.11). In the Arctic winter, the rate of warming of \r\nthe temperature of the coldest nights is about three times the rate \r\nof global warming (Appendix, Figure  11.A.1). Projected changes "


id: 95c10efe937ad2e059f0a0cdb26033a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
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content:
"New studies since AR5 and SR1.5 confirm these assessments. New \r\nliterature since AR5 includes projections of temperature-related \r\nextremes in relation to changes in mean temperatures, projections \r\nbased on CMIP6 simulations, projections based on stabilized global \r\nwarming levels, and the use of new metrics. Constraints for the \r\nprojected changes in hot extremes were also provided (Borodina \r\net al., 2017b; Sippel et al., 2017b; Vogel et al., 2017). Overall, \r\nprojected changes in the magnitude of extreme temperatures over \r\nland are larger than changes in global mean temperature, over mid\u0002latitude land regions in particular (Figures 11.3, 11.11; Fischer et al., \r\n2014; Seneviratne et al., 2016; B.M. Sanderson et al., 2017; Wehner \r\net al., 2018b; Di Luca et al., 2020b). Large warming in hot and cold "


id: d0fb644a831d8d8bef9973b91f3b474e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
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"in temperature extremes can deviate from projected changes in \r\nannual mean warming in the same regions (Figures 11.3, 11.A.1 and \r\n11.A.2; Di Luca et al., 2020b; Wehner, 2020) due to the additional \r\nprocesses that control the response of regional extremes, including, \r\nin particular, soil moisture-evapotranspiration-temperature \r\nfeedbacks for hot extremes in the mid-latitudes and subtropical \r\nregions, and snow/ice-albedo-temperature feedbacks in high\u0002latitude regions. "


id: 23ca372e9297a1893de92ece75277b21
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 3489
content:
"The probability of exceeding a  certain hot extreme threshold will \r\nincrease, while those for cold extreme will decrease with global \r\nwarming (B. Mueller et al., 2016; Lewis et al., 2017b; Suarez\u0002Gutierrez et al., 2020b). The changes tend to scale nonlinearly with \r\nthe level of global warming, with larger changes for more rare \r\nevents (Section 11.2.4; Cross-Chapter Box 11.11; Figures 11.6 and \r\n11.12; Fischer and Knutti, 2015; Kharin et al., 2018; Li et al., 2021). \r\nFor  example, the CMIP5 ensemble projects the frequency of the \r\npresent-day climate 20-year hottest daily temperature to increase \r\nby 80% at the 1.5degC GWL and by 180% at the 2.0degC GWL, and \r\nthe frequency of the present-day climate 100-year hottest daily \r\ntemperature to increase by 200% and more than 700% at the 1.5degC \r\nand 2.0degC warming levels, respectively (Kharin et al., 2018). CMIP6 \r\nsimulations project similar changes (Li et al., 2021)."


id: 58dc0b34af2dc78395f975732fa1627a
created_at: 2023-04-19 13:21:40
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index: climate_gpt_v2
vector_id: 12875
content:
"than that projected for TX90p, consistent with observed past changes \r\n(Lopez-Franca et al., 2016). Under RCP8.5, the number of heatwave \r\ndays are projected to increase for the intra-Americas region for the \r\nend of the 21st century (Angeles-Malaspina et al., 2018). A general \r\ndecrease in the frequency of cold spells and frost days is projected, as \r\nindicated by several indices based on minimum temperature (Lopez\u0002Franca et al., 2016)."


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vector_id: 23999
content:
"In Africa (Table  11.4), evidence includes increases in the intensity \r\nand frequency of hot extremes, such as warm days, warm nights, \r\nand heatwaves, and decreases in the intensity and frequency of cold \r\nextremes, such as cold days and cold nights over the continent, as \r\nprojected by CMIP5, CMIP6, and CORDEX simulations (Giorgi et al., \r\n2014; Engelbrecht et al., 2015; Lelieveld et al., 2016; Russo et al., \r\n2016; Dosio, 2017; Bathiany et al., 2018; Mba et al., 2018; Nangombe \r\net al., 2018; Weber et al., 2018; Kruger et al., 2019; Coppola et al., \r\n2021b; Li et al., 2021). Cold spells are projected to decrease under \r\nall RCPs, and even at low warming levels in Western and Central \r\nAfrica (Diedhiou et al., 2018). The number of cold days is projected to \r\ndecrease in East Africa (Ongoma et al., 2018b)."


id: f27592e56668c58d8b04150a7e2a3022
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 40047
content:
"In Australasia (Table  11.10), evidence includes increases in the \r\nintensity and frequency of hot extremes, such as warm days, warm \r\nnights, and heatwaves, and decreases in the intensity and frequency \r\nof cold extremes, such as cold days and cold nights over the \r\ncontinent, as projected by CMIP5, CMIP6, and CORDEX simulations \r\n(CSIRO and BOM, 2015; Alexander and Arblaster, 2017; Lewis et al., \r\n2017a; Herold et al., 2018; Coppola et al., 2021b; Evans et al., 2021). \r\nOver most of Australia, increases in the intensity and frequency of \r\nhot extremes are projected to be predominantly driven by the long\u0002term increase in mean temperatures (Di Luca et al., 2020b). Future \r\nprojections indicate a decrease in the number of frost days regardless \r\nof the region and season considered (Alexander and Arblaster, 2017; \r\nHerold et al., 2018)."


id: 455fec415a9b3961640991bf0eea1630
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 9426
content:
"In summary, it is virtually certain that further increases in the intensity \r\nand frequency of hot extremes, and decreases in the intensity and \r\nfrequency of cold extremes, will occur throughout the 21st century \r\nand around the world. It is virtually certain that the number of hot \r\ndays and hot nights and the length, frequency, and/or intensity of \r\nwarm spells or heatwaves compared to 1995-2014 will increase \r\nover most land areas. In most regions, changes in the magnitude \r\nof temperature extremes are proportional to global warming levels \r\n(high confidence). The highest increase of temperature of hottest "


id: 727cb686673d9535a4d2220d2558893e
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 17381
content:
"In Central and South America (Table  11.13), evidence includes \r\nincreases in the intensity and frequency of hot extremes, such as warm \r\ndays, warm nights, and heatwaves, and decreases in the intensity and \r\nfrequency of cold extremes, such as cold days and cold nights over the \r\ncontinent, as projected by CMIP5, CMIP6, and CORDEX simulations \r\n(Chou et al., 2014a; Cabre et al., 2016; Lopez-Franca et al., 2016; \r\nStennett-Brown et al., 2017; Coppola et al., 2021b; Li et al., 2021; \r\nVichot-Llano et al., 2021). Over South-Eastern South America during \r\nthe austral summer, the increase in the frequency of TN90p is larger "


id: 4305acab8c48c4492e3fe3a4dec4f753
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 8993
content:
"days is projected in some mid-latitude and semi-arid regions, \r\nat about 1.5 times to twice the rate of global warming (high \r\nconfidence). The highest increase of temperature of coldest days is \r\nprojected in Arctic regions, at about three times the rate of global \r\nwarming (high confidence). The probability of temperature extremes \r\ngenerally increases nonlinearly with increasing global warming \r\nlevels (high confidence). Confidence in assessments depends on the \r\nspatial and temporal scales of the extreme in question, with high \r\nconfidence in projections of temperature-related extremes at global \r\nand continental scales for daily to seasonal scales. There is high \r\nconfidence that, on land, the magnitude of temperature extremes \r\nincreases more strongly than global mean temperature."


id: 19e722c340eed09ee387a2fe234eae87
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 1754
content:
"including tropical/extratropical cyclones and severe convective \r\nstorms, result in changes in extreme precipitation (Section  11.7). \r\nAlso,  changes in sea surface temperatures (SSTs) alter land-sea \r\ncontrast, leading to changes in precipitation extremes near coastal \r\nregions. For example, the projected larger SST increase near the \r\ncoasts of East Asia and India can result in heavier rainfall near these \r\ncoastal areas from tropical cyclones (Mei and Xie, 2016) or torrential \r\nrains (Manda et al., 2014). The warming in the western Indian Ocean \r\nis associated with increases in moisture surges on the low-level \r\nmonsoon westerlies towards the Indian subcontinent, which may \r\nlead to an increase in the occurrence of precipitation extremes over \r\ncentral India (Krishnan et al., 2016; Roxy et al., 2017). "


id: ce3d654edcf945c99b662b7c32a35ef8
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 33617
content:
"Decreases in atmospheric aerosols results in warming and thus an \r\nincrease in extreme precipitation (Samset et al., 2018; Sillmann \r\net al., 2019). Changes in atmospheric aerosols also result in dynamic \r\nchanges such as in tropical cyclones (Takahashi et al., 2017; Strong \r\net al., 2018). Uncertainty in the projections of future aerosol \r\nemissions results in additional uncertainty in the heavy precipitation \r\nprojections of the 21st century (Lin et al., 2016). "


id: afda1efeaaf87d2e3705a0019fe2537f
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 28175
content:
"This section assesses changes in heavy precipitation at global and \r\nregional scales. The main focus is on extreme precipitation at a daily \r\nscale where literature is most concentrated, though extremes of \r\nshorter (sub-daily) and longer (five-day or more) durations are also \r\nassessed to the extent the literature allows."


id: 7ba9fa9fc64ce8708c3949ce35a995f6
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 18968
content:
"The SREX (Chapter 3, Seneviratne et al., 2012) assessed changes in \r\nheavy precipitation in the context of the effects of thermodynamic \r\nand dynamic changes. Box  11.1 assesses thermodynamic and \r\ndynamic changes in a  warming world to aid the understanding \r\nof changes in observations and projections in some extremes and \r\nthe sources of uncertainties (see also Section  8.2.3.2). In general, \r\nwarming increases the atmospheric water-holding capacity following \r\nthe Clausius-Clapeyron (C-C) relation. This thermodynamic effect \r\nresults in an increase in extreme precipitation at a similar rate at the \r\nglobal scale. On a regional scale, changes in extreme precipitation \r\nare further modulated by dynamic changes (Box 11.1). "


id: a5374c6c14bdc835e10001f0c40378a2
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 26295
content:
"Large-scale modes of variability, such as the North Atlantic Oscillation \r\n(NAO), El Nino-Southern Oscillation (ENSO), Atlantic Multi-decadal \r\nVariability (AMV), and Pacific Decadal Variability (PDV) (Annex IV), \r\nmodulate precipitation extremes through changes in environmental \r\nconditions or embedded storms (Section 8.3.2). Latent heating can \r\ninvigorate these storms (Nie et al., 2018; Z. Zhang et al., 2019a); \r\nchanges in dynamics can increase precipitation intensity above \r\nthat expected from the C-C scaling rate (Sections 8.2.3.2 and 11.7; \r\nBox  11.1). Additionally, the efficiency of converting atmospheric \r\nmoisture into precipitation can change as a  result of cloud \r\nmicrophysical adjustment to warming, resulting in changes in the \r\ncharacteristics of extreme precipitation; but changes in precipitation \r\nefficiency in a warming world are highly uncertain (Sui et al., 2020)."


id: ea41b26cd1e1a4d3d412550b7e22265c
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 38570
content:
"In summary, precipitation extremes are controlled by both \r\nthermodynamic and dynamic processes. Warming-induced \r\nthermodynamic change results in an increase in extreme precipitation, \r\nat a  rate that closely follows the C-C relationship at the global \r\nscale (high confidence). The effects of warming-induced changes \r\nin dynamic drivers on extreme precipitation are more complicated, \r\ndifficult to quantify, and are an uncertain aspect of projections. \r\nPrecipitation extremes are also affected by forcings other than \r\nchanges in greenhouse gases, including changes in aerosols, land\u0002use and land-cover change, and urbanization (medium confidence). "


id: 2a88f24e29517a6475d47b2b8b89fe07
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 4710
content:
"It is difficult to separate the effect of global warming from internal \r\nvariability in the observed changes in the modes of variability \r\n(Section  2.4). Future projections of modes of variability are highly \r\nuncertain (Section  4.3.3), resulting in uncertainty in regional \r\nprojections of extreme precipitation. Future warming may amplify \r\nmonsoonal extreme precipitation. Changes in extreme storms, "


id: 9f13f2b2aaccfe8a21fb2c8556109066
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 25210
content:
"Both SREX (Chapter  3, Seneviratne et al., 2012) and AR5 (IPCC, \r\n2014 Chapter 2) concluded it was likely that the number of heavy \r\nprecipitation events over land had increased in more regions than "


id: f2a98d29c4dd8f9df9b413f1e2ccc9ac
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 40094
content:
"it had decreased, though there were wide regional and seasonal \r\nvariations, and trends in many locations were not statistically \r\nsignificant. This assessment has been strengthened with multiple \r\nstudies finding robust evidence of the intensification of extreme \r\nprecipitation at global and continental scales, regardless of spatial \r\nand temporal coverage of observations and the methods of data \r\nprocessing and analysis."


id: 9327a15683d0e6f5b23e670ff4c444be
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 23095
content:
"daily heavy precipitation results in an increase in pluvial floods over \r\nsmaller watersheds (Ghausi and Ghosh, 2020). "


id: 5b228d63027941fe799f6a498cb45622
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 13283
content:
"In Asia (Table 11.8), there is robust evidence that extreme precipitation \r\nhas increased since the 1950s (high confidence), however, this \r\nis dominated by high spatial variability. Increases in Rx1day and \r\nRx5day during 1950-2018 are found over two-thirds of stations. \r\nThe  percentage of stations with statistically significant trends is \r\nlarger than can be expected by chance (Figure  11.13; Sun et al., \r\n2021). An increase in extreme precipitation has also been observed \r\nin various regional studies based on different metrics of extreme "


id: f1f8ee46d0cf5fdcee0d740f7b8c3af0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 39960
content:
"In Small Islands, there is a lack of evidence showing changes in heavy \r\nprecipitation overall. There were increases in extreme precipitation \r\nin Tobago from 1985-2015 (Stephenson et al., 2014; Dookie et al., \r\n2019) and decreases in south-western French Polynesia and the \r\nsouthern subtropics (low confidence) (Table 11.5; Atlas.10). Extreme \r\nprecipitation leading to flooding in the Small Islands has been \r\nattributed in part to tropical cyclones, as well as being influenced by \r\nENSO (Box 11.5; Khouakhi et al., 2016; Hoegh-Guldberg et al., 2018)."


id: 3fd7255ae64444c3f89e288c824b9c95
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 8425
content:
"In summary, the frequency and intensity of heavy precipitation have \r\nlikely increased at the global scale over a majority of land regions \r\nwith good observational coverage. Since 1950, the annual maximum \r\namount of precipitation falling in a day, or over five consecutive days, \r\nhas likely increased over land regions with sufficient observational \r\ncoverage for assessment, with increases in more regions than \r\nthere are decreases. Heavy precipitation has likely increased on the \r\ncontinental scale over three continents (North America, Europe, and \r\nAsia) where observational data are more abundant. There is very low "


id: 61f4e89f14b34cd7129f37b316944856
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 14509
content:
"confidence about changes in sub-daily extreme precipitation due to \r\nthe limited number of studies and available data. "


id: 672221e3d76c3f4f8463093b479167f7
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 15422
content:
"Different generations of CMIP models have improved over time, \r\nthough quite modestly (Flato et al., 2013; Watterson et al., 2014). \r\nImprovements in the representation of the magnitude of the Expert \r\nTeam on Climate Change Detection and Indices (ETCCDI) in CMIP5 \r\nover CMIP3 (Sillmann et al., 2013a; Chen and Sun, 2015a) have \r\nbeen attributed to higher resolution, as higher-resolution models \r\nrepresent smaller areas at individual grid boxes. Additionally, the \r\nspatial distribution of extreme rainfall simulated by high-resolution "


id: 241e3aad3366004457385dcbdced76f7
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 3534
content:
"Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 10, \r\nIPCC, 2014) concluded with medium confidence that anthropogenic \r\nforcing has contributed to a  global-scale intensification of heavy \r\nprecipitation over the second half of the 20th  century. These \r\nassessments were based on the evidence of anthropogenic influence \r\non aspects of the global hydrological cycle, in particular, the human \r\ncontribution to the warming-induced observed increase in atmospheric \r\nmoisture that leads to an increase in heavy precipitation, and limited \r\nevidence of anthropogenic influence on extreme precipitation of \r\ndurations of one and five days."


id: e65a8501b542adb38f5458b90f521889
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 37830
content:
"In general, there is high confidence that historical simulations \r\nby CMIP5 and CMIP6 models of similar horizontal resolutions are \r\ninterchangeable in their performance in simulating the observed \r\nclimatology of extreme precipitation."


id: 5d4f39adb8aa48ae7060299b0685d36b
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 13695
content:
"In summary, there is  high confidence  in the ability of models to \r\ncapture the large-scale spatial distribution of precipitation extremes \r\nover land. The magnitude and frequency of extreme precipitation "


id: 220c0e701498f55529869fe57e1523b7
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 3215
content:
"the combined effect of greenhouse gases and aerosol forcing on \r\nRx1day and Rx5day over North America, Eurasia, and mid-latitude \r\nland regions (Zhang et al., 2013) and of greenhouse gas forcing \r\nin Rx1day and Rx5day in the mid-to-high latitudes, western and \r\neastern Eurasia, and the global dry regions (Paik et al., 2020). These \r\nfindings are corroborated by the detection of human influence in the \r\nfraction of extreme precipitation in the total precipitation over Asia, \r\nEurope, and North America (Dong et al., 2021). Human influence \r\nwas found to have contributed to the increase in frequency and \r\nintensity of regional precipitation extremes in North America during \r\n1961-2010, based on optimal fingerprinting and event attribution \r\napproaches (Kirchmeier-Young and Zhang, 2020). Tabari et al. (2020) \r\nfound the observed latitudinal increase in extreme precipitation \r\nover Europe to be consistent with model-simulated responses to \r\nanthropogenic forcing. "


id: 93b7d65ebfff35e5ccac8350db06e412
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 23196
content:
"Anthropogenic influence may have affected the large-scale \r\nmeteorological processes necessary for extreme precipitation \r\nand the localized thermodynamic and dynamic processes, both \r\ncontributing to changes in extreme precipitation events. Several new \r\nmethods have been proposed to disentangle these effects by either \r\nconditioning on the circulation state or attributing analogues. In \r\nparticular, the extremely wet winter of 2013-2014 in the UK can be \r\nattributed, approximately to the same degree, to both temperature\u0002induced increases in saturation vapour pressure and changes in the \r\nlarge-scale circulation (Vautard et al., 2016; Yiou et al., 2017). There \r\nare multiple cases indicating that very extreme precipitation may \r\nincrease at a rate more than the C-C rate (7% per 1degC of warming) \r\n(Pall et al., 2017; Risser and Wehner, 2017; van der Wiel et al., 2017; \r\nvan Oldenborgh et al., 2017; S.-Y.S. Wang et al., 2018). "


id: e6b8a6217b03dde9f8ade6dd4297539d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 37890
content:
"Event attribution studies found an influence of anthropogenic \r\nactivities on the probability or magnitude of observed extreme \r\nprecipitation events, including European winters (Schaller et al., 2016; \r\nOtto et al., 2018b), extreme 2014 precipitation over the northern "


id: 9ee1ce2bc6f38d78abaa4f11ba52154c
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 25178
content:
"The spatial patterns of the projected changes across different \r\nwarming levels are quite similar, as shown in Figure  11.16, and "


id: ccdcef1e519ed41a0f042db16712edce
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 33377
content:
"In Central and South America (Table  11.14), extreme precipitation \r\nwill likely increase at global warming levels of 2degC and below, but \r\nvery likely increase at higher warming levels for the region as whole. \r\nA  larger increase in global surface temperature leads to a  larger \r\nincrease in extreme precipitation, independent of emissions scenarios \r\n(Li et al., 2021). But there are regional differences in the projection, "


id: 175ae2e077ffdedaf4b004cf127743d0
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 1330
content:
"part of Europe, such as the southern Mediterranean (Tramblay and \r\nSomot, 2018; Lionello and Scarascia, 2020; Coppola et al., 2021a), \r\nuncertain changes over central Europe (Argueso et al., 2012; Croitoru \r\net al., 2013; Rajczak et al., 2013; Casanueva et al., 2014; Patarcic \r\net al., 2014; Paxian et al., 2014; Roth et al., 2014; Fischer and Knutti, \r\n2015; Monjo et al., 2016) and a  strong increase in the remaining \r\nparts, including the Alps region (Gobiet et al., 2014; Donnelly et al., \r\n2017), particularly in winter (Fischer et al., 2015), and in northern \r\nEurope. In a 3degC warmer world, there will be a robust increase in \r\nextreme rainfall over 80% of land areas in northern Europe (Madsen \r\net al., 2014; Donnelly et al., 2017; Cardell et al., 2020). "


id: fc0693f1ec74b800f277927ef2672675
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 41701
content:
"In North America (Table 11.20), the intensity and frequency of extreme \r\nprecipitation will likely increase at the global warming levels of \r\n2degC and below, and very likely increase at higher warming  levels. \r\nAn increase is projected by CMIP6 model simulations (Li et al., \r\n2021) and by previous model generations (Wu, 2015; Easterling \r\net al., 2017; Innocenti et al., 2019), as well as by RCMs (Coppola \r\net al., 2021a). Projections of extreme precipitation over the southern \r\nportion of the continent and over Mexico are more uncertain, with \r\ndecreases possible (Sillmann et al., 2013b; Alexandru, 2018; Coppola \r\net al., 2021a). "


id: d836e1fc9cda19a6474ee0f9c38382b7
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 35340
content:
"In Europe (Table 11.17), extreme precipitation will likely increase at \r\nglobal warming levels of 2degC and below, but very likely increase for \r\nhigher warming levels for the region as whole. The CMIP6 multi\u0002model median projects an increase in the 10- and 50-year return \r\nvalues of Rx1day and Rx5day over a majority of the region at the 2degC \r\nglobal warming level, with more than 95% of the region showing \r\nan increase at higher warming levels (Figure 11.7; C. Li et al., 2021). \r\nThe most intense precipitation events observed today in Europe are \r\nprojected to almost double in occurrence for each 1degC of further global \r\nwarming (Myhre et al., 2019). Extreme precipitation is projected to \r\nincrease in both boreal winter and summer over Europe (Madsen \r\net al., 2014; Christensen et al., 2015; Nissen and Ulbrich, 2017). There \r\nare regional differences, with decreases or no change for the southern "


id: ed207e1316ac0e831f4a87cfac078b6f
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 39095
content:
"In summary, heavy precipitation will generally become more frequent \r\nand more intense with additional global warming. At global warming \r\nlevels of 4degC relative to the pre-industrial, very rare (e.g.,  one in \r\n10 or more years) heavy precipitation events would become more \r\nfrequent and more intense than in the recent past, on the global \r\nscale (virtually certain), and in all continents and AR6 regions: The \r\nincrease in frequency and intensity is extremely likely for most \r\ncontinents and very likely for most AR6 regions. The likelihood \r\nis lower at lower global warming levels and for less-rare heavy \r\nprecipitation events.  At the global scale, the intensification of \r\nheavy precipitation will follow the rate of increase in the maximum "


id: 578f6a6fc86800d832d29beaab0b3852
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 12672
content:
"amount of moisture that the atmosphere can hold as it warms (high \r\nconfidence), of about 7% per 1degC of global warming. The increase in \r\nthe frequency of heavy precipitation events will be non-linear with \r\nmore warming and will be higher for rarer events (high confidence), \r\nwith 10- and 50-year events to be approximately double and triple, \r\nrespectively, at the 4degC warming level. Increases in the intensity of \r\nextreme precipitation events at regional scales will depend on the \r\namount of regional warming as well as changes in atmospheric \r\ncirculation and storm dynamics leading to regional differences \r\nin the rate of heavy precipitation changes (high confidence)."


id: 74d1c218f12a030bb9871cc6a4928920
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 17789
content:
"flooding (Formetta and Feyen, 2019), masking effects of an increase \r\nin extreme precipitation on flood probability in some regions, even \r\nthough they do not eliminate very extreme floods (Vicente-Serrano \r\net al., 2017). This means that an increase in precipitation extremes \r\nmay not always result in an increase in river floods (Sharma et al., \r\n2018; Do et al., 2020). Yet, as very extreme precipitation can become \r\na dominant factor for river floods, there can be some correspondence \r\nin the changes in very extreme precipitation and river floods (Ivancic \r\nand Shaw, 2015; Wasko and Sharma, 2017; Wasko and Nathan, 2019). \r\nThis has been observed in the western Mediterranean (Llasat et al., \r\n2016), in China (Q. Zhang et al., 2015a) and in the USA (Peterson \r\net al., 2013b; Berghuijs et al., 2016; Slater and Villarini, 2016)."


id: ac6b62c9e5246c5c56ee02817c8a7ae2
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 27572
content:
"Since AR5, the number of studies on understanding how floods \r\nmay have changed, and will change in the future, has substantially \r\nincreased. Floods are a complex interplay of hydrology, climate, and \r\nhuman management, and the relative importance of these factors \r\nvaries for different flood types and regions. "


id: aa7b4d30381b73003c7bf6668b504db4
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 27217
content:
"In addition to the amount and intensity of precipitation, the main \r\nfactors for river floods include antecedent soil moisture (Paschalis \r\net al., 2014; Berghuijs et al., 2016; Grillakis et al., 2016; Woldemeskel \r\nand Sharma, 2016) and snow water-equivalent in cold regions \r\n(Sikorska et al., 2015; Berghuijs et al., 2016). Other factors are also \r\nimportant, including stream morphology (Borga et al., 2014; Slater \r\net al., 2015), river and catchment engineering (Pisaniello et al., 2012; \r\nNakayama and Shankman, 2013; Kim and Sanders, 2016), land-use \r\nand land-cover characteristics (Aich et al., 2016; Rogger et al., 2017) \r\nand changes (Knighton et al., 2019), and feedbacks between climate, \r\nsoil, snow, vegetation, etc. (Hall et al., 2014; Ortega et al., 2014; \r\nBerghuijs et al., 2016; Buttle et al., 2016; Teufel et al., 2019). Water \r\nregulation and management have, in general, increased resilience to "


id: 10c4526b0e61c0c520501c4e4f2e2093
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 133
content:
"(high confidence). Changes in extreme precipitation may be used as \r\na proxy to infer changes in some types of flash floods that are more \r\ndirectly related to extreme precipitation (high confidence). "


id: 8b3adf5bc07b9f29ad76eb31cbd80600
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 21660
content:
"In summary, the seasonality of floods has changed in cold regions \r\nwhere snowmelt dominates the flow regime in response to warming \r\n(high confidence). There is low confidence about peak flow trends over \r\npast decades on the global scale, but there are regions experiencing \r\nincreases, including parts of Asia, Southern South America, north\u0002east USA, north-western Europe, and the Amazon, and regions \r\nexperiencing decreases, including parts of the Mediterranean, \r\nAustralia, Africa, and south-western USA. "


id: c9df635e5024c0d4383883147063958d
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 32843
content:
"Hydrological models used to simulate floods are structurally diverse \r\n(Dankers et al., 2014; Mateo et al., 2017; Sen, 2018), often requiring \r\nextensive calibration since sub-grid processes and land-surface \r\nproperties need to be parametrized, irrespective of the spatial \r\nresolutions (Doll et al., 2016; Krysanova et al., 2017). The data used \r\nto drive and calibrate the models are usually of coarse resolution, \r\nnecessitating the use of a wide variety of downscaling techniques \r\n(Muerth et al., 2013). This adds uncertainty not only to the models \r\nbut also to the reliability of the calibrations. The quality of the flood \r\nsimulations also depends on the spatial scale, as flood processes \r\nare different for catchments of different sizes. It is more difficult to \r\nreplicate flood processes for large basins, as water management and \r\nwater use are often more complex for these basins. "


id: ffc7b8085994189f1a639cd5b87a8ac8
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 42389
content:
"Studies that use different regional hydrological models show a large \r\nspread in flood simulations (Dankers et al., 2014; Roudier et al., \r\n2016; Trigg et al., 2016; Krysanova et al., 2017). Regional models \r\nreproduce moderate and high flows reasonably well (0.02-0.1 flow \r\nannual exceedance probabilities), but there are large biases for the \r\nmost extreme flows (0-0.02 annual flow exceedance probability), \r\nindependent of the climatic and physiographic characteristics of the \r\nbasins (S. Huang et al., 2017a). Global-scale hydrological models have \r\neven more challenges, as they struggle to reproduce the magnitude \r\nof the flood hazard (Trigg et al., 2016). Also, the ensemble mean of \r\nmultiple models does not perform better than individual models \r\n(Zaherpour et al., 2018). "


id: 2d1fef19e110e65613c0812f1fba6b5a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 5220
content:
"The use of hydrological models for assessing changes in floods, \r\nespecially for future projections, adds another dimension of \r\nuncertainty on top of uncertainty in the driving climate projections, \r\nincluding emissions scenarios, and in the driving climate models \r\n(both RCMs and GCMs) (Arnell and Gosling, 2016; Hundecha \r\net al., 2016; Krysanova et al., 2017). The differences in hydrological \r\nmodels (Roudier et al., 2016; Thober et al., 2018), as well as post\u0002processing of climate model output for the hydrological models \r\n(Muerth et al., 2013; Maier et al., 2018), add to uncertainty for \r\nflood projections. "


id: e943c1ec951a80adb125e3a085b123a3
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 38399
content:
"In summary, there is medium confidence that simulations for the most \r\nextreme flows by regional hydrological models can have large biases. \r\nGlobal-scale hydrological models still struggle with reproducing the \r\nmagnitude of floods. Projections of future floods are hampered by \r\nthese difficulties and cascading uncertainties, including uncertainties \r\nin emissions scenarios and the climate models that generate inputs. "


id: 6987c8bbc819e2a2f7bae21555856230
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 15834
content:
"there is low confidence in attributing changes in the probability or \r\nmagnitude of flood events to human influence because of a limited \r\nnumber of studies, differences in the results of these studies and \r\nlarge modelling uncertainties. "


id: 4381a1f11210a880bb9cdf076491ad31
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 9102
content:
"Attributing changes in heavy precipitation to anthropogenic activities \r\n(Section 11.4.4) cannot be readily translated to attributing changes \r\nin floods to human activities, because precipitation is only one of \r\nthe multiple factors, albeit an important one, that affect floods. For \r\nexample, Teufel et al. (2017) showed that, while human influence \r\nincreased the odds of the flood-producing rainfall for the 2013 \r\nAlberta flood in Canada, it was not detected to have influenced \r\nthe probability of the flood itself. Schaller et al. (2016) showed \r\nthat human influence on the increase in the probability of heavy \r\nprecipitation translated linearly into an increase in the resulting river \r\nflow of the Thames in the UK in winter 2014, but its contribution to \r\nthe inundation was inconclusive. "


id: 3e9ff280ef8678b3a4dd848f5b8c141a
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 8202
content:
"Gudmundsson et al. (2021) compared the spatial pattern of the observed \r\nregional trends in high river flows (>90th percentile) over 1971-2010 \r\nwith that simulated by global hydrological models. The hydrological \r\nmodels were driven by outputs of climate model simulations under \r\nall historical forcing and pre-industrial forcing conditions. They found \r\ncomplex spatial patterns of extreme river flow trends. They also found \r\nthe observed spatial patterns of trends can be reproduced only if \r\nanthropogenic climate change is considered, and that simulated effects \r\nof water and land management cannot reproduce the observed spatial \r\npattern of trends. As there is only one study and multiple caveats \r\nassociated with the study, including relatively poor observational \r\ndata coverage, there is low confidence about human influence on the \r\nchanges in high river flows on the global scale. "


id: 9a8188deb79caf92c5ea1bf7d03e7e78
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 24425
content:
"At the continental and regional scales, the projected changes in \r\nfloods are uneven in different parts of the world, but there is a larger \r\nfraction of regions with an increase than with a decrease over the \r\n21st  century (Hirabayashi et al., 2013; Dankers et al., 2014; Arnell \r\nand Gosling, 2016; Doll et al., 2018). These results suggest medium \r\nconfidence in flood trends at the global scale, but low confidence in \r\nprojected regional changes. Increases in flood frequency or magnitude \r\nare identified for south-eastern and northern Asia and India (high \r\nagreement across studies), eastern and tropical Africa, and the high \r\nlatitudes of North America (medium agreement), while decreasing "


id: 960b379ee0343f0d6c9d6057edb7d5a3
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 23594
content:
"In summary there is low confidence in the human influence on \r\nthe changes in high river flows on the global scale. In general, "


id: e1b67de8cdcdd4219eba37efc8e512b9
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 36983
content:
"frequency or magnitude is found for central and eastern Europe and \r\nthe Mediterranean (high confidence), and parts of South America, \r\nsouthern and central North America, and south-west Africa (low \r\nconfidence) (Hirabayashi et al., 2013; Dankers et al., 2014; Arnell and \r\nGosling, 2016; Doll et al., 2018). Over South America, most studies \r\nbased on global and regional hydrological models show an increase \r\nin the magnitude and frequency of high flows in the western Amazon \r\n(Guimberteau et al., 2013; Langerwisch et al., 2013; Sorribas et al., \r\n2016; Zulkafli et al., 2016) and the Andes (Hirabayashi et al., 2013; \r\nBozkurt et al., 2018). Section 12.4 provides a detailed assessment of \r\nregional flood projections. "


id: 1ebfa2b9f327ffa93cfe0f8bcec94135
created_at: 2023-04-19 13:21:40
updated_at: 2023-04-19 13:21:40
content_type: text
index: climate_gpt_v2
vector_id: 2612
content:
"This assessment is focused on changes in physical conditions and \r\nmetrics of direct relevance to droughts: (i) precipitation deficits; \r\n(ii)  excess of atmospheric evaporative demand (AED); (iii) soil \r\nmoisture deficits; (iv) hydrological deficits; and e) atmospheric\u0002based indices combining precipitation and AED (Table 11.A.1). In the \r\nregional tables (Section 11.9), the assessment is structured by drought \r\ntypes, addressing: (i) meteorological, (ii) agricultural and ecological, \r\nand (iii) hydrological droughts. Note that the latter two assessments \r\ndirectly inform the Chapter  12 assessment on projected regional \r\nchanges in these climatic impact-drivers (Section  12.4). The text \r\nrefers to AR6 region acronyms (Section 11.9, and see Section 1.4.5)."


