# document

id: 2c57ca364e51bedb6a03ff73129f9000
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5072
content:
"[...] indicators of climate change (Chapter  2), CMIP simulations are \r a key resource for assessing human influence on the climate system. \r Pre-industrial control and historical simulations are of most relevance \r for model evaluation and assessment of internal variability, and these \r simulations are evaluated to assess fitness-for-purpose for attribution, \r which is the focus of this chapter (see also Section 1.5.4). This chapter \r provides the primary evaluation of large-scale indicators of climate \r change in this Report, and is complemented by other fitness-for\u0002purpose evaluations in subsequent chapters. CMIP6 also includes an \r extensive set of idealized and single forcing experiments for attribution "


id: 6dfb5f6213d61c4625b81fad56d71019
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 16613
content:
"The evidence of human influence on the climate system has \r strengthened progressively over the course of the previous five IPCC \r assessments, from the Second Assessment Report that concluded 'the \r balance of evidence suggests a discernible human influence on climate' \r through to the Fifth Assessment Report (AR5) which concluded that 'it \r is extremely likely that human influence caused more than half of the \r observed increase in global mean surface temperature (GMST) from \r 1951 to 2010' (see also Sections 1.3.4 and 3.3.1.1). The AR5 concluded \r that climate models had been developed and improved since the \r Fourth Assessment Report (AR4) and were able to reproduce many \r features of observed climate. Nonetheless, several systematic biases \r were identified (Flato et al., 2013). This chapter additionally builds on \r the assessment of attribution of global temperatures contained in the \r IPCC Special Report on Global Warming of 1.5degC (SR1.5; IPCC, 2018), \r [...]"


id: 62b6932ee9947566ae29f2f321fe46d8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 14658
content:
"In this chapter, we use the Earth System Model Evaluation Tool \r (ESMValTool, Eyring et al., 2020; Lauer et al., 2020; Righi et al., 2020) \r and the NCAR Climate Variability Diagnostic Package (CVDP, Phillips \r et al., 2014) that is included in the ESMValTool to produce most of \r the figures. This ensures traceability of the results and provides an \r additional level of quality control. The ESMValTool code to produce \r the figures in this chapter was released as open source software at \r the time of the publication of this Report (see details in the Chapter \r Data Table, Table SM.3.1). Figures in this chapter are produced either \r using one ensemble member from each model, or using all available \r ensemble members and weighting each simulation by 1/(NMi), where \r N is the number of models and Mi is the ensemble size of the ith \r model, prior to calculating means and percentiles. Both approaches \r ensure that each model used is given equal weight in the figures, and \r [...]"


id: b1e05c8b214b0e6ee6a77729207c2cac
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 28519
content:
"Whereas in previous IPCC assessment reports the comparison of \r simulated and observed climate change was done separately in a \r model evaluation chapter and a chapter on detection and attribution, \r in AR6 these comparisons are integrated together. This has the \r advantage of allowing a single discussion of the full set of explanations \r for any inconsistency in simulated and observed climate change, \r including missing forcings, errors in the simulated response to forcings, \r and observational errors, as well as an assessment of the application \r of detection and attribution techniques to model evaluation. Where \r simulated and observed changes are consistent, this can be interpreted \r both as supporting attribution statements, and as giving confidence in \r simulated future change in the variable concerned (see also Box 4.1). \r However, if a model's simulation of historical climate change has \r been tuned to agree with observations, or if the models used in an \r [...]"


id: 40862905649a845836941e7302e4a12a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8541
content:
"[...] attribution study have been selected or weighted on the basis of the \r realism of their simulated climate response, this information would \r need to be considered in the assessment and any attribution results \r correspondingly tempered. An integrated discussion of evaluation and \r attribution supports such a robust and transparent assessment."


id: 50a15a1d024fbaaabe1c94c09ab4e598
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11461
content:
"This chapter starts with a brief description of methods for detection \r and attribution of observed changes in Section 3.2, which builds on \r the more general introduction to attribution approaches in the Cross\u0002Working Group Box on Attribution in Chapter 1. In this chapter we \r assess the detection of anthropogenic influence on climate on large \r spatial scales and long temporal scales, a concept related to, but \r distinct from, that of the emergence of anthropogenically-induced \r climate change from the range of internal variability on local scales \r and shorter time scales (Section 1.4.2.2). The following sections \r address the climate system component by component, in  each \r case assessing human influence and evaluating climate  models' \r simulations of the relevant aspects of climate and climate \r change. This chapter assesses the evaluation and attribution of global, \r hemispheric, continental and ocean basin-scale indicators of climate \r [...]"


id: 9696b205466503dde344ef3e5f1d26b4
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23699
content:
"[...] change in the atmosphere and at the Earth's surface (Section 3.3), \r cryosphere (Section 3.4), ocean (Section 3.5), and biosphere (Section \r 3.6), and the evaluation and attribution of modes of variability \r (Section  3.7), the period of slower warming in the early 21st century \r (Cross-Chapter Box 3.1) and large-scale changes in extremes (Cross\u0002Chapter Box 3.2). Model evaluation and attribution on sub-continental \r scales are not covered here, since these are assessed in the Atlas and \r in Chapter 10, and extreme event attribution is not covered since it is \r assessed in Chapter 11. Section 3.8 assesses multivariate attribution \r and integrative measures of model performance based on multiple \r variables, as well as process representation in different classes of \r models. The chapter structure is summarized in Figure 3.1."


id: cfda53e1f33e0f9b46d8e0b053f64b2d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33927
content:
"Regression-based methods, also known as fingerprinting methods, \r have been widely used for detection of climate change and attribution \r of the change to different external drivers. Initially, these methods were \r applied to detect changes in global surface temperature, and were \r then extended to other climate variables at different time and spatial \r scales (e.g., Hegerl et al., 1996; Hasselmann, 1997; Allen and Tett, \r 1999; Gillett et al., 2003b; Zhang et al., 2007; Min et al., 2008a, 2011). \r These approaches are based on multivariate linear regression and \r assume that the observed change consists of a linear combination of \r externally forced signals plus internal variability, which generally holds \r for large-scale variables (Hegerl and Zwiers, 2011). The regressors \r are the expected space-time response patterns to different climate \r forcings (fingerprints), and the residuals represent internal variability. \r [...]"


id: a893ca631bf79276ca73a57bba47d5e7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 26867
content:
"[...] Fingerprints are usually estimated from climate model simulations \r following spatial and temporal averaging. A regression coefficient \r which is significantly greater than zero implies that a detectable \r change is identified in the observations. When the confidence interval \r of the regression coefficient includes unity and is inconsistent with \r zero, the magnitude of the model simulated fingerprints is assessed \r to be consistent with the observations, implying that the observed \r changes can be attributed in part to a particular forcing. Variants \r of linear regression have been used to address uncertainty in the \r fingerprints due to internal variability (Allen and Stott, 2003) as well \r as structural model uncertainty (Huntingford et al., 2006)."


id: 8b0ad98445f490531a283dd21c770bc
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21631
content:
"procedure requires an estimate of the inverse covariance matrix of \r the internal variability, and some approaches have been proposed \r for more reliable estimation of this (Ribes et al., 2009). A signal can \r be spuriously detected due to too-small noise, and hence simulated \r internal variability needs to be evaluated with care. Model-simulated \r variability is typically checked through comparing modelled variance \r from unforced simulations with the observed residual variance using \r a standard residual consistency test (Allen and Tett, 1999), or an \r improved one (Ribes and Terray, 2013). Imbers et al. (2014) tested \r the sensitivity of detection and attribution results to different \r representations of internal variability associated with short-memory \r and long-memory processes. Their results supported the robustness \r of previous detection and attribution statements for the global mean \r temperature change but they also recommended the use of a wider \r [...]"


id: 920d14f5921840dc0e16c226465d60e0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 22912
content:
"additivity assumption and likelihood maximization, which estimates \r climate model uncertainty based on an ensemble of opportunity and \r tests whether observations are inconsistent with internal variability \r and consistent with the expected response from climate models. The \r method was further developed by Ribes et al. (2021), who applied \r it to narrow the uncertainty range in the estimated human-induced \r warming. Hannart and Naveau (2018), on the other hand, extended \r the application of standard causal theory (Pearl, 2009) to the context \r of detection and attribution by converting a time series into an event, \r and calculating the probability of causation, an approach which \r maximizes the causal evidence associated with the forcing. On the \r other hand, Schurer et al. (2018) employed a Bayesian framework \r to explicitly consider climate modelling uncertainty in the optimal \r regression method. Application of these approaches to attribution of \r [...]"


id: 9dd8643ac59d1bb97211e340dea28865
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25006
content:
"[...] large-scale temperature changes supports a dominant anthropogenic \r contribution to the observed global warming."


id: 63b2b0a0977f3db0ad6d8c72a2258634
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 14829
content:
"Some recent studies focused on the improved estimation of the \r scaling factor (regression coefficient) and its confidence interval. \r Hannart et al. (2014) described an inference procedure for scaling \r factors which avoids making the assumption that model error and \r internal variability have the same covariance structure. An integrated \r approach to optimal fingerprinting was further suggested in which all \r uncertainty sources (i.e., observational error, model error, and internal \r variability) are treated in one statistical model without a preliminary \r dimension reduction step (Hannart, 2016). Katzfuss et al. (2017) \r introduced a similar integrated approach based on a  Bayesian \r model averaging. On the other hand, DelSole et al. (2019) suggested \r a bootstrap method to better estimate the confidence intervals of \r scaling factors even in a weak-signal regime. It is notable that some \r studies do not optimize fingerprints, as uncertainty in the covariance \r [...]"


id: 277a5fe70cfa42476fcb21131fda7d07
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 4124
content:
"[...] introduces a further layer of complexity, but results in only a limited \r improvement in detection (Polson and Hegerl, 2017)."


id: ccbddebeded2dce359edb47fa12af9ff
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33349
content:
"Another fingerprinting approach uses pattern similarity between \r observations and fingerprints, in which the leading empirical \r orthogonal function obtained from the time-evolving multi-model \r forced simulation is usually defined as a fingerprint (e.g., Santer \r et al., 2013; Marvel et al., 2019; Bonfils et al., 2020). Observations and \r model simulations are then projected onto the fingerprint to measure \r the degree of spatial pattern similarity with the expected physical \r response to a given forcing. This projection provides the signal time \r series, which is in turn tested against internal variability, as estimated \r from long control simulations. As a way to extend this pattern-based \r approach to a high-dimensional detection variable at daily time \r scales, Sippel et al. (2019, 2020) proposed using the relationship \r pattern with a global climate change metric as a fingerprint. To solve \r the high-dimensional regression problem which makes regression \r [...]"


id: 54c6b2285554f41cd82f4be0549a877
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12185
content:
"[...] coefficients not well constrained, they incorporated a statistical \r learning technique based on a regularized linear regression, which \r optimizes a global warming signal by giving lower weight to regions \r with large internal variability."


id: 1e91209213e8930eb77c7c325d0480b8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 2583
content:
"LGM simulation by the Community Earth System Model Version 2.1 \r (CESM2.1) is an exception related to the high equilibrium climate \r sensitivity (ECS) of that model (Section 7.5.6; Kageyama et al., 2021a; \r Zhu et al., 2021). Figure 3.2a illustrates the wide range of simulated \r global LGM temperature responses in both ensembles. CMIP6 models \r tend to underestimate the cooling over land, but agree better with \r oceanic reconstructions. For the mid-Holocene, the regional biases \r found in CMIP5 simulations are similar to those in pre-industrial and \r historical simulations (Harrison et al., 2015; Ackerley et al., 2017), \r suggesting common causes. CMIP5 models underestimate Arctic \r warming in the mid-Holocene (Yoshimori and Suzuki, 2019). CMIP6 \r models simulate a mid-latitude, subtropical, and tropical cooling \r compared to the pre-industrial period, whereas temperature proxies \r indicate a warming (see Section 2.3.1.1.2; Brierley et al., 2020; \r [...]"


id: 23e97815ae45578b76f4ad466b546bf
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3504
content:
"[...] Kaufman et al., 2020), although accounting for seasonal effects in the \r proxies may reduce the discrepancy (Bova et al., 2021). Over the past \r millennium, reconstructed and simulated temperature anomalies, \r internal variability, and forced response agree well over Northern \r Hemisphere continents, but those statistics disagree strongly in \r the Southern Hemisphere, where models seem to overestimate \r the response (PAGES 2k-PMIP3 group, 2015). That disagreement is \r partly explained by the lower quality of the reconstructions in the \r Southern Hemisphere, but model and/or forcing errors may also \r contribute (Neukom et al., 2018). Figure  3.2b shows that land/sea \r warming contrast behaves coherently in model simulations across \r multiple periods, with a slight non-linearity in land warming due to \r a smaller contribution of snow cover to temperature response in \r warmer climates. A multivariate assessment of paleoclimate model \r simulations is carried out in Section 3.8.2."


id: e96286327a9ded08791dcabd536afe7b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38419
content:
"Reconstructions of past temperature from paleoclimate proxies \r (Section 2.3.1.1 and Cross-Chapter Box  2.1) have been used to \r evaluate modelled past climate temperature change patterns. The \r AR5 found that CMIP5 (Taylor et al., 2012) models were able to \r reproduce the large-scale patterns of temperature during the Last \r Glacial Maximum (LGM) (Flato et al., 2013) and simulated a polar \r amplification broadly consistent with reconstructions for warm \r (Pliocene and Eocene) and cold (LGM) periods (Masson-Delmotte \r et al., 2013). Since AR5, a better understanding of temperature \r proxies and their uncertainties and in some cases the forcing applied \r to model simulations has led to better agreement between models \r and reconstructions over a wide range of past climates. For the \r Pliocene and Eocene warm periods, understanding of uncertainties in \r temperature proxies (Hollis et al., 2019; McClymont et al., 2020) and \r [...]"


id: 9bae63226b33f0a0537164fb865bdf3d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 24646
content:
"[...] the boundary conditions used in climate simulations (Haywood et al., \r 2016; Lunt et al., 2017) has improved, and some models now agree \r better with temperature proxies for these time periods compared \r to models assessed in AR5 (Sections 7.4.4.1.2, 7.4.4.2.2 and Cross\u0002Chapter Box 2.4; Zhu et al., 2019; Haywood et al., 2020; Lunt et al., \r 2021). For the Last Interglacial (LIG), improved temporal resolution of \r temperature proxies (Capron et al., 2017) and better appreciation of \r the importance of freshwater forcing (Stone et al., 2016) have clarified \r the reasons behind apparent model-data inconsistencies. Regional \r LIG temperature responses simulated by CMIP6 are within the \r uncertainty ranges of reconstructed temperature responses, except \r in regions where unresolved changes in regional ocean circulation, \r meltwater, or vegetation changes may cause model mismatches \r (Otto-Bliesner et al., 2021). For the LGM, the CMIP5 and CMIP6 \r [...]"


id: bcd44349fb5b7a19a42038ec69d9ae6e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30463
content:
"[...] ensembles compare similarly to new sea surface temperature (SST) \r and surface air temperature (SAT) proxy reconstructions (Figure 3.2a; \r Cleator et al., 2020; Tierney et al., 2020b). The very cold CMIP6 "


id: ee554de70b2b94b80fb0efea65a1dfd7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 39304
content:
"For the historical period, AR5 assessed with very high confidence\r that CMIP5 models reproduced observed large-scale mean surface \r temperature patterns, although errors of several degrees appear in \r elevated regions, like the Himalayas and Antarctica, near the edge \r of the sea ice in the North Atlantic, and in upwelling regions. This \r assessment is updated here for the CMIP6 simulations. Figure  3.3 \r shows the annual mean surface air temperature at 2 m for the \r CMIP5 and CMIP6 multi-model means, both compared to the fifth \r generation European Centre for Medium-Range Weather Forecasts \r (ECMWF) atmospheric reanalysis (ERA5; Section 1.5.2) for the \r period 1995-2014. The distribution of biases is similar in CMIP5 and \r CMIP6 models, as already noted by several studies (Crueger et al., \r 2018; Gainusa-Bogdan et al., 2018; Kuhlbrodt et al., 2018; Lauer \r et al., 2018). Arctic temperature biases seem more widespread in \r [...]"


id: 69dc434b6241fe5edbf2baa331269f74
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 15892
content:
"[...] both ensembles than assessed at the time of AR5. The fundamental \r causes of temperature biases remain elusive, with errors in clouds \r (Lauer et al., 2018), ocean circulation (Kuhlbrodt et al., 2018), winds \r (Lauer et al., 2018), and surface energy budget (Hourdin et al., 2015; \r Seferian et al., 2016; Gainusa-Bogdan et al., 2018) being frequently \r cited candidates. Increasing horizontal resolution shows promise for \r decreasing long-standing biases in surface temperature over large \r regions (Bock et al., 2020). Panels e and f of Figure 3.3 show that \r biases in the mean High-Resolution Model Intercomparison Project \r (HighResMIP, Haarsma et al., 2016) models (see also Table AII.6) are \r smaller than those in the mean of the corresponding lower-resolution \r versions of the same models simulating the same period (see also \r Section 3.8.2.2). However, the bias reduction is modest (Palmer and "


id: 226741676200c1a6de014398d995dbdc
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3275
content:
"7.3.3, and Figure 6.8; Andrews et al., 2020; Dittus et al., 2020; Flynn \r and Mauritsen, 2020). Papalexiou et al. (2020), Tokarska et al. (2020) \r and Stolpe et al. (2021) all report that CMIP6 models on average \r overestimate warming from the 1970s or 1980s to the 2010s, \r although quantitative conclusions depend on which observational \r dataset is compared against (see also Table 2.4). However, Figure 3.4, \r which includes a larger number of models than available to those \r studies, indicates that the CMIP6 multi-model mean tracks observed \r warming better than the CMIP5 multi-model mean after the year \r 2000. The CMIP6 multi-model mean GSAT warming between 1850-\r 1900 and 2010-2019 and associated 5-95% range is 1.09 [0.66 \r to 1.64]  degC. Cross-Chapter Box  2.3 assessed GSAT warming over \r the same period at 1.06 [0.88 to 1.21] degC. So some CMIP6 models \r simulate a warming that is smaller than the assessed observed range, \r [...]"


id: 32c4234cc059701ccc490c90cc354c06
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6201
content:
"[...] and other CMIP6 models simulate a warming that is larger. That \r overestimated warming may be an early symptom of overestimated \r ECS in some CMIP6 models (Section 7.5.6; Meehl et al., 2020; Schlund \r et al., 2020), and has implications for projections of GSAT changes \r (Chapter 4; Liang et al., 2020; Nijsse et al., 2020; Tokarska et al., 2020; \r Ribes et al., 2021). In some models, a large ECS and a strong aerosol \r forcing lead to too large a mid-20th century cooling followed by \r overestimated warming rates in the late 20th century when aerosol \r emissions decrease (Golaz et al., 2019; Flynn and Mauritsen, 2020). \r Temperature biases are driven by both model physics and prescribed \r forcing, which is a challenge for model development."


id: 82967f311f2a71a46e89fd3550386b9a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20103
content:
"Stevens, 2019). In addition, the biases of the limited number of models \r participating in HighResMIP are not entirely representative of overall \r CMIP6 biases, especially in the Southern Ocean, as  indicated by \r comparing panels b and f of Figure 3.3.\r The AR5 assessed with very high confidence that models reproduce \r the general history of the increase in global-scale annual mean \r surface temperature since the year 1850, although AR5 also reported \r that an observed reduction in the rate of warming over the period \r 1998-2012 was not reproduced by the models (Cross-Chapter \r Box 3.1; Flato et al., 2013). Figure 3.2c and Figure 3.4 show time series \r of anomalies in annually and globally averaged surface temperature \r simulated by CMIP5 and CMIP6 models for the past millennium \r and the period 1850 to 2020, respectively, with the baseline set \r to 1850-1900 (see Section 1.4.1). As also indicated by Figure 3.4, \r the spread in simulated absolute temperatures is large (Palmer and \r [...]"


id: 3706c4ed1a7ca2668435129cb7d76220
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6909
content:
"[...] Stevens, 2019). However, the discussion is based on temperature \r anomaly time series instead of absolute temperatures because our \r focus is on evaluation of the simulation of climate change in these \r models, and also because anomalies are more uniformly distributed \r and are more easily deseasonalized to isolate long-term trends (see \r Section 1.4.1). CMIP6 models broadly reproduce surface temperature \r variations over the past millennium, including the cooling that follows \r periods of intense volcanism (medium confidence) (Figure  3.2c). \r Simulated GMST anomalies are well within the uncertainty range \r of temperature reconstructions (medium confidence) since about \r the year 1300, except for some short periods immediately following \r large volcanic eruptions, for which simulations driven by different \r forcing datasets disagree (Figure 3.2c). Before the year 1300, larger \r disagreements between models and temperature reconstructions \r [...]"


id: 253b20decb5dfb73ec49c2f21d2445e4
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3741
content:
"[...] are expected because forcing and temperature reconstructions are \r increasingly uncertain further back in time, but specific causes have \r not been identified conclusively (Ljungqvist et al., 2019; PAGES 2k \r Consortium, 2019) (medium confidence). For the historical period, \r results for CMIP6 shown in Figure 3.4 suggest that the qualitative \r history of surface temperature increase is well reproduced, including \r the increase in warming rates beginning in the 1960s and the \r temporary cooling that follows large volcanic eruptions."


id: 1a544aee05e867e0dc79695d24bc5311
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1832
content:
"Chylek et al. (2020) argue that CMIP5 models overestimate the \r temperature response to volcanic eruptions. Lehner et al. (2016), \r Rypdal (2018) and Stolpe et al. (2021) point instead to missed \r compensating effects on surface temperature change associated \r with internal variability in the El Nino-Southern Oscillation (ENSO) \r or the Atlantic Multi-decadal Oscillation (AMO). An alternative view \r sees those ENSO and AMO responses as expressions of changes in \r climate feedbacks driven by the geographical pattern of SST changes \r (Andrews et al., 2018). At least one model is able to reproduce such \r pattern effects (Gregory and Andrews, 2016). Errors in the volcanic \r forcing prescribed in simulations, including for CMIP6 (Rieger et al., \r 2020), also introduce differences with the observed temperature \r response, independently of the quality of the model physics. In \r addition, comparisons of the modelled temperature  response \r [...]"


id: 52b6a7fd39b8bb90cc5e8410c63cffd3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11827
content:
"[...] to large eruptions over the past millennium to temperature \r reconstructions based on tree rings show a much better agreement \r (Lucke et al., 2019; F. Zhu et al., 2020) than comparisons to the annual, \r multi-temperature proxy reconstructions shown in Figure 3.2c. These \r considerations, and Figures 3.2c and 3.4, suggest that CMIP6 models \r do not systematically overestimate the cooling that follows large \r volcanic eruptions (see also Cross-Chapter Box 4.1)."


id: 36906f8c0975f06a3fcf65f42216d2ff
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6833
content:
"Although virtually all CMIP6 modelling groups report improvements \r in their model's ability to simulate current climate compared to the \r CMIP5 version (Gettelman et al., 2019; Golaz et al., 2019; Mauritsen \r et al., 2019; Swart et al., 2019; Voldoire et al., 2019b; T. Wu et al., \r 2019b; Bock et al., 2020; Boucher et al., 2020; Dunne et al., 2020), it \r does not necessarily follow that the simulation of temperature trends \r is also improved (Bock et al., 2020; Fasullo et al., 2020). The CMIP6 \r multi-model ensemble encompasses observed warming and the \r multi-model mean tracks those observations within 0.2degC over most \r of the historical period. Figure 3.4 confirms the findings of Papalexiou \r et al. (2020), who highlighted based on 29 CMIP6 models that most \r models replicate the period of slow warming  between 1942 and \r 1975 and the late twentieth century warming (1975-2014). The \r CMIP6 multi-model mean is cooler over the period 1980-2000 than \r [...]"


id: 4ad44922fb537be4b3bfcfd59aed6d1c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10406
content:
"[...] both observations and CMIP5 (Figure  3.4; Bock et al., 2020; Flynn \r and Mauritsen, 2020; Gillett et al., 2021). Biases of several tenths \r of a degree in some CMIP6 models over that period may be due \r to an overestimate in aerosol radiative forcing (Sections 6.3.5 and "


id: 456625336cf023a16ddcf7aeb86d4c45
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9430
content:
"Figure 3.3 | Annual mean near-surface (2 m) air temperature (degC) for the period 1995-2014. (a) Multi-model (ensemble) mean constructed with one realization of \r the CMIP6 historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the climatology of the fifth \r generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5). (c) Multi-model mean of the root mean square \r error calculated over all months separately and averaged, with respect to the climatology from ERA5. (d) Multi-model mean bias defined as the difference between the CMIP6 \r multi-model mean and the climatology from ERA5. The difference between the multi-model mean of (e) high-resolution and (f) low-resolution simulations of four HighResMIP \r [...]"


id: 7646fac76d87351d44c8a700e379c574
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 18030
content:
"[...] models and the climatology from ERA5 is also shown. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where >=66% of \r models show change greater than the variability threshold and >=80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, \r where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where >=66% of models show change greater \r than the variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. \r Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least three out of four models than in the corresponding \r [...]"


id: dcdc834147e40a91ae522da18b87959c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 36118
content:
"[...] low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: 8359e811b895db619bd6c5c4a3bac2cb
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20240
content:
"temperature warming over the early 21st century (see Cross-Chapter \r Box 3.1). Evidence coming mostly from paleo studies is mixed on \r whether CMIP5 models underestimate decadal and multi-decadal \r variability in global mean temperature. Schurer et al. (2013) found \r good agreement between internal variability derived from paleo \r reconstructions, estimated as the fraction of variance that is not \r explained by forced responses, and modelled variability, although \r the subset of CMIP5 models they used may have been associated \r with larger variability than the full CMIP5 ensemble. PAGES 2k \r Consortium (2019) found that the largest 51-year trends in both \r reconstructions of global mean temperature and fully forced climate \r simulations over the period 850 to 1850 were almost identical. Zhu \r et al. (2019) showed agreement in the modelled and reconstructed \r temporal spectrum of global surface temperatures on annual to \r [...]"


id: 83c3c5112e63cbda44f3420995fba6f4
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20328
content:
"[...] multi-millennial time scales. However, they suggest that decadal- to \r centennial variability is partly forced by slow orbital changes that \r predate the last millennium. This is consistent with Gebbie and \r Huybers (2019), who showed that the deep ocean has been out \r of equilibrium over that period. Laepple and Huybers (2014) found \r good agreement between modelled and proxy-derived decadal \r ocean temperature variability, but underestimates of variance by \r models by at least a factor of ten at centennial time scales because \r models underestimate the difference between the warm and cold \r periods of the last millennium. Parsons et al. (2020) found that some \r CMIP6 models exhibit much higher multi-decadal variability in GSAT \r than CMIP5 models, with indications that variability in these models "


id: 2b6c74f8db0b7f0c63c02f8867d4e43b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 4889
content:
"in the conclusions from detection and attribution. The AR5 found \r that CMIP5 models simulate realistic variability in global-mean \r surface temperature on decadal time scales, with variability on \r multi-decadal time scales being more difficult to evaluate because \r of the short observational record (Flato et al., 2013). Since AR5, new \r work has characterized the contributions of variability in different \r ocean areas to SST variability, with tropical modes of variability like \r ENSO dominant on time scales of five to ten years, while longer time \r scales see the variance maxima move poleward to the North Atlantic, \r North Pacific, and Southern oceans (Monselesan et al., 2015). There \r may, however, be sizeable, two-way interdependencies between \r ENSO and sea surface temperature variability in different basins \r (Kumar et al., 2014; Cai et al., 2019), and ENSO's influence on global \r surface temperature variability may not be confined only to decadal \r [...]"


id: 20c8a0a6f98e1ac4dfd7b6b3e1d7a53b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 2988
content:
"[...] time scales (Triacca et al., 2014). Studies based on large ensembles \r of 20th and 21st century climate change simulations confirm that \r internal variability has a substantial influence on global warming \r trends over periods shorter than 30-40 years (Kay et al., 2015; Dai \r and Bloecker, 2019). Although the equatorial Pacific seems to be the \r main source of internal variability on decadal time scales, Brown \r et al. (2016a) linked diversity in modelled oceanic convection, sea \r ice, and energy budget in high-latitude regions to overall diversity in \r modelled internal variability."


id: a17f45dcdec6b8b196f2483180edadb7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25627
content:
"Looking at periods preceding the instrumental record, AR5 assessed \r with high confidence that the 20th century annual mean surface \r temperature warming reversed a 5000-year cooling trend in Northern \r Hemisphere mid- to high latitudes caused by orbital forcing, and \r attributed the reversal to anthropogenic forcing with high confidence \r (see also Section 2.3.1.1). Since AR5, the combined response to solar, \r volcanic and greenhouse gas forcing was detected in all Northern \r Hemisphere continents (PAGES 2k-PMIP3 group, 2015) over the \r period 864 to 1840. In contrast, the effect of those forcings was not \r detectable in the Southern Hemisphere (Neukom et al., 2018). Global \r and Northern Hemisphere temperature changes from reconstructions \r over this period have been attributed mostly to volcanic forcing \r (Schurer et al., 2014; McGregor et al., 2015; Otto-Bliesner et al., 2016; \r PAGES 2k Consortium, 2019; Buntgen et al., 2020), with a smaller \r [...]"


id: 131858a271a43e1c38811e41ab6cb98
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 572
content:
"[...] role for changes in greenhouse gas forcing, and solar forcing playing \r a minor role (Schurer et al., 2014; PAGES 2k Consortium, 2019)."


id: 9ffd1d18c197defaea3a59178fe8d3d7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25365
content:
"The previous paragraph took an ensemble-mean view of model \r performance, but individual models disagree on unforced variability. \r Figure 3.6 illustrates the large differences in GSAT variability in unforced \r CMIP6 pre-industrial control simulations, following the method of \r Parsons et al. (2020). Surface temperatures in pre-industrial conditions \r are especially variable in the ten models highlighted in Figure 3.6a, \r and some models substantially exceed the variability seen in CMIP5 \r models (Parsons et al., 2020). Figure 3.6b shows that the distribution \r of warming trends simulated by CMIP6 models in historical simulations \r is clearly distinct from that simulated in unforced pre-industrial control \r simulations. Still, the unforced variability of the five most variable \r models approaches half that observed over the historical period \r under anthropogenically forced conditions (Figure 3.6c; Parsons et al., \r 2020; Ribes et al., 2021). For  the Centre National de la Recherche \r [...]"


id: e5886f6159d5851fc64e2323ce6b7fe8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37694
content:
"[...] Meteorologique (CNRM) models, which are among the most variable, \r the large, low-frequency variability is attributed to strong simulated \r Atlantic Multi-decadal Variability (Seferian et al., 2019; Voldoire et al., \r 2019b), which is difficult to rule out because of the short observational \r record (Section 3.7.7; Cassou et al., 2018). But, importantly, patterns \r of temperature variability simulated by even the most variable models \r differ from the pattern of forced temperature change (Parsons et al., \r 2020). Taken together, this discussion and Figures  3.2, 3.5 and 3.6 \r indicate that the statistics of internal variability in models compare \r well in most cases to observational estimates and temperature proxy \r reconstructions, though some CMIP6 models appear to have higher \r multi-decadal variability than CMIP5 models or proxy reconstructions. \r When used in attribution studies, models with overestimated \r [...]"


id: f16e36db75548c5908e8307cd6463f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 39863
content:
"[...] variability would increase estimated uncertainties and make results \r statistically conservative."


id: 3c83af9b5512d3c66800782fca6e5c21
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 7837
content:
"Focusing now on warming over the historical period, AR5 assessed \r that it was extremely likely that human influence was the dominant \r cause of the observed warming since the mid-20th century, and \r that it was virtually certain that warming over the same period \r could not be explained by internal variability alone. Since AR5 many \r new attribution studies of changes in global surface temperature \r have focused on methodological advances (see also Section 3.2). \r Those advances include better accounting for observational and \r model uncertainties, and internal variability (Ribes and Terray, 2013; \r Hannart, 2016; Ribes et al., 2017; Schurer et al., 2018); formulating \r the attribution problem in a counterfactual framework (Hannart and \r Naveau, 2018); and reducing the dependence of the attribution on \r uncertainties in climate sensitivity and forcing (Otto et al., 2015; \r Haustein et al., 2017, 2019). Studies now account for uncertainties \r [...]"


id: c708b3cc62c8a4e3fc5a2eaa310b0d42
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 32331
content:
"[...] in the statistics of internal variability, either explicitly (Hannart, 2016; \r Hannart and Naveau, 2018; Ribes et al., 2021) or implicitly (Ribes and \r Terray, 2013; Schurer et al., 2018; Gillett et al., 2021), thus addressing \r concerns about over-confident attribution conclusions. Accounting for \r observational uncertainty increases the range of warming attributable \r to greenhouse gases by only 10 to 30% (Jones and Kennedy, 2017; \r Schurer et al., 2018). While some attribution studies estimate \r attributable changes in globally-complete GSAT (Schurer et al., 2018; \r Gillett et al., 2021; Ribes et al., 2021), others attribute changes in \r observational GMST, but this makes little difference to attribution \r conclusions (Schurer et al., 2018). Moreover, based on a synthesis of \r observational and modelling evidence, Cross-Chapter Box 2.3 assesses \r that the current best estimate of the scaling factor between GMST and \r [...]"


id: 30cfd3138c7e242dbc3aebafe106f1e8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5847
content:
"with anthropogenic aerosols cooling temperatures throughout the \r 20th century, especially between 1950 and 1980 when the estimated\r range of aerosol cooling was about 0.1degC to 0.5degC. Jones et al. (2016) \r attributed a warming of 0.87 to 1.22degC per century over the period \r 1906 to 2005 to greenhouse gases, partially offset by a cooling of \r -0.54degC to -0.22degC per century attributed to aerosols. But they also \r found that detection of the greenhouse gas or the aerosol signal \r often fails, because of uncertainties in modelled patterns of change \r and internal variability. That point is illustrated by Figure 3.7, which \r shows two- and three-way fingerprinting regression coefficients for 13 \r CMIP6 models and the corresponding attributable warming ranges, \r derived using HadCRUT4 (Gillett et al., 2021). Regression coefficients \r with an uncertainty range that includes zero mean that detection has \r failed. Models with regression coefficients significantly less than one \r [...]"


id: 322c2f1f5d587154da55d5a300083fb7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6083
content:
"[...] significantly overpredict the temperature response to the corresponding \r forcing. Conversely, models with regression coefficients significantly \r greater than one underpredict the response to these forcings. While \r estimates of warming attributable to anthropogenic influence derived \r using individual models are generally consistent, estimates of warming "


id: fc84d97a248c3c7c940c5182c48945c0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41792
content:
"this is the earliest base period used in attribution literature, and \r this is a reference period used in IPCC SR1.5 and earlier reports. \r It should, however, be noted that Cross-Chapter Box  1.2 assesses \r with medium confidence that there was an anthropogenic warming \r with a likely range of 0.0degC-0.2degC between 1750 and 1850-1900. \r Figure  3.8 also shows the GSAT changes directly simulated in \r response to these forcings in thirteen CMIP6 models. In spite of their \r different methodologies and input datasets, the three attribution \r approaches yield very similar results, with the anthropogenic \r attributable warming range encompassing observed warming, and \r the natural attributable warming being close to zero. The warming \r driven by greenhouse gas increases is offset in part by cooling due \r to other anthropogenic forcing agents, mostly aerosols, although \r uncertainties in these contributions are larger than the uncertainty in \r [...]"


id: ebb6b8f2f919fe91cc1fc1293885d0f2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38832
content:
"[...] the net anthropogenic warming, as discussed above. Estimates based \r on physical understanding of forcing and ECS made by Chapter 7 are \r close to estimates from attribution studies, despite being the products "


id: 3f3dca001d8b799d4d0dc6c4e7c8f57f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8317
content:
"correct sign of the inter-hemispheric contrast when forced with all \r forcings but underestimate its magnitude. Figure 3.9 shows global \r surface temperature change in CMIP6 all-forcing and natural-only \r simulations globally, averaged over continents, and separately over \r land and ocean surfaces. All-forcing simulations encompass observed \r temperature changes for all regions, while natural-only simulations \r fail to do so in recent decades except in Antarctica, based on the \r annual means shown. As stated above, warming results from \r a partial offset of greenhouse gas warming by aerosol cooling. That \r offset is stronger over land than ocean. Regionally, models show \r a large range of possible temperature responses to greenhouse gas \r and aerosol forcing, which complicates single-forcing attribution. \r A more detailed discussion of regional attribution can be found in \r Section 10.4. Over global land surfaces, Chan and Wu (2015) used \r [...]"


id: cea3fae341178f1861f6030aea92c940
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33696
content:
"[...] CMIP5 simulations to attribute a warming trend of 0.3 (2.5%-97.5% \r confidence interval: 0.2-0.36) degC per decade to anthropogenic \r forcing, with natural forcing only contributing 0.05 (0.02-0.06) degC \r per decade. Accounting for unsampled sources of uncertainty and the \r availability of only a single study, their result suggests that it is very \r likely that human influence is the main driver of warming over land."


id: cc27fabf7554c936cd32e32fd950c7c0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33238
content:
"The IPCC SR1.5 best estimate and likely range of anthropogenic \r attributable GMST warming was 1.0 +- 0.2degC in 2017 with respect \r to the period 1850-1900. Here, the best estimate is expressed \r in terms of GSAT and is calculated as the average of the three \r estimates shown in Figure 3.9, yielding a value of 1.07degC. Ranges \r for attributable GSAT warming are derived by finding the smallest \r ranges with a precision of 0.1degC which span all of the 5-95% ranges \r from the attribution studies shown in Figure 3.9. These ranges are \r then assessed as likely rather than very likely because the studies \r may underestimate the importance of the structural limitations of \r climate models, which probably do not represent all possible sources \r of internal variability; use too simple climate models, which may \r underestimate the role of internal variability; or underestimate model \r uncertainty, especially when using model ensembles of limited size \r [...]"


id: 6120b42eb5451608d50ed42959cee02b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 14382
content:
"[...] and inter-dependent models, for example through common errors in \r forcings across models, as discussed above. This leads to a likely range \r for anthropogenic attributable warming in 2010-2019 relative to \r 1850-1900 of 0.8 to 1.3degC in terms of GSAT. This range encompasses \r the best estimate and very likely range of observed GSAT warming of \r 1.06 [0.88 to 1.21] degC over the same period (Cross-Chapter Box 2.3). \r There is medium confidence that the best estimate and likely ranges \r of attributable warming expressed in terms of GMST are equal to \r those for GSAT (Cross-Chapter Box 2.3). Repeating the process for \r other time periods leads to the best estimates and likely ranges listed \r in Table 3.1. GSAT change attributable to natural forcings is -0.1 to \r +0.1degC. The likely range of GSAT warming attributable to greenhouse \r gases is assessed in the same way to be 1.0 to 2.0degC while the GSAT \r change attributable to aerosols, ozone and land-use change is -0.8 \r [...]"


id: e81cf8f7cebbc84fb329f03d3a1be69e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38160
content:
"[...] to 0.0degC. Progress in attribution techniques allows the important \r advance of attributing observed surface temperature warming since \r 1850-1900, instead of since 1951 as was done in AR5."


id: e75f52f50918d88cc9558d7cf66b2b19
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38007
content:
"In summary, since the publication of AR5, new literature has \r emerged that better accounts for methodological and climate model \r uncertainties in attribution studies (Ribes et al., 2017; Hannart and \r Naveau, 2018) and that concludes that anthropogenic warming \r is approximately equal to observed warming over the 1951-2010 \r period. The IPCC SR1.5 reached the same conclusion for 2017 relative \r to 1850-1900 based on anthropogenic warming and associated \r uncertainties calculated using the method of Haustein et al. (2017). \r Moreover, the improved understanding of the causes of the apparent \r slowdown in warming over the beginning of the 21st century and \r the difference in simulated and observed warming trends over this \r period (Cross-Chapter Box 3.1) further improve our confidence in the \r assessment of the dominant anthropogenic contribution to observed \r warming. In deriving our assessments, these considerations are \r [...]"


id: 90fbb6b07bc13a33f8bbd56ccf40ce44
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 22712
content:
"[...] balanced against new literature that raises questions about the ability \r of some models to simulate variability in surface temperatures over \r a  range of time scales (Laepple and Huybers, 2014; Parsons et al., \r 2017; Friedman et al., 2020), and the finding that some CMIP6 models \r exhibit substantially higher multi-decadal internal variability than \r that seen in CMIP5, which remains to be fully understood (Parsons \r et al., 2020; Ribes et al., 2021). Further, uncertainties in simulated \r aerosol-cloud interactions are still large (Section 7.3.3.2.2), resulting \r in very diverse spatial responses of different climate models to "


id: e541c1e71b7e6c83ea2e4f8340181850
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37637
content:
"The IPCC AR5 assessed the likely range of the contribution of internal \r variability to GMST warming to be -0.1 to +0.1degC over the period \r 1951-2010. Since then, several studies have downplayed the \r contribution of internal modes of variability to global temperature \r variability, often by arguing for a forced component to those internal \r modes (Mann et al., 2014; Folland et al., 2018; Haustein et al., \r 2019; Liguori et al., 2020). Haustein et al. (2017) found a 5-95% \r confidence interval of -0.09degC to +0.12degC for the contribution of \r internal variability to warming between 1850-1879 and 2017. \r Ribes et al. (2021) imply a contribution of internal variability of \r -0.02degC +- 0.16degC to warming between 2010-2019 and 1850-1900, \r assuming independence between errors in the observations and in \r the estimate of the forced response. Based on these studies, but \r allowing for unsampled sources of error, we assess the likely range \r [...]"


id: b7ed399cd066b27b160a7d4f38082127
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 29574
content:
"[...] of the contribution of internal variability to GSAT warming between \r 2010-2019 and 1850-1900 to be -0.2degC to +0.2degC."


id: be0722eaeee57b6ddc49d39d3673b407
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30698
content:
"Several studies since AR5 have continued to demonstrate an \r inconsistency between simulated and observed temperature trends \r in the tropical troposphere, with models simulating more warming \r than observations (Mitchell et al., 2013, 2020; Santer et al., 2017a, b; \r McKitrick and Christy, 2018; Po-Chedley et al., 2021). Santer et al. \r (2017b) used updated and improved satellite retrievals to investigate \r model performance in simulating the tropical mid- to upper\u0002troposphere trends, and removed the influence of stratospheric cooling \r by regression. These factors were found to reduce the size of the \r discrepancy in mid- to upper-tropospheric temperature trends between \r models and observations over the satellite era, but a discrepancy \r remained. Santer et al. (2017a) found that during the late 20th century, \r the discrepancies between simulated and satellite-derived mid- to \r upper-tropospheric temperature trends were consistent with internal \r [...]"


id: 179d253a8c28527e42437fa92675814d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1360
content:
"[...] variability, while during most of the early 21st century, simulated \r tropospheric warming was significantly larger than observed, which \r they related to systematic deficiencies in some of the external forcings \r used after year 2000 in the CMIP5 models. However, in CMIP6, \r differences between simulated and observed upper-tropospheric \r temperature trends persist despite updated forcing estimates (Mitchell \r et al., 2020). Figure 3.10 shows that CMIP6 models forced by combined \r anthropogenic and natural forcings overestimate temperature trends \r compared to radiosonde data (Haimberger et al., 2012) throughout \r the tropical troposphere (Mitchell et al., 2020). Over the 1979-2014 \r period, models are more consistent with observations in the lower \r troposphere, and least consistent in the upper troposphere around \r 200 hPa, where biases exceed 0.1degC per decade. Several studies using \r CMIP6 models suggest that differences in climate sensitivity may \r [...]"


id: 35955f99ded10be53d0bdb6c802f418
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6682
content:
"[...] be an important factor contributing to the discrepancy between the \r simulated and observed tropospheric temperature trends (McKitrick \r and Christy, 2020; Po-Chedley et al., 2021), though it is difficult to \r deconvolve the influence of climate sensitivity, changes in aerosol \r forcing and internal variability in contributing to tropospheric warming \r biases (Po-Chedley et al., 2021). Another study found that the absence \r of a hypothesized negative tropical cloud feedback could explain half \r of the upper troposphere warming bias in one model (Mauritsen and \r Stevens, 2015)."


id: 5ec097509a7d0fbf9992046b0c4ac41a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13960
content:
"The IPCC SR1.5 gave a likely range for the human-induced warming rate \r of 0.1degC to 0.3degC per decade in 2017, with a best estimate of 0.2degC per \r decade (Allen et al., 2018). Table 3.1 lists the estimates of attributable \r anthropogenic warming rate over the period 2010-2019 based on the \r three studies that underpin the assessment of GSAT warming (Haustein \r et al., 2017; Gillett et al., 2021; Ribes et al., 2021). Estimates from \r Haustein et al. (2017), based on observed warming, and Ribes et al. \r (2021), based on CMIP6 simulations constrained by observed warming, \r are in good agreement. The Gillett et al. (2021) estimate, also based \r on CMIP6 models, corresponds to a larger anthropogenic attributable \r warming rate, because of a smaller warming rate attributed to natural \r forcing than in Ribes et al. (2021). This disagreement does not support \r a decrease in uncertainty compared to the SR1.5 assessment. So the \r [...]"


id: fc8e0cedb5b5a4fd3332e89e3367da6
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41800
content:
"[...] range for anthropogenic attributable surface temperature warming \r rate of 0.1degC to 0.3degC per decade is again assessed to be likely, with a \r best estimate of 0.2degC per decade."


id: 4b27f1eb3a254f2303ca40e1d3695d6d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10466
content:
"The AR5 assessed as likely that anthropogenic forcings, dominated \r by greenhouse gases, contributed to the warming of the troposphere \r since 1961 (Bindoff et al., 2013). Since then, there has been further \r progress in detecting and attributing tropospheric temperature \r changes. Mitchell et al. (2020) used CMIP6 models to find that the \r main driver of tropospheric temperature changes is greenhouse gases. \r Previous detection of the anthropogenic influence on tropospheric \r warming may have overestimated uncertainties: Pallotta and Santer \r (2020) found that CMIP5 climate models overestimate the observed \r natural variability in global mean tropospheric temperature on time \r scales of 5-20 years. Nevertheless, Santer et al. (2019) found that \r stochastic uncertainty is greater for tropospheric warming than \r stratospheric cooling because of larger noise and slower recovery time \r from the Mount Pinatubo eruption in the troposphere. The detection \r [...]"


id: e61f965ee1d0a6d4a94c49ce0359c496
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37785
content:
"[...] time of the anthropogenic signal in the tropospheric warming can be \r affected by both the model climate sensitivity and the model response \r to aerosol forcing. Volcanic forcing is also important, as models that \r do not consider the influence of volcanic eruptions in the early 21st \r century overestimate the observed tropospheric warming since 1998 \r (Santer et al., 2014). Changes in the amplitude of the seasonal cycle \r of tropospheric temperatures have also been attributed to human \r influence. Santer et al. (2018) found that satellite data and climate "


id: d7df59df054c5fe7fb7609dcce5b78e0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35287
content:
"In summary, studies continue to find that CMIP5 and CMIP6 \r model simulations warm more than observations in the tropical \r mid- and upper-troposphere over the 1979-2014 period (Mitchell \r et al., 2013, 2020; Santer et al., 2017a, b; Suarez-Gutierrez et al., \r 2017; McKitrick and Christy, 2018), and that overestimated surface \r warming is partially responsible (Mitchell et al., 2013; Po-Chedley \r et al., 2021). Some studies point to forcing errors in the CMIP5 \r simulations in the early 21st century as a possible contributor \r (Mitchell et al., 2013; Sherwood and Nishant, 2015; Santer et al., \r 2017a), but CMIP6 simulations use updated forcing estimates yet \r generally still warm more than observations. Although accounting \r for internal variability and residual observational errors can reconcile \r models with observations to some extent (Suarez-Gutierrez et al., \r 2017; Mitchell et al., 2020), some studies suggest that climate \r [...]"


id: 8a6e0d06240b3d56c7330fdd78a9c206
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21522
content:
"[...] sensitivity also plays a role (Mauritsen and Stevens, 2015; McKitrick \r and Christy, 2020; Po-Chedley et al., 2021). Hence, we assess with \r medium confidence that CMIP5 and CMIP6 models continue to \r overestimate observed warming in the upper tropical troposphere \r over the 1979-2014 period by at least 0.1degC per decade, in part "


id: 51217947af42f65a3a69e771b3dabdcc
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11558
content:
"Upper stratospheric temperature changes were not assessed in \r the context of attribution or model evaluation in AR5, but this is \r an area where there has been considerable progress over recent \r years (Section  2.3.1.2.1). Simulated temperature changes in \r chemistry-climate models show good consistency with the reprocessed \r dataset from NOAA STAR but are less consistent with the revised \r UK Met Office record (Karpechko et al., 2018). The latter still shows \r stronger cooling than simulated in chemistry-climate models (Maycock \r et al., 2018). Reanalyses, which assimilate AMSU and SSU datasets, \r indicate an upper-stratospheric cooling from 1979 to 2009 of about \r 3degC at 5 hPa and 4degC at 1 hPa that agrees well with the cooling in \r simulations with prescribed SST and using CMIP5 forcings (Simmons \r et al., 2014). Mitchell (2016) used regularized optimal fingerprinting \r [...]"


id: ff6f407b7720aaf1844deea1c63557a6
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 42345
content:
"[...] techniques to carry out an attribution analysis of annual mid- to upper\u0002stratospheric temperature in response to external forcings. They found \r that anthropogenic forcing has caused a cooling of approximately \r 2degC-3degC in the upper stratosphere over the period of 1979-2015, with \r greenhouse gases contributing two thirds of this change and ozone \r depletion contributing one third. They found a large upper-stratospheric \r temperature change in response to volcanic forcing (0.4degC-0.6degC \r for Mount Pinatubo) but that change is still smaller than the lower\u0002stratospheric signal. Aquila et al. (2016) found that the cooling of the \r middle and upper stratosphere after 1979 is mainly due to changes in \r greenhouse gas concentrations. Volcanic eruptions and the solar cycle \r were found not to affect long-term stratospheric temperature trends \r but to have short-term influences."


id: 37aa30babc4af86bc708daebb334c9fb
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 7003
content:
"For the lower stratosphere, a debate has been ongoing since AR5 \r between studies finding that models underestimate the cooling \r of stratospheric temperature (Santer et al., 2017b), in part because of \r underestimated stratospheric ozone depletion (Eyring et al., 2013; \r Young et al., 2013), and studies finding that lower stratospheric \r temperature trends are within the range of observed trends (Young \r et al., 2013; Maycock et al., 2018). Different observational data and \r different time periods explain the different conclusions. Aquila et al. \r (2016) used forced chemistry-climate models with prescribed SST to \r investigate the influence of different forcings on global stratospheric \r temperature changes. They found that in the lower stratosphere, \r the simulated cooling trend due to increasing greenhouse gases \r was roughly constant over the satellite era, while changes in ODS \r concentrations amplified that stratospheric cooling trend during \r [...]"


id: 21e588a391ee9b8f2336adfaeec39992
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3187
content:
"[...] the era of increasing ozone depletion up until the mid-1990s, with \r a  flattening of the temperature trend over the subsequent period \r over which stratospheric ozone has stabilized (Section 2.2.5.2). \r Mitchell et al. (2020) showed that while models simulate realistic \r trends in tropical lower-stratospheric temperature over the whole \r 1979-2014 period when compared with radiosonde data, they \r tend to overestimate the cooling trend over the ozone depletion era "


id: 29853baa3cade51e1fd0b5fe0d724fbc
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 4524
content:
"In summary, based on the latest updates to satellite observations of \r stratospheric temperature, we assess that simulated and observed \r trends in global mean temperature through the depth of the \r stratosphere are more consistent than based on previous datasets, \r but some differences remain (medium confidence). Studies published \r since AR5 increase our confidence in the simulated stratospheric \r temperature response to greenhouse gas and ozone changes, and \r support an assessment that it is extremely likely that stratospheric \r ozone depletion due to ozone-depleting substances was the main \r driver of the cooling of the lower stratosphere between 1979 and \r the mid-1990s, as expected from physical understanding. Similarly, \r revised observations and new studies support an assessment that it \r is extremely likely that anthropogenic forcing, both from increases in \r greenhouse gas concentrations and depletion of stratospheric ozone \r [...]"


id: 3bcf78f9eb606e0cca54640cf4ad09b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 7711
content:
"[...] due to ozone-depleting substances, was the main driver of upper\u0002stratospheric cooling since 1979."


id: cbbc3eb91283263c6fd0e30c1c367e0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33169
content:
"Updated observational and reanalyses datasets and comparison with model simulations\r Since AR5, there have been version updates and new releases of most observational GMST datasets (Cross-Chapter Box 2.3). All the \r updated products now available consistently find stronger positive trends for 1998-2012 than those assessed in AR5 (Cowtan and Way, \r 2014; Karl et al., 2015; Hausfather et al., 2017; Medhaug et al., 2017; Simmons et al., 2017; Risbey et al., 2018). Simmons et al. (2017) \r reported that the 1998-2012 GMST trends in the updated observational and reanalysis datasets available at that time ranged from \r 0.06degC to 0.14degC per decade, compared with the 0.05degC per decade on average reported in AR5, while the latest data products reported \r in Chapter 2 Table 2.4 show GMST or global mean near-surface air temperature (GSAT) trends over that period ranging from 0.12degC to \r [...]"


id: b476cb82af552fcf4dc59d269047e398
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 28991
content:
"[...] 0.14degC per decade. The lowest trend in Simmons et al. (2017) is from HadCRUT4, now superseded by HadCRUT5, which shows a trend \r of 0.12degC per decade. The upward revision is mainly due to improved sea surface temperature (SST) datasets and infilling of surface \r temperature in locations with missing records in observational products, mainly in the Arctic (see Cross-Chapter Box 2.3 for details)."


id: e2d4940981c1041e9e5fe4299f70f444
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37203
content:
"Studies since AR5 identify Pacific Decadal Variability (PDV) as the leading mode of variability associated with unforced decadal GSAT \r fluctuations, with additional influence from Atlantic Multi-decadal Variability (Annex IV.2.6, IV.2.7; Brown et al., 2015; Dai et al., 2015; \r Steinman et al., 2015; Pasini et al., 2017). PDV transitioned from positive (El Nino-like) to negative (La Nina-like) phases during the \r slow warming period (Figure 3.39f and Cross-Chapter Box 3.1, Figure 1c). Model ensemble members that capture the observed slower \r decadal warming under transient forcing, and time segments of model simulations that show decadal GSAT decreases under fixed \r radiative forcing, also feature negative PDV trends (Cross-Chapter Box 3.1, Figure 1d; Meehl et al., 2011, 2013, 2014; Maher et al., \r 2014; Middlemas and Clement, 2016), suggesting the influence of PDV. This is confirmed by statistical models with the PDV-GSAT \r [...]"


id: 95bf08875dea452d7490ca02eb3af7d6
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23537
content:
"[...] relationship estimated from observations and model simulations (Schmidt et al., 2014; Meehl et al., 2016b; Hu and Fedorov, 2017), \r selected ensemble members and time segments from model simulations where PDV by chance evolves in phase with observations \r over the slow warming period (Huber and Knutti, 2014; Risbey et al., 2014), and coupled model experiments in which PDV evolution is \r constrained to follow the observations (Kosaka and Xie, 2013, 2016; England et al., 2014; Watanabe et al., 2014; Delworth et al., 2015). \r Part of the PDV trend may have been driven by anthropogenic aerosols (Smith et al., 2016); however, this result is model-dependent, \r and internally-driven PDV dominates the forced PDV signal in the CMIP6 multi-model ensemble (Section 3.7.6). It is also notable \r that there is large uncertainty in the magnitude of the PDV influence on GSAT across models (Deser et al., 2017a; C.-Y. Wang et al., \r [...]"


id: 24f6d2d91732570aed4845004f9c4436
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3698
content:
"[...] 2017) and among the studies cited above. In addition to PDV, contributions to the reduced warming trend from wintertime Northern \r Hemisphere atmospheric internal variability, particularly associated with a trend towards the negative phase of the Northern Annular \r Mode/North Atlantic Oscillation (Annex IV.2.1; Guan et al., 2015; Saffioti et al., 2015; Iles and Hegerl, 2017) or the Cold Ocean-Warm \r Land (COWL) pattern (Molteni et al., 2017; Yang et al., 2020) have been suggested, leading to regional continental cooling over a large \r part of Eurasia and North America (Cross-Chapter Box 3.1, Figure 1c; C. Li et al., 2015; Deser et al., 2017a; Gan et al., 2019)."


id: fc1d8d21c86f647b1c282fd0800c35ab
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41718
content:
"CMIP5 historical simulations driven by observed forcing variations ended in 2005 and were extended with RCP scenario simulations \r for model-observation comparisons beyond that date. Post AR5 studies based on updated external forcing show that while no \r net effect of updated anthropogenic aerosols is found on GSAT trends (Murphy, 2013; Gettelman et al., 2015; Oudar et al., 2018), \r natural forcing by moderate volcanic eruptions in the 21st century (Haywood et al., 2014; Ridley et al., 2014; Santer et al., 2014) \r and a prolonged solar irradiance minimum around 2009 compared to the normal 11-year cycle (Lean, 2018) yield a negative \r contribution to radiative forcing, which was missing in CMIP5 (Figure 2.2). This explains part of the difference between observed \r and CMIP5 trends, as shown based on EMIC simulations (Huber and Knutti, 2014; Ridley et al., 2014), statistical and mathematical \r [...]"


id: f1129adcfeb702877c497b953da980e0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 39789
content:
"[...] models (Schmidt et al., 2014; Lean, 2018), and process-based climate models (Santer et al., 2014). However, in a single climate \r model study by Thorne et al. (2015), updating most forcings (greenhouse gas concentrations, solar irradiance, and volcanic and \r anthropogenic aerosols) available when the study was done made no significant difference to the 1998-2012 GMST trend from that \r obtained with original CMIP5 forcing. Potential underestimation of volcanic (negative) forcing may have played a role (Outten et al., \r 2015). In the multi-model ensemble mean, the 1998-2012 GMST trends are almost equal in CMIP5 and CMIP6 (Cross-Chapter \r Box 3.1, Figure 1a), suggesting compensation by a higher transient climate response and equilibrium climate sensitivity in CMIP6 \r than CMIP5 (Section 7.5.6). To summarize, while there is medium confidence that natural forcing that was missing in CMIP5 \r [...]"


id: eb1364290ce8766ca287ff48678dac2d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38713
content:
"[...] contributed to the difference of observed and simulated GMST trends, confidence remains low in the quantitative contribution of \r net forcing updates."


id: e27d0bcf27a9d529344db6054a45a76
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37145
content:
"The early 21st century slower warming was observed in atmospheric temperatures, but the heat capacity of the atmosphere is very \r small compared to that of the ocean. Although there is noticeable uncertainty among observational products (H. Su et al., 2017) and \r observation quality changes through time, global ocean heat content continued to increase during the slower surface warming period \r (very high confidence), at a rate consistent with CMIP5 and CMIP6 historical simulations (Sections 2.3.3.1, 3.5.1.3 and 7.2.2.2). \r There is high confidence that the Earth's energy imbalance was larger in the 2000s than in the 1985-1999 period (Section 7.2.2.1), \r consistent with accelerating ocean heat uptake in the past two decades (Section 3.5.1.3). Internal decadal variability is mainly associated \r with redistribution of heat within the climate system (X.H. Yan et al., 2016; Drijfhout, 2018) while associated top of the atmosphere \r [...]"


id: f60ed03cbf1ff4b0b3e2a93d797bcaa2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 40646
content:
"[...] radiation anomalies are weak (Palmer and McNeall, 2014). Heat redistribution in the top 350 m of the Indian and Pacific Oceans has \r been found to be the main contributor to reduced surface warming during the slower surface warming period (Lee et al., 2015; Nieves \r et al., 2015; F. Liu et al., 2016), consistent with the simulated signature of PDV (England et al., 2014; Maher et al., 2018a; Gastineau \r et al., 2019). Below 700 m, enhanced heat uptake over the slower surface warming period was observed mainly in the North Atlantic and \r Southern Ocean (Chen and Tung, 2014), though whether this was a response to forcing or a unique signature of the slow GMST warming \r has been questioned (W. Liu et al., 2016)."


id: 360dd621ce9c71658fd9e1410ee8155b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6759
content:
"sedimentary paleorecord spanning the last millennium (Tierney et al., \r 2015). This may be a signature of anthropogenic forcing but cannot \r yet be distinguished from natural variability (Hoell et al., 2017; Philip \r et al., 2018). Likewise, tree rings indicate that the 2012-2014 drought \r in the south-western United States was exceptionally severe in the \r context of natural variability over the last millennium, and may have \r been exacerbated by the contribution of anthropogenic temperature \r rise (medium confidence) (Griffin and Anchukaitis, 2014; Williams \r et al., 2015). Furthermore, Williams et al. (2020) used a combination \r of hydrological modelling and tree-ring reconstructions to show that \r the period from 2000 to 2018 was the driest 19-year span in south\u0002western North America since the late 1500s. Nonetheless, tree rings \r also indicate the presence of prolonged megadroughts in western \r North America throughout the last millennium that were more severe \r [...]"


id: ec57f8824f41565230d5a374ff9ec391
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38944
content:
"[...] than 20th and 21st century events (high confidence) (Cook et al., 2004, \r 2010, 2015). These were associated with internal variability (Coats \r et al., 2016; Cook et al., 2016b) and indicate that large-magnitude \r changes in the water cycle may occur irrespective of anthropogenic \r influence (see also McKitrick and Christy, 2019)."


id: cdede7715a8da364da3fc43a5c4d8fb7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33563
content:
"mid-Holocene tended to be underestimated (Flato et al., 2013). \r Further analysis of CMIP5 models confirmed these results but \r has also revealed systematic offsets from the paleoclimate record \r (DiNezio and Tierney, 2013; Hargreaves and Annan, 2014; Harrison \r et al., 2014, 2015; Bartlein et al., 2017; Scheff et al., 2017; Tierney \r et al., 2017). Harrison et al. (2014) concluded that CMIP5 models \r do not perform better in simulating rainfall during the LGM and \r mid-Holocene than earlier model versions despite higher resolution \r and complexity. However, prescribing changes in vegetation and dust \r was found to improve the match to the paleoclimate record (Pausata \r et al., 2016; Tierney et al., 2017) suggesting that vegetation feedbacks \r in the CMIP5 models may be too weak (low confidence) (Hopcroft \r et al., 2017). Brierley et al. (2020) compared the latitudinal gradient \r of annual precipitation changes in the European-African sector \r [...]"


id: 5df17b43799b51a9f962f36a6a8e848f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13831
content:
"[...] simulated by CMIP6 models for the mid-Holocene with pollen-based \r reconstructions and showed that models generally reproduce the \r direction of changes seen in the reconstructions (Figure 3.11). They \r do not show a robust signal in area averaged rainfall over most \r European regions where quantitative reconstructions exist, which "


id: db6b5b5a9ec26f378f682ca4cbc1fb5e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35878
content:
"of the troposphere, and found that the simulated human fingerprint \r pattern was detectable at the 5% level by 2002 in water vapour \r satellite data (from 1988 to 2006). The observed changes matched the \r historical simulations forced by greenhouse gas changes and other \r anthropogenic forcings, and not those due to natural variability alone. \r Then, Santer et al. (2009) repeated this study with CMIP5 models, \r and found that the detection and attribution conclusions were not \r sensitive to model quality. These results demonstrate that the human \r fingerprint is governed by robust and basic physical processes, such \r as the water vapour feedback. Finally, Chung et al. (2014) extended \r this line of research by focusing on the global-mean water vapour \r content in the upper troposphere. Using satellite-based observations \r and sets of CMIP5 climate simulations run under various climate\u0002forcing options, they showed that the observed moistening trend of the \r [...]"


id: 30115e1e1db03d2019ae18e71f6c22de
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5721
content:
"[...] upper troposphere over the 1979-2005 period could not be explained \r by internal variability alone, but is attributable to a combination of \r anthropogenic and natural forcings. This increase in water vapour is \r accompanied by a reduction in mid-tropospheric relative humidity \r and clouds in the subtropics and mid-latitude in both models and \r observations related to changes in the Hadley cell (Section 3.3.3.1.1; \r Lau and Kim, 2015)."


id: 3e6192fe54cd01b57732f972a3c6c76f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8155
content:
"Dunn et al. (2017) confirmed earlier findings that global mean surface \r relative humidity increased between 1973 and 2000, followed by \r a steep decline (also reported in Willett et al., 2014) until 2013, and \r specific humidity correspondingly increased and then remained \r approximately constant (see also Section 2.3.1.3.2), with none of the \r CMIP5 models capturing this behaviour. They noted biases in the mean \r state of the  CMIP5 models' surface relative humidity (and ascribed \r the failure to the representation of land surface processes and their \r response to CO2 forcing), concluding that these biases preclude any \r detection and attribution assessment. On the other hand, Byrne and \r O'Gorman (2018) showed that the positive trend in specific humidity \r continued in recent years and can be detected over land and ocean \r from 1979 to 2016. Moreover, they provided a theory suggesting that \r the increase in annual surface temperature and specific humidity as well \r [...]"


id: 14890cc46372ff72e0cbbc716909e653
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 837
content:
"[...] as the decrease in relative humidity observed over land are linked to \r warming over the neighbouring ocean. They also pointed out that the \r negative trend in relative humidity over land regions is quite uncertain \r and requires further investigation. A recent study has also identified \r an anthropogenically-driven decrease in relative humidity over the \r Northern Hemisphere mid-latitude continents in summer between 1979 \r and 2014, which was underestimated by CMIP5 models (Douville and \r Plazzotta, 2017). Furthermore, in a modelling study Douville et al. (2020) \r showed that this decrease in boreal summer relative humidity over mid\u0002latitudes is related not only to global ocean warming, but also to the \r physiological effect of CO2 on plants in the land surface model."


id: 93bd06c882a95c6b8766e15f4157cd46
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23202
content:
"Water vapour is the most important natural greenhouse gas and its \r amount is expected to increase in a global warming context leading \r to further warming. Particularly important are changes in the upper \r troposphere because there water vapour regulates the strength of \r the water-vapour feedback (Section 7.4.2.2). CMIP5 models have \r been shown to have a wet bias in the tropical upper troposphere \r and a dry bias in the lower troposphere, with the former bias and \r model spread being larger than the latter (Jiang et al., 2012; Tian \r et al., 2013). Tian et al. (2013) also showed that in comparison to the \r AIRS specific humidity, CMIP5 models have the well-known double \r Inter-tropical Convergence Zone (ITCZ) bias in the troposphere from \r 1000 hPa to 300 hPa, especially in the tropical Pacific. Water vapour \r biases in models are dominated by errors in relative humidity \r throughout the troposphere, which are in turn closely related to \r [...]"


id: 4700e15a230b8b399c29df864fa268b5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9694
content:
"[...] errors in large scale circulation; temperature errors dominate near \r the tropopause (Takahashi et al., 2016). Section 7.4.2.2 discusses \r this topic in more detail for CMIP6 models. However, Schroder et al. \r (2019) show that the majority of well-established water vapour \r records are affected by inhomogeneity issues and thus should \r be used with caution (see also Section 2.3.1.3.3). A comparison \r of trends in column water vapour path for 1998-2019 in satellite \r data, a reanalysis, CMIP5 and CMIP6 simulations averaged over \r the near-global ocean reveals that while on average model trends \r are higher than those in observations and a reanalysis, the latter lie \r within the multi-model range (Figure 3.12)."


id: 16eaa2a501a797bb502783185863a7b8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1255
content:
"between increasing heavy precipitation and decreasing light to \r moderate rainfall (Thackeray et al., 2018b), a  characteristic found \r in the observational record (Gu and Adler, 2018). Regional model \r performance is further assessed in Chapter  8 and the Atlas, while \r precipitation extremes are considered in Chapter 11.\r The simulation of annual mean rainfall patterns in the CMIP6 models\r reveals minor improvements compared to those in CMIP5 \r models (Figure 3.13). The persistent biases include the double ITCZ in \r the tropical Pacific (seen as bands of excessive rainfall on both sides \r of the equatorial Pacific in Figure 3.13b,d) and the southward-shifted \r ITCZ in the equatorial Atlantic, which have been linked to the meridional \r pattern of SST bias (S. Zhou et al., 2020) and the reduced sensitivity of \r precipitation to local SST (Good et al., 2021). Tian and Dong (2020) also \r found that all three generations of CMIP models share similar systematic \r [...]"


id: e687aeff73f07b65ca781799c48a0862
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37856
content:
"[...] annual mean precipitation errors in the tropics, but that the double \r ITCZ bias is slightly reduced in CMIP6 models in comparison to CMIP3 \r and CMIP5 models. They also found some improvement in the overly \r intense Indian ocean ITCZ and the too dry South American continent \r except over the Andes. Fiedler et al. (2020) identified improvements in \r the tropical mean spatial correlations and root mean square error of \r the climatology as well as in the day-to-day variability, but found little \r change across CMIP phases in the double ITCZ bias and diurnal cycle. \r The CMIP6 models reproduce better the domain and intensity of the \r global monsoon (see Section 3.3.3.2). Moreover, CMIP6 models better \r represent the storm tracks (Priestley et al., 2020; also Section 3.3.3.3), \r thereby reducing the precipitation biases in the North Atlantic and \r mid-latitudes of the Southern Hemisphere (Figure 3.13b,d). As a result, \r [...]"


id: 3ef1261761f45c1e2586e3d0f994d707
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8259
content:
"[...] pattern correlations between simulated and observed annual mean \r precipitation range between 0.80 and 0.92 for CMIP6 models, compared \r to a range of 0.79 to 0.88 for CMIP5 (Bock et al., 2020). This relative \r improvement may be related to increased model resolution, as found \r when comparing biases in the mean of the HighResMIP models with \r the mean of the corresponding lower-resolution versions of the same \r models (see Figure 3.13e,f), particularly in the tropics and extratropical \r storm tracks. Consistent with this, a recent study using several coupled \r models showed that increasing the atmospheric resolution leads to \r a strong decrease in the precipitation bias in the tropical Atlantic ITCZ \r (see further discussion in Section 3.8.2.2; Vanniere et al., 2019). Based \r on these results we assess that despite some improvements, CMIP6 \r models still have deficiencies in simulating precipitation patterns, \r particularly over the tropical ocean (high confidence)."


id: d3f3972c53bc2f1281b5b4300b7cee1a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 34623
content:
"Since AR5, X. Li et al. (2016b) found that CMIP5 models simulate the \r large scale patterns of annual mean land precipitation and seasonality \r well, as well as reproducing qualitatively the observed zonal mean \r land precipitation trends for the period 1948-2005: models capture \r the drying trends in the tropics and at 45degS and the wetting trend in \r the Northern Hemisphere mid- to high latitudes, but the amplitudes \r of the changes are much smaller than observed. Land precipitation \r was found to show enhanced seasonality in observations (Chou \r et al., 2013), qualitatively consistent with the simulated response to \r anthropogenic forcing (Dwyer et al., 2014). However, models do not \r appear to reproduce the zonal mean trends in the magnitude of the \r seasonal cycle over the period 1948-2005, nor the two-dimensional \r distributions of trends of annual precipitation and seasonality over \r land, but differences may be explainable by internal variability \r [...]"


id: 88ab74a9ed205450cdcdabe3f4cdd9db
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21206
content:
"[...] (X. Li et al., 2016b). However, observed trends in seasonality depend \r on data set used (X. Li et al., 2016b; Marvel et al., 2017), and Marvel \r et al. (2017) found that observed changes in the annual cycle phase \r are consistent with model estimates of forced changes. These \r phase changes are mainly characterized by earlier onset of the wet \r season on the equatorward flanks of the extratropical storm tracks, \r particularly in the Southern Hemisphere. Box 8.2 assesses regional \r changes in water cycle seasonality."


id: 5a33f77f61747a9133aa376cf8439ec3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13120
content:
"Figure 3.13 | Annual-mean precipitation rate (mm day-1) for the period 1995-2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 \r historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the precipitation analysis from the \r Global Precipitation Climatology Project (GPCP) version 2.3 (Adler et al., 2003). (c) Multi-model mean of the root mean square error calculated over all months separately and \r averaged with respect to the precipitation analysis from GPCP version 2.3. (d) Multi-model mean bias, calculated as the difference between the CMIP6 multi-model mean and \r [...]"


id: a81d7eb95b293feb8c3a5afe925b8050
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 26791
content:
"[...] the precipitation analysis from GPCP version 2.3. Also shown is the multi-model mean bias as the difference between the multi-model mean of (e) high resolution and (f) low\u0002resolution simulations of four HighResMIP models and the precipitation analyses from GPCP version 2.3. Uncertainty is represented using the advanced approach. No overlay \r indicates regions with robust signal, where >=66% of models show change greater than the variability threshold and >=80% of all models agree on sign of change; diagonal lines \r indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting \r signal, where >=66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For more information on the advanced \r [...]"


id: bb64e30ce61ff30a22ef80d4f91e97b2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30185
content:
"[...] approach, please refer to the Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least \r three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: 69bcfbe54725040ef957d67e44595196
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 15870
content:
"For the Southern Hemisphere extratropics, Solman and Orlanski (2016) \r found that the observed summertime rainfall increase over high \r latitudes and decrease over mid-latitudes over the period 1979-2010 \r are quasi-zonally symmetric and related to changes in eddy activity. The \r latter were in turn found to be associated with the poleward shift of \r the westerlies due mostly to ozone depletion. Positive rainfall trends \r in the subtropics, particularly over south-eastern South America (see \r also Section 10.4.2.2) and northern and central Australia, have been also \r attributed to stratospheric ozone depletion (Kang et al., 2011; Gonzalez \r et al., 2014) and greenhouse gases (Vera and Diaz, 2015; Saurral et al., \r 2019). During austral winter, wetting at high latitudes and drying at \r mid-latitudes are not zonally homogenous, due to both changes in eddy \r activity and increased lower troposphere humidity. Solman and Orlanski \r [...]"


id: 682cbef1a5698c66d03d860d11e04e52
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 15594
content:
"[...] (2016) associated these climate changes with increases in greenhouse \r gas concentration levels. Recently, Blazquez and Solman (2017) have \r shown that CMIP5 models represent very well the dynamical forcing \r and the frequency of frontal precipitation in the Southern Hemisphere \r winter extratropics, but the amount of precipitation due to fronts is \r overestimated. Chapters 10 and 11 validate in more detail the simulation \r of fronts in climate models (Sections 10.3.3.4.4 and 11.7.2.3)."


id: 532fb33edb1e8fc8bbf0b6b1e81c0920
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11902
content:
"Section 2.3.1.3.6 assesses that there have not been significant trends \r in global average streamflow over the last century, though regional \r trends have been observed, driven in part by internal variability. Only \r a limited number of studies have systematically compared observed \r streamflow trends at continental to global scales with changes \r simulated by global circulation models in a detection and attribution \r setting. H. Yang et al. (2017) did not find a significant correlation \r between observed runoff changes and changes simulated in CMIP5 \r models in most grid cells, consistent with the assessment that observed \r changes are dominated by internal variability. In  a  pan-European \r assessment, Gudmundsson et al. (2017) attributed the spatio\u0002temporal pattern of decreasing streamflow in southern Europe and \r increasing streamflow in northern Europe to anthropogenic climate \r change, but also concluded that additional effects of human water \r [...]"


id: f439f4a1781b9922e26a6dc60fefa995
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 40336
content:
"[...] withdrawals could not be excluded. Focussing on continental runoff \r between 1958 and 2004, Alkama et al. (2013) found a significant \r change only when using reconstructed data over all rivers, and a \r large uncertainty in the estimate of the global streamflow trend due \r to opposing changes over different continents. Gedney et al. (2014) \r detected the influence of aerosols on streamflow in North America \r and Europe, with aerosols having driven an increase in streamflow \r due to reduced evaporation (see Section 8.3.1.5 for details on \r processes). There is also evidence for a detectable anthropogenic \r contribution toward earlier winter-spring streamflows in the north\u0002central US (Kam et al., 2018) and in western Canada (Najafi et al., \r 2017). From a model evaluation perspective, Sheffield et al. (2013) \r reported that CMIP5 models reproduce spatial variations in runoff in \r North America well, though they tend to underestimate it."


id: a084c3db55b355082bb3a5f29d1a1334
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25452
content:
"Overall, studies published since AR5 provide further evidence of an \r anthropogenic influence on precipitation, and therefore we now assess \r that it is likely that human influence has contributed to large-scale \r precipitation changes observed since the mid-20th century. New \r attribution studies strengthen previous findings of a detectable increase \r in mid to high latitude land precipitation over the Northern Hemisphere \r (high confidence). There is medium confidence that human influence \r has contributed to a strengthening of the zonal mean wet tropics-dry \r subtropics contrast, and that tropical rainfall changes follow the wet\u0002gets-wetter, dry-gets-drier paradigm. There is also medium confidence\r that ozone depletion has increased precipitation over the southern high \r latitudes and decreased it over southern mid-latitudes during austral \r summer. Owing to observational uncertainties and inconsistent results \r between studies, we conclude that there is low confidence in the \r [...]"


id: 92e8aefecfee90ca91c830da3ea16b81
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23057
content:
"Chapter 11 assesses that there is high confidence that climate models can reproduce the mean state and overall warming of temperature \r extremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture \r observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time \r periods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human influence on various aspects of \r temperature extremes, at global, continental, and regional scales. This includes attribution to human influence of observed changes \r in intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand \r panel of Cross-Chapter Box 3.2, Figure 1 clearly shows that long-term changes in the global mean annual maximum daily maximum \r [...]"


id: 2b459ccaedf66571b07188964e57263f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 4858
content:
"[...] temperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic \r forcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean \r temperature (Section 3.3.1), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes \r globally and over most continents over the 1951-2015 period (Hu et al., 2020; Seong et al., 2021), though greenhouse gas and aerosol \r influences are less clearly separable in observed changes in cold extremes."


id: a7eb51e3d93aa5e9ff5b7a20371fd89f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 26753
content:
"An important piece of evidence supporting the SREX and AR5 assessment that there is medium confidence that anthropogenic forcing \r has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence \r for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in \r atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly \r associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is \r supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the \r increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models \r [...]"


id: 1528d337d98694376d2a1a62e6a83f90
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 937
content:
"[...] project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation \r is projected to decrease."


id: db40f15d3679fe14837921656217e412
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35847
content:
"The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models \r (high confidence), and there is high confidence in the ability of models to capture the large-scale spatial distribution of precipitation \r extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering \r changes in precipitation intensity averaged over all wet days, there is high confidence that daily mean precipitation intensities have \r increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is likely that \r such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also \r finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation \r [...]"


id: e31e4043737a6e0c029eb878cab976ae
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37255
content:
"[...] than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand "


id: a40ba9e959e7e2731d391e38ca7a0414
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 26050
content:
"CMIP5 models reproduce the mean state of the Walker circulation \r with reasonable fidelity, evidenced by the spatial pattern correlations \r of equatorial zonal mass stream function between models and \r observations being larger than 0.88 (Ma and Zhou, 2016). CMIP5 \r historical simulations on average simulate a significant weakening \r of the Pacific Walker circulation over the 20th century (DiNezio et al., \r 2013; Sandeep et al., 2014; Kociuba and Power, 2015), which is \r also seen in CMIP6 (Figure 3.16d). This weakening is accompanied \r by a reduction of convective activity over the Maritime Continent \r and an enhancement over the central equatorial Pacific (DiNezio \r et al., 2013; Sandeep et al., 2014; Kociuba and Power, 2015). \r In the CMIP6 simulations, greenhouse gas forcing induces this \r weakening (Figure 3.16d), which is consistent with theories based \r on radiative-convective equilibrium (Vecchi et al., 2006; Vecchi and \r [...]"


id: 4b9a490c2eac5ded3f09aa1e77ddbf7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10564
content:
"[...] Soden, 2007) and thermodynamic air-sea coupling (Xie et al., 2010), \r but inconsistent with a theory highlighting the ocean dynamical \r effect which suggests a strengthening in response to greenhouse \r gas increases (Clement et al., 1996; Seager et al., 2019; see also \r Section 7.4.4.2.1). Seager et al. (2019) attributed this inconsistency \r to equatorial Pacific SST biases in the models (Section  3.5.1.2.1). \r However, observational and reanalysis datasets disagree on the sign \r of trends in the Walker Circulation strength over the 1901-2010 \r period (Figure 3.16d), and Section 2.3.1.4.1 assesses low confidence\r in observed long-term Walker Circulation trends. The observational \r uncertainty remains high in the trends since the 1950s (Tokinaga \r et al., 2012; L'Heureux et al., 2013), though both CMIP5 and CMIP6 \r historical simulations span trends of all but one observational data \r set (Figure  3.16e). For this period, external influence simulated \r [...]"


id: ebfcf9d2a040b2c09a09f91fceea9a5d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38883
content:
"[...] in CMIP6 is insignificant due to a partial compensation of forced \r responses to greenhouse gases and aerosols and large internal \r decadal variability (Figure  3.16e). It is notable that while AMIP \r simulations on average show strengthening over both the periods, "


id: 37434512de2692a99241e2fb25281328
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6937
content:
"Figure 3.16 | Model evaluation and attribution of changes in Hadley cell extent and Walker circulation strength. (a-c) Trends in subtropical edge latitude of the \r Hadley cells in (a) the Northern Hemisphere for 1980-2014 annual means and (b, c) Southern Hemisphere for (b) 1980-2014 annual means and (c) 1980/81-1999/2000 \r December-January-February means. Positive values indicate northward shifts. (d-f) Trends in the Pacific Walker circulation strength for (d) 1901-2010, (e) 1951-2010 and \r (f) 1980-2014. Positive values indicate strengthening. Based on CMIP5 historical (extended with RCP4.5), CMIP6 historical, AMIP, pre-industrial control, and single forcing \r simulations along with HadSLP2 and reanalyses. Pre-industrial control simulations are divided into non-overlapping segments of the same length as the other simulations. White \r [...]"


id: 557b1e5e61937825e193ee9dba791982
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12313
content:
"[...] boxes and whiskers represent means, interquartile ranges and 5th and 95th percentiles, calculated after weighting individual members with the inverse of the ensemble size of \r the same model, so that individual models are equally weighted (Section 3.2). The filled boxes represent the 5-95% confidence interval on the multi-model mean trends of the \r models with at least three ensemble members, with dots indicating the ensemble means of individual models. The edge latitude of the Hadley cell is where the surface zonal \r wind velocity changes sign from negative to positive, as described in the Appendix of Grise et al. (2018). The Pacific Walker circulation strength is evaluated as the annual mean \r difference of sea level pressure between 5degS-5degN, 160degW-80degW and 5degS-5degN, 80degE-160degE. Further details on data sources and processing are available in the chapter data \r table (Table 3.SM.1)."


id: 5c85020081c45d2d371eec774c465425
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13539
content:
"Paleoclimate proxy evidence shows that the global monsoon has \r varied with orbital forcing and greenhouse gases (Section 2.3.1.4.2; \r Mohtadi et al., 2016; Seth et al., 2019). These large-magnitude \r intensifications and weakenings in the global monsoon involved \r in some cases orders-of-magnitude changes in precipitation locally \r (Harrison et al., 2014; Tierney et al., 2017). Paleoclimate modelling \r and limited data from past climate states with high CO2 suggest \r that precipitation intensifies in the monsoon domain under elevated \r greenhouse gases, providing context for present and future trends \r (Passey et al., 2009; Haywood et al., 2013; Zhang et al., 2013b). In \r model simulations of the mid-Pliocene, when globally averaged \r temperature was higher than present day, precipitation was larger \r in West African, South Asian and East Asian monsoons than under \r pre-industrial conditions, consistent with proxy evidence (Zhang \r [...]"


id: e8d37e1a96ee0311288285a94e5afa1f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38312
content:
"[...] et al., 2015; Sun et al., 2016, 2018; Corvec and Fletcher, 2017; \r X. Li et al., 2018). Prescott et al. (2019) and R. Zhang et al. (2019) \r find an important role for orbital forcing and CO2 in the mid-Pliocene \r monsoon expansion and intensification. Models are also able to \r capture interhemispherically contrasting monsoon changes in the \r Last Interglacial in response to orbital forcing and greenhouse gases, \r with wetter West African and Asian monsoons and a drier South \r American monsoon as seen in proxies (Govin et al., 2014; Gierz \r et al., 2017; Pedersen et al., 2017). In overall agreement with proxy \r evidence, a model with transient forcing simulates wetting and drying \r respectively of the Southern and Northern Hemisphere monsoons \r during the last deglaciation, with an important contribution from \r Atlantic Meridional Overturning Circulation (AMOC) slowdown \r (Otto-Bliesner et al., 2014; Mohtadi et al., 2016)."


id: 20ddabffeb3a87ee6b4d14e2e5d7d267
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3003
content:
"It is likely that human influence has contributed to the poleward \r expansion of the zonal mean Hadley cell in the Southern Hemisphere \r since the 1980s. This assessment is supported by studies since AR5, \r which consistently find human influence from greenhouse gas \r increases on the expansion, with additional influence from ozone \r depletion in austral summer. For the strong ozone depletion period \r of 1981-2000, human influence is detectable in the summertime \r poleward expansion in the Southern Hemisphere (medium \r confidence). By contrast, there is medium confidence that the \r expansion of the zonal mean Hadley cell in the Northern Hemisphere \r is within the range of internal variability, with contributions from \r PDV and other internal variability. The causes of the observed \r strengthening of the Pacific Walker circulation over the 1980-2014 \r period are not well understood, since the observed strengthening \r trend is outside the range of variability simulated in the coupled \r [...]"


id: d65601d3622542c35d64fdfbd503dbe4
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35020
content:
"[...] models (medium confidence). Large observational uncertainty, lack \r of understanding of the mechanism underlying the poleward Hadley \r cell expansion, and contradicting theories on the greenhouse gas \r influence and uncertainty in the aerosol influence on the Walker \r circulation strength, limit confidence in these assessments."


id: ada423ed7ec67e327a875acc2215f999
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 27790
content:
"Figure 3.17 | Model evaluation of global monsoon domain, intensity, and circulation. (a, b) Climatological summer-winter range of precipitation rate, scaled by \r annual mean precipitation rate (shading) and 850 hPa wind velocity (arrows) based on (a) GPCP and ERA5 and (b) a multi-model ensemble mean of CMIP6 historical simulations \r for 1979-2014. The region enclosed by red lines is the monsoon domain based on the definition by Wang and Ding (2008). (c, d) Five-year running mean anomalies of (c) global \r land monsoon precipitation index defined as the percentage anomaly of the summertime precipitation rate averaged over the monsoon regions over land, relative to its average \r for 1979-2014 (the period indicated by light grey shading) and (d) the tropical monsoon circulation index defined as the vertical shear of zonal winds between 850 and 200 \r [...]"


id: 44e6ea9a1284d0164f2d57b1800a745d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9352
content:
"[...] hPa levels averaged over 0deg-20degN, from 120degW eastward to 120degE in Northern Hemisphere summer (Wang et al., 2013; m s-1) in CMIP5 historical and RCP4.5 simulations, \r and CMIP6 historical and AMIP simulations. Summer and winter are defined for individual hemispheres: May to September is defined as Northern Hemisphere summer and \r Southern Hemisphere winter, and November to March is defined as Northern Hemisphere winter and Summer Hemisphere summer. The numbers of models and simulations \r are given in the legend. The multi-model ensemble mean and percentiles are calculated after weighting individual ensemble members with the inverse of the ensemble size of \r the same model, so that individual models are equally weighted irrespective of ensemble size. Further details on data sources and processing are available in the chapter data \r table (Table 3.SM.1)."


id: 5dd0712942261e5f9b5ebe1eeefed24e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13802
content:
"summer monsoon precipitation (Figure 3.17c). However, the 1960s \r peak in the Northern Hemisphere summer monsoon circulation \r is outside the 5th-95th percentile range of CMIP5 and CMIP6 \r historical simulations for two out of three reanalyses (Figure 3.17d). \r Modelling studies show that greenhouse gas increases act to \r enhance Northern Hemisphere summer monsoon precipitation \r intensity (Liu et al., 2012; Polson et al., 2014; Chai et al., 2018; L. \r Zhang et al., 2018b). Since the mid-20th century, however, modelling \r studies show that this effect was overwhelmed by the influence of \r anthropogenic aerosols in CMIP5 (Polson et al., 2014; Guo et al., \r 2015; Y. Zhang et al., 2018; Giannini and Kaplan, 2019) and in \r CMIP6 (T. Zhou et al., 2020). Weakening of the monsoon circulation \r and reduction of moisture availability are important in this aerosol \r influence (T. Zhou et al., 2020). Besides these human influences, \r [...]"


id: 7152e770703c0e165c165c56bb9129cd
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 17197
content:
"[...] the global monsoon is sensitive to internal variability and natural \r forcing including ENSO and volcanic aerosols on interannual \r time scales and PDV and AMV on decadal to multi-decadal time \r scales (Wang et al., 2013, 2018; F. Liu et al., 2016; Jiang and Zhou, \r 2019; Zuo et al., 2019); though AMV in the 20th century may \r have been partly driven by aerosols, see Section 3.7.7. Indeed, \r AMIP simulations better reproduce the observed multi-decadal \r variations of the global monsoon precipitation and circulation \r (Figure  3.17c,d). Y.  Zhang et al. (2018) find that the multi-model \r ensemble mean trend of global land monsoon precipitation in \r historical simulations, dominated by anthropogenic aerosol forcing \r contributions, emerges out of the 90% range of internally-driven \r trends in pre-industrial control simulations. However, it should \r be noted that CMIP5 models tend to under-represent the PDV \r magnitude (Section 3.7.6), suggesting potential overconfidence \r [...]"


id: eb95e893eaffad0a17f051c2a9d35f27
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38815
content:
"[...] in the detection of the forced signal. An observed enhancement \r in global summer monsoon precipitation since the 1980s is \r accompanied by an intensification of the Northern Hemisphere \r summer monsoon circulation (Figure  3.17c,d). These trends \r appear to be at the extreme of the range of the CMIP6 historical \r simulation ensemble but are well captured by AMIP simulations \r (Figure 3.17c,d). While the precipitation increase is consistent with \r greenhouse gas forcing, the circulation intensification is opposite \r to the simulated response to greenhouse gas forcing, and these \r enhancements have been attributed to PDV and AMV (Wang et al., \r 2013; Kamae et al., 2017)."


id: 56ba19fca87a5f661ecaf3d4d45c4588
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12509
content:
"Since AR5, new research using CMIP5 and CMIP6 models has confirmed \r that increasing the model resolution improves the simulation of \r cyclones and blocking in all seasons albeit with some exceptions and \r caveats (Zappa et al., 2013; Davini et al., 2017; Schiemann et al., 2017, \r 2020; Davini and D'Andrea, 2020; Priestley et al., 2020). New research \r also finds that model performance with respect to the simulation \r of cyclones and that of blocking events are correlated (Zappa et al., \r 2014), suggesting biases in both are aspects of the same underlying \r problems in models (Figure  3.18). In the North Pacific basin the \r annual mean blocking frequency is now well simulated compared to \r earlier evaluations, but substantial errors in the blocking frequency \r remain in the Euro-Atlantic sector (Figure 3.18; Dunn-Sigouin and \r Son, 2013; Davini and D'Andrea, 2016, 2020; Mitchell et al., 2017; \r Woollings et al., 2018b). While there is a resolution dependence \r [...]"


id: 4c5f4afad37c71d70519b2a1478a71ec
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10699
content:
"[...] in the size of this bias, even at very high resolution blocking in the \r Euro-Atlantic sector remains underestimated (Schiemann et al., 2017), \r and there is evidence of a compensation of errors as the resolution is \r increased (Davini et al., 2017). Davini and D'Andrea (2020) show that \r while the simulation of blocking improves with increasing resolution \r in CMIP3, CMIP5, and CMIP6 models, other factors contribute to \r biases, particularly to the underestimation of Euro-Atlantic blocking \r (Schiemann et al., 2020). The persistence of blocking events, typically "


id: 7e59ea72d4b0c762f22ac94d87775d62
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 19394
content:
"In summary, while greenhouse gas increases acted to enhance the global \r land monsoon precipitation over the 20th century (medium confidence), \r consistent with projected future enhancement (Section 4.5.1.5), this \r tendency was overwhelmed by anthropogenic aerosols from the 1950s \r to the 1980s, which contributed to weakening of global land summer \r monsoon precipitation intensity for this period (medium confidence). \r There is medium confidence that the intensification of global monsoon \r precipitation and Northern Hemisphere summer monsoon circulation \r since the 1980s is dominated by internal variability. These assessments \r are supported respectively by multi-model detection and attribution \r studies which find an important role for anthropogenic aerosols \r in the weakening trend, and studies that identify a role for AMV \r and PDV in inducing the Northern Hemisphere summer monsoon \r circulation enhancement since the 1980s. Supported by multi-model \r [...]"


id: 7a78771d13d5c887511fa52657ec577b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 18738
content:
"For the Southern Hemisphere (not considered in AR5), Priestley \r et al. (2020) find considerable improvement in the placement of \r the Southern Ocean storm track during summer (December to \r February) in CMIP6 models versus CMIP5, consistent with a more \r realistic annual mean surface wind maximum latitude in CMIP6 \r than in CMIP5 (Goyal et al., 2021). Relative to CMIP5, both low- and \r high-resolution CMIP6 models have increased track densities south \r of about 55degS and decreased track densities between about 40degS \r and 55degS, in better agreement with observations than CMIP5 models \r (Parsons et al., 2016; Patterson et al., 2019). CMIP5 models and high\u0002resolution CMIP6 models simulate a storm track that is positioned \r too far equatorward, although the bias is smaller in the high\u0002resolution models. By contrast, the low-resolution CMIP6 models \r simulate a storm track that is slightly too far poleward on average \r [...]"


id: 5127b4f503440324f716a5f849be99b4
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11565
content:
"[...] (Priestley et al., 2020). In winter (June to August), the biases found in \r CMIP5 are only slightly improved in CMIP6, with models continuing \r to underestimate the broad maximum cyclone track density in the \r south-eastern Indian Ocean and overestimate the minimum density \r in the south-western South Pacific (Priestley et al., 2020)."


id: 2c575b483311a72ad68c8f9c8c96380d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5071
content:
"Section 2.3.1.4.3 assesses that the extratropical jets and cyclone \r tracks have likely shifted poleward in both hemispheres since the \r 1980s with marked seasonality in trends (medium confidence). For \r the Southern Hemisphere, studies using CMIP5 and other models \r imply that both ozone depletion and increasing greenhouse gases \r have caused substantial atmospheric circulation change since \r the 1960s when concentrations of ozone-depleting substances \r started to increase (Eyring et al., 2013; Iglesias-Suarez et al., \r 2016; Karpechko et al., 2018; Son et al., 2018). In particular, ozone \r depletion, during austral summer, has been linked to a poleward \r shift of the westerly jet and Southern Hemisphere circulation zones \r and a southward expansion of the tropics (Kang et al., 2011), which \r is associated with a strengthening trend of the Southern Annular \r Mode (SAM; Section 3.7.2). This has been well reproduced by \r climate models with prescribed historical ozone concentration or \r [...]"


id: 899352e301c85759dad78d6fff456370
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21377
content:
"The AR5 concluded that 'anthropogenic forcings are very likely to have \r contributed to Arctic sea ice loss since 1979' (Bindoff et al., 2013), based \r on studies showing that models can reproduce the observed decline only \r when including anthropogenic forcings, and formal attribution studies. \r Since the beginning of the modern satellite era in 1979, Northern \r Hemisphere sea ice extent has exhibited significant declines in all \r months with the largest reduction in September (see Section 2.3.2.1.1, \r and Figures 3.20 and 3.21 for more details on observed changes). The \r recent Arctic sea ice loss during summer is unprecedented since 1850 \r (high confidence), but as in AR5 and SROCC there remains only medium \r confidence that the recent reduction is unique during at least the past \r 1000 years due to sparse observations (Sections 2.3.2.1.1 and 9.3.1.1). \r CMIP5 models also simulate Northern Hemisphere sea ice loss over the \r [...]"


id: d6433d26eedb3e58787adae380e06e4c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35010
content:
"[...] satellite era but with large differences among models (e.g., Massonnet \r et al., 2012; Stroeve et al., 2012). The envelope of simulated ice loss \r across model simulations encompasses the observed change, although \r observations fall near the low end of the CMIP5 and CMIP6 distributions \r of trends (Figure 3.20). CMIP6 models on average better capture the \r observed Arctic sea ice decline, albeit with large inter-model spread. Notz \r et al. (2020) found that CMIP6 models better reproduce the sensitivity \r of Arctic sea ice area to CO2 emissions and global warming than earlier \r CMIP models although the models' underestimation of this sensitivity \r remains. Davy and Outten (2020) also found that CMIP6 models can \r simulate the seasonal cycle of Arctic sea ice extent and volume better \r than CMIP5 models. For the assessment of physical processes associated \r with changes in Arctic sea ice, see Section 9.3.1.1."


id: 54acec9b9fa58a695e681a7a7f12ccc5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12171
content:
"Seviour et al. (2016) found that stratosphere-resolving CMIP5 \r models, on average, reproduce the observed frequency of vortex \r splits (one form of SSWs) but with a wide range of model-specific \r biases. Models that produce a better mean state of the polar vortex \r also tend to produce a more realistic SSW frequency (Seviour \r et al., 2016). The mean sea level pressure anomalies occurring in \r CMIP5 model simulations when an SSW is underway, however, \r differ substantially from those in reanalyses (Seviour et al., \r 2016). Unlike stratosphere-resolving models, models with limited \r stratospheric resolution, which make up more than half of the \r CMIP5 ensemble, underestimate the frequency of SSWs (Osprey \r et al., 2013; J. Kim et al., 2017). Taguchi (2017) found a general \r underestimation in CMIP5 models of the frequency of 'major' SSWs \r (which are associated with a break-up of the polar vortex), an \r aspect of an under-representation in those models of dynamical \r [...]"


id: c68ee56aac36fb1ec43e97b42d63c920
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 32236
content:
"[...] variability in the stratosphere. Wu and Reichler (2020) found that \r finer vertical resolution in the stratosphere and a model top above \r the stratopause tend to be associated with a more realistic SSW \r frequency in CMIP5 and CMIP6 models."


id: fc1ed902752b8f796f47b6b87573c43b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41721
content:
"Since AR5, there have been several new detection and attribution \r studies on Arctic sea ice. While the attribution literature has mostly used \r sea ice extent (SIE), it is closely proportional to sea ice area (SIA; Notz, \r 2014), which is assessed in Chapters 2 and 9 and shown in Figures 3.20 \r and 3.21. Kirchmeier-Young et al. (2017) compared the observed time \r series of the September SIE over the period 1979-2012 with those from \r different large ensemble simulations which provide a robust sampling \r of internal climate variability (CanESM2, CESM1, and CMIP5) using an \r optimal fingerprinting technique. They detected anthropogenic signals \r which were separable from the response to natural forcing due to solar \r irradiance variations and volcanic aerosol, supporting previous findings \r (Figure 3.21; Min et al., 2008b; Kay et al., 2011; Notz and Marotzke, \r 2012; Notz and Stroeve, 2016). Using selected CMIP5 models and \r [...]"


id: 1e06e27f0b7a960c99daef9e28f292ee
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 2482
content:
"[...] three independently derived sets of observations, Mueller et al. (2018) \r detected fingerprints from greenhouse gases, natural, and other \r anthropogenic forcings simultaneously in the September Arctic SIE over "


id: ba62ca84726c8ff97a678b437a54fce5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30009
content:
"forced and unforced simulations using multiple climate models and \r found that internal variability explains about 40% of the observed \r September sea ice melting trend, supporting previous studies (Stroeve \r et al., 2012). Based on the large ensembles of CESM1 and CanESM2, \r the September Arctic sea ice extent variance first increases and then \r decreases as SIE declines from its pre-industrial value (Kirchmeier\u0002Young et al., 2017; Mueller et al., 2018) consistent with previous work \r (Goosse et al., 2009), but neither study found a strong sensitivity of \r detection and attribution results to the change in variability. Further \r work has indicated that internally-driven summer atmospheric \r circulation trends with enhanced atmospheric ridges over Greenland \r and the Arctic Ocean, which project on the negative phase of the \r North Atlantic Oscillation (Section 3.7.1), play an important role \r in the observed Arctic sea ice loss (Hanna et al., 2015; Ding et al., \r [...]"


id: 82a3964c7b50c5996bde1f65a6991e88
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20113
content:
"[...] 2017). A fingerprint analysis using the CESM large ensemble suggests \r that this internal variability accounts for 40-50% of the observed \r September Arctic sea ice decline (Ding et al., 2019; England et al., \r 2019). Internally-generated decadal tropical variability and associated \r atmospheric teleconnections were suggested to have contributed to \r the changing atmospheric circulation in the Arctic and the associated \r rapid sea ice decline from 2000 to 2014 (Meehl et al., 2018)."


id: b741c87c9a801658ebf26c59053ed8f6
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 29456
content:
"and declines in the Bellingshausen and Amundsen Seas (Hobbs et al., \r 2016). Observed regional trends have been particularly large in austral \r autumn (see Section 2.3.2.1.2, and also Section 9.3.2.1 for more details \r of regional changes and related physical processes). Starting in austral \r spring of 2016, the ice extent decreased strongly (Turner et al., 2017) and \r has since remained anomalously low (Figure 3.21 and Figure 2.20). This \r decrease has been associated with anomalous atmospheric conditions \r associated with teleconnections from warming in the eastern Indian \r Ocean and a negative Southern Annular Mode (Chenoli et al., 2017; \r Stuecker et al., 2017; Schlosser et al., 2018; Meehl et al., 2019; Purich \r and England, 2019; G. Wang et al., 2019). A decadal-scale warming of \r the near-surface ocean that resulted from strengthened westerlies may \r also have contributed to and helped to sustain the sea ice loss (Meehl \r [...]"


id: d8431a5225257f5797b37df6f24e92ca
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35349
content:
"[...] et al., 2019). Before satellites and on even longer time scales, very \r limited observational data and proxy coverage leads to low confidence \r in all aspects of Antarctic sea ice (Sections 2.3.2.1.2 and 9.3.2.1)."


id: fa7641a472aca1661cb8571815d515d1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41430
content:
"CMIP5 climate models generally simulate Antarctic sea ice loss over \r the satellite era since 1979 (Mahlstein et al., 2013; Turner et al., 2013) \r in contrast to the observed change, and CMIP6 models also simulate \r Antarctic ice loss (Roach et al., 2020; Figure 3.20 and 3.21). A number \r of studies have suggested that this discrepancy may be in part due \r to the role of internal variability in the observed change (Mahlstein \r et al., 2013; Polvani and Smith, 2013; Zunz et al., 2013; Meehl et al., \r 2016c; Turner et al., 2016), including teleconnections associated with \r tropical Pacific variability (Meehl et al., 2016c) and changing surface \r conditions resulting from multi-decadal ocean circulation variations \r (Singh et al., 2019). However, when the spatial pattern is considered, \r trends in the summer and autumn (from 1979-2005) appear outside \r the range of internal variability (Hobbs et al., 2015). This suggests \r [...]"


id: 6c6cc94f9251a42fccb5121487051611
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 16369
content:
"[...] that the models may exhibit an unrealistic simulation of the Antarctic \r sea ice forced response or the internal variability of the system. \r Discrepancies among the models in simulated sea ice variability \r (Zunz et al., 2013), the sea ice climatological state (Roach et al., \r 2018), upper ocean temperature trends (Schneider and Deser, 2018), \r Southern Hemisphere westerly wind trends (Purich et al., 2016), or \r the sea ice response to Southern Annular Mode variations (Ferreira \r et al., 2014; Holland et al., 2017; Kostov et al., 2017; Landrum et al., \r 2017) may all play some role in explaining these differences with the \r observed trends. Increased fresh water fluxes caused by mass loss of \r the Antarctic Ice Sheet (either by melting at the front of ice shelves \r or via iceberg calving) have been suggested as a possible mechanism \r driving the multi-decadal Antarctic sea ice expansion (Bintanja et al., \r 2015; Pauling et al., 2016) but there is a lack of consensus on this \r [...]"


id: 72c11478ba68f93ec4e7293e1cdb0ef3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 17433
content:
"[...] mechanism's impacts (Pauling et al., 2017). A recent study based on \r a decadal prediction system suggests that initializing the state of \r the Antarctic Bottom Water cell allows the system to reproduce the \r observed Antarctic sea ice increase (Zhang et al., 2017), consistent \r with the suggestion that multi-decadal variability associated with \r variations in deep convection has contributed to the observed \r increase in Antarctic sea ice since 1979 (Latif et al., 2013; Zhang \r et al., 2017; L. Zhang et al., 2019) (see also Section 9.3.2.1).\r There have been several studies that aimed to identify causes of the \r observed Antarctic SIE changes. Gagne et al. (2015) assessed \r the consistency of observed and simulated changes in Antarctic SIE for \r an extended period using recovered satellite-based estimates, and found "


id: d4a8ecfbf97a1e7553dc24a374b1918d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 34734
content:
"that the observed trends since the mid-1960s are not inconsistent \r with model simulated trends. Studies based on the satellite period \r also indicate that the observed trends are largely within the range of \r simulated internal variability (Hobbs et al., 2016). A few distinct factors \r that led to the weak signal-to-noise ratio in Antarctic SIE trends have \r been further identified, which include large multi-decadal variability \r (Monselesan et al., 2015), the short observational record (e.g., Abram \r et al., 2013), and the limited model performance at representing \r the complex Antarctic climate system as discussed above (Bintanja \r et al., 2013; Uotila et al., 2014). The short period of comprehensive \r satellite observations, beginning in 1979, makes it challenging to set \r the observed increase between 1979 and 2015, or the subsequent \r decrease, in a long-term context, and to assess whether the difference \r in trend between observations and models, which mostly simulate \r [...]"


id: a2827b4ee67e8204fa336b96fc7660c1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25807
content:
"[...] long-term decreases, is systematic or a rare expression of internal \r variability on decadal to multi-decadal time scales."


id: 541a7bf452b57e6cd5af15f4996af513
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12072
content:
"The AR5 noted the strong linear correlation between Northern \r Hemisphere snow cover extent (SCE) and annual-mean surface air \r temperature in CMIP5 models. It was assessed as likely that there \r had been an anthropogenic contribution to observed reductions in \r Northern Hemisphere snow cover since 1970 (Bindoff et al., 2013). \r The AR5 assessed that CMIP5 models reproduced key features of \r observed snow cover well, including the seasonal cycle of snow cover \r over northern regions of Eurasia and North America, but had more \r difficulties in more southern regions with intermittent snow cover. \r The AR5 also found that CMIP5 models underestimated the observed \r reduction in spring snow cover over this period (Figure  3.22; see \r also Brutel-Vuilmet et al., 2013; Thackeray et al., 2016; Santolaria\u0002Otin and Zolina, 2020). This behaviour has been linked to how \r the snow-albedo feedback is represented in models (Thackeray \r [...]"


id: 285836f6fa867929db23abb91c94b5b1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 4297
content:
"[...] et al., 2018a). The CMIP5 multi-model ensemble has been shown to \r represent the snow-albedo feedback more realistically than CMIP3, \r although models from some individual modelling centres have not \r improved or have even got worse (Thackeray et al., 2018a). There is \r still a systematic overestimation of the albedo of boreal forest covered \r by snow (Thackeray et al., 2015; Y. Li et al., 2016). Consequently, the \r snow albedo feedback might have been overestimated by CMIP5 \r models (Section 9.5.3; Xiao et al., 2017)."


id: bfb4ee974e0e2065901bc36f4b054b80
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31010
content:
"Figure 3.22 | Time series of Northern Hemisphere March-April mean snow \r cover extent (SCE) from observations, CMIP5 and CMIP6 simulations. The \r observations (grey lines) are updated Brown-NOAA (Brown and Robinson, 2011), \r Mudryk et al. (2020), and GLDAS2. CMIP5 (top) and CMIP6 (bottom) simulations \r of the response to natural plus anthropogenic forcing are shown in brown, natural \r forcing only in green, and the pre-industrial control simulation range is presented in \r blue. Five-year mean anomalies are shown for the 1923-2017 period with the x-axis \r representing the centre years of each five-year mean. CMIP5 all forcing simulations \r are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 all forcing \r simulations are extended by using SSP2-4.5 scenario simulations after 2014. Shading \r indicates 5th-95th percentile ranges for CMIP5 and CMIP6 all and natural forcings \r simulations, and solid lines are ensemble means, based on all available ensemble \r [...]"


id: 42a9e244ba07ee76cac5fe3e49f900bd
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8923
content:
"[...] members with equal weight given to each model (Section 3.2). The blue vertical bar \r indicates the mean 5th-95th percentile range of pre-industrial control simulation \r anomalies, based on non-overlapping segments. The numbers in brackets indicate the \r number of models used. Anomalies are relative to the average over 1971-2000. For \r models, SCE is restricted to grid cells with land fraction >=50%. Greenland is excluded \r from the total area summation. Figure is modified from Paik and Min (2020), their \r Figure 1. Further details on data sources and processing are available in the chapter \r data table (Table 3.SM.1)."


id: 19359355b3df7cd86edc6f2e088755a2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1653
content:
"glaciers (Shannon et al., 2019). Moreover, standalone glacier models \r have now been systematically compared in GlacierMIP (Hock et al., \r 2019a; Marzeion et al., 2020). The Antarctic and Greenland Ice Sheets \r were absent in global climate models that pre-date CMIP6 (Eyring \r et al., 2016a), however some preliminary analyses that used results \r from CMIP5 to drive standalone ice-sheet models were included \r in AR5 (Church et al., 2013a). For the first time in CMIP, the latest \r CMIP6 phase includes a coordinated effort to simulate temporally \r evolving ice sheets within the Ice Sheet Model Intercomparison \r Project (ISMIP6; Box  9.3; Nowicki et al., 2016). Our understanding \r of aspects of the global water storage contained in glaciers and ice \r sheets, and their contribution to sea-level rise, has improved since \r AR5 and SROCC (Hock et al., 2019b; Meredith et al., 2019) both in \r models and observations (see assessment of observations and model \r [...]"


id: a58bf7b7472a3a9cd39545cee32f1598
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 26362
content:
"[...] evaluation for the Greenland Ice Sheet in Sections 2.3.2.4.1 and 9.4.1; \r Antarctica in Sections 2.3.2.4.2 and 9.4.2; and glaciers in Sections \r 2.3.2.3 and 9.5.1)."


id: 662c5dd543222a0b1ea46f07ed5faddb
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 15239
content:
"In summary, it is very likely that anthropogenic influence contributed \r to the observed reductions in Northern Hemisphere springtime snow \r cover since 1950. CMIP6 models better represent the seasonality \r and geographical distribution of snow cover than CMIP5 simulations \r (high confidence). Both CMIP5 and CMIP6 models simulate strong \r declines in spring SCE during recent years, in general agreement \r with observations, causing the multi-model mean decreasing trend \r in spring SCE to now better agree with observations than in earlier \r evaluations. Evidence has yet to emerge that interactions between \r vegetation and snow, found problematic in CMIP5, have improved in \r CMIP6 models (Section 9.5.3). Such deficiencies in the representation \r of snow in climate models mean there is medium confidence in the \r simulation of snow cover over the northern continents in CMIP6 model \r simulations. The models consistently link snow extent to surface air \r [...]"


id: 72ae73b5f9f1163c2d8c149e33ef12cc
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 17416
content:
"[...] temperature (Figure 9.24). With warming of near-surface air linked to \r anthropogenic influence, and particularly to greenhouse gas increases \r (Section 3.3.1.1), this provides additional evidence that reductions in \r snow cover are also caused by human activity."


id: 89352c78859d747c1b7026709cd00d7b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21306
content:
"While Chapter  9 (Sections 9.4 and 9.5) discusses process \r understanding for glaciers and ice sheets, as well as evaluation of \r global and regional-scale glacier and ice-sheet models, our focus \r here is on the attribution of large-scale changes in glaciers and ice \r sheets. Land ice in the form of glaciers has been included in CMIP \r climate and Earth system models as components of the land surface \r models for many years. However, their representation is simplified \r and is omitted altogether in the less complex modelling systems. In \r CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) land ice \r area fraction, a component of land surface models, was defined as \r a time-independent quantity, and in most model configurations was \r preset at the simulation initialization as a permanent land feature. \r In CMIP6 considerable progress has been made in improving and \r evaluating the representation of modelled land ice. For glaciers, \r [...]"


id: ead656f19afade3ed77c68a382140b56
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38679
content:
"[...] an example is the expansion of the Joint UK Land Environment \r Simulator (JULES) land surface model to enable elevated tiles, and \r hence more accurately simulate the altitudinal atmospheric effects on "


id: 7855560d2b4d99c897b43f4c3ea0d0ff
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 18378
content:
"A regional study considering 85 Northern Hemisphere glacier systems \r concluded that there is a discernible human influence on glacier mass \r balance, with glacier model simulations driven by CMIP5 historical \r and greenhouse gas-only simulations showing a glacier mass loss, \r whereas those driven by natural-only forced simulations showed a net \r glacier growth (Hirabayashi et al., 2016). In addition, a study of the \r role of climate change in glacier retreat using a simple mass-balance \r model for 37 glaciers worldwide, concluded that observed length \r changes would not have occurred without anthropogenic climate \r change, with observed length variations exceeding those associated \r with internal variability by several standard deviations in many \r cases (Roe et al., 2017). Roe et al. (2021) used the same model to \r estimate that at least 85% of cumulative glacier mass loss since \r 1850 is attributable to anthropogenic influence. While Marzeion \r [...]"


id: 522db30d7001bab48618ee8d1a7cfabe
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11730
content:
"[...] et al. (2014) found that anthropogenic influence contributed only \r 25 +- 35% of glacier mass loss for the period 1851-2010, their "


id: c494af22d94b1bf19f0e44035abb6260
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31896
content:
"(Sasgen et al., 2020; Tedesco and Fettweis, 2020). These results \r support the finding that increased surface melting is associated with \r warming, although atmospheric circulation anomalies, including \r the summer North Atlantic Oscillation (NAO) and variations in \r snowfall play an important role in driving interannual variations \r (Section 9.4.1.1; Sasgen et al., 2020; Tedesco and Fettweis, 2020). \r Further, a coupled ice-sheet-climate model study found emergence \r of decreased surface mass balance prior to the present day in coastal \r locations in Greenland, which dominate the integrated surface mass \r balance (Fyke et al., 2014), suggesting that observed variations \r in surface mass balance in these regions might be expected to be \r distinguishable from internal variability. A CMIP6 simulation of the \r historical period showed stable Greenland surface mass balance up \r to the 1990s, after which it declined due to increased melt and runoff, \r [...]"


id: 85d3ef166fa05ee6e83f67c21b9b6992
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20687
content:
"[...] consistent with a downscaled reanalysis (van Kampenhout et al., \r 2020). Further, all experts surveyed in a structured expert judgement \r exercise examining the causes of the increase in mass loss from \r the Greenland Ice Sheet over the last two decades (Bamber et al., \r 2019) concluded that external forcing was responsible for at least \r 50% of the mass loss. A comparison of Greenland Ice Sheet mass \r loss trends from observations and AR5 model projections for the \r period 2007-2017 found that the magnitude of the observed surface \r mass balance trends was at the top of the AR5 assessed range, \r while mass loss due to changing ice dynamics was near the centre \r of the AR5 range (Slater et al., 2020), providing further evidence of \r consistent anthropogenically-forced mass loss trends in models and \r observations."


id: 5f1b1fd63ec999ccfdb1b59fa5189e3e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 14035
content:
"AR5 assessed that there was low confidence in attributing the causes \r of the observed mass loss from the Antarctic Ice Sheet since 1993 \r (Bindoff et al., 2013). The SROCC assessed that there is medium \r agreement but limited evidence of anthropogenic forcing of Antarctic \r mass balance through both surface mass balance and glacier \r dynamics. It further assessed that Antarctic ice loss is dominated by \r acceleration, retreat and rapid thinning of the major West Antarctic \r Ice Sheet outlet glaciers (very high confidence), driven by melting \r of ice shelves by warm ocean waters (high confidence). Based on \r updated observations, Chapter  2 assesses that there is very high \r confidence that the Antarctic Ice Sheet lost mass between 1992 and \r 2017, and that there is medium confidence that this mass loss has \r accelerated. Models of Antarctic Ice Sheet evolution are evaluated \r in detail in Section 9.4.2.2, which assesses that there is medium \r [...]"


id: 2e84ddb66ea73264b471e796739053b3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5464
content:
"[...] confidence in many ice-sheet processes in Antarctic Ice Sheet models, \r but low confidence in the ocean forcing affecting basal melt rates. \r CMIP5 and CMIP6 models perform similarly in their simulation of \r Antarctic surface mass balance (Section 9.4.2.2, Gorte et al., 2020). "


id: 5625450f34e8f60e6d718d4187ff13ce
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12432
content:
"Detection and attribution studies of change in the Greenland Ice \r Sheet remain challenging (Kjeldsen et al., 2015; Bamber et al., \r 2019). This is in part due to the short observational record (Shepherd \r et al., 2012, 2018, 2020; Bamber et al., 2018; Cazenave et al., 2018; \r Mouginot et al., 2019; Rignot et al., 2019) and the challenges this \r poses to the evaluation of modelling efforts (Section 9.4.1.2). The \r latter require not only dynamic ice-sheet models, but also appropriate \r atmospheric and oceanic conditions to use as a boundary forcing \r to drive the models (Nowicki and Seroussi, 2018; Barthel et al., \r 2020). Nonetheless, new literature since AR5 finds that ice-sheet \r mass balance calculations using reanalysis-driven regional model \r simulations of surface mass balance are found to agree well with \r the observed decrease in ice-sheet mass over the past twenty years \r (Fettweis et al., 2020; Sasgen et al., 2020; Tedesco and Fettweis, \r [...]"


id: 81fdb9fad07e847e8bf78250704a6f69
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 19990
content:
"[...] 2020), consistent with earlier studies (Flato et al., 2013). These \r studies also show that the exceptional melt events observed in 2012 \r and 2019 were associated with exceptional atmospheric conditions "


id: 211adac318e5360269c14315a8c7574
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3044
content:
"Ice discharge around the West Antarctic Ice Sheet is strongly \r influenced by variability in basal melt (Jenkins et al., 2018; Hoffman \r et al., 2019), in particular at decadal and longer time scales (Snow \r et al., 2017). Basal melt rate variability can be induced by wind-driven \r ocean current changes, which may partly be of anthropogenic origin \r via greenhouse gas forcing (Holland et al., 2019). Moreover, ice \r discharge losses from the Antarctic Ice Sheet over the 2007-2017 \r period are close to the centre of the model-based range projected \r in AR5 (Slater et al., 2020). However, expert opinion differs as to \r whether recent Antarctic ice loss from the West Antarctic Ice Sheet \r has been driven primarily by external forcing or by internal variability, \r and there is no consensus (Bamber et al., 2019). Anthropogenic \r influence on the Antarctic surface mass balance, which is expected \r to partially compensate for ice discharge losses through increases in \r [...]"


id: cc98dd12a5762e7fc19f17ba5019cdab
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33318
content:
"[...] snowfall, is currently masked by strong natural variability (Previdi and \r Polvani, 2016; Bodart and Bingham, 2019), and observations suggest \r that it has been close to zero over recent years (see further discussion \r in Section 9.4.2.1; Slater et al., 2020)."


id: 4bbb5ca37d4def0b3d8fa67cad8bd3ef
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10578
content:
"In CMIP3 and CMIP5 models, large SST biases were found in the \r mid- and high latitudes (Flato et al., 2013). In CMIP6, the Northern \r Hemisphere mid-latitude surface temperature biases appear to be \r marginally improved in the multi-model mean when contrasted to \r CMIP5 despite large biases remaining in a few models (Figures 3.23a \r and 3.24). There is a decreased spread of the zonal mean SST error \r between 50degN and 30degS, relative to CMIP5 (Figure  3.24a). On the \r other hand, the Southern Ocean's warm surface temperature bias \r remains (Figure 3.23a; Beadling et al., 2020), and is on average larger \r in CMIP6 than in CMIP5 models (Figures 3.23a and 3.24). This warm \r bias is often associated with persistent overlying atmospheric cloud \r biases (Hyder et al., 2018). Several other large biases also appear \r to remain largely unresolved in CMIP6, particularly warm biases in \r excess of 1degC along the equatorial eastern continental boundaries of \r [...]"


id: cc7885c0338031c70eb41bd8f2810f1b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33291
content:
"In CMIP5, mean state biases in the tropical Pacific Ocean including the \r excessive equatorial cold tongue, erroneous mean thermocline depth \r and slope along the equator remained but were improved relative \r to CMIP3 (Flato et al., 2013). Misrepresentation of the interaction \r between the atmosphere and ocean via the Bjerknes feedback and \r vertical mixing parameterizations, and a bias in winds were among \r the suggested reasons for the persistent biases (Li et al., 2014; Zhu \r and Zhang, 2018). Moving to CMIP6, a reduction of the cold bias in \r the equatorial cold tongue in the central Pacific is found on average \r in the CMIP6 models (Figure  3.24b; Grose et al., 2020; Planton \r et al., 2021), however, this reduced bias is not statistically significant \r when considered across the multi-model ensemble (Planton et al., \r 2021). It is also noteworthy that the longitude of the 28degC isotherm \r is closer to observed in CMIP6 than in CMIP5, with a coincident \r [...]"


id: ba1e33539dcbf4009f8be26ccc05d67
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 29969
content:
"[...] reduction in the CMIP6 inter-model standard deviation (Grose et al., \r 2020). The latter result implies that there is an improvement in the \r representation of the tropical Pacific mean state in CMIP6 models. \r Comparison of biases in individual HighResMIP models with biases \r in lower resolution versions of the same models indicates that there \r is no consistent improvement in SST biases in most of the equatorial \r Pacific with resolution (Figure 3.3e; Bock et al., 2020)."


id: 37ef2504a918d043eea6b1d7181e8e4c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 7049
content:
"Fundamental features such as the mean zonal SST gradient in the \r tropical Atlantic were not reproduced in CMIP5 models. Studies \r have proposed that weaker than observed alongshore winds, \r underestimation of stratocumulus clouds, coarse model resolution, and \r insufficient oceanic cooling due to a deeper thermocline depth and \r weak vertical velocities at the base of the mixed layer in the eastern \r basin, underpinned these tropical Atlantic SST gradient biases (Hourdin \r et al., 2015; Richter, 2015). The SST gradient biases still remain in \r CMIP6. On average the cold bias in the western part of the basin is \r reduced while the warm bias in the eastern part has slightly increased \r (Figure 3.24b,c; Richter and Tokinaga, 2020). Several CMIP6 models, \r however, display large reductions in biases of the zonal SST gradient, \r such that the eastern equatorial Atlantic warm SST bias and associated \r westerly wind biases are mostly eliminated in these models (Richter \r [...]"


id: fb68e034845a3c19823dcfc370f5ae9b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41606
content:
"[...] and Tokinaga, 2020). The high resolution (HighResMIP) CMIP6 models \r show a better representation of the zonal SST gradient (Figure 3.24b,c), \r but some lower resolution models also perform well, suggesting that \r resolution is not the only factor responsible for biases in Tropical \r Atlantic SST (Richter and Tokinaga, 2020)."


id: 8bf13867e8ae4ed0c6b788a1d9ac6472
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21789
content:
"Figure 3.26 | Global ocean heat content in CMIP6 simulations and observations. Time series of observed (black) and simulated (red) global ocean heat content \r anomalies with respect to 1995-2014 for the full ocean depth (left-hand panel); upper layer: 0-700 m (top right-hand panel); intermediate layer: 700-2000 m (middle \r right-hand panel); and the abyssal ocean: >2000 m (bottom right-hand panel). The best estimate observations (black solid line) for the period of 1971-2018, along \r with very likely ranges (black shading) are from Section 2.3.3.1. For the models (1860-2014), ensemble members from 15 CMIP6 models are used to calculate the multi-model \r mean values (red solid line) after averaging across simulations for each independent model. The very likely ranges in the simulations are shown in red shading. Simulation drift \r [...]"


id: f13994b99b98dfc9a93b46be6ec8c662
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 39827
content:
"[...] has been removed from all CMIP6 historical runs using a contemporaneous portion of the linear fit to each corresponding pre-industrial control run (Gleckler et al., 2012). Units \r are zettajoules (ZJ; 1021 joule). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: 3f6850bd2991e6fe1e29eb470a950d7d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8342
content:
"the global intermediate ocean (700-2000 m) warmed substantially \r from 1971 to the present. Further, ocean layer warming contributions \r are reported as 61% (0-700 m), 31% (700-2000 m) and 8% \r (>2000 m) for the 1971 to 2018 period (Table  2.7). CMIP5 model \r simulations replicate this partitioning fairly well for the industrial-era \r (1865 to 2017) throughout the upper (0-700 m, 65%), intermediate \r (700-2000 m, 20%) and deep (>2000 m, 15%) layers (Gleckler et al., \r 2016; Durack et al., 2018). The corresponding warming percentages \r for the multi-model mean of a subset of CMIP6 simulations over the \r 1850-2014 period are 58% for the upper, 21% for the intermediate, \r and 22% for the deep-ocean layers (Figure 3.26). These results are \r consistent with SROCC which assessed that it is virtually certain that \r both the upper and intermediate ocean warmed from 2004 to 2016, \r with an increased rate of warming since 1993 (Bindoff et al., 2019). \r [...]"


id: a39c9ff379c9209ceecf6d783bb7da95
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25990
content:
"[...] The spatial distribution of these changes for different ocean depths \r is assessed in Section 9.2.2.1."


id: 2d6f2923ed44222722b8a508236968a6
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5281
content:
"While ocean assessments have primarily focused on temperature \r changes, improved observational salinity products since the early \r 2000s have supported more assessment of long-term ocean salinity \r change and variability from AR4 (Bindoff et al., 2007) to AR5 across \r both models and observations (Flato et al., 2013; Rhein et al., 2013). \r The AR5 assessed that it was very likely that anthropogenic forcings \r have made a discernible contribution to surface and subsurface \r ocean salinity changes since the 1960s. The SROCC augmented these \r insights, noting that observed high latitude freshening and warming \r have very likely made the surface ocean less dense with a stratification \r increase of between 2.18% and 2.42% from 1970 to 2017 (Bindoff \r et al., 2019). A recent observational analysis has expanded on these \r assessments, suggesting a very marked summertime density contrast \r enhancement across the mixed layer base of 6.2-11.6% per decade, \r [...]"


id: 4247f27fbb673650a766e982154257f3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8860
content:
"[...] driven by changes in temperature and salinity, which is more than six \r times larger than previous estimates (Sallee et al., 2021). An idealized \r ocean modelling study suggests that the enhanced stratification can \r account for a third of the salinity enhancement signal since 1990 \r (Zika et al., 2018). Thus, there has been an expansion of observed \r global- and basin-scale salinity change assessment literature since \r AR5, with many new studies reproducing the key patterns of long\u0002term salinity change reported in AR5 (Rhein et al., 2013), and \r linking these through modelling studies to coincident changes in \r evaporation-precipitation patterns at the ocean surface (Sections \r 2.3.1.3, 3.3.2, 8.2.2.1 and 9.2.2)."


id: 3839b8b716b12303ad68fb981a525ca8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 7097
content:
"Since AR5, the attribution of ocean heat content increases to \r anthropogenic forcing has been further supported by more detection \r and attribution studies. These studies have shown that contributions \r from natural forcing alone cannot explain the observed changes in \r ocean heat content in either the upper or intermediate ocean layers, \r and a response to anthropogenic forcing is clearly detectable in ocean \r heat content (Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., \r 2019). Moreover, a response to greenhouse gas forcing is detectable \r independently of the response to other anthropogenic forcings \r (Bilbao et al., 2019; Tokarska et al., 2019), which has offset part of \r the greenhouse gas induced warming. Further evidence is provided \r by the agreement between observed and simulated changes in \r global thermal expansion associated with the ocean heat content \r increase when both natural and anthropogenic forcings are included \r [...]"


id: c2fced1eed9f7b3c3ea7bd7f5cd5288b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31578
content:
"[...] in the simulations (Section 3.5.3.2), though internal variability plays a \r larger role in driving basin-scale thermosteric sea level trends (Bilbao \r et al., 2015). Over the Southern Ocean, warming is detectable over \r the late 20th century and is largely attributable to greenhouse gases \r (Swart et al., 2018; Hobbs et al., 2021), while other anthropogenic \r forcings such as ozone depletion have been shown to mitigate the \r warming in some of the CMIP5 simulations (Swart et al., 2018; Hobbs \r et al., 2021). The use of the mean temperature above a fixed isotherm \r rather than fixed depth further strengthens a robust detection of the \r anthropogenic response in the upper ocean (Weller et al., 2016), and \r better accounting for internal variability in the upper ocean (Rathore \r et al., 2020), helps explain reported hemispheric asymmetry in ocean \r heat content change (Durack et al., 2014b)."


id: 9e56844601b47b7e64f3928873f8742
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25086
content:
"Considering the bulk of evidence, it is extremely likely that human \r influence has contributed to observed near-surface and subsurface \r salinity changes across the globe since the mid-20th century. All \r available multi-decadal assessments have confirmed that the \r associated pattern of change corresponds to fresh regions becoming \r fresher and salty regions becoming saltier (high confidence). CMIP5 \r and CMIP6 models are only able to reproduce these patterns \r in simulations that include greenhouse gas increases (medium \r confidence). Changes to the coincident atmospheric water cycle and \r ocean-atmosphere fluxes (evaporation and precipitation) are the \r primary drivers of the basin-scale observed salinity changes (high \r confidence). This result is supported by all available observational \r assessments, along with a growing number of climate modelling \r studies targeted at assessing ocean and water cycle changes. \r [...]"


id: b15e54dff79d644fc73f90680575535
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 28431
content:
"[...] The basin-scale changes are consistent across models and intensify \r on centennial scales from the historical period through to the \r projections of future climate (high confidence)."


id: 192252176517c24aee3b5f23988fb1c0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1637
content:
"In an attribution study of the sea-level contributions of glaciers, \r Marzeion et al. (2014) found that between 1991 and 2010, the \r anthropogenic fraction of global glacier mass loss was 69 +- 24% (see \r also Section 3.4.3.1). Slangen et al. (2016) considered all quantifiable \r components of the GMSL budget and showed that anthropogenically \r forced changes account for 69 +- 31% of the observed sea level rise \r over the period 1970 to 2005, whereas natural forcings combined \r with internal variability have a much smaller effect - only contributing \r 9 +- 18% of the change over the same period. These studies indicate \r that about 70% of the combined change in glaciers, ice-sheet surface \r mass balance and thermal expansion since 1970 can be attributed \r to anthropogenic forcing, and that this percentage has increased \r over the course of the 20th century. Detection studies on GMSL \r change in the 20th century (Becker et al., 2014; Dangendorf et al., \r [...]"


id: 1ae13b16e03b254e411c9af523a7d451
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1927
content:
"[...] 2015) found that observed total GMSL change in the 20th century \r was inconsistent with internal variability. Dangendorf et al. (2015) \r determined that for 1900 to 2011 at least 45% of GMSL change is \r human-induced. A study that developed a semi-empirical model to \r link sea-level change to observed GMST change concluded that at \r least 41% of the 20th century sea-level rise would not have happened \r in the absence of the century's increasing GMST and that there was "


id: 7b241454a1b5d2a930bc9a1ac6312106
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 18861
content:
"general features of the AMOC mean state reasonably well, but there is \r a large spread in the latitude and depth of the maximum overturning, \r and the maximum AMOC strength (Figure 3.30a).\r The short length of the observed time-series (RAPID has measured the \r AMOC since 2004), sparse observations, observational uncertainties \r (Sinha et al., 2018), as well as significant observed variability on \r interannual and longer time scales, makes comparison with modelled \r AMOC variability challenging. RAPID observations show that the \r overturning at 26degN was 2.9 Sv weaker in the multi-year average of \r 2008-2012 relative to 2004-2008 and 2.5 Sv weaker in 2012-2017 \r relative to 2004-2008 (Smeed et al., 2014, 2018) (see also Section \r 2.3.3.4.1). As expected, this weakening was accompanied by \r a  significant reduction in northward heat transport (Bryden et al., \r 2020). CMIP5 and CMIP6 models produce a forced weakening of the \r AMOC over the 2012-2017 period relative to 2004-2008, but at \r [...]"


id: 21e838ef0c04f8ec7223b9f8dda0074d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3190
content:
"[...] 26degN the multi-model mean response is substantially weaker than \r the observed AMOC decline over the same period. The discrepancy \r between the modelled multi-model mean (i.e., the forced response) \r and the RAPID observed AMOC changes has led studies to suggest \r that the observed weakening over 2004-2017 is largely due to \r internal variability (Yan et al., 2018). However, comparison of \r observed RAPID AMOC variability with modelled variability also \r reveals that most CMIP5 models appear to underestimate the \r interannual and decadal time scale AMOC variability (Roberts et al., \r 2014; Yan et al., 2018), and, although the overall variance is larger \r in CMIP6 than in CMIP5, similar results are found analysing the \r CMIP6 models (Figure 3.30b,c). It is currently unknown why most \r models underestimate this AMOC variability, or whether they are \r underestimating the internal or externally forced components. \r This underestimation of AMOC variability may also have potential \r [...]"


id: a873d4d21a8648a92c87ef30d3899572
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 26845
content:
"[...] implications for detection and attribution, the relationship between \r AMOC and AMV (see Section 3.7.7), and near-term predictions. \r There is also emerging evidence, based on analysis of freshwater \r transports, that the AMOC in CMIP5-era models is too stable, largely \r due to systematic biases in ocean salinity (W. Liu et al., 2017; \r Mecking et al., 2017). Such a  systematic bias may potentially be \r linked with the underestimation of both simulated AMOC internal \r variability through eddy-mean flow interactions that are poorly \r represented in standard CMIP-class model resolution (Leroux et al., \r 2018), and externally forced change."


id: f96fc4ed8322f18c8ccaf3da722a4568
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41248
content:
"Despite the additional six years or so of observations since AR5, the \r evaluation of the AMOC in models continues to be severely hampered \r by  the geographically sparse and temporally short observational \r record. The longest continuous observational estimates of the AMOC \r are based on measurements taken at 26degN by the RAPID-MOCHA \r array (Smeed et al., 2018). Basic evaluation of the AMOC at 26degN \r shows that the CMIP5 and CMIP6 multi-model mean overturning \r strength is comparable with RAPID (Reintges et al., 2017; Weijer et al., \r 2020), but the model range is large (12-29 sverdrups (Sv)) for CMIP5 \r (Zhang and Wang, 2013); and 10-31 Sv for CMIP6 (Weijer et al., 2020) \r (Figure 3.30a). It is noted that deviations of AMOC strength in CMIP5 \r models have been related to global-scale sea surface temperature \r biases (C. Wang et al., 2014). Both coupled and ocean-only models \r also underestimate the depth of the AMOC cell (Danabasoglu et al., \r [...]"


id: af89c68eae704484217c7e459b2fe0c9
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 28116
content:
"[...] 2014; Weijer et al., 2020; Figure 3.30a). Paleo-climatic evidence has \r also raised questions regarding the accuracy of the representation of \r the strength and depth of the modelled AMOC during past periods \r (Otto-Bliesner et al., 2007; Muglia and Schmittner, 2015). Overall, \r however, both the CMIP5 and CMIP6 model ensembles simulate the "


id: beddcff06d3c837a17ca92909f8aaac5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30846
content:
"As reported in Section 2.3.3.4.1, estimates of AMOC since at least \r 1950, which are generated from observed surface temperatures \r or sea surface height, suggest the AMOC weakened through the \r 20th century (low confidence) (Ezer et al., 2013; Caesar et al., 2018). \r Over the same period, the CMIP5 multi-model mean showed no \r significant net forced response in AMOC (Cheng et al., 2013). However, \r a significant forced change is simulated in the CMIP6 multi-model \r mean, where a clear increase of the AMOC is seen over the 1940-1985 \r period (Figure 3.30e; Menary et al., 2020). Although there is general \r agreement that the influence of greenhouse gases acts to a weaken \r the modelled AMOC (Delworth and Dixon, 2006; Caesar et al., 2018), \r changes in solar, volcanic and anthropogenic aerosol emissions can \r lead to temporary changes in AMOC on decadal- to multi-decadal \r time scales (Delworth and Dixon, 2006; Menary et al., 2013; Menary \r [...]"


id: ce2d26ccb2439f2a5b30d2c2fedbae26
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33608
content:
"Despite notable improvements of CMIP6 models compared to CMIP5 \r models, inherent limitations in the representation of important \r processes at play in the Southern Ocean's horizontal and vertical \r circulation remain (Section 9.2.3.2). For instance, Southern Ocean \r mesoscale eddies are largely parameterized in the current generation \r of climate models and, despite their small spatial scales, they are \r a key element for establishing the ACC and upper overturning cell, as \r well as for their future evolution under changing atmospheric forcing \r (Kuhlbrodt et al., 2012; Downes and Hogg, 2013; Gent, 2016; Downes \r et al., 2018; Poulsen et al., 2018). The absence of ice-sheet coupling \r in the CMIP6 model suite is another important limitation, as basal \r meltwater and calving can influence the circulation, particularly the \r lower cell of the Southern Ocean (Bronselaer et al., 2018; Golledge \r et al., 2019; Lago and England, 2019; Jeong et al., 2020; Moorman \r [...]"


id: bb467f66f9c9ceea99f4a13fc77792cc
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30168
content:
"[...] et al., 2020). We note that early development of global climate \r models with interactive ice-shelf cavities has begun and is showing \r potential to be developed (Jeong et al., 2020)."


id: 645c3d0d124bc9791c6ee7608f453869
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 14936
content:
"The modelled strength of the ACC clearly improved from CMIP3, in \r which the models tended to underestimate the strength of the ACC, \r to CMIP5 (Meijers et al., 2012). This improvement in the realism of \r ACC strength continues from CMIP5 to CMIP6, with the modelled ACC \r strength converging toward the magnitude of observed estimates of \r net flow through the Drake Passage (Beadling et al., 2020). There is, \r however, a small number of models that still display an ACC that \r is much weaker than that observed, while several models also display \r much more pronounced ACC decadal variability than that observed \r (Beadling et al., 2020). The increased realism of the ACC was at least \r partly related to noted improvements in all metrics of the Southern \r Ocean's surface wind stress forcing (Beadling et al., 2020). The most \r notable wind stress forcing improvements were found in the strength \r and the latitudinal position of the zonally-averaged westerly wind \r [...]"


id: 48ca2fad866a58ca6123037263e39625
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10019
content:
"[...] stress maximum (Beadling et al., 2020; Bracegirdle et al., 2020). \r While the two-cell structure of the overturning circulation appears \r to be well captured by CMIP5 models (Sallee et al., 2013; Russell \r et al., 2018), they tend to underestimate the intensity of the lower \r cell overturning, and overestimate the intensity of the upper cell "


id: dcb32533a0f5ab9b10a19d0363a67fe5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 36088
content:
"The AR5 did not make attribution statements on changes in global \r carbon sinks. The IPCC Special Report on Climate Change and Land \r (SRCCL) assessed with high confidence that global vegetation \r photosynthetic activity has increased over the last 2-3 decades \r (Jia et al., 2019). That increase was attributed to direct land use \r and management changes, as well as to CO2 fertilization, nitrogen \r deposition, increased diffuse radiation and climate change (high \r confidence). The AR5 assessed with high confidence that CMIP5 \r Earth System Models (ESMs) simulate the global mean land and \r ocean carbon sinks within the range of observation-based estimates \r (Flato et al., 2013). The IPCC SRCCL, however, noted the remaining \r shortcomings of carbon cycle schemes in ESMs (Jia et al., 2019), \r which for example do not properly incorporate thermal responses of \r respiration and photosynthesis, and frequently omit representations \r [...]"


id: 2127383834c8a37d08a02dc1024dc3a1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3054
content:
"[...] of permafrost thaw (Comyn-Platt et al., 2018), the nitrogen cycle \r (R.Q. Thomas et al., 2015) and its influence on vegetation dynamics \r (Jeffers et al., 2015), the phosphorus cycle (Fleischer et al., 2019), and \r accurate implications of carbon store changes for a range of land use "


id: 8a8dbe0a398175416e78ea855ddea35d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21552
content:
"constrain specific parts of the terrestrial carbon cycle such as plant \r respiration (Huntingford et al., 2017). Figure 3.31 compares historical \r emissions-driven CMIP6 simulations of global mean atmospheric CO2\r concentration and net ocean and land carbon fluxes to the assessed \r CO2 concentration and fluxes from the Global Carbon Project \r (Friedlingstein et al., 2019). For 2014, the CMIP6 models simulate \r a range of CO2 concentrations centred around the observed value of \r 397 ppmv, with a range of 381 to 412 ppmv. GSAT anomalies simulated \r over the historical period are very similar in models that simulate or \r prescribe changes in atmospheric CO2 concentrations (Figures 3.31b \r and 3.4a). Most models simulate realistic temporal evolution of the \r global net ocean and land carbon fluxes, although model spread is \r larger over land (Figure 3.31c,d; see also Sections 3.6.2 and 5.4.5.2, \r and Figure  5.24). Although literature published soon after AR5 \r [...]"


id: 9f0ccbfec459063835839e682a1fe7b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25207
content:
"[...] highlighted the importance of representing nitrogen limitation on \r plant growth (Peng and Dan, 2015; R.Q. Thomas et al., 2015), more \r recent studies note that models without nitrogen limitation can still \r be consistent with the latest estimates of historical carbon cycle \r changes (Arora et al., 2020; Meyerholt et al., 2020). Uncertainties \r in the photosynthetic response to atmospheric CO2 concentrations \r at global scales, shifts in carbon allocation and turnover, land-use \r change (Hoffman et al., 2014; Wieder et al., 2019), and water \r limitation are also important influences on land carbon fluxes."


id: 83fab86414ff44d96f37fc895172ee2c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20373
content:
"This section considers three main large-scale indicators of climate \r change relevant to the terrestrial carbon cycle: atmospheric \r CO2 concentration, atmosphere-land CO2 fluxes, and leaf area \r index. These indicators were chosen because they have been \r the target of attribution studies. Other indicators, like land use \r and management, and wildfires, relate to human influence \r but are discussed in Chapter  5. Chapter  7 discusses energetic \r consequences of changes in the terrestrial carbon cycle in \r Section 7.4.2.5.2. CMIP5 and CMIP6 ESMs are most often run \r with prescribed observed historical changes in atmospheric \r CO2 concentration and diagnose CO2 emissions consistent \r with these. Such calculations require that the models simulate \r realistic changes in the terrestrial carbon cycle over the historical \r period, as changes to land carbon stores will influence the size \r of CO2 emissions consistent with prescribed CO2 pathways, and \r [...]"


id: a8b4b12576b44c9a20071f807655d698
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 26887
content:
"[...] associated remaining carbon budgets (Section 5.5). Such testing of \r existing models is needed while also recognising there are process \r representations still requiring inclusion."


id: e1895a288484bd0b4e0c545734617944
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 36951
content:
"importance of CO2 fertilization is decreased by drought stress, but \r also found only a small contribution from agricultural and land use \r changes. However, many global models do not include nitrogen \r fertilization, changes to crop cultivars or irrigation effects, with the \r latter associated with deficiencies in simulated terrestrial water \r cycling (H. Yang et al., 2018). All these factors influence the capability \r of models to simulate accurately the seasonal cycle in atmosphere\u0002land CO2 exchanges. Model comparisons to the atmospheric CO2\r concentration record for Barrow, Alaska, suggest that models \r underestimate current levels of carbon fixation (Winkler et al., \r 2019) and have deficiencies in their phenological representation of \r greenness levels, particularly for autumn (Z. Li et al., 2018). Based on \r these studies and noting the uncertainty in the processes ultimately \r driving changes in atmospheric CO2 seasonal cycles (Section 5.2.1.4), \r [...]"


id: 34a76ef0d1bba32ef38fa49fb6b8117e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6499
content:
"[...] we assess with medium confidence that fertilization by anthropogenic \r increases in atmospheric CO2 concentrations is the main driver of the \r increase in the amplitude of the seasonal cycle of atmospheric CO2."


id: 78dc856deec1ef7ecb3887330c92c14c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 18451
content:
"All models and observational estimates agree that interannual \r variability in net CO2 uptake is much larger over land than over \r the ocean. Studies demonstrate that regional variations in both \r the trends and the yearly strength of the terrestrial carbon sink are \r considerable. Land carbon uptake is dominated by the extratropical \r northern latitudes (see also Section 5.4.5.3 and Figure  5.25; Ciais \r et al., 2019) because the tropics may have become a net source of \r carbon (Baccini et al., 2017). At local to regional scales, the dominant \r driver of yearly sink strength variations is water availability, but at \r continental to global scales, temperature anomalies are the dominant \r driver (Section 5.2.1.4.2; Jung et al., 2017). The major role of levels \r of water stored in the ground in influencing land-atmosphere CO2\r exchange has also been confirmed through simultaneous analysis \r of satellite gravimetry and atmospheric CO2 levels (Humphrey et al., \r [...]"


id: 7020f449ebbcf984eabc445d40fbf986
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 16977
content:
"[...] 2018). When considered globally, simulated land and ocean carbon \r sinks fall within the range of observation-based estimates with high \r confidence. But there is also high confidence that that apparent \r success arises for the wrong reasons, as models underestimate the \r Northern Hemisphere carbon sink, as discussed in Section 5.4.5.3."


id: 6686d70cb71dec6bc0a20e2ea1644c22
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 15296
content:
"Detection and attribution methods have been applied to leaf area \r index, which represents 'greenness' and general photosynthetic \r productivity (see Section 2.3.4.3). Nitrogen deposition and land \r cover change trends remain small compared to variability, so \r attributing changes in leaf area index to those processes is difficult. \r Using three satellite products and ten land models, Zhu et al. (2016) \r found increases in leaf area index (greening) over 25-50% of global \r vegetated areas, and they attributed 70% of this greening to CO2\r fertilization, although they found that land use change can dominate \r regionally. This is consistent with the attribution study of observed \r greening of Mao et al. (2016), and with Mao et al. (2013) who found \r that CO2 fertilization was the dominant cause of enhanced vegetation \r growth, with latitudinal changes in leaf area index explained by the \r larger land surface warming in the Northern Hemisphere. These \r [...]"


id: e1c650f6c7c741efa91837c8c40f8a10
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37000
content:
"[...] conclusions are also consistent with those of Zhu et al. (2017), \r who found a dominant role for CO2 fertilization in driving leaf area \r index changes in an attribution study in which land models were \r first weighted by performance. However, Chen et al. (2019) has \r challenged these results by showing that greening in India and China \r was driven by land-use change."


id: 42b2be6debbc340101e4bca6ad4f376d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8929
content:
"The seasonal cycle in atmospheric CO2, which is driven by the \r drawdown of carbon by photosynthesis on land during the summer \r and release by respiration during the winter, has increased in \r amplitude since the start of systematic monitoring (Figure 3.32; see \r also Section 2.3.4.1). This trend, which is larger at higher latitudes of \r the Northern Hemisphere, was first reported by Keeling et al. (1996) \r and has continued. Changes in vegetation productivity have also \r been observed, as well as longer growing seasons (Park et al., 2016). \r However, a slow down of the increasing trend has been noted, linked to \r a slow down of both vegetation greening and growing-season length \r increases (Buermann et al., 2018; Z. Li et al., 2018; K. Wang et al., \r 2020). Figure 3.32 shows that CMIP6 terrestrial carbon cycle models \r partially capture the increasing amplitude of the seasonal cycle of the \r land carbon sink, also seen in observational reconstructions. However, \r [...]"


id: 4c18f3ba3bcea9f750b7e57e9dd05846
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10646
content:
"[...] the identification of the human influence that contributes most \r strongly to these changes in the seasonal cycle is debated."


id: 3535a2bb45cdb4e3a72f61c17bc7a162
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6609
content:
"Proposed causes of the trend in the amplitude of the seasonal cycle \r of CO2, and its amplification at higher latitudes, include increases in \r the summer productivity and/or increases in the magnitude of winter \r respiration of northern ecosystems (Barichivich et al., 2013; Graven \r et al., 2013; Forkel et al., 2016; Wenzel et al., 2016), increases in \r productivity throughout the Northern Hemisphere by CO2 fertilization, \r and increases in the productivity of agricultural crops in northern \r mid-latitudes (Gray et al., 2014; Zeng et al., 2014). Recent studies \r have attempted to quantify the different contributions by comparing \r atmospheric CO2 observations with ensembles of land surface \r model simulations. Piao et al. (2017) found that CO2 fertilization of \r photosynthesis is the main driver of the increase in the amplitude \r of the seasonal cycle of atmospheric CO2 but noted that climate \r change drives the latitudinal differences in that increase. North of \r [...]"


id: 9a68cc12dd84853d1d0e2c5a231a74eb
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 24408
content:
"[...] 40degN, Bastos et al. (2019) also found CO2 fertilization to be the most \r likely driver, with warming at northern high latitudes contributing \r a decrease in amplitude, in contrast to earlier conclusions (Graven \r et al., 2013; Forkel et al., 2016), and agricultural and land use \r changes making only a small contribution. For temperate regions \r of the Northern Hemisphere, K. Wang et al. (2020) found that the "


id: acc8dcc94adff2f15fcf7536bf2542be
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 27639
content:
"The AR5 concluded that oxygen concentrations have decreased in \r the open ocean since 1960 and such decreases can be attributed \r in part to human influence with medium confidence. The decrease \r in ocean oxygen content in the upper 1000 m, between 1970 and \r 2010, is further confirmed in SROCC (medium confidence), with \r the oxygen minimum zone expanding in volume (see also Section \r 5.3.3.2). Observed oxygen declines over the last several decades \r (Stendardo and Gruber, 2012; Stramma et al., 2012; Schmidtko et al., \r 2017) match model estimates in the surface ocean (Oschlies et al., \r 2017) but are much larger than model derived estimates in the \r interior (Bopp et al., 2013; Cocco et al., 2013). Some of this difference \r has been interpreted as due to a lack of representation of coastal \r eutrophication in these models (Breitburg et al., 2018), but much of \r it remains unexplained. This disparity is particularly apparent in the \r [...]"


id: ca109b1446ae1acb8422c3a0c016e6dd
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 32876
content:
"[...] eastern Pacific oxygen minimum zone, where some CMIP5 models \r showed increasing trends whereas observations show a strong \r decrease (Cabre et al., 2015). However, proxy reconstructions suggest \r that over the last century the ocean may have in fact undergone \r increases in oxygen in the most oxygen poor regions (Deutsch et al., \r 2014). As discussed in Section 5.3.1, ocean oxygen went through \r wide oscillations on multi-centennial time scales through the last \r deglaciation, with abrupt warming resulting in loss of oxygen in \r subsurface waters of the North Pacific (Praetorius et al., 2015). The \r global upper ocean oxygen inventory is negatively correlated with \r ocean heat content with a regression coefficient comparable to that \r found in ocean models (Ito et al., 2017). Variability and trends in the \r observed upper ocean oxygen concentration are mainly driven by \r the apparent oxygen utilization component with small contributions \r [...]"


id: f5ce35abdb7144ddfa2a2dadabed18a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 40607
content:
"[...] from oxygen solubility, suggesting that changing ocean circulation, \r mixing, and/or biochemical processes, rather than thermally \r induced solubility effects may be the main drivers of observed "


id: d964a7e49bdfd78801284de1775719e0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35547
content:
"As one of the most commonly observed surface parameters, the \r partial pressure of CO2 has been the topic of considerable detection \r and attribution work. In North Atlantic subtropical and equatorial \r biomes, warming has been shown to be a significant and persistent \r contributor to the observed increase in the partial pressure of \r CO2 since the mid-2000s with long-term warming leading to \r a reduction in ocean carbon uptake (Fay and McKinley, 2013), and \r with both the partial pressure of CO2 and associated carbon uptake \r demonstrating strong predictability as a function of interannual to \r decadal climate state (H. Li et al., 2016; Li and Ilyina, 2018). In \r the Southern Ocean however, detection and attribution of surface \r trends in the partial pressure of CO2 has proven more elusive and \r dependent on methodology, with some studies suggesting that \r Southern Ocean carbon uptake slowed from about 1990 to 2006 \r and subsequently strengthened from 2007 to 2010 (Lovenduski \r [...]"


id: 550fad1790985a857524744085692fac
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12239
content:
"[...] et al., 2008; Fay et al., 2014; Ritter et al., 2017). Other studies \r have suggested that poor representation of the seasonal cycle in \r the Southern Ocean may confound the models' ability to represent \r changes in the partial pressure of CO2 in the Southern Ocean (Nevison \r et al., 2016; Mongwe et al., 2018)."


id: 240bf4b5d15e59315ae87e1bf49d3bb2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3521
content:
"Ocean acidification is one of the most detectible metrics of \r environmental change and was well covered in AR5, in which it \r was assessed that the uptake of anthropogenic CO2 had very likely\r resulted in acidification of surface waters (Bindoff et al., 2013). \r Since then, observations and simulations of multi-decadal trends in \r surface carbon chemistry have increased in robustness. The evidence \r on ocean pH decline had further strengthened in SROCC with good \r agreement found between CMIP5 models and observations and an \r assessment that the ocean was continuing to acidify in response to \r ongoing carbon uptake (Bindoff et al., 2019). An observed decrease \r in global surface open ocean pH is assessed in Section 2.3.3.5 to \r be virtually certain to have occurred with a rate of 0.003-0.026 per \r decade for the past 40 years. The ocean acidification has occurred not \r only in the surface layer but also in the interior of the ocean (Sections \r [...]"


id: 64a4b865361f314f6c0ee0c310404d3a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 14990
content:
"[...] 2.3.3.5 and 5.3.3). Rates have been observed to be between -0.015 \r and -0.020 per decade in mode and intermediate waters of the North \r Atlantic through the combined effect of increased anthropogenic and \r remineralized carbon (Rios et al., 2015) and acidification has been \r observed down to 3000 m in the deep water formation regions "


id: 9f2a7104543ad0c3d9932718636fb4f6
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25224
content:
"New studies since AR5 continue to find that CMIP5 models reproduce \r the spatial structure and magnitude of the NAM reasonably well (Lee \r and Black, 2013; Zuo et al., 2013; Davini and Cagnazzo, 2014; Ying \r et al., 2014; Ning and Bradley, 2016; Deser et al., 2017b; Gong et al., \r 2017) although the North Pacific SLP anomalies remain generally too \r strong (Zuo et al., 2013; Gong et al., 2017) and the subtropical North \r Atlantic lobe of SLP anomalies conversely too weak (Ning and Bradley, \r 2016) in many models. Such overall biases noted in both CMIP3 \r and CMIP5 (Davini and Cagnazzo, 2014) persist in CMIP6 historical \r simulations, even though the multi-model multi-member ensemble \r mean spatial correlation between modelled and observed NAM is \r slightly higher (Figure 3.33a,d,g). Regarding the NAO, the majority \r of CMIP5 models very successfully simulate its spatial structure (Lee \r et al., 2019) and its associations with extratropical jet, storm track \r [...]"


id: e6fea9609098b94a22273a62cf3a8dd3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37933
content:
"[...] and blocking variations over a broad North-Atlantic/Europe domain \r (Davini and Cagnazzo, 2014) and over land through teleconnections \r (Volpi et al., 2020). The good performance of the models is confirmed \r in CMIP6 with a marginal improvement of the averaged observation\u0002model spatial correlation (Figure  3.33b,e,h) and better skill based \r on other evaluation metrics (Fasullo et al., 2020). The slight \r underestimation of the SLP anomalies related to the NAO centres \r of actions over the Azores and Greenland-Iceland-Norwegian Seas \r remain unchanged compared to CMIP5."


id: 52726b719cd74ca477c0ad8c9e4eb888
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11787
content:
"CMIP5 models with a model top within the stratosphere seriously \r underestimate the amplitude of the variability of the wintertime \r NAM expression in the stratosphere, in contrast to CMIP5 models \r which extend well above the stratopause (Lee and Black, 2015). \r However, even in the latter models, the stratospheric NAM events, \r and their downward influence on the troposphere, are insufficiently \r persistent (Charlton-Perez et al., 2013; Lee and Black, 2015). \r Increased vertical resolution does not show any significant added \r value in reproducing the structure and magnitude of the tropospheric \r NAM (Lee and Black, 2013) nor in the NAO predictability as assessed \r in a seasonal prediction context with a multi-model approach (Butler \r et al., 2016). On the other hand, there is mounting evidence that \r a correct representation of the Quasi Biennal Oscillation, extratropical \r stratospheric dynamics (the polar vortex and sudden stratospheric \r [...]"


id: 90bf4cadc520ff152d770ff552858861
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 22670
content:
"[...] warmings), and related troposphere-stratosphere coupling, as well as \r their interplay with ENSO, are important for NAO/NAM timing (Scaife \r et al., 2016; Karpechko et al., 2017; Domeisen, 2019; Domeisen et al., \r 2019), in spite of underestimated troposphere-stratosphere coupling \r found in models compared to observations (O'Reilly et al., 2019b)."


id: d127bc0614b1ca7587fe36b75cdb3a27
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 34146
content:
"not statistically significant, the sign of the multi-model mean forced \r response due to natural forcing is consistent with the observed \r reduction of solar activity since the 1980s (Section 2.2.1) whose \r influence would have favoured the negative phase of wintertime \r NAM/NAO based on the fingerprint of the nearly periodical 11-year \r solar cycle extracted from models (Scaife et al., 2013; Andrews et al., \r 2015; Thieblemont et al., 2015) or observations (Gray et al., 2016; \r Ludecke et al., 2020). But such an NAO response to solar forcing \r remains highly uncertain and controversial, being contradicted by \r longer proxy records over the last millennium (Sjolte et al., 2018) \r and modelling evidence (Gillett and Fyfe, 2013; Chiodo et al., \r 2019). For all seasons and for all individual forcings, uncertainties \r remain in the estimation of the forced response in the NAM trend as \r evidenced by considerable model spread (Figure 3.34a) and because \r [...]"


id: e2f6d69abc48bc1b50f945f71ef02457
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37226
content:
"Despite new efforts since AR5 to reconstruct the NAO beyond the \r instrumental record, it is still very challenging to assess the role \r of external forcings in the apparent multi-decadal to centennial \r variability present throughout the last millennium. Large uncertainties \r remain in the reconstructed NAO index that are sensitive to the \r types of proxies and statistical methods (Trouet et al., 2012; Ortega \r et al., 2015; Anchukaitis et al., 2019; Cook et al., 2019; Hernandez \r et al., 2020; Michel et al., 2020) and reconstructed NAO variations \r are often not reproduced using pseudo-proxy approaches in models \r (Lehner et al., 2012; Landrum et al., 2013). At low frequency, it \r remains challenging to evaluate if the observed or reconstructed \r signal corresponds to an actual change in the NAO intraseasonal to \r interannual intrinsic properties or rather to a change in the mean \r background atmospheric circulation changes projecting on a specific \r [...]"


id: 9a255e0b2518d2531d09b1f4f1735483
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 24357
content:
"[...] phase of the mode. Consequently, conflicting results emerge in the \r attribution of reconstructed long-term variations in the NAO to solar \r forcing, whose influence thus remains controversial (Gomez-Navarro \r and Zorita, 2013; Moffa-Sanchez et al., 2014; Ortega et al., 2015; \r Ait Brahim et al., 2018; Sjolte et al., 2018; Xu et al., 2018). Influences \r from major volcanic eruptions appear to be more robust (Ortega et al., \r 2015; Swingedouw et al., 2017) even if some modelling experiments \r question the amplitude of the response, which mostly projects on \r the positive phase of the NAM/NAO (Bittner et al., 2016). The \r forced response is dependent on the strength, seasonal timing and \r location of the eruption but may also depend on the mean climate \r background state (Zanchettin et al., 2013) and/or the phases of the \r main modes of decadal variability such as the AMV (Section 3.7.7; \r Menegoz et al., 2018)."


id: 56d26bb79d1347f5f06a5fd76ce1acd9
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12524
content:
"Additional research has shown that CMIP5 models reproduce \r the spatial structure of the SAM well, but tend to overestimate its \r variability in austral summer at interannual time scales, although this \r variability is within the observational uncertainty (Figure  3.33c,f,i; \r Zheng et al., 2013; Schenzinger and Osprey, 2015). This is related \r to the models' tendency to simulate slightly more persistent SAM \r anomalies in summer compared to reanalyses (Schenzinger and \r Osprey, 2015; Bracegirdle et al., 2020). This may be due in part to \r too weak a  negative feedback from tropospheric planetary waves \r (Simpson et al., 2013). CMIP6 models show improved performance \r in reproducing the spatial structure and interannual variance of the \r SAM in summer based on Lee et al. (2019) diagnostics (Figure 3.33i), \r with a better match of its trend with reanalyses over 1979-2014 \r (Figure 3.33l), more realistic persistence and improved positioning of \r [...]"


id: d8dacec47ea6bae3ecee3698511235cc
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35442
content:
"[...] the westerly jet, which in CMIP5 models on average is located too far \r equatorward (Bracegirdle et al., 2020; Grose et al., 2020). In CMIP5, it \r is also found that models which extend above the stratopause tend \r to simulate stronger summertime trends in the late 20th century \r than their counterparts with tops within the stratosphere (Rea \r et al., 2018; Son et al., 2018), though other differences between \r these sets of models, such as additional physical processes operating \r in the stratosphere or interactive ozone chemistry, may have also \r affected these results (Gillett et al., 2003a; Sigmond et al., 2008; Rea \r et al., 2018). At the surface, Ogawa et al. (2015) demonstrate with an \r atmospheric model the importance of sharp mid-latitude SST gradients \r for stratospheric ozone depletion to affect the SAM in summer. These \r studies imply that the well resolved stratosphere combined with finer \r ocean horizontal resolution has contributed to the stronger simulated \r [...]"


id: d6ead03a060f53ab4cdd7c4f308902a2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35116
content:
"to internal variability could be even lower (Figure 3.34b). Although \r paleo-reconstructions of the SAM index are uncertain and vary in \r terms of long-term trends (Section 2.4.1.2), new reconstructions \r show that the 60-year summertime SAM trend since the mid-20th \r century is outside the 5th-95th percentile range of the trends in the \r pre-industrial variability, which matches the trend range of CMIP5 \r pre-industrial control simulations well (Datwyler et al., 2018).\r In general agreement with AR5, new research continues to indicate \r that both stratospheric ozone depletion and increasing greenhouse \r gases have contributed to the trend of the SAM during austral summer \r toward its positive phase in recent decades (Solomon and Polvani, \r 2016), with the ozone depletion influence dominating (Gerber and \r Son, 2014; Son et al., 2018). In CMIP6 historical simulations there are \r significant positive SAM trends over the 1979-2019 period in austral \r [...]"


id: 18deb066a55da78054786b6262822394
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1594
content:
"[...] summer, although the contribution from ozone forcing evaluated \r with the four available models is not significant (Figure 3.34b). Three \r of these models share the same standard prescribed ozone forcing \r and produce significantly positive SAM trends over an extended \r period (1957-2019). The fourth model, MRI-ESM2-0, has the option \r of interactive ozone chemistry. Its ozone-only experiment is forced \r by prescribed ozone derived from its own historical simulations \r and produces a negative SAM trend associated with weak ozone \r depletion (Morgenstern et al., 2020). Morgenstern et al. (2014) and \r Morgenstern (2021) find an indirect influence of greenhouse gases \r on the SAM via induced ozone changes in coupled chemistry-climate \r simulations, which differ from the prescribed ozone simulations shown \r in Figure 3.34b. Since about 1997, the effective abundance of ozone\u0002depleting halogen has been decreasing in the stratosphere (WMO, \r [...]"


id: 6a84a9bdd9d19b73471a3f93cb6a748
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 16025
content:
"[...] 2018), leading to a stabilization or even a reversal of stratospheric \r ozone depletion (Sections 2.2.5.2 and 6.3.2.2). The ozone stabilization \r and slight recovery since about 2000 may have caused a pause in the \r summertime SAM trend (Figure 3.34c; Saggioro and Shepherd, 2019; \r Banerjee et al., 2020), although some influence from internal variability \r cannot be ruled out. While some studies find an anthropogenic aerosol \r influence on the summertime SAM (Gillett et al., 2013; Rotstayn, \r 2013), recent studies with larger multi-model ensembles find that \r this effect is not robust (Steptoe et al., 2016; Choi et al., 2019), \r consistent with CMIP6 single forcing ensembles (Figure 3.34). In the \r CMIP5 simulations, volcanic stratospheric aerosol has a  significant \r weakening effect on the SAM in autumn and winter (Cross-Chapter \r Box  4.1; Gillett and Fyfe, 2013), but there is no evidence that this \r effect leads to a significant multi-decadal trend since the late 20th \r [...]"


id: c6284191cca16586d5049de522f0f42e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 32173
content:
"[...] century. Beyond external forcing, Fogt et al. (2017) show a significant \r association of tropical SST variability with the summertime SAM \r trend since the mid-20th century in agreement with Lim et al. (2016), \r who, however, demonstrate that such a teleconnection between the \r summertime SAM and El Nino-Southern Oscillation (Annex IV.2.3), \r found in observations, is missing in many CMIP5 models."


id: dc5b067423ab63da999171f1eb5d568a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 36040
content:
"Observations show strong multi-decadal modulation of ENSO variance \r throughout the 20th century, with the most recent period displaying \r larger variability while the mid-century displayed relatively low ENSO \r variability (Figure 2.36; Li et al., 2013; McGregor et al., 2013; Hope \r et al., 2017). As assessed in Section 2.4.2, ENSO amplitude since 1950 \r is higher than over the pre-industrial period from 1850 as far back \r as 1400 (medium confidence), but there is low confidence that it is \r higher than the variability over periods prior to 1400. This reported \r variance increase suggests that external forcing plays a role in the \r ENSO variance changes (Hope et al., 2017). However, large ensembles \r of single model or multiple model simulations do not find strong trends \r in ENSO variability over the historical period, suggesting that external \r forcing has not yet modulated ENSO variability with a magnitude that \r [...]"


id: 4e69158dc660d34029a09294e3632e50
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11070
content:
"[...] exceeds the range of internal variability (Hope et al., 2017; Maher et al., \r 2018b; Stevenson et al., 2019). This is consistent with the Chapter 2 \r assessment that there is no clear evidence for a recent sustained shift \r in ENSO beyond the range of variability on decadal to millennial time \r scales (Section 2.4.2). CMIP5 and CMIP6 models show a decrease in \r ENSO variance in the mid-Holocene (Brown et al., 2020), though not to \r the extent seen in paleo-proxy records (Emile-Geay et al., 2016). This \r suggests that both modelled and observed ENSO respond to changes \r in external forcing, but not necessarily in the same manner."


id: 16a4dc5b2d35d92d25b6a6381c687770
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1197
content:
"Most CMIP5 and CMIP6 models are found to represent the general \r structure of observed SST anomalies during ENSO events well (Kim \r and Yu, 2012; Taschetto et al., 2014; Brown et al., 2020; Grose et al., \r 2020). However, the majority of CMIP5 models display SST anomalies \r that: i) extend too far to the west (Taschetto et al., 2014; Capotondi \r et al., 2015); and ii) have meridional widths that are too narrow (Zhang \r and Jin, 2012) compared to the observations. CMIP6 models display \r a statistically significant improvement in the longitudinal representation \r of ENSO SST anomalies relative to CMIP5 models (Planton et al., 2021), \r however, systematic biases in the zonal extent and meridional width \r remain in CMIP6 models (Fasullo et al., 2020; Planton et al., 2021). The \r ENSO phase asymmetry, where observed strong El Nino events are larger \r and have a shorter duration than strong La Nina events (Ohba and Ueda, \r [...]"


id: fbf1927ea0c44bb930a9b1df35243c0f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41686
content:
"[...] 2009; Frauen and Dommenget, 2010), is also under-represented in both \r CMIP5 and CMIP6 models (Zhang and Sun, 2014; Planton et al., 2021). \r In this instance, both CMIP5 and CMIP6 models typically display El Nino \r events that have a longer duration than those observed, La Nina events \r that have a similar duration to those observed, and there is very little \r asymmetry in the duration of El Nino and La Nina phases (Figure 3.36). \r Roberts et al. (2018) find an improvement in amplitude asymmetry \r in a HighResMIP model, but the under-representation remains."


id: a05113865d04859565e9ec7948424e89
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25414
content:
"Central Pacific-type events (Yeh et al., 2009), but analysis since then \r suggests that this behaviour is (i) consistent with that expected from \r internal variability (Newman et al., 2011); and (ii) not apparent across \r the full CMIP5 ensemble of historical simulations (Taschetto et al., \r 2014). Analysis of single-model large ensembles suggests that changes \r to ENSO event type in response to historical radiative forcing are not \r significant (e.g., Stevenson et al., 2019). These same results, however, \r also suggest that multiple forcings can have significant influences \r on ENSO type and that the net response will depend on the accurate \r representation of the balance of these forcings (Stevenson et al., 2019).\r The climatic effects of ENSO outside the tropical Pacific largely arise \r through atmospheric teleconnections that are induced by ENSO-driven \r changes in deep tropical atmospheric convection and heating (Yeh \r [...]"


id: 74afedc2980ffe415572922fdb28ab7a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 17764
content:
"[...] et al., 2018). The teleconnections to higher latitudes are forced by \r waves that propagate into the extratropics (Hoskins and Karoly, \r 1981) and respectively excite the Pacific-North American pattern \r (Horel and Wallace, 1981) and Pacific-South American pattern (Karoly, \r 1989; Irving and Simmonds, 2016) in the Northern and Southern \r Hemispheres. Given the influence of these teleconnections on climate \r and extremes around the globe, it is important to understand how \r well they are reproduced in CMIP models. What has also become clear \r is that spatial correlations of ENSO's teleconnections calculated over \r relatively short periods (<100 years) may not be the most effective \r way to assess these relationships (Langenbrunner and Neelin, 2013; \r Perry et al., 2020). This is because the spatial patterns are significantly \r affected by internal atmospheric variability on relatively short time \r scales (<100 years; Batehup et al., 2015; Perry et al., 2020). However, \r [...]"


id: 619454df38b5c841c6cb7eda736c7fa0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 14462
content:
"[...] looking at simplified metrics like the agreement in the sign of the \r teleconnections (Langenbrunner and Neelin, 2013), regional average \r teleconnection strength over land (Perry et al., 2020), or a combination \r of both (Power and Delage, 2018) provides a more robust depiction \r of the teleconnection representation. Examining sign agreement for \r the teleconnection patterns, ensembles of CMIP5 AMIP simulations \r display broad spatial regions with high sign agreement with the \r observations, suggesting that the model ensemble is producing \r useful information regarding the teleconnected precipitation signal \r (Langenbrunner and Neelin, 2013). Looking at regional averages of \r CMIP5 historical simulations, Power and Delage (2018) show that \r the average coupled model teleconnection pattern reproduces the \r sign of the observed teleconnections in the majority of the 25 regions \r analysed. The sign agreement between the observed teleconnection \r [...]"


id: 1e2b6c8e2c278f54db1c673f56909d72
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 2505
content:
"[...] and the multi-model mean teleconnection remains strong in CMIP6 \r (18 out of 20 displayed regions; Figure 3.38), and the observed DJF \r (December-January-February) teleconnection strength falls within \r the modelled range in all of the displayed regions for temperature \r and precipitation. Note, however, that while there is broad agreement \r in ENSO teleconnections between CMIP6 models and observations \r during DJF (e.g., Fasullo et al., 2020), there are regions and seasons \r where the modelled teleconnection strength is outside the observed \r range (Chen et al., 2020).\r Most CMIP5 and CMIP6 models exhibit ENSO behaviour during the \r historical period that, to first order, is qualitatively similar to that of \r the observed ENSO. Many studies are now delving deeper into the \r models to understand if they are accurately producing the dynamics \r driving ENSO and its initiation (Jin et al., 2006; Bellenger et al., 2014; "


id: 95529fb42574b9b203fd3946cd1dc18b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23470
content:
"To conclude, ENSO representation in CMIP5 models displayed \r a significant improvement from the representation of ENSO variability \r in CMIP3 models, which displayed much more intermodel spread in \r standard deviation, and stronger biennial periodicity (Guilyardi et al., \r 2012; Flato et al., 2013). In general, there has been no large step \r change in the representation of ENSO between CMIP5 and CMIP6, \r however, CMIP6 models appear to better represent some key ENSO \r characteristics (e.g., Brown et al., 2020; Planton et al., 2021). The \r instrumental record and paleo-proxy evidence through the Holocene \r all suggest that ENSO can display considerable modulations in \r amplitude, pattern and period (see also Section 2.4.2). For the \r period since 1850, there is no clear evidence for a sustained shift \r in ENSO index beyond the range of internal variability. However, \r paleo-proxy evidence indicates with medium confidence that ENSO \r [...]"


id: b07b081eff2e8a02d620c6e9ddce5804
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 28286
content:
"[...] variability since 1950 is greater than at any time between 1400 and \r 1850 (Section 2.4.2). Coupled models display large changes of ENSO \r behaviour in the absence of external forcing changes, and little-to-no \r variance sensitivity to historical anthropogenic forcing. Thus, there is \r low confidence that anthropogenic forcing has led to the changes of \r ENSO variability inferred from paleo-proxy evidence."


id: ad485d970caa9c780d7a8b20636354
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 27727
content:
"NAO/NAM in winter 2019-2020 assessed over the instrumental \r era has been accordingly linked to the record IOD event of autumn \r 2019 (Hardiman et al., 2020), which has been associated with the \r devastating record fire season in Australia (Wang and Cai, 2020).\r The observed Indian Ocean basin-average SST increase on multi-decadal \r and centennial time scales is well represented by CMIP5 historical \r simulations, and has been attributed to the effects of greenhouse gases \r offset in part by the effects of anthropogenic aerosols mainly through \r aerosol-cloud interactions (Dong and Zhou, 2014; Dong et al., 2014b). \r The observed SST trend is larger in the western than eastern tropical \r Indian Ocean, which leads to an apparent upward trend of the IOD \r index, but this trend is statistically insignificant (Section 2.4.3). CMIP5 \r models capture this warming pattern, which may be associated with \r Walker circulation weakening over the Indian Ocean due to greenhouse \r [...]"


id: 6fc98328226ce4a822b85b2a8785558a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 16927
content:
"[...] gas forcing (Dong and Zhou, 2014). However, strong internal decadal \r IOD-like variability and observational uncertainty preclude attribution \r (Cai et al., 2013; Han et al., 2014b; Gopika et al., 2020). Such a positive \r IOD-like change in equatorial zonal SST gradient suggests an increase \r in the frequency of extreme positive events (Cai et al., 2014) and \r skewness (Cowan et al., 2015) of the IOD mode. While there is some \r evidence of an increase in frequency of positive IOD events during the \r second half of the 20th century, the current level of IOD variability is \r not unprecedented in a proxy reconstruction for the last millennium \r (Section 2.4.3; Abram et al., 2020). Besides, the IOD magnitude in \r the late 20th century is not significantly different between CMIP5 \r simulations forced by historical and natural-only forcings, though \r this conclusion is based on only five selected ensemble members \r [...]"


id: a1c399fd41fe08fd5583bdc9a420dd49
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25684
content:
"[...] that realistically reproduce statistical features of the IOD (Blau and \r Ha, 2020). While selected CMIP5 models show weakening (Thielke \r and Molg, 2019) and seasonality changes (Blau and Ha, 2020) \r in IOD-induced rainfall anomalies in tropical eastern Africa, no \r comparison with observational records has been made. Likewise, \r while a strengthening tendency of the ENSO-IOB mode correlation \r and resultant intensification of the IOB mode are found in historical or \r future simulations in selected CMIP5 models (Hu et al., 2014; Tao et al., \r 2015), such a change has not been detected in observational records."


id: 237ed860e9c31899384ec9069b954bc5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3442
content:
"Since AR5, CMIP5 model representation of these modes has been \r analysed in detail, finding that most of the models qualitatively \r reproduce the spatial and seasonal features of the IOB and IOD modes \r (Chu et al., 2014; Liu et al., 2014; W. Tao et al., 2016). Improvements in \r simulating the IOB mode since CMIP3 have been identified in reduced \r multi-model mean biases and inter-model spread (W. Tao et al., 2016). \r CMIP5 models overall capture the transition from the IOD to IOB \r modes during an ENSO event (W. Tao et al., 2016). The IOB mode is \r forced in part through a cross-equatorial wind-evaporation-SST \r feedback triggered by ENSO-forced anomalous ocean Rossby waves \r that propagate to the shallow climatological thermocline dome in the \r tropical south-western Indian Ocean (Du et al., 2009). Consistently, \r models with a deeper climatological thermocline dome produce \r a weaker and less persistent IOB mode (G. Li et al., 2015a; Zheng et al., \r [...]"


id: 99fd11f852c46afdc0e8f26c7baa5b91
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 24326
content:
"[...] 2016). The deep thermocline bias remains in the ensemble mean of \r CMIP5 models due to a common surface easterly wind bias over the \r equatorial Indian Ocean (Lee et al., 2013) associated with weaker South \r Asian summer monsoon circulation (G. Li et al., 2015b). However, the \r influence of this systematic bias may be compensated by other biases, \r resulting in a realistic IOB magnitude (W. Tao et al., 2016). Halder et al. \r (2021) found that CMIP6 models reproduce the IOB mode reasonably \r well, but did not evaluate the progress since CMIP5."


id: 83a1e7b782e253ae4f05b8a10f5c5c59
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20304
content:
"By contrast, the IOD magnitude is overestimated by CMIP5 models on \r average, though with noticeable improvements from CMIP3 models \r (Liu et al., 2014). The overestimation of the IOD magnitude remains \r in most of 34 CMIP6 models examined in McKenna et al. (2020) with \r worsening on average in July and August. A too steep climatological \r thermocline slope along the equator due to the surface easterly wind \r bias in boreal summer and autumn contributes to this IOD magnitude \r bias through an excessively strong Bjerknes feedback in CMIP5 (Liu \r et al., 2014; G. Li et al., 2015b; Hirons and Turner, 2018). The surface \r easterly bias and associated east-west SST gradient bias are not \r improved in CMIP6 (Long et al., 2020; Section 3.5.1.2.3), suggesting \r that the thermocline bias also remains. McKenna et al. (2020) \r additionally find degradation in the positive-negative asymmetry \r of the IOD but an improvement in IOD frequency in a subset of \r [...]"


id: c2633510078d8304f5ffdb880c673844
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31479
content:
"[...] CMIP6 models compared to CMIP5. In terms of teleconnections, the \r equatorial surface easterly wind bias also affects the IOD-associated \r moisture transport anomalies toward tropical eastern Africa \r (Hirons and Turner, 2018) where the IOD is associated with strong \r precipitation anomalies in boreal autumn (Annex IV.2.4). CMIP5 \r and CMIP6 models capture the IOD teleconnection to Southern and \r Central Australian precipitation although it is weaker on average than \r observed, with no clear improvements from CMIP5 to CMIP6 (Grose \r et al., 2020). Strong IOD events could also influence the Northern \r Hemisphere extratropical circulation in winter and in particular the \r NAM (Section 3.7.1), based on interference between forced Rossby \r waves emerging from the Indian Ocean and climatological stationary \r waves (Fletcher and Cassou, 2015). The record positive phase of the "


id: 111b4635602155bba4914d8d17ef21db
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 204
content:
"opposite to observed in two thirds of the CMIP5 models \r (Section 3.5.1.2.2), which clearly affects the simulation of the Atlantic \r Nino and associated dynamics (Munoz et al., 2012; Ding et al., 2015; \r Deppenmeier et al., 2016). The interhemispheric SST gradient is also \r systematically underestimated in models, with a too cold mean state \r in the northern part of the tropical Atlantic ocean and too warm \r conditions in the South Atlantic basin. The seasonality is poorly \r reproduced and the wind-SST coupling is weaker than observed \r so that altogether, and despite AMM-like variability in 20th century \r climate simulations, AMM is not the dominant Atlantic mode in all \r CMIP5 models (Liu et al., 2013; Amaya et al., 2017). These biases in \r mean state translate into biases in modelling the mean ITCZ (Flato \r et al., 2013). Similar biases were found in experiments using CMIP5 \r models but with different climate background states, such as Last \r [...]"


id: d6b40390fec615873b05a7830ce944f1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35077
content:
"[...] Glacial Maximum, mid-Holocene and future scenario simulations \r (Brierley and Wainer, 2018). Analyses of CMIP6 show encouraging \r results in the representation of Atlantic Nino and AMM modes of \r variability in terms of amplitude and seasonality. Some models now \r display reduced biases in the spatial structure of the modes and related \r explained variance but persistent errors still remain on average in the \r timing of the modes and in the coupled nature of the modes, that is, \r the strength of the link between ocean (SST, mixed layer depth) and \r atmospheric (wind) anomalies (Richter and Tokinaga, 2020), as well \r as in the Atlantic Ocean equatorial east-west temperature gradient \r (Section 3.5.1.2.2, Figure 3.24)."


id: 5d7a77b81c3355acddfb53bcc2d68c38
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13731
content:
"Section 2.4.4 assess that there is low confidence in any sustained \r changes to the AZM and AMM variability in instrumental \r observations. Moreover, any attribution of possible human influence \r on the Atlantic modes and associated teleconnections is limited \r by the poor fidelity of CMIP5 and CMIP6 models in reproducing \r the mean tropical Atlantic climate, its seasonality and variability, \r despite hints of some improvement in CMIP6, as well as other \r sources of uncertainties related to limited process understanding \r in the observations (Foltz et al., 2019), the response of the tropical \r Atlantic climate to anthropogenic aerosol forcing (Booth et al., \r 2012; Zhang et al., 2013a) and the presence of strong multi-decadal \r fluctuations related to AMV (Section 3.7.7) and cross-tropical basin \r interactions (Martin-Rey et al., 2018; Cai et al., 2019). The fact that \r most models poorly represent the climatology and variability of the \r [...]"


id: f95c7b4b6dd1b991acb54bf3c3b957
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41231
content:
"[...] tropical Atlantic combined with the short observational record makes \r it difficult to place the recent observed changes in the context of \r internal multi-annual variability versus anthropogenic forcing."


id: b87a65a8817d7aeddaece8a967b62fb6
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 29668
content:
"observational coverage before satellite measurements started, the \r duration of PDV phases appears to be shorter in coupled models \r than in observations, and correspondingly the ratio of decadal to \r interannual variance is underestimated (Figure 3.39e,f; Henley et al., \r 2017). This apparent bias may be associated with overly biennial \r behaviour of Pacific trade wind variability and related ENSO activity, \r leaving too weak variability on decadal time scales (Kociuba and \r Power, 2015). ENSO influence on the extratropical North Pacific \r Ocean at decadal time scales is also very diverse among both CMIP3 \r and CMIP5 models, being controlled by multiple factors (Nidheesh \r et al., 2017). In terms of amplitude, the variance of the PDV index \r after decadal filtering is significantly weaker in the concatenated \r CMIP5 ensemble than the three observational estimates used in \r Figure  3.39e (p <0.1 with an F-test). Consequently, the observed \r [...]"


id: 8b01da40d50ee09068150b5167abab55
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21624
content:
"[...] PDV fluctuations over the historical period often lie in the tails of the \r model distributions (Figure 3.39e,f). Even if one cannot rule out that \r the observed PDV over the instrumental era represents an exceptional \r period of variability, it is plausible that the tendency of the CMIP5 \r models to systematically underestimate the low frequency variance \r is due to an incomplete representation of decadal-scale mechanisms \r in these models. This situation is slightly improved in CMIP6 historical \r simulations but remains a concern (Fasullo et al., 2020). The results of \r McGregor et al. (2018) suggest that the under-representation of the \r variability stems from Atlantic mean SST biases (Section 3.5.1.2.2) \r through inter-basin coupling."


id: 6c7bc0b3b95ddaa129b4c4927ea65614
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 16378
content:
"While PDV is primarily understood as an internal mode of variability \r (Si and Hu, 2017), there are some indications that anthropogenically \r induced SST changes project onto PDV and have contributed to its \r past evolution (Bonfils and Santer, 2011; Dong et al., 2014a; Boo \r et al., 2015; Xu and Hu, 2018). However, the level of evidence is \r limited because of the difficulty in correctly separating internal versus \r externally forced components of the observed SST variations, and \r because it is unclear whether the dynamics of the PDV are operative \r in this forced SST change pattern. Over the last two to three decades \r which encompass the period of slower GMST increase (Cross-Chapter \r Box 3.1), Smith et al. (2016) found that anthropogenic aerosols have \r driven part of the PDV change toward its negative phase. A similar \r result is shown in Takahashi and Watanabe (2016) who found \r intensification of the Pacific Walker circulation in response to aerosol \r [...]"


id: 121c65b6b87e882fb33a832db76111a7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 374
content:
"[...] forcing (Section 3.3.3.1.2). Indeed, CMIP6 models simulate a negative \r PDV trend since the 1980s (Figure 3.39f), which is much weaker than \r internal variability. However, a response to anthropogenic aerosols is \r not robustly identified in a large ensemble of a model (Oudar et al., \r 2018), across CMIP5 models (Hua et al., 2018), or in idealized model \r simulations (Kuntz and Schrag, 2016). Alternatively, inter-basin \r teleconnections associated with the warming of the North Atlantic \r Ocean related to the mid-1990s phase shift of the AMV (McGregor et al., \r 2014; Chikamoto et al., 2016; Kucharski et al., 2016; X. Li et al., 2016a; \r Ruprich-Robert et al., 2017), and also warming in the Indian Ocean \r (Luo et al., 2012; Mochizuki et al., 2016), could have favoured a PDV \r transition to its negative phase in the 2000s. Considering the possible \r influence of external forcing on Indian Ocean decadal variability \r (Section 3.7.4) and AMV (Section 3.7.7), any such human influence \r [...]"


id: ee9a94f5cb96a224d334fb18439ab5c5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 39357
content:
"[...] on PDV would be indirect through changes in these ocean basins, \r and then imported to the Pacific via inter-basin coupling. However, \r this human influence on AMV, and how consistently such inter-basin "


id: 82adce3f53e9fba635eaef5fa1286242
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20124
content:
"The implication of PDV in the observed slowdown of the GMST \r warming rate in the early 2000s (Cross-Chapter Box 3.1) has triggered \r considerable research on decadal climate variability and predictability \r since AR5 (Meehl et al., 2013, 2016b; England et al., 2014; Dai et al., \r 2015; Steinman et al., 2015; Kosaka and Xie, 2016; Cassou et al., \r 2018). Many studies find that the broad spatial characteristics of \r PDV are reasonably well represented in unforced climate models \r (Newman et al., 2016; Henley, 2017) and in historical simulations \r in CMIP5 and CMIP6 (Figure 3.39), although there is sensitivity to \r the methodology used to remove the externally-forced component \r of the SST (Bonfils and Santer, 2011; Xu and Hu, 2018). Compared \r with CMIP3 models, CMIP5 models exhibit overall slightly better \r performance in reproducing PDV and associated teleconnections \r (Polade et al., 2013; Joshi and Kucharski, 2017), and also smaller \r [...]"


id: 112b482c8a6fe85727e72180bc22edcf
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 213
content:
"[...] inter-model spread (Lyu et al., 2016). CMIP6 models on average show \r slightly improved reproduction of the PDV spatial structure than \r CMIP5 models (Figure 3.39a-c; Fasullo et al., 2020). SST anomalies in \r the subtropical South Pacific lobe are, however, too weak relative to \r the equatorial and North Pacific lobes in CMIP5 pre-industrial control \r and historical simulations (Henley et al., 2017), a bias that remains \r in CMIP6 (Figure 3.39b)."


id: 88c30bb11427ac2f70886d50a45229cf
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21226
content:
"atmospheric teleconnections (Martin et al., 2014; Ruprich-Robert \r et al., 2017, 2018; Monerie et al., 2019; Qasmi et al., 2020; Ruggieri \r et al., 2021). Even if debate remains (Clement et al., 2015; Cane \r et al., 2017; Mann et al., 2020), there is now stronger evidence for \r a crucial role of oceanic dynamics in internal AMV that is primarily \r linked to the AMOC and its interplay with the NAO (Zhang et al., \r 2013a; Muller et al., 2015; O'Reilly et al., 2016b, 2019a; Delworth \r et al., 2017; Zhang, 2017; Sun et al., 2019; Kim et al., 2020). However, \r considerable diversity in the spatio-temporal properties of the \r simulated AMV is found in both pre-industrial control and historical \r CMIP5 experiments (Zhang and Wang, 2013; Wills et al., 2019). Such \r model diversity is presumably associated with the wide range of \r coupled processes associated with AMV (Baker et al., 2017; Woollings \r et al., 2018a) including large-scale atmospheric teleconnections and \r [...]"


id: 20cf25399ea4cd4c762e92685826a954
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 2993
content:
"[...] regional feedbacks relating to tropical clouds, Arctic sea ice in the \r subarctic basins and Saharan dust, whose relative importance and \r interactions across time scales are specific to each model (Martin \r et al., 2014; Brown et al., 2016b)."


id: ab4a5ad269615ba08b34eec8d1f2ed58
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 27363
content:
"To conclude, there is high confidence that internal variability has been \r the main driver of the PDV since pre-industrial times, despite some \r modelling evidence for potential external influence. This assessment \r is supported by studies based on large ensemble simulations that \r found the dominance of internally-driven PDV, and the CMIP6-based \r assessment (Figure  3.39). As such, PDV is an important driver of \r decadal internal climate variability which limits detection of human \r influence on various aspects of decadal climate change on global \r to regional scales (high confidence). Model evaluation of PDV is \r hampered by short observational records, spatial incompleteness of \r observations before the satellite observation era, and poor agreement \r among paleoclimate reconstructions. Despite the limitations of these \r model-observation comparisons, CMIP5 models, on average, simulate \r broadly realistic spatial structures of the PDV, but with a clear bias \r [...]"


id: 1a0cb1789107995317a869ff868304ac
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1782
content:
"[...] in the South Pacific (medium confidence). CMIP5 models also very\r likely underestimate PDV magnitude. CMIP6 models tend to show \r better overall performance in spatial structure and magnitude of \r PDV, but there is low confidence in this assessment due to the lack \r of literature."


id: 119e0e3eb4af04fdd58d3dcff144b689
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 291
content:
"Additional studies since AR5 corroborate that CMIP5-era models \r tend to underestimate many aspects of observed AMV and its SST \r fingerprint. On average, the duration of modelled AMV episodes is \r too short, the magnitude of AMV is too weak and its basin-wide \r SST spatial structure is limited by the poor representation of the \r link between the tropical North Atlantic and the subpolar North \r Atlantic/Nordic seas (Martin et al., 2014; Qasmi et al., 2017). Such \r mismatches between observed and simulated AMV (Figure 3.40c-e) \r have been associated with intrinsic model biases in both mean state \r (Menary et al., 2015; Drews and Greatbatch, 2016) and variability \r in the ocean and overlying atmosphere. For instance, compared to \r available observational data CMIP5 models underestimate the ratio \r of decadal to interannual variability of the main drivers of AMV, \r namely the AMOC, NAO and related North Atlantic jet variations \r [...]"


id: d8a7a0b27b6c80ff203937e9ba8cfcc7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35409
content:
"[...] (Section 3.7.1; Bracegirdle et al., 2018; Kim et al., 2018b; Simpson \r et al., 2018; Yan et al., 2018), which has strong implications for the \r simulated temporal statistics of AMV, AMV-induced teleconnections \r (Ault et al., 2012; Menary et al., 2015) and AMV predictability."


id: 100af1a72d3e58d21a86fefe10adbbf5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9
content:
"The increase of AMV variance in CMIP6 models (stronger magnitude \r and longer duration) seems to be explained by the enhanced \r variability in the subpolar North Atlantic SST (Figure 3.40b,c), which \r is particularly pronounced in some models, associated with greater \r variability in the AMOC (Section 3.5.4.1; Voldoire et al., 2019a; Boucher \r et al., 2020) and greater GMST multi-decadal variability (Section 3.3.1 \r and Figure 3.40c-f; Voldoire et al., 2019b; Parsons et al., 2020). The \r decadal variance in SST in the subpolar North Atlantic seems now \r to be slightly overestimated in CMIP6 compared to observational \r estimates, while the AMV-related tropical SST anomalies remain \r weaker in line with CMIP5 (Figure 3.40b). The mechanisms producing \r the tropical-extratropical relationship at decadal time scales \r remain poorly understood despite stronger evidence since AR5 for \r the importance of the subpolar gyre SST anomalies in generating \r [...]"


id: fde0d213f79d3a31667a0fc3fbc5a797
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 42029
content:
"[...] tropical changes through atmospheric teleconnection (Caron \r et al., 2015; Ruprich-Robert et al., 2017; Kim et al., 2020). Significant \r discrepancies remain in the simulated AMV spatial pattern when \r historical simulations are compared to multivariate observations (Yan \r et al., 2018; Robson et al., 2020)."


id: 92693921c176bc9e9033b1f62dd9ce7a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 22978
content:
"Model evaluation of the AMV phenomenon remains difficult because \r of short observational records (especially of detailed process-based \r observations), the lack of stationarity in the variance, spatial patterns \r and frequency of the AMV assessed from modelled SST (Qasmi et al., \r 2017), difficulties in estimating the forced signals in both historical \r simulations and observations (Tandon and Kushner, 2015), and \r because of probable interplay between internally and externally-driven \r processes (Watanabe and Tatebe, 2019). Furthermore, models \r simulate a large range of historical anthropogenic aerosol forcing \r (Smith et al., 2020) and questions often referred to as signal-to-noise \r paradox have been raised concerning the models' ability to correctly \r simulate the magnitude of the response of AMV-related atmospheric \r circulation phenomena, such as the NAO (Section 3.7.1), to both \r internally and externally generated changes (Scaife and Smith, 2018). \r [...]"


id: bae7d5227f9ead92da175efe4e2dad6e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30097
content:
"[...] Related methodological and epistemological uncertainties also call \r into question the relevance of the traditional basin-average SST \r index to assessing the AMV phenomenon (Zanchettin et al., 2014; \r Frajka-Williams et al., 2017; Haustein et al., 2019; Wills et al., 2019)."


id: 59a2236572b58b2b8e633a01db48f95b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13008
content:
"To summarize, results from CMIP5 and CMIP6 models together \r with new statistical techniques to evaluate the forced component \r of modelled and observed AMV, provide robust evidence that \r external forcings have modulated AMV over the historical period. \r In particular, anthropogenic and volcanic aerosols are thought to \r have played a role in the timing and intensity of the negative (cold) \r phase of AMV recorded from the mid-1960s to mid-1990s and \r subsequent warming (medium confidence). However, there is low \r confidence in the estimated magnitude of the human influence. \r The limited level of confidence is primarily explained by difficulties \r in accurately evaluating model performance in simulating AMV. \r The evaluation is severely hampered by short instrumental records \r but also, equally importantly, by the lack of detailed and coherent \r long-term process-based observations (for example of the AMOC, \r aerosol optical depth, surface fluxes and cloud changes), which limit \r [...]"


id: e5de1c5d6fb1d8432dfec60773d798d2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37742
content:
"[...] our process understanding. In addition, studies often rely solely on \r simplistic SST indices that may be hard to interpret (Zhang et al., \r 2016) and may mask critical physical inconsistencies in simulations \r of the AMV compared to observations (Zhang, 2017)."


id: 929a108de458c1f57e63a75573a47bb1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23014
content:
"An analysis of a basket of 16 atmospheric variables (Figure 3.42a) \r assessed across CMIP3, CMIP5, and CMIP6 models but excluding \r high-resolution models participating in HighResMIP, reveals the \r progress made between these three generations of models (Bock \r et al., 2020). Progress is evidenced by the increasing prevalence of \r blue colours (indicating a performance better than the median) for \r the more recent model versions. Additionally, a few CMIP6 models \r outperform the best-performing CMIP5 models. Progress is evident \r across all 16 variables. As noted in AR5, the models typically score \r similarly against both observational reference datasets, indicating \r that indeed uncertainties in these reference datasets are smaller \r than model biases. Several models and model families perform \r better compared to observational references than the median, across \r a majority of the climate variables assessed, and conversely some \r [...]"


id: dc46a1f5485e432e69c9c6e424034037
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 36024
content:
"[...] other models or model families compare more poorly against these \r reference datasets. Such a good correspondence across a range \r of diagnostic fields probing different aspects of climate enhances \r confidence that the improved performances reflect progress in the \r physical realism of these simulations. An alternative explanation, \r that progress is due to a cancellation of errors achieved by model \r tuning, appears improbable given the large number of diagnostic \r fields involved here. However, several instances of poor model \r performance (red colours in Figure  3.42) still exist in the CMIP6 \r ensemble. Family relationships (i.e.  various degrees of shared \r formulations; Knutti et al., 2013) between the models are apparent, \r for example, the GISS, GFDL, CESM, CNRM, and HadGEM/UKESM1/\r ACCESS families score similarly across all atmospheric variables, \r both for the CMIP5 and CMIP6 generations. In the cases of CESM2/\r CESM2(WACCM), CNRM-CM6-1/CNRM-ESM2-1, NorCPM1/\r [...]"


id: fe2e33e99e5a232322f09f54837344e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 42086
content:
"[...] NorESM2-LM, and HadGEM3-GC31-LL/UKESM1-0-LL, the high\u0002complexity model scores as well or better than its lower-complexity \r counterpart, indicating that increasing complexity by adding Earth \r system features, which by removing constraints could be expected to \r degrade a model's performance, does not necessarily do so. Several \r high climate-sensitivity models (Section 7.5; Meehl et al., 2020), in \r particular CanESM5, CESM2, CESM2-WACCM, HadGEM3-GC31-LL, \r and UKESM1-0-LL, score well against the benchmarks. In accordance \r with AR5 and earlier assessments, the multi-model mean, with some \r notable exceptions, is better than any individual model (Annan and \r Hargreaves, 2011; Rougier, 2016)."


id: 7d99ab97dea198a8ed9d677234073f2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 19270
content:
"AR5 found significant differences between models in the simulation \r of mean climate in the CMIP5 ensemble when measured against \r meteorological reanalyses and observations (Flato et al., 2013), see \r also Stouffer et al. (2017). The AR5 determined that for the diagnostic \r fields analysed, the models usually compared similarly against two \r different reference datasets, suggesting that model errors were \r generally larger than observational uncertainties or other differences \r between the observational references. In agreement with previous \r assessments, the CMIP5 multi-model mean generally performed \r better than individual models (Annan and Hargreaves, 2011; Rougier, \r 2016). The AR5 considered 13 atmospheric fields in its assessment \r for the instrumental period but did not assess multi-variate model \r performance in other climate domains (e.g., ocean, land, and sea ice). \r The AR5 found only modest improvement regarding the simulation \r [...]"


id: 8e1853a2f04c3e2662e3040351ea4c7c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 22194
content:
"[...] of climate for two periods of the Earth's history (the Last Glacial \r Maximum and the mid-Holocene) between CMIP5 and previous \r paleoclimate simulations. Similarly, for the modern period only \r modest, incremental progress was found between CMIP3 and \r CMIP5 regarding the simulation of precipitation and radiation. The \r representation of clouds also showed improvement, but remained \r a key challenge in climate modelling (Flato et al., 2013)."


id: 2d652a6d5a0ce74df3e4dba138cb6858
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5272
content:
"In addition to the multivariate assessments of simulations of the \r recent historical period, simulations of selected periods of the Earth's \r more distant history can be used to benchmark climate models by \r exposing them to climate forcings that are radically different from \r the present and recent past (Harrison et al., 2015, 2016; Kageyama \r et al., 2018; Tierney et al., 2020a). These time periods provide an out\u0002of-sample test of models because they are not in general used in the \r process of model development. They encompass a range of climate \r drivers, such as volcanic and solar forcing for the Last Millennium, \r orbital forcing for the mid-Holocene and Last Interglacial, and changes \r in greenhouse gases and ice sheets for the LGM, mid-Pliocene Warm \r Period, and early Eocene (Sections 2.2 and 2.3). These drivers led to \r climate changes, including in surface temperature (Section 2.3.1.1) \r and the hydrological cycle (Section 2.3.1.3.1), which are described \r [...]"


id: a4c8a0fb4d8b68cfaf3a644b381d085e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 26218
content:
"[...] by paleoclimate proxies that have been synthesized to support \r evaluations of models on a global and regional scale. However, the \r more sparse, indirect, and regionally incomplete climate information \r available from paleo-archives motivates a different form of the \r multivariate analysis of simulations covering these periods versus the \r equivalent for the historical period, as described below."


id: eabe6697b5fea3a902cac921f08daa88
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38657
content:
"Using centred pattern correlations (quantifying pattern similarity \r on a scale of -1 to 1, with 1 expressing perfect similarity and 0 no \r relationship) for selected fields, AR5 documented improvements \r between CMIP3 and CMIP5 in surface air temperature, outgoing \r longwave radiation, and precipitation (Figure  9.6 of Flato et al., \r 2013). Little further progress between CMIP3 and CMIP5 was found \r for fields that were already quite well simulated in CMIP3 (such \r as surface air temperature and outgoing longwave radiation). For \r precipitation, the spread reduced because the worst-performing \r models improved. The shortwave cloud radiative effect remained \r relatively poorly simulated with significant inter-model spread \r (e.g.,  Calisto et al., 2014). This comparison of centred pattern \r correlations is designed to help determine the quality of simulation \r of different diagnostics relative to each other, and also to examine \r [...]"


id: a3655f443274f481910b1e78211e15b2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25954
content:
"[...] progress between generations of models. Figure  3.43 shows the \r centred pattern correlations for 16 variables for CMIP3, CMIP5 and \r CMIP6 models. In the ensemble averages, CMIP6 performs better \r than CMIP5 and CMIP3 for near-surface temperature, precipitation, \r mean sea-level pressure, and many other variables. For the variables \r shown, the uncertainties in observational datasets, in particular for "


id: 45562de62f79a1f65cb8f3018758b6ca
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9419
content:
"Figure 3.43 | Centred pattern correlations between models and observations for the annual mean climatology over the period 1980-1999. Results are \r shown for individual CMIP3 (green), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding multi-model \r ensemble averages (long lines). Correlations are shown between the models and the primary reference observational data set (from left to right: ERA5, GPCP-SG, CERES-EBAF, \r CERES-EBAF, CERES-EBAF, CERES-EBAF, JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and \r additional observational datasets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5, NCEP) are shown (solid grey circles) if \r [...]"


id: 84b60d2d1f0f5e479d6ff3e21a9977b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20490
content:
"[...] available. To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed after regridding all datasets to a resolution of 4deg in longitude and \r 5deg in latitude. Figure is updated and expanded from Bock et al. (2020), their Figure 7 CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Further details on data sources and \r processing are available in the chapter data table (Table 3.SM.1)."


id: a17062ed71c93d20cea1bdf12d3b2dd8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25618
content:
"Figure 3.44 | Multivariate synopsis of paleoclimate model results compared to observational references. Data-model comparisons for (a) GSAT anomalies for \r five PMIP4 periods and for regional features for the (b) mid-Holocene and (c) LGM periods, for PMIP3 and PMIP4 models. The results from CMIP6 models are shown as coloured \r dots. In (a) the light orange shading shows the very likely assessed ranges presented in Section 2.3.1.1. In (b) and (c), the regions and variables are defined as follows: North \r America (20degN-50degN, 140degW-60degW), Western Europe (35degN-70degN, 10degW-30degE) and West Africa (0deg-30degN, 10degW-30degE); mean temperature of the coldest month (MTCO; \r degC), mean temperature of the warmest month (MTWA; degC), mean annual precipitation (MAP; mm yr -1). In (b) and (c) the ranges shown for the reconstructions (Bartlein et al. \r [...]"


id: a98b02077da402da9ab5657b2d035493
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 27050
content:
"[...] (2011) for mid-Holocene and Cleator et al. (2020) for LGM) are based on the standard error given at each site: the average and associated standard deviation over each area is \r obtained by computing 1000 times the average of randomly drawn values from the Gaussian distributions defined at each site by the reconstruction mean and standard error; \r the light orange colour shows the +-1 standard deviation of these 1000 estimates. The dots on (b) and (c) show the average of the model output for grid points for which there \r are reconstructions. The ranges for the model results are based on an ensemble of 1000 averages over 50 years randomly picked in the model output time series for each region \r and each variable: the mean +- one standard deviation is plotted for each model. Figure is adapted from Brierley et al. (2020), their Figure S3 for the mid-Holocene; and from \r [...]"


id: 32d05824966b2f80e43a367488a6060a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6206
content:
"[...] Kageyama et al. (2021b), their Figure 12 for the LGM. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1)."


id: 43c12d9a00fc0c33ab285fa8fe928f6e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9145
content:
"Polar amplification in the LGM, mid-Pliocene Warm Period, and \r Early Eocene Climatic Optimum (EECO) simulations is assessed in \r Section 7.4.4.1.2. Here we focus on the mid-Holocene and the LGM, \r which have been a part of AMIP or CMIP through several assessment \r cycles, and as such serve as a reference to quantify regional model-data \r agreement from one IPCC assessment to another. We compare the \r results from 15 CMIP6 models using the PMIP4 protocol (CMIP6-PMIP4), \r with non-CMIP6 models using the PMIP4 protocol, with PMIP3 models, \r and with regional temperature and precipitation changes from proxies \r for the mid-Holocene (Figure  3.44b). For six out of seven variables \r shown, the CMIP6 multi-model mean captures the correct sign of the \r change. For five out of seven of them the CMIP6 ensemble mean is \r within the reconstructed range. For the other two variables (changes in \r the mean temperature of the warmest month over North America and \r [...]"


id: fec96cb6100e4da96d8a73d2fe72bf80
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 42216
content:
"[...] in the mean annual precipitation over West Africa) nearly all PMIP4 \r and PMIP3 models are outside the reconstructed range. CMIP6 models \r show regional patterns of temperature changes similar to the PMIP3 \r ensemble (Brierley et al., 2020), but the slight mid-Holocene cooling in \r PMIP4 compared with PMIP3, probably associated with lower imposed \r mid-Holocene carbon dioxide concentrations (Otto-Bliesner et al., \r 2017), improves the regional model performance for summer and \r winter temperatures (Figure 3.44b). However, this cooling also results \r in a CMIP6 mid-Holocene GSAT that lies further from the assessed \r range (Figure 3.44a). All models show an expansion of the monsoon \r areas from the pre-industrial to the mid-Holocene simulations in the \r Northern Hemisphere, but this expansion in some cases is only large \r enough to cancel out the bias in the pre-industrial control simulations \r (Section 3.3.3.2; Brierley et al., 2020). There is a slight improvement in \r [...]"


id: 407490363774a1157d64a7e8bc569715
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8531
content:
"[...] representing the northward expansion of the West African monsoon \r region in PMIP4 compared with PMIP3 (Figures 3.11 and 3.44b)."


id: bdf8aa73a21a85d0c0ea536ac66b437a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30683
content:
"Fourteen simulations of the LGM climate have been produced \r following the CMIP6-PMIP4 protocol using 11 models, five of \r which are from the latest CMIP6 generation. The multi-model-mean \r global cooling simulated by these models is close to that simulated \r by the CMIP5-PMIP3 ensemble, but the range of results is larger. \r The increase in the range is largely due to the inclusion of CESM2 \r which simulates a much larger cooling than the other PMIP4 models \r (Figure  3.44a). This is consistent with its larger climate sensitivity \r (see also Section 3.3.1.1; Zhu et al., 2021). The other models on \r average also simulate slightly larger cooling in PMIP4 versus PMIP3 \r (Kageyama et al., 2017, 2021a). The PMIP4 multi-model mean is \r within the range of reconstructed regional averages for four out \r of seven regional variables; this is unchanged from PMIP3 but for \r different variables (Figure 3.44c). For all fields, the results of many \r [...]"


id: 59b09c30b200157ca1ecfdca78efdb3d
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13016
content:
"[...] individual models are outside the reconstructed range. For two \r variables out of seven (changes in the mean temperature of the \r warmest month and mean annual precipitation over Western Europe) \r no model is within the range of the reconstructions. This analysis is \r strengthened compared with the equivalent analysis in AR5 because \r it is based on larger and improved reconstructions (Cleator et al., \r 2020). Most CMIP6-PMIP4 models simulate a slightly stronger AMOC \r in the LGM, but no strong deepening of the AMOC (Kageyama et al., \r 2021a), while most other PMIP4 models simulate a strengthening "


id: 937cf9df40fc49277941b31217289ca3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23153
content:
"2020), as well as some aspects of air-sea interactions (P. Wu et al., \r 2019, single model; Bellucci et al., 2021, multi-model). However, warm\u0002biased sea surface temperatures in the Southern Ocean are worse in \r comparison to standard resolution CMIP6 models (Bock et al., 2020). \r The AR5 noted problems with the simulation of clouds in this region \r which were later attributed to a lack of supercooled liquid clouds \r (Bodas-Salcedo et al., 2016). Mesoscale ocean processes are critical \r to maintaining the Southern Ocean stratification and response to \r wind forcing (Marshall and Radko, 2003; Hallberg and Gnanadesikan, \r 2006), and their explicit representation requires even higher ocean \r resolution (Hallberg, 2013). Similarly, atmospheric convection remains \r unresolved even in the highest-resolution climate models participating \r in HighResMIP. However, there is also evidence of improvements \r in the frequency, distribution and interannual variability of tropical \r [...]"


id: 11e0af4227c01d5f4af6e841bcd84a08
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 337
content:
"[...] cyclones in HighResMIP (Roberts et al., 2020a, b), particularly in the \r Northern Hemisphere (see further discussion in Section 11.7.1.3), \r and their interaction with the ocean (Scoccimarro et al., 2017, single \r model), as well as the global moisture budget (Vanniere et al., 2019). \r At higher resolution the track density of tropical cyclones is increased \r practically everywhere where tropical cyclones occur. Simulation of \r some climate extremes is shown to be improved at higher resolution \r including explosively developing extra-tropical cyclones (Vries et al., \r 2019; Jiaxiang et al., 2020), blocking (Section 3.3.3.3; Fabiano et al., \r 2020; Schiemann et al., 2020) and European extreme precipitation \r due to a better representation of the North Atlantic storm track (van \r Haren et al., 2015) and orographic boundary conditions (Schiemann \r et al., 2018)."


id: c8f64171bcab1aaa5b6b237e057a5f36
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 32677
content:
"As discussed in Section 3.3, CMIP6 models reproduce observed \r large-scale mean surface temperature patterns as well as their \r CMIP5 predecessors, but biases in surface temperature in the \r mean of HighResMIP models are smaller than those in the mean \r of the corresponding standard resolution CMIP6 configurations of \r the same models (Section 3.3.1.1 and Figure  3.3). The extent and \r causes of improvements due to increased horizontal resolutions \r in the atmosphere and ocean domains depend on the model \r (Kuhlbrodt et al., 2018; Roberts et al., 2018, 2019; Sidorenko et al., \r 2019), although they typically involve better process representation \r (for  example of ocean currents and atmospheric storms) which \r can lead to reduced biases in top of atmosphere radiation and \r cloudiness.  Precipitation has likewise improved in CMIP6 versus \r CMIP5 models, but biases remain. The high resolution (<25 km) \r class of models participating in HighResMIP compares regionally \r [...]"


id: 3ee41120a4aec405c6aa5373c0931883
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8249
content:
"[...] better against observations than the standard resolution CMIP6 \r models (of order 100 km, Figure 3.13; Section 3.3.2), partly because \r of an improved representation of orographic (mountain-induced) \r precipitation which constitutes a major fraction of precipitation on \r land, but other processes also play an important role (Vanniere et al., \r 2019). However, there are also large parts of the tropical ocean where \r precipitation in high-resolution models is not improved compared to \r standard resolution CMIP6 models (Vanniere et al., 2019)."


id: e75c6e04c2f1486762e3d378364462f3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38002
content:
"In CMIP6 a number of Earth system models have increased the realism \r by which key biogeochemical aspects of the coupled Earth system are \r represented, affecting, for example, the carbon and nitrogen cycles, \r aerosols, and atmospheric chemistry (e.g., Cao et al., 2018; Gettelman \r et al., 2019; Lin et al., 2019; Mauritsen et al., 2019; Seferian et al., \r 2019; Sellar et al., 2019; Sidorenko et al., 2019; Swart et al., 2019; \r Dunne et al., 2020; Seland et al., 2020; Wu et al., 2020; Ziehn et al., \r 2020). In addition to increased process realism, the level of coupling \r between the physical climate and biogeochemical components of \r the Earth system has also been enhanced in some models (Mulcahy \r et al., 2020) as well as across different biogeochemical components \r (see Section 5.4 for a discussion and Table 5.4 for an overview). For \r example, the nitrogen cycle is now simulated in several ESMs (Zaehle \r et al., 2015; Davies-Barnard et al., 2020). This advance accounts \r [...]"


id: 44d8dcd004b5fd5c7ed980c28ad8f0e7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9339
content:
"[...] for the fertilization effect nitrogen availability has on vegetation \r and carbon uptake, reducing uncertainties in the simulations of \r the carbon uptake responses to physical climate change (Section \r 3.6.1) and to CO2 increases (Arora et al., 2020), thus improving \r confidence in the simulated airborne fraction of CO2 emissions \r (Jones and Friedlingstein, 2020) and better constraining remaining \r carbon budgets (Section 5.5). Such advances also allow investigation \r of land-based climate change mitigation options (e.g., through \r changes in land management and associated terrestrial carbon \r uptake (Mahowald et al., 2017; Pongratz et al., 2018)) or interactions \r between different facets of the managed Earth system, such as \r interactions between mitigation efforts targeting climate warming \r and air quality (West et al., 2013). A number of developments also \r explicitly target improved simulation of the past."


id: fd1bc8bfcf4a9f4d764039ca6f3de133
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41883
content:
"Further such ESM developments include: (i) Apart from the nitrogen \r cycle, extending terrestrial carbon cycle models to simulate \r interactions between the carbon cycle and other nutrient cycles, such \r as phosphorus, that are known to play an important role in limiting \r future plant uptake of CO2 (Zaehle et al., 2015). (ii) Introducing \r explicit coupling between interactive atmospheric chemistry and \r aerosol schemes (Gettelman et al., 2019; Sellar et al., 2019), which \r has been shown to affect estimates of historical aerosol radiative \r forcing (Karset et al., 2018). Furthermore, interactive treatment \r of atmospheric chemistry in a full ESM supports investigation of \r interactions between climate and air quality mitigation efforts, such as \r in AerChemMIP (Collins et al., 2017), as well as interactions between \r stratospheric ozone recovery and global warming (Morgenstern \r et al., 2018). (iii) Coupling between components of Earth system \r [...]"


id: 5b2915b2f184805278a5122b3ad1166c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13289
content:
"[...] models has been extended to increase their utility for studying future \r interactions across the full Earth system, such as between ocean \r biogeochemistry and cloud-aerosol processes (Mulcahy et al., 2020), \r and vegetation and impacts on dust production (Kok et al., 2018), \r production of secondary organic aerosols (SOA, Zhao et al., 2017) \r and Equilibrium Climate Sensitivity (ECS), whereby enhanced CO2\r fertilization of land vegetation causes changes in regional surface \r albedo (Andrews et al., 2019). Increased coupling between physical \r climate and biogeochemical processes in a single ESM, along with \r an increased number of interactively represented processes, such as \r permafrost thaw, vegetation, wildfires and continental ice sheets \r increases our ability to investigate the potential for abrupt and \r interactive changes in the Earth system (see Sections 4.7.3 and 5.4.9, \r and Box 5.1). Table 5.4 provides an overview of recent advances in \r [...]"


id: f81e1094311e1ecff4027dc458a87a5e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 41027
content:
"In summary, both high-resolution and high-complexity models have \r been evaluated as part of CMIP6. In comparison with standard \r resolution CMIP6 models, higher resolution probed under the \r HighResMIP activity (Haarsma et al., 2016) improves aspects of the \r simulation of climate (particularly concerning sea surface temperature) \r but discrepancies remain and there are some regions, such as parts of \r the Southern Ocean, where currently attainable resolution produces \r inferior performance (high confidence). Such model behaviour can \r indicate deficiencies in model physics that are not simply associated \r with resolution. In several cases, high-complexity ESMs that include \r additional interactions between Earth system components and thus \r have potential for additional associated model errors nevertheless \r perform as well as their low-complexity counterparts, illustrating that \r interactively simulating these Earth System components as part of \r [...]"


id: c3393d34047c9a500968a7eb3412ab82
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31621
content:
"Results consistently show that such climate models can only reproduce the observed warming (black line in \r FAQ 3.1, Figure 1) when including the effects of human activities (grey band in FAQ 3.1, Figure 1), in particular \r the increasing concentrations of greenhouse gases. These climate models show a dominant warming effect of \r greenhouse gas increases (red band, which shows the warming effects of greenhouse gases by themselves), \r which has been partly offset by the cooling effect of increases in atmospheric aerosols (blue band). By contrast, \r simulations that include only natural processes, including internal variability related to El Nino and other similar \r variations, as well as variations in the activity of the sun and emissions from large volcanoes (green band in \r FAQ 3.1, Figure 1), are not able to reproduce the observed warming. The fact that simulations including only \r [...]"


id: 38237bc95239db2d2e762d8b8d284e97
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 7080
content:
"[...] natural processes show much smaller temperature increases indicates that natural processes alone cannot explain \r the strong rate of warming observed. The observed rate can only be reproduced when human influence is added \r to the simulations."


id: 21ed01f415966341c71e78ec4d020700
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 3193
content:
"Paleoclimatic records (indirect measurements of climate that can extend back many thousands of years) and \r climate models all show that global surface temperatures have changed significantly over a wide range of time \r scales in the past. One of these reasons is natural variability, which refers to variations in climate that are either \r internally generated within the climate system or externally driven by natural changes. Internal natural variability \r corresponds to a redistribution of energy within the climate system (for example via atmospheric circulation\r changes similar to those that drive the daily weather) and is most clearly observed as regional, rather than \r global, fluctuations in surface temperature. External natural variability can result from changes in the Earth's \r orbit, small variations in energy received from the sun, or from major volcanic eruptions. Although large orbital \r [...]"


id: 5d67b708a2cb92fd1d970091d81db41f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13722
content:
"[...] changes are related to global climate changes of the past, they operate on very long time scales (i.e., thousands \r of years). As such, they have displayed very little change over the past century and have had very little influence \r on temperature changes observed over that period. On the other hand, volcanic eruptions can strongly cool the \r Earth, but this effect is short-lived and their influence on surface temperatures typically fades within a decade \r of the eruption."


id: d02698a9af64349376007b174d394efc
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33980
content:
"Climate modelling started in the 1950s and, over the years, models have become increasingly sophisticated as \r computing power, observations and our understanding of the climate system have advanced. The models used \r in the IPCC First Assessment Report published in 1990 correctly reproduced many aspects of climate (FAQ 1.1). \r The actual evolution of the climate since then has confirmed these early projections, when accounting for the \r differences between the simulated scenarios and actual emissions. Models continue to improve and get better \r and better at simulating the large variety of important processes that affect climate. For example, many models \r now simulate complex interactions between different aspects of the Earth system, such as the uptake of carbon \r dioxide by vegetation on land and by the ocean, and the interaction between clouds and air pollutants. While \r some models are becoming more comprehensive, others are striving to represent processes at higher resolution, \r [...]"


id: 92c9777b6f3a5170b8bfbe027532cb74
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23042
content:
"[...] for example to better represent the vortices and swirls in currents responsible for much of the transport of heat \r in the ocean."


id: 404c9470123954d00831d597700fd4f3
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8503
content:
"Sebastian Milinski (Germany), Kyung-Sook Yun (Republic of Korea), Kyle Armour (United  States \r of America), Nicolas Bellouin (United Kingdom/France), Ingo Bethke (Norway/Germany), \r Michael P. Byrne (United Kingdom /Ireland), Christophe Cassou (France), Deliang Chen (Sweden), \r Annalisa Cherchi (Italy), Hannah M. Christensen (United Kingdom), Sarah L. Connors (France/United \r Kingdom), Alejandro Di Luca (Australia, Canada/Argentina), Sybren S. Drijfhout (The Netherlands), \r Christopher G. Fletcher (Canada/United Kingdom, Canada), Piers Forster (United Kingdom), \r Javier  Garcia-Serrano (Spain), Nathan P. Gillett (Canada), Darrell  S.  Kaufmann (United States  of \r America), David  P.  Keller (Germany/United States of America), Ben  Kravitz (United States \r of America), Hongmei Li (Germany/China), Yongxiao Liang (Canada/China), Andrew H. MacDougall \r (Canada), Elizaveta  Malinina (Canada/Russian Federation), Matthew  Menary (France/United \r [...]"


id: bbc2408aa1b4b3be5f654d5f30b8be2a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30262
content:
"[...] Kingdom), William  J.  Merryfield (Canada/United States of America), Seung-Ki Min (Republic \r of Korea), Zebedee R.J. Nicholls (Australia), Dirk Notz (Germany), Brodie Pearson (United States of \r America/United Kingdom), Matthew D. K. Priestley (United Kingdom), Johannes Quaas (Germany), \r Aurelien Ribes (France), Alex C. Ruane (United States of America), Jean-Baptiste Sallee (France), \r Emilia  Sanchez-Gomez (France/Spain), Sonia  I.  Seneviratne (Switzerland), Aimee  B.  A.  Slangen \r (The  Netherlands), Chris Smith (United Kingdom), Malte  F.  Stuecker (United States of America/\r Germany), Ranjini Swaminathan (United Kingdom/India), Peter W. Thorne (Ireland/ United Kingdom), \r Katarzyna  B. Tokarska (Switzerland/Poland), Matthew Toohey (Canada, Germany/Canada), \r Andrew Turner (United Kingdom), Danila Volpi (Italy), Cunde Xiao (China), Giuseppe Zappa (Italy)"


id: 939d16f877dce62ec2fb6234a01a1dc0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23175
content:
"The projections assessed here are mainly based on a new range \r of scenarios, the Shared Socio-economic Pathways (SSPs) used \r in the Coupled Model Intercomparison Project Phase 6 (CMIP6).\r Among the SSPs, the focus is on the five scenarios SSP1-1.9, SSP1-2.6, \r SSP2-4.5, SSP3-7.0, and SSP5-8.5. In the SSP labels, the first number \r refers to the assumed shared socio-economic pathway, and the second \r refers to the approximate global effective radiative forcing (ERF) in \r 2100. Where appropriate, this chapter also assesses new results from \r CMIP5, which used scenarios based on Representative Concentration \r Pathways (RCPs). Additional lines of evidence enter the assessment, \r especially for change in globally averaged surface air temperature \r (GSAT) and global mean sea level (GMSL), while assessment for \r changes in other quantities is mainly based on CMIP6 results. Unless \r noted otherwise, the assessments assume that there will be no major \r [...]"


id: 8fa19c8979f18dc460d69ebb3b3ef761
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 22479
content:
"[...] volcanic eruption in the 21st century. {1.6, 4.2.2, 4.3.2, 4.3.4, 4.6.2, \r Box 4.1, Cross-Chapter Box 4.1, Cross-Chapter Box 7.1, 9.6}"


id: 674edc5e217929ee82c92fc982a49ea1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 15447
content:
"In the near term (2021-2040), a 1.5degC increase in the 20-year \r average of GSAT, relative to the average over the period \r 1850-1900, is very likely to occur in scenario SSP5-8.5, \r likely to occur in scenarios SSP2-4.5 and SSP3-7.0, and more \r likely than not to occur in scenarios SSP1-1.9 and SSP1-2.6.\r The  threshold-crossing time is defined as the midpoint of the  first \r 20-year period during which the average GSAT exceeds the \r threshold. In all scenarios assessed here except SSP5-8.5, the central \r estimate of crossing the 1.5degC threshold lies in the early 2030s. This \r is in the early part of the likely range (2030-2052) assessed in the \r IPCC Special Report on Global Warming of 1.5degC (SR1.5), which \r assumed continuation of the then-current warming rate; this rate has \r been confirmed in the AR6. Roughly half of this difference between \r assessed crossing times arises from a  larger historical warming \r [...]"


id: f7d186f2f19c451676e1d61924f2466b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 40974
content:
"[...] diagnosed in AR6. The other half arises because for central estimates \r of climate sensitivity, most scenarios show stronger warming over \r the near term than was assessed as 'current' in SR1.5 (medium \r confidence). When considering scenarios similar to SSP1-1.9 instead "


id: 5bc32a32bcbc678e3ad5ff0c0c532b6c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13401
content:
"from internal variability (Cox and Stephenson, 2007; Hawkins and \r Sutton, 2009). An additional source of projection uncertainty arises \r from possible future volcanic eruptions and future solar variability. \r Assessment of uncertainty relies on multi-model ensembles such as the \r Coupled Model Intercomparison Project Phase 6 (CMIP6, Eyring et al., \r 2016), single-model initial-condition large ensembles (e.g.,  Kay \r et al., 2015; Deser et al., 2020), and ensembles initialized from the \r observed climate state (decadal predictions, e.g., Smith et al., 2013a; \r Meehl et al., 2014; Boer et al., 2016; Marotzke et al., 2016). Ensemble \r evaluation methods include assessment of model performance and \r independence (e.g., Knutti et al., 2017; Boe, 2018; Abramowitz et al., \r 2019); emergent and other observational constraints (e.g., Allen and \r Ingram, 2002; Hall and Qu, 2006; Cox et al., 2018); and the uncertainty \r assessment of equilibrium climate sensitivity and transient climate \r [...]"


id: e511631cda189517857dba20d6af8f9f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37599
content:
"[...] response in Chapter 7. Ensemble evaluation is assessed in Box 4.1 \r through the inclusion of lines of evidence in addition to the projection \r ensembles, including implications for potential model weighting."


id: 12c5b5567461ca6780d7d898ae6e374e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 507
content:
"Every projection assessment is conditioned on a  particular forcing \r scenario. If sufficient evidence is available, a  detailed probabilistic \r assessment of a physical climate outcome can be performed for each \r scenario separately. By contrast, there is no agreed-upon approach \r to assigning probabilities to forcing scenarios, to the point that it has \r been debated whether such an approach can even exist (e.g., Grubler \r and Nakicenovic, 2001; Schneider, 2001, 2002). Although there were \r some recent attempts to ascribe subjective probabilities to scenarios \r (e.g.,  Ho et  al., 2019; Hausfather and Peters, 2020), and although \r 'feasibility' along different dimensions is an important concept in \r scenario research (see AR6 WGIII Chapter 3), the scenarios used for \r the model-based projections assessed in this chapter do not come \r with statements about their likelihood of actually unfolding in the \r future. Therefore, it is usually not possible to combine responses to \r [...]"


id: 40d1c1d60dd6015610612cdec65303f7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8590
content:
"[...] individual scenarios into an overall probabilistic statement about \r expected future climate. Exceptions to this limit in the assessment \r are possible only under special circumstances, such as for some \r statements about near-term climate changes that are largely \r independent of the scenario chosen (e.g., Section 4.4.1). Beyond this, \r no combination of responses to different scenarios can be assessed \r in this chapter but may be possible in future assessments."


id: e81eb1aa2c38774742e8b017028465a8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38161
content:
"The chapter is organized as follows (Figure  4.1). After Section  4.2 \r on the methodologies used in the assessment, Section 4.3 assesses \r projected changes in key global climate indicators throughout the \r 21st  century, relative to the period 1995-2014, which comprises \r the last 20 years of the historical simulations of CMIP6 (Eyring et al., \r 2016) and hence the most recent past simulated with the observed \r atmospheric composition. The global climate indicators assessed \r include GSAT, global land precipitation, Arctic sea ice area (SIA), \r global mean sea level (GMSL), the Atlantic Meridional Overturning \r Circulation (AMOC), global mean ocean surface pH, carbon \r uptake by land and ocean, the global monsoon, the Northern and \r Southern Annular Modes (NAM and SAM), and the El Nino-Southern \r Oscillation (ENSO). Differently from the assessment for changes \r in other quantities only based on the range of CMIP6 projections, \r [...]"


id: f163307f7c3a05dcc63bf3c904b121dd
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 39851
content:
"[...] additional lines of evidence enter the assessment for GSAT and GMSL \r change. For most results and figures based on CMIP6, one realization \r from each model (the first of the uploaded set) is used. Section 4.3 \r finally synthesizes the assessment of GSAT change using multiple \r lines of evidence in addition to the CMIP6 projection simulations."


id: bff33469df89561e45dd287e838d0a26
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31058
content:
"Similar to the approach used in AR5 (Flato et al., 2013), the primary \r lines of evidence of this chapter are comprehensive climate models \r (atmosphere-ocean general circulation models, AOGCMs) and Earth \r system models (ESMs); ESMs differ from AOGCMs by including \r representations of various biogeochemical cycles. We also build \r on results from ESMs of intermediate complexity (EMICs; Claussen \r et  al., 2002; Eby et  al., 2013) and other types of models where \r appropriate. This chapter focuses on a particular set of coordinated \r multi-model experiments known as model intercomparison projects \r (MIPs). These frameworks recommend and document standards for \r experimental design for running AOGCMs and ESMs to minimize \r the chance of differences in results being misinterpreted. CMIP is an \r activity of the World Climate Research Programme (WCRP), and the \r latest phase is CMIP6 (Eyring et al., 2016). To establish robustness of \r [...]"


id: 87bde33d980ed3e23a181c14b004de48
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 21040
content:
"[...] results, it is vital to assess the performance of these models in terms \r of mean state, variability, and the response to external forcings. \r That evaluation has been undertaken using the CMIP6 'Diagnostic, \r Evaluation and Characterization of Klima' (DECK) and historical \r simulations in Chapter 3 of this Report, which concludes that there \r is high confidence that the CMIP6 multi-model mean captures most \r aspects of observed climate change well (Section 3.8.3.1)."


id: 3612887d1ae74ab51b170b5c2f6b76e2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6765
content:
"In presenting results and evidence, this chapter tries to be as \r comprehensive as possible. In tables we show multi-model mean \r change and 5-95% range for all five SSPs, while in time series \r figures we show multi-model mean change for all five SSPs but for \r clarity 5-95% range only for SSP1-2.6 and SSP3-7.0. Where maps are \r presented, due to space restrictions we focus on showing multi-model \r mean change for SSP1-2.6 and SSP3-7.0. SSP1-2.6 is preferred over \r SSP1-1.9 because the latter has far fewer simulations available. The \r high-end scenarios RCP8.5 or SSP5-8.5 have recently been argued \r to be implausible to unfold (e.g.,  Hausfather and Peters, 2020; see \r Chapter 3 of the AR6 WGIII). However, where relevant we show results \r for SSP5-8.5, for example to enable backwards compatibility with AR5, \r for comparison between emissions-driven and concentration-driven \r simulations, and because there is greater data availability of daily \r [...]"


id: 1ebdcdbbe9b59fd6e7b3a5c143ed1ea5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 2611
content:
"[...] output for SSP5-8.5. When presenting low-likelihood, high-warming \r storylines we also show results from the high-end SSP5-8.5 scenario."


id: 4207b68c1cab9556c8128c50e673b9a7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8818
content:
"Complete backward comparability between CMIP5 and CMIP6 scenarios \r cannot be established for detailed regional assessments, because the \r SSP scenarios include regional forcings - especially from land use and \r aerosols - that are different from the CMIP5 RCPs. Even at a global level, \r a quantitative comparison is challenging between corresponding SSP and \r RCP radiative forcing levels due to differing contributions to the forcing \r (Meinshausen et al., 2020) and evidence of differing model responses \r (Section 4.6.2.2; Wyser et al., 2020). The RCP scenarios assessed in AR5 \r all showed similar, rapid reductions in SLCFs and emissions of SLCF \r precursor species over the 21st century; the CMIP5 projections hence \r did not sample a wide range of possible trajectories for future SLCFs \r (Chuwah et al., 2013). The SSP scenarios assessed in the AR6 offer more \r scope to explore SLCF pathways as they sample a broader range of air \r [...]"


id: 1751cff2b3fb75aa0873e3cafeca43ef
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1324
content:
"[...] quality policy options (Gidden et al., 2019) and relationships of CO2 to \r non-CO2 greenhouse gases (Meinshausen et al., 2020). Section 4.6.2.2 \r assesses RCP and SSP differences. Other MIPs (see Section 4.2.1) have \r been designed to explicitly explore some of the implications of the \r different socio-economic storylines for a given radiative forcing level."


id: e0043a3404be894cd7e98bbb2662bd82
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 36657
content:
"There is also a  wide range of techniques employed to assimilate \r observed information into models in order to generate suitable \r initial conditions (Polkova et  al., 2019). These range in complexity \r from simple relaxation towards observed time series of sea surface \r temperature (SST) (Mignot et  al., 2016) or wind stress anomalies \r (Thoma et al., 2015a, b), to relaxation toward three-dimensional ocean \r and sometimes atmospheric state estimates from various sources \r (e.g.,  Pohlmann et  al., 2013; Knight et  al., 2014; Dunstone et  al., \r 2016), or hybrid relaxation combining surface and tri-dimensional \r restoring as function of ocean basins and depth (Sanchez-Gomez \r et al., 2016), to sophisticated data assimilation methods such as the \r ensemble Kalman filter (Nadiga et al., 2013; Counillon et al., 2014, \r 2016; Msadek et al., 2014; Karspeck et al., 2015; Brune et al., 2018; \r Cassou et  al., 2018; Polkova et  al., 2019), the four-dimensional \r [...]"


id: f24dd84ad2533bb6638d0ff0f4a79170
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 40024
content:
"[...] ensemble-variational hybrid data assimilation (He et al., 2017, 2020) \r and the initialization of sea ice (Guemas et  al., 2016; Kimmritz \r et  al., 2018). In addition, decadal predictions necessarily consist \r of ensembles of forecasts to quantify uncertainty, as discussed in \r Section 4.2.1. A common way to generate an ensemble is through \r sets of initial conditions containing small variations that lead to \r different subsequent climate trajectories. A  variety of methods \r and assumptions has been employed to generate and filter initial\u0002condition ensembles for decadal prediction (e.g., Marini et al., 2016; \r Kadow et al., 2017). As yet, there is no clear consensus as to which \r initialization and ensemble generation techniques are most effective, \r and evaluations of their comparative performance within a  single \r modelling framework are needed (Cassou et al., 2018)."


id: 48fc06c2d69c4acc193ef12c3d8da982
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10060
content:
"A consequence of model imperfections and resulting model systematic \r errors is that estimates of these errors must be removed from the \r prediction to isolate the predicted climate anomaly and the phase \r of the decadal modes of climate variability (Sections 4.4.3.5 and \r 4.4.3.6, and Annex IV, Sections AIV.2.6 and AIV.2.7). Because of the \r tendency for systematic drifts to occur following initialization, bias \r corrections generally depend on time since the start of the forecast, \r often referred to as lead time. In practice, the lead-time-dependent \r biases are calculated using ensemble retrospective predictions, \r also known as hindcasts, and recommended basic procedures for \r such corrections are provided in previous studies (Goddard et  al., \r 2013; Boer et al., 2016). The biases are also dynamically corrected \r during hindcasts and predictions by incorporating the multi-year \r monthly mean analysis increments from the initialization into \r [...]"


id: 4bf28bb76c9e79df654ffd577a646821
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 10628
content:
"[...] the initial condition at each integration step (Wang et al., 2013b). \r Besides mean climate as a function of lead time, further aspects of \r decadal predictions may be biased, such as the modes of variability \r (see Section 3.7 and Annex IV) upon which drift patterns are projected \r (Sanchez-Gomez et al., 2016), and additional correction procedures \r have thus been proposed to remove biases in representing long-term \r trends (Kharin et al., 2012; Kruschke et al., 2016; Balaji et al., 2018; "


id: bbabad73c7cedcf93cad1183c326b0db
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30243
content:
"Ocean initialization techniques generally use one of two strategies. \r Under full-field initialization, estimates of observed climate states \r are represented directly on the model grid. A potential drawback is \r that predictions initialized using the full-field approach tend to drift \r toward the biased climate preferred by the model (Smith et al., 2013a; \r Bellucci et al., 2015b; Sanchez-Gomez et al., 2016; Kroger et al., 2018; \r Nadiga et al., 2019). Such drifts can be as large as, or larger than, \r the climate anomaly being predicted and may therefore obscure \r predicted climate anomalies (Kroger et al., 2018) unless corrected for \r through post-processing. By contrast, anomaly initialization reduces \r drifts by adding observed anomalies (i.e., deviations from mean \r climate) to the mean model climate (Pohlmann et al., 2013; Smith \r et al., 2013a; Thoma et al., 2015b; Cassou et al., 2018), but has the \r disadvantage that the model state is then further from the real world \r [...]"


id: defa646526be817bded8c454b67821b9
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 36487
content:
"[...] from the start of the prediction. For both approaches, unrealistic \r features in the observation data used for initialization may induce \r unrealistic transient behavior (Pohlmann et  al., 2017; Teng et  al., "


id: bff1ab37920bf7a4d9785d1ecd5ab84a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31057
content:
"Skill for multi-year to decadal precipitation forecasts is generally much \r lower than for temperature, although one exception is Sahel rainfall \r (Sheen et al., 2017), due to its dependence on predictable variations \r in North Atlantic SST through teleconnections (Annex IV; Martin and \r Thorncroft, 2014a). Predictive skill on decadal time scales is found for \r extratropical storm-tracks and storm density (Kruschke et al., 2016; \r Schuster et  al., 2019), atmospheric blocking (Schuster et  al., 2019; \r Athanasiadis et al., 2020), the Quasi-Biennial Oscillation (QBO; Scaife \r et al., 2014; Pohlmann et al., 2019) and over the tropical oceans (tropical \r trans-basin variability; Chikamoto et al., 2015). In addition, decadal \r predictions with large ensemble sizes are able to predict multi-annual \r temperature (Peters et al., 2011; Sienz et al., 2016; Borchert et al., 2019; \r Sospedra-Alfonso and Boer, 2020), precipitation (Yeager et al., 2018; \r [...]"


id: 30a61737b64b20f31588c11de189476c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5813
content:
"[...] D.M. Smith et  al., 2019), and atmospheric circulation (Smith et  al., \r 2020) anomalies over certain land regions, although the ensemble\u0002mean magnitudes are much weaker than observed. This discrepancy \r may be symptomatic of an apparent deficiency in climate models that \r causes some predictable signal, such as that associated to the North \r Atlantic Oscillation (NAO; Section AIV.2.1), to be much weaker than \r in nature (Eade et al., 2014; Scaife and Smith, 2018; Strommen and \r Palmer, 2019; Smith et  al., 2020), while others, such as that linked \r to the SAM (Section AIV.2.2), are more consistent with observations \r (Byrne et al., 2019)."


id: 9e9a3df362dbb90f86843fc6d42d5590
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25131
content:
"Skill assessments have shown that initialized predictions can \r out-perform their uninitialized counterparts (Doblas-Reyes et  al., \r 2013; Meehl et al., 2014; Bellucci et al., 2015a; D.M. Smith et al., 2018, \r 2019; Yeager et al., 2018), although such comparisons are sensitive \r to the region and variable considered, multi-model predictions are \r generally more skilful than individual models (Doblas-Reyes et  al., \r 2013; D.M. Smith et al., 2013b, 2019). Considerable skill, especially for \r temperature, can be attributed to external forcings such as changes \r in GHG, aerosol concentrations, and volcanic eruptions. On a global \r scale, this contribution to skill has been found to exceed that from \r the prediction of internal variability except in the early stages (about \r one year for global SST) of the forecast (Corti et al., 2015; Sospedra\u0002Alfonso and Boer, 2020; Bilbao et al., 2021), though idealized potential \r skill measures and observations-based studies suggest that improving \r [...]"


id: c0839258c7a5768207bb679d04ce8a90
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31167
content:
"[...] the prediction of internal variability could extend this crossover to \r longer lead times (Boer et  al., 2013; Arthun et  al., 2017). In some \r cases, part of the skill arises from the ability of initialized predictions \r to capture observed transitions of major modes of climate variability \r (Meehl et al., 2016) such as the Pacific Decadal Variability (PDV) and \r the Atlantic Multi-decadal Variability (AMV; see Sections 4.4.3.5 \r and 4.4.3.6, and Annex IV, Sections AIV.2.6 and AIV.2.7)."


id: 6e340fe1df6b7fb6455f8981c4f94df
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 16653
content:
"Projected climate change is typically represented in this chapter \r for specific future periods. One important source of uncertainty in \r projections presented for fixed future epochs (time-slabs/time-slices) \r is the underlying scenario used; another is the structural uncertainty \r associated with model climate sensitivity. Presenting projections and \r associated measures of uncertainty for specific periods (see Sections 4.4 \r and 4.5) remains the most widely applied methodology towards \r informing climate change impact studies. It is becoming increasingly \r important from the perspective of climate change and mitigation \r policy, however, to present projections also as a function of the change \r in global mean temperature (i.e., global warming levels, GWLs). They \r are expressed either in terms of changes of global mean surface \r temperature (GMST) or GSAT (see  Section  1.6.2 and Cross-Chapter \r Box 2.3). For example, the IPCC SR1.5 (Hoegh-Guldberg et al., 2018) \r [...]"


id: 5087e8bf180366488f0afca29454e969
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 11442
content:
"[...] assessed the regional patterns of warming and precipitation change for \r GMST increase of 1.5degC and 2degC above 1850-1900 levels. Techniques \r used to represent the spatial variations in climate at a given GWL are \r referred to as pattern scaling."


id: 952ad40f8d638eaab3abe5aa4d2feb45
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23449
content:
"In the 'traditional' methodology as applied in AR5 (Collins et al., 2013), \r patterns of climate change in space are calculated as the product of the \r change in GSAT at a given point in time and a spatial pattern of change \r that is constant over time and the scenario under consideration, and \r which may or may not depend on a particular climate model (Allen \r and Ingram, 2002; Mitchell, 2003; Lambert and Allen, 2009; Andrews \r and Forster, 2010; Bony et al., 2013; Lopez et al., 2014). This approach \r assumes that external forcing does not affect the internal variability \r of the climate system, which may be regarded a stringent assumption \r when taking into account decadal and multi-decadal variability (Lopez \r et  al., 2014) and the potential non-linearity of the climate change \r signal. Moreover, pattern scaling is expected to have lower skill for \r variables with large spatial variability (Tebaldi and Arblaster, 2014). \r [...]"


id: 9959fb4f54550fd327dc263eb2bd8e53
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 24198
content:
"[...] Pattern scaling also fails to capture changes in boundaries that move \r poleward such as sea ice extent and snow cover (Collins et al., 2013), \r and temporal frequency quantities such as frost days that decrease \r under warming but are bounded at zero. Spatial patterns are also \r expected to be different between transient and equilibrium simulations \r because of the long adjustment time scale of the deep ocean."


id: dbad7022334973bb03c0f629e7241add
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35940
content:
"An alternative to the traditional pattern scaling approach is the \r time-shift method described by Herger et al. (2015) which is applied in \r this chapter (also called the epoch approach; see Section 4.6.1). When \r applied to a transient scenario such as SSP5-8.5, a future time-slab is \r referenced to a particular increase in the GSAT (e.g., 1.5degC or 2degC of \r global warming above pre-industrial levels). The spatial patterns that \r result represent a  direct scaling of the spatial variations of climate \r change at the particular level of global warming. An important \r advantage of this approach is that it ensures physical consistency \r between the different variables for which changes are presented \r (Herger et al., 2015). The internal variability does not have to be scaled \r and is consistent with the GSAT change. Furthermore, the time-shift \r method allows for a partial comparison of how the rate of increase \r in GSAT influences the regional spatial patterns of climate change. \r [...]"


id: 7e5493a266fd314266b82a9b02020682
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 19390
content:
"[...] For example, spatial patterns of change for global warming of 2degC \r can be compared across the SSP2-4.5 and SSP5-8.5 scenarios. Direct \r comparisons can also be obtained between variations in the regional \r impacts of climate change for the case where global warming stabilizes \r at, for instance, 1.5degC or 2degC of global warming, as opposed to the case \r where the GSAT reaches and then exceeds the 1.5degC or 2degC thresholds \r (Tebaldi and Knutti, 2018). An important potential caveat is that \r forcing mechanisms such as aerosol radiative forcing are represented \r differently in different models, even for the same SSP. This may imply \r different regional aerosol direct and indirect effects, implying different \r regional climate change patterns. Hence, it is important to consider the \r variations in the forcing mechanisms responsible for a specific increase \r in GSAT towards understanding the uncertainty range associated with \r [...]"


id: dceee803f7e5a90dfe2a939a04e95fd8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 36133
content:
"[...] the variations in regional climate change. A minor practical limitation \r of this approach is that stabilization scenarios at 1.5degC or 2degC of global \r warming, such as SSP1-2.6, do not allow for spatial patterns of change \r to be calculated from these scenarios at higher levels of global warming, \r while it is possible in scenarios such as SSP5-8.5 (Herger et al., 2015).\r In this chapter, the spatial patterns of change as a function of GWLs \r (defined in terms of the increase in the GSAT relative to 1850-1900) "


id: 3fbde4844f36a15ef323a285addc7739
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8407
content:
"As discussed extensively in AR5 (Collins et al., 2013), ensemble spread \r in projections performed with different climate models accounts for \r only part of the entire model uncertainty, even when considering the \r uncertainty in the radiative forcing in projections (Vial et al., 2013) \r and forced response. The AR5 uncertainty characterisation (Kirtman \r et  al., 2013) followed Hawkins and Sutton (2009) and diagnosed \r internal variability through a high-pass temporal filter. This approach \r has deficiencies particularly if internal variability manifests on the \r multi-decadal time scales (Deser et al., 2012a; Marotzke and Forster, \r 2015) and is classified as (model) response uncertainty instead of \r internal variability. Single-model initial-condition large ensembles \r revealed that the AR5 approach underestimates the role of internal \r variability uncertainty and overestimates the role of model uncertainty \r [...]"


id: 4893d68037ade08c5351b247f042808e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9983
content:
"[...] (Maher et al., 2018; Stolpe et al., 2018; Lehner et al., 2020) particularly \r at the local scale while yielding a  reasonable approximation for \r uncertainty separation for GSAT (Lehner et al., 2020)."


id: 7fcc23dc51f6a7ab9beca74f7676e6a7
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 19621
content:
"The AR5 assessed with very high confidence that climate models \r reproduce the general features of the global-scale annual mean surface \r temperature increase over the historical period, including the more \r rapid warming in the second half of the 20th century, and the cooling \r immediately following large volcanic eruptions. Furthermore, because \r climate and Earth system models are based on physical principles, they \r were assessed in AR5 to reproduce many important aspects of observed \r climate. Both aspects were argued to contribute to our confidence in the \r models' suitability for their application in quantitative future predictions \r and projections (Flato et al., 2013). This Report assesses (in Section 3.8.2) \r with high confidence that for most large-scale indicators of climate \r change, the recent mean climate simulated by the latest generation \r climate models underpinning this assessment has improved compared \r [...]"


id: 1f8c93fdfa79db053c0041c4c2984daa
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 2760
content:
"[...] to the models assessed in AR5, and with high confidence that the \r multi-model mean captures most aspects of observed climate change \r well. These assessments form the foundation of applying climate and \r Earth system models to the projections assessed in this chapter. Where \r appropriate, the assessment of projected changes is accompanied by an \r assessment of process understanding and model evaluation."


id: c0408987c302f2a526e584352709a27e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31124
content:
"Single-model initial-condition large ensembles thus represent a crucial \r step towards a cleaner separation of model uncertainty and internal \r variability than available for AR5 (Deser et al., 2014, 2016; Saffioti et al., \r 2017; Sippel et al., 2019; Milinski et al., 2020; von Trentini et al., 2020; \r Maher et al., 2021). Novel approaches have been proposed to further \r quantify internal variability in multi-model ensembles (Hingray and Said, \r 2014; Evin et al., 2019; Hingray et al., 2019). For time horizons beyond \r the limit of decadal predictability (Branstator and Teng, 2010; Meehl \r et al., 2014; Marotzke et al., 2016), such as in the CMIP6 projections, \r the simulations are starting from random rather than assimilated \r initial conditions. Internal variability constitutes an uncertainty in the \r projection of the climate in a future period of 10 or 20 years that is \r irreducible, but can be precisely quantified for individual models using \r [...]"


id: 3194a2af7655e6fc7761accc25397f9b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5968
content:
"[...] sufficiently large initial-condition ensembles (Fischer et al., 2013; Deser \r et al., 2016, 2020; Hawkins et al., 2016; Pendergrass et al., 2017; Luo \r et al., 2018; Dai and Bloecker, 2019; Maher et al., 2019)."


id: c0952385640cfec4d48ec49647035fc1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 31182
content:
"Uncertainties in emissions of greenhouse gases and aerosols that affect \r future radiative forcings are represented by selected SSP scenarios \r (Sections 1.6.1 and 4.2.2). In addition to emission uncertainties, \r SSPs represent uncertainties in land use changes (van Vuuren et al., \r 2011; Ciais et al., 2013; O'Neill et al., 2016; Christensen et al., 2018). \r Additional uncertainty comes from climate carbon-cycle feedbacks \r and the residence time of atmospheric constituents, and are at least \r partly accounted for in emissions-driven simulations as opposed to \r concentration-driven simulations (Friedlingstein et al., 2014; Hewitt \r et al., 2016). The climate carbon-cycle feedbacks affect the transient \r climate response to cumulative CO2 emissions (TCRE). Constraining \r this uncertainty is crucial for the assessment of remaining carbon \r budgets consistent with global mean temperature levels (Millar \r et al., 2017; IPCC, 2018a) and is covered in Chapter 5 of this Report. \r [...]"


id: 9feb6f886c44f1d66db80c724956950f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25344
content:
"[...] Finally, there are uncertainties in future solar and volcanic forcing \r (Cross-Chapter Box 4.1)."


id: d605c2c559fd550873a99efb7c67a3d0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 34971
content:
"Most methods for quantifying robustness assume that only one \r realization from each model is applied. There are challenges that \r arise from having heterogeneous multi-model ensembles with many \r members for some models and single members for others (Olonscheck \r and Notz, 2017; Evin et  al., 2019). Furthermore, the methods that \r map model robustness usually ignore that sharing parametrizations \r or entire components across coupled models can lead to substantial \r model interdependence (Fischer et  al., 2011; Kharin et  al., 2012; \r Knutti et al., 2013, 2017; Leduc et al., 2015; Sanderson et al., 2015, \r 2017; Annan and Hargreaves, 2017; Boe, 2018; Abramowitz et  al., \r 2019). This may lead to a  biased estimate of model agreement if \r a substantial fraction of models is interdependent. The methodologies \r and results in this literature since AR5 are higher in quality and \r clarity. However, quantifying and accounting for model dependence \r [...]"


id: 9779603777ed375e1d64215840a17cb2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23861
content:
"[...] in a  robust way remains challenging (Abramowitz et  al.,  2019). \r Furthermore, absence of significant mean change in a certain climate \r variable does not imply absence of substantial impact, because \r there may be substantial change in variability, which is typically not \r mapped (McSweeney and Jones, 2013)."


id: f5672ec8167c8c654f9cd0de70c00a6c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 40541
content:
"Chapter  4 uses the advanced approach, taking into account the \r sign and significance of the change (Cross-Chapter Box Atlas.1, \r approach  C). Where not applicable, such as due to a  lack of the \r necessary model output, the simple method is used taking into account \r only agreement on the sign of the change across the multi-model \r ensemble (Cross-Chapter Box Atlas.1, approach B). The advanced \r approach is similar to the method used in AR5 but isolates conflicting \r signals as proposed in Zappa et  al. (2021). It  uses three mutually \r exclusive categories and distinguishes (i) areas with significant change \r and high model agreement (no overlay), (ii) areas with no change or no \r robust change (diagonal lines), and (iii) areas with significant change \r but low agreement (crossed lines). Category (i) marks areas where \r the climate change signals likely emerge from internal variability, \r where two-thirds or more of the models project changes greater than \r [...]"


id: 3f3b1fac4ff2b877253314c200faf8b8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 8313
content:
"[...] internal variability and 80% or more of the models agree on the sign \r of the change. Category (ii) marks areas where fewer than two-thirds \r of the models project changes greater than internal variability, and \r category (iii) marks areas with significant but conflicting signals, where \r two-thirds or more of the models project changes greater than internal \r variability but less than 80% agree on the sign of the change."


id: f5319aacc42b9d8c1e3bc24387ff7ac
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 40503
content:
"Maps of multi-model mean changes provide an average estimate for \r the forced model climate response to a certain forcing. However, they \r do not include any information on the robustness of the response across \r models nor on the significance of the change with respect to unforced \r internal variability (Tebaldi et  al., 2011). Models can consistently \r show absence of significant change, in which case they should not be \r expected to agree on the sign of a change (e.g., Tebaldi et al., 2011; \r Knutti and Sedlacek, 2013; Fischer et al., 2014). If a multi-model mean \r map of precipitation shows no change, it is unclear whether the models \r consistently project insignificant changes or whether projections span \r both significant increases and significant decreases. Several methods \r have been proposed to distinguish significant conflicting signals from \r agreement on no significant change (Tebaldi et al., 2011; Knutti and \r [...]"


id: bfa9ac0e67d735fc97bf40df567f9b86
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 30995
content:
"[...] Sedlacek, 2013; McSweeney and Jones, 2013; Zappa et al., 2021). A set \r of different methods have been introduced in the literature to display \r model robustness and to put a climate change signal into the context \r of internal variability. Box 12.1 in AR5 provides a detailed assessment \r of different methods of mapping model robustness and Cross-Chapter \r Box Atlas.1 provides an update of recent proposals including the \r methods used in this Report."


id: 7495d1cffad52d5001f5a1f28ffc8469
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 17747
content:
"In this chapter variability is defined as , where\r is the standard deviation of 20-year means in the pre-industrial \r control simulations (see Cross-Chapter Box, Atlas.1). Category (a) \r uses a definition very similar to the AR5 method for stippling, except \r that the model signal is compared to its corresponding internal rather \r than the multi-model mean variability, to account for the substantial \r model differences in pre-industrial internal variability (Parsons et al., \r 2020). Changes smaller than internal variability can have potential \r impacts particularly if they persist over sustained periods such as \r several decades. Finally, even when changes do not exceed variability \r at the grid point level they may exceed variability if aggregated over \r catchment basins, regions, or continents (Cross-Chapter Box Atlas.1). \r Maps of mean changes also ignore potential changes in variability \r addressed by a  more comprehensive assessment of changes in \r [...]"


id: b6c0e15258da067044d82a79f08d6af0
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 29353
content:
"[...] temperature variability (Section  4.5.1) and modes of internal \r variability (Section 4.4.3)."


id: 394322380659dc768e3bd6e55927570b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 7287
content:
"Simulated GSAT over 1995-2014, relative to 1850-1900 period, \r warms by very similar amounts in the two sets of simulations: 0.82degC \r (0.45-1.31) in emissions-driven compared with 0.75degC (0.53-1.09) \r in concentration-driven simulations. By the end of the 21st century, \r warming in emissions-driven simulations is very similar: 4.58degC \r (3.53-6.70), reflecting the slightly lower CO2 concentration \r simulated by the ESMs compared with warming under the \r prescribed CO2 pathway of 4.69degC (3.70-6.77). This difference in \r model-mean response is more than an order of magnitude smaller \r than the 5-95% spread across model projections. The spread in CO2\r concentration, compared with the prescribed default concentration, \r leads to a very small increase by about 0.1degC in the spread of GSAT \r projections, but it is not possible to tell if this is a direct consequence \r of the simulation configuration or internal variability of the model \r [...]"


id: 3512ecdd0d4356ed5a83c9af49b8d68c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 6581
content:
"[...] simulations. These differences due to experimental configuration \r would be smaller still under scenarios with lower CO2 levels, and \r so we assess that results from concentration-driven and emissions\u0002driven configurations do not affect the assessment of GSAT \r projections (high confidence)."


id: c5c0a1ba391f73c90552df0fc72e90d6
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 32099
content:
"Studies focusing on the relationship of sea ice extent and changes in \r external drivers have consistently found a much-reduced likelihood \r of a  practically ice-free Arctic Ocean during summer for global \r warming of 1.5degC than for 2.0degC (Screen and Williamson, 2017; \r Jahn, 2018; Niederdrenk and Notz, 2018; Notz and Stroeve,  2018; \r Sigmond  et  al.,  2018; Olson et  al., 2019). This is shown here in \r a  large initial-condition ensemble of observationally constrained \r model simulations where GSAT are stabilized at 1.5degC, 2.0degC and \r 3.0degC warming relative to 1850-1900 in the RCP8.5 scenario \r (Figure  4.5). Temperature stabilization is achieved by switching \r off all the anthropogenic emissions around the time that GSAT \r first reaches the stabilization thresholds. Simulations have been \r observationally constrained to correct for a model bias in simulated \r historical September sea ice extent. In these simulations, Arctic sea \r [...]"


id: 92c2f8bd71817daeb2b1f650f8bdaea1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23036
content:
"[...] ice coverage in September is simulated, on average, to drop below \r 1 million km2 around 2040, consistent with the AR5 set of assessed \r models (Sigmond et al., 2018). The individual model simulations, for \r which there are twenty for each stabilized temperature level, show "


id: 9ed5b0f50a9a265a331a096751009eb8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25164
content:
"The Northern Annular Mode (NAM) is the leading mode of variability \r in the NH extratropical atmosphere (Section AIV.2.1). Throughout this \r chapter, we use a simple fixed latitude-based NAM index defined as \r the difference in SLP between 35degN and 65degN (Section AIV.2.1; Li and \r Wang, 2003). The NAM index computed from the latitudinal gradient \r in SLP is strongly correlated with variations in the latitudinal position \r and strength of the mid-latitude westerly jets, and with the spatial \r distribution of Arctic sea ice (Caian et al., 2018). Projected changes \r in the position and strength of the mid-latitude westerly jets, storm \r tracks, and atmospheric blocking in both hemispheres are assessed \r in Section 4.5.1.6. The AR5 referred to the NAM, and its synonym the \r Arctic Oscillation (AO), through its regional counterpart, the North \r Atlantic Oscillation (NAO). Here, we use the term NAM to refer also \r to the AO and NAO (Section AIV.2.1), accepting that the AO and NAO \r [...]"


id: 4777a75a9a658bc590c40326b5968733
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 9789
content:
"The Southern Annular Mode (SAM) is the leading mode of large-scale \r extratropical atmospheric variability in the Southern Hemisphere and \r influences most of the southern extratropics (Annex IV, Section AIV.2.2). \r In its positive phase, the SAM characterizes anomalously low pressure \r over the polar cap and high pressure in southern mid-latitudes (Marshall, \r 2003). While there are some zonal asymmetries to the structure of the \r SAM (Section AIV.2.2), it is more symmetric than its NH counterpart \r (Fyfe et  al., 1999). Throughout this chapter, we use a  simple fixed \r latitude-based SAM index defined as the difference in zonal mean SLP \r between 40degS and 65degS (Gong and Wang, 1999; see Section AIV.2.2 for \r discussion of other SAM indices). Although the SAM is often used as \r a proxy for the location of the mid-latitude westerly wind belt, trends \r in the SAM can reflect a combination of changes in jet position, width, \r [...]"


id: d7c98ac287a6f511e145701b0e6bef30
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35274
content:
"[...] and strength. The changes in the Southern Hemisphere circulation \r associated with the SAM influence surface wind stress (Wang et al., \r 2014) and hence affect the Southern Ocean."


id: 549400f2f3bd5faa1b03c87ab26675cd
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12160
content:
"Among a range of indices proposed for representing ENSO, we use \r the most prominent one, the Nino 3.4 index, defined as the average \r equatorial SST or precipitation across the central equatorial Pacific \r (5degS-5degN, 170degW-120degW; Section AIV.2.3). Here, we consider the \r evolution of the amplitude of Nino 3.4 index for SST and precipitation \r over the 21st  century as projected by CMIP6 models. Analysis of \r CMIP6 models shows there is no robust model consensus on the \r forced changes in the amplitude of ENSO SST variability even under \r the high-emissions scenarios SSP3-7.0 and SSP5-8.5, but a significant \r increasing trend in the amplitude of ENSO precipitation variability is \r projected across the 21st century in the four SSPs (Figure 4.10). This \r is broadly consistent with results from CMIP5 models (Christensen \r et al., 2013; Power et al., 2013; Cai et al., 2015; Chen et al., 2017; \r Wengel et al., 2018), recent studies with CMIP6 models (Brown et al., \r [...]"


id: 2badcbee455da2573150135d3b5120fe
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 4946
content:
"[...] 2020; Fredriksen et al., 2020; Freund et al., 2020; Yun et al., 2021), \r and large initial-condition ensemble experiments (Maher et al., 2018; \r Zheng et al., 2018; Haszpra et al., 2020)."


id: cf581fa8d3322315e08459329239b075
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33828
content:
"and Figure 4.11a,b; Brunner et al., 2020; Liang et al., 2020; Nijsse \r et  al., 2020; Tokarska et  al., 2020; Ribes et  al., 2021), as well \r as the AR6-updated assessment of ECS and TCR in Section  7.5. \r The approaches of (Liang et al., 2020; Tokarska et al., 2020; Ribes \r et al., 2021) have first been extended to all 20-year averaging periods \r between 2000 and 2100. For each 20-year period, the 5%, 50%, and \r 95% percentile GSAT values of these three constrained CMIP6 results \r are averaged percentile by percentile (Figure 4.11c). Then, an emulator \r based on a two-layer energy balance model (e.g., Held et al., 2010) \r is driven by the Chapter 7-derived ERF. The emulator parameters are \r chosen such that the central estimate, lower bound of the very likely \r range, and upper bound of the very likely range of climate feedback \r parameter and ocean heat uptake coefficient take the values that \r map onto the corresponding combination of ECS (3degC, 2degC and \r [...]"


id: ba2d971e9d3bd10a3a7011f2c3e186b1
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 29980
content:
"[...] 5degC, respectively) and TCR (1.8degC, 1.2degC and 2.4degC, respectively) \r of Section 7.5 (see Box 4.1). As a final step, the constrained-CMIP6 \r and the emulator-based 5%, 50%, and 95% percentile GSAT values \r are averaged percentile by percentile (Figure 4.11c,d and Table 4.5). \r Constrained CMIP6 results and the ECS- and TCR-based emulator \r thus contribute one-half each to the GSAT assessment. Because the \r emulator results and (Ribes et al., 2021) represent the forced response \r only, and averaging over the other two individual estimates (Liang \r et al., 2020; Tokarska et al., 2020) further reduces the contribution \r from internal variability, the assessed GSAT time series are assumed \r to represent purely the forced response."


id: f2154738674ac51d8262c331f729b1ae
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 39980
content:
"the subtropics, precipitation response is driven primarily by the fast \r adjustment to CO2 forcing (He and Soden, 2017). In addition to the \r response to GHG forcing, forcing from natural and anthropogenic \r aerosols exert impacts on regional patterns of precipitation \r (Section  10.3.1; Ramanathan et  al., 2005; Bollasina et  al., 2011; \r Polson et al., 2014; Krishnan et al., 2016; L. Liu et al., 2018; Shawki \r et al., 2018). The large uncertainties in near-term regional precipitation \r projections arise due to the interplay between internal variability and \r anthropogenic external forcing (Endo et al., 2018; Wang et al., 2021). \r Uncertainties in future aerosol emissions scenarios contribute to \r uncertainties in regional precipitation projections (Wilcox et al., 2020). \r Aerosol changes induce a drying in the SH tropical band compensated \r by wetter conditions in the NH counterpart (Acosta Navarro et al., 2017). \r [...]"


id: 82ad53c64035e8ac42cd00a8a223e952
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20120
content:
"[...] The spatially uneven distribution of the aerosol forcing may also induce \r changes in tropical precipitation caused by shifts in the mean location \r of the intertropical convergence zone (ITCZ) (Hwang et al., 2013; Ridley \r et al., 2015; Voigt et al., 2017). Because of the large uncertainty in the \r aerosol radiative forcing and the dynamical response to the aerosol \r forcing there is medium confidence in the impacts of aerosols on near\u0002"


id: 9f32031175880396349171adff4a4c60
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 25229
content:
"Figure 4.13 | Near-term change of seasonal mean precipitation. Displayed are projected spatial patterns of CMIP6 multi-model mean change (%) in (top) December-January-\r February (DJF) and (bottom) June-July-August (JJA) precipitation from SSP1-2.6 and SSP3-7.0 in 2021-2040 relative to 1995-2014. The number of models used is indicated \r in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show \r a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change \r or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals \r [...]"


id: d8cb2ee64d2dec62d8ed9fd7ff942a24
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 35435
content:
"[...] where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on \r data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 5538902e16225f370c6c7ac48546ada8
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 12270
content:
"The AR5 assessed that for RCP8.5, Arctic sea ice coverage in September \r will drop below 1 million km2, or become practically ice-free, at some \r point between 2040 and 2060 (Collins et al., 2013). Since AR5, there \r has been substantial progress in understanding the response of \r Arctic sea ice to near-term changes in external forcing. In particular, \r it is very likely that different trajectories of the near-term evolution \r of anthropogenic forcing cause distinctly different likelihood ranges \r for very low sea ice coverage to occur over the next two decades \r (Notz and Stroeve, 2018). For example, there is an unlikely drop of \r September Arctic sea ice coverage to below 1 million km2 before 2040 \r for RCP 2.6, and a likely drop of September Arctic sea ice coverage \r to below 1 million km2 before 2040 for RCP 8.5 (medium confidence \r given the single study). The much higher likelihood of a practically \r sea ice free Arctic Ocean during summer before 2040 in RCP8.5 \r [...]"


id: ce5b31afeab16e9197d0c69fc346c9c4
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 33646
content:
"[...] compared to RCP2.6 is consistent with related studies assessed in \r SROCC that find a substantially increased likelihood of an ice-free \r Arctic Ocean for 2.0degC compared to 1.5degC mean global warming \r relative to pre-industrial levels (Screen and Williamson, 2017; Jahn, \r 2018; Niederdrenk and Notz, 2018; Notz and Stroeve, 2018; Sigmond \r et al., 2018; Olson et al., 2019)."


id: 739bde94709d33aab6b4c4ae19ac36e2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 17595
content:
"The global land monsoon precipitation index, defined as the area\u0002weighted precipitation rate in the global land monsoon domain, tends \r to increase in the near term under all five core SSPs (Figure 4.14a) (Chen \r et al., 2020), but changes are small compared to the intermodel spread \r in the historical period. The Northern Hemisphere summer monsoon \r circulation index, defined as the vertical shear of zonal winds between \r 850 and 200 hPa averaged in a zone stretching from Mexico eastward \r to the Philippines (0deg-20degN, 120degW-120degE), tends to decrease under \r four of the five SSP scenarios (Figure 4.14b), potentially offsetting \r monsoon precipitation increase. Projected changes in the global \r monsoon circulation are also uncertain, because they are influenced \r by internal variability such as AMV and PDV (see Section  3.3.3.2) \r and structural differences across models. In the near-term, for CMIP6 \r projections (Figure 4.14a), the multi-model mean (5-95% range) of \r [...]"


id: b8dd016223e59aead0cc7c035e29a258
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 29729
content:
"[...] global land monsoon precipitation change is 1.9% (-0.4 to 4.9%), \r 1.6% (-1.0 to 5.2%), 1.3% (-1.7 to 3.7%), and 1.9% (-0.8 to 5.2%) \r under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively."


id: 14b8b9004fdfed1ac36d02da41fb253
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 872
content:
"On seasonal to interannual time scales, there is new evidence since \r AR5 that initialized predictions show lower potential predictability \r for the boreal winter NAO than the correlation skill with respect \r to observations (Eade et  al., 2014; Baker et  al., 2018; Scaife and \r Smith, 2018; Athanasiadis et  al., 2020). This has been referred to \r in the literature as a 'signal-to-noise paradox' and means that very \r large ensembles of predictions are needed to isolate the predictable \r component of the NAO. While the processes that contribute to the \r predictability of the winter NAO on seasonal time scales may be \r distinct from the processes that drive multi-decadal trends, there is \r emerging evidence that initialized predictions also underrepresent the \r predictability of the winter NAO on decadal time scales (D.M. Smith \r et al., 2019). Post-processing and aggregation of initialized predictions \r may therefore reveal significant skill for predicting the winter NAO on \r [...]"


id: 5b1973a59ce0feacbaaeaaacc5bb5f52
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13278
content:
"[...] decadal time scales (Smith et al., 2020). Considering these new results \r since AR5, in the near-term it is likely that any anthropogenic forced \r signal in the NAM will be of comparable magnitude or smaller than \r natural internal variability in the NAM (medium confidence)."


id: 3108ec0e55e2c47fb935136fc49b675a
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 5876
content:
"Since AR5, there have been advances in understanding the role of \r internal climate variability for projected near-term SH circulation \r trends (Solomon and Polvani, 2016). A large initial-condition ensemble \r following the RCP4.5 emissions scenario shows a monotonic positive \r SAM trend in austral winter. In austral summer, the SAM trend over \r the first half of the 21st century is weaker compared to the strongly \r positive trend observed and simulated over the late 20th  century. \r In that model, the number of realizations required to identify \r a  detectable change in decadal mean austral winter SAM index \r from a year 2000 reference state decreased to below five by around \r 2025-2030 (Solomon and Polvani, 2016). However, in December-\r January-February (DJF) the same criterion is not met until the second \r half of the 21st century, owing to the near-term opposing effects of \r ozone recovery and GHGs on the austral-summer SAM. In austral \r [...]"


id: e3fca8ff413c922b84f477ac781d9b45
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 37401
content:
"[...] summer, forced changes in the SAM index in the near-term are \r therefore likely to be smaller than changes due to internal variability \r (Figure 4.17b; Barnes et al., 2014; Solomon and Polvani, 2016)."


id: d5afd6d759118ebfda043c267e6ddaa2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 34907
content:
"There are no specific studies focusing on near-term changes in \r tropical Atlantic modes; nevertheless, decadal predictions show \r that although the North Atlantic stands out in most CMIP5 models \r as the primary region where skill might be improved because of \r initialization, encouraging results have also been found in the \r tropical Atlantic (Meehl et  al., 2014). The effect of initialization in \r the tropical Atlantic is not only visible in surface temperature but also \r in the subsurface ocean (Corti et al., 2015). In particular, initialization \r improves the skill via remote ocean conditions in the North Atlantic \r subpolar gyre and tropical Pacific, which influence the tropical \r Atlantic through atmospheric teleconnections (Dunstone et al., 2011; \r Vecchi et  al., 2014; Garcia-Serrano et  al., 2015). Improvements of \r some aspects of climate prediction systems (initialization techniques, \r large ensembles, increasing model resolution) have also led to skill \r [...]"


id: 1647384fc959fe021f19f6496e0b9c82
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 1137
content:
"[...] improvements over the tropical Atlantic (Pohlmann et  al., 2013; \r Monerie et al., 2017; Yeager and Robson, 2017)."


id: f02bc65793bae903845eb3d88fcd82bd
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 39642
content:
"The Atlantic Multi-decadal Variability (AMV) is a large-scale climate \r mode accounting for the main fluctuations in North Atlantic SST on \r multi-decadal time scales (Section AIV.2.7). The AMV influences air \r temperatures and precipitation over adjacent and remote continents, \r and its undulations can partially explain the observed variations in \r the NH mean temperatures (Steinman et al., 2015). The origin of this \r variability is still uncertain. Ocean dynamics (e.g.,  changes in the \r AMOC), external forcing, and local atmospheric forcing all seem to \r play a role (Menary et al., 2015; Ruprich-Robert and Cassou, 2015; \r Brown et al., 2016; Cassou et al., 2018; Wills et al., 2019). Recent \r studies have discussed that the ocean dynamics play an active role \r in generating AMV (Oelsmann et al., 2020) and its interplay with the \r NAO (Vecchi et  al., 2017; R. Zhang et  al., 2019; Kim et  al., 2020), \r although natural and anthropogenic external forcing might be \r [...]"


id: 733a736ed14eb19426a9cf1d056b733c
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 17523
content:
"[...] crucial in modulating its amplitude and timing (Bellucci et al., 2017; \r Bellomo et  al., 2018; Andrews et  al., 2020; Borchert et  al., 2021; \r Mann et al., 2021; see Sections 3.7.7 and AIV.2.7)."


id: 6aa78871ca293341ba4054a5f92de4fe
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 16046
content:
"Encouraging results about the prediction of land precipitation linked \r to the warm AMV phase (Section 3.7.7 and Annex IV, Figure AIV.2.7) \r on a  two-to-nine-year time scale are reported in the multi-model \r study by D.M. Smith et  al. (2019). Positive correlations with \r observations are found in the Sahel, South America, the Maritime \r Continent. Analyses from large-ensemble decadal prediction systems \r such as the community Earth system model decadal prediction large \r ensemble (CESM-DPLE; Yeager et al., 2018) show an improvement \r with respect to the CMIP5 decadal hindcasts (Martin and Thorncroft, \r 2014b) in forecasting Sahel precipitation over three to seven years, \r which is consistent with the current understanding of AMV impact \r over Africa (Mohino et al., 2016; D.M. Smith et al., 2019). CESM-DPLE \r predicts drought conditions over the Sahel through 2020, which is \r compatible with a shift towards a negative phase of AMV as a result \r [...]"


id: f641f624c8424d5cc7cd9ce98785ed81
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 40684
content:
"[...] of a  weakening of the AMOC, advocated by a  number of studies \r (Hermanson et al., 2014; Robson et al., 2014; Yeager et al., 2015)."


id: 681d38885bd557d248b426b0f261c83e
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 15583
content:
"Building on CMIP6 results for the effects of reducing SLCF \r emissions from a baseline of SSP3-7.0, the overall contribution of \r SLCFs to GSAT changes in the marker SSPs are now quantified using \r a simple climate model emulator. For consistency with Section 6.7.2 \r and Figure  6.22, the basket of SLCF compounds considered \r includes aerosols, ozone, methane, black carbon on snow and \r hydrofluorocarbons (HFCs) with lifetimes of less than 50 years. In \r the five marker SSPs considered, the net effect of SLCFs contributes \r to a higher GSAT in the near, mid- and long term (Table 4.6 and \r Section  6.7.2). In the SSP1-1.9 and SSP1-2.6 scenarios, SLCFs \r contribute to a higher GSAT by a central estimate of around 0.3degC \r compared to 1995-2014 across the three-time horizons. In the \r long-term, the 0.3C warming due to SLCFs in SSP1-2.6 can be \r compared to the assessed very likely GSAT change for this period of \r 0.5degC-1.5degC (Section 4.3.4 and Table 4.5). The SSP2-4.5, SSP3-7.0 \r [...]"


id: 804f80801e7c0ff3acf27b393f12de0f
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 19704
content:
"[...] and SSP5-8.5 scenarios all show a larger SLCF effect on GSAT in \r the long term relative to the near term. In SSP3-7.0, the long\u0002term warming due to SLCFs by 0.7degC can be compared with the \r assessed very likely GSAT anomaly for this period of 2.0degC -3.7degC \r (Section 4.3.4). In summary, it is very likely that changes in SLCFs \r contribute to an overall warmer GSAT over the near, mid- and long \r term in the five SSP scenarios considered (Table 4.6, Section 6.7.2 \r and Figure 6.22)."


id: e849451c61c44556ee5d9cc389a6c10
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38201
content:
"Mitigation of SLCFs affects future climate projections and could \r alter the time of emergence of anthropogenic climate change \r signals. The AR5  assessed that emission reductions aimed at \r decreasing local air pollution could have a  near-term warming \r impact on climate (high confidence) (Kirtman et al., 2013). Because \r of their shorter lifetimes, reductions in emissions of SLCF species \r mainly influence near-term GSAT trends (Chalmers et  al., 2012; \r Shindell et  al., 2017; Shindell and Smith, 2019), but on decadal \r time scales the near-term response to even very large reductions \r in SLCFs may be difficult to detect in the presence of large internal \r climate variability (Samset et  al., 2020). The changes in SLCF \r emissions during the COVID-19 pandemic has resulted in a small \r net radiative forcing without a discernible impact on GSAT (Cross\u0002Chapter Box 6.1). SLCF mitigation also leads to a higher GSAT in \r [...]"


id: 8688c86461ba604459e81e2de6f622d5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 20838
content:
"[...] the mid- to long-term (Smith and Mizrahi, 2013; Stohl et al., 2015; \r Hienola et al., 2018) and can influence peak warming during the \r 21st century (Rogelj et al., 2014; Hienola et al., 2018). This section \r focuses on the total effect of SLCF changes on GSAT projections \r in the SSP scenarios. A more detailed breakdown of the separate \r climate effects of SLCF species and precursor species can be found \r in Sections 6.7.2 and 6.7.3."


id: 938f596590039866738a1f7cab6acda5
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 23164
content:
"temperature variability in the Northern Hemisphere during the past \r 2,500 years, with cooling persisting for up to ten years after some \r of the largest eruptive episodes (Sigl et al., 2015). Repeated clusters \r of volcanic eruptions can induce a net negative radiative forcing that \r results in a centennial- and global-scale cooling trend via a decline \r in mixed-layer oceanic heat content (McGregor et  al., 2015). The \r response to multi-decadal changes in volcanic forcing (representing \r clusters of eruptions) shows similar cooling in both simulations and \r reconstructions of NH temperature. Volcanic eruptions generally \r result in decreased global precipitation for up to a few years following \r the eruption (Iles and Hegerl, 2014, 2015; Man et  al., 2014), with \r climatologically wet regions drying and climatologically dry regions \r wetting (medium confidence), which is opposite to the response \r under global warming (Held and Soden, 2006; Iles et al., 2013; Zuo \r [...]"


id: d0dfb96a8c99bf988a9ce78080214264
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 34095
content:
"[...] et  al., 2019a, b). El Nino-like warming appears after large volcanic \r eruptions, as seen in both observations (Adams et al., 2003; McGregor \r et al., 2010; Khodri et al., 2017) and climate model simulations (Ohba \r et al., 2013; Pausata et al., 2015; Colose et al., 2016; Stevenson et al., \r 2016; Khodri et al., 2017; Predybaylo et al., 2017; Zuo et al., 2018). \r The large tropical eruptions are coincident with positive Indian Ocean \r dipole events (Maher et al., 2015)."


id: 74b7d4699a574b59420c387e35496105
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 17773
content:
"In AR5, uncertainty due to future volcanic activity was not considered \r in the assessment of the CMIP5 21st  century climate projections \r (Taylor  et  al., 2012; O'Neill et  al., 2016). Since AR5, there has been \r considerable progress in quantifying the impacts of volcanic eruptions \r on decadal climate prediction and longer-term climate projections \r (Meehl et al., 2015; Swingedouw et al., 2015, 2017; Timmreck et al., \r 2016; Bethke et al., 2017; Illing et al., 2018). By exploring 60 possible \r volcanic futures under RCP4.5, it has been demonstrated that the \r inclusion of time-varying volcanic forcing may enhance climate \r variability on annual-to-decadal time scales (Bethke et  al., 2017). \r Consistent with a  tropospheric cooling response, the change in \r ensemble spread in the volcanic cases is skewed towards lower GSAT \r relative to the non-volcanic cases (Cross-Chapter Box 4.1, Figure 1). \r In  these simulations with multiple volcanic forcing futures there is: \r [...]"


id: ea98b3ecfe416afdc8e03dfd963b2da2
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 38634
content:
"[...] (i) an increase in the frequency of extremely cold individual years; (ii) an \r increased likelihood of decades with negative GSAT trend (decades \r with negative GSAT trends become 50% more commonplace); \r (iii)  later anthropogenic signal emergence (the mean time at which \r the signal of global warming emerges from the noise of natural \r climate variability is delayed almost everywhere) (high confidence); \r and (iv) a 10% overall reduction in global land monsoon precipitation \r and a 20% overall increase in the ensemble spread (Man et al., 2021)."


id: 9b86325c06e4916186d7e065b2c4da11
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24610
content:
"Another factor that could substantially alter projections in the \r near-term would be the occurrence of a  large explosive volcanic \r eruption, or even a decadal to multi-decadal sequence of small-to\u0002moderate volcanic eruptions as witnessed over the early 21st century \r (Cross-Chapter Box 4.1; Santer et al., 2014). An eruption similar to \r the last large tropical eruption, Mount Pinatubo in the Philippines \r in June 1991, is expected to cause substantial Northern Hemisphere \r (NH) cooling, peaking between 0.09degC and 0.38degC and lasting for \r three to five years, as indicated by climate model simulations over the \r past millennium (e.g., Jungclaus et al., 2010). Phase 3 of Paleoclimate \r Modelling Intercomparison Project (PMIP3) simulated a  significant \r NH cooling in response to individual volcanic events (peaks between \r 0.1degC and 0.5degC, depending on model, during the first year after \r the eruption) that lasts for three to five years. On a regional scale, \r [...]"


id: 65c085de9cfee9409c8893992098db24
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 15163
content:
"[...] the double volcanic events that occurred in 536 and 540 CE resulted \r in a cooling of 2degC (Buntgen et al., 2016; Toohey et al., 2016)."


id: f00555ab9285223330599cd00caf9ebb
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39608
content:
"Due to the direct radiative effect of volcanic stratospheric aerosols, large volcanic eruptions lead to an overall decrease of GSAT, which can \r extend to multi-decadal or century time scales in the case of clustered volcanism (Section 3.3.1.1; Schurer et al., 2013; McGregor et al., \r 2015; Sigl et al., 2015; Kobashi et al., 2017; Zambri et al., 2017; Bronnimann et al., 2019; Neukom et al., 2019). Large eruptions also increase \r the frequency of extremely cold individual years and the likelihood of cooling trends occurring in individual decades (Cross-Chapter \r Box 3.1 and Section 4.4.4; Paik and Min, 2018). Re-dating of ice core chronologies now confirms that the coldest decades of the past \r approximately 2000 years are the outcome of volcanic eruptions (Sigl et al., 2015; Buntgen et al., 2016; Toohey et al., 2016; Neukom et al., \r 2019). CMIP5 and CMIP6 models reproduce the decreased GSAT that follows periods of intense volcanism. New reconciliations between \r [...]"


id: 99f649c1756f9afa41b1abffa3d53e34
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24317
content:
"[...] simulations and proxy-based reconstructions of past eruptions have been achieved through better Earth System Model representation \r of volcanic plume chemical compositions (Legrande et al., 2016; Marshall et al., 2020; F. Zhu et al., 2020). Yet, remaining disagreements \r reflect differences in the volcanic forcing datasets used in the simulations (medium confidence) (Section 3.3.1.1 and Figure 3.2c)."


id: 34aa12c82a635f2c808ea38f60ffb34f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 6502
content:
"Although incomplete, proxy records show large impacts upon contemporary society from eruptions such as 1257 Samalas and 1815 \r Tambora, the latter resulting in 'the year without a summer' with multiple harvest failures across the Northern Hemisphere (e.g., Raible \r et al., 2016). Comparing CMIP5 multi-model simulations with observations has improved understanding of the hydrological responses \r to 20th century eruptions, particularly global land monsoon drying, and associated uncertainties (Section 3.3.2.3). Global mean land \r precipitation decreases for up to a few years following the eruption, with climatologically wet regions drying and dry regions wetting \r (Sections 3.3.2.3 and 4.4.4). Changes in monsoon circulations occur with a general weakening of tropical precipitation (Section 8.5.2.3) \r and a decrease in extreme precipitation over global monsoon regions (Section 11.4.4). Monsoon precipitation in one hemisphere tends \r [...]"


id: 9daf23d825538f4cc2617e1d11f155ca
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24974
content:
"[...] to be enhanced by eruptions occurring in the other hemisphere or reduced if they occur in the same hemisphere (Sections 3.3.2.3 \r and 8.5.2.3). Volcanic eruptions have been linked to the onset of El Nino followed by La Nina although this connection remains \r contentious (Adams et al., 2003; Bradley et al., 2003; McGregor et al., 2010; Khodri et al., 2017; F. Liu et al., 2018; Sun et al., 2019; "


id: 574f79567b84e913014aaf86d4d6b95d
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12615
content:
"Given the unpredictability of individual eruptions, volcanic forcing is prescribed as a constant background loading in CMIP6 models \r (Eyring et al., 2016). This means the effects of potential large volcanic eruptions are largely absent from model projections, and few studies \r have addressed the potential implications on 21st century warming. One study considered future scenarios with hypothetical volcanic \r eruptions consistent with levels of Common Era volcanic activity (Bethke et al., 2017) under RCP4.5 and found that climate projections \r could be substantially altered (Cross-Chapter Box 4.1, Figure 1). Although temporary, close to pre-industrial level temperatures could be \r experienced globally for a few years after a 1257 Samalas-sized eruption. Several other key climate indicators are also changed substantially, \r consistent with evidence from past events. Bethke et al. (2017) suggest that an eruption early in the 21st century could delay the timing \r [...]"


id: 6af83f736e0a033b0dd010b907feca6
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 16111
content:
"[...] of crossing 1.5degC global warming by several years. Clustered eruptions would have substantial impact upon GSAT evolution throughout \r the century (Cross-Chapter Box 4.1, Figure 1), and could have far-reaching implications, as observed for past eruptions. For near-term \r response options, decadal prediction models can update 21st-century projections once a volcanic eruption occurs (Timmreck et al., 2016)."


id: 9ce10fe235fae3847ff889df18183f8f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24834
content:
"Figure 4.19 | Mid- and long-term change of annual mean surface temperature. Displayed are projected spatial patterns of multi-model mean change in annual mean \r near-surface air temperature (degC) in 2041-2060 and 2081-2100 relative to 1995-2014 for (top) SSP1-2.6 and (bottom) SSP3-7.0. The number of models used is indicated in \r the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show \r a change greater than the internal-variability threshold (see Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with \r no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of \r [...]"


id: 9ba6a44330b9f592284f6027b7f6828c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24638
content:
"[...] conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. \r Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 7b96e9bad23647e13734099c491b58e6
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 18953
content:
"It is virtually certain that future average warming will be higher over \r land than over the ocean. Section 2.2.1 of SRCCL (G. Jia et al., 2019) \r assessed that it is certain that land temperatures have increased \r more than global mean temperatures since the pre-industrial period. \r This so-called land-ocean warming contrast is a striking feature of \r observed trends (Lambert and Chiang, 2007; Byrne and O'Gorman, \r 2018) and projected changes in surface-air temperature (Sutton \r et  al., 2007; Joshi and Gregory, 2008; Dong et  al., 2009; Lambert \r et al., 2011; Drost et al., 2012; Bayr and Dommenget, 2013; Byrne \r and O'Gorman, 2013b; Izumi et al., 2013; Joshi et al., 2013). Between \r 1979 and 2016, average temperature over land increased by 42% \r more than over the ocean (Byrne and O'Gorman, 2018). A  similar \r warming contrast is found in CMIP5 projections though with large \r differences across models and latitudes (Sutton et al., 2007; Drost \r [...]"


id: 69b94dcf74bb89308fbddf2d38583825
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 15866
content:
"[...] et al., 2012; Byrne and O'Gorman, 2013b; Joshi et al., 2013), which \r is also consistent with paleoclimate evidence (Izumi et  al., 2013; \r Schmidt et al., 2014). The ratio of land-to-ocean warming is greater \r than one for almost all regions (high confidence) and is larger for \r dry subtropical continents (about 1.5) than for moist regions in the \r tropics and mid-latitudes (about 1.2; Byrne and O'Gorman, 2013a). \r Projected warming over land and ocean only is shown in Table 4.2 \r for different scenarios, and the global average ratio of land-to-ocean \r warming in CMIP6 is 1.5 with a likely range of 1.4 to 1.7, which is \r consistent with estimates based on CMIP5."


id: 9fa70bed013f3f05ccef6633e3c2f0f5
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 25300
content:
"A variety of mechanisms contribute to Arctic amplification \r (Section 7.4.4.1.1). While surface-albedo feedbacks associated with \r the loss of sea ice and snow have long been known to play important \r roles (Arrhenius, 1896; Manabe and Stouffer, 1980; Robock, 1983; \r Hall, 2004), it is now recognized that temperature (lapse-rate and \r Planck) feedbacks also contribute to Arctic amplification through \r a less efficient longwave radiative damping to space with warming \r at high latitudes (Winton, 2006; Pithan and Mauritsen, 2014; Goosse \r et al., 2018; Stuecker et al., 2018). Increases in poleward atmospheric \r latent heat transport and oceanic heat transport also contribute to \r Arctic warming (Holland and Bitz, 2003; Bitz et al., 2006; Lee et al., \r 2011, 2017; Alexeev and Jackson, 2013; Marshall et al., 2014, 2015; \r Woods and Caballero, 2016; Nummelin et al., 2017; Singh et al., 2017; \r Merlis and Henry, 2018; Oldenburg et al., 2018; Armour et al., 2019; \r [...]"


id: 3b3944acb80a38e465482a54c44ee89b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 7608
content:
"[...] Beer et al., 2020). Projected reduction in the strength of the AMOC \r over the 21st century is expected to reduce Arctic warming, but even \r a strong AMOC reduction would not eliminate Arctic amplification \r entirely (medium confidence) (Liu et  al., 2017; Y. Liu et  al., 2018; \r Wen et al., 2018)."


id: dff47a073e978a1e41a33a2686168403
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 36641
content:
"Since AR5, a robust physical understanding of the warming contrast \r been developed. A simple theory based on atmospheric dynamics and \r moisture transport shows that surface-air temperature and relative \r humidity over land are strongly coupled, and demonstrates that the \r warming contrast occurs because air over land is drier than over \r the ocean (Joshi et al., 2008; Byrne and O'Gorman, 2013a, b, 2018). \r The warming contrast causes land relative humidity to decrease \r (Byrne and O'Gorman, 2016, 2018; Chadwick et al., 2016) and this \r feeds back on and strengthens the warming contrast. Differences \r in land-relative humidity responses across models are the primary \r cause of uncertainty in the land-ocean warming contrast (Byrne \r and O'Gorman, 2013b). These land-relative humidity changes are \r ultimately controlled by moisture transport between the land and \r ocean boundary layers (Byrne and O'Gorman, 2016; Chadwick et al., \r [...]"


id: e71bc8d9b3def15bab7cb42fb3acad7c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 37956
content:
"[...] 2016) and are also sensitive to characteristics of land surfaces that \r are challenging to model, including stomatal conductance and soil \r moisture (Berg et al., 2016; Zarakas et al., 2020)."


id: cf71ddf874665615ba52f9435826a13c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 33849
content:
"There remains substantial uncertainty in the magnitude of projected \r Arctic amplification (Smith et  al., 2020), with the Arctic warming \r ranging from two to four times the global average in models (Holland \r and Bitz, 2003; Nummelin et  al., 2017). This uncertainty primarily \r stems from different representations of polar surface-albedo, lapse\u0002rate, and cloud feedbacks, and from different projected poleward \r energy transport changes (Holland and Bitz, 2003; Crook et al., 2011; \r Mahlstein and Knutti, 2011; Pithan and Mauritsen, 2014; Bonan \r et al., 2018). The magnitude of Arctic amplification may also depend \r on the mix of radiative forcing agents (Najafi et al., 2015; Sand et al., \r 2016; Stjern et al., 2019) such as the contribution of ozone depleting \r substances (Polvani et al., 2020). Tropospheric aerosol emissions tend \r to reduce simulated Arctic warming over the middle of the 20th century \r (Gagne et al., 2017b) and consequently aerosol emission reductions \r [...]"


id: c8c54d8256b73fb1a3dcbb19bbd1d0aa
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 32645
content:
"Climate models project a weaker polar amplification in the SH than in \r the NH under transient warming (Figure 4.19). Model simulations (Hall, \r 2004; Danabasoglu and Gent, 2009; Li et al., 2013) and paleoclimate \r proxies indicate polar amplification in both hemispheres near \r equilibrium, but generally with less warming in the Antarctic than \r the Arctic (Section 7.4.4.1.2). The primary driver of delayed warming \r of the southern high latitudes is the upwelling in the Southern Ocean \r and associated ocean heat uptake that is then transported away \r from Antarctica by northward flowing surface waters (Frolicher et al., \r 2015; Marshall et al., 2015; Armour et al., 2016; W. Liu et al., 2018), \r although asymmetries in feedbacks between the poles also play \r a role (Section 7.4.4.1.1). Changes in westerly surface winds over the \r Southern Ocean have the potential to affect the rate of sea-surface \r warming, but there is currently low confidence in even the sign of \r [...]"


id: 508fa20f35416afb0fa87276f9ba3c5a
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 11444
content:
"[...] the effect based on a diverse range of climate model responses to \r wind changes (Marshall et  al., 2014; Ferreira et  al., 2015; Kostov \r et al., 2017; Seviour et al., 2019). A substantial increase in freshwater \r input to the ocean from the Antarctic ice sheet could further slow the \r emergence of SH polar amplification by cooling the Southern Ocean \r surface (Bronselaer et  al., 2018; Golledge et  al., 2019; Schloesser \r et al., 2019), but this process is not represented in current climate \r models which lack dynamic ice sheets. Thus, while there is high \r confidence that the SH high latitudes will warm by more than the \r tropics on centennial time scales, there is low confidence that such \r a feature will emerge this century (Section 7.4.4.1)."


id: ac9ff48318ae53ba9770f68c69988491
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 27615
content:
"in variability of annual and particularly seasonal mean temperature, \r including (i) a reduction in mid- to high-latitude winter temperature \r variability and (ii) an increase in summer temperature variability \r over land in the tropics and subtropics (Huntingford et  al., 2013; \r Holmes et  al., 2016; see Figure  4.21). The multi-ensemble average \r across seven single-model initial-condition large ensembles projects \r a consistent reduction in year-to-year December -January-February \r (DJF) variability around about 50degN-70degN and June-July-August \r (JJA) variability around 55degS-70degS along the edge of the sea ice- and \r snow-covered region (Figure 4.21). There is growing evidence that \r year-to-year and day-to-day temperature variability decreases in \r winter over northern mid- to high-latitudes (Fischer et  al., 2011; \r De Vries et al., 2012; Screen, 2014; Schneider et al., 2015; Holmes \r et al., 2016; Borodina et al., 2017; Tamarin-Brodsky et al., 2020) which \r [...]"


id: ca1642a70cfb0facf9d5b8b2091bf620
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 32887
content:
"[...] implies that the lowest temperatures rise more than the respective \r climatological mean temperatures (medium confidence). Over the \r NH, reduced high-latitude temperature variability is associated \r with disproportionally large warming in source region of cold-air \r advection due to Arctic amplification and land-sea contrast (De Vries \r et al., 2012; Screen, 2014; Holmes et al., 2016). It has further been \r argued that a reduction in snow and sea ice coverage from partly \r to completely snow- and ice-free ocean and land surface would \r substantially reduce cold-season temperature variability (Gregory and \r Mitchell, 1995; Fischer et al., 2011; Borodina et al., 2017) and lead to \r a shortening of the cold season and earlier onset of the warm season \r (Cassou and Cattiaux, 2016). Mid-latitudinal winter temperature \r variability is further affected by a complex interplay of a multitude \r of processes including potential changes in atmospheric circulation, \r [...]"


id: 10cb97e68ad61a746f51a0f6bd70cb68
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 141
content:
"[...] but there is low confidence in the dominant contribution of Arctic \r warming compared to other drivers (Cross-Chapter Box 10.1)."


id: 10f0be70b42451be1468eafec5ed7fd8
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 170
content:
"Figure  4.21 | Percentage change in interannual variability of (left) December -January-February (DJF) and (right) June-July-August (JJA) mean \r temperature averaged across seven large initial condition ensembles. Average changes across seven single-model, initial-condition large ensembles are shown for \r RCP8.5 in 2081-2100 (and where not available for 2080-2099) relative to 1995-2014. Standard deviations are calculated across all members of the large ensembles for every \r given year to avoid inflation due to the underlying trend and then averaged across the period. Changes are averaged across the ensembles MPI-GE (100 members, Maher \r et al., 2019), CanESM2 (50 members, Kirchmeier-Young et al., 2017), NCAR-CESM (30 members, Kay et al., 2015), GFDL-CM3 (20 members, Sun et al., 2018), GFDL-ESM2M \r (30 members, Rodgers et al., 2015), CSIRO-Mk3-6-0 (30 members, Jeffrey et al., 2013) and EC-EARTH (16 members, Hazeleger et al., 2010). Also see Deser et al. (2020) for \r [...]"


id: 5757e41310c85e87cc687e65df0629da
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12626
content:
"[...] further information on those ensembles. Diagonal lines indicate areas with low model agreement where fewer than 80% of the models agree on the sign of the change, and \r no overlay areas with high model agreement where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available \r in the chapter data table (Table 4.SM.1)."


id: 782b22d4632091277c7607555b193a7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 18354
content:
"CMIP6 projections show warming throughout the troposphere by the \r end of this century and a mix of warming and cooling in the stratosphere \r depending on the emissions scenario (Figure 4.22). The patterns of \r tropospheric temperature change are highly consistent with those \r derived from earlier generations of climate models as assessed in \r AR5, AR4 and TAR. In SSP1-2.6, the multi-model mean warming \r remains below 3degC everywhere in the troposphere except near the \r surface in the Arctic; this is similar to the findings in AR5 based on \r CMIP5 models for RCP2.6. In SSP3-7.0, the zonal mean tropospheric \r warming is also largest in the tropical upper troposphere, reaching \r more than 5degC, and near the surface in the Arctic where warming \r exceeds 8degC (Figure 4.22). It is likely that the warmer projected GSAT \r in the unconstrained CMIP6 model ensemble contributes to larger \r warming in the tropical upper troposphere and in the Arctic lower \r [...]"


id: 3906e30227efcab739e51b109b163927
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 7239
content:
"[...] troposphere. This assessment is based on the understanding of polar \r amplification assessed in Chapter  7 (Section  7.4.4.1), and at low \r latitudes is based on the understanding of moist convective processes \r as well as the relationship between CMIP5- and CMIP6-simulated \r surface temperatures and tropical upper tropospheric warming over \r the historical period (Section 3.3.1.2)."


id: 4bec440aee1dce0ff9fb01bf8dc878b8
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 10617
content:
"Figure  4.23 | Long-term changes in seasonal mean relative humidity. Displayed are projected spatial patterns of multi-model mean change (%) in seasonal \r (top) December -January-February (DJF) and (bottom) June-July-August (JJA) mean near-surface relative humidity in 2081-2100 relative to 1995-2014, for (left) SSP1-2.6 \r and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from \r internal variability, that is, where at least 66% of the models show a change greater than the internal variability threshold (Section 4.2.6) and at least 80% of the models agree \r on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the \r [...]"


id: dfda6b836aee0e31c206fbd2a83b24c3
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 36629
content:
"[...] internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but \r fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 9d261e11cf3618aa167d00729c292d8e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24885
content:
"The thermodynamic response to global warming is associated with \r a 'wet get wetter' mechanism, with enhanced moisture flux leading \r to subtropical dry regions getting drier and tropical and mid-latitude \r wet regions getting wetter (Held and Soden, 2006; Chou et al., 2009). \r Recent studies suggest that the dry-get-drier argument does not hold, \r especially over subtropical land regions (Greve et  al., 2014; Feng \r and Zhang, 2015; Greve and Seneviratne, 2015). The discrepancy \r may be partly arising due to differences in model climatologies \r and by change in the location of wet and dry regions (Polson and \r Hegerl, 2017). Over the 21st century, significant rate of precipitation \r change is associated with a spatial stabilization and intensification \r of moistening and drying patterns (Chavaillaz et  al., 2016). In the \r tropics, weakening of circulation leads to a 'wet gets drier, dry gets \r wetter' pattern (Chadwick et  al., 2013). Climate model agreement \r [...]"


id: e2cfd2c644a4d0ce496407af89ef7ffe
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 37200
content:
"[...] for precipitation change in the tropics is lower than for other regions \r (Knutti and Sedlacek, 2013; McSweeney and Jones, 2013). Sources \r of inter-model uncertainty in regional rainfall projections arise from \r circulation changes (Kent et al., 2015; Chadwick, 2016) and spatial \r shifts in convection and convergence, associated with SST pattern \r change and land-sea thermal contrast change (Kent et  al., 2015; \r Chadwick et  al., 2017) with a  secondary contribution from the \r response to direct CO2 forcing (Chadwick, 2016). Factors governing \r changes in large-scale precipitation patterns are assessed in detail in \r Sections 8.2.2 and 10.4.1."


id: 665e06046c4daaf72dc7467cc1649b77
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 15272
content:
"Long-term multi-model mean change in seasonal precipitation \r (JJA and DJF) from CMIP6 models (Figure  4.24) shows substantial \r regional differences and seasonal contrast. Changes in seasonal \r precipitation under SSP1-2.6 are small compared to internal \r variability. Consistent with the AR5, patterns of precipitation \r change are very likely to increase in the high latitudes especially \r during local winter and over tropical oceans under SSP3-7.0 (high \r confidence). CMIP6 projections show an increase in precipitation \r over larger parts of the monsoon regions and decreases in many \r subtropical regions including the Mediterranean, southern Africa \r and south-west Australia (medium confidence). The large-scale \r patterns of precipitation shown in Figure 4.24 are consistent with the \r patterns presented in Section 8.4.1.3. Precipitation changes exhibit \r strong seasonal characteristics (Box 8.2), and, in many regions, the \r [...]"


id: 4cb9a3412aceb81a79fb8ecbafd18e6e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 10763
content:
"[...] sign of the precipitation changes varies with season. Precipitation \r variability is projected to increase over a majority of global land area, \r as assessed in Chapter 8 (Section 8.4.1.3.3), over a wide range of \r time scales in response to warming (Pendergrass et al., 2017)."


id: 42e033750b957a61e472ca039cc703cf
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 8958
content:
"Since AR5, progress has been achieved in understanding changes \r in patterns and rates of precipitation with GSAT rise. The projected \r precipitation changes can be decomposed into a part that is related \r to atmospheric circulation referred to as dynamical component \r and a  part related to water vapour changes, the thermodynamic \r component. Based on process understanding and modelling (Flaschner \r et al., 2016; Samset et al., 2016), global mean precipitation will very \r likely increase by 1-3% per degC of GSAT warming (Section  8.2.1). \r The increase in atmospheric water vapour is a robust change under \r global warming, the sensitivity of global precipitation change to \r warming is smaller (2% per degC) as compared to water vapour change \r (7% per degC; Held and Soden, 2006). Global energy balance places \r a  strong constraint on the global mean precipitation (Allen and \r Ingram, 2002; Pendergrass and Hartmann, 2014; Myhre et al., 2018; \r [...]"


id: 9275e20e8d43596f9c2de500451277d9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 22988
content:
"Figure 4.24 | Long-term change of seasonal mean precipitation. Displayed are projected spatial patterns of multi-model mean change (%) in (top) December-January-\r February (DJF) and (bottom) June-July-August (JJA) mean precipitation in 2081-2100 relative to 1995-2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models \r used is indicated in the top right of the maps. No map overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at \r least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal \r lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. \r [...]"


id: 6c7f1182ea4e483c2f0945b193a7c96c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 16379
content:
"[...] Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all \r models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: c0fc40008fa63896fa7b7076adcdb593
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 31257
content:
"Since AR5, there has been progress in understanding physical \r mechanisms for the projected changes in global monsoon and \r quantifying the sources of uncertainty in projections. The increase in \r global monsoon precipitation under warming is primarily attributed \r to the increase of moisture convergence, which comes mainly from \r the thermodynamic effect due to increasing atmospheric moisture \r but is partly offset by reduced convergence (W. Zhang et  al., \r 2019; Chen et  al., 2020). The dynamic effect, such as monsoon \r circulation changes, dominates regional differences in the projected \r monsoon  precipitation  changes (Chen et  al., 2020). Specifically, \r NH monsoon precipitation will increase more strongly than its \r SH counterpart, due to an increase in hemispheric temperature \r difference between the NH and SH, enhancement of the Hadley \r circulation, and atmospheric moistening, countered by stabilization \r of the troposphere (Lee and Wang, 2014). The seasonality of \r [...]"


id: 1ce7de247db4d558ebd674f9cffaa5bb
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 2265
content:
"[...] global monsoon rainfall is projected to be enhanced in response to \r warming, featuring a greater wet-dry season contrast (Lee and Wang \r 2014; Zhang et al. 2019). In addition, the interannual variability of \r global monsoon rainfall is projected to intensify mainly over land, \r with a strengthened relationship between global monsoon and ENSO \r (Hsu et al., 2013; Wang et al., 2020, 2021)."


id: f6656ae31cf8dff46e7b65d64dee8026
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 40714
content:
"The AR5 assessed that mean sea level pressure is projected to decrease \r in high latitudes and to increase in mid-latitudes. Such a pattern is \r associated with a poleward shift in the storm track and an increase \r in the annular mode index. This broad pattern is also found in CMIP6 \r models (Figure 4.25). Under SSP1-2.6, the pattern in sea level pressure \r change resembles that for SSP3-7.0, but the amplitudes are small \r compared to internal variability in 20-year means (Figure 4.25). One \r exception is found in the SH mid-latitudes, where pressure robustly \r increases in SSP3-7.0 in both austral summer and winter, but shows \r no robust change in SSP1-2.6. This is likely attributable to the larger \r GHG forcing in SSP3-7.0 compared to SSP1-2.6, which contributes \r to a poleward shift of the SH mid-latitude circulation and becomes \r relatively more important than the effect of ozone recovery which \r drives an equatorward shift in the circulation (see Section 4.5.3.1 on \r [...]"


id: 5773dffff4c096ff30b45a9f89c59841
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12649
content:
"[...] the Southern Annular Mode; Barnes and Polvani, 2013; Barnes et al., \r 2014; Bracegirdle et al., 2020b). The poleward shift in SH mid-latitude \r circulation in SSP3-7.0 likely contributes to the wetting trend at high \r southern latitudes (Figure 4.25)."


id: ddc69c6192ef6591803ae3882ab781a7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 36281
content:
"For the uncertainty in mean monsoon precipitation projections, the \r model uncertainty is the dominant contributor throughout the century \r and explains more than 70% of the inter-model variance during near \r term, mid-term, and long term. The contribution of internal variability \r is only important at the beginning in early decades, while scenario \r uncertainty becomes important at the end of the 21st  century. The \r sources of uncertainty for the mean and extreme monsoon precipitation \r mainly differ in the long-term projection, when the contribution of \r scenario uncertainty is comparable to the model uncertainty for extreme \r precipitation (Zhou et al., 2020). Although the magnitude of internal \r variability differs between CMIP5 models and single-model, initial\u0002condition large ensembles, the impact is only evident in the beginning \r decades. For the mid- and long term, the magnitude difference does not \r alter that model uncertainty is the dominant source of uncertainty in \r [...]"


id: eda26a1096f7727882b3d6fb4e495ed4
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39165
content:
"Under both SSP1-2.6 and SSP3-7.0 there is a strengthening and lifting \r of the subtropical jets in both hemispheres (Figure 4.26), consistent \r with the response to large-scale tropospheric warming found in earlier \r generations of climate models (Collins et al., 2013). In the SH, GHG \r emissions tend to force a poleward shift of the jet, but this is opposed, \r particularly in austral summer, by the stratospheric ozone hole recovery \r (Barnes and Polvani, 2013; Barnes et  al., 2014; Bracegirdle et  al., \r 2020b). Consistent with sea level pressure changes, CMIP6 models \r project a strengthening and poleward shift of the SH jet in austral \r summer and winter under SSP3-7.0, but smaller and non-robust \r changes in SH mid-latitude zonal winds under SSP1-2.6 (Figure 4.26; \r see also Section 4.5.3.1). CMIP6 models show an improved simulation \r of the SH jet stream latitude (Bracegirdle et al., 2020a; Curtis et al., \r 2020). This has been linked to a reduction in the projected poleward \r [...]"


id: 62d4a39a9ebc2658df7679e7c681f60c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 14684
content:
"[...] shift of the SH jet in austral summer compared to the CMIP5 models \r (Curtis et al., 2020; Goyal et al., 2021), although differences in the \r pattern of SST response may also play a  role (Wood  et  al., 2020). \r In the NH extratropics, the changes in lower-tropospheric zonal-mean "


id: 83793d722dc6b8d2743829c7dad4fca5
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 20269
content:
"Figure 4.25 | Long-term change of seasonal-mean sea level pressure. Displayed are projected spatial patterns of multi-model mean change in (top) December-January-\r February (DJF) and (bottom) June-July-August (JJA) mean sea level pressure (hPa) in 2081-2100 relative to 1995-2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number \r of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at \r least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal \r lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed \r [...]"


id: a86db26d4bc4c163ec3596c342423f46
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 26840
content:
"[...] lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree \r on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: f55bd4d39706c1c9985f75a2429ae929
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 40538
content:
"with an eastward extension into Europe (Li et al., 2018; Peings et al., \r 2018; Simpson et al., 2019a; Harvey et al., 2020; Oudar et al., 2020). \r Simulations indicate that most of the changes in winter storminess \r over the Euro-Atlantic region will occur only after exceeding the 1.5degC \r warming level (Barcikowska et al., 2018).\r Progress since AR5 has improved understanding of the climate change \r aspects that can drive these different, and potentially opposite, \r responses in the mid-latitude jets and storm tracks. A poleward shift \r of the jets and storm tracks is expected in response to an increase \r in the atmospheric stratification and in the upper-tropospheric \r equator-to-pole meridional temperature gradient, while it is opposed \r by the decrease in the meridional temperature gradient in the \r lower troposphere associated with the polar amplification of global \r warming (Harvey et  al., 2014; Shaw et  al., 2016). Recent analyses \r [...]"


id: 1efeaa070024c6e6f2f5d00f21b5b8cd
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 2657
content:
"[...] have identified additional climate aspects that can drive mid-latitude \r jet changes, including patterns in sea surface warming (Mizuta \r et al., 2014; Langenbrunner et al., 2015; Ceppi et al., 2018; Wood et al., \r 2020), land-sea warming contrast (Shaw and Voigt, 2015), loss of sea \r ice (Deser et al., 2015; Harvey et al., 2015; Screen et al., 2018b; Zappa \r et al., 2018), and changes in the strength of the stratospheric polar \r vortex (Manzini et al., 2014; Grise and Polvani, 2017; Simpson et al., \r 2018; Ceppi and Shepherd, 2019). From an energetics perspective, \r the uncertainty in the response of the jet streams depends on the \r response of clouds, their non-spatially uniform radiative feedbacks \r shaping the meridional profile of warming (Ceppi et al., 2014; Voigt \r and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and \r Shepherd, 2017; Lipat et  al., 2018; Albern et  al., 2019; Voigt et  al., \r 2019). Climate models seem to underestimate the forced component \r [...]"


id: 1130cb26cd7607d11318d283bba50bdb
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 218
content:
"[...] of the year-to-year variability in the atmospheric circulation, \r particularly in the North Atlantic sector (Scaife and Smith, 2018), \r which suggests some relevant dynamical processes may not be well \r represented. Whether and how this may affect long-term projections \r is unknown. In conclusion, due to the influence from competing \r dynamical drivers and the absence of observational evidence, there \r is medium confidence in a projected poleward shift of the NH zonal\u0002mean low-level westerlies in autumn and summer and low confidence\r in the other seasons. There is also overall low confidence in projected \r regional changes in the NH low-level westerlies, particularly for the \r North Atlantic basin in boreal winter."


id: d6bf1b29a89e3f81acba92946dcf41e7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35090
content:
"Regarding the dynamical intensity of the storm tracks (Section 11.7.2), \r the number of ETCs associated with intense surface wind speeds and \r undergoing explosive pressure deepening are projected to strongly \r decrease in the NH winter (Seiler and Zwiers, 2016; Chang, 2018). \r The weakening of surface winds of ETCs in the NH is attributed to \r the reduced low-level baroclinicity from SST and sea ice changes \r (Harvey et al., 2014; Seiler and Zwiers, 2016; J. Wang et al., 2017a). \r There are, however, regional exceptions such as in the northern North \r Pacific, where explosive and intense ETCs are projected to increase \r in association with the poleward shift of the jet and increased upper\u0002level baroclinicity (Seiler and Zwiers, 2016). Eddy kinetic energy \r and intense cyclone activity are also projected to decrease in the \r NH summer in association with a  weakening of the jet (Lehmann \r et al., 2014; Chang et al., 2016). However, explosive cyclones tend \r [...]"


id: f5920fcbe97df08d8bd4f9aa11fa582b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 40567
content:
"[...] to be too weak in climate models (Seiler and Zwiers, 2016; Priestley \r et al., 2020), though this bias seems to be reduced in high-resolution \r simulations (Jiaxiang et al., 2020). Furthermore, models may not fully \r capture the contribution of the future increase in mesoscale latent "


id: aef566f87426354be0a61e58359bfb9b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 28024
content:
"The anthropogenic forced signal in extratropical atmospheric circulation \r may well be small compared to internal variability (Deser et al., 2012b, \r 2014) and, as assessed in AR5, there is generally low agreement across \r models in many aspects of regional atmospheric circulation change \r particularly in the NH (Shepherd, 2014). The latter means that, in some \r regions, a multi-model average perspective of atmospheric circulation \r change represents a  small residual after averaging over large \r intermodel spread. This is in strong contrast to thermodynamic aspects \r of climate change, such as surface temperature change, for which \r model results are generally highly consistent (see, e.g., Figure 4.19). \r Furthermore, models share systematic biases in some aspects of \r extratropical atmospheric circulation such as mid-latitude jets, which \r can have complex implications for understanding forced changes \r (Simpson and Polvani, 2016). Given these issues, an emerging field \r [...]"


