# document

id: 891b2041756ebc586f63f8e0bfb2683b
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 21291
content:
"Constructing useful climate information requires considering all available sources in order to capture the fullest \r possible representation of projected changes and distilling the information in a way that meets the needs of the \r stakeholders and communities impacted by the changes. For example, climate scientists can provide information \r on future changes by using simulations of global and/or regional climate and inferring changes in the weather \r behaviour influencing a region. An effective distillation process (FAQ 10.1, Figure 1) engages with the intended \r recipients of the information, especially stakeholders whose work involves non-climatic factors, such as human \r health, agriculture or water resources. The distillation evaluates the accuracy of all information sources \r (observations, simulations, expert judgement), weighs the credibility of possible conflicting information, and \r [...]"


id: f262ec59162ef67db6aab3b5455a9d15
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 40039
content:
"[...] arrives at climate information that includes estimating the confidence a user should have in it. Producers of \r climate data should further recognize that the geographic regions and time periods governing stakeholders' \r interest (for example, the growing season of an agricultural zone) may not align well with the time and space \r resolution of available climate data; thus additional model development or data processing may be required to \r extract useful climate information."


id: a5217b365c3f281684aa0e4ee766c9c8
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 26280
content:
"One way to distil complex information for stakeholder applications is to connect this information to experiences \r stakeholders have already had through storylines as plausible unfoldings of weather and climate events related \r to stakeholders' experiences. Dialogue between stakeholders and climate scientists can determine the most \r relevant experiences to evaluate for possible future behaviour. The development of storylines uses the experience \r and expertise of stakeholders, such as water-resource managers and health professionals, who seek to develop \r appropriate response measures. Storylines are thus a pathway through the distillation process that can make \r climate information more accessible and physically comprehensible. For example, a storyline may take a common \r experience like an extended drought, with depleted water availability and damaged crops, and show how \r droughts may change in the future, perhaps with even greater precipitation deficits or longer duration. With \r [...]"


id: 96514af70d9faffc02d2a95cfbb5dcae
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 23640
content:
"[...] appropriate choices, storylines can engage nuances of the climate information in a meaningful way by building \r on common experiences, thus enhancing the information's usefulness."


id: 46a81a63cb1f495a48b3461eeebf375e
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 9636
content:
"Mathias Hauser (Switzerland), Megan Kirchmeier-Young (Canada/United States of America), \r Lisa  V.  Alexander (Australia), Richard P. Allan (United Kingdom), Mansour  Almazroui \r (Saudi  Arabia), Lincoln M. Alvez (Brazil), Margot Bador (France, Australia/France), \r Rondrotiana  Barimalala (South  Africa/Madagascar), Richard A. Betts (United Kingdom), \r Suzana J. Camargo (United States of America/Brazil, United States of America), Pep G. Canadell \r (Australia), Erika Coppola (Italy), Markus G. Donat (Spain/Germany, Australia), Herve Douville \r (France), Robert J. H. Dunn (United  Kingdom/Germany,United Kingdom), Erich Fischer \r (Switzerland), Hayley J. Fowler (United  Kingdom), Nathan  P.  Gillett (Canada), Peter  Greve \r (Austria/Germany), Michael Grose (Australia), Lukas  Gudmundsson (Switzerland/\r Germany, Iceland), Jose Manuel Guttierez (Spain), Lofti Halimi (Algeria), Zhenyu  Han \r (China), Kevin  Hennessy (Australia), Richard  G.  Jones (United  Kingdom), Yeon-Hee Kim \r [...]"


id: d9d90b75e9f5fa6732b6ff166e3ca483
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 35625
content:
"[...] (Republic of Korea), Thomas Knutson (United States of America), June-Yi Lee (Republic of Korea), \r Chao Li (China), Georges-Noel T. Longandjo (South Africa/Democratic Republic of the Congo), \r Kathleen L.  McInnes (Australia), Tim  R.  McVicar (Australia), Malte Meinshausen (Australia/\r Germany), Seung-Ki Min (Republic of Korea), Ryan  S.  Padron  Flasher (Switzerland/Ecuador, \r United States of America), Christina M. Patricola (United States of America), Roshanka Ranasinghe \r (The  Netherlands/Sri Lanka, Australia), Johan  Reyns (The Netherlands/Belgium), Joeri  Rogelj \r (United Kingdom/Belgium), Alex C. Ruane (United States of America), Daniel  Ruiz Carrascal \r (United States of America/Colombia), Bjorn  H.  Samset (Norway), Jonathan  Spinoni (Italy), "


id: f075e2f74b3c280177969f0febeb168a
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 39685
content:
"This chapter assesses changes in weather and climate extremes \r on regional and global scales, including observed changes and \r their attribution, as well as projected changes. The extremes \r considered include temperature extremes, heavy precipitation \r and pluvial floods, river floods, droughts, storms (including \r tropical cyclones), as well as compound events (multivariate and \r concurrent extremes). The assessment focuses on land regions \r excluding Antarctica. Changes in marine extremes are addressed \r in Chapter  9  and Cross-Chapter Box  9.1. Assessments of past \r changes and their drivers are from 1950 onward, unless indicated \r otherwise. Projections for changes in extremes are presented for \r different levels of global warming, supplemented with information \r for the conversion to emissions scenario-based projections (Cross\u0002Chapter Box  11.1 and Table  4.2). Since the IPCC Fifth Assessment \r Report (AR5), there have been important new developments and \r [...]"


id: a7707d0bf39c958cd78b31ff39f6e12c
created_at: 2023-04-19 13:22:42
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content_type: text
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content:
"[...] knowledge advances on changes in weather and climate extremes, in \r particular regarding human influence on individual extreme events, \r on changes in droughts, tropical cyclones, and compound events, and \r on projections at different global warming levels (1.5degC-4degC). These, \r together with new evidence at regional scales, provide a  stronger \r basis and more regional information for the AR6 assessment on \r weather and climate extremes."


id: 1fa54efb7347a232ab9635012b10ac85
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
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"Since AR5, the confidence about past and future changes \r in weather and climate extremes has increased due to \r better physical understanding of processes, an increasing \r proportion of the scientific literature combining different \r lines of evidence, and improved accessibility to different \r types of climate models (high confidence). There have \r been improvements in some observation-based datasets, \r including reanalysis data (high confidence). Climate models \r can reproduce the sign (direction) of changes in temperature \r extremes observed globally and in most regions, although the \r magnitude of the trends may differ (high confidence). Models \r are able to capture the large-scale spatial distribution of precipitation \r extremes over land (high confidence). The intensity and frequency of \r extreme precipitation simulated by Coupled Model Intercomparison \r Project Phase 6 (CMIP6) models are similar to those simulated by \r [...]"


id: 2660f6e20f10aba85be63e7927c8fc85
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 3922
content:
"[...] CMIP5 models (high confidence). Higher horizontal model resolution \r improves the spatial representation of some extreme events \r (e.g., heavy precipitation events), in particular in regions with highly \r varying topography (high confidence). {11.2, 11.3, 11.4}"


id: 819de749fa0272b16d881ea2ca385d2d
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 19933
content:
"The frequency and intensity of hot extremes will continue to \r increase and those of cold extremes will continue to decrease, \r at global and continental scales and in nearly all inhabited \r regions1 with increasing global warming levels. This will be the \r case even if global warming is stabilized at 1.5degC. Relative to present\u0002day conditions, changes in the intensity of extremes would be at least \r double at 2degC, and quadruple at 3degC of global warming, compared \r to changes at 1.5degC of global warming. The number of hot days and \r hot nights and the length, frequency, and/or intensity of warm spells \r or heatwaves will increase over most land areas (virtually certain). In \r most regions, future changes in the intensity of temperature extremes \r will very likely be proportional to changes in global warming, and up \r to two to three times larger (high confidence). The highest increase \r of temperature of hottest days is projected in some mid-latitude and \r [...]"


id: 59821d40fa59cb9c0eed83b6ec2a7227
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 12984
content:
"[...] semi-arid regions and in the South American Monsoon region, at \r about 1.5 times to twice the rate of global warming (high confidence). \r The highest increase of temperature of coldest days is projected \r in Arctic regions, at about three times the rate of global warming \r (high confidence). The frequency of hot temperature extreme events \r will very likely increase nonlinearly with increasing global warming, \r with larger percentage increases for rarer events. {11.2, 11.3, 11.9; \r Table 11.1; Figure 11.3}"


id: e3a882c29406b6ad86f646e48a4515a8
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 37346
content:
"More regions are affected by increases in agricultural and \r ecological droughts with increasing global warming (high \r confidence). Several regions will be affected by more severe \r agricultural and ecological droughts even if global warming \r is stabilised at 2degC, including MED, WSAF, SAM and SSA (high \r confidence), and ESAF, MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, \r WCE, NCA, WNA and CNA (medium confidence). Some regions \r are also projected to be affected by more severe agricultural and \r ecological droughts at 1.5degC (MED, WSAF, ESAF, SAU, NSA, SAM, \r SSA, CNA, medium confidence). At 4degC of global warming, about \r 50% of all inhabited AR6 regions would be affected by increases \r in agricultural and ecological droughts (WCE, MED, CAU, EAU, SAU, \r WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA, CNA, ENA, \r WNA, WSAF, ESAF, MDG: medium confidence or higher), and only two \r regions (NEAF, SAS) would experience decreases in agricultural and \r [...]"


id: f2b3e4bbd0ad092f2802b302c51b93eb
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 40099
content:
"[...] ecological drought (medium confidence). There is high confidence\r that the projected increases in agricultural and ecological droughts \r are strongly affected by evapotranspiration increases associated with \r enhanced atmospheric evaporative demand. Several regions are \r projected to be more strongly affected by hydrological droughts with \r increasing global warming (at 4degC of global warming: NEU, WCE, \r EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, \r MDG: medium confidence or higher). There is low confidence that \r effects of enhanced atmospheric carbon dioxide (CO2) concentrations \r on plant water-use efficiency alleviate extreme  agricultural and \r ecological droughts in conditions  characterized by  limited soil \r moisture and enhanced atmospheric evaporative demand. There is \r also low confidence that these effects will substantially reduce global \r plant transpiration and the severity of hydrological droughts. There is \r [...]"


id: 4f5e26bd51771e06afc942f7573d817c
created_at: 2023-04-19 13:22:42
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"[...] high confidence that the land carbon sink will become less efficient \r due to soil moisture limitations and associated drought conditions \r in some regions in higher-emissions scenarios, in particular under \r global warming levels above 4degC. {11.6, 11.9, Cross-Chapter Box 5.1}"


id: c7ad018332158abf05fcabdfeecd455
created_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 32437
content:
"SREX report, IPCC, 2012) provided a comprehensive assessment on \r changes in extremes and how exposure and vulnerability to extremes \r determine the impacts and likelihood of disasters. Chapter  3  of \r that report (Seneviratne et al., 2012, hereafter also referred to as \r SREX Chapter  3) assessed physical aspects of extremes, and laid \r a foundation for the follow-up IPCC assessments. Several chapters \r of the IPCC Fifth Assessment Report (AR5) (IPCC, 2013) addressed \r climate extremes with respect to observed changes (Hartmann et al., \r 2013), model evaluation (Flato et al., 2013), attribution (Bindoff \r et al., 2013), and projected long-term changes (Collins et al., 2013). \r Assessments were also provided in the IPCC Special Report on Global \r Warming of 1.5degC (SR1.5) (IPCC, 2018; Hoegh-Guldberg et al., 2018), \r on climate change and land (SRCCL; (IPCC, 2019a), and on oceans \r and the cryosphere (SROCC; IPCC, 2019b). These assessments are the \r [...]"


id: c16742ff1cc3acc53003995c505c95a6
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 31330
content:
"The assessment of projected future changes in extremes is presented \r as function of different global warming levels (Section  11.2.4 and \r Cross-Chapter Box  11.1). This provides traceability and comparison \r to the SR1.5 assessment (Hoegh-Guldberg et al., 2018, hereafter \r referred to as SR1.5 Chapter 3). Also, this is useful for decision makers \r as actionable information, as much of the mitigation policy discussion \r and adaptation planning can be tied to the level of global warming. \r For example, regional changes in extremes, and thus their impacts, can \r be linked to global mitigation efforts. There is also the advantage of \r separating uncertainty in future projections due to regional responses \r as a  function of global warming levels from other factors such as \r differences in global climate sensitivity and emissions scenarios (Cross\u0002Chapter Box  11.1). Information is also provided on the translation \r between information provided at global warming levels and for single \r [...]"


id: 1c5df5e1b50c1cecb5166d5c60ab2182
created_at: 2023-04-19 13:22:42
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content_type: text
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"[...] emissions scenarios (Cross-Chapter Box  11.1). This facilitates easier \r comparison with the AR5 assessment and with some analyses provided \r in other chapters as function of emissions scenarios."


id: 9463b234e001481084e4b5cf2fe630bc
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 23314
content:
"At the global scale, and also at the regional scale to some extent, \r many of the changes in extremes are a  direct consequence of \r enhanced radiative forcing, and the associated global warming and/\r or resultant increase in the water-holding capacity of the atmosphere, \r as well as changes in vertical stability and meridional temperature \r gradients that affect climate dynamics (see Box 11.1). Widespread \r observed and projected increases in the intensity and frequency of \r hot extremes, together with decreases in the intensity and frequency \r of cold extremes, are consistent with global and regional warming \r (Section 11.3 and Figure 11.2). Extreme temperatures on land tend \r to increase more than the global mean temperature (Figure 11.2), \r due in large part to the land-sea warming contrast, and additionally \r to regional feedbacks in some regions (Section 11.1.6). Increases in \r the intensity of temperature extremes scale robustly, and in general \r [...]"


id: 80f256822519ac39179f0b674e8e9e73
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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vector_id: 19824
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"[...] linearly, with global warming across different geographical regions \r in projections up to 2100, with minimal dependence on emissions \r scenarios (Section 11.2.4, Figure 11.3, and Cross-Chapter Box 11.1; \r Seneviratne et al., 2016; Wartenburger et al., 2017; Kharin et al., \r 2018). The frequency of hot temperature extremes (see Figure 11.6), \r the number of heatwave days and the length of heatwave seasons \r in various regions also scale well, but nonlinearly (because of \r threshold effects, Section  11.2.1), with global mean temperatures \r (Wartenburger et al., 2017; Y. Sun et al., 2018a). "


id: 3828a2d1c5c6edaa789fc12b7cef6961
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 7083
content:
"Since AR5, the attribution of extreme weather events, or the \r investigation of changes in the frequency and/or magnitude of \r individual and local- and regional-scale extreme weather events due \r to various drivers (Section 11.2.3 and Cross-Working Group Box 1.1) \r has provided evidence that greenhouse gases and other external \r forcings have affected individual extreme weather events. The events \r that have been studied are geographically uneven. For example, \r extreme rainfall events in the UK (Schaller et al., 2016; Vautard et al., \r 2016; Otto et al., 2018b) or heatwaves in Australia (King et al., 2014; \r Perkins-Kirkpatrick et al., 2016; Lewis et al., 2017b) have spurred more \r studies than other events. Many highly impactful extreme weather \r events have not been studied in the event attribution framework. \r Studies in the developing world are also generally lacking. This is due \r to various reasons (Section 11.2) including lack of observational data, \r [...]"


id: f07c9e8701e8182f6758db50fcb90643
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 39687
content:
"[...] lack of reliable climate models and other problems (Otto et al., 2020). \r While the events that have been studied are not representative of all \r extreme events that occurred, and results from these studies may also \r be subject to selection bias, the large number of event attribution \r studies provide evidence that changes in the properties of these \r local and individual events are in line with expected consequences \r of human influence on the climate and can be attributed to external \r drivers (Section  11.9). Figure  11.4 summarizes assessments of \r observed changes in temperature extremes, in heavy precipitation \r and in droughts, and their attribution in a map form. "


id: 769d80fe4df04b4370a034666c88e028
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 18089
content:
"An increase in the concentration of greenhouse gases in the atmosphere leads to the warming of tropospheric air and the Earth's \r surface. This direct thermodynamic effect leads to warmer temperatures everywhere, with an increase in the frequency and intensity \r of warm extremes, and a decrease in the frequency and intensity of cold extremes. The initial increase in temperature leads to other \r thermodynamic responses and feedbacks affecting the atmosphere and the surface. These include an increase in the water vapour \r content of the atmosphere (water vapour feedback, see Section 7.4.2.2) and a change in the vertical profile of temperature (lapse rate \r feedback, see Section 7.4.2.2). While the water vapour feedback always amplifies the initial temperature increases (positive feedback), \r the lapse rate feedback amplifies near-surface temperature increases (positive feedback) in mid- and high latitudes but reduces \r [...]"


id: ce254845584e9c0b5560a59bdf962486
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 33601
content:
"[...] temperature increases (negative feedback) in tropical regions (Pithan and Mauritsen, 2014). "


id: a2fa852ea8937b46976db6baeb8807f8
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 25889
content:
"Changes in temperature also control changes in water vapour through increases in evaporation and in the water-holding capacity of \r the atmosphere (Section 8.2.1). At the global scale, column-integrated water vapour content increases roughly following the Clausius-\r Clapeyron (C-C) relation, with an increase of approximately 7% per 1degC of global-mean surface warming (Section 8.2.1). Nonetheless, \r at regional scales, water vapour increases differ from this C-C rate due to several reasons (Section 8.2.2), including a change in \r weather regimes and limitations in moisture transport from the ocean, which warms more slowly than land (Byrne and O'Gorman, \r 2018). Observational studies (Fischer and Knutti, 2016; Sun et al., 2021) have shown that the observed rate of increased precipitation \r extremes is similar to the C-C rate at the global scale. Climate model projections show that the increase in water vapour leads to \r [...]"


id: 953a0f5f9b31e6f9e78b5e573f428a87
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 23454
content:
"[...] robust increases in precipitation extremes everywhere, with a magnitude that varies between 4% and 8%per 1degC of surface warming \r (thermodynamic contribution, Box 11.1, Figure 1b). At regional scales, climate models show that the dynamic contribution (Box 11.1, \r Figure 1c) can be substantial and strongly modify the projected rate of change of extreme precipitation (Box 11.1, Figure 1a) with large \r regions in the subtropics showing robust reductions and other areas (e.g., equatorial Pacific) showing robust amplifications (Box 11.1, \r Figure 1c). However, the dynamic contributions show large differences across models and are more uncertain than thermodynamic \r contributions (Box 11.1, Figure 1c; Shepherd, 2014; Trenberth et al., 2015; Pfahl et al., 2017). "


id: d411675749c649d991c1feaf7669272e
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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"Box 11.1, Figure 1: Multi-model Coupled Model Intercomparison Project Phase 5 (CMIP5) mean fractional changes (in % per degree of warming). \r (a) changes in annual maximum precipitation (Rx1day); (b) changes in Rx1day due to the thermodynamic contribution; and (c) changes in Rx1day due to the dynamic \r contribution estimated as the difference between the total changes and the thermodynamic contribution. Changes were derived from a linear regression for the period \r 1950-2100. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where >=80% of models (n=22) agree \r on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the \r simple approach, please refer to the Cross-Chapter Box Atlas 1. A detailed description of the estimation of dynamic and thermodynamic contributions is given in Pfahl \r [...]"


id: afb8888000dd749c69297af3bd3a2625
created_at: 2023-04-19 13:22:42
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content:
"[...] et al. (2017). Figure adapted from Pfahl et al. (2017), originally published in Nature Climate Change/Springer Nature. Further details on data sources and processing \r are available in the chapter data table (Table 11.SM.9). "


id: 5d24515a374808b8212ceecc996abc67
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 13659
content:
"Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms that result from the extra \r latent heat released from the thermodynamic increases in moisture (Lackmann, 2013; Willison et al., 2013; Marciano et al., 2015; \r Nie et al., 2018; Mizuta and Endo, 2020). The extra latent heat released within storms has been shown to increase precipitation \r extremes by strengthening convective updrafts and the intensity of the cyclonic circulation (e.g., Molnar et al., 2015; Nie et al., 2018), \r although weakening effects have also been found in mid-latitude cyclones (e.g., Kirshbaum et al., 2017). Additionally, the increase \r in latent heat can also suppress convection at larger scales due to atmospheric stabilization (Nie et al., 2018; Tandon et al., 2018; \r Kendon et al., 2019). As these dynamic effects result from feedback processes within storms where convective processes are crucial, \r [...]"


id: 5bdfb94403ada624eaa35a53654bf738
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 13422
content:
"[...] their proper representation might require improving the horizontal/vertical resolution, the formulation of parametrizations, or both, \r in current climate models (i.e., Kendon et al., 2014; Westra et al., 2014; Ban et al., 2015; Meredith et al., 2015; Prein et al., 2015; \r Nie et al., 2018)."


id: 34fcae17522f65eeca19ce415d8721ef
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 6566
content:
"Atmospheric large-scale circulation patterns and associated \r atmospheric dynamics are important determinants of the regional \r climate (Chapter 10). As a result, they are also important to the magnitude, \r frequency, and duration of extremes (Box 11.4). Aspects of changes in \r large-scale circulation patterns are assessed in Chapters 2, 3, 4 and 8, \r and representative atmospheric and oceanic modes are described in \r Annex IV. This subsection provides some general concepts, through \r a couple of examples, on why the uncertainty in the response of large\u0002scale circulation patterns to external forcing can cascade to uncertainty \r in the response of extremes to external forcings. Details for specific \r types of extremes are covered in the relevant subsections. For example, \r the occurrence of the El Nino-Southern Oscillation (ENSO) influences \r precipitation regimes in many areas, favouring droughts in some regions \r [...]"


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content:
"[...] and heavy rains in others (Box 11.4). The extent and strength of the Hadley \r circulation influences regions where tropical and extratropical cyclones \r occur, with important consequences for the characteristics of extreme \r precipitation, drought, and winds (Section 11.7). Changes in circulation \r patterns associated with land-ocean heat contrast, which affect the \r monsoon circulations (Section 8.4.2.4), lead to heavy precipitation along \r the coastal regions in East Asia (Freychet et al., 2015). As a result, changes \r in the spatial and/or temporal variability of the atmospheric circulation in \r response to warming affect characteristics of weather systems such \r as tropical cyclones (Sharmila and Walsh, 2018), storm tracks (Shaw \r et al., 2016), and atmospheric rivers (Section 11.7; Waliser and Guan, \r 2017). Changes in weather systems come with changes in the frequency \r and intensity of extreme winds, extreme temperatures, and extreme \r [...]"


id: 42a67b6fbb2ae97326efb5f48a193361
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
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content:
"[...] precipitation, on the backdrop of thermodynamic responses of extremes \r to warming (Box 11.1). Floods are also affected by large-scale circulation \r modes, including ENSO, the North Atlantic Oscillation (NAO), the Atlantic \r Multi-decadal Variability (AMV), and the Pacific Decadal Variability (PDV) \r (Kundzewicz et al., 2018; Annex IV). Aerosol forcing, through changes \r in patterns of sea surface temperatures (SSTs), also affects circulation \r patterns and tropical cyclone activities (Takahashi et al., 2017)."


id: 1857a0c79e6be41ff9fe41419d885b93
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 1487
content:
"Land use can affect regional extremes, in particular hot extremes, \r in several ways (high confidence). This includes effects of land \r management (e.g.,  cropland intensification, irrigation, double \r cropping) as well as of land cover changes (deforestation; Sections \r 11.3.2 and 11.6). Some of these processes are not well represented \r (e.g.,  effects of forest cover on diurnal temperature cycle) or not \r integrated (e.g.,  irrigation) in climate models (Sections 11.3.2 and \r 11.3.3). Overall, the effects of land-use forcing may be particularly \r relevant in the context of low-emissions scenarios, which include \r large land-use modifications, for instance those associated with the \r expansion of biofuels, bioenergy with carbon capture and storage, \r or re-/afforestation to ensure negative emissions, as well as with the \r expansion of food production (e.g., SR1.5, Chapter 3; Cross-Chapter \r Box 5.1 in this Report; van Vuuren et al., 2011; Hirsch et al., 2018). \r [...]"


id: 7935c17eb0528ec936ee832b93c2ceba
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 18519
content:
"[...] There are also effects on the water cycle through freshwater use \r (Section 11.6 and Cross-Chapter Box 5.1)."


id: c888e795154d148e0e5273d67abfea3f
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 32587
content:
"Aerosol forcing also has a strong regional footprint associated with \r regional emissions, which affects temperature and precipitation \r extremes (high confidence) (Sections 11.3 and 11.4). From around the \r 1950s to 1980s, enhanced aerosol loadings led to regional cooling \r due to decreased global solar radiation ('global dimming') which \r was followed by a phase of 'global brightening' due to a reduction \r in aerosol loadings (Chapters 3 and 7; Wild et al., 2005). King et al. \r (2016b) show that aerosol-induced cooling delayed the timing of \r a significant human contribution to record-breaking heat extremes in \r some regions. However, the decreased aerosol loading since the 1990s \r has led to an accelerated warming of hot extremes in some regions. \r Based on Earth system model (ESM) simulations, Dong et al. (2017) \r suggest that a  substantial fraction of the warming of the annual \r hottest days in Western Europe since the mid-1990s has been due to \r [...]"


id: dcbec95eac9061c10fe8909b78e2965d
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 36099
content:
"[...] decreases in aerosol concentrations in the region. Dong et al. (2016b) \r also identify non-local effects of decreases in aerosol concentrations \r in Western Europe, which they estimate played a dominant role in \r the warming of the hottest daytime temperatures in north-east Asia \r since the mid-1990s, via induced coupled atmosphere-land surface \r and cloud feedbacks, rather than a direct impact of anthropogenic \r aerosol changes on cloud condensation nuclei. "


id: e205d5011ddeabc8066490f22fc42050
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 37047
content:
"In general, changes in atmospheric large-scale circulation due to \r external forcing are uncertain, but there are some robust changes \r (Sections 2.3.1.4 and 8.2.2.2). Among them, there has been a very likely \r widening of the Hadley circulation since the 1980s and the extratropical \r jets and cyclone tracks have likely been shifting poleward since the \r 1980s (Section  2.3.1.4). The poleward expansion affects drought \r occurrence in some regions (Section  11.6), and results in poleward \r shifts of tropical cyclones and storm tracks (Sections 11.7.1 and \r 11.7.2). Although it is very likely that the amplitude of ENSO variability \r will not robustly change over the 21st century (Section 4.3.3.2), the \r frequency of extreme ENSO events (Box 11.4), defined by precipitation \r threshold, is projected to increase with global warming (Section 6.5 \r of SROCC). This would have implications for projected changes in \r extreme events affected by ENSO, including droughts over wide areas \r [...]"


id: bad74cb09b16da2082df937aa5f5883a
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 30083
content:
"[...] (Section 11.6; Box 11.4) and tropical cyclones (Section 11.7.1). A case \r study is provided for extreme ENSO events in 2015-2016 in Box 11.4 \r to highlight the influence of ENSO on extremes. "


id: e5dc46eab7a74d45dd12dce82b096097
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 37740
content:
"In addition to regional forcings, regional feedback mechanisms can \r also substantially affect extremes (high confidence) (Sections 11.3, \r 11.4 and 11.6). In particular, soil moisture feedbacks play an important \r role for extremes in several mid-latitude regions, leading to a marked \r additional warming of hot extremes compared to mean global warming \r (Seneviratne et al., 2016; Bathiany et al., 2018; Miralles et al., 2019), \r which is superimposed on the known land-sea contrast in mean \r warming (Vogel et al., 2017). Soil moisture-atmosphere feedbacks \r also affect drought development (Section 11.6). Additionally, effects of \r land surface conditions on circulation patterns have also been reported \r (Koster et al., 2016; Sato and Nakamura, 2019). These regional feedbacks \r are also associated with substantial spread in models (Section 11.3), \r and contribute to the identified higher spread of regional projections of \r [...]"


id: 6062e88f5fadf6ab37bd10bec2b10172
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 14264
content:
"[...] temperature extremes as a function of global warming, compared with \r the spread resulting from the differences in projected global warming \r (global transient climate responses) in climate models (Seneviratne \r and Hauser, 2020). In addition, there are also feedbacks between soil \r moisture content and precipitation occurrence, generally characterized \r by negative spatial feedbacks and positive local feedbacks (Taylor \r et al., 2012; Guillod et al., 2015). Climate model projections suggest \r that these feedbacks are relevant for projected changes in heavy \r precipitation (Seneviratne et al., 2013). However, there is evidence that \r climate models do not capture the correct sign of the soil moisture-\r precipitation feedbacks in several regions, in particular spatially, and/\r or in some cases also temporally (Taylor et al., 2012; Moon et al., 2019). \r In the Northern Hemisphere high latitudes, the snow- and ice-albedo \r [...]"


id: c6c31455b72fd709e4a71e44c5ebb584
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 32275
content:
"[...] feedback, along with other factors, is projected to largely amplify \r temperature increases (e.g., Pithan and Mauritsen, 2014), although the \r effect on temperature extremes is still unclear. It also remains unclear \r whether snow-albedo feedbacks in mountainous regions might have \r an effect on temperature and precipitation extremes(e.g., Gobiet et al., "


id: c89df6ba8981b9c2c311f8542a3495e9
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 32613
content:
"The SREX (Chapter 3) assigned low confidence to changes in low-likelihood, high-impact (LLHI) events (termed 'low-probability high\u0002impact scenarios'). Such events are often not anticipated and thus sometimes referred to as 'surprises'. There are several types of LLHI \r events. Abrupt changes in mean climate are addressed in Chapter 4. Unanticipated LLHI events can either result from tipping points \r in the climate system (Section 1.4.4.3), such as the shutdown of the Atlantic thermohaline circulation (SROCC Chapter 6; Collins \r et al., 2019) or the drydown of the Amazonian rainforest (SR1.5 Chapter 3, Hoegh-Guldberg et al., 2018; Drijfhout et al., 2015), or \r from uncertainties in climate processes, including climate feedbacks, that may enhance or damp extremes either related to global or \r regional climate responses (Seneviratne et al., 2018a; Sutton, 2018). The low confidence does not by itself exclude the possibility of \r [...]"


id: 276dcd26efb843f94105f7216add100e
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 4117
content:
"[...] such events occuring, rather it indicates a poor state of knowledge. Such outcomes, while improbable, could be associated with very \r high impacts, and are thus highly relevant from a risk perspective (see Section 1.4.3 and Box 11.4; Sutton, 2018, 2019). Alternatively, \r high impacts can occur when different extremes occur at the same time, or in short succession at the same location, or in several \r regions with shared vulnerability (e.g., food-basket regions Gaupp et al., 2019). These 'compound events' are assessed in Section 11.8, \r and Box 11.4 provides a case study example. "


id: 55c5066825c1f1848058f96baa6b9e10
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 12361
content:
"Difficulties persist in determining the likelihood of occurrence and time frame of potential tipping points and LLHI events. However, \r new literature has emerged on unanticipated and LLHI events. There are some events that are sufficiently rare that they have not been \r observed in meteorological records, but whose occurrence is nonetheless plausible within the current state of the climate system - \r see examples below and in McCollum et al. (2020). The rare nature of such events and the limited availability of relevant data makes \r it difficult to estimate their occurrence probability and thus gives little evidence on whether to include such hypothetical events in \r planning decisions and risk assessments. The estimation of such potential surprises is often limited to events that have historical \r analogues (including before the instrumental records began, Wetter et al., 2014), albeit the magnitude of the event may differ. \r [...]"


id: 2d834ad942ec1c26dca741aeeaf26259
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 5301
content:
"[...] Additionally, there is also a limitation of available resources to exhaust all plausible trajectories of the climate system. As a result, there \r will still be events that cannot be anticipated. These events can be surprises to many in that the events have not been experienced, \r although their occurrence could be inferred by statistical means or physical modelling approaches (Chen et al., 2017; van Oldenborgh \r et al., 2017; Harrington and Otto, 2018a). Another approach focusing on the estimation of low-probability events and of events \r whose likelihood of occurrence is unknown consists in using physical climate models to create a physically self-consistent storyline of \r plausible extreme events and assessing their impacts and driving factors in past (Section 11.2.3) or future conditions (Section 11.2.4) \r (Hazeleger et al., 2015; Shepherd, 2016; Zappa and Shepherd, 2017; Cheng et al., 2018; Shepherd et al., 2018; Sutton, 2018; Schaller \r et al., 2020; Wehrli et al., 2020). "


id: 36a358eba01b863b5326008b3062ffe2
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 6847
content:
"One factor making such events hard to anticipate is the fact that we now live in a non-stationary climate, and that the framework of \r reference for adaptation is continuously moving. As an example, the concurrent heatwaves that occurred across the Northern Hemisphere \r in the summer of 2018 were considered very unusual and were unprecedented given the total area that was concurrently affected (Drouard \r et al., 2019; Kornhuber et al., 2019; Toreti et al., 2019; Vogel et al., 2019); however, the probability of this event under 1degC global warming \r was found to be about 16% (Vogel et al., 2019), which is not particularly low. Similarly, the 2013 summer temperature over eastern \r China was the hottest on record at the time, but it had an estimated recurrence interval of about four years in the climate of 2013 \r (Sun et al., 2014). Furthermore, when other aspects of the risk, vulnerability, and exposure are historically high or have recently increased \r [...]"


id: 54bf9eefcb102537841a13e1aa9dc582
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 12181
content:
"[...] (see WGII, Chapter 16, Section 16.4), relatively moderate extremes can have very high impacts (Otto et al., 2015b; Philip et al., 2018b). \r As warming continues, the climate moves further away from its historical state we are familiar with, resulting in an increased likelihood of \r unprecedented events and surprises. This is particularly the case under high global warming levels - for example, the climate of the late \r 21st century under high-emissions scenarios, above 4degC of global warming (Cross-Chapter Box 11.1)."


id: cd86a284ef7266ff4ee532d3abec29ba
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 33490
content:
"patterns with amplification of resulting responses to human-induced global warming. Also multivariate extremes at single locations \r pose specific challenges to anticipation (Section 11.8), with low likelihoods in the current climate but the probability of occurrence of \r such compound events strongly increasing with increasing global warming levels (Vogel et al., 2020a). Therefore, in order to estimate \r whether, and at what level of global warming, very high impacts arising from extremes would occur, the spatial extent of extremes \r and the potential of compounding extremes need to be assessed. Sections 11.3, 11.4, 11.7 and 11.8 highlight increasing evidence that \r temperature extremes, higher intensity precipitation accompanying tropical cyclones, and compound events such as dry/hot conditions \r conducive to wildfire or storm surges resulting from sea level rise and heavy precipitation events, pose widespread threats to societies \r [...]"


id: 377a1c399eaaebca7240d09f71aa2214
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 6970
content:
"[...] already at relatively low warming levels. Studies have already shown that the probability for some recent extreme events is so small in \r the undisturbed world that these events were extremely unlikely to occur without human influence (Section 11.2.4). Box 11.2, Table 1, \r provides examples of projected changes in LLHI extremes (single extremes, compound events) of potential relevance for impact and \r adaptation assessments showing that today's very rare events can become commonplace in a warmer future."


id: fa99886d250fd380839100af3029b6f
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 41462
content:
"Changes in extremes have also been examined from two \r perspectives:  changes in the frequency for a  given magnitude of \r extremes; or changes in the magnitude for a particular return period \r (frequency). Changes in the probability of extremes (e.g., temperature \r extremes) depend on the rarity of the extreme event that is assessed, \r with a  larger change in probability associated with a  rarer event \r (e.g.,  Kharin et al., 2018). However, changes in the magnitude \r represented by the return levels of the extreme events may not be \r as sensitive to the rarity of the event. While the answers to the two \r different questions are related, their relevance may differ for distinct \r audiences. Conclusions regarding the respective contribution of \r greenhouse gas forcing to changes in magnitude versus frequency \r of extremes may also differ (Otto et al., 2012). Correspondingly, the \r sensitivity of changes in extremes to increasing global warming is \r [...]"


id: 62c0ca97453f179169c9ead987524d07
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 14670
content:
"[...] also dependent on the definition of the considered extremes. In the \r case of temperature extremes, changes in magnitude have been \r shown to often depend linearly on global surface temperature \r (Seneviratne et al., 2016; Wartenburger et al., 2017), while changes in \r frequency tend to be nonlinear and can, for example, be exponential \r for increasing global warming levels (Fischer and Knutti, 2015; Kharin \r et al., 2018). When similar damage occurs once a  fixed threshold \r is exceeded, it is more important to ask a  question regarding \r changes in the frequency. But when the exceedance of this fixed \r threshold becomes a normal occurrence in the future, this can lead \r to a  saturation in the change of probability (Harrington and Otto, \r 2018a). Also, if the impact of an event increases with the intensity \r of the event, it would be more relevant to examine changes in the \r magnitude. Finally, adaptation to climate change might change \r [...]"


id: ff49132d156504bac558fece62beaa5a
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 42321
content:
"In the literature, an event is generally considered extreme if the value \r of a variable exceeds (or lies below) a threshold. The thresholds have \r been defined in different ways, leading to differences in the meaning \r of extremes that may share the same name. For example, two sets \r of metrics for the frequency of hot/warm days have been used in \r the literature. One set counts the number of days when maximum \r daily temperature is above a relative threshold defined as the 90th or \r higher percentile of maximum daily temperature for the calendar day \r over a base period. An event based on such a definition can occur at \r any time of the year, and the impact of such an event would differ \r depending on the season. The other set counts the number of days \r in which maximum daily temperature is above an absolute threshold \r such as 35degC, because exceeding this temperature can sometimes \r cause health impacts (however, these impacts may depend on \r [...]"


id: f0cf8153a19f26dbf969abcaa158ef51
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 39729
content:
"[...] location and whether ecosystems and the population are adapted \r to such temperatures). While both types of hot extreme indices \r have been used to analyse changes in the frequency of hot/warm \r events, they represent different events that occur at different times "


id: 43dae5c04a71a650e321f24e747de19b
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 9168
content:
"In general, changes in temperature extremes from various reanalyses \r were most consistent with gridded observations after about 1980, \r but larger differences were found during the pre-satellite era (Donat \r et al., 2014b). Overall, lower agreement across reanalysis datasets \r was found for extreme precipitation changes, although temporal \r and spatial correlations against observations were found to be still \r significant. In regions with sparse observations (e.g., Africa and parts \r of South America), there is generally less agreement for extreme \r precipitation between different reanalysis products, indicating \r a  consequence of the lack of an observational constraint in these \r regions (Donat et al., 2014b, 2016a). More recent reanalyses, such as \r ERA5 (Hersbach et al., 2020), seem to have improved over previous \r products, at least over some regions (e.g., Mahto and Mishra, 2019; \r Gleixner et al., 2020; Sheridan et al., 2020). Caution is needed when \r [...]"


id: 2a6f4acdd477a1a08105743753674278
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content:
"[...] reanalysis data products are used to provide additional information \r about past changes in these extremes in regions where observations \r are generally lacking. "


id: 7f003f814d9a686aca07c3c258f727c8
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 19495
content:
"Extreme weather and climate events occur on time scales of \r hours (e.g.,  convective storms that produce heavy precipitation) \r to days (e.g.,  tropical cyclones, heatwaves), to seasons and years \r (e.g., droughts). A robust determination of long-term changes in these \r events can have different requirements for the spatial and temporal \r scales and sample size of the data. In general, it is more difficult \r to determine long-term changes for events of fairly large temporal \r duration, such as 'megadroughts' that last several years or longer \r (e.g., Ault et al., 2014), because of the limitations of the observational \r sample size. Literature that studies changes in extreme precipitation \r and temperature often uses indices representing specifics of extremes \r that are derived from daily precipitation and temperature values. \r Station-based indices would have the same issues as those for the \r mean climate regarding the quality, availability, and homogeneity of \r [...]"


id: ddd813219b928e88cab40c81cf0cc9c
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
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index: climate_gpt_v2
vector_id: 36290
content:
"[...] the data. For the purpose of constructing regional information and/\r or for comparison with model outputs, such as model evaluation, \r and detection and attribution, these station-based indices are often \r interpolated onto regular grids. Two different approaches, involving \r two different orders of operation, have been used in producing such \r gridded datasets. "


id: 6bf46e778f63cb16c103ef676d0f104f
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 16292
content:
"Many factors affect confidence in information on pre-instrumental extremes. First, the geographical coverage of paleoclimate \r reconstructions of extremes is not spatially uniform (Smerdon and Pollack, 2016) and depends on both the availability of archives and \r records, which are environmentally dependent, and also the differing attention and focus from the scientific community. In Australia, \r for example, the paleoclimate network is sparser than for other regions, such as Asia, Europe and North America, and synthesized \r products rely on remote proxies and assumptions about the spatial coherence of precipitation between remote climates (Cook et al., \r 2016c; Freund et al., 2017). Second, pre-instrumental evidence of extremes may be focused on understanding archetypal extreme \r events, such as the climatic consequences of the 1815 eruption of Mount Tambora, Indonesia (Veale and Endfield, 2016). These studies \r [...]"


id: 10992b436eb3adce3719a1fbaa6151dd
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 102
content:
"[...] provide narrow evidence of extremes in response to specific forcings (M. Li et al., 2017) for specific epochs. Third, natural archives may \r provide information about extremes in one season only and may not represent all extremes of the same types. "


id: cc4d08d86082fcd52e310bd125539dec
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 33264
content:
"Evidence of shorter duration extreme event types, such as floods and tropical storms, is further restricted by the comparatively low \r chronological controls and temporal resolution (e.g., monthly, seasonal, yearly, multiple years) of most archives compared to the \r events (e.g., minutes to days). Natural archives may be sensitive only to intense environmental disturbances, and so only sporadically \r record short-duration or small spatial-scale extremes. Interpreting sedimentary records as evidence of past short-duration extremes is \r also complex and requires a clear understanding of natural processes (Wilhelm et al., 2019). For example, paleoflood reconstructions \r of flood recurrence and intensity produced from geological evidence (e.g., river and lake sediments), speleothems (Denniston and \r Luetscher, 2017), botanical evidence (e.g.,  flood damage to trees, or tree ring reconstructions), and floral and faunal evidence \r [...]"


id: 7500c2d88263e2d876cf3e48f7144d4
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 17821
content:
"[...] (e.g.,  diatom fossil assemblages) require understanding of sediment sources and flood mechanisms. Pre-instrumental records of \r tropical storm intensity and frequency (also called paleotempest records) derived from overwash deposits of coastal lake and marsh \r sediments are difficult to interpret. Many factors have an impact on whether disturbances are deposited in archives (Muller et al., \r 2017) and deposits may provide sporadic and incomplete preservation histories (e.g., Tamura et al., 2018). "


id: cdee8fd5873edb43cc3b73032bd20421
created_at: 2023-04-19 13:22:42
updated_at: 2023-04-19 13:22:42
content_type: text
index: climate_gpt_v2
vector_id: 33564
content:
"There is high confidence in the occurrence of long-duration and severe drought events during the Common Era for many locations, \r although their severity compared to recent drought events differs between locations and the lengths of reconstruction provided. \r Recent observed drought extremes in some regions - such as the eastern Mediterranean Levant (Cook et al., 2016a), California in the \r USA (Cook et al., 2014b; Griffin and Anchukaitis, 2014), and in the Andes (Garreaud et al., 2017; Dominguez-Castro et al., 2018) - do \r not have precedents within the multi-century periods reconstructed in these studies, in terms of duration and/or severity. In some \r regions (in south-western North America (Asmerom et al., 2013; Cook et al., 2015), the Great Plains region (Cook et al., 2004), the \r Middle East (Kaniewski et al., 2012), and China (Gou et al., 2015)), recent drought extremes may have been exceeded in the Common \r [...]"


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index: climate_gpt_v2
vector_id: 305
content:
"[...] Era. In further locations, there is conflicting evidence for the severity of pre-instrumental droughts compared to observed extremes, "


id: 2e5a43e1f15e0afec55a727b8ae1e883
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"There is high confidence that the magnitude of floods over the Common Era exceeded observed records in some locations, including \r Central Europe and eastern Asia. Recent literature supports the AR5 assessments of floods (Masson-Delmotte et al., 2013). For example, \r high temporally resolved records provide evidence of Common Era floods exceeding the probable maximum flood levels in the Upper \r Colorado River, USA (Greenbaum et al., 2014) and peak discharges that are double gauge levels along the middle Yellow River, China \r (Liu et al., 2014). Further studies demonstrate pre-instrumental or early instrumental differences in flood frequency compared to \r the instrumental period, including reconstructions of high and low flood frequency in the European Alps (e.g., Swierczynski et al., \r 2013; Amann et al., 2015) and Himalayas (Ballesteros Canovas et al., 2017). The combination of extreme historical flood episodes \r [...]"


id: 83eb664e15b102423a719f2a98ad576f
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"[...] determined from documentary evidence also increases confidence in the determination of flood frequency and magnitude, compared \r to using geomorphological archives alone (Kjeldsen et al., 2014). In regions, such as Europe and China, that have rich historical flood \r documents, there is strong evidence of high-magnitude flood events over pre-instrumental periods (Kjeldsen et al., 2014; Benito et al., \r 2015; Macdonald and Sangster, 2017). A key feature of paleoflood records is variability in flood recurrence at centennial timescales \r (Wilhelm et al., 2019), although constraining climate-flood relationships remains challenging. Pre-instrumental floods often occurred \r in considerably different contexts in terms of land use, irrigation, and infrastructure, and may not provide direct insight into modern \r river systems, which further prevents long-term assessments of flood changes being made based on these sources. "


id: b27ea890bae47028f6c73fc2a6d14252
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"are considered and often utilize fully coupled climate models (Sun \r et al., 2014). Other more conditional approaches involve prescribing \r certain aspects of the climate system. These range from prescribing \r the pattern of the surface ocean change at the time of the event \r (e.g.,  Hoerling et al., 2013, 2014), often using Atmospheric Model \r Intercomparison Project (AMIP) style global models, where the \r choice of sea surface temperature and ice patterns influences the \r attribution results (Sparrow et al., 2018), to prescribing the large\u0002scale circulation of the atmosphere and using weather forecasting \r models or methods (e.g., Pall et al., 2017; Patricola and Wehner, 2018; \r Wehner et al., 2018a). These highly conditional approaches have also \r been called 'storylines' (Cross-Working Group Box  1.1; Shepherd, \r 2016) and can be useful when applied to extreme events that are \r too rare to otherwise analyse, or where the specific atmospheric \r [...]"


id: ccf6e64c6228fb0e115623eeabd979c6
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"[...] conditions were central to the impact. These methods are also used \r to enable the use of very high-resolution simulations in cases were \r lower-resolution models do not simulate the regional atmospheric \r dynamics well (Shepherd, 2016; Shepherd et al., 2018). However, the \r imposed conditions limit an overall assessment of the anthropogenic \r influence on an event, as the fixed aspects of the analysis may also \r have been affected by climate change. For instance, the specified \r initial conditions in the highly conditional hindcast attribution \r approach often applied to tropical cyclones (e.g.,  Takayabu \r et al., 2015; Patricola and Wehner, 2018) permit only a conditional \r statement about the magnitude of the storm if similar large-scale \r meteorological patterns could have occurred in a  world without \r climate change, thus precluding any attribution statement about \r the change in frequency if used in isolation. Combining conditional \r [...]"


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"[...] assessments of changes in the intensity with a multi-model approach \r does allow for the latter as well (Shepherd, 2016)."


id: 6af1b8315e380d9f7fb4852baf29f04c
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"Attribution science concerns the identification of causes for changes \r in characteristics of the climate system (e.g., trends, single extreme \r events). A general overview and summary of methods of attribution \r science is provided in  the Cross-Working Group Box  1.1. Trend \r detection using optimal fingerprinting methods is a well-established \r field, and has been assessed in AR5 (Chapter 10, Bindoff et al., 2013), \r and Section 3.2.1 of this Report. There are specific challenges when \r applying optimal fingerprinting to the detection and attribution of \r trends in extremes and on regional scales where the lower signal\u0002to-noise ratio is a  challenge. In particular, the method generally \r requires the data to follow a Normal (Gaussian) distribution, which \r is often not the case for extremes. However, recent studies showed \r that extremes can be transformed to a  Gaussian distribution, for \r example, by averaging over space, so that optimal fingerprinting \r [...]"


id: d9910fdb5541e2e79e5fa5896a6013cc
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"[...] techniques can still be used (Wen et al., 2013; Zhang et al., 2013; \r Wan et al., 2019). Non-stationary extreme value distributions, which \r allow for the detailed detection and attribution of regional trends in \r temperature extremes, have also been used (Z. Wang et al., 2017a). "


id: 6ea0faeb08bdd73e6a5b50abf5d9fe4d
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"The outcome of event attribution is dependent on the definition of \r the event (Leach et al., 2020), as well as the framing (Otto et al., \r 2016; Christidis et al., 2018; Jezequel et al., 2018) and uncertainties \r in observations and modelling. Observational uncertainties arise in \r estimating the magnitude of an event as well as its rarity (Angelil \r et al., 2017). Results of attribution studies can also be very sensitive \r to the choice of climate variables (Sippel and Otto, 2014; Wehner \r et al., 2016). Attribution statements are also dependent on the spatial \r (Uhe et al., 2016; Cattiaux and Ribes, 2018; Kirchmeier-Young et al., \r 2019) and temporal (Harrington, 2017; Leach et al., 2020) extent \r of event definitions, as events of different scales involve different \r processes (W. Zhang et al., 2020) and large-scale averages generally \r yield higher attributable changes in magnitude or probability due \r to the smoothing out of noise. In general, confidence in attribution \r [...]"


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"[...] statements for large-scale heat and lengthy extreme precipitation \r events have higher confidence than shorter and more localized events, \r such as extreme storms, an aspect also relevant for determining the \r emergence of signals in extremes or the confidence in projections \r (see also Cross-Chapter Box Atlas.1). "


id: 3a1ac4e434ec7a5d8e175d11a15edf69
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"However, since AR5, the attribution of extreme weather events has \r emerged as a growing field of climate research with an increasing \r body of literature (see series of supplements to the annual State \r of the Climate report (Peterson et al., 2012, 2013a; Herring et al., \r 2014, 2015, 2016, 2018), including the number of approaches to \r examining extreme events (described in Easterling et al., 2016; \r Otto, 2017; Stott et al., 2016)). A commonly used approach - often \r called the risk-based approach in the literature, and referred to \r here as the 'probability-based approach'  - produces statements \r such as 'anthropogenic climate change made this event type twice \r as likely' or 'anthropogenic climate change made this event 15% \r more intense'. This is done by estimating probability distributions of \r the index characterizing the event in today's climate, as well as in \r a counterfactual climate, and either comparing intensities for a given \r [...]"


id: d02286fd17620552961c36c361b91a58
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"[...] occurrence probability (e.g.,  1-in-100-year event) or probabilities \r for a  given magnitude (see FAQ 11.3). There are a  number of \r different analytical methods encompassed in the probability-based \r approach, building on observations and statistical analyses (e.g., van \r Oldenborgh et al., 2012), optimal fingerprint methods (Sun et al., \r 2014), regional climate and weather forecast models (e.g., Schaller \r et al., 2016), global climate models (GCMs) (e.g., Lewis and Karoly, \r 2013), and large ensembles of atmosphere-only GCMs (e.g.,  Lott \r et al., 2013). A key component in any event attribution analysis is \r the level of conditioning on the state of the climate system. In the \r least conditional approach, the combined effect of the overall \r warming and changes in the large-scale atmospheric circulation "


id: de6ff963a0be5513a2c352c0c54577a0
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"The lack of model evaluation, in particular in early event attribution \r studies, has led to criticism of the emerging field of attribution science \r as a whole (Trenberth et al., 2015) and of individual studies (Angelil \r et al., 2017). In this regard, the storyline approach (Shepherd, 2016) \r provides an alternative option that does not depend on the model's \r ability to represent the circulation reliably. In addition, several ways \r of quantifying statistical uncertainty (Paciorek et al., 2018) and model \r evaluation (Lott and Stott, 2016; Philip et al., 2018b, 2020) have been \r employed to evaluate the robustness of event attribution results. \r For the unconditional probability-based approach, multi-model and \r multi-approach (e.g.,  combining observational analyses and model \r experiments) methods have been used to improve the robustness of \r event attribution (Hauser et al., 2017; Otto et al., 2018a; Philip et al., \r [...]"


id: cd5de44eb0b0b65721d2e37fe8b0012
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"While regional changes in many types of extremes do scale robustly \r with global surface temperature, generally irrespective of emissions \r scenarios (Section  11.1.4, Figures 11.3, 11.6 and 11.7 and Cross\u0002Chapter Box 11.1), effects of local forcing can distort this relation. \r For example, emissions scenarios with the same radiative forcing \r can have different regional extreme precipitation responses resulting \r from different aerosol forcing (Z. Wang et al., 2017b). Another \r example is related to forcing from land-use and land cover changes \r (Section  11.1.6). Climate models often either overestimate or \r underestimate observed changes in annual maximum daily maximum \r temperature, depending on the region and considered models (Donat \r et al., 2017; Vautard et al., 2020). Part of the discrepancies may be \r due to the lack of representation of some land forcings, in particular \r crop intensification and irrigation (N.D. Mueller et al., 2016; Findell \r [...]"


id: a7b0e9e4275e7cfff07950930b9e19bf
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"[...] et al., 2017; Thiery et al., 2017, 2020). Since these local forcings are \r not represented, and their future changes are difficult to project, \r these can be important caveats when using GWL scaling to project \r future changes for these regions. However, these caveats also apply \r to the use of scenario-based projections."


id: 8705306ee958bbb06da0a74ffef47aae
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"The most important quantity used to characterize past and future \r climate change is global warming relative to its pre-industrial \r level. Changes in global warming are linked quasi-linearly to \r global cumulative carbon dioxide (CO2) emissions (IPCC, 2013), \r and for their part, changes in regional climate, including many \r types of extremes, scale quasi-linearly with changes in global \r warming, often independently of the underlying emissions scenarios \r (SR1.5 Chapter  3; Seneviratne et al., 2016; Matthews et al., 2017; \r Wartenburger et al., 2017; Kharin et al., 2018; Y. Sun et al., 2018a; \r Tebaldi and Knutti, 2018; Beusch et al., 2020; Li et al., 2021). \r In addition, the use of global warming levels in the context of global \r policy documents - in particular the 2015 Paris Agreement (UNFCCC, \r 2016) implies that information on changes in the climate system, \r and specifically extremes, as a  function of global warming are of \r [...]"


id: 3b110f4cffc9a5aebbd79a6271e289fd
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"[...] particular policy relevance. Cross-Chapter Box  11.1 provides an \r overview on the translation between information at global warming \r levels (GWLs) and scenarios."


id: a4cee8d2c747bda1556d9f7097ae55f9
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"Wartenburger et al., 2017; Betts et al., 2018; Dosio and Fischer, 2018; Samset et al., 2019; Tebaldi et al., 2020; see Sections 4.6.1, 8.5.3, \r 9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4 for further details). The regional response patterns at given GWLs have been found to be consistent \r across different scenarios for many climate variables (Cross-Chapter Box 11.1 Figure 2; Pendergrass et al., 2015; Seneviratne et al., \r 2016; Wartenburger et al., 2017; Seneviratne and Hauser, 2020). The consistency tends to be higher for temperature-related variables \r than for variables in the hydrological cycle or variables characterizing atmospheric dynamics, and for intermediate to high-emissions \r scenarios than for low-emissions scenarios (e.g.,  for mean precipitation in the Representative Concentration Pathway (RCP) 2.6 \r scenario: Pendergrass et al., 2015; Wartenburger et al., 2017). Nonetheless, Cross-Chapter Box 11.1 Figure 2 illustrates that, even for \r [...]"


id: 99968845dd3280c167f0d78dd1292bc2
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"[...] mean precipitation, which is known to be forcing dependent (Sections 4.6.1 and 8.5.3), scenario differences in the response pattern at \r a given GWL are smaller than model uncertainty and internal variability in many regions (Herger et al., 2015). The response pattern is \r further found to be broadly consistent between models that reach a GWL relatively early, and those that reach it later under a given \r Shared Socio-economic Pathway (SSP; see Cross-Chapter Box 11.1, Figure 2g,h)."


id: 7c39bf3a21235b97bf43a3d7fdc7fd40
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"The limited scenario dependence of the GWL-based response for many variables implies that the regional response to emissions \r scenarios can be split in almost independent contributions of: (i) the transient global warming response to scenarios (see Chapter 4); \r and (ii) the regional response as function of a given GWL, which has also been referred to as 'regional climate sensitivity' (Seneviratne \r and Hauser, 2020). This property has also been used to develop regionally resolved emulators for global climate models, using global \r surface temperature as input (Beusch et al., 2020; Tebaldi et al., 2020). Analyses of the CMIP6 and CMIP5 multi-model ensembles \r shows that the GWL-based responses are very similar for temperature and precipitation extremes across the ensembles (Seneviratne \r and Hauser, 2020; Wehner, 2020; Li et al., 2021). This is despite their difference in global warming response (Chapter 4), confirming \r [...]"


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"[...] a substantial decoupling between the two responses (global warming vs GWL-based regional response) for these variables. Thus, the \r GWL approach isolates the uncertainty in the regional climate response from the global warming uncertainty induced by scenario, \r global mean model response and internal variability (Cross-Chapter Box, Figure 1)."


id: 62142cf3df78011c00f167ba5e4d67ff
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"Cross-Chapter Box 11.1, Figure 2 | (a-c) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean precipitation change at 2degC \r global warming level (GWL) (20-year mean) in three different Shared Socio-economic Pathway (SSP) scenarios relative to 1850-1900. All models \r reaching the corresponding GWL in the corresponding scenario are averaged. The number of models averaged across is shown at the top right of the panel. The maps \r for the other two SSP scenarios SSP1-1.9 (five models only) and SSP3-7.0 (not shown) are consistent. (d-f) Same as (a-c) but for annual mean temperature. (g)\r Annual mean temperature change at 2degC in CMIP6 models with high warming rate reaching the GWL in the corresponding scenario before the earliest year of the \r assessed very likely range (Section 4.3.4). (h) Climate response at 2degC GWL across all SSP1-1.9, SSP2-2.6, SSP2-4.5. SSP3-7.0 and SSP5-8.5 in all other models not \r [...]"


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"[...] shown in (g). The close agreement of (g) and (h) demonstrates that the mean temperature response at 2degC is not sensitive to the rate of warming, and thereby the \r global mean surface air temperature (GSAT) warming of the respective models in 2081-2100. 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 the sign of change; \r diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate \r regions with conflicting signal, where >=66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For \r more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1."


id: 8aeb170d91e5c25d48e8c412697aa483
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"In the model-based asssessment of Chapters 4, 8, 10, 11, 12 and the Atlas, the estimation of changes at GWLs are generally defined as \r the 20-year time period in which the mean global surface air temperature (GSAT; Cross-Chapter Box 2.3) first exceeds a certain anomaly \r relative to 1850-1900 - for simulations that start after 1850, relative to all years up to 1900 (Cross-Chapter Box Figure 3). The years \r when each individual model reaches a given GWL for CMIP6 and CMIP5 can be found in Hauser et al. (2021). The changes at given GWLs \r are identified for each ensemble member (for all scenarios) individually. Thereby, a given GWL is potentially reached a few years earlier \r or later in different realizations of the same model due to internal variability, but the temperature averaged across the 20-year period \r analysed in any simulation is consistent with the GWL. Instead of blending the information from the different scenarios, the Interactive \r [...]"


id: 44d83edf8caab94decb4c8567471803f
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"[...] Atlas can be used to compare the GWL spatial patterns and timings across the different scenarios (see Section Atlas 1.3.1). "


id: c778434eaeb661c81afea97327046c77
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"2019). Soil-moisture-temperature feedbacks affect past and present\u0002day heatwaves in observations and model simulations, both locally \r (Miralles et al., 2014a; Cowan et al., 2016, 2020; Hauser et al., 2016; \r Meehl et al., 2016; Wehrli et al., 2019) and beyond the regions of \r feedback occurrence through changes in regional circulation patterns \r (Stefanon et al., 2014; Koster et al., 2016; Sato and Nakamura, 2019). \r The uncertainty due to the representation of land-atmosphere \r feedbacks in ESMs is a cause of discrepancy between observations \r and simulations (Clark et al., 2006; Mueller and Seneviratne, 2014; \r Meehl et al., 2016). The decrease of plant transpiration or the \r increase of stomata resistance under enhanced CO2 concentrations \r is a direct CO2 forcing of land temperatures (warming due to reduced \r evaporative cooling), which contributes to higher warming on land \r [...]"


id: 5c08a2c9d4d0b10a2f1c7d4feaed36cc
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"[...] (Lemordant et al., 2016; Vicente-Serrano et al., 2020b). The snow/ice\u0002albedo feedback plays an important role in amplifying temperature \r variability in the high latitudes (Diro et al., 2018) and can be the \r largest contributor to the rapid warming of cold extremes in the mid\u0002and high latitudes of the Northern Hemisphere (Gross et al., 2020). "


id: 9478deed50e8d72b4cae59f53228b52c
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"Extreme temperature events are associated with large-scale \r meteorological patterns (Grotjahn et al., 2016). Quasi-stationary \r anticyclonic circulation anomalies or atmospheric blocking events are \r linked to temperature extremes in many regions, such as in Australia \r (Parker et al., 2014; Perkins-Kirkpatrick et al., 2016), Europe (Brunner \r et al., 2017, 2018; Schaller et al., 2018), Eurasia (Yao et al., 2017), \r Asia (Chen et al., 2016; Ratnam et al., 2016; Rohini et al., 2016), \r and North America (Yu et al., 2018, 2019; Zhang and Luo, 2019). \r Mid-latitude planetary wave modulations affect short-duration \r temperature extremes such as heatwaves (Perkins, 2015; Kornhuber \r et al., 2020). The large-scale modes of variability (Annex IV) affect \r the strength, frequency and persistence of these meteorological \r patterns and, hence, temperature extremes. For example, cold \r and warm extremes in the mid-latitudes are associated with \r [...]"


id: 283f5ff980c9874a58dba752ecc7ff70
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"[...] atmospheric circulation patterns such as the Pacific-North American \r (PNA) pattern, as well as atmosphere-ocean coupled modes such \r as Pacific Decadal Variability (PDV), the North Atlantic Oscillation \r (NAO), and Atlantic Multi-decadal Variability (AMV) (Section 11.1.5; \r Kamae et al., 2014; Johnson et al., 2018; Ruprich-Robert et al., 2018; \r Yu et al., 2018, 2020; Muller et al., 2020; Qasmi et al., 2021). Changes \r in the modes of variability in response to warming would therefore \r affect temperature extremes (Clark and Brown, 2013; Horton \r et al., 2015). The level of confidence in those changes varies, both \r in the observations and in future projections, affecting the level of \r confidence in changes in temperature extremes in different regions. \r As highlighted in Chapters 2  to 4  of this Report, it is likely that \r there have been observational changes in the extratropical jets and \r mid-latitude jet meandering (Section  2.3.1.4.3 and Cross-Chapter \r [...]"


id: b857cf3999dd3831cf31b4d24fd7997d
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"[...] Box  10.1). There is low confidence in possible effects of Arctic \r warming on mid-latitude temperature extremes (Cross-Chapter \r Box 10.1). A large portion of the multi-decadal changes in extreme \r temperature remains after the removal of the effect of these modes \r of variability, and can be attributed to human influence (Kamae et al., \r 2017b; Wan et al., 2019). Thus, global warming dominates changes in \r temperature extremes at the regional scale and it is very unlikely that \r dynamic responses to greenhouse-gas induced warming would alter \r the direction of these changes. "


id: e6f3734e26e6141a61bbae835c26211
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"Regional external forcings, including land-use changes and \r emissions of anthropogenic aerosols, play an important role in the \r changes of temperature extremes in some regions (high confidence) \r (Section  11.1.6). Deforestation may have contributed to about \r one third of the warming of hot extremes in some mid-latitude \r regions since the pre-industrial time (Lejeune et al., 2018). Aspects \r of agricultural practice, including no-till farming, irrigation, and \r overall cropland intensification, may cool hot temperature extremes \r (Davin et al., 2014; N.D. Mueller et al., 2016). For instance, cropland \r intensification has been suggested to be responsible for a cooling of \r the highest temperature percentiles in Midwest USA (N.D. Mueller \r et al., 2016). Irrigation has been shown to be responsible for \r a cooling of hot temperature extremes of up to 1degC-2degC in many \r mid-latitude regions in the present climate (Thiery et al., 2017, 2020), \r [...]"


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"[...] a process not represented in most of state-of-the-art ESMs (CMIP5, \r CMIP6). Double cropping may have led to increased hot extremes \r in the inter-cropping season in part of China (Jeong et al., 2014). \r Rapid increases in summer warming in western Europe and north\u0002east Asia since the 1980s are linked to a reduction in anthropogenic \r aerosol precursor emissions over Europe (Nabat et al., 2014; Dong \r et al., 2016b, 2017), in addition to the effect of increased greenhouse \r gas forcing (see also Section  10.1.3.1). This effect of aerosols on \r temperature-related extremes is also noted for declines in short-lived \r anthropogenic aerosol emissions over North America (Mascioli et al., \r 2016). On the local scale, the urban heat island (UHI) effect results \r in higher temperatures in urban areas than in their surrounding \r regions, and contributes to warming in regions of rapid urbanization, \r in particular for nighttime temperature extremes (Box 10.3; Phelan \r [...]"


id: 343633554e16a7e0a2ec9b1e32646d30
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"[...] et al., 2015; Chapman et al., 2017; Y. Sun et al., 2019). But these local \r and regional forcings are generally not or not well represented in the \r CMIP5 and CMIP6 simulations (see also Section 11.3.3), contributing \r to uncertainty in model simulated changes. \r In summary, greenhouse gas forcing is the dominant driver leading \r to the warming of temperature extremes. At regional scales, changes \r in temperature extremes are modulated by changes in large-scale \r patterns and modes of variability, feedbacks including soil-moisture-\r evapotranspiration-temperature or snow/ice-albedo-temperature "


id: c9fcc19f543cb4e93cd5c685e505b7b1
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"warming of TXx in Europe and north-western South America, and \r a particularly high warming of TNn in the Arctic. Consistent with the \r observed warming in global surface temperature (Section 2.3.1.2) \r and the observed trends in TXx and TNn, the frequency of TX90p \r has increased, while that of cold nights (TN10p) has decreased since \r the 1950s: Nearly all land regions showed statistically significant \r decreases in TN10p (Alexander, 2016; Dunn et al., 2020), though \r trends in TX90p are variable with some decreases in Southern South \r America, mainly during austral summer (Rusticucci et al., 2017). \r A decrease in the number of cold spell days is also observed over \r nearly all land surface areas (Easterling et al., 2016) and in the \r northern mid-latitudes in particular (van Oldenborgh et al., 2019). \r These observed changes are also consistent when a  new global \r land surface daily air temperature dataset is analysed (P. Zhang \r [...]"


id: 2415c5e9b28e105f550e0acec70e3077
created_at: 2023-04-19 13:22:42
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"[...] et al., 2019). Warming trends in temperature extremes globally, \r and in most land areas, over the path  century are also found to \r be consistent in a  range of observation-based datasets (Fischer \r and Knutti, 2014; Donat et al., 2016a; Dunn et al., 2020), with the \r extremes related to daily minimum temperatures changing faster \r than those related to daily maximum temperatures (Dunn et al., \r 2020; see Figure 11.2). Seasonal variations in trends in temperature\u0002related extremes have been demonstrated. A  warming in warm\u0002season temperature extremes is detected, even during the 'slower \r surface global warming' period from the late 1990s to early 2010s \r (Cross-Chapter Box 3.1; Kamae et al., 2014; Seneviratne et al., 2014; \r Imada et al., 2017). Many studies of past changes in temperature \r extremes for particular regions or countries show trends consistent \r with this global picture, as summarized below and in Tables 11.4, \r 11.7, 11.10, 11.13, 11.16 and 11.19. "


id: 771864e069cbda08af295765486259af
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"Since SREX (IPCC, 2012) and AR5 (IPCC, 2014), many regional\u0002scale studies have examined trends in temperature extremes using \r different metrics that are based on daily temperatures, such as the \r Commission for Climatology/World Climate Research Program/\r Commission for Oceanography and Marine Meteorology joint Expert \r Team on Climate Change Detection and Indices (ETCCDI) indices \r (Dunn et al., 2020). The additional observational records, along with \r a stronger warming signal, show very clearly that changes observed \r at the time of AR5 (IPCC, 2014) continued, providing strengthened \r evidence of an increase in the intensity and frequency of hot extremes \r and decrease in the intensity and frequency of cold extremes. While \r the magnitude of the observed trends in temperature-related \r extremes varies depending on the region, spatial and temporal \r scales, and metric assessed, evidence of a  warming effect is \r overwhelming, robust, and consistent. In particular, an increase \r [...]"


id: d11fff5716065c735d26f585b899ac9f
created_at: 2023-04-19 13:22:42
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"[...] in the intensity and frequency of hot extremes is almost always \r associated with an increase in the hottest temperatures and in the \r number of heatwave days. It is also the case for changes (decreases) \r in cold extremes. For this reason, and to simplify the presentation, \r the phrase 'increase in the intensity and frequency of hot extremes' \r is used to represent, collectively, an increase in the magnitude of \r extreme day and/or night temperatures, in the number of warm days \r and/or nights, and in the number of heatwave days. Changes in cold \r extremes are assessed similarly. "


id: 8d5131876025a7c2e83c155dbfaab062
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index: climate_gpt_v2
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"In Africa (Table  11.4), while it is difficult to assess changes in \r temperature extremes in parts of the continent because of a  lack \r of data, evidence of an increase in the intensity and frequency of \r hot extremes and decrease in the intensity and frequency of cold \r extremes is clear and robust in regions where data are available. \r These include an increase in the frequency of warm days and nights \r and a decrease in the frequency of cold days and nights with high \r confidence (Donat et al., 2013a, 2014b; Kruger and Sekele, 2013; \r Chaney et al., 2014; Filahi et al., 2016; Moron et al., 2016; Ringard \r et al., 2016; Barry et al., 2018; Gebrechorkos et al., 2018) and an \r increase in heatwaves (Russo et al., 2016; Ceccherini et al., 2017). The \r increase in TNn is more notable than in TXx (Figure 11.9). Cold spells \r occasionally strike subtropical areas, but are likely to have decreased \r in frequency (Barry et al., 2018). The frequency of cold events has likely\r [...]"


id: 98c75b305c2cbcf70637a40e80525213
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"[...] decreased in South Africa (Song et al., 2014; Kruger and Nxumalo, \r 2017), North Africa (Filahi et al., 2016; Driouech et al., 2021), and \r the Sahara (Donat et al., 2016a). Over the whole continent, there \r is medium confidence in an increase in the intensity and frequency \r of hot extremes and decrease in the intensity and frequency of cold \r extremes; it is likely that similar changes have also occurred in areas \r with poor data coverage, as warming is widespread and as projected \r future changes are similar over all regions (Section 11.3.5)."


id: 4d497a63c45043b869d83e47da20d260
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"2020), as well as in numerous regional studies (Table 11.7). The area \r fraction with extreme warmth in Asia increased during 1951-2016 \r (Imada et al., 2018). The frequency of warm extremes increased and \r the frequency of cold extremes decreased in East Asia (B. Zhou et al., \r 2016; Chen and Zhai, 2017; Yin et al., 2017; W. Lee et al., 2018; Qian \r et al., 2019) and west Asia (Acar Deniz and Gonencgil, 2015; Erlat \r and Turkes, 2016; Rahimi and Hejabi, 2018; Rahimi et al., 2018) with \r high confidence. The duration of heat extremes has also lengthened \r in some regions, for example, in southern China (Luo and Lau, \r 2016), but there is medium confidence of heat extremes increasing \r in frequency in South Asia (AlSarmi and Washington, 2014; Sheikh \r et al., 2015; Mazdiyasni et al., 2017; Zahid et al., 2017; Nasim et al., \r 2018; Khan et al., 2019; Sen Roy, 2019). Warming trends in daily \r temperature extremes indices have also been observed in central \r [...]"


id: ae73ee56ce9d160613ab44028f2a8b8e
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"[...] Asia (Hu et al., 2016; Feng et al., 2018), the Hindu Kush Himalaya (Sun \r et al., 2017), and South East Asia (Supari et al., 2017; Cheong et al., \r 2018). The intensity and frequency of cold spells in all Asian regions \r have been decreasing since the beginning of the 20th century (high \r confidence) (Sheikh et al., 2015; Donat et al., 2016a; Dong et al., \r 2018; van Oldenborgh et al., 2019)."


id: a74b5d409a7921097a2c4365d2a9f1c3
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"In Central and South America (Table 11.13), there is high confidence\r that observed hot extremes (TN90p, TX90p) have increased, and \r cold extremes (TN10p, TX10p) have decreased over recent decades, \r though trends vary among different extremes types, datasets, and \r regions (Skansi et al., 2013; Dittus et al., 2016; Rusticucci et al., 2017; \r Meseguer-Ruiz et al., 2018; Salvador and de Brito, 2018; Dereczynski \r et al., 2020; Dunn et al., 2020; Olmo et al., 2020). An increase in \r the intensity and frequency of heatwave events was also observed \r between 1961 and 2014 in an area covering most of South America \r (Ceccherini et al., 2016; Geirinhas et al., 2018). However, there \r is medium confidence that warm extremes (TXx and TX90p) have \r decreased in the last decades over the central region of South-Eastern \r South America (SES) during austral summer (Tencer and Rusticucci, \r 2012; Skansi et al., 2013; Rusticucci et al., 2017; Wu and  Polvani, \r [...]"


id: bc8caa9fd7818161668b575158f6b009
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"[...] 2017). There is medium confidence that TNn extremes are warming \r faster than TXx extremes, with the largest warming rates observed \r over North-East Brazil (NEB) and Northern South America (NSA) for \r cold nights (Skansi et al., 2013). "


id: b07903f16ffa125db4213203df2b35e5
created_at: 2023-04-19 13:22:42
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"In Australasia (Table 11.10), there is very robust evidence for very \r likely increases in the number of warm days and warm nights and \r decreases in the number of cold days and cold nights since 1950 \r (Lewis and King, 2015; Jakob and Walland, 2016; Alexander and \r Arblaster, 2017). The increase in extreme minimum temperatures \r occurs in all seasons over most of Australia and typically exceeds the \r increase in extreme maximum temperatures (X.L. Wang et al., 2013b; \r Jakob and Walland, 2016). However, some parts of Southern Australia \r have shown stable or increased numbers of frost days since the \r 1980s (Dittus et al., 2014) (see also Section 11.3.4). Similar positive \r trends in extreme minimum and maximum temperatures have been \r observed in New Zealand, in particular in the autumn and winter \r seasons, although they generally show higher spatial variability \r (Caloiero, 2017). In the tropical Western Pacific region, spatially \r [...]"


id: 8e7317a109abae8650591989271ac99e
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"In North America (Table  11.19), there is very robust evidence for \r a very likely increase in the intensity and frequency of hot extremes \r and decrease in the intensity and frequency of cold extremes for the \r whole continent, though there are substantial spatial and seasonal \r variations in the trends. Minimum temperatures display warming \r consistently across the continent, while there are more contrasting \r trends in the annual maximum daily temperatures in parts of the \r USA (Figure 11.9; Lee et al., 2014; van Oldenborgh et al., 2019; Dunn \r et al., 2020). In Canada, there is a  clear increase in the intensity \r and frequency of hot extremes and decrease in the intensity and \r frequency of cold extremes (Vincent et al., 2018). In Mexico, a clear \r warming trend in TNn was found, particularly in the northern arid \r region (Montero-Martinez et al., 2018). The number of warm days has \r increased and the number of cold days has decreased (Garcia-Cueto \r [...]"


id: 613085d06e23e03969e8830a081d9996
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"[...] et al., 2019). Cold spells have undergone a reduction in magnitude \r and intensity in all regions of North America (Bennett and Walsh, \r 2015; Donat et al., 2016a; Grotjahn et al., 2016; Vose et al., 2017; \r Garcia-Cueto et al., 2019; van Oldenborgh et al., 2019). "


id: 90e4b267ee58c91cb1e9bd2a336b08da
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"In summary, it is virtually certain that there has been an increase in \r the number of warm days and nights and a decrease in the number of \r cold days and nights on the global scale since 1950. Both the coldest \r extremes and hottest extremes display increasing temperatures. It is \r very likely that these changes have also occurred at the regional \r scale in Europe, Australasia, Asia, and North America. It is virtually \r certain that there has been increases in the intensity and duration \r of heatwaves and in the number of heatwave days at the global \r scale. These trends likely occur in Europe, Asia, and Australia. There \r is medium confidence in similar changes in temperature extremes in \r Africa and high confidence in South America; the lower confidence is \r due to reduced data availability and fewer studies. Annual minimum \r temperatures on land have increased about three times more than \r global surface temperature since the 1960s, with particularly strong \r [...]"


id: 7387e9f747d711094e90bddaaff23acd
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"The AR5 assessed that CMIP3 and CMIP5 models generally captured \r the observed spatial distributions of the mean state and that the \r inter-model range of simulated temperature extremes was similar \r to the spread estimated from different observational datasets; \r the models generally captured trends in the second half of the \r 20th  century for indices of extreme temperature, although they \r tended to overestimate trends in hot extremes and underestimate \r trends in cold extremes (Flato et al., 2013). Post-AR5 studies on \r the CMIP5 models' performance in simulating mean and changes \r in temperature extremes continue to support the AR5 assessment \r (Fischer and Knutti, 2014; Sillmann et al., 2014; Ringard et al., 2016; \r Borodina et al., 2017b; Donat et al., 2017; Di Luca et al., 2020b). Over \r Africa, the observed warming in temperature extremes is captured by \r CMIP5 models, although it is underestimated in Western and Central \r [...]"


id: a83fb82536f73216590796757c6e178a
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content:
"[...] Africa (Sherwood et al., 2014; Diedhiou et al., 2018). Over East Asia, \r the CMIP5 ensemble performs well in reproducing the observed \r trend in temperature extremes averaged over China (Dong et al., \r 2015). Over Australia, the multi-model mean performs better than \r individual models in capturing observed trends in gridded station\u0002based ETCCDI temperature indices (Alexander and Arblaster, 2017). "


id: 48070a07e370b24f0a1f8509e0b4a4fa
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content:
"Trends in some measures of heatwaves are also observed at the \r global scale. Globally averaged heatwave intensity, heatwave \r duration, and the number of heatwave days have significantly \r increased from 1950-2011 (Perkins, 2015). There are some regional \r differences in trends in characteristics of heatwaves, with significant \r increases reported in Europe (Russo et al., 2015; Forzieri et al., 2016; \r Sanchez-Benitez et al., 2020) and Australia (CSIRO and BOM, \r 2016;  Alexander and Arblaster, 2017). In Africa, there is medium \r confidence that heatwaves, regardless of the definition, have been \r becoming more frequent, longer-lasting, and hotter over more \r than three decades (Fontaine et al., 2013; Mouhamed et al., 2013; \r Ceccherini et al., 2016, 2017; Forzieri et al., 2016; Moron et al., 2016; \r Russo et al., 2016). The majority of heatwave characteristics examined \r in China between 1961 and 2014 show increases in heatwave days, \r [...]"


id: 262bb1b0019e679235350b4f7b90e681
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content:
"[...] consistent with warming (You et al., 2017; Xie et al., 2020). Increases \r in the frequency and duration of heatwaves are also observed in \r Mongolia (Erdenebat and Sato, 2016) and India (Ratnam et al., 2016; \r Rohini et al., 2016). In the UK, the lengths of short heatwaves have \r increased since the 1970s, while the lengths of long heatwaves (more \r than 10 days) have decreased over some stations in the south-east "


id: faf079b680604ac8f22a8bf43a263481
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content:
"CMIP6 models better represent some of the underlying processes \r leading to extreme temperatures, such as seasonal and diurnal \r variability and synoptic-scale variability (Di Luca et al., 2020a). Whether \r these improvements are sufficient to enhance our understanding of \r past changes, or to reduce uncertainties in future projections, remains \r unclear. The relative error estimates in the simulation of various indices \r of temperature extremes in the available CMIP6 models show that \r no single model performs the best on all indices, and the multi-model \r ensemble seems to outperform any individual model due to its reduction \r in systematic bias (Kim et al., 2020). Figure 11.10 show errors in the \r 1979-2014 average annual TXx and annual TNn simulated by available \r CMIP6 models in comparison with HadEX3 and ERA5 (Kim et al., 2020; \r Wehner et al., 2020; Li et al., 2021). While the magnitude of the model \r error depends on the reference dataset, the model evaluations drawn \r [...]"


id: 1876b83c9abdd3bd1b90ab935c817abe
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"[...] from different reference datasets are quite similar. In general, models \r reproduce the spatial patterns and magnitudes of both cold and hot \r temperature extremes quite well. There are also systematic biases. \r Hot extremes tend to be too cool in mountainous and high-latitude \r regions, but too warm in the eastern USA and South America. For cold \r extremes, CMIP6 models are too cool, except in north-eastern Eurasia \r and the southern mid-latitudes. Errors in seasonal mean temperatures \r are uncorrelated with errors in extreme temperatures and are often of \r opposite sign (Wehner et al., 2020). "


id: f61d896b93e58c57bd0150cb436a2f92
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"Several regional climate models (RCMs) have also been evaluated in \r terms of their performance in simulating the climatology of extremes \r in various regions of the Coordinated Regional Downscaling \r Experiment (CORDEX) (Giorgi et al., 2009), especially in East Asia \r (Ji and Kang, 2015; Yu et al., 2015; Park et al., 2016; Bucchignani \r et al., 2017; Gao et al., 2017a; Niu et al., 2018; Y. Sun et al., 2018b; \r Wang et al., 2019), Europe (Vautard et al., 2013, 2021; Smiatek \r et al., 2016; Gaertner et al., 2018; Cardoso et al., 2019; Lorenz et al., \r 2019; Jacob et al., 2020; Kim et al., 2020), and Africa (J. Kim et al., \r 2014; Diallo et al., 2015; Dosio, 2017; Samouly et al., 2018; Mostafa \r et al., 2019). Compared to GCMs, RCM simulations show an added \r value in simulating temperature-related extremes, though this \r depends on topographical complexity and the parameters employed \r (see  Section  10.3.3). The improvement with resolution is noted in \r [...]"


id: 1b576799062e9e69c5d7289a0f3b4c6
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vector_id: 1986
content:
"[...] East Asia (Park et al., 2016; W. Zhou et al., 2016; Shi et al., 2017; Hui \r et al., 2018). However, in the European CORDEX ensemble, different \r aerosol climatologies with various degrees of complexity were used \r in projections (Bartok et al., 2017; Lorenz et al., 2019) and the land \r surface models used in the RCMs do not account for physiological CO2\r effects on photosynthesis leading to enhanced water-use efficiency \r and decreased evapotranspiration (Schwingshackl et al., 2019), \r which could lead to biases in the representation of temperature \r extremes in these projections (Boe et al., 2020). In addition, there \r are key cold biases in temperature extremes over areas with complex \r topography (Niu et al., 2018). Over North America, 12 RCMs were \r evaluated over the ARCTIC-CORDEX region (Diaconescu et al., 2018). \r Models performed well at simulating climate indices related to mean \r air temperature and hot extremes over most of the Canadian Arctic, \r [...]"


id: 185d27056b7ada3dbfc58c1efffef94d
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"[...] with the exception of the Yukon region where models displayed \r the largest biases related to topographic effects. Two RCMs were \r evaluated against observed extremes indices over North America \r over the period 1989-2009, with a cool bias in minimum temperature "


id: f7067821a5e314537e67401851247f59
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"in the frequency or intensity of cold extremes to human influence, \r dominated by anthropogenic greenhouse gas emissions, on global \r and continental scales, and for many AR6 regions. These include \r attribution of changes in the magnitude of annual TXx, TNx, TXn, and \r TNn, based on different observational datasets including, HadEX2 \r and HadEX3, CMIP5 and CMIP6 simulations, and different statistical \r methods (Kim et al., 2016; Z. Wang et al., 2017a; Seong et al., 2021). \r As is the case for an increase in mean temperature (Section 3.3.1), \r an increase in extreme temperature is mostly due to greenhouse gas \r forcing, offset by aerosol forcing. The aerosols' cooling effect is clearly \r detectable over Europe and Asia (Seong et al., 2021). As much as 75% \r of the moderate daily hot extremes (above 99.9th percentile) over \r land are due to anthropogenic warming (Fischer and Knutti, 2015). \r New results are found to be more robust due to the extended period \r [...]"


id: 8465f99ed7ba1fa059cd6c8cf6c932d6
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"[...] that improves the signal-to-noise ratio. The effect of anthropogenic \r forcing is clearly detectable and attributable in the observed changes \r in these indicators of temperature extremes, even at country and \r sub-country scales, such as in Canada (Wan et al., 2019). Changes in \r the number of warm nights, warm days, cold nights, and cold days, \r and other indicators such as the Warm Spell Duration Index (WSDI), \r are also attributed to anthropogenic influence (Christidis and Stott, \r 2016; Hu et al., 2020). "


id: edf5d4aa4a62db436080568922cef9
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content:
"Some land forcings are not well represented in climate models. \r As  highlighted in the Special Report on Climate Change and Land \r (SRCCL) Chapter 2, there is high agreement that temperate deforestation \r leads to summer warming and winter cooling (Anderson et al., 2011; \r Galos et al., 2011, 2013; Anderson-Teixeira et al., 2012; Chen et al., \r 2012; Wickham et al., 2013; Zhao and Jackson, 2014; Ahlswede and \r Thomas, 2017; Bright et al., 2017; Strandberg and Kjellstrom, 2019), \r which has substantially contributed to the warming of hot extremes in \r the northern mid-latitudes over the course of the 20th century (Lejeune \r et al., 2018) and in recent years (Strandberg and Kjellstrom, 2019). \r However, observed forest effects on the seasonal and diurnal cycle of \r temperature are not well-captured in several ESMs: while observations \r show a cooling effect of forest cover compared to non-forest vegetation \r during daytime (Li et al., 2015), in particular in arid, temperate, and \r [...]"


id: 823b17e10c1489b4b594daf478402f5c
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content:
"[...] tropical regions (Alkama and Cescatti, 2016), several ESMs simulate \r a warming of daytime temperatures for regions with forest versus non\u0002forest cover (Lejeune et al., 2017). Also irrigation effects, which can \r lead to regional cooling of temperature extremes, are generally not \r integrated in current generations of ESMs (Section 11.3.1)."


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"Regional studies, including for Asia (Dong et al., 2018; Lu et al., 2018), \r Australia (Alexander and Arblaster, 2017), and Europe (Christidis and \r Stott, 2016), found similar results. A  clear anthropogenic signal is \r also found in the trends in the Combined Extreme Index (CEI) for \r North America, Asia, Australia, and Europe (Dittus et al., 2016). While \r various studies have described increasing trends in several heatwave \r metrics (heatwave duration, the number of heatwave days, etc.) in \r different regions (e.g.,  Cowan et al., 2014; Bandyopadhyay et al., \r 2016; M. Sanderson et al., 2017), few recent studies have explicitly \r attributed these changes to causes; most of them stated that observed \r trends are consistent with anthropogenic warming. The detected \r anthropogenic signals are clearly separable from the response to \r natural forcing, and the results are generally insensitive to the use \r of different model samples, as well as different data availability, \r [...]"


id: 4cb4b828f5560f6effcf5ddfd7b0bc6
created_at: 2023-04-19 13:22:45
updated_at: 2023-04-19 13:22:45
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index: climate_gpt_v2
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content:
"[...] indicating robust attribution. Studies of monthly, seasonal, and \r annual records in various regions (Kendon, 2014; Lewis and King, \r 2015; Bador et al., 2016; Meehl et al., 2016; C. Zhou et al., 2019) and \r globally (King, 2017) show an increase in the breaking of hot records \r and a decrease in the breaking of cold records (King, 2017). Changes \r in anthropogenically attributable record-breaking rates are noted to \r be largest over the Northern Hemisphere land areas (Shiogama et al., \r 2016). Yin and Sun (2018) found clear evidence of an anthropogenic \r signal in the changes in the number of frost and ice days, when \r multiple model simulations were used. In some key wheat-producing \r regions of Southern Australia, increases in frost days or frost season \r length have been reported (Dittus et al., 2014; Crimp et al., 2016); \r these changes are linked to decreases in rainfall, cloud-cover, and \r subtropical ridge strength, despite an overall increase in regional \r [...]"


id: 42849d1b8b587e3a396d5da1864c9950
created_at: 2023-04-19 13:22:45
updated_at: 2023-04-19 13:22:45
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index: climate_gpt_v2
vector_id: 8896
content:
"severity and probability of the hottest month by more than 80% of \r the available observational area on the global scale. Christidis and \r Stott (2014) provide clear evidence that warm events have become \r more probable because of anthropogenic forcings. Sun et al. (2014) \r found that human influence has caused a more than 60-fold increase \r in the probability of the extreme warm 2013 summer in eastern \r China since the 1950s. Human influence is found to have increased \r the probability of the historically hottest summers in many regions of \r the world, both in terms of mean temperature (B. Mueller et al., 2016) \r and wet bulb globe temperature (WBGT; C. Li et al., 2017). In most \r regions of the Northern Hemisphere, changes in the probability of \r extreme summer average WBGT were found to be about an order \r of magnitude larger than changes in the probability of extreme hot \r summers estimated by surface air temperature (C. Li et al., 2017). \r [...]"


id: f410be55ff9d7395a98fb5d4b6421ed2
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 40310
content:
"[...] In addition to these generalized, global-scale approaches, extreme \r event studies have found an attributable increase in the probability \r of hot annual and seasonal temperatures in many locations, \r including Australia (Knutson et al., 2014b; Lewis and Karoly, 2014), \r China (Sun et al., 2014; Sparrow et al., 2018; Zhou et al., 2020), Korea \r (Y.-H. Kim et al., 2018) and Europe (King et al., 2015b). "


id: 4f63bdbfc8832703f1648d29f790a5f7
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 11256
content:
"The relative strength of anthropogenic influences on temperature \r extremes is regionally variable, in part due to differences in changes \r in atmospheric circulation, land-surface feedbacks, and other \r external drivers such as aerosols. For example, in the Mediterranean \r and over western Europe, risk ratios on the order of 100 have been \r found (Kew et al., 2019; Vautard et al., 2020), whereas in the USA, \r changes are much less pronounced. This is probably a reflection of \r the land-surface feedback enhanced extreme 1930s temperatures \r that reduce the rarity of recent extremes, in addition to the definition \r of the events and framing of attribution analyses (e.g., spatial and \r temporal scales considered). Local forcing may mask or enhance \r the warming effect of greenhouse gases. In India, short-lived \r aerosols or an increase in irrigation may be masking the warming \r effect of greenhouse gases (Wehner et al., 2018c). Irrigation and \r [...]"


id: 611f76de316c18559d2a0b9b6180eb0b
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 14381
content:
"[...] crop intensification have been shown to lead to a cooling in some \r regions, in particular in North America, Europe, and India (high \r confidence) (N.D. Mueller et al., 2016; Thiery et al., 2017, 2020; Chen \r and Dirmeyer, 2019). Deforestation has contributed about one third \r of the total warming of hot extremes in some mid-latitude regions \r since pre-industrial times (Lejeune et al., 2018). Despite all of these \r differences, and larger uncertainties at the regional scale, nearly \r all studies demonstrated that human influence has contributed \r to an increase in the frequency or intensity of hot extremes and to \r a decrease in the frequency or intensity of cold extremes. "


id: d987f43f81e944fb8462ce8b8fe2cbcb
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 35569
content:
"There have also been many extreme event attribution studies that \r examined short-duration temperature extremes, including daily \r temperatures, temperature indices, and heatwave metrics. Examples \r of these events from different regions are summarized in various \r annual Explaining Extreme Events supplements of the Bulletin of \r the American Meteorological Society (Peterson et al., 2012, 2013a; \r Herring et al., 2014, 2015, 2016, 2018, 2019, 2020), including \r a  number of approaches to examine extreme events (described in \r Easterling et al., 2016; Stott et al., 2016; Otto, 2017). Several studies \r of recent events from 2016 onwards have determined an infinite \r risk ratio (a fraction of attributable risk, or FAR, of 1), indicating that \r the occurrence probability for such events is close to zero in model \r simulations without anthropogenic influences (see Herring et al., \r 2018, 2019, 2020; Imada et al., 2019; Vogel et al., 2019). Though it \r [...]"


id: 634e1148fc7f2d739806d254bad67446
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 14768
content:
"[...] is difficult to accurately estimate the lower bound of the uncertainty \r range of the FAR in these cases (Paciorek et al., 2018), the fact that \r those events are so far outside the envelop of the models with only \r natural forcing indicates that it is extremely unlikely for those events \r to occur without human influence. "


id: 4f61d7da98f3bff62043d96fe655431
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 11253
content:
"In summary, long-term changes in various aspects of long- and \r short-duration extreme temperatures, including intensity, frequency, \r and duration have been detected in observations and attributed to \r human influence at global and continental scales. It is extremely \r likely that human influence is the main contributor to the observed \r increase in the intensity and frequency of hot extremes and the \r observed decrease in the intensity and frequency of cold extremes \r on the global scale. It is very likely that this applies on continental \r scales as well. Some specific recent hot extreme events would have \r been extremely unlikely to occur without human influence on the \r climate system. Changes in aerosol concentrations have affected \r trends in hot extremes in some regions, with the presence of aerosols \r leading to attenuated warming, in particular from 1950 to 1980. \r Crop intensification, irrigation and no-till farming have attenuated \r [...]"


id: b97c81e21cb3c5aa1228f403b05e54a3
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 29846
content:
"[...] increases in summer hot extremes in some regions, such as Central \r North America (medium confidence)."


id: 74fe5075329724e0bc8d78d0f86d7e22
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 17818
content:
"the present-day 20-year return values of TXx and the running three\u0002day average of the daily maximum temperature every other year, on \r average, under the Representative Concentration Pathway 8.5 (RCP8.5) \r scenario, as opposed to only 34% under RCP4.5. By the end of the \r century, these area fractions increase to 92% and 62%, respectively. \r Such nonlinearities in the characteristics of future regional extremes are \r shown, for instance, for Europe (Dosio and Fischer, 2018; Spinoni et al., \r 2018b; Lionello and Scarascia, 2020), Asia (Guo et al., 2017; Harrington \r and Otto, 2018b; King et al., 2018), and Australia (Lewis et al., 2017a) \r under various global warming thresholds. The nonlinear increase in \r fixed-threshold indices (e.g., based on a percentile for a given reference \r period, or on an absolute threshold) as a function of global warming \r is consistent with a  linear warming of the absolute temperature of \r [...]"


id: e5126b81fc73d2aadc32992e09c2b44b
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 37600
content:
"[...] the temperature extremes (e.g., Whan et al., 2015). Compared to the \r historical climate, warming will result in strong increases in heatwave \r area, duration and magnitude (Vogel et al., 2020b). These changes are \r mostly due to the increase in mean seasonal temperature, rather than \r changes in temperature variability, though the latter can have an effect \r in some regions (Brown, 2020; Di Luca et al., 2020b; Suarez-Gutierrez \r et al., 2020a)."


id: 2e667065006784613ea33c2fc195814a
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 5437
content:
"Projections of temperature-related extremes in RCMs in the CORDEX \r regions demonstrate robust increases under future scenarios and can \r provide information on finer spatial scales than GCMs (e.g., Coppola \r et al., 2021b). Five RCMs in the CORDEX-East Asia region project \r increases in the 20-year return values of temperature extremes \r (summer maxima), with models that exhibit warm biases projecting \r stronger warming (Park and Min, 2019). Similarly, in the African \r domain, future increases in TX90p and TN90p are projected (Dosio, \r 2017; Mostafa et al., 2019). This regional-scale analysis provides \r fine-scale information, such as distinguishing the increase in TX90p \r over sub-equatorial Africa (Democratic Republic of the Congo, \r Angola, and Zambia) with values over the Gulf of Guinea, Central \r African Republic, South Sudan, and Ethiopia. Empirical statistical \r downscaling has also been used to produce more robust estimates \r [...]"


id: 79251f636a8f004e474059ae592232cc
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 18502
content:
"[...] for future heatwaves compared to RCMs based on large multi-model \r ensembles (Furrer et al., 2010; Keellings and Waylen, 2014; Wang \r et al., 2015; Benestad et al., 2018)."


id: 450745c2be872e95efe32e1e044f820b
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 9371
content:
"In all continental regions, including Africa (Table  11.4), Asia \r (Table  11.7), Australasia (Table  11.10), Central and South America \r (Table 11.13), Europe (Table 11.16), North America (Table 11.19) and \r at the continental scale, it is very likely that the intensity and frequency \r of hot extremes will increase and the intensity and frequency of cold \r extremes will decrease compared with the 1995-2014 baseline, even \r under 1.5degC global warming. Those changes are virtually certain\r to occur under 4degC global warming. At the regional scale, and for \r almost all AR6 regions, it is likely that the intensity and frequency of \r hot extremes will increase and the intensity and frequency of cold \r extremes will decrease compared with the 1995-2014 baseline, \r even under 1.5degC global warming. Those changes are virtually \r certain to occur under 4degC global warming. Exceptions include lower \r confidence in the projected decrease in the intensity and frequency of \r [...]"


id: b63d86ebfbcb32ff4bcabc484050c62b
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"[...] cold extremes compared with the 1995-2014 baseline under 1.5degC \r of global warming (medium confidence) and 4degC of global warming \r (very likely) in Northern Central America, Central North America, and \r Western North America."


id: 9197631857816aba9e22b4cfdfa0da7b
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 22841
content:
"In Europe (Table 11.16), evidence includes increases in the intensity \r and frequency of hot extremes, such as warm days, warm nights, \r and heatwaves, and decreases in the intensity and frequency of cold \r extremes, such as cold days and cold nights over the continent, as \r projected by CMIP5, CMIP6, and CORDEX simulations (Lau and Nath, \r 2014; Ozturk et al., 2015; Russo et al., 2015; Schoetter et al., 2015; \r Vogel et al., 2017; Winter et al., 2017; Jacob et al., 2018; Lhotka et al., \r 2018; Rasmijn et al., 2018; Suarez-Gutierrez et al., 2018; Cardoso \r et al., 2019; Lionello and Scarascia, 2020; Molina et al., 2020; Coppola \r et al., 2021b; Li et al., 2021). Increases in heatwaves are greater over \r the southern Mediterranean and Scandinavia (Forzieri et al., 2016; \r Abaurrea et al., 2018; Dosio and Fischer, 2018; Rohat et al., 2019). \r The biggest increases in the number of heatwave days are expected \r for southern European cities (Guerreiro et al., 2018a; Junk et al., \r [...]"


id: 67120087349b7a67d20c481990df1304
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 15401
content:
"[...] 2019), and Central European cities will see the biggest increases in \r maximum heatwave temperatures (Guerreiro et al., 2018a)."


id: 9a4520e808798fe508529bed878cc4c
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 24384
content:
"In Asia (Table  11.7), evidence includes increases in the intensity \r and frequency of hot extremes, such as warm days, warm nights, and \r heatwaves, and decreases in the intensity and frequency of cold \r extremes, such as cold days and cold nights over the continent, as \r projected by CMIP5, CMIP6, and CORDEX simulations (Sillmann \r et al., 2013b; Zhou et al., 2014; R. Zhang et al., 2015; Zhao et al., \r 2015; Pal and Eltahir, 2016; Singh and Goyal, 2016; Xu et al., 2017; \r Gao et al., 2018; Han et al., 2018; Shin et al., 2018; Sui et al., 2018; \r L. Li et al., 2019; Zhu et al., 2020). More intense heatwaves of longer \r durations and occurring at a  higher frequency are projected over \r India (Murari et al., 2015; Mishra et al., 2017) and Pakistan (Nasim \r et al., 2018). Future mid-latitude warm extremes, similar to those \r experienced during the 2010 event, are projected to become more \r extreme, with temperature extremes increasing potentially by 8.4degC \r [...]"


id: 5926246aaf9a37ea18b01a13b1a65cf4
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index: climate_gpt_v2
vector_id: 12927
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"[...] (RCP8.5) over north-west Asia (van der Schrier et al., 2018). Over \r West and East Siberia, and Russian Far East, an increase in extreme \r heat durations is expected in all scenarios (Sillmann et al., 2013b; \r Kattsov et al., 2017; Reyer et al., 2017). In the MENA regions (Arabian \r Peninsula and Western Central Asia), extreme temperatures could \r increase by almost 7degC by 2100 under RCP8.5 (Lelieveld et al., 2016)."


id: cfd7fad58f7b1d8538b99813ff7fd7f6
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 33925
content:
"In North America (Table 11.19), evidence includes increases in the \r intensity and frequency of hot extremes, such as warm days, warm \r nights, and heatwaves, and decreases in the intensity and frequency \r of cold extremes, such as cold days and cold nights over the \r continent, as projected by CMIP5, CMIP6, and CORDEX simulations \r (Grotjahn et al., 2016; Vose et al., 2017; Alexandru, 2018; C. Li et al., \r 2018, 2021; C. Yang et al., 2018; X. Zhang et al., 2019; Coppola \r et al., 2021b). Projections of temperature extremes for the end of \r the 21st century show that warm days and nights are very likely to \r increase, and cold days and nights are very likely to decrease in all \r regions. There is medium confidence in large increases in warm days \r and warm nights in summer, particularly over the USA, and in large \r decreases in cold days in Canada in autumn and winter (Grotjahn \r et al., 2016; Vose et al., 2017; Alexandru, 2018; C. Li et al., 2018, 2021; \r [...]"


id: 313515486a0686411a06ff7e5cf0b76a
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index: climate_gpt_v2
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content:
"[...] C. Yang et al., 2018; X. Zhang et al., 2019; Coppola et al., 2021b). \r Minimum winter temperatures are projected to rise faster than mean \r winter temperatures (Underwood et al., 2017). Projections for the \r end of the century under RCP8.5 showed the four-day cold spell that \r happens on average once every five years is projected to warm by \r more than 10degC. CMIP5 models do not project current 1-in-20-year \r annual minimum temperature extremes to recur over much of the \r continent (Wuebbles et al., 2014)."


id: 84865ea732d291347e05920ca67e7ed0
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 20461
content:
"There has been new evidence of the effect of local land-use and \r land-cover change on heavy precipitation. There is a growing set of \r literature linking increases in heavy precipitation in urban centres \r to urbanization (Argueso et al., 2016; Y. Zhang et al., 2019b). \r Urbanization intensifies extreme precipitation, especially in the \r afternoon and early evening, over the urban area and its downwind \r region (medium confidence) (Box  10.3). There are four possible \r mechanisms: (i) increases in atmospheric moisture due to horizontal \r convergence of air associated with the urban heat island effect (Shastri \r et al., 2015; Argueso et al., 2016); (ii) increases in condensation due \r to urban aerosol emissions (Han et al., 2011; Sarangi et al., 2017); \r (iii)  aerosol pollution that impacts cloud microphysics (Box  8.1; \r Schmid and Niyogi, 2017); and (iv) urban structures that impede \r atmospheric motion (Shepherd, 2013; Ganeshan and Murtugudde, \r [...]"


id: ecc0ee66e06cf7e9cbf18c72a8c5bf6f
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index: climate_gpt_v2
vector_id: 39016
content:
"[...] 2015; Paul et al., 2018). Other local forcing, including reservoirs \r (Woldemichael et al., 2012), irrigation (Devanand et al., 2019), or \r large-scale land-use and land-cover change (Odoulami et al., 2019), \r can also affect local extreme precipitation."


id: baa6367a31088e78ef29469335213b2d
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 30051
content:
"There is a  considerable body of literature examining scaling of \r sub-daily precipitation extremes, conditional on day-to-day air or \r dew-point temperatures (Westra et al., 2014; Fowler et al., 2021). \r This scaling, also termed 'apparent scaling' (Fowler et al., 2021), is \r robust when different methodologies are used in different regions, \r ranging between the C-C and two-times the C-C rate (e.g., Formayer \r and Fritz, 2017; Lenderink et al., 2017; Burdanowitz et al., 2019). \r This is confirmed when sub-daily precipitation data collected from \r multiple continents (Lewis et al., 2019) are analysed in a consistent \r manner using different methods (Ali et al., 2021). It has been \r hoped that apparent scaling might be used to help understand past \r and future changes in extreme sub-daily precipitation. However, \r apparent scaling samples multiple synoptic weather states, mixing \r thermodynamic and dynamic factors that are not directly relevant for \r [...]"


id: d32f3c532e1225bec30151b419935746
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 34501
content:
"[...] climate change responses (Section 8.2.3.2; Prein et al., 2016b; Bao \r et al., 2017; X. Zhang et al., 2017; Drobinski et al., 2018; Sun et al., \r 2020). The spatial pattern of apparent scaling is different from those \r of projected changes over Australia (Bao et al., 2017) and North \r America (Sun et al., 2020) in regional climate model simulations. \r It thus remains difficult to use the knowledge about apparent scaling \r to infer past and future changes in extreme sub-daily precipitation \r according to observed and projected changes in local temperature. "


id: 36b8b82b3f9e3d1c4ca13e1a82dd624e
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index: climate_gpt_v2
vector_id: 6855
content:
"The average annual maximum precipitation amount in a  day \r (Rx1day) has significantly increased since the mid-20th century over \r land (Du et al., 2019; Dunn et al., 2020) and in the humid and dry \r regions of the globe (Dunn et al., 2020). The percentage of observing \r stations with statistically significant increases in Rx1day is larger \r than expected by chance, while the percentage of stations with \r statistically significant decreases is smaller than expected by chance, \r over the global land as a  whole and over North America, Europe, \r and Asia (Figure 11.13; Sun et al., 2021) and over global monsoon \r regions (Zhang and Zhou, 2019) where data coverage is relatively \r good. The addition of the past decade of observational data shows \r a more robust increase in Rx1day over the global land region (Sun \r et al., 2021). Light, moderate, and heavy daily precipitation has all \r intensified in a gridded daily precipitation dataset (Contractor et al., \r [...]"


id: 3699b9c6f4354d9fed97dbf60d7f5db3
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updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 6839
content:
"[...] 2020a). Daily mean precipitation intensities have increased since \r the mid-20th century in a majority of land regions (high confidence) \r (Section 8.3.1.3). The probability of precipitation exceeding 50 mm/\r day increased during 1961-2018 (Benestad et al., 2019). The globally \r averaged annual fraction of precipitation from days in the top 5% \r (R95pTOT) has also significantly increased (Dunn et al., 2020). The \r increase in the magnitude of Rx1day in the 20th century is estimated \r to be at a  rate consistent with C-C scaling with respect to global \r mean temperature (Fischer and Knutti, 2016; Sun et al., 2021). Studies \r on past changes in extreme precipitation of durations longer than \r a  day are more limited, though there are some studies examining \r long-term trends in annual maximum five-day precipitation (Rx5day). \r On global and continental scales, long-term changes in Rx5day are \r similar to those of Rx1day in many aspects (Zhang and Zhou 2019; \r [...]"


id: 65a43f1102ce717ad16d15d859e526a4
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index: climate_gpt_v2
vector_id: 15145
content:
"[...] Sun et al., 2021). As discussed below, at the regional scale, changes in \r Rx5day are also similar to those of Rx1day where there are analyses \r of changes in both Rx1day and Rx5day. "


id: 59dbf843cfd47c781498e495c29b9ef6
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 13052
content:
"In Africa (Table 11.5), evidence shows an increase in extreme daily \r precipitation for the late half of the 20th century over the continent \r where data are available; there is a  larger percentage of stations \r showing significant increases in extreme daily precipitation than \r decreases (Sun et al., 2021). There are increases in different metrics \r relevant to extreme precipitation in various regions of the continent \r (Chaney et al., 2014; Harrison et al., 2019; Dunn et al., 2020; Sun \r et al., 2021). There is an increase in extreme precipitation events in \r Southern Africa (Weldon and Reason, 2014; Kruger et al., 2019) and \r a general increase in heavy precipitation over East Africa, the Greater \r Horn of Africa (Omondi et al., 2014). Over sub-Saharan Africa, \r increases in the frequency and intensity of extreme precipitation \r have been observed over the well-gauged areas during 1950-2013; \r however, this covers only 15% of the total area of sub-Saharan Africa \r [...]"


id: f8fd9808ed1c7aacfd3ff15f5219810
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index: climate_gpt_v2
vector_id: 41168
content:
"[...] (Harrison et al., 2019). There is medium confidence about the increase \r in extreme precipitation for some regions where observations are \r more abundant, but for Africa as whole, there is low confidence \r because of a general lack of continent-wide systematic analysis, the \r sporadic nature of available precipitation data over the continent, \r and spatially non-homogenous trends in places where data are \r available (Donat et al., 2014a; Mathbout et al., 2018b; Alexander \r et al., 2019; Funk et al., 2020)."


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"Overall, there is a lack of systematic analysis of long-term trends in \r sub-daily extreme precipitation at the global scale. Often, sub-daily \r precipitation data have only sporadic spatial coverage and are of \r limited length. Additionally, the available data records are far shorter \r than needed for a robust quantification of past changes in sub-daily \r extreme precipitation (C. Li et al., 2019a). Despite these limitations, \r there are studies in regions of almost all continents that generally \r indicate intensification of sub-daily extreme precipitation, although \r there remains low confidence in an overall increase at the global \r scale. Studies include an increase in extreme sub-daily rainfall in \r summer over South Africa (Sen Roy and Rouault, 2013), annually \r in Australia (Guerreiro et al., 2018b), over 23 urban locations in \r India (Ali and Mishra, 2018), in Peninsular Malaysia (Syafrina et al., \r 2015), and in eastern China in the summer season during 1971-2013 \r [...]"


id: ac99f92be8fa619ebd88d2014563456a
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"[...] (Xiao  et al., 2016). In some regions in Italy (Arnone et al., 2013; \r Libertino et al., 2019) and in the USA during 1950-2011 (Barbero \r et al., 2017), there is also an increase. In general, an increase in sub\u0002"


id: 221de26e36f6bdcc85db1c42625c066d
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"precipitation and spatial and temporal coverage of the data. These \r include an increase in daily precipitation extremes over central Asia \r (Hu et al., 2016), most of South Asia (Zahid and Rasul, 2012; Pai et al., \r 2015; Sheikh et al., 2015; Adnan et al., 2016; Malik et al., 2016; Dimri \r et al., 2017; Priya et al., 2017; Roxy et al., 2017; Hunt et al., 2018; \r Kim et al., 2019; Wester et al., 2019), the Arabian Peninsula (Rahimi \r and Fatemi, 2019; Almazroui and Saeed, 2020; Atif et al., 2020), \r South East Asia (Siswanto et al., 2015; Supari et al., 2017; Cheong \r et al., 2018); the north-west Himalaya (Malik et al., 2016), parts of \r East Asia (Baek et al., 2017; Nayak et al., 2017; Ye and Li, 2017), the \r western Himalayas since the 1950s (Ridley et al., 2013; Dimri et al., \r 2015; Madhura et al., 2015), West and East Siberia, and Russian Far \r East (Donat et al., 2016a). A decrease was found over the eastern \r [...]"


id: 174ad4ffb2cfccd197c738869a09d203
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"[...] Himalayas (Sheikh et al., 2015; Talchabhadel et al., 2018). Increases \r have been observed over Jakarta (Siswanto et al., 2015), but Rx1day \r over most parts of the Maritime Continent has decreased (Villafuerte \r and Matsumoto, 2015). Trends in extreme precipitation over China \r are mixed with increases and decreases (G. Fu et al., 2013; Jiang \r et al., 2013; Ma et al., 2015; Yin et al., 2015; Xiao et al., 2016) and \r are not significant over China as whole (Jiang et al., 2013; Hu et al., \r 2016; Ge et al., 2017; Deng et al., 2018; He and Zhai, 2018; W. Li et al., \r 2018a; Tao et al., 2018; M. Liu et al., 2019b; Chen et al., 2021). With \r few exceptions, most South East Asian countries have experienced an \r increase in rainfall intensity, but with a reduced number of wet days \r (Donat et al., 2016a; Cheong et al., 2018; Naveendrakumar et al., \r 2019), though large differences in trends exists if the trends are \r estimated from different datasets, including gauge-based, remotely \r [...]"


id: 9164ed3f011396ba733aa29b7a336f5f
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"[...] sensed, and reanalysis data, over a relatively short period (Kim et al., \r 2019). There is a significant increase in heavy rainfall (>100 mm day-\r 1\r ) and a significant decrease in moderate rainfall (5-100 mm day-1) \r in central India during the South Asian monsoon season (Deshpande \r et al., 2016; Roxy et al., 2017). "


id: 2cd547172acefe15a8b744f959afc26f
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"In Central and South America (Table  11.14), evidence shows an \r increase in extreme precipitation, but in general there is low\r confidence; while continent-wide analyses produced wetting trends \r are not robust. Rx1day increased at more stations than it decreased \r in South America between 1950 and 2018 (Sun et al., 2021). Over \r the period 1950-2010, both Rx5day and R99p increased over large \r regions of South America, including North-Western South America, \r Northern South America, and South-Eastern South America (Skansi \r et al., 2013). There are large regional differences. A decrease in daily \r extreme precipitation is observed in north-eastern Brazil (Skansi \r et al., 2013; Bezerra et al., 2018; Dereczynski et al., 2020). Trends \r in extreme precipitation indices were not statistically significant \r over the period 1947-2012 within the Sao Francisco River basin in \r the Brazilian semi-arid region (Bezerra et al., 2018). An increase in \r [...]"


id: 346e91abce85c14bcdbd23b6fe71520b
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index: climate_gpt_v2
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content:
"[...] extreme rainfall is observed in the Amazon with medium confidence\r (Skansi et al., 2013) and in South-Eastern South America with \r high confidence (Skansi et al., 2013; Valverde and Marengo, 2014; \r Barros et al., 2015; Avila et al., 2016; Wu and Polvani, 2017; Lovino \r et al., 2018; Dereczynski et al., 2020). Among all sub-regions, South\u0002Eastern South America shows the highest rate of increase for rainfall \r extremes, followed by the Amazon (Skansi et al., 2013). Increases \r in the intensity of heavy daily rainfall events have been observed in \r the southern Pacific and in the Titicaca basin (Skansi et al., 2013; \r Huerta and Lavado-Casimiro, 2021). In Southern Central America, \r trends in annual precipitation are generally not significant, although \r small (but significant) increases are found in Guatemala, El Salvador, \r and Panama (Hidalgo et al., 2017). Small positive trends were found \r in multiple extreme precipitation indices over the Caribbean region \r [...]"


id: d220dce59edd6eae6b613bb642815f90
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content:
"In Europe (Table 11.17), there is robust evidence that the magnitude \r and intensity of extreme precipitation has very likely increased since \r the 1950s. There is a  significant increase in Rx1day and Rx5day \r during 1950-2018 in Europe as a  whole (Sun et al., 2021, also \r Figure  11.13). The number of stations with increases far exceeds \r those with decreases in the frequency of daily rainfall exceeding its \r 90th or 95th percentile in century-long series (Cioffi et al., 2015). The \r five-, 10-, and 20-year events of one-day and five-day precipitation \r during 1951-1960 became more common since the 1950s (van \r den Besselaar et al., 2013). There can be large discrepancies among \r studies and regions and seasons (Croitoru et al., 2013; Willems, \r 2013; Casanueva et al., 2014; Roth et al., 2014; Fischer et al., 2015); \r evidence for increasing extreme precipitation is more frequently \r observed for summer and winter, but not in other seasons (Madsen \r [...]"


id: b21d8271b62606badbd75722c343d75a
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content:
"[...] et al., 2014; Helama et al., 2018). An increase is observed in central \r Europe (Volosciuk et al., 2016; Zeder and Fischer, 2020), and in \r Romania (Croitoru et al., 2016). Trends in the Mediterranean region \r are in general not spatially consistent (Reale and Lionello, 2013), with \r decreases in the western Mediterranean and some increases in the \r eastern Mediterranean (Rajczak et al., 2013; Casanueva et al., 2014; \r de Lima et al., 2015; Gajic-Capka et al., 2015; Sunyer et al., 2015; \r Pedron et al., 2017; Serrano-Notivoli et al., 2018; Ribes et al., 2019). \r In the Netherlands, the total precipitation contributed from extremes \r higher than the 99th percentile doubles per 1degC increase in warming \r (Myhre et al., 2019), though extreme rainfall trends in Northern \r Europe may differ in different seasons (Irannezhad et al., 2017). "


id: c42a149108b9cfa23e9fa820ac12284c
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"In Australasia (Table  11.11), available evidence has not shown \r an increase or a  decrease in heavy precipitation over Australasia \r as a  whole (medium confidence), but heavy precipitation tends \r to increase over Northern Australia (particularly the north-west) \r and decrease over the eastern and southern regions (e.g., Jakob and \r Walland, 2016; Guerreiro et al., 2018b; Dey et al., 2019b; Dunn \r et al., 2020; Sun et al., 2021). Available studies that used long\u0002term observations since the mid-20th  century showed nearly as \r many stations with an increase as those with a decrease in heavy \r precipitation (Jakob and Walland, 2016) or slightly more stations \r with a decrease than with an increase in Rx1day and Rx5day (Sun \r et al., 2021), or strong differences in Rx1day trends with increases \r over Northern Australia and Central Australia in general, but mostly \r decreases over Southern Australia and Eastern Australia (Dunn et al., \r [...]"


id: 3dfdcbaa20354d20f6132b9a3831693d
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 8076
content:
"[...] 2020). Over New Zealand, decreases are observed for moderate-\r heavy precipitation events, but there are no significant trends for very \r heavy events (more than 64 mm in a day) for the period 1951-2012. \r The number of stations with an increase in very wet days is similar \r to that with a decrease during 1960-2019 (MfE and Stats NZ, 2020). \r Overall, there is low confidence in trends in the frequency of heavy \r rain days, with mostly decreases over New Zealand (Harrington and \r Renwick, 2014; Caloiero, 2015). "


id: f0ec93cb08eb6af0d7813a5a557cc344
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 39753
content:
"In North America (Table  11.20), there is robust evidence that the \r magnitude and intensity of extreme precipitation has very likely\r increased since the 1950s. Both Rx1day and Rx5day have significantly \r increased in North America during 1950-2018 (Sun et al., 2021, also \r Figure 11.13). There is, however, regional diversity. In Canada, there \r is a  lack of detectable trends in observed annual maximum daily \r (or shorter duration) precipitation (Shephard et al., 2014; Mekis et al., \r 2015; Vincent et al., 2018). In the USA, there is an overall increase in \r one-day heavy precipitation, both in terms of intensity and frequency \r (Villarini et al., 2012; Donat et al., 2013b; Wu, 2015; Easterling et al., \r 2017; H. Huang et al., 2017; Howarth et al., 2019; Sun et al., 2021), \r except for the southern USA (Hoerling et al., 2016) where internal \r variability may have played a substantial role in the lack of observed \r increases. In Mexico, increases are observed in R10mm and R95p \r [...]"


id: fb44e8bb2ee02e206e4ea441d447bc9f
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index: climate_gpt_v2
vector_id: 41580
content:
"[...] (Donat et al., 2016a), very wet days over the cities (Garcia-Cueto \r et al., 2019) and in total precipitation (PRCPTOT) and Rx1day (Donat \r et al., 2016b). "


id: 2f102122c7b4ad3278753479c5cc9a8
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 5549
content:
"models is generally more comparable to observations (Sillmann \r et al., 2013b; Kusunoki, 2017, 2018b; Scher et al., 2017) as these \r models tend to produce more realistic storms compared to coarser \r models (Section 11.7.2). Higher horizontal resolution alone improves \r simulation of extreme precipitation in some models (Wehner et al., \r 2014; Kusunoki, 2017, 2018b), but this is insufficient in other models \r (Bador et al., 2020) as parametrization also plays a significant role \r (M. Wu et al., 2020). A simple comparison of climatology may not \r fully reflect the improvements of the new models that have more \r comprehensive process formulations (Di Luca et al., 2015). Dittus et al. \r (2016) found that many of the eight CMIP5 models they evaluated \r reproduced the observed increase in the difference between areas \r experiencing an extreme high (90%) and an extreme low (10%) \r proportion of the annual total precipitation from heavy precipitation \r [...]"


id: 679204879b63397b1fb6b22a220b9a45
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content:
"[...] (R95p/PRCPTOT) for Northern Hemisphere regions. Additionally, \r CMIP5 models reproduced the relation between changes in extreme \r and non-extreme precipitation: an increase in extreme precipitation \r is at the cost of a decrease in non-extreme precipitation (Thackeray \r et al., 2018), a  characteristic found in the observational record \r (Gu and Adler, 2018)."


id: 863ad1d37b872a94e76f8a54d4e8f321
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 20769
content:
"Evaluating climate model competence in simulating heavy \r precipitation extremes is challenging due to a  number of factors, \r including the lack of reliable observations and the spatial scale \r mismatch between simulated and observed data (Avila et al., 2015; \r Alexander et al., 2019). Simulated precipitation represents areal \r means, but station-based observations are conducted at point \r locations and are often sparse. The areal-reduction factor, the ratio \r between pointwise station estimates of extreme precipitation and \r extremes of the areal mean, can be as large as 130% at CMIP6 \r resolutions (about 100 km) (Gervais et al., 2014). Hence, the order in \r which gridded station based extreme values are constructed (i.e., if \r the extreme values are extracted at the station first and then gridded, \r or if the daily station values are gridded and then the extreme values \r are extracted) represents different spatial scales of extreme \r [...]"


id: ee4c204dccbe8c41454b0c181bae2c6f
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vector_id: 39293
content:
"[...] precipitation and needs to be taken into account in model evaluation \r (Wehner et al. 2020). This aspect has been considered in some studies. \r Reanalysis products are used in place of station observations for their \r spatial completeness as well as spatial-scale comparability (Sillmann \r et al., 2013a; Kim et al., 2020; Li et al., 2021). However, reanalyses \r share similar parametrizations to the models themselves, reducing \r the objectivity of the comparison. "


id: dc427d816c5c37534706c2bb6f316d8
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 36022
content:
"The CMIP6 models perform reasonably well in capturing large-scale \r features of precipitation extremes, including intense precipitation \r extremes in the intertropical convergence zone (ITCZ), and weak \r precipitation extremes in dry areas in the tropical regions (Li et al., \r 2021) but a double-ITCZ bias over the equatorial central and eastern \r Pacific that appeared in CMIP5 models remains (Section 3.3.2.3). There \r are also regional biases in the magnitude of precipitation extremes \r (Kim et al., 2020). The models also have difficulties in reproducing \r detailed regional patterns of extreme precipitation, such as over the \r north-east USA (Agel and Barlow, 2020), though they performed \r better for summer extremes over the USA (Akinsanola et al., 2020). The \r comparison between climatologies in the observations and in model \r simulations shows that the CMIP6 and CMIP5 models that have similar \r horizontal resolutions also have similar model evaluation scores, \r [...]"


id: 4e2a594a7c179bdc973347688d2ef21c
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 11020
content:
"In general, extreme precipitation in CMIP6 models tends to be \r somewhat larger than in CMIP5 models (Li et al., 2021), reflecting \r smaller spatial scales of extreme precipitation represented by slightly \r higher-resolution models (Gervais et al., 2014). This is confirmed by \r Kim et al. (2020), who showed that Rx1day and Rx5day simulated \r by CMIP6 models tend to be closer to point estimates of HadEX3 \r data (Dunn et al., 2020) than those simulated by CMIP5. Figure 11.14 \r shows the multi-model ensemble bias in mean Rx1day over the \r period 1979-2014 from 21 available CMIP6 models when compared \r with observations and reanalyses. Measured by global land root\u0002mean-square error, the model performance is generally consistent \r across different observed/reanalysis data products for the extreme \r precipitation metric (Figure 11.14). The magnitude of extreme area \r mean precipitation simulated by the CMIP6 models is consistently \r [...]"


id: 4adff5c1e363634f6d42f4f175084a80
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 10414
content:
"[...] smaller than the point estimates of HadEX3, but the model values \r are more comparable to those of areal-mean values (Figure 11.14) \r of the ERA5 reanalysis or REGEN (Contractor et al., 2020b). Taylor\u0002plot-based performance metrics reveal strong similarities in the \r patterns of extreme precipitation errors over land regions between \r CMIP5 and CMIP6 (Srivastava et al., 2020; Wehner et al., 2020) and \r between annual mean precipitation errors and Rx1day errors for both \r generations of models (Wehner et al., 2020)."


id: bb040413bb81ec954a7caa3bd09a4337
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 30119
content:
"Since AR5 there has been new and robust evidence and improved \r understanding of human influence on extreme precipitation. In \r particular, detection and attribution analyses have provided consistent \r and robust evidence of human influence on extreme precipitation \r of one- and five-day durations at global to continental scales. \r The observed increases in Rx1day and Rx5day over the Northern \r Hemisphere land area during 1951-2005 can be attributed to the \r effect of combined anthropogenic forcing, including greenhouse \r gases and anthropogenic aerosols, as simulated by CMIP5 models \r and the rate of intensification with regard to warming is consistent \r with C-C scaling (Zhang et al., 2013). This is confirmed to be robust \r when an additional nine years of observational data and the CMIP6 \r model simulations were used (Cross-Chapter Box 3.2, Figure 1; Paik \r et al., 2020). The influence of greenhouse gases is attributed as the \r [...]"


id: c83294f41cb6d15c92ee8f534fb553fe
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 32532
content:
"[...] dominant contributor to the observed intensification. The global \r average of Rx1day in the observations is consistent with simulations \r by both CMIP5 and CMIP6 models under anthropogenic forcing, \r but not under natural forcing (Cross-Chapter Box 3.2, Figure 1). The \r observed increase in the fraction of annual total precipitation falling \r into the top fifth or top first percentiles of daily precipitation can \r also be attributed to human influence at the global scale (Dong \r et al., 2021). The CMIP5 models were able to capture the fraction \r of land experiencing a strong intensification of heavy precipitation \r during 1960-2010 under anthropogenic forcing, but not in unforced \r simulations (Fischer et al., 2014). But the models underestimated \r the observed trends (Borodina et al., 2017a). Human influence also \r significantly contributed to the historical changes in record-breaking \r one-day precipitation (Shiogama et al., 2016). There is also limited \r [...]"


id: 907e76d4b00b4bc689ba021e172053be
created_at: 2023-04-19 13:22:46
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content:
"[...] evidence of the influences of natural forcing. Substantial reductions in \r Rx5day and Simple Daily Intensity Index (SDII) for daily precipitation \r intensity over the global summer monsoon regions occurred during \r 1957-2000 after explosive volcanic eruptions (Paik and Min, 2018). \r The reduction in post-volcanic eruption extreme precipitation in the \r simulations is closely linked to the decrease in mean precipitation, for \r which both thermodynamic effects (moisture reduction due to surface \r cooling) and dynamic effects (monsoon circulation weakening) play \r important roles. \r There has been new evidence of human influence on extreme \r precipitation at continental scales, including the detection of "


id: 74f11217990ec227c3f92df31fde31c9
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 17809
content:
"Studies using regional climate models (RCMs), for example, CORDEX \r (Giorgi et al., 2009) over Africa (Dosio et al., 2015; Klutse et al., 2016; \r Pinto et al., 2016; Gibba et al., 2019), Australia, East Asia (Park et al., \r 2016), Europe (Prein et al., 2016a; Fantini et al., 2018), and parts \r of North America (Diaconescu et al., 2018) suggest that extreme \r rainfall events are better captured in RCMs compared to their host \r GCMs due to their ability to address regional characteristics, for \r example, topography and coastlines. However, CORDEX simulations \r do not show good skill over South Asia for heavy precipitation, and \r do not add value with respect to their GCM source of boundary \r conditions (Mishra et al., 2014b; S. Singh et al., 2017). The evaluation \r of models in simulating regional processes is discussed in detail in \r Section  10.3.3.4. The high-resolution simulation of mid-latitude \r winter extreme precipitation over land is of similar magnitude to \r [...]"


id: 7ba00962bf2253fba7add92822f3596
created_at: 2023-04-19 13:22:46
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vector_id: 18959
content:
"[...] point observations. Simulation of summer extreme precipitation has \r a large bias when compared with observations at the same spatial \r scale. Simulated extreme precipitation in the tropics also appears to \r be too large, indicating possible deficiencies in the parametrization \r of cumulus convection at this resolution. Indeed, precipitation \r distributions at both daily and sub-daily time scales are much \r improved with a convection-permitting model (Belusic et al., 2020) \r over Western Africa (Berthou et al., 2019b), East Africa (Finney et al., \r 2019), North America and Canada (Cannon and Innocenti, 2019; \r Innocenti et al., 2019) and over Belgium in Europe (Vanden Broucke \r et al., 2019). "


id: dd351fde0480a306504dac6e5273154a
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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vector_id: 36188
content:
"Mediterranean (Vautard et al., 2015), parts of the USA for individual \r events (Knutson et al., 2014a; Szeto et al., 2015; Eden et al., 2016; \r van Oldenborgh et al., 2017), extreme rainfall in 2014 over Northland, \r New Zealand (Rosier et al., 2015) or China (Burke et al., 2016; Sun \r and Miao, 2018; Yuan et al., 2018b; Zhou et al., 2018). However, \r for other heavy rainfall events, studies identified a lack of evidence \r about anthropogenic influences (Imada et al., 2013; Schaller et al., \r 2014; Otto et al., 2015c; Siswanto et al., 2015). There are also studies \r where results are inconclusive because of limited reliable simulations \r (Christidis et al., 2013b; Angelil et al., 2016). Overall, both the spatial \r and temporal scales on which extreme precipitation events are \r defined are important for attribution; events defined on larger scales \r have larger signal-to-noise ratios and thus the signal is more readily \r [...]"


id: 4e7f1caba760b57afa1ab5ec1d277d1b
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index: climate_gpt_v2
vector_id: 11087
content:
"[...] detectable. At the current level of global warming, there is a strong \r enough signal to be detectable for large-scale extreme precipitation \r events, but the chance of detecting such signals for smaller-scale \r events decreases (Kirchmeier-Young et al., 2019)."


id: 71eb44e02d54fa09a20de89b308a825c
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 17290
content:
"Evidence of human influence on extreme precipitation at regional \r scales is more limited and less robust. In north-west Australia, the \r increase in extreme rainfall since 1950 can be related to increased \r monsoonal flow due to increased aerosol emissions, but cannot be \r attributed to an increase in greenhouse gases (Dey et al., 2019a). \r Anthropogenic influence on extreme precipitation in China was \r detected in one study (H. Li et al., 2017), but not in another using \r different detection and data-processing procedures (W. Li et al., \r 2018a), indicating the lack of robustness in the detection results. \r A still weak signal-to-noise ratio seems to be the main cause for the \r lack of robustness, as detection would become robust 20 years in \r the future (W. Li et al., 2018a). Krishnan et al. (2016) attributed the \r observed increase in heavy rain events (intensity >100 mm day-1) \r in the post-1950s over central India to the combined effects of \r [...]"


id: 9aaaf2b6eed93f0b732b5f2cdfbee7ee
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vector_id: 24451
content:
"[...] greenhouse gases, aerosols, land-use and land-cover changes, and \r rapid warming of the equatorial Indian Ocean SSTs. Roxy et al. (2017) \r and Devanand et al. (2019) showed that the increase in widespread \r extremes over the South Asian Monsoon during 1950-2015 is due \r to the combined impacts of the warming of the Western Indian \r Ocean (Arabian Sea) and the intensification of irrigation water \r management over India. "


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"In summary, most of the observed intensification of heavy precipitation \r over land regions is likely due to anthropogenic influence, for which \r greenhouse gases emissions are the main contributor. New and \r robust evidence since AR5 includes attribution to human influence \r of the observed increases in annual maximum one-day and five\u0002day precipitation and in the fraction of annual precipitation falling \r in heavy events. The evidence since AR5 also includes a  larger \r fraction of land showing enhanced extreme precipitation and \r a  larger probability of record-breaking one-day precipitation than \r expected by chance, both of which can only be explained when \r anthropogenic greenhouse gas forcing is considered. Human \r influence has contributed to the intensification of heavy precipitation \r in three continents where observational data are more abundant \r (high confidence) (North America, Europe and Asia). On the spatial \r [...]"


id: ba8db5bdc767087d4390690e4825d7f3
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"[...] scale of AR6 regions, there is limited evidence of human influence on \r extreme precipitation, but new evidence is emerging; in particular, \r studies attributing individual heavy precipitation events  found \r that human influence was a  significant driver of the events, \r particularly in the winter season. "


id: b96515a3fd8c69394e04c5fc7018fe31
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 29831
content:
"The AR5 concluded it is very likely that extreme precipitation events \r will be more frequent and more intense over most of the mid\u0002latitude land masses and wet tropics in a  warmer world (Collins \r et al., 2013). Post-AR5 studies provide more and robust evidence\r to support the previous assessments. These include an observed \r increase in extreme precipitation (Section 11.4.3) and human causes \r of past changes (Section  11.4.4), as well as projections based on \r either GCM and/or RCM simulations. The CMIP5 models project \r that the rate of increase in Rx1day with warming is independent \r of the forcing scenario (Section  8.5.3.1; Pendergrass et al., 2015) \r or forcing mechanism (Sillmann et al., 2017a). This is confirmed \r in CMIP6 simulations (Sillmann et al., 2019; Li et al., 2021). In \r particular, for extreme precipitation that occurs once a year or less \r frequently, the magnitudes of the rates of change per 1degC change \r [...]"


id: 24b023969c031c84bc5c80d25ffabe85
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 3652
content:
"[...] in global mean temperature are similar, regardless of whether the \r temperature change is caused by increases in carbon dioxide (CO2), "


id: 94b5b2e37680527e8ba4310ef15f1130
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"confirmed by near-linear scaling between extreme precipitation and \r global warming levels at regional scales (Seneviratne and Hauser, \r 2020). Internal variability modulates changes in heavy rainfall (Wood \r and Ludwig, 2020), resulting in different changes in different regions \r (Seneviratne and Hauser, 2020). Extreme precipitation nearly always \r increases across land areas with larger increases at higher global \r warming levels, except in very few regions, such as Southern Europe \r around the Mediterranean Basin at low warming levels (Table 11.17). \r The very likely ranges of the multi-model ensemble changes across \r all land grid boxes in the 50-year return values for Rx1day and \r Rx5day between 1.5degC and 1degC warming levels are above zero for \r all continents except Europe, with the lower bound of the likely \r range above zero over Europe (Li et al., 2021). Decreases in extreme \r precipitation are confined mostly to subtropical ocean areas and are \r [...]"


id: ff5818e83e0b7a1572b5aa5605e80db4
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 42329
content:
"[...] highly correlated to decreases in mean precipitation due to storm \r track shifts. These subtropical decreases can extend to nearby land \r areas in individual realizations. "


id: ea52de6827f3baa964d9dd83b1d30fea
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 38580
content:
"Projected increases in the probability of extreme precipitation of \r fixed magnitudes are nonlinear and show larger increases for more \r rare events (Figures 11.7 and 11.15; Fischer and Knutti, 2015; Kharin \r et al., 2018; Li et al., 2021). The CMIP5 model projected increases \r in the probability of high (99th and 99.9th) percentile precipitation \r between 1.5degC and 2degC warming scenarios are consistent with what \r can be expected based on observed changes (Fischer and Knutti, \r 2015), providing confidence in the projections. The CMIP5 model \r simulations show that the frequency for present-day climate 20-year \r extreme precipitation is projected to increase by 10% at the 1.5degC \r global warming level, and by 22% at the 2.0degC global warming \r level, while the increase in the frequency for present-day climate \r 100-year extreme precipitation is projected to increase by 20% and \r more than 45% at the 1.5degC and 2.0degC warming levels, respectively \r [...]"


id: 58d697b33e136e39c431801e62c4bb
created_at: 2023-04-19 13:22:46
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content:
"[...] (Kharin et al., 2018). CMIP6 simulations with SSP scenarios show that \r the frequency of 10-year and 50-year events will be approximately \r doubled and tripled, respectively, at a very high warming level of 4degC \r (Figure 11.7; Li et al., 2021). "


id: 5c09e3c0c7b2459b442a102977fa0da6
created_at: 2023-04-19 13:22:46
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content_type: text
index: climate_gpt_v2
vector_id: 13453
content:
"methane (CH4), solar forcing, or sulphate (SO4) (Sillmann et al., 2019). \r In some models - CESM1 in particular - the extreme precipitation \r response to warming may follow a quadratic relation (Pendergrass \r et al., 2019). Figure  11.15 shows changes in the 10- and 50-year \r return values of Rx1day at different warming levels as simulated by \r the CMIP6 models. The median value of the scaling over land, across \r all Shared Socio-economic Pathway (SSP) scenarios and all models, \r is close to 7% per 1degC of warming for the 50-year return value of \r Rx1day. It is just slightly smaller for the 10- and 50-year return \r values of Rx5day (Li et al., 2021). The 90% ranges of the multimodel \r ensemble changes across all land grid boxes in the 50-year return \r values for Rx1day and Rx5day do not overlap between 1.5degC and \r 2degC warming levels (Li et al., 2021), indicating that a small increment \r such as 0.5degC in global warming can result in a significant increase \r [...]"


id: 2c4612e58e18b6b562bc6e8be165befc
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"[...] in extreme precipitation. Projected long-period Rx1day return value \r changes are larger than changes in mean Rx1day and with larger \r relative changes for more rare events (Pendergrass, 2018; Mizuta and \r Endo, 2020; Wehner, 2020). The rate of change of moderate extreme \r precipitation may depend more on the forcing agent, similar to the \r mean precipitation response to warming (Lin et al., 2016, 2018). \r Thus, there is high confidence that extreme precipitation that occurs \r once a year or less frequently increases proportionally to the amount \r of surface warming, and the rate of change in precipitation is not \r dependent on the underlying forcing agents of warming."


id: ad822213222b3e4de388549f9d7c3d36
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 27767
content:
"There is a limited number of studies on the projections of extreme \r hourly precipitation. The ability of GCMs to simulate hourly \r precipitation extremes is limited (Morrison et al., 2019) and very \r few modelling centres archive sub-daily and hourly precipitation \r prior to CMIP6 experiments. RCM simulations project an increase in \r extreme sub-daily precipitation in North America (C. Li et al., 2019b) \r and Sweden (Olsson and Foster, 2013), but these models still do not \r explicitly resolve convective processes that are important for properly \r simulating extreme sub-daily precipitation. Simulations by RCMs \r that explicitly resolve convective processes (convection-permitting \r models) are limited in length and only available in a  few regions \r because of high computing costs. Yet, a  majority of the available \r convection-permitting simulations project increases in the intensities \r of extreme sub-daily precipitation events, with the amount similar to \r [...]"


id: b2e5586c87ba5dbea446aee2f0dfb52c
created_at: 2023-04-19 13:22:46
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content:
"[...] or higher than the C-C scaling rate (Kendon et al., 2014, 2019; Ban \r et al., 2015; Prein et al., 2016b; Helsen et al., 2020; Fowler et al., 2021). \r An increase is projected in extreme sub-daily precipitation over Africa \r (Kendon et al., 2019); East Africa (Finney et al., 2020) and Western \r Africa (Berthou et al., 2019a; Fitzpatrick et al., 2020), even for areas \r where parametrized RCMs project a decrease; in Europe (Hodnebrog "


id: 94f44eb8e66bc1bb45c2a30222450e88
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 23409
content:
"there are regional differences, extreme precipitation is projected \r to increase in almost all sub-regions, though there can be spatial \r heterogeneity within sub-regions, such as in India (Shashikanth et al., \r 2018) and South East Asia (Ohba and Sugimoto, 2019). In East and \r South East Asia, there is high confidence that extreme precipitation \r is projected to intensify (Seo et al., 2014; Zhou et al., 2014; Y. Xu \r et al., 2016; Nayak et al., 2017; X. Wang et al., 2017; Y. Wang et al., \r 2017; Guo et al., 2018; D. Li et al., 2018; Sui et al., 2018). Extreme \r daily precipitation is also projected to increase in South Asia (Xu \r et al., 2017; Han et al., 2018; Shashikanth et al., 2018). The extreme \r precipitation indices, including Rx5day, R95p, and days of heavy \r precipitation (i.e., R10mm), are all projected to increase under the \r RCP4.5 and RCP8.5 scenarios in central and northern Asia (Xu et al., \r 2017; Han et al., 2018). A general wetting across the whole Tibetan \r [...]"


id: 3ec129fecda9aa4ecab9671c53c0e2d9
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 8229
content:
"[...] Plateau and the Himalayas is projected, with increases in heavy \r precipitation in the 21st  century (Palazzi et al., 2013; Zhou et al., \r 2014; Rajbhandari et al., 2015; R. Zhang et al., 2015; Wu et al., 2017; \r Gao et al., 2018; Paltan et al., 2018). Agreement in projected changes \r by different models is low in regions of complex topography such \r as Hindu-Kush Himalayas (Roy et al., 2019), but CMIP5, CMIP6 and \r CORDEX-CORE simulations consistently project an increase in heavy \r precipitation in higher latitude areas, such as West and East Siberia, \r and Russian Far East (high confidence) (Coppola et al., 2021a). "


id: 1df770939fcc4405f361248623323834
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 2465
content:
"et al., 2019; Chan et al., 2020); as well as in the continental USA \r (Prein et al., 2016b). Overall, while limited, the available evidence \r points to an increase in extreme sub-daily precipitation in the future. \r Studies on future changes in extreme precipitation for a month or \r longer are limited. One study projects an increase in extreme monthly \r precipitation in Japan under 4degC global warming for around 80% of \r stations in the summer (Hatsuzuka and Sato, 2019). \r In Africa (Table 11.5), extreme precipitation will likely increase under \r warming levels of 2degC or below (compared to pre-industrial values) \r and very likely increase at higher warming levels. Simulations by \r CMIP5, CMIP6 and CORDEX regional models project an increase \r in daily extreme precipitation between 1.5degC and 2.0degC warming \r levels. The pattern of change in heavy precipitation under different \r scenarios or warming levels is similar with larger increases for \r [...]"


id: 88a7e3f557f391008e2b8227c8d6bac0
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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vector_id: 21203
content:
"[...] higher warming levels (e.g., Nikulin et al., 2018; Li et al., 2021). With \r increases in warming, extreme precipitation is projected to increase \r in the majority of land regions in Africa (Mtongori et al., 2016; Pfahl \r et al., 2017; Diedhiou et al., 2018; Dunning et al., 2018; Akinyemi \r and Abiodun, 2019; Giorgi et al., 2019). Over Southern Africa, heavy \r precipitation will likely increase by the end of the 21st century under \r RCP 8.5 (Dosio, 2016; Pinto et al., 2016; Abiodun et al., 2017; Dosio \r et al., 2019). However, heavy rainfall amounts are projected to \r decrease over western South Africa (Pinto et al., 2018) as a result \r of a projected decrease in the frequency of the prevailing westerly \r winds south of the continent that translates into fewer cold fronts \r and closed mid-latitudes cyclones (Engelbrecht et al., 2009; Pinto \r et al., 2018). Heavy precipitation will likely increase by the end of the \r [...]"


id: a3bbdc280c01b5eaf23f914803a95dbb
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"[...] century under RCP8.5 in West Africa (Diallo et al., 2016; Dosio, 2016; \r Sylla et al., 2016; Abiodun et al., 2017; Akinsanola and Zhou, 2019; \r Dosio et al., 2019) and is projected to increase (high confidence) \r in Central Africa (Fotso-Nguemo et al., 2018, 2019; Sonkoue et al., \r 2019) and eastern Africa (Thiery et al., 2016; Ongoma et al., 2018a). \r In north-east and central east Africa, extreme precipitation intensity \r is projected to increase across CMIP5, CMIP6 and CORDEX-CORE \r (high confidence) in most areas annually (Coppola et al., 2021a), \r but the trends differ from season to season in all future scenarios \r (Dosio et al., 2019). In northern Africa, there is low confidence in \r the projected changes in heavy precipitation, either due to a lack of \r agreement among studies on the sign of changes (Sillmann et al., \r 2013a; Giorgi et al., 2014) or due to insufficient evidence. "


id: 5d88912e13bdbb83866f3405a0cf9c19
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 13739
content:
"In Australasia (Table  11.11), most CMIP5 models project an \r increase in Rx1day under RCP4.5 and RCP8.5 scenarios for the late \r 21st century (CSIRO and BOM, 2015; Alexander and Arblaster, 2017; \r Grose et al., 2020) and the CMIP6 multi-model median projects an \r increase in the 10- and 50-year return values of Rx1day and Rx5day \r at a rate between 5% and 6% per 1degC of near-surface global mean \r warming (Figure 11.7; Li et al., 2021). Yet, there is large uncertainty \r in the increase because projected changes in dynamic processes \r lead to a  decrease in Rx1day that can offset the thermodynamic \r increase over a large portion of the region (Box 11.1, Figure 1; Pfahl \r et al., 2017). Projected changes in moderate extreme precipitation \r (the 99th percentile of daily precipitation) by RCMs under RCP8.5 \r for 2070-2099 are mixed, with more regions showing decreases \r than increases (Evans et al., 2021). It is likely that daily rainfall \r [...]"


id: b8b2bbb4278911fcce463aba844294fa
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 29699
content:
"[...] extremes such as Rx1day will increase at the continental scale for \r global warming levels at or above 3degC. Daily rainfall extremes are \r projected to increase at the 2.0degC global warming level (medium \r confidence), and there is low confidence in changes at the 1.5degC.\r Projected changes show important regional differences with very \r likely increases over Northern Australia (Alexander and Arblaster, \r 2017; Herold et al., 2018; Grose et al., 2020) and New Zealand (MfE, \r 2018) where projected dynamic contributions are small (Box  11.1 \r Figure 1; Pfahl et al., 2017) and medium confidence on increases over \r central, eastern, and Southern Australia where dynamic contributions \r are substantial and can affect local phenomena (CSIRO and BOM, \r 2015; Pepler et al., 2016; Bell et al., 2019; Dowdy et al., 2019). "


id: 79b877fc7f77e3beea1c2bc63a99b80b
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 18618
content:
"In Asia (Table 11.8), extreme precipitation will likely increase at global \r warming levels of 2degC and below, but very likely increase at higher \r warming levels for the region as whole. The CMIP6 multi-model \r median projects an increase in the 10- and 50-year return values \r of Rx1day and Rx5day over more than 95% of regions, even at the \r 2degC warming level, with larger increases at higher warming levels, \r independent of emissions scenarios (Li et al., 2021, also Figure 11.7). \r The CMIP5 models produced similar projections. Both heavy rainfall \r and rainfall intensity are projected to increase (Zhou et al., 2014; Guo \r et al., 2016, 2018; Y. Xu et al., 2016; Endo et al., 2017; Han et al., 2018; \r G. Kim et al., 2018). A half-degree difference in warming between the \r 1.5degC and 2.0degC warming levels can result in a detectable increase \r in extreme precipitation over the region (Li et al., 2021), in the \r Asian-Australian monsoon region (Chevuturi et al., 2018), and over \r [...]"


id: bfdd7a0f65bb7eedf070452ea7080c63
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 31045
content:
"and projected changes for more moderate extreme precipitation \r are also more uncertain. Extreme precipitation, represented by \r the number of days with daily precipitation exceeding 50 mm and the \r annual fraction of precipitation falling during days with the top 10% \r daily precipitation amount, is projected to increase on the eastern \r coast of Southern Central America, but to decrease along the Pacific \r coasts of El Salvador and Guatemala (Imbach et al., 2018). Chou et al. \r (2014b) and Giorgi et al. (2014) projected an increase in extreme \r precipitation over South-Eastern South America and the Amazon. \r Projected changes in moderate extreme precipitation represented \r by the 99th percentile of daily precipitation by different models \r under different emissions scenarios, even at high warming levels, \r are mixed: increases are projected for all regions by the CORDEX\u0002CORE and CMIP5 simulations, while increases for some regions and \r [...]"


id: e1878c3e037e15671fdbbe7d512749fc
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content:
"[...] decreases for other regions are projected by CMIP6 simulations \r (Coppola et al., 2021a). Extreme precipitation is projected to increase \r in the La Plata basin (Cavalcanti et al., 2015; Carril et al., 2016). Taylor \r et al. (2018) projected a decrease in days with intense rainfall in the \r Caribbean under 2degC global warming by the 2050s under RCP4.5 \r relative to 1971-2000. "


id: 4e07da2f9fd3383c64451096d2090f1b
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 10990
content:
"In regions with a seasonal snow cover, snowmelt is the main cause \r of extreme river flooding over large areas (Pall et al., 2019). Extensive \r snowmelt combined with heavy and/or long-duration precipitation \r can cause significant floods (D. Li et al., 2019; Krug et al., 2020). \r Changes in floods in these regions can be uncertain because of \r the compounding and competing effects of the responses of snow \r and rain to warming that affect snowpack size: warming results \r in an increase in precipitation, but also a  reduction in the time \r period of snowfall accumulation (Teufel et al., 2019). An increase in \r atmospheric CO2 enhances water-use efficiency by plants (Roderick \r et al., 2015; Milly and Dunne, 2016; Swann et al., 2016; Swann, \r 2018); this could reduce evapotranspiration and contribute to the \r maintenance of soil moisture and streamflow levels under enhanced \r atmospheric CO2 concentrations (Yang et al., 2019). This mechanism \r [...]"


id: 6db7f6af90efd0cc674feb80e7e23886
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content:
"[...] would suggest an increase in the magnitude of some floods in the \r future (Kooperman et al., 2018). But this effect is uncertain as an \r increase in leaf area index, and vegetation coverage could also result \r in overall larger water consumption (Matyas and Sun, 2014; Mankin \r et al., 2019; Teuling et al., 2019), and there are also other CO2-related \r mechanisms that come into play (Cross-Chapter Box 5.1). "


id: cd5c07d374dd796b35008bf8e405e861
created_at: 2023-04-19 13:22:46
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vector_id: 33467
content:
"Floods are the inundation of normally dry land, and are classified \r into types (e.g., pluvial floods, flash floods, river floods, groundwater \r floods, surge floods, coastal floods) depending on the space and time \r scales and the major factors and processes involved (Section 8.2.3.2; \r Nied et al., 2014; Aerts et al., 2018). Flooded area is difficult to \r measure or quantify and, for this reason, many of the existing studies \r on changes in floods focus on streamflow. Thus, this section assesses \r changes in flow as a proxy for river floods, in addition to some types \r of flash floods. Pluvial and urban floods  - types of flash floods \r resulting from the precipitation intensity exceeding the capacity of \r natural and artificial drainage systems - are directly linked to extreme \r precipitation. Because of this link, changes in extreme precipitation \r are the main proxy for inferring changes in pluvial and urban floods \r [...]"


id: fab165d75b7a98f0344429046b22a926
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updated_at: 2023-04-19 13:22:46
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vector_id: 41482
content:
"[...] (see also Section  12.4), assuming there is no additional change \r in the surface condition. Changes in these types of floods are not \r assessed in this section, but can be inferred from the assessment of \r changes in heavy precipitation in Section 11.4. Coastal floods due to \r extreme sea levels and flood changes at regional scales are assessed \r in Section 12.4. "


id: 93d18304b96453676dab78767f9e6fdc
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 23217
content:
"Various factors, such as extreme precipitation (Cho et al., 2016; Archer \r and Fowler, 2018), glacier lake outbursts (Schneider et al., 2014; \r Schwanghart et al., 2016), or dam breaks (Biscarini et al., 2016) can \r cause flash floods. Very intense rainfall, along with a high fraction \r of impervious surfaces can result in flash floods in urban areas \r (Hettiarachchi et al., 2018). Because of this direct connection, changes \r in very intense precipitation can translate to changes in urban flood \r potential (Rosenzweig et al., 2018), though there can be a spectrum \r of urban flood responses to this flood potential (Smith et al., 2013), \r as many factors, such as the overland flow rate and the design of \r urban (Falconer et al., 2009) and storm water drainage systems \r (Maksimovic et al., 2009), can play an important role. Nevertheless, \r changes in extreme precipitation are the main proxy for inferring \r changes in some types of flash floods, (which are addressed in \r [...]"


id: 2b351007a38ebb6f9de6385b22ca9932
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 4847
content:
"[...] Section 12.4), given the relation between extreme precipitation and \r pluvial floods, the very limited literature on urban and pluvial floods \r (e.g., Skougaard Kaspersen et al., 2017), and limitations of existing \r methodologies for assessing changes in floods (Archer et al., 2016). "


id: 71a2410c31b6191088b4259887cfc493
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 17241
content:
"The SREX (Seneviratne et al., 2012) assessed low confidence for \r observed changes in the magnitude or frequency of floods at the \r global scale. This assessment was confirmed by AR5 (Hartmann et al., \r 2013). The SR1.5 (Hoegh-Guldberg et al., 2018) found increases \r in flood frequency and extreme streamflow in some regions, but \r decreases in other regions. While the number of studies on flood \r trends has increased since AR5, and there were also new analyses \r after the release of SR1.5 (Berghuijs et al., 2017; Bloschl et al., 2019; \r Gudmundsson et al., 2019), hydrological literature on observed \r flood changes is heterogeneous, focusing at regional and sub\u0002regional basin scales, making it difficult to synthesize at the global \r and sometimes regional scales. The vast majority of studies focus on \r river floods using streamflow as a proxy, with limited attention to \r urban floods. Streamflow measurements are not evenly distributed \r [...]"


id: ca241901ce61ec46b1ed930c6951ac74
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 32901
content:
"[...] over space, with gaps in spatial coverage, and their coverage in many \r regions of Africa, South America, and parts of Asia is poor (e.g., Do \r et al., 2017), leading to difficulties in detecting long-term changes in \r floods (Slater and Villarini, 2017). See also Section 8.3.1.5."


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"Peak flow trends are characterized by high regional variability and \r lack overall statistical significance of a decrease or an increase over \r the globe as a  whole. Of more than 3500 streamflow stations in \r the USA, central and Northern Europe, Africa, Brazil, and Australia, \r 7.1% stations showed a  significant increase, and 11.9% stations \r showed a significant decrease in annual maximum peak flow during \r 1961-2005 (Do et al., 2017). This is in direct contrast to the global \r and continental scale intensification of short-duration extreme \r precipitation (Section 11.4.2). There may be some consistency over \r large regions (see Gudmundsson et al., 2019), in high streamflows \r (>90th percentile), including a decrease in some regions (e.g., in the \r Mediterranean) and an increase in others (e.g., northern Asia), but \r gauge coverage is often limited. On a continental scale, a decrease \r seems to dominate in Africa (Tramblay et al., 2020) and Australia \r [...]"


id: 421bc6dd6091a1b0463872574fc7841
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"[...] (Ishak et al., 2013; Wasko and Nathan, 2019), an increase in the \r Amazon (Barichivich et al., 2018), and trends are spatially variable \r in other continents (Q. Zhang et al., 2015b; Bai et al., 2016; Do et al., \r 2017; Hodgkins et al., 2017). In Europe, flow trends have large spatial \r differences (Hall et al., 2014; Mediero et al., 2015; Kundzewicz et al., \r 2018; Mangini et al., 2018), but there appears to be a  pattern of \r increase in north-western Europe, and a decrease in southern and \r eastern Europe in annual peak flow during 1960-2000 (Bloschl et al., \r 2019). In North America, peak flow has increased in north-east USA \r and decreased in south-west USA (Peterson et al., 2013b; Armstrong \r et al., 2014; Mallakpour and Villarini, 2015; Archfield et al., 2016; Burn \r and Whitfield, 2016; Wehner et al., 2017; Neri et al., 2019). There are \r important changes in the seasonality of peak flows in regions where \r snowmelt dominates, such as northern North America (Burn and \r [...]"


id: e669013c3225138b168e866de3186602
created_at: 2023-04-19 13:22:46
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"[...] Whitfield, 2016; Dudley et al., 2017) and Northern Europe (Bloschl \r et al., 2017), corresponding to strong winter and spring warming. "


id: 2f239c6853a55b1c41f3f3f617bdf6c9
created_at: 2023-04-19 13:22:46
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"There are very few studies focused on the attribution of long-term \r changes in floods, but there are studies on changes in flood events. \r Most of the studies focus on flash floods and urban floods, which \r are closely related to intense precipitation events (Hannaford, 2015). \r In other cases, event attribution focused on runoff using hydrological \r models, and examples include river basins in the UK (Section 11.4.4; \r Schaller et al., 2016; Kay et al., 2018), the Okavango River in Africa \r (Wolski et al., 2014), and the Brahmaputra River in Bangladesh (Philip \r et al., 2019). Findings about anthropogenic influences vary between \r different regions and basins. For some flood events, the probability of \r high floods in the current climate is lower than in a climate without \r an anthropogenic influence (Wolski et al., 2014), while in other \r cases anthropogenic influence leads to more intense floods (Cho \r et al., 2016; Pall et al., 2017; van der Wiel et al., 2017; Philip et al., \r [...]"


id: 9b89963ae6a4f279a59397a57fcc8ca8
created_at: 2023-04-19 13:22:46
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vector_id: 24615
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"[...] 2018a; Teufel et al., 2019). Factors such as land-cover change and \r river management can also increase the probability of high floods \r (Ji et al., 2020). These, along with model uncertainties and the lack \r of studies overall, suggest a low confidence in general statements to \r attribute changes in flood events to anthropogenic climate change. \r A few individual regions have been well studied, which allows for \r high confidence in the attribution of increased flooding in these \r cases. For example, flooding in the UK following increased winter \r precipitation (Schaller et al., 2016; Kay et al., 2018) can be attributed \r to anthropogenic climate change (Schaller et al., 2016; Vautard et al., \r 2016; Yiou et al., 2017; Otto et al., 2018b). "


id: cefcc2480a775c29a0e627a31337b15c
created_at: 2023-04-19 13:22:46
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content:
"The SREX (Chapter  3, Seneviratne et al., 2012) stressed the low \r availability of studies on flood projections under different emissions \r scenarios, and concluded that there was low confidence in \r projections of flood events given the complexity of the mechanisms \r driving floods at the regional scale. The AR5 WGII report (Chapter 3, \r Jimenez Cisneros et al., 2014) assessed with medium confidence the \r pattern of future flood changes, including flood hazards increasing \r over about half of the globe (parts of southern and South East Asia, \r tropical Africa, north-east Eurasia, and South America) and flood \r hazards decreasing in other parts of the world, despite uncertainties \r in GCMs and their coupling to hydrological models. The SR1.5 \r (Chapter  3, Hoegh-Guldberg et al., 2018) assessed with medium \r confidence that global warming of 2degC would lead to an expansion \r of the fraction of global area affected by flood hazards, compared to \r [...]"


id: eda7d5ac4aee3cae44664e2197799bd9
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content:
"[...] conditions at 1.5degC of global warming, as a consequence of changes \r in heavy precipitation. "


id: fb64d4cd613bb9c33f0015f4b15dbcdb
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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"The majority of new studies that produce future flood projections \r based on hydrological models do not typically consider aspects \r that are also important to actual flood severity or damages, such \r as flood prevention measures (Neumann et al., 2015; Sen, 2018), \r flood control policies (Barraque, 2017), and future changes in land \r cover (see also Section  8.4.1.5). At the global scale, Alfieri et al. \r (2017) used downscaled projections from seven GCMs as input to \r drive a hydrodynamic model. They found successive increases in the \r frequency of high floods in all continents except Europe, associated \r with increasing levels of global warming (1.5degC, 2degC, 4degC). These \r results are supported by Paltan et al. (2018), who applied a simplified \r runoff aggregation model forced by outputs from four GCMs. S. Huang \r et al. (2018) used three hydrological models forced with bias-adjusted \r outputs from four GCMs to produce projections for four river basins \r [...]"


id: 23569c2a1cb2540591d188e870adbf77
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content:
"[...] including the Rhine, Upper Mississippi, Upper Yellow, and Upper \r Niger under 1.5degC, 2degC, and 3degC global warming. This study found \r diverse projections for different basins, including a  shift towards \r earlier flooding for the Rhine and the Upper Mississippi, a substantial \r increase in flood frequency in the Rhine only under the 1.5degC and 2degC \r scenarios, and a decrease in flood frequency in the Upper Mississippi \r under all scenarios. "


id: 702f51d0a3ecc74e2c08dd1f25d6cfbe
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"Similar to many other extreme events, droughts occur as \r a combination of thermodynamic and dynamic processes (Box 11.1). \r Thermodynamic processes contributing to drought, which are \r modified by greenhouse gas forcing both at global and regional \r scales, are mostly related to heat and moisture exchanges, and are \r also partly modulated by plant coverage and physiology. They affect, \r for instance, atmospheric humidity, temperature, and radiation, \r which in turn affect precipitation and/or evapotranspiration in some \r regions and time frames. However, dynamic processes are particularly \r important to explain drought variability on different time scales, \r from a few weeks (flash droughts) to multiannual (megadroughts). \r There is low confidence in the effects of greenhouse gas forcing \r on changes in atmospheric dynamic (Section  2.4; Section  4.3.3), \r and on associated changes in drought occurrence. Thermodynamic \r [...]"


id: b7672960bc9d7aa5515588bda7823303
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content:
"[...] processes are thus the main driver of drought changes in a warming \r climate (high confidence)."


id: faa5f71eba033b62390c7feb84b4ea02
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"Droughts refer to periods of time with substantially below\u0002average moisture conditions, usually covering large areas, during \r which limitations in water availability result in negative impacts \r for various components of natural systems and economic sectors \r (Wilhite and Pulwarty, 2017; Ault, 2020). Depending on the \r variables used to characterize it and the systems or sectors being \r impacted, drought may be classified in different types (Figure 8.6 \r and Appendix Table 11.A.1) such as meteorological (precipitation \r deficits), agricultural (e.g.,  crop yield reductions or failure, often \r related to soil moisture deficits), ecological (related to plant water \r stress that causes e.g.,  tree mortality), or hydrological droughts \r (e.g.,  water shortage in streams or storages such as reservoirs, \r lakes, lagoons, and groundwater; see Glossary). The distinction of \r [...]"


id: d032e0b34adf27e516f882b5488ef68b
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content:
"[...] drought types is not absolute, as drought can affect different sub\u0002domains of the Earth system concomitantly, but sometimes also \r asynchronously, including propagation from one drought type to \r another (Brunner and Tallaksen, 2019). Because of this, drought \r cannot be characterized using a single universal definition (Lloyd\u0002Hughes, 2014) or directly measured based on a  single variable \r (SREX Chapter  3; Wilhite and Pulwarty, 2017). Drought can \r happen on a wide range of timescales - from 'flash droughts' on \r a scale of weeks, and characterized by a sudden onset and rapid \r intensification of drought conditions (Hunt et al., 2014; Otkin et al., \r 2018; Pendergrass et al., 2020) to multi-year or decadal rainfall \r deficits  - sometimes termed 'megadroughts' (see Glossary; Ault \r et al., 2014; Cook et al., 2016b; Garreaud et al., 2017). Droughts \r are often analysed using indices that are measures of drought \r [...]"


id: 46fca98b71a76996330c3950830cd704
created_at: 2023-04-19 13:22:46
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content:
"[...] severity, duration and frequency (Sections 8.3.1.6, 8.4.1.6, 12.3.2.6 \r and 12.3.2.7, and Table  11.A.1). There are many drought indices \r published in the scientific literature, as also highlighted in SREX \r (SREX Chapter 3). These can range from anomalies in single variables \r (e.g.,  precipitation, soil moisture, runoff, evapotranspiration) to \r indices combining different atmospheric variables. "


id: e35c6c4327a5e7adcbd106d9a61294ab
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index: climate_gpt_v2
vector_id: 37298
content:
"Lack of precipitation is generally the main factor controlling drought \r onset. There is high confidence that atmospheric dynamics, which \r vary on interannual, decadal and longer time scales, is the dominant \r contributor to variations in precipitation deficits in the majority of \r world regions (Dai, 2013; Miralles et al., 2014b; Seager and Hoerling, \r 2014; Burgman and Jang, 2015; Dong and Dai, 2015; Schubert et al., \r 2016; Raymond et al., 2018; Baek et al., 2019; Drumond et al., 2019; \r Herrera-Estrada et al., 2019; Gimeno et al., 2020; Mishra, 2020). \r Precipitation deficits are driven by dynamic mechanisms taking \r place on different spatial scales, including synoptic processes  - \r atmospheric rivers and extratropical cyclones, blocking and ridges \r (Section 11.7; Sousa et al., 2017), dominant large-scale circulation \r patterns (Kingston et al., 2015), and global ocean-atmosphere \r coupled patterns such as inter-decadal Pacific Oscillation (IPO), \r [...]"


id: 9c45c33f1dd89c213ade0ccb18a31cb2
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"[...] Atlantic Multi-decadal Oscillation (AMO) and El Nino-Southern \r Oscillation (ENSO; Dai and Zhao, 2017). These various mechanisms \r occur on different scales, are not independent, and substantially \r interact with one another. Also regional moisture recycling and land-\r atmosphere feedbacks play an important role for some precipitation \r anomalies (see below). "


id: e506f1d1b4fb747346eb970ba6c0b98b
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content:
"On subseasonal to decadal scales, temporal variations in AED are \r strongly controlled by circulation variability (Williams et al., 2014; \r Chai et al., 2018; Martens et al., 2018), but thermodynamic processes \r also play a  fundamental role and, under human-induced climate \r change, dominate the changes in AED. Atmospheric warming due \r to increased atmospheric CO2 concentrations increases AED by \r means of enhanced VPD in the absence of other influences (Scheff \r and Frierson, 2015). Because of the greater warming over land than \r over oceans (Sections 2.3.1.1 and 11.3), the saturation pressure of \r water vapour increases more over land than over oceans; oceanic \r air masses advected over land thus contain insufficient water vapour \r to keep pace with the greater increase in saturation vapour pressure \r over land (Sherwood and Fu, 2014; Byrne and O'Gorman, 2018; \r Findell et al., 2019). Land-atmosphere feedbacks are also important \r [...]"


id: de40a5b00bc7241c5b80293f2b12981d
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vector_id: 36362
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"[...] in affecting atmospheric moisture content and temperature, with \r resulting effects on relative humidity and VPD (Box 11.1; Berg et al., \r 2016; Haslinger et al., 2019; S. Zhou et al., 2019)."


id: c07ab26fd4ef8d7e2e21847c0d8f80cd
created_at: 2023-04-19 13:22:46
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"Atmospheric evaporative demand (AED) quantifies the maximum \r amount of actual evapotranspiration (ET) that can happen from land \r surfaces if they are not limited by water availability (Table 11.A.1). \r AED is affected by radiative and aerodynamic components. For this \r reason, the atmospheric dryness, often quantified with the relative \r humidity or the vapour pressure deficit (VPD), is not equivalent to \r the AED, as other variables are also highly relevant, including solar \r radiation and wind speed (Hobbins et al., 2012; McVicar et al., 2012a; \r Sheffield et al., 2012). AED can be estimated using different methods \r (McMahon et al., 2013), and those solely based on air temperature \r (e.g., Hargreaves, Thornthwaite) usually overestimate it in terms of \r magnitude and temporal trends (Sheffield et al., 2012), in particular, \r in the context of substantial background warming. Physically-based \r combination methods such as the Penman-Monteith equation are \r [...]"


id: f252bc11231fad71129c3fcf0684882f
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"[...] more adequate and recommended since 1998 by the United Nations \r Food and Agriculture Oganization (Pereira et al., 2015). For this reason, \r the assessment of this Chapter, when considering atmospheric\u0002based drought indices, only includes AED estimates using the latter \r (see also Section 11.9). AED is generally higher than ET, since AED \r represents an upper bound for ET. Hence, an AED increase does not \r necessarily lead to increased ET (Milly and Dunne, 2016), in particular \r under drought conditions given soil moisture limitation (Bonan et al., \r 2014; Berg et al., 2016; Konings et al., 2017; Stocker et al., 2018). \r In general, AED is highest in regions where ET is lowest (e.g., desert \r areas), further illustrating the decoupling between the two variables \r under limited soil moisture. "


id: c8de23bcd3c93cdbaaf2e4f3e842e1ec
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content:
"Soil moisture shows an important correlation with precipitation \r variability (Khong et al., 2015; Seager et al., 2019), but ET also plays \r a substantial role in further depleting moisture from soils, in particular \r in humid regions during periods of precipitation deficits (Teuling \r et al., 2013; Padron et al., 2020). In addition, soil moisture plays \r a role in drought self-intensification under dry conditions in which ET \r is decreased and leads to higher AED (Miralles et al., 2019), an effect \r that can also contribute to triggering flash droughts (Otkin et al., \r 2016, 2018; DeAngelis et al., 2020; Pendergrass et al., 2020). If soil \r moisture becomes limited, ET is reduced, which may decrease the \r rate of soil drying, but can also lead to further atmospheric dryness \r through various feedback loops (Seneviratne et al., 2010; Miralles \r et al., 2014a, 2019; Teuling, 2018; Vogel et al., 2018; S. Zhou et al., \r [...]"


id: b588b94204c61d1f36d9125579c9a383
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"[...] 2019; Liu et al., 2020). The process is complex since vegetation cover \r plays a role in modulating albedo and in providing access to deeper \r stores of water (both in the soil and groundwater). Also, changes in \r land cover and in plant phenology may alter ET (Sterling et al., 2013; \r Woodward et al., 2014; Frank et al., 2015; Doll et al., 2016; Ukkola \r et al., 2016; Trancoso et al., 2017; Hao et al., 2019; Lian et al., 2020). \r Snow depth has strong and direct impacts on soil moisture in many \r systems (Gergel et al., 2017; Williams et al., 2020). "


id: 18feb5c3e6af498c5f5a657c8f5f8d37
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content:
"(Anderegg et al., 2012, 2016; Rowland et al., 2015). Also carbon \r assimilation by plants strongly depends on soil moisture (Hartzell \r et al., 2017), with implications for carbon starvation and plant \r dying if soil moisture deficits are prolonged (Sevanto et al., 2014). \r These mechanisms explain that soil moisture deficits are usually \r more relevant than AED excess to explain gross primary production \r anomalies and vegetation stress, mostly in sub-humid and semi-arid \r regions (Stocker et al., 2018; Liu et al., 2020). High CO2 concentrations \r are shown to potentially decrease plant ET and increase plant water\u0002use efficiency, affecting soil moisture levels, but this effect interacts \r with other CO2 physiological and radiative effects (Section 11.6.5.2 \r and Cross-Chapter Box 5.1), and has less relevance under low soil \r moisture (Morgan et al., 2011; Z. Xu et al., 2016; Nackley et al., 2018; \r Diksaityte et al., 2019). ESMs represent both surface (around 10cm) \r [...]"


id: 4ff476bf4d8cd3f788ce63e054851be6
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vector_id: 11357
content:
"[...] and total column soil moisture, whereby total soil moisture is of more \r direct relevance for root water uptake, in particular by trees. There is \r evidence that surface soil moisture projections are substantially drier \r than total soil moisture projections, and may overestimate drying of \r relevance for most vegetation (Berg et al., 2017a)."


id: ba17280974f016341709b07bfdc133c5
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vector_id: 29963
content:
"Although there are many atmospheric-based drought indices, two are \r assessed in this chapter: the Palmer Drought Severity Index (PDSI) and \r the Standardized Precipitation Evapotranspiration Index (SPEI). The \r PDSI has been widely used to monitor and quantify drought severity \r (Dai et al., 2018), but is affected by some constraints (SREX Chapter 3; \r Mukherjee et al., 2018a). Although the calculation of the PDSI is based \r on a soil water budget, the PDSI is essentially a climate drought index \r that mostly responds to the precipitation and the AED (van der Schrier \r et al., 2013; Vicente-Serrano et al., 2015; Dai et al., 2018). The SPEI also \r combines precipitation and AED, being equally sensitive to these two \r variables (Vicente-Serrano et al., 2015). The SPEI is more sensitive to \r AED than the PDSI (Cook et al., 2014a; Vicente-Serrano et al., 2015), \r although under humid and normal precipitation conditions, the effects \r [...]"


id: 97efe839a3560944a6c521b2137b24bf
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content:
"[...] of AED on the SPEI are small (Tomas-Burguera et al., 2020). Given \r the limitations associated with temperature-based AED estimates \r (Section  11.6.1.2), only studies using the Penman-Monteith-based \r SPEI and PDSI (hereafter SPEI-PM and PDSI-PM) are considered in this \r assessment and in the regional tables in Section 11.9."


id: 8ef9ad4ddc44ac4a4740608cee9592f0
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"Drivers of streamflow and surface water deficits are complex \r and strongly depend on the hydrological system analysed \r (e.g., streamflows in the headwaters, medium course of the rivers, \r groundwater, highly regulated hydrological basins). Soil hydrological \r processes, which control the propagation of meteorological droughts \r throughout different parts of the hydrological cycle (Van Loon and \r Van Lanen, 2012), are spatially and temporally complex (Herrera\u0002Estrada et al., 2017; S. Huang et al., 2017b) and difficult to quantify \r (Van Lanen et al., 2016; Apurv et al., 2017; Caillouet et al., 2017; \r Konapala and Mishra, 2017; Hasan et al., 2019). The physiographic \r characteristics of the basins also affect how droughts propagate \r throughout the hydrological cycle (Van Loon and Van Lanen, 2012; \r Van Lanen et al., 2013; Van Loon, 2015; Konapala and Mishra, 2020; \r Veettil and Mishra, 2020). In addition, the assessment of groundwater \r [...]"


id: bb355b7ba728e2e9fdded4a0aa0a055b
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"[...] deficits is very difficult given the complexity of processes that \r involve natural and human-driven feedbacks and interactions with \r the climate system (Taylor et al., 2013). Streamflow and surface \r water deficits are affected by land cover, groundwater and soil \r characteristics (Van Lanen et al., 2013; Van Loon and Laaha, 2015; \r Barker et al., 2016; Tijdeman et al., 2018), as well as human activities \r (water management and demand, damming) and land-use changes \r (Section 11.6.4.3; Van Loon et al., 2016; He et al., 2017; Veldkamp \r et al., 2017; J. Wu et al., 2018; Y. Xu et al., 2019; Jehanzaib et al., \r 2020). Finally, snow and glaciers are relevant for water resources \r in some regions. For instance, warming affects snowpack levels \r (Dierauer et al., 2019; Huning and AghaKouchak, 2020), as well as \r the timing of snow melt, thus potentially affecting the seasonality \r and magnitude of low flows (Barnhart et al., 2016)."


id: bc13f781b03a95702cb44e6c72363f78
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"estimates provide an indication of possible trends in the influence of \r AED on drought. Given the observed global temperature increases \r (Sections 2.3.1.1 and 11.3) and dominant decrease in relative \r humidity over land areas (Simmons et al., 2010; Willett et al., 2014), \r VPD has increased globally (Barkhordarian et al., 2019; Yuan et al., \r 2019). Pan evaporation has increased as a  consequence of VPD \r changes in several AR6 regions, such as East Asia (Li et al., 2013; \r Z.  Sun et al., 2018; M.-Z. Yang et al., 2018), Western and Central \r Europe (Mozny et al., 2020), the Mediterranean, (Azorin-Molina et al., \r 2015) and Central and Southern Australia (Stephens et al., 2018). \r Nevertheless, there is an important regional variability in observed \r trends, and in other AR6 regions pan evaporation has decreased - \r for example, in North Central America (Brena-Naranjo et al., 2017) \r and in the Tibetan Plateau (C. Zhang et al., 2018)). Physical models \r [...]"


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"[...] also show an important regional diversity, with an increase in New \r Zealand (Salinger and Porteous, 2014) and the Mediterranean (Gocic \r and Trajkovic, 2014; Azorin-Molina et al., 2015; Piticar et al., 2016), \r a decrease in South Asia (Jhajharia et al., 2015), and strong spatial \r variability in North America (Seager et al., 2015b). This variability is \r driven by the role of other meteorological variables affecting AED. \r Changes in solar radiation as a consequence of solar dimming and \r brightening may affect trends (Section  7.2.2.2; Kambezidis et al., \r 2012; Wang and Yang, 2014; Sanchez-Lorenzo et al., 2015). Wind \r speed is also relevant (McVicar et al., 2012b), and studies suggest \r a reduction of the wind speed in some regions (Z. Zhang et al., 2019b) \r that could compensate the role of the VPD increase. Nevertheless, the \r VPD trend seems to dominate the overall AED trends, compared to \r the effects of trends in wind speed and solar radiation (Wang et al., \r [...]"


id: cb4e02a7d73742e1e870fb9377921c26
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"Strong precipitation deficits have been recorded in recent decades \r in the Amazon (2005, 2010), south-western China (2009-2010), \r south-western North America (2011-2014), Australia (1997-2009), \r California (2014), the middle East (2012-2016), Chile (2010-2015), \r the Great Horn of Africa (2011), among others (van Dijk et al., 2013; \r Mann and Gleick, 2015; Rowell et al., 2015; Marengo and Espinoza, \r 2016; Dai and Zhao, 2017; Garreaud et al., 2017, 2020; Marengo \r et al., 2017; Brito et al., 2018; Cook et al., 2018). Global studies \r generally show no significant trends in SPI time series (Orlowsky \r and Seneviratne, 2013; Spinoni et al., 2014), and in derived drought \r frequency and severity data (Spinoni et al., 2019), with very few \r regional exceptions (Section  11.9 and Figure  11.17). Long-term \r decreases in precipitation are found in some AR6 regions in Africa \r (Central Africa and East Southern Africa), and several regions in South \r [...]"


id: 89b579ada21d18a98933d4313bc38bb
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 21401
content:
"[...] America (North-Eastern South America, South American Monsoon, \r South-Western South America, and Southern South America) \r (Section 11.9). Evidence of precipitation-based drying trends is also \r found in Western Africa, consistent with studies based on CDD trends \r (Figure 11.17; Chaney et al., 2014; Donat et al., 2014b; Barry et al., \r 2018; Dunn et al., 2020), however, there is a partial recovery of the \r rainfall trends since the 1980s in this region (Section  10.4.2.1). \r Some AR6 regions show a  decrease in meteorological drought, \r including Northern Australia, Central Australia, Northern Europe \r and Central North America (Section 11.9). Other regions either do \r not show substantial trends in long-term meteorological drought, or \r they display mixed signals depending on the considered time frame \r and sub-regions, such as in Southern Australia (Gallant et al., 2013; \r Delworth and Zeng, 2014; Alexander and Arblaster, 2017; Spinoni \r [...]"


id: f7f2f919417664da6e848cb46b0cec78
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
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content:
"[...] et al., 2019; Dunn et al., 2020; Rauniyar and Power, 2020) and the \r Mediterranean (Camuffo et al., 2013; Gudmundsson and Seneviratne, \r 2016; Spinoni et al., 2017; Stagge et al., 2017; Caloiero et al., 2018; \r Pena-Angulo et al., 2020b; see also Section 11.9 and Atlas.8.2)."


id: 757313f510aa6b4270f0feb081fb35da
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 17892
content:
"There are limited long-term measurements of soil moisture from \r ground observations (Dorigo et al., 2011; Qiu et al., 2016; Quiring \r et al., 2016), which impedes their use in the analysis of trends. Among \r the few existing observational studies covering at least two decades, \r several studies have investigated trends in ground soil moisture in \r East Asia (Section 11.9; Chen and Sun, 2015b; Liu et al., 2015; Qiu \r et al., 2016). Alternatively, microwave-based satellite measurements \r of surface soil moisture have also been used to analyse trends (Dorigo \r et al., 2012; Jia et al., 2018). Although there is regional evidence that \r microwave-based soil moisture estimates can capture well drying \r trends in comparison with ground soil moisture observations (Jia \r et al., 2018), there is only medium confidence in the derived trends, \r since satellite soil moisture data are affected by inhomogeneities \r (Dorigo et al., 2015; Rodell et al., 2018; Preimesberger et al., 2021). \r [...]"


id: 64d5176f1068af9d993bab0e4e885034
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 15013
content:
"[...] Furthermore, microwave-based satellites only sense surface soil \r moisture, which differs from root-zone soil moisture (Berg et al., \r 2017a), although relationships can be derived between the two \r (Brocca et al., 2011). Several studies have also analysed long-term \r soil moisture time series from observation-driven land-surface \r or hydrological models, including land-based reanalysis products \r (Albergel et al., 2013; Jia et al., 2018; Gu et al., 2019b; Markonis \r et al., 2021). Such models have also been used to assess changes in \r land water availability, estimated as precipitation minus ET, which \r is equal to the sum of soil moisture and runoff (Greve et al., 2014; \r Padron et al., 2020). "


id: b7b9ee66f7ae64e55e4ce7454b7b17b3
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 29544
content:
"Overall, evidence from global studies suggests that several land \r regions have been affected by increased soil moisture drying or water \r balance drying in past decades, despite some spread among products \r (Albergel et al., 2013; Greve et al., 2014; Gu et al., 2019b; Padron \r et al., 2020). Drying has not only occurred in dry regions but also in \r humid regions (Greve et al., 2014). Some studies have specifically \r addressed changes in soil moisture at regional scale (Section 11.9). \r For AR6 regions, several studies suggest an increase in the frequency \r and areal extent of soil moisture deficits, with examples in East Asia \r (Cheng et al., 2015; Y. Qin et al., 2015; Jia et al., 2018), Western and \r Central Europe (Trnka et al., 2015b), and the Mediterranean (Hanel \r et al., 2018; Moravec et al., 2019; Markonis et al., 2021). Nonetheless, \r some analyses also show no long-term trends in soil drying in some \r AR6 regions - for example, in Eastern North America (Park Williams \r [...]"


id: 984fd0edd444f9581fa5cb610a44a81
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 24005
content:
"[...] et al., 2017) and Central North America (Seager et al., 2019), as well \r as in North Eastern Africa (Kew et al., 2021). The soil moisture drying \r trends identified in both global and regional studies are generally \r related to increases in ET (associated with higher AED) rather than \r decreases in precipitation, as identified on global land for trends in \r water balance in the dry season (Padron et al., 2020), as well as for \r some regions (Teuling et al., 2013; Cheng et al., 2015; Trnka et al., \r 2015a; van Der Linden et al., 2019; X. Li et al., 2020). "


id: f9a8e86f3d196c8c9157be0826098448
created_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 41287
content:
"flow reconstructions (Caillouet et al., 2017; Barker et al., 2019) and \r observations (Vicente-Serrano et al., 2019). In the Mediterranean \r region, there is high confidence in hydrological drought intensification \r (Section  11.9; Giuntoli et al., 2013; Lorenzo-Lacruz et al., 2013; \r Gudmundsson et al., 2019). In south-eastern South America there \r is a  decrease in the severity of hydrological droughts (Rivera and \r Penalba, 2018). In North America, depending on the methods, datasets \r and study periods, there are differences between studies that suggest \r an increase (Shukla et al., 2015; Udall and Overpeck, 2017) versus \r a  decrease in hydrological drought frequency (Mo and Lettenmaier, \r 2018), but in general there is strong spatial variability (Poshtiri and \r Pal, 2016). Streamflow observation reference networks of near-natural \r catchments have also been used to isolate the effect of climate trends \r on hydrological drought trends in a few regions, but these show limited \r [...]"


id: ae6117c90f97e56fb8da2a87611cea52
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 27908
content:
"[...] trends in Northern Europe and Western and Central Europe (Stahl et al., \r 2010; Bard et al., 2015; Harrigan et al., 2018), North America (Dudley \r et al., 2020) and most of Australia, with the exception of Eastern and \r Southern Australia (X.S. Zhang et al., 2016). Given the low availability \r of observations, there are few studies analysing trends of drought \r severity in the groundwater. Nevertheless, some studies suggest \r a noticeable response of groundwater droughts to climate variability \r (Lorenzo-Lacruz et al., 2017) and increased drought frequency and \r severity associated with warming, probably as a  consequence of \r enhanced ET induced by higher AED (Maxwell and Condon, 2016). This \r is supported by studies in Northern Europe (Bloomfield et al., 2019) \r and North America (Condon et al., 2020)."


id: e4e2547404fe331e7ae1abf4120c5311
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 37554
content:
"trends in comparison with observations (Nasrollahi et al., 2015). \r Meteorological drought trends in the CMIP5 ensemble showed \r substantial disagreements compared with observations (Orlowsky \r and Seneviratne, 2013; Knutson and Zeng, 2018) including \r a  tendency to overestimate drying, in particular in mid- to high \r latitudes (Knutson and Zeng, 2018). The CMIP6 models display \r a better performance in reproducing long-term precipitation trends \r or seasonal dynamics in some studies in Southern South America \r (Rivera and Arnould, 2020), East Asia (Xin et al., 2020), southern \r Asia (Gusain et al., 2020), and south-western Europe (Pena-Angulo \r et al., 2020b), but there is still too limited evidence to allow for \r an assessment of possible differences in performance between \r CMIP5 and CMIP6. Furthermore, ESMs are generally found to \r underestimate the severity of precipitation deficits and the dry day \r frequencies in comparison to observations (Fantini et al., 2018; \r [...]"


id: e61db49befd5845cb46a3ffd43821727
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 37784
content:
"[...] Ukkola et al., 2018). This is probably related to shortcomings in \r the simulation of persistent weather events in the mid-latitudes \r (Section  10.3.3.3). ESMs also show a  tendency to underestimate \r precipitation-based drought persistence at monthly to decadal \r time scales (Ault et al., 2014; Moon et al., 2018). The overall inter\u0002model spread in the projected frequency of precipitation deficits is \r also substantial (Touma et al., 2015; Zhao et al., 2016; Engstrom \r and Keellings, 2018). Moreover, there are spatial differences in the \r spread, which is higher in the regions where enhanced drought \r conditions are projected and under high-emissions scenarios \r (Orlowsky and Seneviratne, 2013). Nonetheless, some event \r attribution studies have concluded that droughts at regional scales \r can be adequately simulated by some climate models (Schaller \r et al., 2016; Otto et al., 2018c)."


id: cacca615897b9f008bf26437f578c340
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 33021
content:
"increase the severity of agricultural and ecological droughts compared \r to meteorological droughts (Garcia-Herrera et al., 2019; Williams et al., \r 2020), reduce soil moisture during the dry season (Padron et al., 2020), \r increase plant water stress (Allen et al., 2015; Grossiord et al., 2020; \r Solander et al., 2020) and trigger more severe forest fires (Abatzoglou \r and Williams, 2016; Turco et al., 2019; Nolan et al., 2020). A number \r of regional studies based on these drought indices have also shown \r stronger drying trends in comparison to trends in precipitation-based \r indices in the following AR6 regions (see also Section  11.9): NSA \r (R. Fu et al., 2013; Marengo and Espinoza, 2016), SCA (Hidalgo et al., \r 2017), WCA (Tabari and Aghajanloo, 2013; Sharafati et al., 2020), SAS \r (Niranjan Kumar et al., 2013), NEAF (Zeleke et al., 2017), WSAF (Edossa \r et al., 2016), NWN and NEN (Bonsal et al., 2013), EAS (Yu et al., 2014; \r [...]"


id: 59a2e8be72ec237e3f6d9ab41f12da91
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 13009
content:
"[...] Chen and Sun, 2015b; L. Li et al., 2020; Liang et al., 2020; Z. Wu et al., \r 2020) and MED (Kelley et al., 2015; Stagge et al., 2017; Gonzalez\u0002Hidalgo et al., 2018; Mathbout et al., 2018a). "


id: 622b06b6333e82b6de299ca4edaba50e
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 14544
content:
"Few AR6 regions show observed increases in meteorological drought \r (Section  11.9), mostly in Africa and South America (NES: high \r confidence; WAF, CAF, ESAF, SAM, SWS, SSA, SAS: medium confidence); \r a  few others show a  decrease (WSB, ESB, NAU, CAU, NEU, CNA: \r medium confidence). There are stronger signals indicating observed \r increases in agricultural and ecological drought (Section 11.9), which \r highlights the role of increased ET, driven by increased AED, for these \r trends (Sections 11.6.2.3 and11.6.2.5). Past increases in agricultural \r and ecological droughts are found on all continents and several \r regions (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, WCE, \r NES: medium confidence), while decreases are found only in one \r AR6 region (NAU: medium confidence). The more limited availability \r of datasets makes it more difficult to assess historical trends in \r hydrological drought at regional scale (Section  11.9). Increasing \r [...]"


id: 76b059a5899bd0a52225c153d899532a
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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index: climate_gpt_v2
vector_id: 18096
content:
"[...] (MED: high confidence; WAF, EAS, SAU: medium confidence) and \r decreasing (NEU, SES: medium confidence) trends in hydrological \r droughts have only been observed in a few regions."


id: 4d16e69862027af59a826f3272ae1c09
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 10833
content:
"Overall, there are contrasting results on the performance of land \r surface models and climate models in representing soil moisture. \r Some studies suggest that soil moisture anomalies are well captured \r by land surface models driven with observation-based forcing \r (Dirmeyer et al., 2006; Albergel et al., 2013; Xia et al., 2014; Balsamo \r et al., 2015; Reichle et al., 2017; Spennemann et al., 2020), but other \r studies report limited agreement in the representation of interannual \r soil moisture variability (Stillman et al., 2016; Yuan and Quiring, 2017; \r Ford and Quiring, 2019) and noticeable seasonal differences in model \r skill in some regions (Xia et al., 2014, 2015). Models with good skill \r can nonetheless display biases in absolute soil moisture (Xia et al., \r 2014; Gu et al., 2019a), but these are not necessarily of relevance \r for the simulation of surface water fluxes and drought anomalies \r (Koster et al., 2009). There is also substantial inter-model spread \r [...]"


id: bcae911964bee3372b2921ae18ee986f
created_at: 2023-04-19 13:22:46
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content_type: text
index: climate_gpt_v2
vector_id: 30443
content:
"[...] (Albergel et al., 2013), particularly for the root-zone soil moisture \r (Berg et al., 2017a). "


id: c3e2c7f16fbef95de8858bd33bf2082c
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 31744
content:
"Streamflow and groundwater are not directly simulated by ESMs, \r which only simulate runoff, but they are generally represented in \r hydrological models (Prudhomme et al., 2014; Giuntoli et al., 2015), \r which are typically driven in a stand-alone manner by observed or \r simulated climate forcing. The simulation of hydrological deficits is \r much more problematic than the simulation of mean streamflow or \r peak flows (Fundel et al., 2013; Stoelzle et al., 2013; Velazquez et al., \r 2013; Staudinger et al., 2015), since models tend to be too responsive \r to the climate forcing and do not satisfactorily capture low flows \r (Tallaksen and Stahl, 2014). Simulations of hydrological drought \r metrics show uncertainties related to the contribution of both GCMs \r and hydrological models (Bosshard et al., 2013; Giuntoli et al., 2015; \r Samaniego et al., 2017; Vetter et al., 2017), but hydrological models \r forced by the same climate input data also show a  large spread \r [...]"


id: 667673579115be9a2aa63d352520f84
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 15290
content:
"[...] (van Huijgevoort et al., 2013; Ukkola et al., 2018). At the catchment \r scale, the hydrological model uncertainty is higher than both GCM \r and downscaling uncertainty (Vidal et al., 2016), and the hydrological \r models show issues in representing drought propagation throughout \r the hydrological cycle (Barella-Ortiz and Quintana Segui, 2019). \r A study on the evaluation of streamflow droughts in seven global \r (hydrological and land surface) models compared with observations \r in near-natural catchments of Europe showed a substantial spread \r among models, an overestimation of the number of drought events, \r and an underestimation of drought duration and drought-affected \r area (Tallaksen and Stahl, 2014). "


id: 92317f91d5784ee996cd281a5c6f19ad
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 22938
content:
"Regarding the performance of regional and global climate models, an \r evaluation of an ensemble of RCM simulations for Europe (Stegehuis \r et al., 2013) shows that these models display overly strong drying \r in early summer, resulting in an excessive decrease of latent heat \r fluxes, with potential implications for more severe droughts in dry \r environments (Teuling, 2018; van Der Linden et al., 2019). Compared \r with a range of observational ET estimates, CMIP5 models show an \r overestimation of ET on annual scale, but an ET underestimation in \r boreal summer in many Northern Hemisphere mid-latitude regions, \r also suggesting a  tendency towards excessive soil drying (Mueller \r and Seneviratne, 2014), consistent with identified biases in soil\u0002moisture-temperature coupling (Donat et al., 2018; Vogel et al., \r 2018; Selten et al., 2020). Land surface models used in ESMs display \r a bias in their representation of the sensitivity of interannual land \r [...]"


id: 1df4375bf365d60452724e960f3b67
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 2460
content:
"[...] carbon uptake to soil moisture conditions, which appears related to \r a limited range of soil moisture variations compared to observations \r (Humphrey et al., 2018)."


id: ef708e656af54b085b82589cee6d4a63
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 39499
content:
"Attribution studies for recent meteorological drought events are \r available for various regions. In Western and Central Europe, a multi\u0002method and multi-model attribution study on the 2015 Central \r European drought did not find conclusive evidence for whether \r human-induced climate change was a driver of the rainfall deficit, \r as the results depended on model and method used (Hauser et al., \r 2017). In the Mediterranean region, a human contribution was found \r in the case of the 2014 meteorological drought in the southern Levant \r based on a single-model study (Bergaoui et al., 2015). In Africa, there \r is some evidence of a  contribution of human emissions to single \r meteorological drought events, such as the 2015-2017 southern \r African drought (Funk et al., 2018a; Yuan et al., 2018a; Pascale et al., \r 2020), and the three-year (2015-2017) drought in the western Cape \r Town region of South Africa (Otto et al., 2018c). An attributable \r [...]"


id: 247cb5f21857236f657594b47e121388
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 3610
content:
"[...] signal was not found in droughts that occurred in different years \r with different spatial extents in the last decade in North and South \r Eastern Africa (Marthews et al., 2015; Uhe et al., 2017; Otto et al., \r 2018a; Philip et al., 2018b; Kew et al., 2021). However, an attributable \r increase in 2011 long rain failure was identified (Lott et al., 2013). \r Further studies have attributed some African meteorological drought \r events to large-scale modes of variability, such as the strong 2015 \r El Nino (Box 11.4; Philip et al., 2018b) and increased SSTs overall \r (Funk et al., 2015a, 2018b). Natural variability was dominant in \r the California droughts of 2011-2012 to 2013-2014 (Seager et al., \r 2015a). In Asia, no climate change signal was found in the record dry \r spell over Singapore and Malaysia in 2014 (Mcbride et al., 2015) or \r the drought in central south-west Asia in 2013-2014 (Barlow and \r Hoell, 2015). Nevertheless, the South East Asia drought of 2015 has \r [...]"


id: 82dab5e56e1a29a2c00f1771e96db725
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 20150
content:
"[...] been attributed to anthropogenic warming effects (Shiogama et al., \r 2020). Recent droughts occurring in South America, specifically in \r the southern Amazon region in 2010 (Shiogama et al., 2013) and \r in north-east South America in 2014 (Otto et al., 2015b) and 2016 \r (Martins et al., 2018) were not attributed to anthropogenic climate \r change. Nevertheless, the central Chile drought between 2010 \r and 2018 has been suggested to be partly associated to global \r warming (Boisier et al., 2016; Garreaud et al., 2020). The 2013 New \r Zealand meteorological drought was attributed to human influence \r by Harrington et al. (2014, 2016) based on fully coupled CMIP5 \r models, but no corresponding change in the dry end of simulated \r precipitation from a stand-alone atmospheric model was found by \r Angelil et al. (2017).   \r Event attribution studies also highlight a  complex interplay of \r anthropogenic and non-anthropogenic climatological factors for "


id: 64de0f21e5473e033fe19a1bfa1b9ea0
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 15019
content:
"There are only two AR6 regions where there is at least medium \r confidence that human-induced climate change has contributed \r to changes in meteorological droughts (Section  11.9). In South\u0002Western South America, there is medium confidence that human\u0002induced climate change has contributed to an increase in \r meteorological droughts (Boisier et al., 2016; Garreaud et al., 2020), \r while in Northern Europe, there is medium confidence that it has \r contributed to a decrease in meteorological droughts (Section 11.9; \r Gudmundsson and Seneviratne, 2016). In other AR6 regions, there \r is inconclusive evidence in the attribution of long-term trends, but \r a human contribution to single meteorological events or sub-regional \r trends has been identified in some instances (Section 11.9; see also \r below). In the Mediterranean region, some studies have identified \r a  precipitation decline or increase in meteorological drought \r [...]"


id: 4dd2ee11ba6d29c719a8cf0afde67453
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 10962
content:
"[...] probability for time frames since the early or mid 20th century, and \r a possible human contribution to these trends (Hoerling et al., 2012; \r Gudmundsson and Seneviratne, 2016; Knutson and Zeng, 2018), also \r on sub-regional scale in Syria from 1930 to 2010 (Kelley et al., 2015). \r On the contrary, other studies have not identified precipitation and \r meteorological drought trends in the region for the long term (Camuffo \r et al., 2013; Paulo et al., 2016; Vicente-Serrano et al., 2021) and also \r from the mid 20th century (Norrant and Douguedroit, 2006; Stagge \r et al., 2017). There is evidence of substantial internal variability in \r long-term precipitation trends in the region (Section 11.6.2.1), which \r limits the attribution of human influence on variability and trends \r of meteorological droughts from observational records (Kelley et al., \r 2012; Pena-Angulo et al., 2020b). In addition, there are important \r sub-regional trends showing mixed signals (Section 11.9; MedECC, \r [...]"


id: 4093396d4c45ebe19e649e873045cd84
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 8550
content:
"In other regions, the influence of human water uses can be more \r important to explain hydrological drought trends (Y. Liu et al., 2016; \r Mohammed and Scholz, 2016). There is medium confidence that \r human-induced climate change has contributed to an increase of \r hydrological droughts in the Mediterranean (Giuntoli et al., 2013; \r Vicente-Serrano et al., 2014; Gudmundsson et al., 2017), but also \r medium confidence that changes in land use and terrestrial water \r management contributed to these trends (Section 11.9; Teuling et al., \r 2019; Vicente-Serrano et al., 2019). A  global study with a  single \r hydrological model estimated that human water consumption has \r intensified the magnitude of hydrological droughts by 20-40% \r over the last 50 years, and that the human water use contribution \r to hydrological droughts was more important than climatic factors \r in the Mediterranean, and central USA, as well as in parts of Brazil \r [...]"


id: 77a0157a4bc7cf185076d48224c95e6e
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 18251
content:
"[...] (Wada et al., 2013). However, Gudmundsson et al. (2021) concluded \r that the contribution of human water use is smaller than that of \r anthropogenic climate change to explain spatial differences in the \r trends of low flows based on a multi-model analysis. There is still \r limited evidence and thus low confidence in assessing these trends at \r the scale of single regions, with few exceptions (Section 11.9)."


id: f291b5ac672ebfdc0a72b06a67df0f59
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 40077
content:
"There is a  growing number of studies on the detection and \r attribution of long-term changes in soil moisture deficits. Mueller \r and Zhang (2016) concluded that anthropogenic forcing contributed \r significantly to soil moisture drying in the warm season in the \r Northern Hemisphere from 1951 to 2005 and also led to an increase \r in the land surface area affected by soil moisture deficits, which can \r be reproduced by CMIP5 models only if anthropogenic forcings are \r involved. Gu et al. (2019b) similarly identified a  global-scale soil \r moisture drying tendency in land surface model data from the Global \r Land Data Assimilation System 2 over the time frame 1948-2005, \r which was attributed to anthropogenic forcing based on evaluation \r with CMIP5 models using optimal fingerprinting. Padron et al. (2019) \r analysed long-term reconstructed and CMIP5 simulated dry season \r water availability, defined as precipitation minus ET (i.e., equivalent \r [...]"


id: 6a3474eceb42fda181b6888ca82079f5
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 15963
content:
"[...] to soil moisture and runoff availability), also related to agricultural \r and ecological droughts. They found an intensification of dry-season \r precipitation minus evapotranspiration deficits over a predominant \r fraction of the land area in the last three decades, which can only be \r explained by anthropogenic forcing and is mostly related to increases \r in ET. Similarly, Williams et al. (2020) concluded that human\u0002induced climate change contributed to the strong soil moisture \r deficits recorded in the last two decades in Western North America \r through VPD increases associated with higher air temperatures and \r lower air humidity. There are few studies analysing the attribution \r of particular episodes of soil moisture deficits to anthropogenic \r influence. Nevertheless, the available modelling studies coincide \r in supporting an anthropogenic attribution associated with more \r extreme temperatures, exacerbating AED and increasing ET, and \r [...]"


id: 2c8d13d8963e9957b74025f01fb615d7
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 5105
content:
"[...] thus depleting soil moisture, as observed in southern Europe in 2017 \r (Garcia-Herrera et al., 2019) and in Australia in 2018 (Lewis et al., \r 2020) and 2019 (van Oldenborgh et al., 2021), the latter event having \r strong implications in the propagation of widespread megafires \r (Nolan et al., 2020).  "


id: f0fe1ea8a73bfed27aaf2fc4f914e677
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 39767
content:
"Different studies using atmospheric-based drought indices suggest \r an attributable anthropogenic signal, characterized by the increased \r frequency and severity of droughts (Cook et al., 2018), associated to \r increased AED (Section 11.6.4.2). The majority of studies are based on \r the PDSI-PM. Williams et al. (2015) and Griffin and Anchukaitis (2014) \r concluded that increased AED has had an increased contribution to \r drought severity over the last decades, and played a dominant role in \r the intensification of the 2012-2014 drought in California. The same \r temporal pattern and physical mechanism was stressed by Z. Li et al. \r (2017) in central Asia. Marvel et al. (2019) compared tree ring-based \r reconstructions of the PDSI-PM over the past millennium with PDSI\u0002PM estimates based on output from CMIP5 models. The comparisons \r suggested a contribution of greenhouse gas forcing to the changes \r since the beginning of the 20th century, although characterized with \r [...]"


id: 291619d15ba02d8cbbf230356215014
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 4436
content:
"[...] temporal differences that could be driven by temporal variations \r in the aerosol forcing. This was in agreement with the dominant \r external forcings of aridification at global scale between 1950 and \r 2014 (Bonfils et al., 2020). In the Mediterranean region, there is \r medium confidence of drying attributable to antropogenic forcing \r as a consequence of the strong AED increase (Gocic and Trajkovic, \r 2014; Azorin-Molina et al., 2015; Liuzzo et al., 2016; Macek et al., \r 2018), which has enhanced the severity of drought events (Vicente\u0002Serrano et al., 2014; Stagge et al., 2017; Gonzalez-Hidalgo et al., "


id: 2c6a15d5c6415825e825844d26709602
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 5080
content:
"regional and global scales. The assessment of drought mechanisms \r under future climate change scenarios depends on the model used \r (Section  11.6.3). Moreover, uncertainties in drought projections are \r affected by the consideration of plant physiological responses to \r increasing atmospheric CO2 (Cross-Chapter Box 5.1; Milly and Dunne, \r 2016; Greve et al., 2019; Mankin et al., 2019; Yang et al., 2020), the \r role of soil-moisture-atmosphere feedbacks for changes in water \r balance and aridity (Berg et al., 2016; Zhou et al., 2021), and statistical \r issues related to considered drought time scales (Vicente-Serrano \r et al., 2020c). Nonetheless, the extensive literature available since AR5 \r allows a substantially more robust assessment of projected changes in \r droughts, also subdivided in different drought types (meteorological \r drought, agricultural and ecological drought, and hydrological drought). \r This includes assessments of projected changes in droughts, including \r [...]"


id: 3ee411a301d68e85c9c3a9cbbe620ac0
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 8250
content:
"[...] changes at 1.5degC, 2degC and 4degC of global warming, for all AR6 regions \r (Section 11.9). Projected changes show increases in drought frequency \r and intensity in several regions as function of global warming (high \r confidence). There are also substantial increases in drought hazard \r probability from 1.5degC to 2degC global warming and for further additional \r increments of global warming (high confidence) (Figures 11.18 and \r 11.19). These findings are based on both CMIP5 and CMIP6 analyses \r (Section 11.9; Wartenburger et al., 2017; Greve et al., 2018; L. Xu et al., \r 2019), and strengthen the conclusions of SR1.5 Chapter 3. "


id: a7aa7d6046b77e1d6d93bb435747a7
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 26712
content:
"The regional evidence on attribution for single AR6 regions generally \r shows low confidence for a human contribution to observed trends \r in meteorological droughts at regional scale, with few exceptions \r (Section 11.9). There is medium confidence that human influence has \r contributed to increases in agricultural and ecological droughts in \r the dry season in some regions and has led to an overall increase in the \r affected land area. At regional scales, there is medium confidence\r in a  contribution of human-induced climate change to increases \r in agricultural and ecological droughts in the Mediterranean and \r Western North America (Section 11.9). There is medium confidence\r that human-induced climate change has contributed to an increase \r in hydrological droughts in the Mediterranean region, but also \r medium confidence in contributions from other human influences, \r including water management and land use (Section  11.9). Several \r [...]"


id: e800477853a9fe47af2532b3af4ec224
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 38130
content:
"[...] meteorological and agricultural and ecological drought events have \r been attributed to human-induced climate change, even in regions \r where no long-term changes are detected (medium confidence). \r However, a lack of attribution to human-induced climate change has \r also been shown for some events (medium confidence)."


id: a6de5de3624792a2407166c8af3fd95f
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 26568
content:
"Studies based on CMIP5, CMIP6 and Coordinated Regional \r Climate Downscaling Experiment (CORDEX) projections show \r a consistent signal in the sign and spatial pattern of projections of \r precipitation deficits. Global studies based on these multi-model \r ensemble projections (Orlowsky and Seneviratne, 2013; Martin, \r 2018; Spinoni et al., 2020; Ukkola et al., 2020; Coppola et al., \r 2021b) show particularly strong signal-to-noise ratios for increasing \r meteorological droughts in the following AR6 regions: MED, ESAF, \r WSAF, SAU, CAU, NCA, SCA, NSA and NES (Section 11.9). There is also \r substantial evidence of changes in meteorological droughts at 1.5degC \r versus 2degC of global warming from global studies (Wartenburger \r et al., 2017; L. Xu et al., 2019). The patterns of projected changes \r in mean precipitation are consistent with the changes in the \r drought duration, but they are not consistent with the changes in \r [...]"


id: 7cb20de5a5c0df2bd4cceb32425e22b9
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 19133
content:
"[...] drought intensity (Ukkola et al., 2020). In general, CMIP6 projections \r suggest a stronger increase of the probability of precipitation deficits \r than CMIP5 projections (Cook et al., 2020; Ukkola et al., 2020). \r Projections for the number of CDDs in CMIP6 (Figure  11.19) for \r different levels of global warming relative to 1850-1900 show similar \r spatial patterns as projected precipitation deficits. The robustness of \r the patterns in projected precipitation deficits identified in the global \r studies is also consistent with results from regional studies (Giorgi \r et al., 2014; Marengo and Espinoza, 2016; Pinto et al., 2016; J. Huang \r et al., 2018; Maure et al., 2018; Nangombe et al., 2018; Tabari and \r Willems, 2018; Abiodun et al., 2019; Dosio et al., 2019). "


id: 36022d5c64d5fc68012c1488cac1bfdb
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 6751
content:
"In summary, human influence has contributed to increases in \r agricultural and ecological droughts in the dry season in some \r regions due to increases in evapotranspiration (medium confidence). \r The increases in evapotranspiration have been driven by increases in \r atmospheric evaporative demand induced by increased temperature, \r decreased relative humidity and increased net radiation over affected \r land areas (high confidence). There is low confidence that human \r influence has affected trends in meteorological droughts in most \r regions, but medium confidence that they have contributed to the \r severity of some single events. There is medium confidence that \r human-induced climate change has contributed to increasing trends \r in the probability or intensity of recent agricultural and ecological \r droughts, leading to an increase of the affected land area. Human\u0002induced climate change has contributed to global-scale change in \r [...]"


id: b1ffa57e0e35e2ecbbeb7e717177a4a2
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 28541
content:
"[...] low flow, but human water management and land-use changes are \r also important drivers (medium confidence)."


id: 5c255225e26f1e7d55d6e7dd28ffd693
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 13467
content:
"In Central America, projections suggest an increase in mid-summer \r meteorological drought (Imbach et al., 2018) and increased CDD \r (Chou et al., 2014a; Giorgi et al., 2014; Nakaegawa et al., 2014). In \r the Amazon, there is also a projected increase in dryness (Marengo \r and Espinoza, 2016), which is the combination of a projected increase \r in the frequency and geographic extent of meteorological drought in \r the eastern Amazon, and an opposite trend in the west (Duffy et al., \r 2015). In South-Western South America, there is a projected increase \r of CDD (Chou et al., 2014a; Giorgi et al., 2014) and in Chile, drying \r is projected to prevail (Boisier et al., 2018). In the South America \r monsoon region, an increase in CDD is projected (Chou et al., 2014a; \r Giorgi et al., 2014), but a decrease is projected in South-Eastern and \r Southern South America (Giorgi et al., 2014). In Central America, \r mid-summer meteorological drought is projected to intensify during \r [...]"


id: 5eac7fd021d22d6acf6e9288b9b0ac74
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 13944
content:
"Several studies suggest that increasing atmospheric CO2 could \r lead to reduced leaf stomatal conductance, which would increase \r water-use efficiency and reduce plant water needs, thus limiting ET \r (Cross-Chapter Box 5.1; Roderick et al., 2015; Milly and Dunne, 2016; \r Swann et al., 2016; Greve et al., 2017; Scheff et al., 2017; Lemordant \r et al., 2018; Swann, 2018). The implemention of a  CO2-dependent \r land resistance parameter has been suggested for the estimation \r of AED (Yang et al., 2019). Nevertheless, there are other relevant \r mechanisms, as soil moisture deficits and VPD also play an important \r role in the control of the leaf stomatal conductance (Z. Xu et al., 2016; \r Menezes-Silva et al., 2019; Grossiord et al., 2020), and a  number \r of ecophysiological and anatomical processes affect the response \r of plant physiology under higher atmospheric CO2 concentrations \r (Cross-Chapter Box  5.1; Mankin et al., 2019; Menezes-Silva et al., \r [...]"


id: 703ee17ae96ecef9830847313d2e7055
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 17003
content:
"[...] 2019). The benefits of the atmospheric CO2 for plant stress and \r agricultural and ecological droughts would be minimal precisely \r during dry periods given stomatal closure in response to limited \r soil moisture (Allen et al., 2015; Z. Xu et al., 2016). In addition, CO2\r effects on plant stomatal conductance could not entirely compensate \r for the increased demand associated with warming (Liu and Sun, \r 2017); in large tropical and subtropical regions (e.g., southern Africa, \r the Amazon, the Mediterranean and southern North America), AED \r is projected to increase, even considering the possible CO2 effects \r on land resistance (Vicente-Serrano et al., 2020a). Moreover, these \r CO2 effects would not affect the direct evaporation from soil and \r water bodies, which is very relevant in the reservoirs of warm areas \r (Friedrich et al., 2018). Because of these uncertainties, there is low \r confidence whether increased CO2-induced water-use efficiency \r [...]"


id: 6863f92c1921808bc528bb99ab45ad6f
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 15633
content:
"[...] in vegetation will substantially reduce global plant transpiration \r and will diminish the frequency and severity of soil moisture and \r streamflow deficits associated with the radiative effect of higher CO2\r concentrations (Cross-Chapter Box 5.1)."


id: 129c3fd9a4bb53cf163e3fe87f6e741c
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 475
content:
"(Lu et al., 2019), extending the regions affected by severe soil \r moisture deficits over most of south and central Europe (Lehner \r et al., 2017; Ruosteenoja et al., 2018; Samaniego et al., 2018; van Der \r Linden et al., 2019), southern North America (Cook et al., 2019), South \r America (Orlowsky and Seneviratne, 2013), southern Africa (Lu et al., \r 2019), East Africa (Rowell et al., 2015), Southern Australia (Kirono \r et al., 2020), India (Mishra et al., 2014a) and East Asia (Figure 11.19; \r Cheng et al., 2015). Projected changes in total soil moisture display \r less widespread drying than those for surface soil moisture (Berg \r et al., 2017a), but still more than for precipitation (Cross-Chapter \r Box 11.1, Figures 2a,b,c). The severity of droughts based on surface \r soil moisture in future projections is stronger than projections \r based on precipitation and runoff (Dai et al., 2018; Vicente-Serrano \r et al., 2020c). Nevertheless, in many parts of the world where soil \r [...]"


id: da942d76b4e03c4ec0d35a029208931b
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 35751
content:
"[...] moisture is projected to decrease, the signal-to-noise ratio among \r models is low; only the projections in the Mediterranean, Europe, \r the south-western USA, and southern Africa show a  high signal\u0002to-noise ratio in soil moisture projections (Figure  11.19; Lu et al., \r 2019). Increases in soil moisture deficits are found to be statistically \r signicant at regional scale in the Mediterranean region, southern \r Africa and western South America for changes as small as 0.5degC in \r global warming, based on differences between +1.5degC and +2degC of \r global warming (Wartenburger et al., 2017). Several other regions \r are affected when considering changes in droughts for higher \r changes in global warming (Section 11.9 and Figure 11.19). Seasonal \r projections of drought frequency for boreal winter (December-\r January-February) and summer (June-July-August), from CMIP6 \r multi-model ensemble for 1.5degC, 2degC and 4degC global warming "


id: d4840b8a99b40d6c3bc4b785ccdbffd1
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 34709
content:
"Studies show a stronger drying in projections based on atmospheric\u0002based drought indices compared to ESM projections of changes in \r soil moisture (Berg and Sheffield, 2018) and runoff (Yang et al., 2019). \r It has been suggested that this difference is due to physiological CO2\r effects (Section  11.6.5.2; Roderick et al., 2015; Milly and Dunne, \r 2016; Swann et al., 2016; Lemordant et al., 2018; Scheff, 2018; \r Swann, 2018; Greve et al., 2019; Yang et al., 2020). Nonetheless, there \r is evidence that differences in projections between atmospheric\u0002based drought indices and water-balance metrics from ESMs are not \r alone due to CO2-plant effects (Berg et al., 2016; Scheff et al., 2021). \r Differences can also be related to the fact that AED is an upper bound \r for ET in dry regions and conditions (Section 11.6.1.2) and that soil \r moisture stress limits increases in ET in projections (Section 11.6.5.2; \r Berg et al., 2016; Zhou et al., 2021). In general, atmospheric-based \r [...]"


id: ea16fcc932133ab7e77fc40225cc8f22
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 38536
content:
"[...] indices show more drying than total column soil moisture (Berg and \r Sheffield, 2018; Cook et al., 2020; Scheff et al., 2021), but are more \r consistent with projected increases in surface soil moisture deficits \r (Dirmeyer et al., 2013; Dai et al., 2018; Lu et al., 2019; Cook et al., \r 2020; Vicente-Serrano et al., 2020c). "


id: d933c673df64068aab1b68e08cb5546
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 35510
content:
"Some studies support wetting tendencies as a response to a warmer \r climate when considering globally averaged changes in runoff over \r land (Roderick et al., 2015; Greve et al., 2017; Y. Yang et al., 2018), \r and streamflow projections respond to enhanced CO2 concentrations \r in CMIP5 models (Yang et al., 2019). Nevertheless, when focusing \r regionally on low-runoff periods, model projections also show an \r increase of hydrological droughts in large world regions (Wanders \r and Van Lanen, 2015; Dai et al., 2018; Vicente-Serrano et al., 2020c). \r In general, the frequency of hydrological deficits is projected to \r increase over most of the continents, although with regionally \r and seasonally differentiated effects (Section  11.9), with medium \r confidence of increase in the following AR6 regions: WCE, MED, \r SAU, WCA, WNA, SCA, NSA, SAM, SWS, SSA, WSAF, ESAF and MDG \r (Section 11.9; Forzieri et al., 2014; Prudhomme et al., 2014; Giuntoli \r [...]"


id: 66f7f5bdffea8221d3a43513299e85ae
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 15383
content:
"[...] et al., 2015; Wanders and Van Lanen, 2015; Roudier et al., 2016; Marx \r et al., 2018; Cook et al., 2019; Zhao et al., 2020). However, there \r are large uncertainties related to the hydrological/impact model used \r (Prudhomme et al., 2014; Schewe et al., 2014; Gosling et al., 2017), \r limited signal-to-noise ratio (due to model spread) in several regions \r (Giuntoli et al., 2015), and also uncertainties in the projection of future \r human activities, including water demand and land cover changes, \r which may represent more than 50% of the projected changes in \r hydrological droughts in some regions (Wanders and Wada, 2015). "


id: 8ea9d164031a25b9d6a8f5b5bacdfb80
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 22285
content:
"Atmospheric-based drought indices are not metrics of soil moisture \r or runoff (Section 11.6.1.5) so their projections may not necessarily \r reflect the same trend of online simulated soil moisture and runoff. \r Independently of effects on the land water balance, atmospheric\u0002based drought indices will reflect the potential vegetation stress \r resulting from deficits between available water and enhanced \r AED, even in conditions with no or low ET. Under dry conditions, \r the enhanced AED associated with human forcing would increase \r plant water stress (Brodribb et al., 2020), with effects on widespread \r forest dieback and mortality (Anderegg et al., 2013; Williams et al., \r 2013; Allen et al., 2015; McDowell and Allen, 2015; McDowell et al., \r 2016, 2020), and stronger risk of megafires (Flannigan et al., 2016; \r Podschwit et al., 2018; Clarke and Evans, 2019; Varela et al., 2019). \r For these reasons, there is high confidence that the future projections \r [...]"


id: a18515421322948cd6696977bc06b789
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 25632
content:
"[...] of enhanced drought severity showed by the PDSI-PM and the SPEI\u0002PM are representative of more frequent and severe plant stress \r episodes and more severe agricultural and ecological drought \r impacts in some regions. "


id: 1fcac73faa05c019f594fb73c925b4ea
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 2798
content:
"WNA and CNA (medium confidence). At 4degC of global warming, \r even more regions would be affected by agricultural and ecological \r droughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, \r NES, SAM, SWS, SSA, NCA, CNA, ENA, WNA, WSAF, ESAF and MDG). \r Some regions are also projected to experience less agricultural \r and ecological drought with global warming (medium confidence; \r NEAF, SAS). There is high confidence that the projected increases in \r agricultural and ecological droughts are strongly affected by AED \r increases in a warming climate, although ET increases are projected \r to be smaller than those in AED due to soil moisture limitations and \r CO2 effects on leaf stomatal conductance. Enhanced atmospheric \r CO2 concentrations lead to enhanced water-use efficiency in plants \r (medium confidence), but there is low confidence that it can alleviate \r agricultural and ecological droughts, or hydrological droughts, at \r [...]"


id: 4898f6ee641b3a92a3bb86a8ad81cfc0
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 9990
content:
"The tables in Section  11.9 provide assessed projected changes in \r metorological drought, agricultural and ecological drought, and \r hydrological droughts. The assessment shows that several regions will \r be affected by more severe agricultural and ecological droughts even \r if global warming is stabilized at 2degC, including MED, WSAF, SAM and \r SSA (high confidence), and ESAF, MDG, EAU, SAU, SCA, CAR, NSA, \r NES, SWS, WCE, NCA, WNA and CNA (medium confidence). Some \r regions are also projected to be affected by more severe agricultural \r and ecological droughts at 1.5degC (MED, WSAF, ESAF, SAU, NSA, SAM, \r SSA, can; medium confidence) At 4degC of global warming, even more \r regions would be affected by agricultural and ecological droughts \r (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, \r SWS, SSA, NCA, CNA, ENA, WNA, WSAF, ESAF and MDG). NEAF, SAS \r are also projected to experience less agricultural and ecological \r [...]"


id: 1ccf6bdbae01c4bb195ca67b300ca684
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"[...] drought with global warming (medium confidence). Projected \r changes in meteorological droughts are, overall, less extended but \r also affect several AR6 regions, at 1.5degC and 2degC (MED, EAU, SAU, \r SCA, NSA, NCA, WSAF, ESAF, MDG) and 4degC of global warming \r (WCE, MED, EAU, SAU, SEA, SCA, CAR, NWS, NSA, NES, SAM, SWS, \r SSA, NCA, ENA, WAF, WSAF, ESAF, MDG). Several regions are also \r projected to be affected by more hydrological droughts at 1.5degC and \r 2degC (WCE, MED, WNA, WSAF, ESAF) and 4degC of global warming (NEU, \r WCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, \r ESAF, MDG). To illustrate the changes in both intensity and frequency \r of drought in the regions where strongest changes are projected, \r Figure  11.18 displays changes in the intensity and frequency of \r soil moisture drought under different global warming levels (1.5degC, \r 2degC, 4degC) relative to the 1851-1900 baseline based on CMIP6 \r [...]"


id: 74c25f48081bfb3a94b909848cae7041
created_at: 2023-04-19 13:22:46
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content:
"[...] simulations under different SSP forcing scenarios averaged over \r \"drying regions\", i.e. AR6 regions for which there is at least medium \r confidence in increase in agricultural and ecological drought at 2degC \r of global warming. The 90% uncertainty ranges for the projected \r changes in both intensity and frequency are above zero, indicating \r significant increase in both intensity and frequency of drought in \r these regions as whole."


id: 1181d40f321c1d3ea5d31a4ec3148ffb
created_at: 2023-04-19 13:22:46
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content:
"Projected changes in meteorological droughts are overall less \r extended than for agricultural and ecological droughts, but also \r affect several AR6 regions, even at 1.5degC and 2degC of global warming. \r Several regions are also projected to be more strongly affected by \r hydrological droughts with increasing global warming (NEU, WCE, \r EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, \r MDG). Increased soil moisture limitation and associated changes in \r droughts are projected to lead to increased vegetation stress in many \r regions, with implications for the global land carbon sink (Cross\u0002Chapter Box 5). There is high confidence that the global land carbon \r sink will become less efficient due to soil moisture limitations and \r associated drought conditions in some regions in higher-emissions \r scenarios, especially under global warming levels above 4degC; \r however, there is low confidence on how these water cycle feedbacks \r [...]"


id: 79932d12bd76414ff57243cd75015a4d
created_at: 2023-04-19 13:22:46
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content:
"[...] will play out in lower-emissions scenarios (at 2degC global warming or \r lower; Cross-Chapter Box5.1)."


id: d4917b0c12f4a6e275209c6b55da532d
created_at: 2023-04-19 13:22:46
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content:
"Figure 11.19 | Projected changes in (a-c) the number of consecutive dry days (CDD), (d-f) annual mean soil moisture over the total column, and (g-l) the \r frequency and intensity of 1-in-10-year soil moisture drought for the June-to-August and December-to-February seasons at 1.5degC, 2degC, and 4degC of global \r warming compared to the 1850-1900 baseline. The unit for soil moisture change is the standard deviation of interannual variability in soil moisture during 1850-1900. \r Standard deviation is a widely used metric in characterizing drought severity. A projected reduction in mean soil moisture by one standard deviation corresponds to soil moisture \r conditions typical of about 1-in-6-year droughts during 1850-1900 becoming the norm in the future. Results are based on simulations from the Coupled Model Intercomparison \r [...]"


id: c075934db56eba0a8a0669abd7358bcd
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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content:
"[...] Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers \r in the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, \r where >=80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more \r information on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in CDDs are also \r displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9)."


id: a588293f7cb6402f357912c1529e5af8
created_at: 2023-04-19 13:22:46
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content:
"and interannual (e.g., the El Nino-Southern Oscillation and Pacific \r and Atlantic Meridional Modes), to inter-decadal (e.g.,  Atlantic \r Multidecadal Variability and Pacific Decadal Variability). This broad \r range of natural variability makes detection of anthropogenic \r effects difficult, and uncertainties in the projected changes of these \r modes of variability increase uncertainty in the projected changes \r in TC activity. Aerosol forcing also affects sea surface temperature \r (SST) patterns and cloud microphysics, and it is likely that observed \r changes in TC activity are partly caused by changes in aerosol forcing \r (Evan et al., 2011; Ting et al., 2015; Sobel et al., 2016, 2019; Takahashi \r et al., 2017; Zhao et al., 2018; Reed et al., 2019). Among possible \r changes from these drivers, there is medium confidence that the \r Hadley cell has widened and will continue to widen in the future \r (Sections 2.3, 3.3 and 4.5). This likely causes latitudinal shifts of TC \r [...]"


id: a38633a707aa7e0daad9c46152752760
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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vector_id: 25979
content:
"[...] tracks (Sharmila and Walsh, 2018). Regional TC activity changes are \r also strongly affected by projected changes in SST warming patterns \r (Yoshida et al., 2017), which are highly uncertain (Chapters 4 and 9)."


id: f5779cf2f5d2e26dcc5a3d9ed910bb72
created_at: 2023-04-19 13:22:46
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content:
"Since SREX, several IPCC Reports also assessed storms. The AR5 \r (Chapter  2, Hartmann et al., 2013) assessment observed with \r low confidence long-term trends in TC metrics, but revised the \r statement from SREX to state that it is virtually certain that there \r are increasing trends in North Atlantic TC activity since the 1970s, \r with medium confidence that anthropogenic aerosol forcing has \r contributed to these trends. The AR5 concluded that it is likely that \r TC precipitation and mean intensity will increase and more likely \r than not that the frequency of the strongest storms will increase \r with continued greenhouse gas warming. Confidence in projected \r trends in overall TC frequency remained low. Confidence in observed \r and projected trends in hail storm and tornado events also remained \r low. The SROCC (Chapter 6, Collins et al., 2019) assessed past and \r projected TCs and ETCs, supporting the AR5 conclusions with some \r [...]"


id: 785711becea5f062e1c497ba7c6f6320
created_at: 2023-04-19 13:22:46
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"[...] additional detail. Literature subsequent to AR5 adds support to the \r likelihood of increasing trends in TC intensity, precipitation, and \r frequency of the most intense storms, while some newer studies \r have added uncertainty to projected trends in overall frequency. \r A growing body of literature since AR5 on the poleward migration \r of TCs led to a  new assessment in SROCC of low confidence that \r the migration in the western North Pacific represents a detectable \r climate change contribution from anthropogenic forcing. The SR1.5 \r (Chapter 3, Hoegh-Guldberg et al., 2018) essentially confirmed the \r AR5 assessment of TCs and ETCs, adding that heavy precipitation \r associated with TCs is projected to be higher at 2degC compared to \r 1.5degC global warming (medium confidence)."


id: 748c42d43ed6a17173d8e5848d7a95ab
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content:
"There are previous and ongoing efforts to homogenize the best-track \r data (Elsner et al., 2008; Kossin et al., 2013, 2020; Choy et al., 2015; \r Landsea, 2015; Emanuel et al., 2018) and there is substantial literature \r that finds positive trends in intensity-related metrics in the best-track \r during the 'satellite period', which is generally limited to around the \r past 40 years (Kang and Elsner, 2012; Kishtawal et al., 2012; Kossin \r et al., 2013, 2020; Mei and Xie, 2016; Zhao et al., 2018; Tauvale and \r Tsuboki, 2019). When best-track trends are tested using homogenized \r data, the intensity trends generally remain positive, but are smaller in \r amplitude (Kossin et al., 2013; Holland and Bruyere, 2014). Kossin et al. \r (2020) extended the homogenized TC intensity record to the period \r 1979-2017 and identified significant global increases in major TC \r exceedance probability of about 6% per decade. In addition to trends \r [...]"


id: 2a2b4e1f96e5d46aaa6a651fbd90837
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"[...] in TC intensity, there is evidence that TC intensification rates and the \r frequency of rapid intensification events have increased within the \r satellite era (Kishtawal et al., 2012; Balaguru et al., 2018; Bhatia et al., \r 2018). The increase in intensification rates is found in the best-track \r and the homogenized intensity data."


id: 8c8c32658bb9130c229aa0723e5cc437
created_at: 2023-04-19 13:22:46
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"period. A similarly reliable subset of the data representing TC landfall \r frequency over Australia shows a decreasing trend in Eastern Australia \r since the 1800s (Callaghan and Power, 2011), as well as in other parts \r of Australia since 1982 (Chand et al., 2019; Knutson et al., 2019). \r A  paleoclimate proxy reconstruction shows that recent levels of TC \r interactions along parts of the Australian coastline are the lowest \r in the past 550-1500 years (Haig et al., 2014). Existing TC datasets \r show substantial inter-decadal variations in basin-wide TC frequency \r and intensity in the western North Pacific, but a statistically significant \r north-westward shift in the western North Pacific TC tracks since the \r 1980s (T.-C. Lee et al., 2020). In the case of the North Indian Ocean, \r analyses of trends are highly dependent on the details of each analysis \r (e.g., pre- and/or post-monsoon season period, or Bay of Bengal and/\r [...]"


id: e3ab577d146dda0d12b79945336135af
created_at: 2023-04-19 13:22:46
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content:
"[...] or Arabian Sea region). The most consistent trends are an increase in \r the occurrence of the most intense TCs, and a decrease in the overall \r TC frequency, in particular in the Bay of Bengal (Sahoo and Bhaskaran, \r 2016; Balaji et al., 2018; Singh et al., 2019; Baburaj et al., 2020). In \r the South Indian Ocean (SIO), an increase in the occurrence of the \r most intense TCs has been noted; however, there are well-known \r data quality issues there (Kuleshov et al., 2010; Fitchett, 2018). When \r the SIO data are homogenized, a significant increase is found in the \r fractional proportion of global Category 3-5 TC instances (6-hourly \r intensity estimates during the lifetime of each TC) to all Category 1-5 \r instances (Kossin et al., 2020). "


id: bbf1676599f92c04d1811e59eb911c9d
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"Subsequent to AR5, two metrics have been analysed that are argued \r to be comparatively less sensitive to data issues than frequency- and \r intensity-based metrics. Trends in these metrics have been identified \r over the past 70 years or more (Knutson et al., 2019). The first metric - \r the mean latitude where TCs reach their peak intensity  - exhibits \r a global and regional poleward migration during the satellite period \r (Kossin et al., 2014). The poleward migration can influence TC hazard \r exposure and risk (Kossin et al., 2016a) and is consistent with the \r independently observed expansion of the tropics (Lucas et al., 2014). \r The migration has been linked to changes in the Hadley circulation \r (Altman et al., 2018; Sharmila and Walsh, 2018; Studholme and \r Gulev, 2018). The migration is also apparent in the mean locations \r where TCs exhibit eyes (Knapp et al., 2018), which is when TCs are \r most intense. Part of the Northern Hemisphere poleward migration \r [...]"


id: fec6749546b5d2f3a163925ba30c6bd9
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"[...] is due to basin-wide changes in TC frequency (Kossin et al., 2014, \r 2016b; Moon et al., 2015, 2016) and the trends, as expected, can \r be sensitive to the time period chosen (Tennille and Ellis, 2017; \r Kossin, 2018; Song and Klotzbach, 2018) and to subsetting of the \r data by intensity (Zhan and Wang, 2017). The poleward migration is \r particularly pronounced and well-documented in the western North \r Pacific basin (Kossin et al., 2016a; Oey and Chou, 2016; Liang et al., \r 2017; Nakamura et al., 2017; Altman et al., 2018; Daloz and Camargo, "


id: c9238cf91c2643044c495299bf6b7434
created_at: 2023-04-19 13:22:46
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vector_id: 32713
content:
"Figure 11.20 | Summary schematic of past and projected changes in tropical cyclone (TC), extratropical cyclone (ETC), atmospheric river (AR), and severe \r convective storm (SCS) behaviour. Global changes (blue shading) from top to bottom: (i) Increased mean and maximum rain rates in TCs, ETCs, and ARs [past (low \r confidence due to lack of reliable data) and projected (high confidence)]; (ii) Increased proportion of stronger TCs [past (medium confidence) and projected (high confidence)]; \r (iii) Decrease or no change in global frequency of TC genesis [past (low confidence due to lack of reliable data) and projected (medium confidence)]; and (iv) Increased and \r decreased ETC wind speed, depending on the region, as storm tracks change [past (low confidence due to lack of reliable data) and projected (medium confidence)]. Regional \r [...]"


id: c58ea9bee12c73d71276acd43fb8cdff
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"[...] changes, from left to right: (i) Poleward TC migration in the western North Pacific and subsequent changes in TC exposure [past (medium confidence) and projected (medium\r confidence)]; (ii) Slowdown of TC forward translation speed over the contiguous USA and subsequent increase in TC rainfall [past (medium confidence) and projected (low\r confidence due to lack of directed studies)]; and (iii) Increase in mean and maximum SCS rain rate and increase in spring SCS frequency and season length over the contiguous \r USA [past (low confidence due to lack of reliable data) and projected (medium confidence)]. "


id: b1041e3c9625163e3d2df7549af588df
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"A second metric that is argued to be comparatively less sensitive \r to data issues than frequency- and intensity-based metrics is TC \r translation speed (Kossin, 2018), which exhibits a global slowdown \r in the best-track data over the period 1949-2016. TC translation \r speed is a  measure of the speed at which TCs move across the \r Earth's surface, and is very closely related to local rainfall amounts \r (i.e.,  a slower translation speed causes greater local rainfall). TC \r translation speed also affects structural wind damage and coastal \r storm surge by changing the hazard event duration. The slowdown is \r observed in the best-track data from all basins except the Northern \r Indian Ocean, and is also found in a number of regions where TCs \r interact directly with land. The slowing trends identified in the best\u0002track data by Kossin (2018) have been argued to be largely due to \r data heterogeneity. Moon et al. (2019) and Lanzante (2019) provide \r [...]"


id: 8d5d4f6436d3f94e73f12f783384129e
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"[...] evidence that meridional TC track shifts project onto the slowing \r trends, and argue that these shifts are due to the introduction of \r satellite data. Kossin (2019) provides evidence that the slowing trend \r is real by focusing on Atlantic TC track data over the contiguous USA \r in the 118-year period 1900-2017, which are generally considered \r reliable. In this period, mean TC translation speed has decreased by \r 17%. The slowing TC translation speed is expected to increase local \r rainfall amounts, which would increase coastal and inland flooding. \r In combination with slowing translation speed, abrupt TC track \r direction changes  - that can be associated with track 'meanders' \r or 'stalls'  - have become increasingly common along the North \r American coast since the mid-20th century, leading to more rainfall \r in the region (Hall and Kossin, 2019). "


id: 6d96f46a0558f937f820b3d91ecb2c7c
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"Confidence in the projection of intense TCs, such as those of \r Category 4-5, generally becomes higher as the resolution of the \r models becomes higher. The Coupled Model Intercomparison Project \r Phases 5 and 6 (CMIP5/6) class climate models (around 100-200 km \r grid spacing) cannot simulate TCs of Category 4-5 intensity. They do \r simulate storms of relatively high vorticity that are at best described \r as 'TC-like', but metrics such as storm counts are highly dependent \r on tracking algorithms (Camargo, 2013; Wehner et al., 2015; Zarzycki \r and Ullrich, 2017; Roberts et al., 2020a). High-resolution GCMs \r (around 10-60 km grid spacing), as used in HighResMIP (Haarsma \r et al., 2016; Roberts et al., 2020a), begin to capture some structures \r of TCs more realistically, as well as produce intense TCs of Category \r 4-5 despite the effects of parametrized deep cumulus convection \r processes (Murakami et al., 2015; Wehner et al., 2015; Yamada \r [...]"


id: 140b1039a7480d4986ad67d031425bbf
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"[...] et al., 2017; Roberts et al., 2018; Moon et al., 2020). Convection\u0002permitting models (around 1-10 km grid-spacing), such as used \r in some dynamical downscaling studies, provide further realism \r with capturing TC eye-wall structures (Tsuboki et al., 2015). Model \r characteristics besides resolution, especially details of convective \r parametrization, can influence a model's ability to simulate intense \r TCs (Reed and Jablonowski, 2011; Zhao et al., 2012; He and Posselt, \r 2015; D. Kim et al., 2018; Zhang and Wang, 2018; Camargo et al., \r 2020). However, models' dynamical cores and other physics also \r affect simulated TC properties (Reed et al., 2015; Vidale et al., 2021). \r Both wide-area regional and global convection-permitting models \r without the need for parameterized convection are becoming more \r useful for TC regional model projection studies (Tsuboki et al., 2015; \r Kanada et al., 2017a; Gutmann et al., 2018) and global model \r [...]"


id: 2bc106aa9fa3cd8ad6e5c98321421ad9
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"[...] projection studies (Satoh et al., 2015, 2017; Yamada et al., 2017), \r as they capture more realistic TC eye wall structures (Kinter III et al., \r 2013) and are becoming more useful for investigating changes in TC \r structures (Kanada et al., 2013; Yamada et al., 2017). Large ensemble \r simulations of GCMs with 60 km grid spacing provide TC statistics \r that allow more reliable detection of changes in the projections, \r which are not well captured in any single experiment (Yoshida et al., \r 2017; Yamaguchi et al., 2020). Variable resolution global models offer \r an alternative to regional models for individual TC or basin-wide \r simulations (Yanase et al., 2012; Zarzycki et al., 2014; Harris et al., \r 2016; Reed et al., 2020; Stansfield et al., 2020). Computationally less \r intense than equivalent uniform resolution global models, they also \r do not require lateral boundary conditions, thus reducing this source \r [...]"


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"[...] of error (Hashimoto et al., 2016). Confidence in the projection of TC \r statistics and properties is increased by the use of higher-resolution \r models with more realistic simulations."


id: 3cc092597960c2cf17904ee29fd381c
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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content:
"Accurate projections of future TC activity have two principal \r requirements: accurate representation of changes in the relevant \r environmental factors (e.g.,  SSTs) that can affect TC activity, and \r accurate representation of actual TC activity in given environmental \r conditions. In particular, models' capacity to reproduce historical trends \r or interannual variabilities of TC activity is relevant to the confidence in \r future projections. One test of the models is to evaluate their ability to \r reproduce the dependency of the TC statistics in the different basins in \r the real world, in addition to their capability of reproducing atmospheric \r and ocean environmental conditions. For the evaluation of projections \r of TC-relevant environmental variables, AR5 confidence statements \r were based on global surface temperature and moisture, but not on \r the detailed regional structure of SST and atmospheric circulation \r changes such as steering flows and vertical shear, which affect \r [...]"


id: a80902ffd18626d8967ae1bc78f477c8
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
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vector_id: 26775
content:
"There is general agreement in the literature that anthropogenic \r greenhouse gases and aerosols have measurably affected observed \r oceanic and atmospheric variability in TC-prone regions (see \r Chapter  3). This underpinned the SROCC assessment of medium \r confidence that humans have contributed to the observed increase in \r Atlantic hurricane activity since the 1970s (Chapter 5, Bindoff et al., \r 2013). Literature subsequent to AR5 lends further support to this \r statement (Knutson et al., 2019). However, there is still no consensus \r on the relative magnitude of human and natural influences on past \r changes in Atlantic hurricane activity, and particularly on which \r factor has dominated the observed increase (Ting et al., 2015) and \r it remains uncertain whether past changes in Atlantic TC activity \r are outside the range of natural variability. A  recent result using \r high-resolution dynamical model experiments suggested that the \r [...]"


id: 2b0bbf4852cfff60e2e92a9b897c0fad
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content:
"[...] observed spatial contrast in TC trends cannot be explained only by \r multi-decadal natural variability, and that external forcing plays an \r important role (Murakami et al., 2020). Observational evidence for \r significant global increases in the proportion of major TC intensities \r (Kossin et al., 2020) is consistent with both theory and numerical \r modelling simulations, which generally indicate an increase in mean \r TC peak intensity and the proportion of very intense TCs in a warming \r world (Knutson et al., 2015, 2020; Walsh et al., 2015, 2016). In \r addition, high-resolution coupled model simulations provide support \r that natural variability alone is unlikely to explain the magnitude \r of the observed increase in TC intensification rates and upward TC \r intensity trend in the Atlantic basin since the early 1980s (Bhatia \r et al., 2019; Murakami et al., 2020)."


id: 40bc19a43076352fb4cdaeec3b742dd5
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vector_id: 8575
content:
"found no statistically significant impact of anthropogenic climate \r change on storm intensity, while projections in a  warmer world \r showed significant strengthening. However, Magnusson et al. (2014) \r found that, in European Centre for Medium-Range Weather Forecast \r (ECMWF) simulations, the simulated cyclone depth and intensity, \r as well as precipitation, were larger when the model was driven \r by the warmer actual SSTs than the climatological average SSTs. In \r Super Typhoon Haiyan, which struck the Philippines on 8 November \r 2013, Takayabu et al. (2015) took an event attribution approach \r with cloud system-resolving (around 1  km) downscaling ensemble \r experiments to evaluate the anthropogenic effect on typhoons, and \r showed that the intensity of the simulated worst-case storm in the \r actual conditions was stronger than that in a hypothetical condition \r without historical anthropogenic forcing in the model. However, \r [...]"


id: 17bb6fc4e5eeafe192a187f468a9c72f
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vector_id: 1381
content:
"[...] in a  similar approach with two coarser parametrized convection \r models, Wehner et al. (2019) found conflicting human influences \r on Haiyan's intensity. Patricola and Wehner (2018) found little \r evidence of an attributable change in intensity of hurricanes Katrina \r (2005), Irma (2017), and Maria (2017) using a  regional climate \r model configured between 3  km and 4.5 km resolution. They did, \r however, find attributable increases in heavy precipitation totals. \r These results imply that higher resolution, such as in a convective \r permitting 5 km or less mesh model, is required to obtain a robust \r anthropogenic intensification of a strong TC by simulating realistic \r rapid intensification (Kanada and Wada, 2016; Kanada et al., 2017a), \r and that whether the TC intensification can be attributed to the \r recent warming depends on the case."


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"The dominant factor in the extreme rainfall amounts during Hurricane \r Harvey's passage onto the USA in 2017 was its slow translation speed. \r But studies published after the event have argued that anthropogenic \r climate change contributed to an increase in rain rate, which \r compounded the extreme local rainfall caused by the slow translation. \r Emanuel (2017) used a large set of synthetically-generated storms \r and concluded that the occurrence of extreme rainfall as observed \r in Harvey was substantially enhanced by anthropogenic changes \r to the larger-scale ocean and atmosphere characteristics; Trenberth \r et al. (2018) linked Harvey's rainfall totals to the anomalously large \r ocean heat content from the Gulf of Mexico; and van Oldenborgh \r et al. (2017) and Risser and Wehner (2017) applied extreme value \r analysis to extreme rainfall records in the Houston, Texas region, both \r attributing large increases to climate change. Large precipitation \r [...]"


id: e56c1e684c42ff3c706b686978cb0a4d
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 37671
content:
"[...] increases during Harvey due to global warming were also found \r using climate models (van Oldenborgh et al., 2017; S.-Y.S. Wang et al., \r 2018). Harvey precipitation totals were estimated in these papers to \r be three to 10 times more probable due to climate change. A best \r estimate from a regional climate and flood model is that urbanization \r increased the risk of the Harvey flooding by a factor of 21 (W. Zhang \r et al., 2018), using a regional climate and flood model, found that \r surface roughness from urbanization increased the risk of the Harvey \r flooding by a  factor of 21. Anthropogenic effects on precipitation \r increases were also predicted in advance from a forecast model for \r Hurricane Florence in 2018 (Reed et al., 2020). "


id: fdeae8f788184cc9142366effe6ca8fc
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 42042
content:
"The recent active TC seasons in some basins have been studied to \r determine whether there is anthropogenic influence. For 2015, \r Murakami et al. (2017b) explored the unusually high TC frequency \r near Hawaii and in the eastern Pacific basin. W. Zhang et al. (2016b) \r considered unusually high Accumulated Cyclone Energy (ACE) in the \r western North Pacific; and S.-H. Yang et al. (2018) and Yamada et al. \r (2019) looked at TC intensification in the western North Pacific. These \r studies suggest that the anomalous TC activity in 2015 was not solely \r explained by the effect of an extreme El Nino (see Box  11.4) and \r that there was also an anthropogenic contribution, mainly through \r the effects of SSTs in subtropical regions. In the post-monsoon \r seasons of 2014 and 2015, tropical storms with lifetime maximum \r winds greater than 46 m s-1 were first observed over the Arabian \r [...]"


id: 3b19c9a89733fb3da674a2edfcb8484e
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 7585
content:
"[...] Sea, and Murakami et al. (2017a) showed that the probability of late\u0002season severe tropical storms is increased by anthropogenic forcing \r compared to the preindustrial era. Murakami et al. (2018) concluded \r that the active 2017 Atlantic hurricane season was mainly caused \r by pronounced SSTs in the tropical North Atlantic and that these \r types of seasonal events will intensify with projected anthropogenic \r forcing. The trans-basin SST change, which might be driven by \r anthropogenic aerosol forcing, also affects TC activity. Takahashi \r et al. (2017) suggested that a decrease in sulphate aerosol emissions \r caused about half of the observed decreasing trends in TC genesis \r frequency in the south-eastern region of the western North Pacific \r during 1992-2011."


id: 205b2020af9046062ec8411fc31ef08b
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 2911
content:
"Changes in SST and atmospheric temperature and moisture play \r a  role in tropical cyclogenesis (Walsh et al., 2015). Reductions in \r vertical convective mass flux due to increased tropical stability have \r been associated with a  reduction in cyclogenesis (Held and Zhao, \r 2011; Sugi et al., 2012). Satoh et al. (2015) further posit that the \r robust simulated increase in the number of intense TCs, and hence \r increased vertical mass flux associated with intense TCs, must lead \r to a decrease in overall TC frequency because of this association. The \r Genesis Potential Index can be modified to mimic the TC frequency \r decreases of a model by altering the treatment of humidity (Camargo \r et al., 2014). This supports the idea that increased mid-tropospheric \r saturation deficit (Emanuel et al., 2008) controls TC frequency, \r but the approach remains empirical. Other possible controlling \r factors, such as a  decline in the number of seeds (held constant \r [...]"


id: 520c34ed4467994a34ef3191ecb2a486
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 11710
content:
"[...] in Emanuel's downscaling approach, or dependent on the genesis \r index formulation in the approach proposed by C.-Y. Lee et al., 2020) \r caused by increased atmospheric stability have been proposed, \r but questioned as an important factor (Patricola et al., 2018). The \r resolution of atmospheric models affects the number of seeds, hence \r TC genesis frequency (Vecchi et al., 2019; Sugi et al., 2020; Yamada \r et al., 2021). The diverse and sometimes inconsistent projected \r changes in global TC frequency by high-resolution models indicate \r that better process understanding and improvement of the models \r are needed to raise confidence in these changes. "


id: fda53f6f6b8136b254b772919002bf47
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 41976
content:
"There is not an established theory for the drivers of future changes \r in the frequency of TCs. Most, but not all, high-resolution global \r simulations project significant reductions in the total number of TCs, \r with the bulk of the reduction at the weaker end of the intensity \r spectrum as the climate warms (Knutson et al., 2020). Recent \r exceptions based on high-resolution coupled model results are \r noted in Bhatia et al. (2018) and Vecchi et al. (2019). Vecchi et al. \r (2019) showed that the representation of synoptic-scale seeds for TC \r genesis in their high-resolution model causes different projections of \r global TC frequency, and there is evidence for a decrease in cyclone \r seeds in some projected TC simulations (Sugi et al., 2020; Yamada \r et al., 2011). However, other research indicates that TC seeds are not \r an independent control on climatological TC frequency, rather the \r seeds covary with the large-scale controls on TCs (Patricola et al., \r [...]"


id: ae3ea65cb5601a8a481c31a6ea2572ca
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 27892
content:
"[...] 2018). While empirical genesis indices derived from observations and \r reanalysis describe well the observed subseasonal and interannual \r variability of current TC frequency (Camargo et al., 2007, 2009; Tippett \r et al., 2011; Menkes et al., 2012), they fail to predict the decreased TC \r frequency found in most high-resolution model simulations (Zhang \r et al., 2010; Camargo, 2013; Wehner et al., 2015), as they generally \r project an increase as the climate warms. This suggests a limitation \r of the use of the empirical genesis indices for projections of TC \r genesis, in particular due to their sensitivity to the humidity variable \r considered in the genesis index for these projections (Camargo et al., \r 2014). In a different approach, a statistical-dynamical downscaling \r framework assuming a constant seeding rate with warming (Emanuel, \r 2013, 2021) exhibits increases in TC frequency consistent with \r genesis indices-based projections, while downscaling with a different \r [...]"


id: b0e42bccab76ed7a8607b3a9ffacd590
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 28351
content:
"[...] model leads to two different scenarios depending on the humidity \r variable considered (C.-Y. Lee et al., 2020). This disparity in the sign \r of the projected change in global TC frequency, and the difficulty in \r explaining the mechanisms behind the different signed responses, \r further emphasize the lack of process understanding of future "


id: 51b023e457ac538985cdcd2c1692b132
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 11656
content:
"Most TC-permitting model simulations (10-60 km or finer grid \r spacing) are consistent in their projection of increases in the \r proportion of intense TCs (Category 4-5), as well as an increase in \r the intensity of the strongest TCs defined by maximum wind speed \r or central pressure fall (Murakami et al., 2012; Tsuboki et al., 2015; \r Wehner et al., 2018a; Knutson et al., 2020). The general reduction \r in the total number of TCs, which is concentrated in storms weaker \r than or equal to Category 1, contributes to this increase. The models \r are somewhat less consistent in projecting an increase in the \r frequency of Category 4-5 TCs (Wehner et al., 2018a; Knutson et al., \r 2020). The projected increase in the intensity of the strongest TCs is \r consistent with theoretical understanding (e.g., Emanuel, 1987) and \r observations (e.g.,  Kossin et al., 2020). For a  2degC global warming, \r the median proportion of Category 4-5 TCs increases by 13%, \r [...]"


id: 42a8a497d5cc3241030dbf9a2a4bf63a
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 8921
content:
"[...] while the median global TC frequency decreases by 14%, which \r implies that the median of the global Category 4-5 TC frequency is \r slightly reduced by 1% or almost unchanged (Knutson et al., 2020). \r Murakami et al. (2020) projected a decrease in TC frequency over the \r coming century in the North Atlantic due to greenhouse warming, \r as consistent with Dunstone et al. (2013), and a  reduction in TC \r frequency almost everywhere in the tropics in response to +1% \r CO2 forcing. Exceptions include the central North Pacific (Hawaii \r region), east of the Philippines in the North Pacific, and two relatively \r small regions in the northern Arabian Sea and Bay of Bengal. These \r projections can vary substantially between ocean basins, possibly "


id: bf2667d5ce9ec960ee91c8cba051765e
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 30906
content:
"TC occurrence in the central North Pacific (Yamada et al., 2017; \r Yoshida et al., 2017; Wehner et al., 2018a; Roberts et al., 2020b). \r The observed poleward expansion of the latitude of maximum TC \r intensity in the western North Pacific is consistently reproduced \r by the CMIP5 models and downscaled models, and these models \r show further poleward expansion in the future; the projected mean \r migration rate of the mean latitude where TCs reach their lifetime\u0002maximum intensity is 0.2+-0.1deg from CMIP5 model results, while it \r is 0.13+-0.04deg from downscaled models in the western North Pacific \r (Kossin et al., 2014, 2016a). In the North Atlantic, while the location \r of TC maximum intensity does not show clear poleward migration \r observationally (Kossin et al., 2014), it tends to migrate poleward \r in projections (Garner et al., 2017). The poleward migration is less \r robust among models and observations in the Indian Ocean, eastern \r [...]"


id: cbdacbe3f41663cade2e587c149f8898
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 33189
content:
"[...] North Pacific, and South Pacific (e.g.,  Tauvale and Tsuboki, 2019; \r Ramsay et al. 2018; Cattiaux et al. 2020). There is presently no clear \r consensus in projected changes in TC translation speed (Knutson \r et al., 2020), although recent studies suggest a slowdown outside \r of the tropics (Kossin, 2019; Yamaguchi et al., 2020; G. Zhang et al., \r 2020), but regionally there can even be an acceleration of the storms \r (Hassanzadeh et al., 2020)."


id: 686d555aba5a902ee31247b992c8a46c
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 15639
content:
"A projected increase in global average TC rain rates of about 12% \r for a 2degC global warming is consistent with the Clausius-Clapeyron \r scaling of saturation-specific humidity (Knutson et al., 2020). \r Increases substantially greater than Clausius-Clapeyron scaling are \r projected in some regions, which is caused by increased low-level \r moisture convergence due to projected TC intensity increases in those \r regions (Knutson et al., 2015; Phibbs and Toumi, 2016; Patricola and \r Wehner, 2018; M. Liu et al., 2019a). Projections of TC precipitation \r using large-ensemble experiments (Kitoh and Endo, 2019) show \r that the annual maximum one-day precipitation total is projected \r to increase, except for the western North Pacific where only a small \r change (or even a reduction) is projected, mainly due to a projected \r decrease of TC frequency. They also show that the 10-year return \r value of extreme Rx1day associated with TCs will greatly increase \r [...]"


id: b06cbb8bf6605b4b77f1147fab9ddf93
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 28276
content:
"[...] in a region extending from Hawaii to the south of Japan. TC tracks \r and the location of topography relative to TCs significantly affect \r precipitation, thus, in general, areas on the eastern and southern \r faces of mountains have more impacts of TC precipitation changes \r (Hatsuzuka et al., 2020). Projection studies using variable-resolution \r models in the North Atlantic (Stansfield et al., 2020) indicate that TC\u0002related precipitation rates within North Atlantic TCs and the amount \r of hourly precipitation due to TC are projected to increase by the end \r of the century compared to a  historical simulation. However, the \r annual average TC-related Rx5day over the eastern USA is projected \r to decrease because of a reduction in landfalling TCs. RCM studies \r with around 25-50 km grid spacing are used to study projected \r changes in TCs. The projected changes of TCs in South East Asia \r simulated by RCMs are consistent with those of most GCMs, showing \r [...]"


id: d8ff0078b92b27fd072e8a15643942f2
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 35472
content:
"[...] a  decrease in TC frequency and an increase in the amount of TC\u0002associated precipitation or an increase in the frequency of intense \r TCs (Redmond et al., 2015; Gallo et al., 2019). \r Projected changes in TC tracks or TC areas of occurrence in the \r late 21st  century vary considerably among available studies, \r although there is better agreement in the western North Pacific. \r Several studies project either poleward or eastward expansion \r of TC occurrence over the western North Pacific region, and more "


id: 167801d0903ba00d4ac0353c76313bc4
created_at: 2023-04-19 13:22:46
updated_at: 2023-04-19 13:22:46
content_type: text
index: climate_gpt_v2
vector_id: 1170
content:
"The spatial extent, or 'size', of the TC wind field is an important \r determinant of storm surge and damage. No detectable anthropogenic \r influences on TC size have been identified to date, because TCs in \r observations vary in size substantially (Chan and Chan, 2015) and \r there is no definite theory on what controls TC size, although this \r is an area of active research (Chavas and Emanuel, 2014; Chan \r and Chan, 2018). However, projections by high-resolution models \r indicate future broadening of TC wind fields when compared to \r TCs of the same categories (Yamada et al., 2017), while Knutson \r et al. (2015) simulate a reasonable interbasin distribution of TC size \r climatology, but project no statistically significant change in global \r average TC size. A plausible mechanism is that, as the tropopause \r height becomes higher with global warming, the eye wall areas \r become wider because the eye walls are inclined outward with \r [...]"


id: c6f3cf216625bda6813713548c2a3d86
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 32313
content:
"[...] height to the tropopause. This effect is only reproduced in high\u0002resolution convection-permitting models capturing eye walls, and \r such modelling studies are not common. Moreover, the projected TC \r size changes are generally on the order of 10% or less, and these size \r changes are still highly variable between basins and studies. Thus, the \r projected change in both magnitude and sign of TC size is uncertain. "


id: f10da1a3b2c36ef6df12251dfc596464
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 39783
content:
"Section 2.3.1.4.3 concluded that there is overall low confidence in \r recent changes in the total number of ETCs over both hemispheres, \r and that there is medium confidence in a poleward shift of the storm \r tracks over both hemispheres since the 1980s. Overall, there is also \r low confidence in past-century trends in the number and intensity \r of the strongest ETCs due to the large interannual and decadal \r variability (Feser et al., 2015; Reboita et al., 2015; Wang et al., 2016; \r Varino et al., 2019) and due to temporal and spatial heterogeneities \r in the number and type of assimilated data in reanalyses, particularly \r before the satellite era (Krueger et al., 2013; Tilinina et al., 2013; \r Befort et al., 2016; Chang and Yau, 2016; Wang et al., 2016). There \r is medium confidence that the agreement among reanalyses and \r detection and tracking algorithms is higher when considering stronger \r cyclones (Neu et al., 2013; Pepler et al., 2015; Wang et al., 2016). \r [...]"


id: 6207500623990653069a3e411541ff8
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 14520
content:
"[...] Over the Southern Hemisphere, there is high confidence that the total \r number of ETCs with low central pressures (<980 hPa) has increased \r between 1979 and 2009, with all eight reanalyses considered by \r Wang et al. (2016) showing positive trends, and five of them showing \r statistically significant trends. Similar results were found by Reboita \r et al. (2015) using a different detection and tracking algorithm and \r a  single reanalysis product. Over the Northern Hemisphere, there \r is high agreement among reanalyses that the number of cyclones \r with low central pressures (<970 hPa) has decreased in summer and \r winter during the period 1979-2010 (Tilinina et al., 2013; Chang \r et al., 2016). However, changes exhibit substantial decadal variability \r and do not show monotonic trends since the 1980s. For example, \r over the Arctic and North Atlantic, Tilinina et al. (2013) showed that \r the number of cyclones with very low central pressure (<960 hPa) \r [...]"


id: bd340f20a098c394c8ad1b49fcb6529
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 30537
content:
"[...] increased from 1979 to 1990 and then declined until 2010 in all five \r reanalyses considered. Over the North Pacific, the number of cyclones \r with very low central pressure reached a peak around 2000 and then \r decreased until 2010 in the five reanalyses considered (Tilinina et al., \r 2013). Overall, however, it should be noted that characterising trends \r in the dynamical intensity of ETCs (e.g.,  wind speeds) using the \r absolute central pressure is problematic because the central pressure \r depends on the background mean sea level pressure, which varies \r seasonally and regionally (e.g., Befort et al., 2016). "


id: f4ceb70f2ad2a9c2e94b775dda2e2235
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 40439
content:
"In the North Atlantic, vertical wind shear, which inhibits TC genesis \r and intensification, varies in a quasi-dipole pattern, with one centre \r of action in the tropics and another along the south-east USA \r coast (Vimont and Kossin, 2007). This pattern of variability creates \r a protective barrier of high shear along the USA coast during periods \r of heightened TC activity in the tropics (Kossin, 2017), and appears \r to be a  natural part of the Atlantic ocean-atmosphere climate \r system (Ting et al., 2019). Greenhouse gas forcing in CMIP5 and the \r Community Earth System Model Large Ensemble (Kay et al., 2015) \r simulations, however, erodes the pattern and degrades the natural \r shear barrier along the USA coast. Following the RCP8.5 emissions \r scenario, the magnitude of the erosion of the barrier equals the \r amplitude of past natural variability (time of emergence) by the \r mid-21st century (Ting et al., 2019). The projected reduction of shear \r [...]"


id: a4162aca1dc4140d95218011f9fdcfb5
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 26059
content:
"[...] along the USA East Coast with warming is consistent among studies \r (e.g., Vecchi and Soden, 2007)."


id: 867a428d7dc30a077f806b8753b96d4
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 20826
content:
"and 6% in December-January-February and June-July-August \r according to the RCP4.5 scenario (2070-2099 vs. 1976-2005). Over \r the North Pacific, Chang (2014) showed that CMIP5 models project \r a decrease in the frequency of ETCs, with the largest central pressure \r perturbation (i.e.,  the depth, strongly related with low-level wind \r speeds) by the end of the century according to simulations using \r the RCP8.5 scenario. Using projections from CMIP5 GCMs under \r the RCP8.5 scenario (1981-2000 to 2081-2100), Seiler and Zwiers \r (2016b) projected a  northward shift in the number of explosive \r ETCs in the northern Pacific, with fewer and weaker events south, \r and more frequent and stronger events north of 45degN. Using 19 \r CMIP5 GCMs under the RCP8.5 scenario, Kar-Man Chang (2018) \r found a significant decrease in the number of ETCs associated with \r extreme wind speeds (2081-2100 vs. 1980-99) over the Northern \r Hemisphere (average decrease of 17%) and over some smaller \r [...]"


id: 56d7d201f81aaaaaf8f9ece3e3563daf
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 12526
content:
"Priestley et al., 2020). There is high confidence that \r the underestimation of the intensity of ETCs is associated with the \r coarse horizontal resolution of climate models, with higher \r horizontal resolution models, including HighResMIP and CORDEX, \r usually showing better performance (Colle et al., 2013; Zappa \r et al., 2013a; Di Luca et al., 2016; Trzeciak et al., 2016; Seiler et al., \r 2018; Gao et al., 2020; Priestley et al., 2020). The improvement by \r higher-resolution models is found, even when comparing models \r and reanalyses after post-processing data to a common resolution \r (Zappa et al., 2013a; Di Luca et al., 2016; Priestley et al., 2020). \r The systematic bias in the intensity of ETCs has also been linked to \r the inability of current climate models to resolve diabatic processes, \r particularly those related to the release of latent heat (Willison et al., \r 2013; Trzeciak et al., 2016) and the formation of clouds (Govekar \r [...]"


id: bc1ac0e834449d762d6558e4ddd13653
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 30332
content:
"[...] et al., 2014). There is medium confidence that climate models \r simulate well the spatial distribution of precipitation associated with \r ETCs over the Northern Hemisphere, together with some of the main \r features of the ETC life cycle, including the maximum in precipitation \r occurring just before the peak in dynamical intensity (e.g., vorticity) \r as observed in a reanalysis and observations (Hawcroft et al., 2018). \r There is, however, large observational uncertainty in ETC-associated \r precipitation (Hawcroft et al., 2018) and limitations in the simulation \r of frontal precipitation, including overly low rainfall intensity over \r mid-latitude oceanic areas in both hemispheres (Catto et al., 2015)."


id: 906d294ff2b8213683ee871179fab6ee
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 22613
content:
"Over the Southern Hemisphere, future changes (RCP8.5 scenario; \r 1980-1999 to 2081-2100) in extreme ETCs were studied by Chang \r (2017) using 26 CMIP5 models, and a  variety of intensity metrics \r (850-hPa vorticity, 850-hPa wind speed, mean sea level pressure and \r near-surface wind speed). They found that the number of extreme \r cyclones is projected to increase by at least 20% and as much as \r 50%, depending on the specific metric used to define extreme ETCs. \r Increases in the number of strong cyclones appear to be robust across \r models and for most seasons, although they show strong regional \r variations, with increases occurring mostly over the southern flank of \r the storm track, consistent with a shift and intensification of the storm \r track. Overall, there is medium confidence that projected changes \r in the dynamical intensity of ETCs depend on the resolution and \r formulation (e.g.,  explicit or implicit representation of convection) \r [...]"


id: ec572f217b6d67df9b7e7edd85c454df
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 38943
content:
"As reported in AR5 and in Section 8.4.2.8, despite small changes in \r the dynamical intensity of ETCs, there is high confidence that the \r precipitation associated with ETCs will increase in the future (Zappa \r et al., 2013b; Marciano et al., 2015; Pepler et al., 2016; Michaelis et al., \r 2017; Yettella and Kay, 2017; Zhang and Colle, 2017; Barcikowska \r et al., 2018; Hawcroft et al., 2018; Zarzycki, 2018; Kodama et al., \r 2019; Bevacqua et al., 2020a; Reboita et al., 2021). There is high \r confidence that increases in precipitation will follow increases in \r low-level water vapour (i.e., about 7% per 1degC of surface warming; \r see Box 11.1) and will be larger for higher warming levels (Zhang \r and Colle, 2017). There is medium confidence that precipitation \r changes will show regional and seasonal differences due to distinct \r changes in atmospheric humidity and dynamical conditions (Zappa \r et al., 2015; Hawcroft et al., 2018), with decreases in some specific \r [...]"


id: 1115a2f56d0f7538a0e2a51e18af5c0a
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 199
content:
"[...] regions such as the Mediterranean (Zappa et al., 2015; Barcikowska \r et al., 2018). There is high confidence that snowfall associated \r with winter ETCs will decrease in the future, because increases in \r tropospheric temperatures lead to a lower proportion of precipitation \r falling as snow (O'Gorman, 2014; Rhoades et al., 2018; Zarzycki, \r 2018). However, there is medium confidence that extreme snowfall \r events associated with winter ETCs will change little in regions \r where snowfall will be supported in the future (O'Gorman, 2014; \r Zarzycki, 2018)."


id: 36bb67f5b02b21442827144673b4067b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 6857
content:
"The frequency of ETCs is expected to change, primarily following \r a poleward shift of the storm tracks as discussed in Section 4.5.1.6 \r (see also Figure  4.31) and Section  8.4.2.8. There is medium \r confidence that changes in the dynamical intensity (e.g.,  wind \r speeds) of ETCs will be small, although changes in the location of \r storm tracks can lead to substantial changes in local extreme wind \r speeds (Zappa et al., 2013b; Chang, 2014; Li et al., 2014; Seiler and \r Zwiers, 2016b; Yettella and Kay, 2017; Barcikowska et al., 2018; Kar\u0002Man Chang, 2018). Yettella and Kay (2017) detected and tracked \r ETCs over both hemispheres in an ensemble of 30 Community Earth \r System Model Large Ensemble simulations, differing only in their \r initial conditions, and found that changes in mean wind speeds \r around ETC centres are often negligible between present (1986-\r 2005) and future (2081-2100) periods. Using 19 CMIP5 models, \r [...]"


id: bc7d9b040211952212fcefc60db58770
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 30407
content:
"[...] Zappa et al. (2013b) found an overall reduction in the number of \r cyclones associated with low-troposphere (850-hPa) wind speeds \r larger than 25 m s-1 over the North Atlantic and Europe with the \r number of the 10% strongest cyclones decreasing by about 8% "


id: 3bf1d8aab9b0e99a1a314f83366d3f99
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 7739
content:
"Johnson, 2005). Many of the recent severe rainfall events in Japan \r are associated with band-shaped precipitation systems (Kunii et al., \r 2016; Oizumi et al., 2018; Tsuguti et al., 2018; Kato, 2020), suggesting \r common characteristics of severe precipitation, at least in East Asia. \r The convective modes of severe storms in the USA can be classified \r into rotating or linear modes and preferable environmental conditions \r for these modes, such as vertical shear, have been identified (Trapp \r et al., 2005; Smith et al., 2013; Allen, 2018). Cloud microphysics \r characteristics of MCSs were examined and the roles of warm rain \r processes on extreme precipitation were emphasized recently (Sohn \r et al., 2013; Hamada et al., 2015; Hamada and Takayabu, 2018). \r Idealized studies also suggest the importance of ice and mixed-phase \r processes of cloud microphysics on extreme precipitation (Sandvik \r et al., 2018; Bao and Sherwood, 2019). However, it is unknown \r [...]"


id: 65780a9d182a3fd5aefcfd8fcd931de0
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 15118
content:
"[...] whether the types of MCS are changing in recent periods or observed \r ubiquitously all over the world."


id: 22a34ebd6ddc796ce1a1aa4d5e647227
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 3311
content:
"Severe convective storms are convective systems that are associated \r with extreme phenomena such as tornadoes, hail, heavy precipitation \r (rain or snow), strong winds, and lightning. The assessment of changes \r in severe convective storms in SREX (Chapter 3, Seneviratne et al., \r 2012) and AR5 (Chapter 12, Collins et al., 2013) is limited and focused \r mainly on tornadoes and hail storms. The SREX assessed that there \r is low confidence in observed trends in tornadoes and hail because \r of data inhomogeneities and inadequacies in monitoring systems. \r Subsequent literature assessed in the Climate Science Special Report \r (Kossin et al., 2017) led to the assessment of the observed tornado \r activity over the 2000s in the USA, with a decrease in the number \r of days per year with tornadoes and an increase in the number of \r tornadoes on these days (medium confidence). However, there \r is low confidence in past trends for hail and severe thunderstorm \r [...]"


id: ff73d363086257d7dce9a6a0e5324649
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 42348
content:
"[...] winds. Climate models consistently project environmental changes \r that would support an increase in the frequency and intensity of \r severe thunderstorms that combine tornadoes, hail, and winds \r (high confidence), but there is low confidence in the details of the \r projected increase. Regional aspects of severe convective storms and \r details of the assessment of tornadoes and hail are also assessed in \r Section 12.3.3.2 (tornadoes), Section 12.3.4.5 (hail), Section 12.4.5.3 \r (Europe), Section  12.4.6.3 (North America), and Section  12.7.2 \r (regional gaps and uncertainties)."


id: 2c9f7ac9b4c273bb2b18b11976d729f3
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 5113
content:
"Severe convective storms occur under conditions preferable for deep \r convection, that is, conditionally unstable stratification, sufficient \r moisture, both in lower and middle levels of the atmosphere, and \r a  strong vertical shear. These large-scale environmental conditions \r are viewed as necessary conditions for the occurrence of severe \r convective systems, or the resulting tornadoes and lightning, and \r the relevance of these factors strongly depends on the region \r (e.g.,  Antonescu et al., 2016a; Allen, 2018; Tochimoto and Niino, \r 2018). Frequently used metrics are atmospheric static stability, \r moisture content, convective available potential energy (CAPE) and \r convective inhibition, wind shear or helicity, including storm-relative \r environmental helicity (Tochimoto and Niino, 2018; Elsner et al., \r 2019). These metrics, largely controlled by large-scale atmospheric \r circulations or synoptic weather systems, such as TCs and ETCs, \r [...]"


id: 86acc617dc85b501a814c8b358510e24
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 20862
content:
"[...] are then generally used to examine severe convective systems. In \r particular, there is high confidence that CAPE in the tropics and the \r subtropics increases in response to global warming (M.S. Singh et al., \r 2017), as supported by theoretical studies (Singh and O'Gorman, \r 2013; Seeley and Romps, 2015; Romps, 2016; Agard and Emanuel, \r 2017). The uncertainty, however, arises from the balance between \r factors affecting severe storm occurrence. For example, the warming \r of mid-tropospheric temperatures leads to an increase in the freezing \r level, which leads to increased melting of smaller hailstones, while \r there may be some offset by stronger updrafts driven by increasing \r CAPE, which would favour the growth of larger hailstones, leading to \r less melting when falling (Allen, 2018; Mahoney, 2020). "


id: 2b753f1f0022afbd88db95a74aa04e70
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4901
content:
"Severe convective storms are sometimes embedded in synoptic\u0002scale weather systems, such as TCs, ETCs, and fronts (Kunkel et al., \r 2013). They are also generated as individual events as mesoscale \r convective systems (MCSs) and mesoscale convective complexes \r (MCCs, a  special type of a  large, organized and long-lived MCS), \r without being clearly embedded within larger-scale weather systems. \r In addition to the general vigorousness of precipitation, hail, and \r winds associated with MCSs, characteristics of MCSs are viewed \r in new perspectives in recent years, probably because of both the \r development of dense mesoscale observing networks and advances in \r high-resolution mesoscale modelling (Sections 11.7.3.2 and 11.7.3.3). \r The horizontal scale of MCSs is discussed with their organization \r of the convective structure, and  it is examined with a  concept of \r 'convective aggregation'  in recent years (Holloway et al., 2017). \r [...]"


id: a8d4001d50e0c65cb3bbbcda10193e43
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 26913
content:
"[...] MCSs sometimes take a linear shape and stay almost stationary with \r successive production of cumulonimbus on the upstream side (back\u0002building type convection), and cause heavy rainfall (Schumacher and "


id: 3674c3087acd4cf4abf11cd82177b84f
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 6818
content:
"Studies on trends in severe convective storms and their ingredients \r outside of the USA are limited. Westra et al. (2014) found that there \r is an increase in the intensity of short-duration convective events \r (minutes to hours) over many regions of the world, except eastern \r China. In Europe, a climatology of tornadoes shows an increase in \r detected tornadoes between 1800 and 2014, but this trend might be \r affected by the density of observations (Antonescu et al., 2016a, b). \r An increase in the trend in extreme daily rainfall is found in south\u0002eastern France, where MCSs play a  key role in this type of event \r (Blanchet et al., 2018; Ribes et al., 2019). Trend analysis of the mean \r annual number of days with thunderstorms since 1979 in Europe \r indicates an increase over the Alps and central, south-eastern, and \r eastern Europe, with a decrease over the south-west (Taszarek et al., \r 2019). In the Sahelian region, Taylor et al. (2017) analysed MCSs \r [...]"


id: d4515634d68e22f62aabbc28a2fc4adc
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 34679
content:
"[...] using satellite observations since 1982 and showed an increase in the \r frequency of extreme storms. In Bangladesh, the annual number of \r propagating MCSs decreased significantly during 1998-2015 based \r on TRMM precipitation data (Habib et al., 2019). Prein and Holland \r (2018) estimated the hail hazard from large-scale environmental \r conditions using a statistical approach and showed increasing trends \r in the USA, Europe, and Australia. However, trends in hail on regional \r scales are difficult to validate because of an insufficient length of \r observations and inhomogeneous records (Allen, 2018). The high \r spatial variability of hail suggests it is reasonable that there would be \r local signals of both positive and negative trends, and the trends that \r are occurring in hail globally are uncertain. In China, the total number \r of days that have either a thunderstorm or hail have decreased by \r about 50% from 1961 to 2010, and the reduction in these severe \r [...]"


id: 538c03f04d9ae43f904980c4c7abf7e2
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 11972
content:
"[...] weather occurrences correlates strongly with the weakening of the \r East Asian summer monsoon (Q. Zhang et al., 2017). More regional \r aspects of severe convective storms are detailed in Chapter 12."


id: 2834cf9bc05ed65578dd79e1d25420e7
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4272
content:
"Observed trends in severe convective storms or MCSs are not well \r documented, but the climatology of MCSs has been analysed in \r specific regions (North America, South America, Europe, Asia; regional \r aspects of convective storms are separately assessed in Chapter 12). \r As the definition of severe convective storms varies depending on \r the literature, it is not straightforward to make a synthesizing view \r of observed trends in severe convective storms in different regions. \r However, analysis using satellite observations provides a global view \r of MCSs (Kossin et al., 2017). The global distribution of thunderstorms \r is captured (Zipser et al., 2006; Liu and Zipser, 2015) by using \r the satellite precipitation measurements by the Tropical Rainfall \r Measuring Mission (TRMM) and Global Precipitation Mission (GPM) \r (Hou et al., 2014). The climatological characteristics of MCSs are \r provided by satellite analyses in South America (Durkee and Mote, \r [...]"


id: 289d4122c73e73d05f75744e09ac8678
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4358
content:
"[...] 2010; Rasmussen and Houze, 2011; Rehbein et al., 2018) and those of \r MCCs in the Maritime Continent by Trismidianto and Satyawardhana \r (2018). Analysis of the environmental conditions favourable for \r severe convective events indirectly indicates the climatology and \r trends of severe convective events (Allen et al., 2018; Taszarek et al., \r 2018, 2019), though favourable conditions depend on the location, \r such as the difference for tornadoes associated with ETCs between \r the USA and Japan (Tochimoto and Niino, 2018)."


id: 5f74e67514ce89f9aa475ab774cd21ba
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 14104
content:
"Observed trends in severe convective storms are highly regionally \r dependent. In the USA, it is indicated that there is no significant \r increase in convective storms, and hail and severe thunderstorms \r (Kunkel et al., 2013; Kossin et al., 2017). There is an upward trend \r in the frequency and intensity of extreme precipitation events in the \r USA (high confidence) (Kunkel et al., 2013; Easterling et al., 2017), and \r MCSs have increased in occurrence and precipitation amounts since \r 1979 (limited evidence) (Feng et al., 2016). Significant interannual \r variability of hailstone occurrences is found in the Southern Great \r Plains of the USA (Jeong et al., 2020). The mean annual number \r of tornadoes has remained relatively constant, but their variability \r of occurrence has increased since the 1970s, particularly over the \r 2000s, with a  decrease in the number of days per year, but an \r increase in the number of tornadoes on these days (Brooks et al., \r [...]"


id: 53daea458880d21a4520e97e1f3d910f
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 12026
content:
"[...] 2014; Elsner et al., 2015, 2019; Kossin et al., 2017; Allen, 2018). There \r has been a shift in the distribution of tornadoes, with increases in the \r mid-south of the USA and decreases over the High Plains (Gensini \r and Brooks, 2018). Trends in MCSs are relatively more visible for "


id: b154b6cac5a6847931615c8b1e577f45
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 28427
content:
"denoted as convection-permitting models or storm-resolving models \r (Section  10.3.1). Convection-permitting models are becoming \r available to run over a wide domain, such as a continental scale or even \r over the global area, and show realistic climatological characteristics \r of MCSs (Prein et al., 2015; Guichard and Couvreux, 2017; Satoh et al., \r 2019). Such high-resolution simulations are computationally too \r expensive to perform at the larger domain and for long periods, and \r alternative methods by using an RCM with dynamical downscaling \r are generally used (Section  10.3.1). Convection-permitting models \r are used as the flagship project of CORDEX to particularly study \r projections of thunderstorms (Section 10.3.3). Simulations of North \r American MCSs by a  convection-permitting model conducted by \r Prein et al. (2020) were able to capture the main characteristics of \r the observed MCSs, such as their size, precipitation rate, propagation \r [...]"


id: f083504a318b6b6fb77dda9d9762e8fb
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 39693
content:
"[...] speed, and lifetime. Cloud-permitting model simulations in Europe \r also showed sub-daily precipitation realistically (Ban et al., 2014; \r Kendon et al., 2014). Evaluation of precipitation conducted using \r convection-permitting simulations around Japan showed that finer \r resolution improves intense precipitation (Murata et al., 2017). MCSs \r over Africa simulated using convection-permitting models showed \r better extreme rainfall (Kendon et al., 2019) and diurnal cycles and \r convective rainfall over land than the coarser-resolution RCMs or \r GCMs (Stratton et al., 2018; Crook et al., 2019)."


id: c29f9e4b7c094a5cf82493540ebdfb51
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 31528
content:
"Future projections of severe convective storms are usually studied \r either by analysing the environmental conditions simulated by \r climate models, or by a time-slice approach with higher-resolution \r convection-permitting models by comparing simulations downscaled \r with climate model results under historical conditions and those \r under hypothesized future conditions (Kendon et al., 2017; Allen, \r 2018). Up to now, individual studies using convection-permitting \r models gave projections of extreme events associated with severe \r convective storms in local regions, and it is not generally possible \r to obtain global or general views of projected changes of severe \r convective storms. Prein et al. (2017) investigated future projections \r of North American MCS simulations and showed an increase in \r MCS frequency and an increase in total MCS precipitation volume \r by the combined effect of increases in maximum precipitation \r [...]"


id: f6086f3282d7e23e8f891ca79994cc86
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 40642
content:
"[...] rates associated with MCSs and increases in their size. Rasmussen \r et al. (2020) investigated future changes in the diurnal cycle of \r precipitation by capturing organized and propagating convection \r and showed that weak-to-moderate convection will decrease, and \r strong convection will increase in frequency in the future. Ban et al. \r (2015) found that the day-long and hour-long precipitation events \r in summer intensify in the European region covering the Alps. \r Kendon et al. (2019) showed future increases in extreme three\u0002hourly precipitation in Africa. Murata et al. (2015) investigated \r future projections of precipitation around Japan and showed \r a decrease in monthly mean precipitation in the eastern Japan Sea \r region in December, suggesting that convective clouds become \r shallower in the future in the winter over the Japan Sea."


id: d01d0cbfe46fd0287305d967e4339585
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 33969
content:
"Extreme winds are defined here in terms of the strongest near\u0002surface wind speeds that are generally associated with extreme \r storms, such as TCs, ETCs, and severe convective storms. In previous \r IPCC reports, near-surface wind speed (including extremes), has not \r been assessed as a variable in its own right, but rather in the context \r of other extreme atmospheric or oceanic phenomena. The exception \r was the SREX report (Seneviratne et al., 2012), which specifically \r examined past changes and projections of mean and extreme near\u0002surface wind speeds. A strong decline in extreme winds compared to \r mean winds was reported for the continental northern mid-latitudes. \r Due to the small number of studies and uncertainties in terrestrial\u0002based surface wind measurements, the findings were assigned low \r confidence in SREX. The AR5 reported a  weakening of mean and \r maximum winds from the 1960s or 1970s to the early 2000s in the \r [...]"


id: 65325edb92c14396117efe5c132325c7
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 15073
content:
"[...] tropics and mid-latitudes, and increases in high latitudes, but with \r low confidence in changes in the observed surface winds over land \r (Hartmann et al., 2013). Observed trends in mean wind speed over \r land and the ocean are assessed in Section  2.3.1.4.4. Aspects of \r climate impact-drivers for winds are addressed in Sections 12.3.3 \r and 12.5.2, and their regional changes are assessed in Section 12.4."


id: 39447642ff4cf3848dee2cdeeaaf1766
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 7290
content:
"In the USA, projected changes in the environmental conditions show \r an increase in CAPE and no changes or decreases in the vertical \r wind shear, suggesting favourable conditions for an increase in \r severe convective storms in the future, but the interpretation of how \r tornadoes or hail will change is an open question because  of the \r strong dependence on shear (Brooks, 2013). Diffenbaugh et al. \r (2013) showed robust increases in the occurrence of the favourable \r environments for severe convective storms with increased CAPE \r and stronger low-level wind shear in response to future global \r warming. A downscaling approach showed that the variability of the \r occurrence of severe convective storms increases in spring in late \r 21st-century simulations (Gensini and Mote, 2015). Future changes in \r hail occurrence in the USA examined through convection-permitting \r dynamical downscaling suggested that the hail season may begin \r [...]"


id: 48a0280d4ee56d30bef624869a6f96d5
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 9996
content:
"[...] earlier in the year and exhibit more interannual variability, with \r increases in the frequency of large hail in broad areas over the USA \r (Trapp et al., 2019). There is medium confidence that the frequency \r and variability of the favourable environments for severe convective \r storms will increase in spring, and low confidence for summer \r and autumn (Diffenbaugh et al., 2013; Gensini and Mote, 2015; \r Hoogewind et al., 2017). The occurrence of hail events in Colorado in \r the USA was examined by comparing both present-day and projected \r future climates using high-resolution model simulations capable of \r resolving hailstorms (Mahoney et al., 2012), which showed that \r hail is almost eliminated at the surface in the future in most of the \r simulations, despite more intense future storms and significantly \r larger amounts of hail generated in-cloud."


id: 8c27bd12175494368d54e7fa09718146
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 21830
content:
"Observationally, although not specifically addressing extreme \r wind speed changes, negative surface wind speed trends (stilling) \r were found in the tropics and mid-latitudes of both hemispheres \r of -0.014 m s-1 yr-1, while positive trends were reported at high \r latitudes poleward of 70 degrees, based on a review of 148 studies \r (McVicar et al., 2012b). An earlier study attributed the stilling to \r both changes in atmospheric circulation and an increase in surface \r roughness due to an overall increase in vegetation cover (Vautard \r et al., 2010). Since then, a number of studies have mostly confirmed \r these general negative mean-wind trends based on anemometer \r data for Spain (Azorin-Molina et al., 2017), Turkey, (Dadaser-Celik \r and Cengiz, 2014), the Netherlands, (Wever, 2012), Saudi Arabia, \r (Rehman, 2013), Romania, (Marin et al., 2014), and China (Chen \r et al., 2013). Lin et al. (2013) note that wind speed variability over \r [...]"


id: 3d6fef3a0dfda289c68ab58b4eb5d973
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 7982
content:
"[...] China is greater at high-elevation locations compared to those \r closer to mean sea level. Hande et al. (2012), using radiosonde \r data, found an increase in surface wind speed on Macquarie \r Island of Australia. "


id: ec91a4106ad327316a3820dd95f760c8
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 38980
content:
"A number of new studies have examined surface wind speeds over \r the ocean using ship-based measurements, satellite altimeters, and \r Special Sensor Microwave/Imagers (Tokinaga and Xie, 2011; Zieger \r et al., 2014). It has been noted that wind speed trends tend to be \r stronger in altimeter measurements, although the spatial patterns \r of change are qualitatively similar in both instruments (Zieger et al., \r 2014). Q. Liu et al. (2016) found positive trends in surface wind \r speeds over the Arctic Ocean in 20 years of satellite observations. \r Small positive trends in mean wind speed were found in 33 years \r of satellite data, together with larger trends in the 90th percentile \r values over global oceans (Ribal and Young, 2019). These results \r were consistent with an earlier study that found a positive trend in \r 1-in-100-year wind speeds (Young et al., 2012). A positive change \r in mean wind speeds was found for the Arabian Sea and the Bay of \r [...]"


id: bf2cf9419e0b3da2135840569e51673c
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 30908
content:
"Changes in extreme winds are associated with changes in the \r characteristics (locations, frequencies, and intensities) of extreme \r storms, including TCs, ETCs, and severe convective storms. For TCs, \r as assessed in Section  11.7.1.5, it is projected that the average \r peak TC wind speeds will increase globally with warming, while the \r global frequency of TCs over all categories will decrease or remain \r unchanged; the average location where TCs reach their peak wind \r intensity will migrate poleward in the western North Pacific Ocean \r as the tropics expand with warming. Frequency, intensities, and \r geographical distributions of extreme wind events associated with \r TCs will change according to these TC changes. For ETCs, by the \r end of the century, CMIP5 models show that the number of ETCs \r associated with extreme winds will significantly decrease in the mid\u0002and high latitudes of the Northern Hemisphere in winter, with the \r [...]"


id: d67c0bc98ca4d7076aa26335008da96
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 35043
content:
"[...] projected decrease being larger over the Atlantic (Kar-Man Chang, \r 2018), while it will significantly increase irrespective of the season \r in the Southern Hemisphere (Section 11.7.2.4; Chang, 2017). Over \r the ocean in the subtropics, a large ensemble of 60-km global model \r simulations indicated that extreme winds associated with storm \r surges will intensify over 15-35degN in the Northern Hemisphere \r (Mori et al., 2019). However, extreme surface wind speeds will \r mostly decrease due to decreases in the number and intensity of TCs \r over most tropical areas of the Southern Hemisphere (Mori et al., \r 2019). The projected changes in the frequency of extreme winds are \r associated with the future changes in TCs and ETCs."


id: b129c1c39400e914ad94484544d58436
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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content:
"Further definitions of compound events have emerged since SREX. \r Zscheischler et al. (2018) defined compound events broadly as 'the \r combination of multiple drivers and/or hazards that contributes to \r societal or environmental risk'. This definition is used in the present \r assessment, because of its clear focus on the risk framework \r established by the IPCC, and also highlighting that compound events \r may not necessarily result from dependent drivers. Compound events \r have been classified into: preconditioned events, where a weather\u0002driven or climate-driven precondition aggravates the impacts \r of a  climatic impact-driver; multivariate events, where multiple \r drivers and/or climatic impact-drivers lead to an impact; temporally \r compounding events, where a  succession of hazards leads to an \r impact; and spatially compounding events, where hazards in multiple \r connected locations cause an aggregated impact (Zscheischler et al., \r [...]"


id: 2d0901b0f0cfa366cdf46240c245995b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
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content:
"[...] 2020). Drivers include processes, variables, and phenomena in the \r climate and weather domain that may span over multiple spatial \r and temporal scales. Hazards (such as floods, heatwaves, wildfires; \r also termed 'climatic impact-drivers' in this report, see Chapter 12) \r are usually the immediate physical precursors to negative impacts, \r but can occasionally have positive outcomes (Flach et al., 2018). The \r present assessment focuses on the physical dimension of changes in \r compound events, as it is part of the IPCC AR6 Working Group I Report."


id: 6520b35eed0d3d09472dd2f3c2c80ac1
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 15056
content:
"The combination of two or more - not necessarily extreme - weather \r or climate events that occur: i) at the same time; ii) in close succession; \r or iii) concurrently in different regions, can lead to extreme impacts \r that are much larger than the sum of the impacts due to the occurrence \r of individual extremes alone. This is because multiple stressors can \r exceed the coping capacity of a system more quickly. The contributing \r events can be of similar types (clustered multiple events) or of \r different types (Zscheischler et al., 2020). Many major weather- and \r climate-related catastrophes are inherently of a  compound nature \r (Zscheischler et al., 2019). This has been highlighted for a  broad \r range of hazards, such as droughts, heatwaves, wildfires, coastal \r extremes, and floods (Westra et al., 2016; AghaKouchak et al., 2020; \r Ridder et al., 2020). Co-occurring extreme precipitation and extreme \r winds can result in infrastructural damage (Martius et al., 2016); the \r [...]"


id: 8e9dda701601d467829c224cdd8990ba
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
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content:
"[...] compounding of storm surge and precipitation extremes can cause \r coastal floods (Wahl et al., 2015); the combination of drought and \r heat can lead to tree mortality (Section 11.6; Allen et al., 2015); and \r wildfires increase occurrences of hailstorms and lightning (Y. Zhang \r et al., 2019a). Compound storm types consisting of co-located \r cyclone, front and thunderstorm systems have a  higher chance of \r causing extreme rainfall and extreme winds than individual storm \r types (Dowdy and Catto, 2017). Extremes may occur at similar times \r at different locations (De Luca et al., 2020a, b) but affect the same \r system, for instance, spatially concurrent climate extremes affecting \r crop yields and food prices (Singh et al., 2018; Anderson et al., 2019). \r Studies also show an increasing likelihood for breadbasket regions to \r be concurrently affected by climate extremes with increasing global "


id: 27c3b79acca522ccdafd01a9046122ba
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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content:
"coast and the North Sea (Bevacqua et al., 2019). The probability of \r flood occurrence can be assessed via the dependence between storm \r surge and river flow (Bevacqua et al., 2020b, c). For instance, the \r occurrence of a North Sea storm surge in close succession with an \r extreme Rhine or Meuse river discharge is much more probable due \r to their dependence, compared to if both events were independent \r (Kew et al., 2013; Klerk et al., 2015). Significant dependence between \r high sea levels and high river discharge are found for more than \r half of the available station observations, which are mostly located \r around the coasts of North America, Europe, Australia, and Japan \r (Ward et al., 2018). Combining global river discharge with a global \r storm surge model, hotspots of compound flooding have been \r discovered that are not well covered by observations in some regions, \r including Madagascar, Northern Morocco, Vietnam, and Taiwan of \r [...]"


id: 451f36251e575ff9be69339242ee5d7a
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
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content:
"[...] China (Couasnon et al., 2020).  In  the Dutch Noorderzijlvest area, \r there is more than a two-fold increase in the frequency of exceeding \r the highest warning level compared to the case if storm surge and \r heavy precipitation were independent (van den Hurk et al., 2015). In \r other regions and seasons, the dependence can be insignificant (W. \r Wu et al., 2018) and there can be significant seasonal and regional \r differences in the storm surge-heavy precipitation relationship. \r Assessments of flood probabilities are often not based on actual flood \r measurements; instead, they are estimated from its main drivers, \r including astronomical tides, storm surge, heavy precipitation, and \r high streamflow. Such single driver analyses might underestimate \r flood probabilities if multiple correlated drivers contribute to flood \r occurrence (e.g., van den Hurk et al., 2015). "


id: d348b2f0e54927fc2a6276b0a9354c20
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 34516
content:
"Finally, impacts may occur because of large multivariate anomalies in \r the climate drivers, if systems are adapted to historical multivariate \r climate variability (Flach et al., 2017). For instance, ecosystems \r are typically adapted to the local covariability of temperature and \r precipitation such that a bivariate anomaly may have a large impact, \r even though neither temperature nor precipitation may be extreme \r based on a  univariate assessment (Mahony and Cannon, 2018). \r Given that almost all systems are affected by weather and climate \r phenomena at multiple space-time scales (Raymond et al., 2020), it \r is natural to consider extremes in a  compound event framework. \r It should be noted, however, that multi-hazard dependencies can also \r decrease risk, for instance when hazards are negatively correlated \r (Hillier et al., 2020). Despite this recognition, the literature on past \r and future changes in compound events has been limited, but is \r [...]"


id: eb2a24b39abcab369d218a7fb0d62e4d
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content:
"[...] growing. This section assesses examples of types of compound \r events in available literature. "


id: 22013e1e87dfc20cdb01982c97707e82
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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content:
"Aspects of concurrent extremes in coastal and estuarine environments \r have increased in frequency and/or magnitude over the last century in \r some regions. These include an increase in the dependence between \r heavy precipitation and storm surge over the last century, leading \r to more frequent co-occurring storm surge and heavy precipitation \r events in the present day along USA coastlines (Wahl et al., 2015). In \r Europe, the probability of compound flooding occurrence increases \r most strongly along the Atlantic coast and the North Sea under \r strong warming. This increase is mostly driven by an intensification \r of precipitation extremes and aggravated flooding probability due \r to sea level rise (Bevacqua et al., 2019). At the global scale and \r under a  high-emissions scenario, the concurrence probability of \r meteorological conditions driving compound flooding would increase \r by more than 25%, on average, along coastlines worldwide by 2100, \r [...]"


id: 72ef886d2323dc5bd035a488553e8a9d
created_at: 2023-04-19 13:22:47
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content:
"[...] compared to the present (Bevacqua et al., 2020c). Sea level extremes \r and their physical impacts in the coastal zone arise from a complex \r set of atmospheric, oceanic, and terrestrial processes that interact \r on a range of spatial and temporal scales and will be modified by \r a changing climate, including sea level rise (McInnes et al., 2016). \r Interactions between sea level rise and storm surges (Little et al., \r 2015), and sea level and fluvial flooding (Moftakhari et al., 2017) are "


id: b57a259b7b63ac78679a364c84211d13
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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vector_id: 29154
content:
"anthropogenic climate change via a  significant increase in vapour \r pressure deficit, a primary driver of wildfires. A study of the western \r USA examined the correlation between historical water-balance \r deficits and annual area burned, across a range of vegetation types, \r from temperate rainforest to desert (McKenzie and Littell, 2017). \r The relationship between temperature and dryness, and wildfire, \r varied with ecosystem type, and the fire-climate relationship was \r nonstationary and vegetation-dependent. In many fire-prone regions, \r such as the Mediterranean and China's Daxing'anling region, \r projections for increased severity of future drought and heatwaves \r may lead to an increased frequency of wildfires relative to observed \r climatology (Tian et al., 2017; Ruffault et al., 2018). Observations \r show a long-term trend towards more dangerous weather conditions \r for bushfires in many regions of Australia, which is attributable (at \r [...]"


id: 6eea797f82b891c3948d7be409080ebd
created_at: 2023-04-19 13:22:47
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content:
"[...] least in part) to anthropogenic climate change (Dowdy, 2018). There \r is emerging evidence that recent regional surges in wildland fires are \r being driven by changing weather extremes (Cross-Chapter Box 3; Jia \r et al., 2019; SRCCL Chapter 2). Between 1979 and 2013, the global \r burnable area affected by long fire weather seasons doubled, and \r the mean length of the fire weather season increased by 19% (Jolly \r et al., 2015). However, at the global scale, the total burned area has \r been decreasing between 1998 and 2015 due to human activities \r mostly related to changes in land use (Andela et al., 2017). Given \r the projected high confidence increase in compound hot and dry \r conditions, there is high confidence that fire weather conditions \r will become more frequent at higher levels of global warming in \r some regions. This assessment is also consistent with Chapter 12's \r examination of regional projected changes in fire weather. The \r [...]"


id: 969e12f1df568d86eaa8ba5416c9d522
created_at: 2023-04-19 13:22:47
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content:
"[...] SRCCL (Chapter 2) assessed with high confidence that future climate \r variability is expected to enhance the risk and severity of wildfires in \r many biomes such as tropical rainforests."


id: d7685b06fee71a312d66b2c4aef35af7
created_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 35202
content:
"The probability of co-occurring meteorological droughts and \r heatwaves has increased in the observational period in many regions \r and will continue to do so under unabated warming (Herrera-Estrada \r and Sheffield, 2017; Zscheischler and Seneviratne, 2017; Hao et al., \r 2018; Sarhadi et al., 2018; Alizadeh et al., 2020; Wu et al., 2021). Overall, \r projections of increases in co-occurring drought and heatwaves are \r reported in northern Eurasia (Schubert et al., 2014), Europe (Orth \r et al., 2016a; Sedlmeier et al., 2018), south-east Australia (Kirono \r et al., 2017), multiple regions of the USA (Diffenbaugh et al., 2015; \r Herrera-Estrada and Sheffield, 2017), north-west China (X. Li et al., \r 2019; Kong et al., 2020) and India (Sharma and Mujumdar, 2017). The \r dominant signal is related to the increase in heatwave occurrence, \r which has been attributed to anthropogenic forcing (Section 11.3.4). \r This means that, even if drought occurrence is unaffected, compound \r [...]"


id: 6a21fa6bc211575f991cc310a782a6f7
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content:
"Occurrence of concurrent or near-concurrent extremes in \r different parts of a region, or in different locations around the \r world, challenges adaptation and risk management capacity. This \r can occur as a result of natural climate variability, as climates \r in different parts of the world are interconnected through large\u0002scale atmospheric-oceanic teleconnections. In addition, in \r a  warming climate, the probability of having several locations \r being affected simultaneously by, for example, hot extremes and \r heatwaves increases strongly as a function of global warming, \r with detectable changes even for changes as small as +0.5degC \r of additional global warming (Sections 11.2.4 and 11.3, Cross\u0002chapter Box  11.1). Recent articles have highlighted the risks \r associated with concurrent extremes over large spatial scales \r (e.g.,  Lehner and Stocker, 2015; Boers et al., 2019; Gaupp \r et al., 2019). There is evidence that such global-scale extremes \r [...]"


id: 637fea390745b3b9545ca727a3d9e9fe
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content:
"[...] associated with hot temperature extremes are increasing in \r occurrence (Sippel et al., 2015; Vogel et al., 2019). Hereafter, the \r focus is on two case studies of recent global-scale events that \r featured concurrent extremes in several regions across the world. \r The first focuses on concurrent extremes driven by variability in \r tropical Pacific sea surface temperatures (SSTs) associated with the 2015-2016 extreme El Nino, while the second addresses the \r impacts of global warming combined with abnormal atmospheric circulation patterns in the 2018 boreal spring/summer. \r Box 11.4, Figure 1 | Analysis of the percentage of land area affected \r by temperature extremes larger than two (blue) or three (orange) \r standard deviations in June-July-August (JJA) between 30degN and \r 80degN using a normalization. This figure shows a substantial increase in the \r overall land area affected by very strong hot extremes since 1990. Adapted from \r Sippel et al. (2015)."


id: c42c59f0b0dea12acedf4d0b437568cd
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content:
"El Nino-Southern Oscillation (ENSO) is one of the phenomena that have the ability to bring multitudes of extremes in different \r parts of the world, especially in extreme El Nino (Annex IV.2.3) cases. Additionally, the background climate warming associated with \r greenhouse gas forcing can significantly exacerbate extremes in parts of the world, even under normal El Nino conditions. The 2015-\r 2016 extreme El Nino event was one of the three extreme El Nino events since the 1980s and the availability of satellite rainfall \r observations. According to some measures, it was the strongest El Nino in the past 145 years (Barnard et al., 2017). The 2015-2016 \r warmth was unprecedented at the central equatorial Pacific (Nino4: 5degN-5degS, 150degE-150degW), and this exceptional warmth was \r unlikely to have occurred entirely naturally, appearing to reflect an anthropogenically forced trend (Newman et al., 2018). In particular, \r [...]"


id: 6945f65043900a9f64d18bdb1ba26b3d
created_at: 2023-04-19 13:22:47
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content:
"[...] its signal was seen in very high monthly global mean surface temperature (GMST) values in late 2015 and early 2016, contributing \r to the highest record of GMST in 2016 (Section 2.3.1.1). Both the ENSO amplitude and the frequency of high-magnitude events since \r 1950 is higher than over the pre-industrial period (medium confidence) (Section 2.4.2), suggesting that global extremes similar to \r those associated with the 2015-2016 extreme El Nino would occur more frequently under further increases in global warming. A brief \r summary of extreme events that happened in 2015-2016 is provided in Sections 6.2.2 and 6.5.1.1 of the Special Report on the Ocean \r and Cryosphere in a Changing Climate (SROCC). We provide some highlights illustrating extremes that occurred in different parts of \r the world during the 2015-2016 extreme El Nino in Box 11.4, Table 1, as well as in the short summary that follows."


id: 92817d1ce3ce02d70cef776e634053f0
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content:
"Several regions were strongly affected by droughts in 2015, including Indonesia, Australia, the Amazon region, Ethiopia, southern Africa, \r and Europe. As a result, global measurements of land water anomalies were particularly low in that year (Humphrey et al., 2018). In 2015, \r Indonesia experienced a severe drought and forest fire, causing pronounced impact on economy, ecology and human health due to haze \r crisis (Field et al., 2016; Huijnen et al., 2016; Patra et al., 2017; Hartmann et al., 2018). The northern part of Australia experienced high \r temperatures and low precipitation between late 2015 and early 2016, and the extensive mangrove trees were damaged along the \r Gulf of Carpentaria in Northern Australia (Duke et al., 2017). The Amazon region experienced the most intense droughts of this century \r in 2015-2016. This drought was more severe than the previous major droughts that occurred in the Amazon in 2005 and 2010 (Lewis \r [...]"


id: d6d1f80eab0965cd2d13a7d28d520f46
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content:
"[...] et al., 2011; Erfanian et al., 2017; Panisset et al., 2018). The 2015-2016 Amazon drought impacted the entirety of South America north \r of 20degS during the austral spring and summer (Erfanian et al., 2017). It also increased forest fire incidence by 36% compared to the \r preceding 12 years (Aragao et al., 2018) and, as a consequence, increased the biomass burning outbreaks and the carbon monoxide (CO) \r concentration in the area, affecting air quality (Ribeiro et al., 2018). This out-of-season drought affected the water availability for human \r consumption and agricultural irrigation. It also left rivers with very low water levels and large sandbanks, preventing ship transportation \r of food, medicines, and fuels (INMET, 2017). Eastern African countries were impacted by drought in 2015. The drought in Ethiopia was the \r worst in several decades and was associated with the 2015-2016 extreme El Nino (Blunden and Arndt, 2016; Philip et al., 2018b). It was "


id: f3d07c1a27add017e6edbf4b7794fc09
created_at: 2023-04-19 13:22:47
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content:
"In 2015, tropical cyclone activity was notably high in the North Pacific (Blunden and Arndt, 2016). Over the western North Pacific, there \r were 13 Category 4 and 5 tropical cyclones (TCs), more than twice the area's typical annual value of 6.3 (W. Zhang et al., 2016b). \r Similarly, a record-breaking number of TCs were observed in the eastern North Pacific, particularly in the western part of that domain \r (Collins et al., 2016; Murakami et al., 2017b). These extraordinary TC activities were related to the average SST anomaly during that \r year, which were associated with the 2015-2016 extreme El Nino and the positive phase of the Pacific Meridional Mode (Murakami \r et al., 2017b; Hong et al., 2018; Yamada et al., 2019). However, it has been suggested that the intense TC activities in both the western \r and the eastern North Pacific in 2015 were not only due to the El Nino, but also to a contribution of anthropogenic forcing (Murakami \r [...]"


id: 26c810acc7ac3a88587eba2ee8939670
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"[...] et al., 2017b; S.-H. Yang et al., 2018). The impact of the Indian Ocean SST was also suggested to contribute to the extreme TC activity \r in the western North Pacific in 2015 (Zhan et al., 2018). In contrast, in Australia, it was the least active TC season since satellite records \r began in 1969-1970 (Blunden and Arndt, 2017)."


id: c448ac0ca3c407ad0a1b5e56c18fe98f
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"Global-scale temperature extremes and concurrent precipitation extremes in boreal 2018 spring and summer\r In the 2018 boreal spring-summer season (May-August), wide areas of the mid-latitudes in the Northern Hemisphere experienced \r heat extremes and (in part) enhanced drought (Box 11.4, Figure 2; Kornhuber et al., 2019; Vogel et al., 2019). The reported impacts \r included (Vogel et al., 2019): 90 deaths from heat strokes in Quebec (Canada); 1469 deaths from heat strokes in Japan (Shimpo et al., \r 2019); 48 heat-related deaths in the Republic of Korea (Min et al., 2020); heat warnings affecting 90,000 students in the USA; fires \r in numerous countries (Canada (British Columbia), USA (California), Finland (Lapland), Latvia); crop losses in the UK, Germany and \r Switzerland (Vogel et al., 2019) and overall in central and Northern Europe (leading to yield reductions of up to 50% for the main \r [...]"


id: 8c2cbeaab2a0fd0012378eaca8ba855
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content:
"[...] crops (Toreti et al., 2019); fish deaths in Switzerland; and melting of roads in the Netherlands and the UK, among others. In addition \r to the numerous hot and dry extremes, an extremely heavy rainfall event occurred over wide areas of Japan from 28 June to 8 July \r 2018 (Tsuguti et al., 2018), which was followed by a heatwave (Shimpo et al., 2019). The heavy precipitation event caused more than \r 230 deaths in Japan, and was named 'the Heavy Rain Event of July 2018'. "


id: aa36b5db877ac2be2acfaa893d27f165
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content:
"global mean temperature anomaly around that time (for 2017; SR1.5). This study also found that events similar to the 2018 May-July \r temperature extremes would approximately occur two out of three years under +1.5degC of global warming, and every year under +2degC \r of global warming. Imada et al. (2019) also suggest that the mean annual occurrence of extreme hot days in Japan will be expected to \r increase by 1.8 times under a global warming level of 2degC above pre-industrial levels. Kawase et al. (2020) showed that the extreme \r rainfall in Japan during this event was increased by approximately 7% due to recent rapid warming around Japan. Imada et al. (2020) \r showed that the probability of the Heavy Rain Event of July 2018 in Japan was increased from 0.22% to 2.00% due to anthropogenic \r warming. Hence, it is virtually certain that these 2018 concurrent events would not have occurred without human-induced global \r [...]"


id: d12286ccb95e397d9be8084bbce1e6f6
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content:
"[...] warming. Concurrent events of this type are also projected to happen more frequently under higher levels of global warming. However, \r there is currently low confidence in projected changes in the frequency or strength of the anomalous circulation patterns leading to \r concurrent extremes (e.g., Cross-Chapter Box 10.1). "


id: fbb6e308dcf49ebe62a1dc75e0501630
created_at: 2023-04-19 13:22:47
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vector_id: 41654
content:
"In summary, the 2015-2016 extreme El Nino and the 2018 boreal spring/summer extremes were two examples of recent concurrent \r extremes. The El Nino event in 2015-2016 was one of the three extreme El Nino events since the 1980s, and there are many extreme \r events concurrently observed in this period including droughts, heavy precipitation, and more frequent intense tropical cyclones. Both \r the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period (medium \r confidence), suggesting that global extremes similar to those associated with the 2015-2016 extreme El Nino would occur more \r frequently under further increases in global warming. The 2018 boreal spring/summer extremes were characterized by heat extremes \r and enhanced droughts in wide areas of the mid-latitudes in the Northern Hemisphere and extremely heavy rainfall in East Asia. These \r [...]"


id: 960c7225cabf3771a788cdc7094b284d
created_at: 2023-04-19 13:22:47
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vector_id: 23595
content:
"[...] concurrent events were generally related to the abnormal condition of the jet and North Pacific Subtropical High, but also amplified \r by background global warming. It is virtually certain that these 2018 concurrent extreme events would not have occurred without \r human-induced global warming. Recent years have seen a more frequent occurrence of such concurrent events. However, it is still \r unknown which types of concurrent extreme events could occur under increasing global warming."


id: 3c8e47af6adc6ec4ecc304c59fccf3df
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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vector_id: 7846
content:
"This section complements the assessments of changes in temperature \r extremes (Section  11.3), heavy precipitation (Section  11.4), and \r droughts (Section  11.6), by providing additional regional details. \r Regional changes in floods are assessed in Chapter  12. Owing to \r the large number of regions and space limitations, the regional \r assessment for each of the AR6 reference regions (see Section 1.5.2.2 \r for a  description) is presented here in a  set of tables. The tables \r are organized according to types of extremes (temperature, \r heavy precipitation, droughts) for Africa (Tables 11.4-11.6), Asia \r (Tables  11.7-11.9), Australasia (Tables 11.10-11.12), Central and \r South America (Tables 11.13-11.15), Europe (Tables 11.16-11.18), \r and North America (Tables 11.19-11.21). Each table contains \r regional assessments for observed changes, the human contribution \r to the observed changes, and projections of changes in these \r [...]"


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index: climate_gpt_v2
vector_id: 38928
content:
"[...] extremes at 1.5degC, 2degC and 4degC of global warming. A synthesis of \r regional changes in hot extremes, heavy precipitation, agricultural \r and ecological droughts, and hydrological droughts can be found in \r the Chapter 11 Appendix in Table 11.A.2."


id: 9bb0c517bcd2f052d848b0e6b6ec6b74
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"Tables 11.5, 11.8, 11.11, 11.14, 11.17, and 11.20 include assessments \r for past changes in heavy precipitation events and their attribution, as \r well as future projections. The evidence is mostly drawn from changes \r in metrics based on one-day or five-day precipitation amounts, as \r addressed in Section  11.4. Similar to temperature extremes, the \r assessment of changes in heavy precipitation uses global studies, \r including Dunn et al. (2020) and Sun et al. (2021) for observed \r changes, and Li et al. (2021) and the Chapter  11 Supplementary \r Material (11.SM) for projected changes using the CMIP6 multi-model \r ensemble. For attribution, Paik et al. (2020) provided continental \r analyses where data coverage was sufficient, but no attribution \r studies based on global data are available for the regional scale. \r For each region, regional studies, and studies based on the CMIP5 \r multi-model ensemble or CORDEX simulations, are also considered in \r [...]"


id: 2de71f4fdae17657da69c3ab30d752fd
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"Tables 11.4, 11.7, 11.10, 11.13, 11.16, and 11.19 include assessments \r for past temperature extremes and their attribution, as well as future \r projections. The evidence is mostly drawn from changes in metrics based \r on daily maximum and minimum temperatures, similar to those used \r in Section 11.3. The regional assessments start from global studies that \r used consistent analyses for all regions globally with sufficient data. \r This includes Dunn et al. (2020) for observed changes, and Li et al. (2021) \r and the Chapter 11 Supplementary Material (11.SM) for projections \r with the CMIP6 multi-model ensemble. Evidence from regional studies, \r and those based on the CMIP5 multi-model ensemble or CORDEX \r simulations, are then used to refine the confidence assessments. \r For attribution, Seong et al. (2020) provide a consistent analysis for \r AR6 regions, and Z. Wang et al. (2017a) for SREX regions. Additional \r regional studies, including event attribution analyses (Section  11.2), \r [...]"


id: e7be80fe69ffec4060f1e48dacc2a85c
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"[...] are used when available. In some regions that were not analysed \r in Seong et al. (2020), and those with no known event attribution \r studies, medium confidence of a  human contribution is assessed: \r when there is strong evidence of changes from observations that are \r in the direction of model-projected changes for the future; when the \r magnitude of projected changes increases with global warming; and \r where there is no other evidence to the contrary. This assessment is \r further supported by an understanding of how temperature extremes \r change with the mean temperature and overwhelming evidence of \r a  human contribution to the observed larger-scale changes in the \r mean temperature and temperature extremes. "


id: e0f600fe0d61e1d903a27c8ee55a4ba3
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 36843
content:
"Meteorological droughts are assessed based on observed and \r projected changes in precipitation-only metrics such as the \r Standardized Precipitation Index (SPI) and Consecutive Dry Days \r (CDD). Observed changes are assessed based on two global \r studies, Dunn et al. (2020) for CDD, and Spinoni et al. (2019) for SPI. \r For  projections, evidence for changes at 1.5degC and 2degC of global \r warming is drawn from L. Xu et al. (2019) and Touma et al. (2015) \r (based on RCP8.5 for 2010-2054 compared to 1961-2005) for SPI \r (CMIP5) and the Chapter  11 Supplementary Material (11.SM) for \r CDD (CMIP6). For projections at 4degC of global warming, evidence is \r drawn from several sources, including Touma et al. (2015) and Spinoni \r et al. (2020) for SPI (from CMIP5 and CORDEX, respectively), and \r 11.SM for CDD (CMIP6). No global-scale studies are available for the \r attribution of meteorological drought, so this assessment is based on \r [...]"


id: 181488abfde848efdde9bc8697269a6b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 1446
content:
"Climate change will also manifest differently for different weather regimes and can lead to contrasting changes \r in average and extreme conditions. Observations of the recent past and climate model projections show that, \r in most places, changes in daily temperatures are dominated by a general warming where the climatological \r average and extreme values are shifted towards higher temperatures, making warm extremes more frequent and \r cold extremes less frequent. The top panels in FAQ 11.1, Figure 1 show projected changes in surface temperature \r for long-term average conditions (left) and for extreme hot days (right) during the warm season (summer in mid\u0002to high latitudes). Projected increases in long-term average temperature differ substantially between different \r places, varying from less than 3degC in some places in central South Asia and southern South America to over 7degC \r [...]"


id: 4d3e6ee527c4dab4e3837e13a9c4c07
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"[...] in some places in North America, North Africa and the Middle East. Changes in extreme hot days follow changes \r in average conditions quite closely, although, in some places, the warming rates for extremes can be intensified \r (e.g., southern Europe and the Amazon basin) or weakened (e.g., northern Asia and Greenland) compared to \r average values."


id: 9ba440f4fcc0c3887ad83bfdf07247a6
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 24635
content:
"Recent observations and global and regional climate model projections point to changes in precipitation \r extremes (including both rainfall and snowfall extremes) differing drastically from those in average precipitation. \r The bottom panels in FAQ 11.1, Figure 1 show projected changes in the long-term average precipitation (left) \r and in heavy precipitation (right). Averaged precipitation changes show striking regional differences, with \r substantial drying in places such as southern Europe and northern South America and wetting in places such as \r the Middle East and southern South America. Changes in extreme precipitation are much more uniform, with \r systematic increases over nearly all land regions. The physical reasons behind the different responses of averaged \r and extreme precipitation are now well understood. The intensification of extreme precipitation is driven by \r [...]"


id: be5d0fd21ac73498b67ab702eb808eac
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 30755
content:
"[...] the increase in atmospheric water vapour (about 7% per 1degC of warming near the surface), although this is \r modulated by various dynamical changes. In contrast, changes in average precipitation are driven not only by \r moisture increases but also by slower processes that constrain future changes over the globe to only 2-3% per \r 1degC of warming near the surface."


id: 91930c684cb1a9877180a136ebed471e
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 22835
content:
"Human-induced climate change has already affected many aspects of the climate system. In addition to the increase \r in global surface temperature, many types of weather and climate extremes have changed. In most regions, the \r frequency and intensity of hot extremes have increased and those of cold extremes have decreased. The frequency \r and intensity of heavy precipitation events have increased at the global scale and over a majority of land regions. \r Although extreme events such as land and marine heatwaves, heavy precipitation, drought, tropical cyclones, and \r associated wildfires and coastal flooding have occurred in the past and will continue to occur in the future, they \r often come with different magnitudes or frequencies in a warmer world. For example, future heatwaves will last \r longer and have higher temperatures, and future extreme precipitation events will be more intense in several \r [...]"


id: b47b3aab0e014cf728313fdec0db8807
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 28999
content:
"[...] regions. Certain extremes, such as extreme cold, will be less intense and less frequent with increasing warming. "


id: db9d3dcd86cdcf68ad43220b2f3580a0
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 35929
content:
"It is common to question whether human-caused climate change caused a major weather- and climate-related \r disaster. When extreme weather and climate events do occur, both exposure and vulnerability play an important \r role in determining the magnitude and impacts of the resulting disaster. As such, it is difficult to attribute a specific \r disaster directly to climate change. However, the relatively new science of event attribution enables scientists to \r attribute aspects of specific extreme weather and climate events to certain causes. Scientists cannot answer directly \r whether a particular event was caused by climate change, as extremes do occur naturally, and any specific weather \r and climate event is the result of a complex mix of human and natural factors. Instead, scientists quantify the relative \r importance of human and natural influences on the magnitude and/or probability of specific extreme weather \r [...]"


id: a44b47fc1b23f93b2048b120fbee84b4
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 26099
content:
"[...] events. Such information is important for disaster risk reduction planning, because improved knowledge about \r changes in the probability and magnitude of relevant extreme events enables better quantification of disaster risks."


id: 168f0d9aa3892453d779de86ec084733
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 1181
content:
"On a case-by-case basis, scientists can now quantify the contribution of human influences to the magnitude and \r probability of many extreme events. This is done by estimating and comparing the probability or magnitude of \r the same type of event between the current climate - including the increases in greenhouse gas concentrations \r and other human influences - and an alternate world \r where the atmospheric greenhouse gases remained \r at pre-industrial levels. FAQ 11.3 Figure  1  illustrates \r this approach using differences in temperature and \r probability between the two scenarios as an example. \r Both the pre-industrial (blue) and current (red) \r climates experience hot extremes, but with different \r probabilities and magnitudes. Hot extremes of a given \r temperature have a  higher probability of occurrence \r in the warmer current climate than in the cooler pre\u0002industrial climate. Additionally, an extreme hot event \r of a particular probability will be warmer in the current \r [...]"


id: 6a658152992c7acd56a338456baf5258
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 15999
content:
"[...] climate than in the pre-industrial climate. Climate model \r simulations are often used to estimate the occurrence \r of a specific event in both climates. The change in the \r magnitude and/or probability of the extreme event \r in the current climate compared to the pre-industrial \r climate is attributed to the difference between the two \r scenarios, which is the human influence.\r FAQ 11.3: Climate change and extreme events\r Extreme events have become more probable and more intense. \r Many of these changes can be attributed to human influence \r on the climate.\r Same temperature \r but more probable\r Current climate\r Same probability \r but hotter Pre-industrial \r climate\r Cooler Temperature Warmer\r Annual \r chance \r of event\r More \r probable\r Probabilities are lower \r for hotter (more extreme) events\r Less \r probable"


id: 9e5d6e9ae87c68900a0a819f8ec7f731
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 25088
content:
"FAQ 11.3, Figure  1 | Changes in climate result in changes in the \r magnitude and probability of extremes. Example of how temperature \r extremes differ between a climate with pre-industrial greenhouse gases (shown \r in blue) and the current climate (shown in orange) for a representative region. \r The horizontal axis shows the range of extreme temperatures, while the vertical \r axis shows the annual chance of each temperature event's occurrence. Moving \r towards the right indicates increasingly hotter extremes that are more rare (less \r probable). For hot extremes, an extreme event of a particular temperature in the \r pre-industrial climate would be more probable (vertical arrow) in the current \r climate. An event of a certain probability in the pre-industrial climate would \r be warmer (horizontal arrow) in the current climate. While the climate under \r greenhouse gases at the pre-industrial level experiences a  range of hot \r [...]"


