Editing IPCC:AR6/WGI/Chapter-11 (section)

== 11.9 Regional Information on Extremes ==

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This section complements the assessments of changes in temperature extremes ( [[#11.3|Section 11.3]] ), heavy precipitation ( [[#11.4|Section 11.4]] ), and droughts ( [[#11.6|Section 11.6]] ), by providing additional regional details. Regional changes in floods are assessed in Chapter 12. Owing to the large number of regions and space limitations, the regional assessment for each of the AR6 reference regions (see [[IPCC:Wg1:Chapter:Chapter-1#1.5.2.2|Section 1.5.2.2]] for a description) is presented here in a set of tables. The tables are organized according to types of extremes (temperature, heavy precipitation, droughts) for Africa (Tables 11.4–11.6), Asia (Tables 11.7–11.9), Australasia (Tables 11.10–11.12), Central and South America (Tables 11.13–11.15), Europe (Tables 11.16–11.18), and North America (Tables 11.19–11.21). Each table contains regional assessments for observed changes, the human contribution to the observed changes, and projections of changes in these extremes at 1.5°C, 2°C and 4°C of global warming. A synthesis of regional changes in hot extremes, heavy precipitation, agricultural and ecological droughts, and hydrological droughts can be found in the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Appendix in Table 11.A.2.

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=== 11.9.1 Overview ===

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Sections 11.9.2, 11.9.3 and 11.9.4 provide brief summaries of the underlying evidence used to derive the regional assessments for temperature extremes, heavy precipitation events, and droughts, respectively. The assessments take into account evidence from studies based on global datasets (global studies), as well as regional studies. Global studies include analyses for all continents and AR6 regions with sufficient data coverage, and provide an important basis for cross-region consistency, as the same data and methods are used for all regions. However, individual regional studies may include additional information that is missed in global studies, and thus provide an important regional calibration for the assessment.

The assessments are presented using the calibrated confidence and likelihood language (Box 1.1). ''Low confidence'' is assessed when there is ''limited evidence'' , either because of a lack of available data in the region and/or a lack of relevant studies. ''Low confidence'' is also assessed when there is a lack of agreement on the evidence of a change, which may be due to large variability or inconsistent changes depending on the considered sub-regions, time frame, models, assessed metrics, or studies. In cases when the evidence is strongly contradictory, for example, with substantial regional changes of opposite sign, ‘mixed signal’ is indicated. With an assessment of ''low confidence'' , the direction of change is not indicated in the tables. A direction of change (increase or decrease) is provided with an assessment of ''medium confidence'' , ''high confidence'' , ''likely'' , or higher likelihood levels. Likelihood assessments are only provided in the case of ''high confidence'' . In some cases, there may be confidence in a small or no change.

For projections, changes are assessed at three global warming levels (GWLs; Cross-Chapter Box 11.1): 1.5°C, 2°C and 4°C. The assessments use literature based both on GWL projections and scenario-based projections. In the case of literature on scenario-based projections, a mapping between scenarios/time frames and GWLs was performed, as documented in Cross-Chapter Box 11.1. Projections of changes in temperature and precipitation extremes are assessed relative to two different baselines: the recent past (1995–2014) and pre-industrial (1850–1900). With smaller changes relative to the variability, in particular because droughts happen on longer timescales compared to extremes of daily temperature and precipitation, it is more difficult to distinguish changes in drought relative to the recent past. As such, changes in droughts are assessed relative to the pre-industrial baseline, unless indicated otherwise.

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=== 11.9.2 Temperature Extremes ===

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Tables 11.4, 11.7, 11.10, 11.13, 11.16, and 11.19 include assessments for past temperature extremes and their attribution, as well as future projections. The evidence is mostly drawn from changes in metrics based on daily maximum and minimum temperatures, similar to those used in [[#11.3|Section 11.3]] . The regional assessments start from global studies that used consistent analyses for all regions globally with sufficient data. This includes [[#Dunn--2020|Dunn et al. (2020)]] for observed changes, and [[#Li--2021|Li et al. (2021)]] and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) for projections with the CMIP6 multi-model ensemble. Evidence from regional studies, and those based on the CMIP5 multi-model ensemble or CORDEX simulations, are then used to refine the confidence assessments. For attribution, Seong et al. (2020) provide a consistent analysis for AR6 regions, and Z. [[#Wang--2017a|Wang et al. (2017a)]] for SREX regions. Additional regional studies, including event attribution analyses ( [[#11.2|Section 11.2]] ), are used when available. In some regions that were not analysed in Seong et al. (2020), and those with no known event attribution studies, ''medium confidence'' of a human contribution is assessed: when there is strong evidence of changes from observations that are in the direction of model-projected changes for the future; when the magnitude of projected changes increases with global warming; and where there is no other evidence to the contrary. This assessment is further supported by an understanding of how temperature extremes change with the mean temperature and overwhelming evidence of a human contribution to the observed larger-scale changes in the mean temperature and temperature extremes.

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=== 11.9.3 Heavy Precipitation ===

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Tables 11.5, 11.8, 11.11, 11.14, 11.17, and 11.20 include assessments for past changes in heavy precipitation events and their attribution, as well as future projections. The evidence is mostly drawn from changes in metrics based on one-day or five-day precipitation amounts, as addressed in [[#11.4|Section 11.4]] . Similar to temperature extremes, the assessment of changes in heavy precipitation uses global studies, including [[#Dunn--2020|Dunn et al. (2020)]] and [[#Sun--2021|Sun et al. (2021)]] for observed changes, and [[#Li--2021|Li et al. (2021)]] and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) for projected changes using the CMIP6 multi-model ensemble. For attribution, [[#Paik--2020|Paik et al. (2020)]] provided continental analyses where data coverage was sufficient, but no attribution studies based on global data are available for the regional scale. For each region, regional studies, and studies based on the CMIP5 multi-model ensemble or CORDEX simulations, are also considered in the assessments for past changes, attribution, and projections.

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=== 11.9.4 Droughts ===

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Tables 11.6, 11.9, 11.12, 11.15, 11.18, and 11.21 provide regional assessments on past, attributed and projected changes in droughts. The assessment is subdivided in three drought categories corresponding to four drought types: i) meteorological droughts, ii) agricultural and ecological droughts, and iii) hydrological droughts (see [[#11.6|Section 11.6]] ). A list of metrics and global studies used for the assessments is provided below. The evidence from global studies is complemented in each continent with evidence from regional studies. An overview of studies considered for the assessments in projections is provided in Table 11.3.

Meteorological droughts are assessed based on observed and projected changes in precipitation-only metrics such as the Standardized Precipitation Index (SPI) and Consecutive Dry Days (CDD). Observed changes are assessed based on two global studies, [[#Dunn--2020|Dunn et al. (2020)]] for CDD, and [[#Spinoni--2019|Spinoni et al. (2019)]] for SPI. For projections, evidence for changes at 1.5°C and 2°C of global warming is drawn from L. [[#Xu--2019|]] [[#Xu--2019|Xu et al. (2019)]] and [[#Touma--2015|Touma et al. (2015)]] (based on RCP8.5 for 2010–2054 compared to 1961–2005) for SPI (CMIP5) and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) for CDD (CMIP6). For projections at 4°C of global warming, evidence is drawn from several sources, including [[#Touma--2015|Touma et al. (2015)]] and [[#Spinoni--2020|Spinoni et al. (2020)]] for SPI (from CMIP5 and CORDEX, respectively), and 11.SM for CDD (CMIP6). No global-scale studies are available for the attribution of meteorological drought, so this assessment is based on regional detection and attribution or event attribution studies.

Agricultural and ecological droughts are primarily assessed based on observed and projected changes in total column soil moisture, complemented by evidence on changes in surface soil moisture, water-balance (precipitation minus evapotranspiration (ET)) and metrics driven by precipitation and atmospheric evaporative demand (AED) such as the SPEI and PDSI ( [[#11.6|Section 11.6]] ). In the latter, only studies including estimates based on the Penman–Monteith equation (SPEI-PM and PDSI-PM) are considered because of biases associated with temperature-only approaches ( [[#11.6|Section 11.6]] ). ''Medium'' to ''high confidence'' in drying was assigned in the assessment for arid regions if a signal was also identifiable in total soil moisture in addition to surface soil moisture or metrics that combine AED and precipitation, which tend to dry more in these regions. For observed changes, evidence is drawn from several sources: [[#Padrón--2020|Padrón et al. (2020)]] for changes in precipitation minus ET, as well as soil moisture from the multi-model Land Surface Snow and Soil Moisture Model Intercomparison Project within CMIP6 (11.SM; [[#van%20Den%20Hurk--2016|van Den Hurk et al., 2016]] ); [[#Greve--2014|Greve et al. (2014)]] for changes in precipitation minus ET, and precipitation minus AED; [[#Spinoni--2019|Spinoni et al. (2019)]] for changes in SPEI-PM; and [[#Dai--2017|Dai and Zhao (2017)]] for changes in PDSI-PM.

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'''Table 11.3 |''' '''Global analyses considered for the assessments of drought projections.''' MET refers to meteorological droughts, AGR/ECOL to agricultural and ecological droughts, and HYDR to hydrological droughts.

{| class="wikitable"
|-
! Reference

! Model Data <sup>a</sup>

! Index <sup>a</sup>

! Drought Type

! Projection Horizons

! Baseline

|-
| 11.SM

| CMIP6

| CDD, Soil moisture (total, surface)

| MET

| 1.5°C, 2°C, 4°C

| 1850–1900

|-
| [[#Cook--2020|Cook et al. (2020)]]

| CMIP6

| Soil moisture (total, surface), runoff (total, surface)

| AGR/ECOL, HYDR

| 2071–2011, SSP1-2.6 (about 2°C, Cross-Chapter Box 11.1; Table 4.2)

2071–2011, SSP3-7-3 (about 4°C, Cross-Chapter Box 11.1; Table 4.2)

| 1850–1900

|-
| L. [[#Xu--2019|]] [[#Xu--2019|Xu et al. (2019)]]

| CMIP5

| SPI, soil moisture (total, surface)

| MET, AGR/ECOL

| 1.5°C, 2°C

| 1971–2000

|-
| [[#Touma--2015|Touma et al. (2015)]]

| CMIP5

| SPI, SRI

| MET, HYDR

| 2010–2054, RCP8.5 (about 1.5°C; Cross-Chapter Box 11.1 and 11.SM.1)

2055–2099, RCP8.5 (about 3.5°C, Cross-Chapter Box 11.1 and 11.SM.1)

| 1961–2005

|-
| [[#Spinoni--2020|Spinoni et al. (2020)]]

| CORDEX (CMIP5 driving GCMs, RCMs)

| SPI

| MET

| 2071–2100, RCP4.5 (about 2.5°C, Cross-Chapter Box 11.1 and 11.SM.1)

2071–2100, RCP8.5 (about 4.5°C, Cross-Chapter Box 11.1 and 11.SM.1)

| 1981–2010

|-
| [[#Naumann--2018|Naumann et al. (2018)]]

| One GCM (EC-EARTH3-HR v3.1) driven with SST fields from seven CMIP5 GCMs

| SPEI-PM

| AGR/ECOL

| 1.5°C, 2°C, (3°C)

| 0.6°C

|-
| [[#Vicente-Serrano--2020c|Vicente-Serrano et al. (2020c)]]

| CMIP5

| SPEI-PM

| AGR/ECOL

| 2070–2100, RCP8.5 (about 4.5°C, Cross-Chapter Box 11.1 and 11.SM.1)

| 1970–2000

|-
| [[#Giuntoli--2015|Giuntoli et al. (2015)]]

| ISI-MIP (six GHMs and five CMIP5 GCMs)

| Low-flows days

| HYDR

| 2066–2099, RCP8-5 (about 4°C, Cross-Chapter Box 11.1 and 11.SM.1)

| 1972–2005

|-
| J. [[#Zhai--2020|]] [[#Zhai--2020|Zhai et al. (2020)]]

| One GHM (VIC) driven by four CMIP5 GCMs

| Extreme low runoff

| HYDR

| 1.5°C, 2°C

| 2006–2015

|}

<sup>a</sup> CMIP5 and CMIP6: Coupled Model Intercomparison Project Phases 5/6; CORDEX: Coordinated Regional Downscaling Experiment; GCMs: global climate models; RCMs: regional climate models; SST: sea surface temperatures; ISI-MIP: Inter-Sectoral Impact Model Intercomparison Project; GHMs: Global Hydrological Models; CDD: consecutive dry days index; SPI: Standardized Precipitation Index; SRI: Standardized Runoff Index; SPEI-PM: Penman–Monteith-based Standardized Precipitation Evapotranspiration Index.

For projections at 1.5°C of global warming, evidence is drawn from: L. [[#Xu--2019|]] [[#Xu--2019|Xu et al. (2019)]] , based on CMIP5; 11.SM based on CMIP6 for changes in total column and surface soil moisture; and from [[#Naumann--2018|Naumann et al. (2018)]] for changes in SPEI-PM, based on EC-Earth simulations driven with SSTs from seven CMIP5 Earth system models. For projections at 2°C of global warming, evidence is drawn from L. [[#Xu--2019|]] [[#Xu--2019|Xu et al. (2019)]] based on CMIP5, and [[#Cook--2020|Cook et al. (2020)]] (SSP1-2.6, 2071–2100 compared to pre-industrial) and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) based on CMIP6, for changes in total column and surface soil moisture; evidence is also drawn from [[#Naumann--2018|Naumann et al. (2018)]] for changes in SPEI-PM. For projections at 4°C of global warming, evidence is mostly drawn from: [[#Cook--2020|Cook et al. (2020)]] (SSP3-7.0, 2071–2100) and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) based on CMIP6 for changes in total column and surface soil moisture; and from [[#Vicente-Serrano--2020c|Vicente-Serrano et al. (2020c)]] for changes in SPEI-PM based on CMIP5. No global-scale studies with regional-scale information are available for the attribution of agricultural and ecological droughts, so this assessment is based on regional detection and attribution or event attribution studies.

Hydrological droughts are assessed based on observed and projected changes in low flows, complemented by information on changes in mean runoff. For observed changes, evidence is drawn from three studies ( [[#Dai--2017|Dai and Zhao, 2017]] ; [[#Gudmundsson--2019|Gudmundsson et al., 2019]] , 2021). For projected changes at 1.5°C of global warming, evidence is drawn from [[#Touma--2015|Touma et al. (2015)]] based on analyses of the Standardized Runoff Index (SRI) (CMIP5, based on 2010–2054 compared to 1961–2005), complemented with regional studies when available. For projected changes at 2°C of global warming, evidence is also drawn from [[#Cook--2020|Cook et al. (2020)]] for changes in runoff in CMIP6 (Scenario SSP1-2.6, 2071–2100), and from J. [[#Zhai--2020|]] [[#Zhai--2020|Zhai et al. (2020)]] for changes in low flows based on simulations with a single model. For projected changes at 4°C of global warming, evidence is drawn from: [[#Touma--2015|Touma et al. (2015)]] based on CMIP5 analyses of SRI; [[#Cook--2020|Cook et al. (2020)]] for changes in surface and total runoff based on CMIP6; and [[#Giuntoli--2015|Giuntoli et al. (2015)]] for changes in low flows based on the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) based on six Global Hydrological Models (GHMs) and five GCMs, including an analysis of inter-model signal-to-noise ratio. One global-scale study with regional-scale information is available for the attribution of hydrological droughts ( [[#Gudmundsson--2021|Gudmundsson et al., 2021]] ), but only in a few AR6 regions. This information was complemented with evidence from regional detection and attribution, and event attribution studies when available.

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