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

=== 11.1.4 Effects of Greenhouse Gas and Other External Forcings on Extremes ===

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The SREX, AR5, and SR1.5 assessed that there is evidence from observations that some extremes have changed since the mid-20th century, that some of the changes are a result of anthropogenic influences, and that some observed changes are projected to continue into the future. Additionally, other changes are projected to emerge from natural climate variability under enhanced global warming (SREX Chapter 3; AR5 Chapter 10).

At the global scale, and also at the regional scale to some extent, many of the changes in extremes are a direct consequence of enhanced radiative forcing, and the associated global warming and/or resultant increase in the water-holding capacity of the atmosphere, as well as changes in vertical stability and meridional temperature gradients that affect climate dynamics (see Box 11.1). Widespread observed and projected increases in the intensity and frequency of hot extremes, together with decreases in the intensity and frequency of cold extremes, are consistent with global and regional warming ( [[#11.3|Section 11.3]] and Figure 11.2). Extreme temperatures on land tend to increase more than the global mean temperature (Figure 11.2), due in large part to the land–sea warming contrast, and additionally to regional feedbacks in some regions ( [[#11.1.6|Section 11.1.6]] ). Increases in the intensity of temperature extremes scale robustly, and in general linearly, with global warming across different geographical regions in projections up to 2100, with minimal dependence on emissions scenarios ( [[#11.2.4|Section 11.2.4]] , Figure 11.3,and Cross-Chapter Box 11.1; [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Kharin--2018|Kharin et al., 2018]] ). The frequency of hot temperature extremes (see Figure 11.6), the number of heatwave days and the length of heatwave seasons in various regions also scale well, but nonlinearly (because of threshold effects, [[#11.2.1|Section 11.2.1]] ), with global mean temperatures ( [[#Wartenburger--2017|Wartenburger et al., 2017]] ; Y. [[#Sun--2018a|]] [[#Sun--2018|Sun et al., 2018]] a ).

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'''Figure 11.2 |''' '''Time series of observed temperature anomalies for global average annual mean temperature (black), land average annual mean temperature (green), land average annual hottest daily maximum temperature (TXx, purple), and land average annual coldest daily minimum temperature (TNn, blue).''' Global and land mean temperature anomalies are relative to their 1850–1900 means and are based on the multi-product mean annual time series assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.3|Section 2.3.1.1.3]] (see text for references). TXx and TNn anomalies are relative to their respective 1961–1990 means and are based on the HadEX3 dataset ( [[#Dunn--2020|Dunn et al., 2020]] ) using values for grid boxes with at least 90% temporal completeness over 1961–2018. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Changes in annual maximum one-day precipitation (Rx1day) are proportional to mean global surface temperature changes, at about 7% increase per 1°C of warming, that is, following the Clausius–Clapeyron relation (Box 11.1), both in observations ( [[#Westra--2013|Westra et al., 2013]] ) and in future projections ( [[#Kharin--2013|Kharin et al., 2013]] ) at the global scale. Extreme short-duration precipitation in North America also scales with global surface temperature ( [[#Prein--2016b|Prein et al., 2016b]] ; [[#Li--2019a|]] [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|C. Li et al., 2019]] a ). At the local and regional scales, changes in extremes are also strongly modulated and controlled by regional forcings and feedback mechanisms ( [[#11.1.6|Section 11.1.6]] ), whereby some regional forcings, for example, associated with changes in land cover and land use or aerosol emissions, can have non-local or some (non-homogeneous) global-scale effects. In general, there is ''high confidence'' in changes in extremes due to global-scale thermodynamic processes (i.e., global warming, mean moistening of the air) as the processes are well understood, while the confidence in those related to dynamic processes or regional and local forcing, including regional and local thermodynamic processes, is much lower due to multiple factors (see the following subsection and Box 11.1).

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[[File:8e0a33b4b21ac32fe7915a86e48b0251 IPCC_AR6_WGI_Figure_11_3.png]]

'''Figure 11.3 |''' '''Regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and the global land area (except Antarctica), against changes in global mean surface air temperature (GSAT) as simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) models under different Shared Socio-economic Pathway (SSP) forci''' '''ng scena''' '''ri''' '''os, SSP1''' '''-1.9''' ''', SSP1''' '''-2.6''' ''', SSP2''' '''-4.5,''' '''SSP3-7.''' ''0, and SSP5-8.5.'' Changes in TXx and GSAT are relative to the 1850–1900 baseline, and changes in GSAT are expressed as global warming level. ''(a)'' Individual models from the CMIP6 ensemble (grey), the multi-model median under three selected SSPs (colours), and the multi-model median (black); ''(b) to (l)'' Multi-model median for the pooled data for individual AR6 regions. Numbers in parentheses indicate the linear scaling between regional TXx and GSAT. The black line indicates the 1:1 reference scaling between TXx and GSAT. See Atlas.1.3.2 for the definition of regions. Changes in TXx are also displayed in the Interactive Atlas. For details on the methods, see Supplementary Material 11.SM.2.

Since AR5, the attribution of extreme weather events, or the investigation of changes in the frequency and/or magnitude of individual and local- and regional-scale extreme weather events due to various drivers ( [[#11.2.3|Section 11.2.3]] and Cross-Working Group Box 1.1) has provided evidence that greenhouse gases and other external forcings have affected individual extreme weather events. The events that have been studied are geographically uneven. For example, extreme rainfall events in the UK ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Vautard--2016|Vautard et al., 2016]] ; [[#Otto--2018b|Otto et al., 2018b]] ) or heatwaves in Australia ( [[#King--2014|King et al., 2014]] ; [[#Perkins-Kirkpatrick--2016|Perkins-Kirkpatrick et al., 2016]] ; [[#Lewis--2017b|Lewis et al., 2017b]] ) have spurred more studies than other events. Many highly impactful extreme weather events have not been studied in the event attribution framework. Studies in the developing world are also generally lacking. This is due to various reasons ( [[#11.2|Section 11.2]] ) including lack of observational data, lack of reliable climate models and other problems ( [[#Otto--2020|Otto et al., 2020]] ). While the events that have been studied are not representative of all extreme events that occurred, and results from these studies may also be subject to selection bias, the large number of event attribution studies provide evidence that changes in the properties of these local and individual events are in line with expected consequences of human influence on the climate and can be attributed to external drivers ( [[#11.9|Section 11.9]] ). Figure 11.4 summarizes assessments of observed changes in temperature extremes, in heavy precipitation and in droughts, and their attribution in a map form.

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'''Figure 11.4 |''' '''Overview of observed changes for cold, hot, and wet extremes and their potential human contribution.''' Shown are the direction of change and the confidence in: 1) the observed changes in cold and hot as well as wet extremes across the world; and 2) whether human-induced climate change contributed to causing these changes (attribution). In each region changes in extremes are indicated by colour (orange – increase in the type of extreme; blue – decrease; both colours – changes of opposing direction within the region, with the signal depending on the exact event definition; grey – there are no changes observed; and no fill – the data/evidence is too sparse to make an assessment). The squares and dots next to the symbol indicate the level of confidence for observing the trend and the human contribution, respectively. The more black dots/squares, the higher the level of confidence. The information on this figure is based on regional assessment of the literature on observed trends, detection and attribution and event attribution in [[#11.9|Section 11.9]] .

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Box 11.1 | Thermodynamic and Dynamic Changes in Extremes Across Scales

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Changes in weather and climate extremes are determined by local exchanges in heat, moisture, and other related quantities (thermodynamic changes) and those associated with atmospheric and oceanic motions (dynamic changes). While thermodynamic and dynamic processes are interconnected, considering them separately helps to disentangle the roles of different processes contributing to changes in climate extremes (e.g., [[#Shepherd--2014|Shepherd, 2014]] ).

'''Temperature extremes'''

An increase in the concentration of greenhouse gases in the atmosphere leads to the warming of tropospheric air and the Earth’s surface. This direct thermodynamic effect leads to warmer temperatures everywhere, with an increase in the frequency and intensity of warm extremes, and a decrease in the frequency and intensity of cold extremes. The initial increase in temperature leads to other thermodynamic responses and feedbacks affecting the atmosphere and the surface. These include an increase in the water vapour content of the atmosphere (water vapour feedback, see [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.2|Section 7.4.2.2]] ) and a change in the vertical profile of temperature (lapse rate feedback, see [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.2|Section 7.4.2.2]] ). While the water vapour feedback always amplifies the initial temperature increases (positive feedback), the lapse rate feedback amplifies near-surface temperature increases (positive feedback) in mid- and high latitudes but reduces temperature increases (negative feedback) in tropical regions ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ).

Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations and model simulations show that temperature increases, including extreme temperatures, are amplified in areas where seasonal snow cover is reduced due to decreases in surface albedo (see [[#11.3.1|Section 11.3.1]] ). In some mid-latitude areas, temperature increases are amplified by the higher atmospheric evaporative demand ( [[#Fu--2014|Fu and Feng, 2014]] ; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ) that results in a drying of soils in some regions ( [[#11.6|Section 11.6]] ), leading to increased sensible heat fluxes (soil-moisture – temperature feedback, see Sections 11.1.6 and 11.3.1 for more background). Other thermodynamic feedback processes include changes in the water-use efficiency of plants under enhanced atmospheric carbon dioxide (CO <sub>2</sub> ) concentrations that can reduce the overall transpiration, and thus also enhance temperature in projections (Sections 8.2.3.3, 11.1.6, 11.3 and 11.6).

Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the characteristics of weather patterns (e.g., [[#Suarez-Gutierrez--2020a|Suarez-Gutierrez et al., 2020a]] ). For example, a robust thermodynamic effect of polar amplification is a weakened north-south temperature gradient, which amplifies the warming of cold extremes in the Northern Hemisphere mid- and high latitudes because of the reduction of cold air advection ( [[#Holmes--2015|Holmes et al., 2015]] ; [[#Schneider--2015|Schneider et al., 2015]] ; [[#Gross--2020|Gross et al., 2020]] ). Much less robust is the dynamic effect of polar amplification ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.4.1|Section 7.4.4.1]] ) and the reduced low-altitude meridional temperature gradient that has been linked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent increases in temperature extremes (Cross-Chapter Box 10.1; [[#Francis--2012|Francis and Vavrus, 2012]] ; Coumou et al. , 2015, 2018; Mann et al., 2017).

'''Precipitation extremes'''

Changes in temperature also control changes in water vapour through increases in evaporation and in the water-holding capacity of the atmosphere ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ). At the global scale, column-integrated water vapour content increases roughly following the Clausius–Clapeyron (C-C) relation, with an increase of approximately 7% per 1°C of global-mean surface warming ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ). Nonetheless, at regional scales, water vapour increases differ from this C-C rate due to several reasons ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.2|Section 8.2.2]] ), including a change in weather regimes and limitations in moisture transport from the ocean, which warms more slowly than land ( [[#Byrne--2018|Byrne and O’Gorman, 2018]] ). Observational studies ( [[#Fischer--2016|Fischer and Knutti, 2016]] ; [[#Sun--2021|Sun et al., 2021]] ) have shown that the observed rate of increased precipitation extremes is similar to the C-C rate at the global scale. Climate model projections show that the increase in water vapour leads to robust increases in precipitation extremes everywhere, with a magnitude that varies between 4% and 8% per 1°C of surface warming (thermodynamic contribution, Box 11.1, Figure 1b). At regional scales, climate models show that the dynamic contribution (Box 11.1, Figure 1c) can be substantial and strongly modify the projected rate of change of extreme precipitation (Box 11.1, Figure 1a) with large regions in the subtropics showing robust reductions and other areas (e.g., equatorial Pacific) showing robust amplifications (Box 11.1, Figure 1c). However, the dynamic contributions show large differences across models and are more uncertain than thermodynamic contributions (Box 11.1, Figure 1c; [[#Shepherd--2014|Shepherd, 2014]] ; [[#Trenberth--2015|Trenberth et al., 2015]] ; [[#Pfahl--2017|Pfahl et al., 2017]] ).

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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). (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 contribution estimated as the difference between the total changes and the thermodynamic contribution. Changes were derived from a linear regression for the period 1950–2100. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models (n=22) agree on the sign of change; diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 1. A detailed description of the estimation of dynamic and thermodynamic contributions is given in Pfahl 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 are available in the chapter data table (Table 11.SM.9).

Dynamic contributions can occur in response to changes in the vertical and horizontal distribution of temperature (thermodynamics) and can affect the frequency and intensity of synoptic and subsynoptic phenomena, including tropical cyclones, extratropical cyclones, fronts, mesoscale-convective systems and thunderstorms. For example, the poleward shift and strengthening of the Southern Hemisphere mid-latitude storm tracks ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ) can modify the frequency or intensity of extreme precipitation. However, the precise way in which dynamic changes will affect precipitation extremes is unclear due to several competing effects ( [[#Shaw--2016|Shaw et al., 2016]] ; [[#Allan--2020|Allan et al., 2020]] ).

Box 11.1

Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms that result from the extra latent heat released from the thermodynamic increases in moisture ( [[#Lackmann--2013|Lackmann, 2013]] ; Willison et al. , 2013; Marciano et al. , 2015; Nie et al. , 2018; [[#Mizuta--2020|Mizuta and Endo, 2020]] ). The extra latent heat released within storms has been shown to increase precipitation extremes by strengthening convective updrafts and the intensity of the cyclonic circulation (e.g., [[#Molnar--2015|Molnar et al., 2015]] ; [[#Nie--2018|Nie et al., 2018]] ), although weakening effects have also been found in mid-latitude cyclones (e.g., [[#Kirshbaum--2017|Kirshbaum et al., 2017]] ). Additionally, the increase in latent heat can also suppress convection at larger scales due to atmospheric stabilization ( [[#Nie--2018|Nie et al., 2018]] ; [[#Tandon--2018|Tandon et al., 2018]] ; [[#Kendon--2019|Kendon et al., 2019]] ). As these dynamic effects result from feedback processes within storms where convective processes are crucial, their proper representation might require improving the horizontal/vertical resolution, the formulation of parametrizations, or both, 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; Nie et al., 2018).

'''Droughts'''

Droughts are also affected by thermodynamic and dynamic processes (Sections 8.2.3.3 and 11.6). Thermodynamic processes affect droughts by increasing atmospheric evaporative demand ( [[#Martin--2018|Martin, 2018]] ; [[#Gebremeskel%20Haile--2020|Gebremeskel Haile et al., 2020]] ; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ) through changes in air temperature, radiation, wind speed, and relative humidity. Dynamic processes affect droughts through changes in the occurrence, duration and intensity of weather anomalies, which are related to precipitation and the amount of sunlight ( [[#11.6|Section 11.6]] ). While atmospheric evaporative demand increases with warming, regional changes in aridity are affected by increasing land–ocean warming contrast, vegetation feedbacks and responses to rising CO <sub>2</sub> concentrations, and dynamic shifts in the location of the wet and dry parts of the atmospheric circulation in response to climate change, as well as internal variability ( [[#Byrne--2015|Byrne and O’Gorman, 2015]] ; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Allan--2020|Allan et al., 2020]] ).

In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall increases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify heatwaves, droughts and heavy precipitation events when they occur ( ''high confidence'' ). Dynamic processes are often indirect responses to thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is ''low confidence'' in how dynamic changes affect the location and magnitude of extreme events in a warming climate.

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