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

=== 11.3.1 Mechanisms and Drivers ===

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The SREX (IPCC, 2012) and AR5 (IPCC, 2014) concluded that greenhouse gas forcing is the dominant factor for the increases in the intensity, frequency, and duration of warm extremes and the decrease in those of cold extremes. This general global-scale warming is modulated by large-scale atmospheric circulation patterns, as well as by feedbacks such as soil moisture-evapotranspiration–temperature and snow/ice-albedo–temperature feedbacks, and local forcings such as land-use change or changes in aerosol concentrations at the regional and local scales (Sections 11.1.5 and 11.1.6, and Box 11.1). Therefore, changes in temperature extremes at regional and local scales can have heterogeneous spatial distributions. Changes in the magnitudes (or intensities) of extreme temperatures are often larger than changes in global surface temperature, because of larger warming on land than on the ocean surface ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] ), and because of feedbacks, though they are of similar magnitude to changes in the local mean temperature (Figure 11.2).

Extreme temperature events are associated with large-scale meteorological patterns ( [[#Grotjahn--2016|Grotjahn et al., 2016]] ). Quasi-stationary anticyclonic circulation anomalies or atmospheric blocking events are linked to temperature extremes in many regions, such as in Australia ( [[#Parker--2014|Parker et al., 2014]] ; [[#Perkins-Kirkpatrick--2016|Perkins-Kirkpatrick et al., 2016]] ), Europe ( [[#Brunner--2017|Brunner et al., 2017]] , 2018; [[#Schaller--2018|Schaller et al., 2018]] ), Eurasia ( [[#Yao--2017|Yao et al., 2017]] ), Asia ( [[#Chen--2016|Chen et al., 2016]] ; [[#Ratnam--2016|Ratnam et al., 2016]] ; [[#Rohini--2016|Rohini et al., 2016]] ), and North America ( [[#Yu--2018|Yu et al., 2018]] , 2019; [[#Zhang--2019|Zhang and Luo, 2019]] ). Mid-latitude planetary wave modulations affect short-duration temperature extremes such as heatwaves ( [[#Perkins--2015|Perkins, 2015]] ; [[#Kornhuber--2020|Kornhuber et al., 2020]] ). The large-scale modes of variability (Annex IV) affect the strength, frequency and persistence of these meteorological patterns and, hence, temperature extremes. For example, cold and warm extremes in the mid-latitudes are associated with atmospheric circulation patterns such as the Pacific-North American (PNA) pattern, as well as atmosphere–ocean coupled modes such as Pacific Decadal Variability (PDV), the North Atlantic Oscillation (NAO), and Atlantic Multi-decadal Variability (AMV) ( [[#11.1.5|Section 11.1.5]] ; [[#Kamae--2014|Kamae et al., 2014]] ; [[#Johnson--2018|Johnson et al., 2018]] ; [[#Ruprich-Robert--2018|Ruprich-Robert et al., 2018]] ; [[#Yu--2018|Yu et al., 2018]] , 2020; [[#Müller--2020|Müller et al., 2020]] ; [[#Qasmi--2021|Qasmi et al., 2021]] ). Changes in the modes of variability in response to warming would therefore affect temperature extremes ( [[#Clark--2013|Clark and Brown, 2013]] ; [[#Horton--2015|Horton et al., 2015]] ). The level of confidence in those changes varies, both in the observations and in future projections, affecting the level of confidence in changes in temperature extremes in different regions. As highlighted in Chapters 2 to 4 of this Report, it is ''likely'' that there have been observational changes in the extratropical jets and mid-latitude jet meandering ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] and Cross-Chapter Box 10.1). There is ''low confidence'' in possible effects of Arctic warming on mid-latitude temperature extremes (Cross-Chapter Box 10.1). A large portion of the multi-decadal changes in extreme temperature remains after the removal of the effect of these modes of variability, and can be attributed to human influence ( [[#Kamae--2017b|Kamae et al., 2017b]] ; [[#Wan--2019|Wan et al., 2019]] ). Thus, global warming dominates changes in temperature extremes at the regional scale and it is ''very unlikely'' that dynamic responses to greenhouse-gas induced warming would alter the direction of these changes.

Land–atmosphere feedbacks strongly modulate regional- and local-scale changes in temperature extremes ( ''high confidence'' ) ( [[#11.1.6|Section 11.1.6]] ; [[#Seneviratne--2013|Seneviratne et al., 2013]] ; [[#Lemordant--2016|Lemordant et al., 2016]] ; [[#Donat--2017|Donat et al., 2017]] ; [[#Sillmann--2017b|Sillmann et al., 2017b]] ; [[#Hirsch--2019|Hirsch et al., 2019]] ). This effect is particularly notable in mid-latitude regions where the drying of soil moisture amplifies high temperatures, especially through increases in sensible heat flux ( [[#Whan--2015|Whan et al., 2015]] ; [[#Douville--2016|Douville et al., 2016]] ; [[#Vogel--2017|Vogel et al., 2017]] ). Land–atmosphere feedbacks amplifying temperature extremes also include boundary-layer feedbacks and effects on atmospheric circulation ( [[#Miralles--2014a|Miralles et al., 2014a]] ; [[#Schumacher--2019|Schumacher et al., 2019]] ). Soil-moisture–temperature feedbacks affect past and present-day heatwaves in observations and model simulations, both locally ( [[#Miralles--2014a|Miralles et al., 2014a]] ; [[#Cowan--2016|Cowan et al., 2016]] , 2020; [[#Hauser--2016|Hauser et al., 2016]] ; [[#Meehl--2016|Meehl et al., 2016]] ; [[#Wehrli--2019|Wehrli et al., 2019]] ) and beyond the regions of feedback occurrence through changes in regional circulation patterns ( [[#Stéfanon--2014|Stéfanon et al., 2014]] ; [[#Koster--2016|Koster et al., 2016]] ; [[#Sato--2019|Sato and Nakamura, 2019]] ). The uncertainty due to the representation of land–atmosphere feedbacks in ESMs is a cause of discrepancy between observations and simulations ( [[#Clark--2006|Clark et al., 2006]] ; [[#Mueller--2014|Mueller and Seneviratne, 2014]] ; [[#Meehl--2016|Meehl et al., 2016]] ). The decrease of plant transpiration or the increase of stomata resistance under enhanced CO <sub>2</sub> concentrations is a direct CO <sub>2</sub> forcing of land temperatures (warming due to reduced evaporative cooling), which contributes to higher warming on land ( [[#Lemordant--2016|Lemordant et al., 2016]] ; [[#Vicente-Serrano--2020b|Vicente-Serrano et al., 2020b]] ). The snow/ice-albedo feedback plays an important role in amplifying temperature variability in the high latitudes ( [[#Diro--2018|Diro et al., 2018]] ) and can be the largest contributor to the rapid warming of cold extremes in the mid- and high latitudes of the Northern Hemisphere ( [[#Gross--2020|Gross et al., 2020]] ).

Regional external forcings, including land-use changes and emissions of anthropogenic aerosols, play an important role in the changes of temperature extremes in some regions ( ''high confidence'' ) ( [[#11.1.6|Section 11.1.6]] ). Deforestation may have contributed to about one third of the warming of hot extremes in some mid-latitude regions since the pre-industrial time ( [[#Lejeune--2018|Lejeune et al., 2018]] ). Aspects of agricultural practice, including no-till farming, irrigation, and overall cropland intensification, may cool hot temperature extremes ( [[#Davin--2014|Davin et al., 2014]] ; N.D. [[#Mueller--2016|]] [[#Mueller--2016|Mueller et al., 2016]] ). For instance, cropland intensification has been suggested to be responsible for a cooling of the highest temperature percentiles in Midwest USA (N.D. [[#Mueller--2016|]] [[#Mueller--2016|Mueller et al., 2016]] ). Irrigation has been shown to be responsible for a cooling of hot temperature extremes of up to 1°C–2°C in many mid-latitude regions in the present climate (Thieryet al., 2017, 2020), a process not represented in most of state-of-the-art ESMs (CMIP5, CMIP6). Double cropping may have led to increased hot extremes in the inter-cropping season in part of China ( [[#Jeong--2014|Jeong et al., 2014]] ). Rapid increases in summer warming in western Europe and north-east Asia since the 1980s are linked to a reduction in anthropogenic aerosol precursor emissions over Europe ( [[#Nabat--2014|Nabat et al., 2014]] ; [[#Dong--2016b|Dong et al., 2016b]] , 2017), in addition to the effect of increased greenhouse gas forcing (see also [[IPCC:Wg1:Chapter:Chapter-10#10.1.3.1|Section 10.1.3.1]] ). This effect of aerosols on temperature-related extremes is also noted for declines in short-lived anthropogenic aerosol emissions over North America ( [[#Mascioli--2016|Mascioli et al., 2016]] ). On the local scale, the urban heat island (UHI) effect results in higher temperatures in urban areas than in their surrounding regions, and contributes to warming in regions of rapid urbanization, in particular for nighttime temperature extremes (Box 10.3; [[#Phelan--2015|Phelan et al., 2015]] ; [[#Chapman--2017|Chapman et al., 2017]] ; Y. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ). But these local and regional forcings are generally not or not well represented in the CMIP5 and CMIP6 simulations (see also [[#11.3.3|Section 11.3.3]] ), contributing to uncertainty in model simulated changes.

In summary, greenhouse gas forcing is the dominant driver leading to the warming of temperature extremes. At regional scales, changes in temperature extremes are modulated by changes in large-scale patterns and modes of variability, feedbacks including soil-moisture–evapotranspiration–temperature or snow/ice–albedo–temperature feedbacks, and local and regional forcings such as land-use and land-cover changes, or aerosol concentrations, and decadal and multi-decadal natural variability. This leads to heterogeneity in regional changes and their associated uncertainties ( ''high confidence'' ). Changes in anthropogenic aerosol concentrations have ''likely'' affected trends in hot extremes in some regions. Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as the Midwestern USA ( ''medium confidence'' ). Urbanization has ''likely'' exacerbated the effects of global warming in cities, in particular for nighttime temperature extremes.

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