Editing IPCC:AR6/WGI/Chapter-4 (section)

==== 4.5.1.1 Near-surface Air Temperature ====

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Patterns of near-surface air temperature changes show widespread warming by 2041–2060 and 2081–2100 (Figure 4.19) for all SSPs relative to 1995–2014. The area fraction experiencing warming increases with the level of global mean warming. As GSAT continues to increase, it is ''very likely'' that by the middle and the end of the 21st century most of the global land and ocean areas will be warmer than in 1995–2014 ( ''high confidence'' , [[#4.3.1.1|Section 4.3.1.1]] ).

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[[File:d43b7f71006864008c202a2838895a53 IPCC_AR6_WGI_Figure_4_19.png]]

'''Figure 4.19 |''' '''Mid-and long-term change of annual mean surface temperature.''' Displayed are projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2041–2060 and 2081–2100 relative to 1995–2014 for '''(top)''' SSP1-2.6 and '''(bottom)''' SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and ''likely'' emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (see [[#4.2.6|Section 4.2.6]] ) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

The multi-model mean temperature change pattern (Figure 4.19) shows some robust key characteristics that are independent of the time horizon and scenario, such as a land–ocean warming contrast, amplified warming over the Arctic region, assessed below, or the comparatively small warming or even cooling in the North Atlantic subpolar gyre (Section 9.2.1.1). Furthermore, changes in aerosol concentrations and land use and land management can have a direct imprint on the regional warming pattern ( [[#Bright--2017|Bright et al., 2017]] ; [[#Kasoar--2018|Kasoar et al., 2018]] ). Note that the global average of the pattern shown in Figure 4.19 corresponds to CMIP6 multi-model mean GSAT warming ( [[#4.3.1|Section 4.3.1]] ) and is thus somewhat warmer than the warming pattern consistent with the central estimate of the GSAT range assessed in [[#4.3.4|Section 4.3.4]] . Since the regional mean warming scales well with global warming levels independent of the emissions scenario ( [[#4.2.4|Section 4.2.4]] ), the key characteristics of the spatial pattern assessed here are largely independent of the difference between CMIP6 multi-model global mean and assessed global GSAT change.

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===== 4.5.1.1.1 Land–ocean warming contrast =====

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It is ''virtually certain'' that future average warming will be higher over land than over the ocean. [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] of SRCCL (G. [[#Jia--2019|]] [[#Jia--2019|Jia et al., 2019]] ) assessed that it is certain that land temperatures have increased more than global mean temperatures since the pre-industrial period. This so-called land–ocean warming contrast is a striking feature of observed trends ( [[#Lambert--2007|Lambert and Chiang, 2007]] ; [[#Byrne--2018|Byrne and O’Gorman, 2018]] ) and projected changes in surface-air temperature ( [[#Sutton--2007|Sutton et al., 2007]] ; [[#Joshi--2008|Joshi and Gregory, 2008]] ; [[#Dong--2009|Dong et al., 2009]] ; [[#Lambert--2011|Lambert et al., 2011]] ; [[#Drost--2012|Drost et al., 2012]] ; [[#Bayr--2013|Bayr and Dommenget, 2013]] ; [[#Byrne--2013b|Byrne and O’Gorman, 2013b]] ; [[#Izumi--2013|Izumi et al., 2013]] ; [[#Joshi--2013|Joshi et al., 2013]] ). Between 1979 and 2016, average temperature over land increased by 42% more than over the ocean ( [[#Byrne--2018|Byrne and O’Gorman, 2018]] ). A similar warming contrast is found in CMIP5 projections though with large differences across models and latitudes ( [[#Sutton--2007|Sutton et al., 2007]] ; [[#Drost--2012|Drost et al., 2012]] ; [[#Byrne--2013b|Byrne and O’Gorman, 2013b]] ; [[#Joshi--2013|Joshi et al., 2013]] ), which is also consistent with paleoclimate evidence ( [[#Izumi--2013|Izumi et al., 2013]] ; [[#Schmidt--2014|Schmidt et al., 2014]] ). The ratio of land-to-ocean warming is greater than one for almost all regions ( ''high confidence'' ) and is larger for dry subtropical continents (about 1.5) than for moist regions in the tropics and mid-latitudes (about 1.2; [[#Byrne--2013a|Byrne and O’Gorman, 2013a]] ). Projected warming over land and ocean only is shown in Table 4.2 for different scenarios, and the global average ratio of land-to-ocean warming in CMIP6 is 1.5 with a ''likely'' range of 1.4 to 1.7, which is consistent with estimates based on CMIP5.

Since AR5, a robust physical understanding of the warming contrast been developed. A simple theory based on atmospheric dynamics and moisture transport shows that surface-air temperature and relative humidity over land are strongly coupled, and demonstrates that the warming contrast occurs because air over land is drier than over the ocean ( [[#Joshi--2008|Joshi et al., 2008]] ; [[#Byrne--2013a|Byrne and O’Gorman, 2013a]] , b, 2018). The warming contrast causes land relative humidity to decrease ( [[#Byrne--2016|Byrne and O’Gorman, 2016]] , 2018; [[#Chadwick--2016|Chadwick et al., 2016]] ) and this feeds back on and strengthens the warming contrast. Differences in land-relative humidity responses across models are the primary cause of uncertainty in the land–ocean warming contrast ( [[#Byrne--2013b|Byrne and O’Gorman, 2013b]] ). These land-relative humidity changes are ultimately controlled by moisture transport between the land and ocean boundary layers ( [[#Byrne--2016|Byrne and O’Gorman, 2016]] ; [[#Chadwick--2016|Chadwick et al., 2016]] ) and are also sensitive to characteristics of land surfaces that are challenging to model, including stomatal conductance and soil moisture ( [[#Berg--2016|Berg et al., 2016]] ; [[#Zarakas--2020|Zarakas et al., 2020]] ).

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===== 4.5.1.1.2 Polar amplification =====

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It is ''very likely'' that under all SSPs the warming in the Arctic will be more pronounced than in the global average over the 21st century. Since AR5 the understanding of the physical mechanisms driving polar amplification has improved.

The Arctic surface is projected to warm more than the global average over the 21st century, with annual-average Arctic warming of about 3°C (SSP1-2.6), 10°C (SSP3-7.0) and 12°C in (SSP5-8.5) by 2081–2100 relative to 1995–2014 (Figure 4.19). This phenomenon, known as polar or Arctic amplification, is a ubiquitous feature of the response to GHG forcing simulated by climate models ( [[#Manabe--1975|Manabe and Wetherald, 1975]] , 1980; [[#Manabe--1980|Manabe and Stouffer, 1980]] ; [[#Robock--1983|Robock, 1983]] ; [[#Hansen--1984|Hansen et al., 1984]] ; [[#Manabe--1991|Manabe et al., 1991]] ; [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Winton--2006|Winton, 2006]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ) and has been observed over recent decades concurrent with Arctic sea ice loss ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1|Section 2.3.2.1]] ; [[#Serreze--2011|Serreze and Barry, 2011]] ). Based on robust scientific understanding and agreement across multiple lines of evidence (Section 7.4.4.1), there is ''high confidence'' that the rate of Arctic surface warming will continue to exceed the global average over the 21st century.

A variety of mechanisms contribute to Arctic amplification (Section 7.4.4.1.1). While surface-albedo feedbacks associated with the loss of sea ice and snow have long been known to play important roles ( [[#Arrhenius--1896|Arrhenius, 1896]] ; [[#Manabe--1980|Manabe and Stouffer, 1980]] ; [[#Robock--1983|Robock, 1983]] ; [[#Hall--2004|Hall, 2004]] ), it is now recognized that temperature (lapse-rate and Planck) feedbacks also contribute to Arctic amplification through a less efficient longwave radiative damping to space with warming at high latitudes ( [[#Winton--2006|Winton, 2006]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Goosse--2018|Goosse et al., 2018]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ). Increases in poleward atmospheric latent heat transport and oceanic heat transport also contribute to Arctic warming ( [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Bitz--2006|Bitz et al., 2006]] ; [[#Lee--2011|Lee et al., 2011]] , [[#Lee--2017|Lee et al., 2017]] ; [[#Alexeev--2013|Alexeev and Jackson, 2013]] ; [[#Marshall--2014|Marshall et al., 2014]] , 2015; [[#Woods--2016|Woods and Caballero, 2016]] ; [[#Nummelin--2017|Nummelin et al., 2017]] ; [[#Singh--2017|Singh et al., 2017]] ; [[#Merlis--2018|Merlis and Henry, 2018]] ; [[#Oldenburg--2018|Oldenburg et al., 2018]] ; [[#Armour--2019|Armour et al., 2019]] ; [[#Beer--2020|Beer et al., 2020]] ). Projected reduction in the strength of the AMOC over the 21st century is expected to reduce Arctic warming, but even a strong AMOC reduction would not eliminate Arctic amplification entirely ( ''medium confidence'' ) ( [[#Liu--2017|Liu et al., 2017]] ; [[#Liu--2018|Liu et al., 2018]] ; [[#Wen--2018|Wen et al., 2018]] ).

There remains substantial uncertainty in the magnitude of projected Arctic amplification ( [[#Smith--2020|Smith et al., 2020]] ), with the Arctic warming ranging from two to four times the global average in models ( [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Nummelin--2017|Nummelin et al., 2017]] ). This uncertainty primarily stems from different representations of polar surface-albedo, lapse-rate, and cloud feedbacks, and from different projected poleward energy transport changes ( [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Crook--2011|Crook et al., 2011]] ; [[#Mahlstein--2011|Mahlstein and Knutti, 2011]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Bonan--2018|Bonan et al., 2018]] ). The magnitude of Arctic amplification may also depend on the mix of radiative forcing agents ( [[#Najafi--2015|Najafi et al., 2015]] ; [[#Sand--2016|Sand et al., 2016]] ; [[#Stjern--2019|Stjern et al., 2019]] ) such as the contribution of ozone depleting substances ( [[#Polvani--2020|Polvani et al., 2020]] ). Tropospheric aerosol emissions tend to reduce simulated Arctic warming over the middle of the 20th century ( [[#Gagné--2017b|Gagné et al., 2017b]] ) and consequently aerosol emission reductions in observations and SSP scenarios enhance simulated Arctic warming over recent and future decades (Section 6.4.3; [[#Gagné--2015|Gagné et al., 2015]] ; [[#Acosta%20Navarro--2016|Acosta Navarro et al., 2016]] ; [[#Wobus--2016|Wobus et al., 2016]] ; [[#Wang--2018|Wang et al., 2018]] ).

Climate models project a weaker polar amplification in the SH than in the NH under transient warming (Figure 4.19). Model simulations ( [[#Hall--2004|Hall, 2004]] ; [[#Danabasoglu--2009|Danabasoglu and Gent, 2009]] ; [[#Li--2013|Li et al., 2013]] ) and paleoclimate proxies indicate polar amplification in both hemispheres near equilibrium, but generally with less warming in the Antarctic than the Arctic (Section 7.4.4.1.2). The primary driver of delayed warming of the southern high latitudes is the upwelling in the Southern Ocean and associated ocean heat uptake that is then transported away from Antarctica by northward flowing surface waters ( [[#Frölicher--2015|Frölicher et al., 2015]] ; [[#Marshall--2015|Marshall et al., 2015]] ; [[#Armour--2016|Armour et al., 2016]] ; [[#Liu--2018|Liu et al., 2018]] ), although asymmetries in feedbacks between the poles also play a role (Section 7.4.4.1.1). Changes in westerly surface winds over the Southern Ocean have the potential to affect the rate of sea-surface warming, but there is currently ''low confidence'' in even the sign of the effect based on a diverse range of climate model responses to wind changes ( [[#Marshall--2014|Marshall et al., 2014]] ; [[#Ferreira--2015|Ferreira et al., 2015]] ; [[#Kostov--2017|Kostov et al., 2017]] ; [[#Seviour--2019|Seviour et al., 2019]] ). A substantial increase in freshwater input to the ocean from the Antarctic ice sheet could further slow the emergence of SH polar amplification by cooling the Southern Ocean surface ( [[#Bronselaer--2018|Bronselaer et al., 2018]] ; [[#Golledge--2019|Golledge et al., 2019]] ; [[#Schloesser--2019|Schloesser et al., 2019]] ), but this process is not represented in current climate models which lack dynamic ice sheets. Thus, while there is ''high confidence'' that the SH high latitudes will warm by more than the tropics on centennial time scales, there is ''low confidence'' that such a feature will emerge this century (Section 7.4.4.1).

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====== Seasonal warming patterns ======

The warming pattern shows distinct seasonal characteristics. The majority of models show a stronger hemispheric winter than summer warming over land poleward of about 55°N and 55°S (Figure 4.20) and thereby a reduced amplitude of the temperature cycle ( [[#Dwyer--2012|Dwyer et al., 2012]] ; [[#Donohoe--2013|Donohoe and Battisti, 2013]] ). On the other hand, over most of the subtropics and mid-latitudinal land regions except for parts of Asia, models project stronger warming in hemispheric summer than winter ( [[#Donohoe--2013|Donohoe and Battisti, 2013]] ; [[#Santer--2018|Santer et al., 2018]] ), leading to an amplification of the seasonal cycle. This phenomenon has been studied particularly in the case of the amplified summer warming over the Mediterranean region ( [[#Seager--2014a|Seager et al., 2014a]] ; [[#Kröner--2017|Kröner et al., 2017]] ; [[#Brogli--2019|Brogli et al., 2019]] ).

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[[File:ed083936f8e40287a47ab4d11c8445d8 IPCC_AR6_WGI_Figure_4_20.png]]

'''Figure 4.20''' '''|''' '''Difference of surface temperature change between June–July–August (JJA) and December–January–February (DJF).''' Displayed are spatial patterns of multi-model mean difference in projected warming in JJA minus warming in DJF in 2081–2100 relative to 1995–2014 for '''(left)''' SSP1-2.6 and '''(right)''' SSP3-7.0. Diagonal lines mark areas where fewer than 80% of the models agree on the sign of change, and no overlay where at least 80% of the models agree. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

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===== 4.5.1.1.3 Changes in temperature variability =====

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It has long been recognized that along with mean temperatures also variance and skewness of the temperature distribution may be changing ( [[#Gregory--1995|Gregory and Mitchell, 1995]] ; [[#Mearns--1997|Mearns et al., 1997]] ). By amplifying or dampening changes in the tail of temperature distribution such changes are potentially highly relevant to extremes (Section 11.3.1) and pose a serious challenge to adaptation measures. Changes in temperature variability can occur from diurnal to multi-decadal time scales and from the local to the global scale with potentially even opposing signals in different seasons and at the different spatial scales

Changes in GSAT variability are poorly understood. Based on model experiments it has been suggested that unforced variability of GSAT tends to decrease in a warmer world as a result of reduced albedo variability in high latitudes resulting from melting snow and sea ice ( [[#Huntingford--2013|Huntingford et al., 2013]] ; [[#Brown--2017|Brown et al., 2017]] ), but ''confidence'' remains ''low'' and an observed change has not been detected. An assessment of changes in global temperature variability is inherently challenging due to the interplay of unforced internal variability and forced changes.

Changes in tropical temperature variability may arise from changes in the amplitude of ENSO ( [[#4.5.3.2|Section 4.5.3.2]] ). Over the extratropics, several studies have identified robust large-scale patterns of changes in variability of annual and particularly seasonal mean temperature, including (i) a reduction in mid- to high-latitude winter temperature variability and (ii) an increase in summer temperature variability over land in the tropics and subtropics ( [[#Huntingford--2013|Huntingford et al., 2013]] ; [[#Holmes--2016|Holmes et al., 2016]] ; see Figure 4.21). The multi-ensemble average across seven single-model initial-condition large ensembles projects a consistent reduction in year-to-year December–January–February (DJF) variability around about 50°N–70°N and June–July–August (JJA) variability around 55°S–70°S along the edge of the sea ice- and snow-covered region (Figure 4.21). There is growing evidence that year-to-year and day-to-day temperature variability decreases in winter over northern mid- to high-latitudes ( [[#Fischer--2011|Fischer et al., 2011]] ; [[#De%20Vries--2012|De Vries et al., 2012]] ; [[#Screen--2014|Screen, 2014]] ; [[#Schneider--2015|Schneider et al., 2015]] ; [[#Holmes--2016|Holmes et al., 2016]] ; [[#Borodina--2017|Borodina et al., 2017]] ; [[#Tamarin-Brodsky--2020|Tamarin-Brodsky et al., 2020]] ) which implies that the lowest temperatures rise more than the respective climatological mean temperatures ( ''medium confidence'' ). Over the NH, reduced high-latitude temperature variability is associated with disproportionally large warming in source region of cold-air advection due to Arctic amplification and land–sea contrast ( [[#De%20Vries--2012|De Vries et al., 2012]] ; [[#Screen--2014|Screen, 2014]] ; [[#Holmes--2016|Holmes et al., 2016]] ). It has further been argued that a reduction in snow and sea ice coverage from partly to completely snow- and ice-free ocean and land surface would substantially reduce cold-season temperature variability ( [[#Gregory--1995|Gregory and Mitchell, 1995]] ; [[#Fischer--2011|Fischer et al., 2011]] ; [[#Borodina--2017|Borodina et al., 2017]] ) and lead to a shortening of the cold season and earlier onset of the warm season ( [[#Cassou--2016|Cassou and Cattiaux, 2016]] ). Mid-latitudinal winter temperature variability is further affected by a complex interplay of a multitude of processes including potential changes in atmospheric circulation, but there is ''low confiden'' ce in the dominant contribution of Arctic warming compared to other drivers ( [[IPCC:Wg1:Chapter:Chapter-10#cross-chapter-box-10.1|Cross-Chapter Box 10.1]] ).

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[[File:fcdb5520d6e52e8460074f9c7ca3f057 IPCC_AR6_WGI_Figure_4_21.png]]

'''Figure''' '''4.21 |''' '''Percentage change in interannual variability of (left) December–January–February (DJF) and (right) June–July–August (JJA) mean temperature averaged across seven large initial condition ensembles.''' Average changes across seven single-model, initial-condition large ensembles are shown for RCP8.5 in 2081–2100 (and where not available for 2080–2099) relative to 1995–2014. Standard deviations are calculated across all members of the large ensembles for every given year to avoid inflation due to the underlying trend and then averaged across the period. Changes are averaged across the ensembles MPI-GE (100 members, [[#Maher--2019|Maher et al., 2019]] ), CanESM2 (50 members, [[#Kirchmeier-Young--2017|Kirchmeier-Young et al., 2017]] ), NCAR-CESM (30 members, [[#Kay--2015|Kay et al., 2015]] ), GFDL-CM3 (20 members, [[#Sun--2018|Sun et al., 2018]] ), GFDL-ESM2M (30 members, [[#Rodgers--2015|Rodgers et al., 2015]] ), CSIRO-Mk3-6-0 (30 members, [[#Jeffrey--2013|Jeffrey et al., 2013]] ) and EC-EARTH (16 members, [[#Hazeleger--2010|Hazeleger et al., 2010]] ). Also see [[#Deser--2020|Deser et al. (2020)]] for further information on those ensembles. Diagonal lines indicate areas with low model agreement where fewer than 80% of the models agree on the sign of the change, and no overlay areas with high model agreement where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In JJA, the multi-model average projects an increase in year-to-year JJA variability over Central Europe and North America (Figure 4.21). In particular an increase in daily to interannual summer temperature variability has been projected over central Europe as a result of larger year-to-year variability in soil moisture conditions varying between a wet and dry regime and leading to enhanced land–atmosphere interaction ( [[#Seneviratne--2006|Seneviratne et al., 2006]] ; [[#Fischer--2012|Fischer et al., 2012]] ; [[#Holmes--2016|Holmes et al., 2016]] ). Furthermore, the amplified warming in the source regions of warm-air advection due to land–ocean warming contrast and amplified Mediterranean warming ( [[#Seager--2014a|Seager et al., 2014a]] ; [[#Brogli--2019|Brogli et al., 2019]] ), may lead to disproportionally strong warming of the hottest days and summers and thereby increased variability. Enhanced temperature variability is further projected over some land regions in the subtropics and tropics ( [[#Bathiany--2018|Bathiany et al., 2018]] ).

In summary, there is ''medium confidence'' that continued warming will regionally lead to increased and decreased year-to-year temperature variability in the extratropics and there is ''medium confidence'' that year-to-year temperature variability will decrease over parts of the mid- to high- latitudes of the winter hemisphere.

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