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

==== 4.4.1.2 Spatial Patterns of Surface Warming ====

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Consistent with AR5 and earlier assessments, Figure 4.12 shows for SSP1-2.6 and SSP3-7.0 that the largest warming occurs at high latitudes, particularly in boreal winter in the Arctic ( [[#4.5.1.1|Section 4.5.1.1]] ), and larger warming over land than over the ocean ( [[#4.5.1.1|Section 4.5.1.1]] ). In both scenarios, the increase in seasonal mean surface temperatures over many NH land regions exceeds 1°C relative to 1995–2014. In the near term, the two scenarios show surface temperature changes that are similar in magnitude. The trajectories for well-mixed GHGs, and as a consequence the effective radiative forcing, in the scenarios have not yet diverged that much ( [[#O’Neill--2016|O’Neill et al., 2016]] ; [[#Riahi--2017|Riahi et al., 2017]] ). Based on the currently available CMIP6 models, regions that do not show robust warming in the near-term include the northern North Atlantic, parts of India, parts of North America and Eurasia in winter, and the subtropical eastern Pacific in the Southern Hemisphere.

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[[File:01a88c300b9a720e313ba0e3579d1219 IPCC_AR6_WGI_Figure_4_12.png]]

'''Figure 4.12''' '''|''' '''Near-term change of seasonal mean surface temperature.''' Displayed are projected spatial patterns of CMIP6 multi-model mean change (°C) in '''(top)''' December–January–February (DJF) and '''(bottom)''' June–July–August (JJA) near-surface air temperature for 2021–2040 from SSP1-2.6 and SSP3-7.0 relative to 1995–2014. 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 ( [[#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 ERF patterns from aerosols and well-mixed GHGs are distinct (Chapter 7), and warming patterns therefore depend on the precise mix of forcing agents in the scenarios. The spatial efficacies – the change in surface temperature per unit ERF – for CO <sub>2</sub> , sulphate and black carbon aerosols and solar forcing have been recently evaluated in climate models ( [[#Modak--2016|Modak et al., 2016]] , 2018; [[#Duan--2018|Duan et al., 2018]] ; [[#Modak--2019|Modak and Bala, 2019]] ; [[#Richardson--2019|Richardson et al., 2019]] ). On average, the spatial patterns of near-surface warming are largely similar for different external drivers ( [[#Xie--2013|Xie et al., 2013]] ; [[#Richardson--2019|Richardson et al., 2019]] ; [[#Samset--2020|Samset et al., 2020]] ), despite the patterns of forcing being different and despite the large spread across different models ( [[#Richardson--2019|Richardson et al., 2019]] ).

Internal variability in near-surface temperature change is large in many regions, particularly in mid-latitudes and polar regions ( [[#Hawkins--2012|Hawkins and Sutton, 2012]] ). Projections from individual realizations can therefore exhibit divergent regional responses in the near-term in areas where the amplitude of a forced signal is relatively small compared to internal variability ( [[#Deser--2012b|Deser et al., 2012b]] , 2014, 2016).

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