Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.3.3.5 Performance at Simulating Regional Feedbacks ====

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Both SRCCL ( [[#Jia--2019|Jia et al., 2019]] ) and SROCC ( [[#Hock--2019|Hock et al., 2019]] ) highlight the weaknesses of climate models at simulating atmosphere–surface feedbacks. The performance at simulating some of these feedbacks is assessed below (climate feedbacks in urban areas are discussed in Box 10.3).

The snow-albedo feedback contributes to enhanced warming at high elevations ( [[IPCC:Wg1:Chapter:Chapter-8#8.5|Section 8.5]] ; [[#Pepin--2015|Pepin et al., 2015]] ). Global models often do not simulate it realistically due to their misrepresentation of orography in complex terrain ( [[#Hall--2014|Hall, 2014]] ; [[#Walton--2015|Walton et al., 2015]] ). The elevation dependence of historical warming, which is partly caused by the snow-albedo effect, is realistically represented across Europe by the ENSEMBLES RCMs ( [[#Kotlarski--2015|Kotlarski et al., 2015]] ). Some EURO-CORDEX RCMs simulate a spring snow–albedo feedback close to that observed, whereas others considerably overestimate it ( [[#Winter--2017|Winter et al., 2017]] ). In a multi-physics ensemble RCM experiment, the cold bias in north-eastern Europe is amplified by the albedo feedback ( [[#García-Díez--2015|García-Díez et al., 2015]] ). For the Rocky Mountains, RCM simulations generally reproduce the observed spatial and seasonal variability in snow cover, but strongly overestimate the snow albedo ( [[#Minder--2016|Minder et al., 2016]] ). There is ''high confidence'' ( ''medium evidence'' and ''high agreement'' ) that RCMs considerably improve the representation of the snow-albedo effect in complex terrain.

Soil-moisture feedbacks influence changes in both temperature and precipitation. More than 30% of CMIP5 models overestimate the influence of preceding precipitation (a proxy for soil moisture) on temperature extremes in Europe and the USA ( [[#Donat--2018|Donat et al., 2018]] ), and many CMIP5 models simulate an unrealistic influence of evaporation on temperature extremes for wet regions in Europe and the US ( [[#Ukkola--2018|Ukkola et al., 2018]] ). RCMs were found to realistically simulate the correlation between latent and sensible heat fluxes and temperature (coupling strength) over Africa ( [[#Knist--2017|Knist et al., 2017]] ; [[#Careto--2018|Careto et al., 2018]] ) and in northern and southern Europe, but to overestimate it in central Europe ( [[#Knist--2017|Knist et al., 2017]] ). Land surface models driven by global reanalysis agreed relatively well with observations. However, the coupling strength varied strongly across models at the regional scale, and a realistic partitioning of the incoming radiation into latent and sensible heat fluxes did not necessarily result in a realistic soil moisture-temperature coupling ( [[#Gevaert--2018|Gevaert et al., 2018]] ; [[#Boé--2020a|Boé et al., 2020a]] ).

Evaluating the representation of soil-moisture–precipitation feedbacks in climate models is challenging as different processes may induce feedbacks including moisture recycling, boundary-layer dynamics and mesoscale circulation. Moreover, the effects of soil moisture on precipitation may be region and scale dependent and may even change sign depending on the strength of the background flow ( [[#Taylor--2013|Taylor et al., 2013]] ; [[#Froidevaux--2014|Froidevaux et al., 2014]] ; [[#Guillod--2015|Guillod et al., 2015]] ; [[#Larsen--2016|Larsen et al., 2016]] ; [[#Tuttle--2016|Tuttle and Salvucci, 2016]] ). On seasonal-to-interannual time scales, CMIP5 models showed a stronger soil-moisture–precipitation feedback than estimated by satellite data ( [[#Levine--2016|Levine et al., 2016]] ). [[#Taylor--2013|Taylor et al. (2013)]] found that convection-permitting RCMs perform well at simulating surface-induced mesoscale circulations in daytime convection and the observed negative soil moisture feedback, whereas an RCM with parametrized convection, even when run at the same resolution, simulated an unrealistic positive feedback. There is ''medium evidence'' and ''high agreement'' that simulations at convection-permitting resolution are required to realistically represent soil-moisture–precipitation feedbacks.

Ocean–atmosphere RCMs have successfully been used to understand and simulate phenomena involving strong regional feedbacks like tropical cyclones in the Indian Ocean ( [[#Samson--2014|Samson et al., 2014]] ), Indian summer monsoon ( [[#Samanta--2018|Samanta et al., 2018]] ), East Asian summer monsoon ( [[#Zou--2016|Zou et al., 2016]] ), near coastline intense precipitation in the Mediterranean ( [[#Berthou--2015|Berthou et al., 2015]] , 2018), air-sea fluxes influencing heat and humidity advection over land ( [[#Sevault--2014|Sevault et al., 2014]] ; [[#Lebeaupin%20Brossier--2015|Lebeaupin Brossier et al., 2015]] ; [[#Akhtar--2018|Akhtar et al., 2018]] ) or snow bands in the Baltic region ( [[#Pham--2017|Pham et al., 2017]] ). The positive impact of ocean-coupling on the simulation of strongly convective phenomena such as Medicanes, a class of severe cyclones in the Mediterranean, can only be diagnosed when using relatively fine atmospheric resolution of about 10 km ( [[#Akhtar--2014|Akhtar et al., 2014]] ; [[#Flaounas--2018|Flaounas et al., 2018]] ; [[#Gaertner--2018|Gaertner et al., 2018]] ). A positive impact of ocean coupling has been quantified in marginal sea regions with reduced large-scale influence (e.g., in the Baltic Sea area during weak phases of the NAO and thus weak influence of Atlantic westerlies ( [[#Kjellström--2005|Kjellström et al., 2005]] ; [[#Pham--2018|Pham et al., 2018]] ). There is some evidence that coupled ocean components also positively impact RCM simulations of inland climates such as precipitation extremes in central Europe ( [[#Ho-Hagemann--2017|Ho-Hagemann et al., 2017]] ; [[#Akhtar--2019|Akhtar et al., 2019]] ). There ''is high confidence'' that coupled ocean–atmosphere RCMs improve the representation of ocean–atmosphere feedbacks and related phenomena.

The influence of ice-sheet mass balance on regional climate, explored with global and regional models by ( [[#Noël--2018|Noël et al., 2018]] ; [[#Fettweis--2020|Fettweis et al., 2020]] ), is discussed in [[IPCC:Wg1:Chapter:Chapter-9#9.4|Section 9.4]] .

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