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

==== 11.6.3.3 Soil Moisture Deficits ====

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The performance of climate models for representing soil moisture deficits shows more uncertainty than for precipitation deficits since, in addition to the uncertainties related to cloud and precipitation processes, there is uncertainty related to the representation of complex soil hydrological and boundary-layer processes ( [[#van%20den%20Hurk--2011|van den Hurk et al., 2011]] ; [[#Lu--2019|Lu et al., 2019]] ; [[#Quintana-Seguí--2020|Quintana-Seguí et al., 2020]] ). Another limitation is the lack of observations, particularly for soil moisture, in most regions ( [[#11.6.2.3|Section 11.6.2.3]] ) and the paucity of land surface property data to parametrize land surface models, in particular soil types, soil properties and depth ( [[#Xia--2015|Xia et al., 2015]] ). The spatial resolution of models is an additional limitation since the representation of some land–atmosphere feedbacks and topographic effects requires detailed resolution ( [[#Nicolai-Shaw--2015|Nicolai-Shaw et al., 2015]] ; Van Der Linden et al., 2019). In addition to climate models, land surface and hydrological models are also used to derive historical and projected trends in soil moisture and related land water variables ( [[#Albergel--2013|Albergel et al., 2013]] ; [[#Cheng--2015|Cheng et al., 2015]] ; [[#Gu--2019b|Gu et al., 2019b]] ; [[#Padrón--2020|Padrón et al., 2020]] ; [[#Markonis--2021|Markonis et al., 2021]] ; [[#Pokhrel--2021|Pokhrel et al., 2021]] ).

Overall, there are contrasting results on the performance of land surface models and climate models in representing soil moisture. Some studies suggest that soil moisture anomalies are well captured by land surface models driven with observation-based forcing ( [[#Dirmeyer--2006|Dirmeyer et al., 2006]] ; [[#Albergel--2013|Albergel et al., 2013]] ; [[#Xia--2014|Xia et al., 2014]] ; [[#Balsamo--2015|Balsamo et al., 2015]] ; [[#Reichle--2017|Reichle et al., 2017]] ; [[#Spennemann--2020|Spennemann et al., 2020]] ), but other studies report limited agreement in the representation of interannual soil moisture variability ( [[#Stillman--2016|Stillman et al., 2016]] ; [[#Yuan--2017|Yuan and Quiring, 2017]] ; [[#Ford--2019|Ford and Quiring, 2019]] ) and noticeable seasonal differences in model skill in some regions ( [[#Xia--2014|Xia et al., 2014]] , 2015). Models with good skill can nonetheless display biases in absolute soil moisture ( [[#Xia--2014|Xia et al., 2014]] ; [[#Gu--2019a|Gu et al., 2019a]] ), but these are not necessarily of relevance for the simulation of surface water fluxes and drought anomalies ( [[#Koster--2009|Koster et al., 2009]] ). There is also substantial inter-model spread ( [[#Albergel--2013|Albergel et al., 2013]] ), particularly for the root-zone soil moisture ( [[#Berg--2017a|Berg et al., 2017a]] ).

Regarding the performance of regional and global climate models, an evaluation of an ensemble of RCM simulations for Europe ( [[#Stegehuis--2013|Stegehuis et al., 2013]] ) shows that these models display overly strong drying in early summer, resulting in an excessive decrease of latent heat fluxes, with potential implications for more severe droughts in dry environments ( [[#Teuling--2018|Teuling, 2018]] ; [[#van%20Der%20Linden--2019|van Der Linden et al., 2019]] ). Compared with a range of observational ET estimates, CMIP5 models show an overestimation of ET on annual scale, but an ET underestimation in boreal summer in many Northern Hemisphere mid-latitude regions, also suggesting a tendency towards excessive soil drying ( [[#Mueller--2014|Mueller and Seneviratne, 2014]] ), consistent with identified biases in soil-moisture–temperature coupling ( [[#Donat--2018|Donat et al., 2018]] ; [[#Vogel--2018|Vogel et al., 2018]] ; [[#Selten--2020|Selten et al., 2020]] ). Land surface models used in ESMs display a bias in their representation of the sensitivity of interannual land carbon uptake to soil moisture conditions, which appears related to a limited range of soil moisture variations compared to observations ( [[#Humphrey--2018|Humphrey et al., 2018]] ).

For future projections, the spread of soil moisture outputs among different ESMs is more important than internal variability and scenario uncertainty, and the bias is strongly related to the sign of the projected change ( [[#Ukkola--2018|Ukkola et al., 2018]] ; [[#Lu--2019|Lu et al., 2019]] ; [[#Selten--2020|Selten et al., 2020]] ). The CMIP5 ESMs that project more drying and warming in mid-latitude regions show a substantial bias in soil-moisture–temperature coupling ( [[#Donat--2018|Donat et al., 2018]] ; [[#Vogel--2018|Vogel et al., 2018]] ). Although CMIP6 and CMIP5 simulations for soil moisture changes are similar overall, some differences are found in projections in a few regions ( [[#11.9|Section 11.9]] ; [[#Cook--2020|Cook et al., 2020]] ). There is still ''limited evidence'' to assess whether there are substantial differences in model performance in the two ensembles, but improvements in modelling aspects relevant for soil moisture have been reported for precipitation ( [[#11.6.3.2|Section 11.6.3.2]] ), and a better performance has been found in CMIP6 for the representation of long-term trends in soil moisture in continental USA ( [[#Yuan--2021|Yuan et al., 2021]] ). Despite the mentioned model limitations, the representation of soil moisture processes in ESMs uses physical and biological understanding of the underlying processes, which can well represent the temporal anomalies associated with temporal variability and trends in climate. In summary, there is ''medium confidence'' in the representation of soil moisture deficits in ESMs and related land surface and hydrological models.

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