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

==== 10.3.3.6 Performance at Simulating Regional Drivers of Climate and Climate Change ====

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Dust, with its regional character in both emissions and climatic influences, has traditionally been specified in climate simulations with a climatological estimate. In CMIP5 models, the influence of vegetation changes on mineral dust is largely underestimated while the influence of surface wind and precipitation are overestimated, resulting in a low bias of dust load ( [[#Pu--2018|Pu and Ginoux, 2018]] ). Interactive dust emission modules that simulate the dust optical depth in most of the key emission regions have only been recently introduced ( [[#Pu--2018|Pu and Ginoux, 2018]] ). However, coarse dust is underestimated in global models ( [[#Adebiyi--2020|Adebiyi and Kok, 2020]] ). Simulations of future changes in dust are hindered by the uncertainties in future regional wind and precipitation as the climate warms ( [[#Evan--2016|Evan et al., 2016]] ), in the effect of CO <sub>2</sub> fertilization on source extent ( [[#Huang--2017|Huang et al., 2017]] ), in the dust feedbacks ( [[#Evans--2019|Evans et al., 2019]] ), and in the effect of human activities that change land use and disturb the soil, including cropping and livestock grazing, recreation and urbanization, and water diversion for irrigation ( [[#Ginoux--2012|Ginoux et al., 2012]] ).

Volcanoes also provide forcings with a marked regional impact (Cross-Chapter Box 4.1). This implies that models are expected to capture these effects ( [[#Bethke--2017|Bethke et al., 2017]] ). Both proxy analyses and simulations have demonstrated reduced Asian monsoon precipitation after tropical and Northern Hemisphere (NH) volcanic eruptions due to reduced humidity and divergent circulation ( [[#Man--2014|Man and Zhou, 2014]] ; [[#Zhuo--2014|Zhuo et al., 2014]] ; F. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Stevenson--2016|Stevenson et al., 2016]] ). Global model experiments ( [[#Zanchettin--2013|Zanchettin et al., 2013]] ; [[#Ortega--2015|Ortega et al., 2015]] ; [[#Sjolte--2018|Sjolte et al., 2018]] ; [[#Michel--2020|Michel et al., 2020]] ) have suggested that tropical volcanic eruptions (larger than the one from Mount Pinatubo in 1991) may lead to a positive phase of the winter NAO in the following few years (with an uncertainty on the exact years affected), but this influence is not well-reproduced in climate models and requires very large ensembles ( [[#Driscoll--2012|Driscoll et al., 2012]] ; [[#Toohey--2014|Toohey et al., 2014]] ; [[#Swingedouw--2017|Swingedouw et al., 2017]] ; [[#Ménégoz--2018b|Ménégoz et al., 2018b]] ). The ability to simulate the effect of volcanic aerosol in global models is evaluated in VolMIP ( [[#Zanchettin--2016|Zanchettin et al., 2016]] ). Given the relevance of volcanic aerosol, a good knowledge of the initial conditions is important because the response has proven to be sensitive to them ( [[#Ménégoz--2018a|Ménégoz et al., 2018a]] ; [[#Zanchettin--2019|Zanchettin et al., 2019]] ). A few decadal prediction systems have illustrated that current systems can predict some aspects of regional climate a few years in advance ( [[#Swingedouw--2017|Swingedouw et al., 2017]] ; [[#Illing--2018|Illing et al., 2018]] ; [[#Ménégoz--2018a|Ménégoz et al., 2018a]] ; [[#Hermanson--2020|Hermanson et al., 2020]] ). However, a better performance requires information about volcanic location ( [[#Haywood--2013|Haywood et al., 2013]] ; [[#Pausata--2015|Pausata et al., 2015]] ; [[#Stevenson--2016|Stevenson et al., 2016]] ; F. [[#Liu--2018a|]] [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] a ), strength ( [[#Emile-Geay--2008|Emile-Geay et al., 2008]] ; H.-G. [[#Lim--2016|]] [[#Lim--2016|Lim et al., 2016]] ; F. [[#Liu--2018b|]] [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] b ), and seasonality ( [[#Stevenson--2017|Stevenson et al., 2017]] ; [[#Sun--2019a|Sun et al., 2019a]] , b).

Some recent regional climate changes can only be simulated by climate models if anthropogenic aerosols are correctly included (Sections 10.4.2.1, 10.6.3 and 10.6.4; Chapters 6 and 8). Examples of the importance of correctly representing anthropogenic aerosols are the recent enhanced warming over Europe ( [[#Nabat--2014|Nabat et al., 2014]] ; [[#Dong--2017|Dong et al., 2017]] ), the cooling over the East Asian monsoon region, leading to a weakening of the monsoon ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4|Section 8.3.2.4]] ; [[#Song--2014|Song et al., 2014]] ; Q. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ), as well as changes in the monsoons of West Africa (Sections 8.3.2.4 and 10.4.2.1) and South Asia (Sections 8.3.2.4 and 10.6.3; [[#Undorf--2018|Undorf et al., 2018]] ). The relevance of appropriately representing anthropogenic aerosols has been widely studied in regional models ( [[#Boé--2020a|Boé et al., 2020a]] ; [[#Gutiérrez--2020|Gutiérrez et al., 2020]] ), with an advantage for models with interactive aerosol schemes ( [[#Drugé--2019|Drugé et al., 2019]] ; [[#Nabat--2020|Nabat et al., 2020]] ). Without a fully coupled chemistry module, radiative forcing can be simulated by including simple models of sulphate chemistry or specifying the optical properties from observations and prescribing the effect of aerosols on the cloud-droplet number ( [[#Fiedler--2017|Fiedler et al., 2017]] , 2019; [[#Stevens--2017|Stevens et al., 2017]] ). In all cases, the specification of the aerosol load limits the trustworthiness of the simulations at the regional scale when enough detail is not provided ( [[#Samset--2019|Samset et al., 2019]] ; [[#Shonk--2020|Shonk et al., 2020]] ; Z. [[#Wang--2021|]] [[#Wang--2021|Wang et al., 2021]] ).

The inclusion of irrigation in global and regional models over the South Asian monsoon region ( [[#10.6.3|Section 10.6.3]] ) has been found to be important to represent the monsoon circulation and rainfall correctly ( [[#Lucas-Picher--2011|Lucas-Picher et al., 2011]] ; [[#Guimberteau--2012|Guimberteau et al., 2012]] ; [[#Shukla--2014|Shukla et al., 2014]] ; [[#Tuinenburg--2014|Tuinenburg et al., 2014]] ; [[#Cook--2015a|Cook et al., 2015a]] ; [[#Devanand--2019|Devanand et al., 2019]] ). Similarly, the inclusion of irrigation over northern India and western Pakistan could be important for the correct simulation of precipitation over the Upper Indus Basin in northern Pakistan ( [[#Saeed--2013|Saeed et al., 2013]] ). Irrigation in the East African Sahel inhibits rainfall over the irrigated region and instead enhances rainfall to the east, coherent with both observations and theoretical understanding of the local circulation anomalies induced by the lower surface air temperatures over the irrigated region ( [[#Alter--2015|Alter et al., 2015]] ). Although several studies show how modelled irrigation reduces daytime temperature extremes, few compare modelled results with observations. Global model studies have found improvements in simulated surface temperature when including irrigation ( [[#Thiery--2017|Thiery et al., 2017]] ), in particular in areas where the model used has a strong land-atmosphere coupling ( [[#Chen--2019|Chen and Dirmeyer, 2019]] ). An RCM study over the North China Plain showed that the inclusion of irrigation led to a better representation of the observed nighttime warming ( [[#Chen--2018|Chen and Jeong, 2018]] ).

There is ''medium confidence'' that representing irrigation is important for a realistic simulation of South Asian monsoon precipitation. There is ''limited evidence'' that including irrigation in climate models improves the simulation of maximum and minimum daily temperatures as well as precipitation for other regions.

Regional land-radiation management, including modifying the albedo through, for instance, no-tillage practices, has been suggested as a measure to decrease regional maximum daily temperatures (see review in [[#Seneviratne--2018|Seneviratne et al., 2018]] ), but although modelled results and theoretical understanding are coherent, few studies have verified the results with observations. [[#Hirsch--2018|Hirsch et al. (2018)]] is an exception, showing that implementing minimal tillage, crop residue management and crop rotation in a global model over regions where it is practiced, improves the simulation of surface heat fluxes.

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