Editing IPCC:AR6/WGI/Chapter-8 (section)

===== 8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation =====

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In AR5 Chapter 7, there was ''low confidence'' in the representation of cloud–aerosol interactions in climate models. Despite progresses in this field since AR5, cloud–aerosol interactions remain a major obstacle to understanding climate and severe weather ( [[#Varble--2018|Varble, 2018]] ). High aerosol concentrations have been observed to suppress rain in water clouds ( [[#Campos%20Braga--2017|Campos Braga et al., 2017]] ; [[#Fan--2020|Fan et al., 2020]] ). However, such aerosol effects are muted in GCMs, which tend to produce precipitation from shallow clouds too frequently at the expense of rain intensity ( [[#Suzuki--2015|Suzuki et al., 2015]] ; [[#Jing--2017|Jing et al., 2017]] ). This arises from incomplete knowledge of how clouds adjust to aerosol primary effects such as cloud condensation nuclei (CCN). The adjustment occurs mainly as a dynamic response to the impacts of CCN on cloud droplet size and number concentrations on precipitation-forming processes ( [[#Rosenfeld--2008|Rosenfeld et al., 2008]] ; [[#Goren--2014|Goren and Rosenfeld, 2014]] ; [[#Koren--2014|Koren et al., 2014]] ; [[#Camponogara--2018|Camponogara et al., 2018]] ). Uncertainties are large for deep clouds, as their processes are much more complex and include also the impacts of aerosols on ice-precipitation processes. Aerosols can substantially invigorate ( [[#Rosenfeld--2008|Rosenfeld et al., 2008]] ; [[#Koren--2014|Koren et al., 2014]] ; [[#Fan--2018|Fan et al., 2018]] ) and electrify ( [[#Thornton--2017|Thornton et al., 2017]] ; Q. [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ) deep tropical convective clouds. High-resolution atmospheric simulations suggest that high aerosol concentrations can increase environmental humidity by producing clouds that mix more condensed water into the surrounding air, which in turn favours large-scale ascent and strong convective events ( [[#Abbott--2021|Abbott and Cronin, 2021]] ). Further assessment of uncertainties in aerosol – cloud interactions for shallow water clouds is provided in [[IPCC:Wg1:Chapter:Chapter-7#7.3.3.2|Section 7.3.3.2]] .

A major challenge in representing convective clouds and related precipitation events in GCMs is a lack of sophisticated cloud microphysics in convective parametrization schemes (e.g., [[#Fan--2016|Fan et al., 2016]] ). Most of these schemes only include simple microphysical treatments, such as direct partition between cloud condensation and precipitation, and do not include advanced treatment of conversion among different types of hydrometeors. As such these schemes are unable to simulate microphysical cloud and precipitation responses to aerosol-related perturbations in cloud droplet concentration and ice crystals (see Box 8.1), or perturbations in thermodynamical states from global warming. Efforts have been made to include more advanced cloud microphysical treatment in cumulus parametrizations ( [[#Song--2011|Song and Zhang, 2011]] ; [[#Grell--2014|Grell and Freitas, 2014]] ; [[#Berg--2015|Berg et al., 2015]] ) or to use explicit cloud microphysics schemes in climate models with a ‘super parametrization’ ( [[#Wang--2015|Wang et al., 2015]] ), which have been shown to improve the performance in simulating cloud properties and precipitation. However, few of these improvements have been incorporated into CMIP6 climate models so the projected precipitation response to anthropogenic perturbation may still be hindered by the inadequate microphysical treatment in cumulus parametrization ( [[#Smith--2020|Smith et al., 2020]] ).

In summary, there is still ''low confidence'' in the simulated influence of the aerosol microphysical effects on future precipitation changes.

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