Editing IPCC:AR6/WGI/Chapter-1 (section)

===== 1.5.3.1.2 Representation of physical and chemical processes in ESMs =====

<div id="h4-11-siblings" class="h4-siblings"></div>

Atmospheric models include representations of physical processes such as clouds, turbulence, convection and gravity waves that are not fully represented by grid-scale dynamics. The CMIP6 models have undergone updates in some of their parameterization schemes compared to their CMIP5 counterparts, with the aim of better representing the physics and bringing the climatology of the models closer to newly available observational datasets. Most notable developments are to schemes involving radiative transfer, cloud microphysics, and aerosols, in particular a more explicit representation of the aerosol indirect effects through aerosol-induced modification of cloud properties. Broadly, aerosol–cloud microphysics has been a key topic for the aerosol and chemistry modelling communities since AR5, leading to improved understanding of the climate influence of short-lived climate forcers, but they remain the single largest source of spread in ESM calculations of climate sensitivity ( [[#Meehl--2020|Meehl et al., 2020]] ), with numerous parameterization schemes in use (Section 6.4; [[#Gettelman--2016|Gettelman and Sherwood, 2016]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ). The treatment of droplet size and mixed-phase clouds (liquid and ice) was found to lead to changes in the climate sensitivity (Glossary) of some models between AR5 and AR6 (Section 7.4; [[#Bodas-Salcedo--2019|Bodas-Salcedo et al., 2019]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ).

The representation of ocean and cryosphere processes has also evolved significantly since CMIP5. The explicit representation of ocean eddies, due to increased grid resolution (typically, from 1° to ¼°), is a major advance in a number of CMIP6 ocean model components ( [[#Hewitt--2017|]] [[#Hewitt--2017|Hewitt et al., 2017]] ). Advances in sea ice models have been made, for example through correcting known shortcomings in CMIP5 simulations, in particular the persistent underestimation of the rapid decline in summer Arctic sea ice extent ( [[#Rosenblum--2016|Rosenblum and Eisenman, 2016]] , 2017; [[#Turner--2017|Turner and Comiso, 2017]] ; [[#Notz--2018|Notz and Stroeve, 2018]] ). The development of glacier and ice-sheet models has been motivated and guided by an improved understanding of key physical processes, including grounding line dynamics, stratigraphy and microstructure evolution, sub-shelf melting, and glacier and ice-shelf calving, among others ( [[#Faria--2014|Faria et al., 2014]] , 2018; [[#Hanna--2020|Hanna et al., 2020]] ). The resolution of ice-sheet models has continuously increased, including the use of nested grids, sub-grid interpolation schemes, and adaptive mesh approaches ( [[#Cornford--2016|Cornford et al., 2016]] ), mainly for a more accurate representation of grounding-line migration and data assimilation ( [[#Pattyn--2018|Pattyn, 2018]] ). Ice-sheet models are increasingly interactively coupled with global and regional climate models, accounting for the height–mass-balance feedback ( [[#Vizcaino--2015|Vizcaino et al., 2015]] ; [[#Le%20clec’h--2019|Le clec’h et al., 2019]] ), and enabling a better representation of ice-ocean processes, in particular for the Antarctic Ice Sheet ( [[#Asay-Davis--2017|Asay-Davis et al., 2017]] ).

Sealevel rise is caused by multiple processes acting on multiple time scales: ocean warming, glaciers and ice-sheet melting, change in water storage on land, and glacial isostatic adjustment (Box 9.1) but no single model can represent all these processes (Section 9.6). In this Report, the contributions are computed separately (Figure 9.28) and merged into a common probabilistic framework and updated from AR5 (Section 9.6; [[#Church--2013|Church et al., 2013]] ; [[#Kopp--2014|Kopp et al., 2014]] ).

Another notable development since AR5 is the inclusion of stochastic parameterizations of sub-grid processes in some comprehensive climate models ( [[#Sanchez--2016|Sanchez et al., 2016]] ). Here, the deterministic differential equations that govern the dynamical evolution of the model are complemented by knowledge of the stochastic variability in unresolved processes. While not yet widely implemented, the approach has been shown to improve the forecasting skill of weather models, to reduce systematic biases in global models ( [[#Berner--2017|Berner et al., 2017]] ; [[#Palmer--2019|Palmer, 2019]] ) and to influence simulated climate sensitivity ( [[#Strommen--2019|Strommen et al., 2019]] ).

<div id="1.5.3.1.3" class="h4-container"></div>

<span id="representation-of-biogeochemistry-including-the-carbon-cycle"></span>