Editing IPCC:AR6/WGI/Chapter-3 (section)

==== 3.8.2.2 Process Representation in Different Classes of Models ====

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Based on new scientific insights and newly available observations, many improvements have been made to models from CMIP5 to CMIP6, including changes in the representation of physics of the atmosphere, ocean, sea ice, and land surface. In many cases, changes in the detailed representation of cloud and aerosol processes have been implemented. The new generation of CMIP6 climate models also features increases in spatial resolution, as well as inclusion of additional Earth system processes and new components (see further details in [[IPCC:Wg1:Chapter:Chapter-1#1.5.3.1|Section 1.5.3.1]] and in Tables AII.5 and AII.6). Such changes to model physics and resolution are often designed to improve the fitness-for-purpose of a model such as for projecting regional aspects of climate (Section 10.3) or to more fully represent feedbacks to make the models more fit for long-term climate projections affected for example by carbon cycle feedbacks (see also ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.3.1|Section 1.5.3.1]] ).

Factors affecting model performance include resolution, the type of dynamical core (spectral, finite difference or finite volume), physics parameters and parameterisations, model structure, for example, many of the coupled HighResMIP models ( [[#Haarsma--2016|Haarsma et al., 2016]] ) use the NEMO ocean model, affecting model diversity, and the range and degree of process realism (e.g., for aerosols, atmospheric chemistry and other Earth System components). This section particularly explores the influence of model resolution and of complexity on model performance (see also Section 8.5.1).

A key advance in CMIP6 compared to CMIP5 is the presence of high-resolution models that have participated in HighResMIP. Resolution alone can significantly affect a model’s performance, with some effects propagating to the global scale. Recent studies have shown that enhancing the horizontal resolution of models is seen to significantly affect aspects of large-scale circulation as well as improve the simulation of small-scale processes and extremes when compared to CMIP3 and CMIP5 models (see also Section 11.4.3; [[#Haarsma--2016|Haarsma et al., 2016]] ), with some models approaching 10 km resolution in the atmosphere ( [[#Kodama--2021|Kodama et al., 2021]] ) or ocean ( [[#Caldwell--2019|Caldwell et al., 2019]] ; [[#Gutjahr--2019|Gutjahr et al., 2019]] ; [[#Roberts--2019|Roberts et al., 2019]] ; [[#Chang--2020|Chang et al., 2020]] ; [[#Semmler--2020|Semmler et al., 2020]] ).

As discussed in Section [[#_idTextAnchor000|3.3]] , CMIP6 models reproduce observed large-scale mean surface temperature patterns as well as their CMIP5 predecessors, but biases in surface temperature in the mean of HighResMIP models are smaller than those in the mean of the corresponding standard resolution CMIP6 configurations of the same models ( [[#3.3.1.1|Section 3.3.1.1]] and Figure 3.3). The extent and causes of improvements due to increased horizontal resolutions in the atmosphere and ocean domains depend on the model ( [[#Kuhlbrodt--2018|Kuhlbrodt et al., 2018]] ; [[#Roberts--2018|Roberts et al., 2018]] , 2019; [[#Sidorenko--2019|Sidorenko et al., 2019]] ), although they typically involve better process representation (for example of ocean currents and atmospheric storms) which can lead to reduced biases in top of atmosphere radiation and cloudiness. Precipitation has likewise improved in CMIP6 versus CMIP5 models, but biases remain. The high resolution (&lt;25 km) class of models participating in HighResMIP compares regionally better against observations than the standard resolution CMIP6 models (of order 100 km, Figure 3.13; [[#3.3.2|Section 3.3.2]] ), partly because of an improved representation of orographic (mountain-induced) precipitation which constitutes a major fraction of precipitation on land, but other processes also play an important role ( [[#Vannière--2019|Vannière et al., 2019]] ). However, there are also large parts of the tropical ocean where precipitation in high-resolution models is not improved compared to standard resolution CMIP6 models ( [[#Vannière--2019|Vannière et al., 2019]] ).

Additionally, the representation of surface and deeper ocean mean temperature is improved in models with higher horizontal resolution (Sections 3.5.1.1 and 3.5.1.2) with systematic improvements in coupled tropical Atlantic sea surface temperature and precipitation biases at higher resolutions ( [[#Roberts--2019|Roberts et al., 2019]] , single model; [[#Vannière--2019|Vannière et al., 2019]] , multi-model), the North Atlantic cold bias ( [[#Bock--2020|Bock et al., 2020]] , multi-model; [[#Roberts--2018|Roberts et al., 2018]] , 2019; [[#Caldwell--2019|Caldwell et al., 2019]] ; all single models) as well as deep-ocean biases ( [[#Small--2014|Small et al., 2014]] ; [[#Griffies--2015|Griffies et al., 2015]] ; [[#Caldwell--2019|Caldwell et al., 2019]] ; [[#Gutjahr--2019|Gutjahr et al., 2019]] ; [[#Roberts--2019|Roberts et al., 2019]] ; [[#Chang--2020|Chang et al., 2020]] , all single model studies). Atlantic ocean transports (heat and volume) are also generally improved compared to observations ( [[#Grist--2018|Grist et al., 2018]] ; [[#Caldwell--2019|Caldwell et al., 2019]] ; [[#Docquier--2019|Docquier et al., 2019]] ; [[#Roberts--2019|Roberts et al., 2019]] , 2020c; [[#Chang--2020|Chang et al., 2020]] ), as well as some aspects of air-sea interactions (P. [[#Wu--2019|Wu et al., 2019]] , single model; [[#Bellucci--2021|Bellucci et al., 2021]] , multi-model). However, warm-biased sea surface temperatures in the Southern Ocean are worse in comparison to standard resolution CMIP6 models ( [[#Bock--2020|Bock et al., 2020]] ). The AR5 noted problems with the simulation of clouds in this region which were later attributed to a lack of supercooled liquid clouds ( [[#Bodas-Salcedo--2016|Bodas-Salcedo et al., 2016]] ). Mesoscale ocean processes are critical to maintaining the Southern Ocean stratification and response to wind forcing ( [[#Marshall--2003|Marshall and Radko, 2003]] ; [[#Hallberg--2006|Hallberg and Gnanadesikan, 2006]] ), and their explicit representation requires even higher ocean resolution ( [[#Hallberg--2013|Hallberg, 2013]] ). Similarly, atmospheric convection remains unresolved even in the highest-resolution climate models participating in HighResMIP. However, there is also evidence of improvements in the frequency, distribution and interannual variability of tropical cyclones in HighResMIP ( [[#Roberts--2020a|Roberts et al., 2020a]] , b), particularly in the Northern Hemisphere (see further discussion in Section 11.7.1.3), and their interaction with the ocean ( [[#Scoccimarro--2017|Scoccimarro et al., 2017]] , single model), as well as the global moisture budget ( [[#Vannière--2019|Vannière et al., 2019]] ). At higher resolution the track density of tropical cyclones is increased practically everywhere where tropical cyclones occur. Simulation of some climate extremes is shown to be improved at higher resolution including explosively developing extra-tropical cyclones ( [[#Vries--2019|Vries et al., 2019]] ; [[#Jiaxiang--2020|Jiaxiang et al., 2020]] ), blocking ( [[#3.3.3.3|Section 3.3.3.3]] ; [[#Fabiano--2020|Fabiano et al., 2020]] ; [[#Schiemann--2020|Schiemann et al., 2020]] ) and European extreme precipitation due to a better representation of the North Atlantic storm track ( [[#van%20Haren--2015|van Haren et al., 2015]] ) and orographic boundary conditions ( [[#Schiemann--2018|Schiemann et al., 2018]] ).

In CMIP6 a number of Earth system models have increased the realism by which key biogeochemical aspects of the coupled Earth system are represented, affecting, for example, the carbon and nitrogen cycles, aerosols, and atmospheric chemistry (e.g., [[#Cao--2018|Cao et al., 2018]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ; [[#Lin--2019|Lin et al., 2019]] ; [[#Mauritsen--2019|Mauritsen et al., 2019]] ; [[#Séférian--2019|Séférian et al., 2019]] ; [[#Sellar--2019|Sellar et al., 2019]] ; [[#Sidorenko--2019|Sidorenko et al., 2019]] ; [[#Swart--2019|Swart et al., 2019]] ; [[#Dunne--2020|Dunne et al., 2020]] ; [[#Seland--2020|Seland et al., 2020]] ; [[#Wu--2020|Wu et al., 2020]] ; [[#Ziehn--2020|Ziehn et al., 2020]] ). In addition to increased process realism, the level of coupling between the physical climate and biogeochemical components of the Earth system has also been enhanced in some models ( [[#Mulcahy--2020|Mulcahy et al., 2020]] ) as well as across different biogeochemical components (see Section 5.4 for a discussion and Table 5.4 for an overview). For example, the nitrogen cycle is now simulated in several ESMs ( [[#Zaehle--2015|Zaehle et al., 2015]] ; [[#Davies-Barnard--2020|Davies-Barnard et al., 2020]] ). This advance accounts for the fertilization effect nitrogen availability has on vegetation and carbon uptake, reducing uncertainties in the simulations of the carbon uptake responses to physical climate change ( [[#3.6.1|Section 3.6.1]] ) and to CO <sub>2</sub> increases ( [[#Arora--2020|Arora et al., 2020]] ), thus improving confidence in the simulated airborne fraction of CO <sub>2</sub> emissions ( [[#Jones--2020|Jones and Friedlingstein, 2020]] ) and better constraining remaining carbon budgets (Section 5.5). Such advances also allow investigation of land-based climate change mitigation options (e.g., through changes in land management and associated terrestrial carbon uptake ( [[#Mahowald--2017|Mahowald et al., 2017]] ; [[#Pongratz--2018|Pongratz et al., 2018]] )) or interactions between different facets of the managed Earth system, such as interactions between mitigation efforts targeting climate warming and air quality ( [[#West--2013|West et al., 2013]] ). A number of developments also explicitly target improved simulation of the past.

Further such ESM developments include: (i) Apart from the nitrogen cycle, extending terrestrial carbon cycle models to simulate interactions between the carbon cycle and other nutrient cycles, such as phosphorus, that are known to play an important role in limiting future plant uptake of CO <sub>2</sub> ( [[#Zaehle--2015|Zaehle et al., 2015]] ). (ii) Introducing explicit coupling between interactive atmospheric chemistry and aerosol schemes ( [[#Gettelman--2019|Gettelman et al., 2019]] ; [[#Sellar--2019|Sellar et al., 2019]] ), which has been shown to affect estimates of historical aerosol radiative forcing ( [[#Karset--2018|Karset et al., 2018]] ). Furthermore, interactive treatment of atmospheric chemistry in a full ESM supports investigation of interactions between climate and air quality mitigation efforts, such as in AerChemMIP ( [[#Collins--2017|Collins et al., 2017]] ), as well as interactions between stratospheric ozone recovery and global warming ( [[#Morgenstern--2018|Morgenstern et al., 2018]] ). (iii) Coupling between components of Earth system models has been extended to increase their utility for studying future interactions across the full Earth system, such as between ocean biogeochemistry and cloud-aerosol processes ( [[#Mulcahy--2020|Mulcahy et al., 2020]] ), and vegetation and impacts on dust production ( [[#Kok--2018|Kok et al., 2018]] ), production of secondary organic aerosols (SOA, [[#Zhao--2017|Zhao et al., 2017]] ) and Equilibrium Climate Sensitivity (ECS), whereby enhanced CO <sub>2</sub> fertilization of land vegetation causes changes in regional surface albedo ( [[#Andrews--2019|Andrews et al., 2019]] ). Increased coupling between physical climate and biogeochemical processes in a single ESM, along with an increased number of interactively represented processes, such as permafrost thaw, vegetation, wildfires and continental ice sheets increases our ability to investigate the potential for abrupt and interactive changes in the Earth system (see Sections 4.7.3 and 5.4.9, and Box 5.1). Table 5.4 provides an overview of recent advances in representing the carbon cycle in ESMs.

In summary, both high-resolution and high-complexity models have been evaluated as part of CMIP6. In comparison with standard resolution CMIP6 models, higher resolution probed under the HighResMIP activity ( [[#Haarsma--2016|Haarsma et al., 2016]] ) improves aspects of the simulation of climate (particularly concerning sea surface temperature) but discrepancies remain and there are some regions, such as parts of the Southern Ocean, where currently attainable resolution produces inferior performance ( ''high confidence'' ). Such model behaviour can indicate deficiencies in model physics that are not simply associated with resolution. In several cases, high-complexity ESMs that include additional interactions between Earth system components and thus have potential for additional associated model errors nevertheless perform as well as their low-complexity counterparts, illustrating that interactively simulating these Earth System components as part of the climate system is now well established.

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