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

===== 8.5.1.1.1 Atmospheric convection =====

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Moist convection is fundamental to the water cycle through its vertical transport of momentum, heat, and moisture across the atmosphere. It is particularly active in the tropics where it contributes to more than half of annual precipitation and to the development of severe weather events. Given limitations in computing resources, the current-generation GCMs cannot yet represent small-scale cloud processes and consequently shallow and deep convection is determined by sub-grid-scale parametrizations. While such parametrizations can be evaluated against field observations (e.g., [[#Abdel-Lathif--2018|Abdel-Lathif et al., 2018]] ), it remains challenging to estimate convective entrainment that is valid for both shallow and deep convection (G.J. [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|Zhang et al., 2016]] ). Comparisons between regional projections with explicit compared with parametrized convection also highlight the limitations of parametrized convection for assessing climate change ( [[#Kendon--2019|Kendon et al., 2019]] ; [[#Jackson--2020|Jackson et al., 2020]] ).

Atmospheric convection is particularly important for a realistic simulation of tropical precipitation intensities ( [[#Pendergrass--2014a|Pendergrass and Hartmann, 2014a]] ; [[#Kendon--2019|Kendon et al., 2019]] ). Many CMIP5 models produce rainfall at water vapour amounts lower than in observations ( [[#Takahashi--2018|Takahashi, 2018]] ), as well as too light and too frequent precipitation events ( [[#Sun--2015|Sun et al., 2015]] ; [[#Trenberth--2017|Trenberth et al., 2017]] ). Such biases can be explained by a lack of convective inhibition ( [[#Rochetin--2014a|Rochetin et al., 2014a]] , b) and by too much convective and too little non-convective precipitation ( [[#Chen--2019|Chen and Dai, 2019]] ). Tropical convection controls the amount of precipitable water simulated over the equatorial Indian Ocean, which has been identified as a key metric for differentiating model skill in simulating South Asian monsoon precipitation ( [[#Hagos--2019|Hagos et al., 2019]] ). Many models have difficulty in adequately simulating the diurnal cycle of precipitation over land ( [[#Couvreux--2015|Couvreux et al., 2015]] ), the rainfall intensity distribution associated with the West African monsoon ( [[#Roehrig--2013|Roehrig et al., 2013]] ), and the intensity of tropical cyclones (Sections 10.3.3.4 and 11.7.1.3), phenomena for which atmospheric convection also plays a key role.

Since AR5, there have been improvements in the representation of convective clouds and related precipitation in GCMs. For instance, the drizzle issue (too light and too frequent rainfall events) has led to modifications in the deep convection triggering scheme (Rochetin et al. , 2014b; Han et al. , 2017; Xie et al. , 2018; Wu et al. , 2019) . Although high-resolution studies have highlighted these limitations, most GCMs still rely on a convective available potential energy (CAPE) closure which has been adapted to various cloud regimes ( [[#Bechtold--2014|Bechtold et al., 2014]] ; [[#Han--2017|Han et al., 2017]] ; [[#Walters--2019|Walters et al., 2019]] ) or evaluated against convection-permitting models (CPMs; J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] a). To increase the sensitivity of convection to tropospheric humidity, several models now include a representation of deep convective entrainment dependent on relative humidity (Bechtold et al. , 2008; Han et al. , 2017; M. Zhao et al. , 2018; Walters et al. , 2019) . Other efforts have focused on the improvement of shallow convection and low-level cloudiness due to their major contribution to uncertainty in climate sensitivity ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.4|Section 7.4.2.4]] ). A cloud-regime-based study however highlights an apparent disconnection between cloud and precipitation processes in GCMs ( [[#Tan--2018|Tan et al., 2018]] ), suggesting that a good representation of clouds does not lead to systematic improvement in simulated precipitation. A global simulation in which the parametrized convection is switched off shows a strong influence of parametrized convection on daily precipitation extremes(P. [[#Maher--2018|]] [[#Maher--2018|Maher et al., 2018]] ). Regional simulations at a 25km resolution suggest that an explicit deep convection can be beneficial even at such a relatively coarse resolution ( [[#Vergara-Temprado--2020|Vergara-Temprado et al., 2020]] ). Perturbed physics ensembles (PPE, [[IPCC:Wg1:Chapter:Chapter-1#1.4.4|Section 1.4.4]] ) make it possible to identify parameters in the convection scheme that are most important in determining future precipitation changes ( [[#Bernstein--2016|Bernstein and Neelin, 2016]] ).

Since AR5, spatial aggregation of tropical convection has also received growing attention in both observational ( [[#Holloway--2017|Holloway et al., 2017]] ) and modelling studies ( [[#Muller--2015|Muller and Bony, 2015]] ; [[#Wing--2017|Wing et al., 2017]] ; [[#Tan--2018|Tan et al., 2018]] ). The '''changing degree of convective organization was highlighted as a key mechanism for dynamic changes in extreme precipitation ( [[#Pendergrass--2020a|Pendergrass, 2020a]] ).''' Yet, convective parametrizations do not represent all aspects of mesoscale convective systems ( [[#Hourdin--2013|Hourdin et al., 2013]] ; [[#Park--2019|Park et al., 2019]] ). This is related to the complexity of mechanisms involved from synoptic to mesoscale dynamics, which are only partially resolved by models. Cloud-resolving models (CRMs, [[#8.5.1.2.2|Section 8.5.1.2.2]] ) represent a useful benchmark for improving the parametrization of mesoscale convective systems. Machine learning can also be used to parametrize moist convection after training the model with a conventional or a super parametrization scheme ( [[#Gentine--2018|Gentine et al., 2018]] ; [[#O’Gorman--2018|O’Gorman and Dwyer, 2018]] ), but has not yet been used in the CMIP framework.

While some global modelling centres have reported progress in their parametrization of convection and in their simulation of seasonal, daily and sub-daily precipitation (e.g., [[#Danabasoglu--2020|Danabasoglu et al., 2020]] ; [[#Roehrig--2020|Roehrig et al., 2020]] ), CMIP6 models as a whole only show limited improvements in their simulation of the tropical precipitation climatology compared to CMIP5 (Figure 3.10; [[#Fiedler--2020|Fiedler et al., 2020]] ). For instance, the double-ITCZ syndrome is still prominent ( [[#Tian--2020|Tian and Dong, 2020]] ) despite being reduced in some models (e.g., [[#Qin--2018|Qin and Lin, 2018]] ) . This systematic bias was shown to arise from atmospheric processes including cloud feedbacks ( [[#Tian--2015|Tian, 2015]] ; [[#Dixit--2018|Dixit et al., 2018]] ; [[#Talib--2018|Talib et al., 2018]] ) and the SST threshold at which deep convection occurs in the tropics ( [[#Oueslati--2015|Oueslati and Bellon, 2015]] ; [[#Xiang--2017|Xiang et al., 2017]] ; [[#Adam--2018|Adam et al., 2018]] ). Such biases can also arise from a too weak sensitivity of seasonal tropical precipitation to local SSTs compared with observations ( [[#Good--2021|Good et al., 2021]] ). These biases are large enough to alter forced precipitation changes, and consequently limit our confidence in projected precipitation changes ( [[#Samanta--2019|Samanta et al., 2019]] ; [[#Aadhar--2020|Aadhar and Mishra, 2020]] ). Observational constraints can be used to narrow model response uncertainties ( [[#DeAngelis--2015|DeAngelis et al., 2015]] ; G. [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|Li et al., 2017]] ; [[#Ham--2018|Ham et al., 2018]] ; [[#Watanabe--2018|Watanabe et al., 2018]] ), although there is still no consensus that model selection or weighting is a reliable alternative to the ‘one-model-one-vote’ approach used in [[#8.4|Section 8.4]] (Box 4.1). The detrimental influence of model errors can also be mitigated by focusing on phenomena or events (Polson and Hegerl, 2017; [[#Weller--2017|Weller et al., 2017]] ), implementing bias adjustment techniques ( [[IPCC:Wg1:Chapter:Chapter-10#10.2.3.2|Section 10.2.3.2]] ), or adopting a non-probabilistic storyline approach (Zappa and Shepherd, 2017).

In summary, since AR5 empirical convective parametrization schemes and associated precipitation biases have improved in some but not all global climate models. There is still ''low confidence'' in their ability to accurately simulate the spatio-temporal features of present-day precipitation, especially in the tropics where a double-ITCZ bias is still apparent in many models. While such biases limit the reliability of precipitation projections in some cases, there is currently only ''medium confidence'' that model selection or weighting is a better alternative to the one-model-one-vote approach (Box 4.1). Improved water cycle projections can be achieved by focusing on phenomena or weather events, such as a thermodynamic intensification of convective events ( ''high confidence'' , [[#8.2.2.1|Section 8.2.2.1]] ), however accurate quantitative estimates are currently hampered by complex, model-dependent dynamical responses ( [[#8.2.2.2|Section 8.2.2.2]] ).

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