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

=== 8.5.1 Model Uncertainties of Relevance for the Water Cycle ===

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Model response uncertainty is typically estimated as the inter-model spread (range) projected by a set of climate models for a given emissions scenario. It is best estimated at the end of a high-emissions scenario when internal variability has a limited contribution to total uncertainty (Figure 8.23). Even for aggregated quantities, like decadal-mean precipitation averaged over relatively large domains, model response uncertainty is substantial and can exceed scenario uncertainty ( [[#Hawkins--2011|Hawkins and Sutton, 2011]] ; [[#Lehner--2020|Lehner et al., 2020]] , 1.5.4, 4.4.1.3). This can also be true for other water cycle variables such as soil moisture, runoff and streamflow at the regional scale, either derived directly from global climate models (GCMs) or produced by ‘offline’ using global hydrological models (GHMs) driven by the same GCMs (Orlowsky and Seneviratne, 2013; [[#Giuntoli--2015|Giuntoli et al., 2015]] , 2018; [[#Chegwidden--2019|Chegwidden et al., 2019]] ). Although some of the model response uncertainty is related to climatological biases ( [[#Grose--2017|Grose et al., 2017]] ; G. [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|Li et al., 2017]] ; [[#Lehner--2019|Lehner et al., 2019]] ; [[#Samanta--2019|Samanta et al., 2019]] ), model biases are not the only way to assess the reliability of climate projections (compare with Box 4.1). Therefore, our focus here is on the representation of key processes that are not completely resolved in current-generation GCMs ( [[#8.5.1.1|Section 8.5.1.1]] ) and on the model improvements associated with increased horizontal resolution ( [[#8.5.1.2|Section 8.5.1.2]] ).

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==== 8.5.1.1 Fitness-for-purpose and Poorly Constrained Key Processes ====

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The AR5 ( [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] recognized that the simulation of clouds and precipitation remains challenging for state-of-the-art GCMs. Model development and evaluation have continued since AR5, with a particular emphasis on the representation of new model components, like interactive vegetation, aerosols and biogeochemical cycles. For example, the comparison of simulated tropical precipitation across three successive generations of CMIP models (including CMIP6) indicates overall little improvement for the summer monsoons, the double-ITCZ bias, the diurnal cycle and the frequency of precipitation ( [[#Fiedler--2020|Fiedler et al., 2020]] ). Some of these issues are related to inherent model limitations in three specific areas: atmospheric convection, cloud – aerosol interactions and land surface processes (ocean and cryosphere-related processes are addressed in Chapter 9). These limitations do not weaken the overall progress made in the large-scale simulation of present-day climate (FAQ 3.3 and [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.3|Section 3.3.2.3]] ), even though the improvement of CMIP6 compared with CMIP5 models is limited (Figure 3.12) and is generally less systematic or obvious at the regional scale (e.g., Gusain et al. , 2020; Monerie et al. , 2020; Oudar et al. , 2020a) . Instead, they call for a careful interpretation of hydrological projections with the full range of plausible outcomes, rather than only considering the most likely scenarios ( [[#Sutton--2018|Sutton, 2018]] , 2019).

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===== 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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===== 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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===== 8.5.1.1.3 Land surface processes =====

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Land surface processes determine the partitioning of net surface radiation into sensible, latent and ground heat fluxes, the partitioning of precipitation into evapotranspiration and runoff, and the net terrestrial carbon flux at the Earth’s surface. They are relevant for simulating the terrestrial water cycle responses to climate change, as well as the response to land use change (FAQ 8.1). Even basic land surface properties such as albedo ( [[#Terray--2018|Terray et al., 2018]] ) or the ratio of transpiration to total evaporation ( [[#Chang--2018|Chang et al., 2018]] ) still need to be improved in state-of-the-art coupled GCMs. Runoff sensitivities are also not well constrained in these models, which display a large spread for the present-day climate, influencing simulated changes under global warming ( [[#Lehner--2019|Lehner et al., 2019]] ). Earth System Models (ESMs) incorporate some combined biophysical and biogeochemical processes to a limited extent, and many relevant processes about how plants and soils interactively respond to climate changes are yet to be considered (e.g., Y. [[#Liu--2020|]] [[#Liu--2020|]] [[#Liu--2020|Liu et al., 2020]] ). Consequently, land surface processes and their atmospheric coupling contribute to the range in water cycle projections ( [[#Jia--2019|Jia et al., 2019]] ).

Since AR5, development of new and existing processes in land surface models (LSMs) have been evaluated. These include soil freezing and permafrost (Vergnes et al. , 2014; Chadburn et al. , 2015; K. Yang et al. , 2018; Gao et al. , 2019) , soil and snow hydrology ( [[#Brunke--2016|Brunke et al., 2016]] ; [[#Decharme--2016|Decharme et al., 2016]] ), glaciers ( [[#Shannon--2019|Shannon et al., 2019]] ), surface waters and rivers ( [[#Decharme--2012|Decharme et al., 2012]] ), as well as vegetation ( [[#Bartlett--2015|Bartlett and Verseghy, 2015]] ; [[#Betts--2015|Betts et al., 2015]] ; [[#Knauer--2015|Knauer et al., 2015]] ; [[#Tang--2015|Tang et al., 2015]] ) and the representation of hydraulic gradients throughout the soil–plant–atmosphere continuum (Bonan et al., 2014). Such land surface model developments have led to significant improvements in global offline hydrological simulations driven by observed atmospheric forcings (e.g., [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ; [[#Decharme--2019|Decharme et al., 2019]] ).

Progress in the representation of land surface heterogeneity has been made, in the form of improved mapping of root zone storage capacity (Wang-Erlandsson et al., 2016), improved vegetation stand, disturbance and fire dynamics (F. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ; [[#Fisher--2018|Fisher et al., 2018]] ; [[#Haverd--2018|Haverd et al., 2018]] ; [[#Yue--2018|Yue et al., 2018]] ; [[#Zou--2019|Zou et al., 2019]] ), better representation of urban surfaces (Box 10.3), and the explicit representation of inland water bodies ( [[#Gu--2015|Gu et al., 2015]] ; [[#Verseghy--2017|Verseghy and MacKay, 2017]] ). The representation of realistic snow and vegetation cover significantly affects the simulation of the land surface energy and water budgets at multiple time scales (Loranty et al., 2014; [[#Bartlett--2015|Bartlett and Verseghy, 2015]] ; [[#Thackeray--2015|Thackeray et al., 2015]] ; [[#Qiu--2016|Qiu et al., 2016]] ; [[#Thackeray--2016|Thackeray and Fletcher, 2016]] ; L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ; [[#Alessandri--2017|Alessandri et al., 2017]] ). Groundwater remains inadequately represented in many models, which limits our current understanding of the two-way interactions between groundwater and the rest of the hydrologic cycle (R.G. Taylor et al. , 2013a; Leng et al. , 2014; Vergnes et al. , 2014; Pokhrel et al. , 2015; [[#Maxwell--2016|Maxwell and Condon, 2016]] ; [[#Collins--2017|Collins, 2017]] ; Scanlon et al. , 2018; Condon et al. , 2020) . Land management exerts an increasing influence on the water cycle ( [[#Abbott--2019|Abbott et al., 2019]] ) whose representation in the current-generation climate models is generally incomplete ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.7.2|Section 10.3.3.7.2]] ).

Aside from land surface models (LSMs), global hydrological models (GHMs) have been further developed for off-line simulations of the hydrological impacts of both climate change and water management (Jiménez Cisneros et al. , 2014; Schewe et al. , 2014; Döll et al. , 2016, 2018; Pokhrel et al. , 2016, 2017; Veldkamp et al. , 2018) . GHMs can equal or outweigh the contribution of GCMs to uncertainties in hydrological projections at the regional scale (Giuntoli et al., 2015). Historical GHM simulations are currently not sufficient to improve regional water cycle projections, due to modelling uncertainties in both the driving GCMs and land surface hydrology (Pechlivanidis et al. , 2017; Samaniego et al. , 2017; Hattermann et al. , 2018; Krysanova et al. , 2018) . Biophysical vegetation processes are still not accounted for in many GHMs, which may lead to inadequate projections of terrestrial runoff and water resources. However, hydrological models that do simulate these effects often disagree ( [[#Prudhomme--2014|Prudhomme et al., 2014]] ), so do not necessarily provide the added value of a more sophisticated representation of vegetation processes and land surface conditions ( [[#Döll--2016|Döll et al., 2016]] ).

Since AR5, there has been increasing recognition of the need to better understand the role of land–atmosphere coupling and related feedbacks (Joetzjer et al. , 2014; Berg et al. , 2016; Catalano et al. , 2016; [[#Berg--2018a|Berg and Sheffield, 2018a]] ; Santanello et al. , 2018) . This has led to the development of dedicated field campaigns (Song et al., 2016; [[#Phillips--2017|Phillips et al., 2017]] ; [[#Dirmeyer--2018|Dirmeyer et al., 2018]] ), remotely sensed observations (Ferguson and Wood, 2011; [[#Roundy--2017|Roundy and Santanello, 2017]] ), and tailored diagnostics (Tawfik et al., 2015a, b; [[#Miralles--2016|Miralles et al., 2016]] , 2019; [[#Dirmeyer--2017|Dirmeyer and Halder, 2017]] ). Dynamic vegetation models have been introduced in global ESMs but they need further evaluation (Medlyn et al., 2015; [[#Prentice--2015|Prentice et al., 2015]] ; [[#Cantú--2018|Cantú et al., 2018]] ; [[#Franks--2018|Franks et al., 2018]] ) to provide valuable information on potential vegetation feedbacks. Plant migration and mortality, increased disturbances from wild fires, insects and extreme events, interactive nitrogen cycle, or the impact of increased levels of tropospheric ozone are often ignored or poorly represented in the current-generation of ESMs (Bonan and Doney, 2018; [[#Fisher--2018|Fisher et al., 2018]] ).

The physiological response of plants to increasing atmospheric CO <sub>2</sub> is generally accounted for, but only using empirical models of stomatal conductance that are characterized by a single critical parameter of intrinsic water-use efficiency (Franks et al., 2017, 2018). This reflects a lack of structural diversity and caution about the consensus of the photosynthesis response to increasing CO <sub>2</sub> ( [[#Knauer--2015|Knauer et al., 2015]] ; [[#Huang--2016|Huang et al., 2016]] ), which has implications for the ability of the current-generation models to account for uncertainty in future evapotranspiration changes. Most CMIP5 models underestimate the ratio of plant transpiration to total terrestrial evapotranspiration, which may suggest that they also underestimate the impact of plant physiology on the water cycle (Lian et al., 2018). Plant hydraulics are not explicitly considered in many land surface models, which may lead to an underestimation of the influence of the increasing atmospheric moisture stress on plant transpiration under climate change (Massmann et al. , 2019; Grossiord et al. , 2020; Y. Liu et al. , 2020) . Most ESMs underestimate the water use efficiency measured at many sites and, consequently overestimate the ratio of evapotranspiration to precipitation (J. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ).

In summary, since AR5 substantial advances have been made in the representation of land surface processes in current-generation Earth System Models (ESMs). Offline hydrological models allow the application of bias-adjusted atmospheric forcings, but there is ''low confidence'' of an improved response compared to coupled climate models, given their inherent limitations (Box 10.2). While improvements in the representation of complex land surface feedbacks relevant to the water cycle are needed, there is currently ''low confidence'' that they will systematically improve the reliability of water cycle projections.

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==== 8.5.1.2 Added Value of Increased Horizontal Model Resolution ====

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Coarse spatial resolution of climate models has often been considered a key limitation in global climate projections ( [[#Di%20Luca--2015|Di Luca et al., 2015]] ; [[#Roberts--2018|Roberts et al., 2018]] ). Proposed and tested solutions include a uniform or regional increase in the resolution of GCMs, or the use of regional climate models (RCMs). The increase in computing resources has also led to the development of convection-permitting models ( [[#Prein--2015|Prein et al., 2015]] ), which have been integrated over larger domains, but are still unsuitable for CMIP simulations. Statistical downscaling tools are also widely used to generate fine-scale regional climate information necessary for climate impacts and adaptation studies. A comprehensive assessment of the added value of increased spatial resolution and of the benefits and shortcomings of statistical downscaling tools are addressed in [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.3|Section 10.3.3]] ).

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===== 8.5.1.2.1 High-resolution global climate models =====

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Since AR5, horizontal resolution has increased in most global climate models, which has led to several improvements in the simulation of the water cycle (see also [[IPCC:Wg1:Chapter:Chapter-10#10.3.1.1|Section 10.3.1.1]] ), not only in areas with steep or complex orography, but also over the tropical oceans and within the North Pacific and North Atlantic storm tracks (Piazza et al. , 2016; Roberts et al. , 2018; Bui et al. , 2019; [[#Chen--2019|Chen and Dai, 2019]] ; Vannière et al. , 2019) . Yet, the added value of higher resolution global climate models is not systematic (Johnson et al. , 2016; Ogata et al. , 2017; D. Huang et al. , 2018; Mahajan et al. , 2018; Vannière et al. , 2019) and needs careful assessment ( [[#Haarsma--2016|Haarsma et al., 2016]] ; [[#Caldwell--2019|Caldwell et al., 2019]] ). Several AGCM studies suggest that increased spatial resolution leads to better simulation of the atmospheric moisture transport from ocean to land, the geographical distribution of annual mean precipitation ( [[#Demory--2014|Demory et al., 2014]] ), and the frequency distribution of daily precipitation intensities (L. [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|Zhang et al., 2016]] ; [[#Chen--2019|Chen and Dai, 2019]] ) including extremes in many( [[#Jacob--2014|Jacob et al., 2014]] ; [[#Westra--2014|Westra et al., 2014]] ), but not all cases ( [[#Bador--2020|Bador et al., 2020]] ).

Part of the improvement in simulated precipitation accuracy is related to improved simulation of the frequency and/or mean intensity of tropical ( [[#Roberts--2015|Roberts et al., 2015]] ; [[#Walsh--2015|Walsh et al., 2015]] ) and extratropical ( [[#Hawcroft--2016|Hawcroft et al., 2016]] ) cyclones. Idealized regional experiments also show that the North Atlantic storm track response to global warming can be amplified in higher resolution models ( [[#Willison--2015|Willison et al., 2015]] ). Increased atmospheric horizontal resolution can be also important for simulating Northern Hemisphere (NH) blockings ( [[#Davini--2017|Davini et al., 2017]] ; [[#Schiemann--2017|Schiemann et al., 2017]] ) and synoptic features of the East Asian summer monsoon ( [[#Yao--2017|Yao et al., 2017]] ; [[#Kusunoki--2018|Kusunoki, 2018]] ). Variable resolution based on grid stretching may be a valuable alternative for simulating regional phenomena like monsoons (Sabin et al. , 2013; Krishnan et al. , 2016) or tropical cyclones ( [[#Harris--2016|Harris et al., 2016]] ; [[#Chauvin--2017|Chauvin et al., 2017]] ), while avoiding inconsistencies in the forcings or physics that can be found in RCMs driven by GCMs ( [[#Boé--2020|Boé et al., 2020]] ; [[#Tapiador--2020|Tapiador et al., 2020]] ).

Increasing horizontal model resolution in CMIP5 and CMIP6 models leads to a systematic increase in global mean precipitation, enhanced moisture advection to land in close connection with increased orographic precipitation, and a partial reduction of the long-standing double ITCZ bias ( [[#Demory--2014|Demory et al., 2014]] ; [[#Caldwell--2019|Caldwell et al., 2019]] ; [[#Vannière--2019|Vannière et al., 2019]] ). Recent studies based on HighResMIP simulations ( [[#Haarsma--2016|Haarsma et al., 2016]] ) confirm the added value of increased horizontal resolution (at least 50 km in the atmosphere and 25 km in the ocean) for the simulation of tropical ( [[#Roberts--2020|Roberts et al., 2020]] ) and extratropical cyclones ( [[#Priestley--2020b|Priestley et al., 2020b]] ). CMIP6 model biases in annual mean precipitation are only slightly reduced at higher resolution (Figure 3.10).

High resolution representation of the land surface is also important for simulating many features of the terrestrial water cycle, such as orographic precipitation, snow, runoff and streamflow in complex topography areas ( [[#Zhao--2015|Zhao and Li, 2015]] ). However, the added value may be easier to assess in offline rather than online land surface simulations ( [[#Döll--2016|Döll et al., 2016]] ) given the possible use of bias-corrected atmospheric forcings. Offline high-resolution GHMs are routinely used to monitor water resources or to assess the hydrological impacts of bias-adjusted global climate projections ( [[#Davie--2013|Davie et al., 2013]] ; S. [[#Huang--2017|Huang et al., 2017]] , 2018). Yet, the development and calibration of ‘hyper-resolution’ hydrological models, with gridcells of typically 100 m to 1 km, raises a number of issues given the lack of comprehensive surface or subsurface information ( [[#Bierkens--2015|Bierkens et al., 2015]] ) and the lack of coupling with the atmosphere (Berg and Sheffield, 2018a).

In summary, there is ''high confidence'' that increasing horizontal resolution in GCMs can reduce a number of systematic model errors of relevance for the water cycle, including synoptic circulation and the statistics of daily precipitation. High-resolution GCMs and GHMs provide improved representation of land surfaces, including topography, vegetation and land use change, which are required to accurately simulate changes in the terrestrial water cycle. However, there is ''low confidence'' that the higher horizontal resolution simulations currently available provide more accurate projections of the large-scale features of the water cycle.

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===== 8.5.1.2.2 Regional climate models and convective-permitting models =====

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Regional Climate Models (RCMs) are used to dynamically downscale global model simulations for a particular region (usually at a spatial resolution of the order of 10 to 50 km; see [[IPCC:Wg1:Chapter:Chapter-10#10.3.3|Section 10.3.3]] ). The AR5 reported that RCMs are useful for regions with variable topography and for small-scale phenomena. However, they inherit biases from their driving GCMs and thus may lack physical consistency with them. Since AR5, the application of RCMs has largely increased due to international model intercomparison projects such as CLARIS-LPB ( [[#Sánchez--2015|Sánchez et al., 2015]] ). Many studies have focused on present-day climatological precipitation, showing with ''high confidence'' improvements in its monthly to seasonal accumulation and spatial distribution ( [[#Dosio--2015|Dosio et al., 2015]] ; [[#Giorgi--2016|Giorgi et al., 2016]] ; [[#Bozkurt--2019|Bozkurt et al., 2019]] ; [[#Falco--2019|Falco et al., 2019]] ; [[#Di%20Virgilio--2020|Di Virgilio et al., 2020]] ), although the modelling of precipitation remains the ‘Achilles heel’ of both GCMs and RCMs and should be considered cautiously when informing regional climate change adaptation strategies ( [[#Tapiador--2019b|Tapiador et al., 2019b]] ).

Regional Convective Permitting Models (CPMs), typically run at a resolution less than 10 km, have been implemented over increasingly large domains. Compared to models with parametrized convection ( [[#8.5.1.1.1|Section 8.5.1.1.1]] ), they generally show improved simulation of key features of the water cycle such as orographic precipitation, sea breeze dynamics, the diurnal cycle in precipitation, soil-moisture–precipitation feedbacks, daily precipitation persistence, sub-daily to daily precipitation intensities and related extremes ( [[#8.2.3.2|Section 8.2.3.2]] ; Birch et al. , 2015; Prein et al. , 2015; Kendon et al. , 2017; Leutwyler et al. , 2017; Willetts et al. , 2017; [[#Hohenegger--2018|Hohenegger and Stevens, 2018]] ; Berthou et al. , 2019b; [[#Takahashi--2019|Takahashi and Polcher, 2019]] ; Fumière et al. , 2020; Scaff et al. , 2020; Caillaud et al. , 2021) . A growing number of studies have also assessed the potential added value of using CPMs for regional climate projections (Ban et al. , 2015; Giorgi et al. , 2016; Fosser et al. , 2017; Kendon et al. , 2017, 2019; C. Liu et al. , 2017; Lenderink et al. , 2019; Rasmussen et al. , 2020; see also [[IPCC:Wg1:Chapter:Atlas|Atlas]] 5.6.3) . Although projected changes in rainfall occurrence in CPMs are broadly and qualitatively consistent with the results of GCMs and RCMs ( [[#Kendon--2017|Kendon et al., 2017]] ), there is a tendency towards stronger changes in both wet and dry extremes (Berthou et al. , 2019a; Kendon et al. , 2019; Lenderink et al. , 2019; Finney et al. , 2020a) . While both GCMs and RCMs project an overall decrease in summer precipitation over the Alps, RCMs simulate an increase over the high Alpine elevations that is not present in the global simulations ( [[#Giorgi--2016|Giorgi et al., 2016]] ).

Recent studies based on both GCMs and CPMs indicate that both CAPE and convective inhibition will increase in a warmer climate ( [[#8.2.3.2|Section 8.2.3.2]] ; J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] a), consistent with a shift from moderate to less frequent but stronger convective events ( [[#Rasmussen--2020|Rasmussen et al., 2020]] ). If underestimated by models with parametrized convection, such a mechanism could explain the underestimation of both projected increase in precipitation extremes ( [[#Borodina--2017|Borodina et al., 2017]] ; [[#Yin--2018|Yin et al., 2018]] ) and land surface drying ( [[#Douville--2017|Douville and Plazzotta, 2017]] ) in the extratropics. CMIP5 models with a larger increase in extreme precipitation also exhibit larger declines or smaller increases in light to moderate events ( [[#Thackeray--2018|Thackeray et al., 2018]] ).

In summary, there is ''high confidence'' that dynamical downscaling using limited area models adds value in simulating precipitation and related water cycle processes at the regional scale, especially in complex orography areas ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.5.1|Section 10.3.3.5.1]] ). There is ''high confidence'' that the explicit simulation of atmospheric convection can improve the representation of weather phenomena, including the life cycle of convective storms and related precipitation extremes. Even with an improved simulation of small-scale processes, there is only ''medium confidence'' that there will be an improvement in RCM-based water cycle projections as they rely on GCM boundary conditions.

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