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

==== 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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