Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.3.3.4 Performance at Simulating Regional Phenomena and Processes ====

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Regional climate is shaped by a wide range of weather phenomena occurring at scales from about 2000 km to 2 km (Figure 10.3). These modulate the influence of large-scale atmospheric phenomena and create the characteristic and potentially severe weather conditions. The climate in different regions will be affected by different mesoscale phenomena, of which several may be relevant. A skilful representation of these phenomena is a necessary condition for providing credible and relevant climate information for a given region and application. Therefore, it is important to understand the strengths and weaknesses of different model types in simulating these phenomena. The performance of different dynamical climate model types to simulate a selection of relevant mesoscale weather phenomena is assessed here.

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===== 10.3.3.4.1 Convection including tropical cyclones =====

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Convection is the process of vertical mixing due to atmospheric instability. Deep moist convection is associated with thunderstorms and severe weather such as heavy precipitation and strong wind gusts. Convection may occur in single locations, in spatially extended severe events such as supercells, and organized into larger mesoscale convective systems such as squall lines or tropical cyclones, and embedded in fronts (see below). Shallow and deep convection are not explicitly simulated but parametrized in standard global and regional models. In consequence, these models suffer from several biases. AR5 has stated that many CMIP3 and CMIP5 models simulate the peak in the diurnal cycle of precipitation too early, but increasing resolution and better parametrizations help to mitigate this problem ( [[#Flato--2014|Flato et al., 2014]] ). Similar issues arise for RCMs with parametrized deep convection ( [[#Prein--2015|Prein et al., 2015]] ), which also tend to overestimate high cloud cover ( [[#Langhans--2013|Langhans et al., 2013]] ; [[#Keller--2016|Keller et al., 2016]] ).

Non-hydrostatic RCMs at convection-permitting resolution (4 km and finer) improve features such as the initiation and diurnal cycle of convection ( [[#Zhu--2012|Zhu et al., 2012]] ; [[#Prein--2013a|Prein et al., 2013a]] , b; [[#Fosser--2015|Fosser et al., 2015]] ; [[#Stratton--2018|Stratton et al., 2018]] ; [[#Sugimoto--2018|Sugimoto et al., 2018]] ; [[#Finney--2019|Finney et al., 2019]] ; [[#Berthou--2020|Berthou et al., 2020]] ; [[#Ban--2021|Ban et al., 2021]] ; [[#Pichelli--2021|Pichelli et al., 2021]] ), the triggering of convection by orographic lifting ( [[#Langhans--2013|Langhans et al., 2013]] ; [[#Fosser--2015|Fosser et al., 2015]] ), and maximum vertical wind speeds in convective cells ( [[#Meredith--2015a|Meredith et al., 2015a]] ). Also spatial patterns of precipitation ( [[#Prein--2013a|Prein et al., 2013a]] , b; [[#Stratton--2018|Stratton et al., 2018]] ), precipitation intensities ( [[#Prein--2015|Prein et al., 2015]] ; [[#Fumière--2020|Fumière et al., 2020]] ; [[#Ban--2021|Ban et al., 2021]] ; [[#Pichelli--2021|Pichelli et al., 2021]] ), the scaling of precipitation with temperature ( [[#Ban--2014|Ban et al., 2014]] ), cloud cover ( [[#Böhme--2011|Böhme et al., 2011]] ; [[#Langhans--2013|Langhans et al., 2013]] ) and its resultant radiative effects ( [[#Stratton--2018|Stratton et al., 2018]] ), as well as the annual cycle of tropical convection ( [[#Hart--2018|Hart et al., 2018]] ) are improved. Phenomena such as supercells, mesoscale convective systems, or the local weather associated with squall lines are not captured by global models and standard RCMs. Convection-permitting RCM simulations, however, have been shown to realistically simulate supercells ( [[#Trapp--2011|Trapp et al., 2011]] ), mesoscale convective systems, their life cycle and motion ( [[#Prein--2017|Prein et al., 2017]] ; [[#Crook--2019|Crook et al., 2019]] ), and heavy precipitation associated with a squall line ( [[#Kendon--2014|Kendon et al., 2014]] ). There is ''high confidence'' that simulations at convection-permitting resolution add value to the representation of deep convection and related phenomena.

Convection is the key ingredient of tropical cyclones. An intercomparison of high-resolution AGCM simulations ( [[#Shaevitz--2014|Shaevitz et al., 2014]] ) showed that tropical cyclone intensities appeared to be better represented with increasing model resolution. [[#Takayabu--2015|Takayabu et al. (2015)]] have compared simulations of typhoon Haiyan at different resolutions ranging from 20 km to 1 km (Figure 10.8). While the eyewall structure in the precipitation pattern was strongly smoothed in the coarse resolution simulations, it was well-resolved at the highest resolution. [[#Gentry--2010|Gentry and Lackmann (2010)]] found similar improvements in simulating hurricane Ivan for horizontal resolutions between 8 km and 1 km. High-resolution coupled ocean–atmosphere simulations improve the representation of the radial structure of core convection and thereby the rapid intensification of the cyclone ( [[#Kanada--2017b|Kanada et al., 2017b]] ). There is ''high confidence'' that convection-permitting resolution is required to realistically simulate the three-dimensional structure of tropical cyclones.

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[[File:1b451650234bba076f5e7d74e3147fc1 IPCC_AR6_WGI_Figure_10_8.png]]

'''Figure 10.8''' '''|''' '''Hourly accumulated precipitation profiles (mm hour''' –1 ''') around the eye of Typhoon Haiyan.''' Represented by '''(a)''' Global Satellite Mapping of Precipitation (GSMaP) data (multi-satellite observation), '''(b)''' Guiuan radar (PAGASA), '''(c)''' Weekly Ensemble Prediction System (WEPS) data (JMA; 60 km), '''(d)''' NHRCM (20 km), '''(e)''' NHRCM (5 km), and '''(f)''' WRF (1 km) models. Panels (b), (d–f) are adapted from [[#Takayabu--2015|Takayabu et al. (2015)]] , CCBY3.0 https://creativecommons.org/licenses/by/3.0 . Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

Initial studies with convection-permitting global models suggests that improvements in representing convection, as described for RCMs above, have a positive impact on the tropical and extratropical atmospheric circulation and, thus, regional climate ( [[#Satoh--2019|Satoh et al., 2019]] ; [[#Stevens--2019|Stevens et al., 2019]] ; see also [[IPCC:Wg1:Chapter:Chapter-8#8.5.1.2|Section 8.5.1.2]] and Chapter 7). Computational constraints currently limit these simulations to a length of few months only, such that they cannot yet be used for routine climate change studies.

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===== 10.3.3.4.2 Mountain wind systems =====

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Mountain slope and valley winds are localized thermally generated diurnal circulations that have a strong influence on temperature and precipitation patterns in mountain regions. During the day, heating of mountain slopes induces upslope winds; during the night this circulation reverses. This phenomenon is not realistically represented by global models and coarse-resolution RCMs. RCM simulations at 4 km resolution showed good skill in simulating the diurnal cycle of temperature and wind on days of weak synoptic forcing in the Rocky Mountains ( [[#Letcher--2017|Letcher and Minder, 2017]] ) as well as in simulating the mountain-plain wind circulation over the Tianshan mountains in central Asia ( [[#Cai--2019|Cai et al., 2019]] ), while in the Alps, a 1 km resolution has been required ( [[#Zängl--2004|Zängl, 2004]] ).

Föhn winds are synoptically-driven winds across a mountain range that are warm and dry due to adiabatic warming in the downwind side. In an RCM study for the Japanese Alps, [[#Ishizaki--2009|Ishizaki and Takayabu (2009)]] found that at least 10 km resolution was required to realistically simulate the basic characteristics of Föhn events.

Synoptically-forced winds may be channelled and accelerated in long valleys. For instance, the Tramontana, Mistral and Bora are northerly winds blowing down-valley from central France and the Balkans into the Mediterranean ( [[#Flaounas--2013|Flaounas et al., 2013]] ). In winter, these winds may cause severe cold air outbreaks along the coast. [[#Flaounas--2013|Flaounas et al. (2013)]] have shown that a GCM with a horizontal resolution of roughly 3.75° longitude/1.875° latitude (roughly 400 km × 200 km depending on latitude) is unable to reproduce these winds because of the coarse representation of orography. Fifty-kilometre RCM simulations did not realistically represent the Mistral ( [[#Obermann--2018|Obermann et al., 2018]] ) and Bora winds ( [[#Belušić--2018|Belušić et al., 2018]] ), but simulations at 12 km added substantial value. Similarly, [[#Cholette--2015|Cholette et al. (2015)]] found that a 30 km RCM resolution was not sufficient to adequately simulate the channelling of winds in the St Lawrence River Valley in eastern Canada, whereas a 10 km resolution was.

There is ''high confidence'' that climate models with resolutions of around 10 km or finer are necessary for realistically simulating mountain wind systems such as slope and valley winds and the channelling of winds in valleys.

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===== 10.3.3.4.3 Coastal winds and lake effects =====

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Simulating coastal climates and the influence of big lakes are a modelling challenge, due to the complex coastlines, the different heat capacities of land and water, the resulting wind system, and differential evaporation. The AR5 concluded that RCMs can add value to the simulation of coastal climates.

Summer coastal low-level jets off the mid-latitude western continental coasts are forced by the semi-permanent subtropical anticyclones, inland thermal lows, strong across-shore temperature contrasts in upwelling regions, and high coastal topography. They are important factors in shaping regional climate by, for instance, preventing onshore advection of humidity and thereby causing aridity in the Iberian Peninsula ( [[#Soares--2014|Soares et al., 2014]] ), or by transporting moisture towards precipitating regions as in the North American monsoon ( [[#Bukovsky--2013|Bukovsky et al., 2013]] ).

Reanalyses and most global models do not well resolve the details of coastal low-level jets ( [[#Bukovsky--2013|Bukovsky et al., 2013]] ; [[#Soares--2014|Soares et al., 2014]] ), but they are still able to represent annual and diurnal cycles and interannual variability ( [[#Cardoso--2016|Cardoso et al., 2016]] ; [[#Lima--2019|Lima et al., 2019]] ). [[#Bukovsky--2013|Bukovsky et al. (2013)]] found RCM simulations at a 50 km resolution to improve the representation of the coastal low-level jet in the Gulf of California and the associated precipitation pattern compared to the driving global models. [[#Lucas-Picher--2017|Lucas-Picher et al. (2017)]] find indirect evidence via precipitation patterns that 12 km simulations further improve the representation. [[#Soares--2014|Soares et al. (2014)]] demonstrated that an 8 km resolution RCM simulated a realistic three-dimensional structure of the Iberian coastal low-level jet, and the surface winds compare well with observations. [[#Lucas-Picher--2017|Lucas-Picher et al. (2017)]] showed that a 0.44° resolution RCM underestimated winds along the Canadian east coast, whereas a 0.11° resolution version simulated more realistic 10 metre wind speed. Also, the Etesian winds in the Aegean Sea were realistically simulated by 12 km resolution RCMs ( [[#Dafka--2018|Dafka et al., 2018]] ).

A particularly relevant coastal phenomenon is the sea breeze, which is caused by the differential heating of water and land during the diurnal cycle and typically reaches several tens of kilometres inland. Reanalyses and global models have too coarse a resolution to realistically represent this phenomenon, such that they typically underestimate precipitation over islands and misrepresent its diurnal cycle ( [[#Lucas-Picher--2017|Lucas-Picher et al., 2017]] ). RCMs improve the representation of sea breezes and thereby precipitation in coastal areas and islands. Over Cuba and Florida only a 12 km-resolution RCM is able to realistically simulate the inland propagation of precipitation during the course of the day ( [[#Lucas-Picher--2017|Lucas-Picher et al., 2017]] ). RCM simulations at 20 km horizontal resolution realistically represented the sea breeze circulation in the Mediterranean Gulf of Lions including the intensity, direction and inward propagation ( [[#Drobinski--2018|Drobinski et al., 2018]] ). Even though a coupled ocean–atmosphere simulation improved the representation of diurnal SST variations, the sea breeze representation itself was not improved.

Big lakes modify the downwind climate. In particular during winter they are relatively warm compared to the surrounding land, provide moisture, destabilize the passing air column and produce convective systems. The increase in friction when moving air reaches land causes convergence and uplift, and may trigger precipitation. [[#Gula--2012|Gula and Peltier (2012)]] found that a state-of-the-art GCM does not realistically simulate these effects over the North American Great Lakes, but a 10 km RCM better represents them and thereby simulates realistic downwind precipitation patterns, in particular enhanced snowfall during the winter season. Similar results were found by [[#Wright--2013|Wright et al. (2013)]] , [[#Notaro--2015|Notaro et al. (2015)]] and [[#Lucas-Picher--2017|Lucas-Picher et al. (2017)]] . In a convection permitting simulation of the Lake Victoria region, a too strong nocturnal land breeze resulted in unrealistically high precipitation ( [[#Finney--2019|Finney et al., 2019]] ).

There is ''high confidence'' that climate models with sufficiently high resolution are necessary for realistically simulating lake and coastal weather including coastal low-level jets, lake and sea breezes, as well as lake effects on rainfall and snow.

In regions like Fenno-Scandinavia or central-eastern Canada, very large fractions of land are covered by small and medium sized lakes. Other regions have fewer but larger lakes, such as central-eastern Africa, the eastern border between the USA and Canada, and central Asia. In these regions it has been considered essential to include a lake model in an RCM to realistically represent regional temperatures ( [[#Samuelsson--2010|Samuelsson et al., 2010]] ; [[#Deng--2013|Deng et al., 2013]] ; [[#Mallard--2014|Mallard et al., 2014]] ; [[#Thiery--2015|Thiery et al., 2015]] ; [[#Pietikäinen--2018|Pietikäinen et al., 2018]] ), as well as remote effects ( [[#Spero--2016|Spero et al., 2016]] ). The most common approach in RCMs is the two-layer lake model, including a lake-ice model, with parametrized vertical temperature profiles ( [[#Mironov--2010|Mironov et al., 2010]] ; [[#Golosov--2018|Golosov et al., 2018]] ). For the Caspian Sea, it is found that a three-dimensional ocean model simulated the SST fields better than a one-dimensional lake model when coupled to the same RCM ( [[#Turuncoglu--2013|Turuncoglu et al., 2013]] ).

There is ''medium evidence'' and ''high agreement'' that it is important to include interactive lake models in RCMs to improve the simulation of regional temperature, in particular in seasonally ice-covered areas with large fractions of lakes. There is ''medium evidence'' of the local influence of lakes on snow and rainfall as well as the importance of including lakes in regional climate simulations.

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===== 10.3.3.4.4 Fronts =====

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Weather fronts are two-dimensional surfaces separating air masses of different characteristics and are a key element of mid-latitude cyclones. In particular cold fronts are regions of relatively strong uplift and hence often associated with severe weather (e.g., [[#Schemm--2016|Schemm et al., 2016]] ). Stationary or slowly moving fronts may cause extended heavy precipitation. The evaluation of how climate models represent fronts, however, remains limited. [[#Catto--2014|Catto et al. (2014)]] found in both ERA-Interim and CMIP5 models that frontal frequency and strength were realistically simulated, albeit with some biases in the location. Follow-up investigations, for boreal and austral winter ( [[#Catto--2015|Catto et al., 2015]] ) found frontal precipitation frequency to be too high and the intensity too low, but these compensating biases resulted in only a small total precipitation bias. [[#Blázquez--2018|Blázquez and Solman (2018)]] found similar results for Southern Hemisphere (SH) winter, and also showed that CMIP5 models typically overestimate the fraction of frontal precipitation compared to total precipitation. As for the reference, the ERA-Interim reanalysis misrepresents conditional symmetric instability associated with fronts, and the corresponding precipitation ( [[#Glinton--2017|Glinton et al., 2017]] ). Only a few studies evaluating fronts in RCMs have been conducted. [[#Kawazoe--2013|Kawazoe and Gutowski (2013)]] diagnosed strong temperature gradients associated with extreme winter precipitation in the North American Regional Climate Change Assessment Program (NARCCAP) RCM ensemble ( [[#Mearns--2012|Mearns et al., 2012]] ) and found the models agreed well with gradients in a reanalysis. De Jesus et al. (2016) diagnosed the representations of cold fronts over southern Brazil in two RCMs, finding that they were only underestimated by about 5% across the year, but in one RCM, summer cold fronts were underestimated by 17%. An RCM-based reanalysis suggests that high-resolution RCM simulations improve the representation of orographic influences on fronts ( [[#Jenkner--2009|Jenkner et al., 2009]] ).

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