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

==== 10.3.3.8 Performance at Simulating Historical Regional Climate Changes ====

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This section assesses how well climate models perform at realistically simulating historical regional climatic trends. Current global model ensembles reproduce global to continental-scale surface temperature trends at multi-decadal to centennial time scales (CMIP5, CMIP6), but underestimate precipitation trends (CMIP5) (Sections 3.3.1 and 3.3.2). For regional trends, AR5 concluded that the CMIP5 ensemble cannot be taken as a reliable representation of reality and that the true uncertainty can be larger than the simulated model spread ( [[#Kirtman--2014|Kirtman et al., 2014]] ). Case studies of regional trend simulations by global models can be found in Sections 10.4.1 and 10.6, and region-by-region assessments in the Atlas. A key limitation for assessing the representation of regional observed trends by single transient simulations of global models (or downscaled versions thereof) is the strong amplitude of internal variability compared to the forced signal at the regional scale ( [[#10.3.4.3|Section 10.3.4.3]] ). Even on multi-decadal time scales, an agreement between observed and individual simulated trends would be expected to occur only by chance (Laprise, 2014).

In the context of downscaling, the ability of downscaling methods to reproduce observed trends when driven with boundary conditions or predictors taken from reanalysis data (which reproduce the observed internal variability on long time scales) can be assessed. For temperature in the continental USA, reanalysis-driven RCMs skilfully simulated recent spring and winter trends, but did not reproduce summer and autumn trends, ( [[#Bukovsky--2012|Bukovsky, 2012]] ). Over Central America, observed warming trends were reproduced ( [[#Cavazos--2020|Cavazos et al., 2020]] ). In contrast, a reanalysis-driven coupled atmosphere–ocean RCM covering the Mediterranean could not reproduce the observed SST trend ( [[#Sevault--2014|Sevault et al., 2014]] ).

Similar studies have been carried out for statistical downscaling and bias adjustment using predictors from reanalyses (or in case of bias adjustment, dynamically downscaled reanalyses). For a range of different perfect prognosis methods, [[#Huth--2015|Huth et al. (2015)]] found that simulated temperature trends were too strong for winter and too weak for summer. The performance was similar for the different methods, indicating the importance of choosing informative predictors. Similarly, Maraun et al. (2019b) found that the performance of perfect prognosis methods depends mostly on the predictor and domain choice (for instance, temperature trends were only captured by those methods including surface temperature as predictor). Bias adjustment methods reproduced the trends of the driving reanalysis, apart from quantile mapping methods, which deteriorated these trends.

RCM experiments are often set up such that changes in forcing agents are included only via the boundary conditions, but not explicitly included inside the domain. [[#Jerez--2018|Jerez et al. (2018)]] demonstrated that not including time-varying GHG concentrations within the RCM domain may misrepresent temperature trends by 1–2°C per century. Including the past trend in anthropogenic sulphate aerosols in reanalysis-driven RCM simulations substantially improved the representation of recent brightening and warming trends in Europe ( [[#Nabat--2014|Nabat et al., 2014]] ; see Sections 10.3.3.6 and 10.6.4, and Atlas.8.4). Similarly, [[#Bukovsky--2012|Bukovsky (2012)]] argued that RCMs may not capture observed summer temperature trends in the USA because changes in land cover are not taken into account. [[#Barlage--2015|Barlage et al. (2015)]] have revealed that including the behaviour of groundwater in land schemes increases the performance of an RCM model to represent climate variability in the central USA. [[#Hamdi--2014|Hamdi et al. (2014)]] found that an RCM that did not incorporate the historical urbanization in the land-use, land-cover scheme is not able to reproduce the warming trend observed in urban stations, with a larger bias for the minimum temperature trend.

Overall, there is ''high confidence'' that including all relevant forcings is a prerequisite for reproducing historical trends.

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