Abstract
Abstract. In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44∘ (∼ 50 km), 0.22∘ (∼ 25 km), and 0.11∘ (∼ 12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM–RCM modeling chain.
Highlights
Dynamical downscaling of global climate models (GCMs) with a regional climate model (RCM) is an approach employed to obtain higher spatial and temporal resolved climate information at the regional to local scales (Rummukainen, 2016; Giorgi, 2019; Gutowski et al, 2016; Jacob et al, 2020)
Three global near-surface temperature datasets are considered for the evaluation of the simulations: first, the Global Historical Climatology Network version 2 and the Climate Anomaly Monitoring System (GHCN2+CAMS, Fan and van den Dool, 2008), which combine two large individual datasets of station observations; second, a dataset collected by the University of DELaware (UDEL), including a large number of station temperature data, both from the GHCN2 and, more extensively, from the archive of Willmott and Matsuura (2001); third, time-series datasets produced by the Climatic Research Unit (CRU) at the University of East Anglia, which is based on an archive of monthly mean temperatures provided by more than 4000 weather stations distributed around the world (University of East Anglia Climatic Research Unit et al, 2008)
As more computing power became available, modeling groups were able to run their model at a higher horizontal resolution, resulting in the CORDEX framework recommending the RCMs to be run with a horizontal grid spacing of 25 km (12 km for Europe) instead of 50 km, which was initially suggested by Giorgi et al (2009)
Summary
Dynamical downscaling of global climate models (GCMs) with a regional climate model (RCM) is an approach employed to obtain higher spatial and temporal resolved climate information at the regional to local scales (Rummukainen, 2016; Giorgi, 2019; Gutowski et al, 2016; Jacob et al, 2020) These GCM–RCM model chain data are typically used as the basis for impact studies and long-term adaptation planning by impact modeling groups, stakeholders, and national climate assessment reports (Ahrens et al, 2014; Kjellström et al, 2016; Dalelane et al, 2018; Rineau et al, 2019; Sørland et al, 2020; Sterl et al, 2020; Vanderkelen et al, 2020). Compared to earlier projects such as PRUDENCE (Christensen and Christensen, 2007) and ENSEMBLES (van der Linden and Mitchell, 2009), the number of simulations has increased by more than 400 % (Christensen et al, 2019)
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