Regional climate model simulation of projected 21st century climate change over an all-Africa domain: Comparison analysis of nested and driving model results

Abstract

[1] We analyze a transient climate change simulation for the 21st century over a large all-Africa domain carried out with the RegCM3 regional model driven by the ECHAM5 global model. We focus the analysis on a comparison between the driving and nested model runs. For present climate, the two models show temperature and precipitation biases of similar magnitude but different spatial patterns. In particular the bias patterns in the regional model driven by ECHAM5 are more similar to those of a regional simulation driven by ERA Interim reanalysis fields than to the bias patterns in the present climate simulation by ECHAM5 itself. In the transient simulation, while the temperature changes are strongly driven by the global model, the precipitation change patterns are more different between the global and regional models, particularly over the West Africa and Sahel regions. A targeted analysis suggests that this is due to the different simulation by the two models of the local response to El Niño-Southern Oscillation forcing and of local soil moisture/precipitation feedbacks. Our results thus indicate that local processes and internal model physics and characteristics are important elements in determining the precipitation change signal simulated by the nested regional model in this large domain experiment, especially over equatorial and tropical regions. This adds an element of uncertainty that needs to be address through the use of ensembles of regional model experiments as planned in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project.

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

“…Model uncertainty also increases with prediction lead time over all subregions. RCM simulation results are also influenced by the internal physics and boundary conditions from GCMs, as discussed in others' studies (Mariotti et al, 2011;Syed et al, 2012). More reliable future climate information and uncertainty quantification could be provided by coupling large ensembles of GCMs and RCMs under different emission scenarios.…”

A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability

“…The studies reviewed here suggest that RCMs have dynamic downscaling ability only under certain conditions, including adequate LBCs and proper domain setting, convective schemes, land surface parameterizations, initializations, and numerical schemes, as well as sufficiently large domains. Through interactions of these processes in the regional domain, the RCMs are able to provide added value compared with the data used for LBCs in some aspects (e.g., Mariotti et al, 2011;Xue et al, 2001Xue et al, , 2007Liang et al, 2004;Seth et al, 2007;Solman and Pessacg, 2012). Any significant weaknesses in one of these aspects would cause an RCM to lose its dynamic downscaling ability.…”

Validating the dynamic downscaling ability of WRF for East Asian summer climate

“…Due to the interactions of these physical processes, any significant weaknesses in one of these aspects could impede the RCMs from adding information in their dynamic downscaling (Mariotti et al 2011;Solman and Pessacg 2012). This is why RCMs with different convective and radiation schemes and land surface parameterizations produce significant different output when compared with those using different parameterizations but only one single model (De Elia et al 2008;Pielke 2013).…”