Select Representative General Circulation Model Runs Using Envelope-Based Technique

Abstract A subset of ensemble general circulation models (GCMs), known as representative GCMs (RGCMs), is typically used in climate change analysis to reduce computational cost. This study presents a new envelope-based workflow for RGCM selection beginning with history matching to better preserve the full ensemble uncertainty range and replicate past climate characteristics, in contrast to an envelope-based standard workflow that begins with capturing climatic means. Our workflow contains a three-stage selection process that leverages monthly ground-truth data from the reference period 1981–2005, along with monthly simulation outputs for the periods 1981–2010 and 2071–2100 from GCM runs in phase 5 of the Coupled Model Intercomparison Project (CMIP5), starting with an initial pool of 105 and 77 runs under representative concentration pathways 4.5 and 8.5, respectively. The performance of the proposed workflow is compared with the standard workflow using the Kolmogorov–Smirnov test measuring D max , the largest absolute difference between two cumulative distributions of changes in mean air temperature Δ T and precipitation Δ P from the full set of GCM runs and RGCM runs, and the mean-square error (MSE) evaluating reproduction of past climate data. The comparisons for Texas show that the new workflow reduces mean D max for Δ T by 23% and Δ P by 20%, while improving the mean MSE by 105% and 6% for monthly average of mean air temperature and precipitation, respectively. Further evaluation in Bihar, India, and New York State, United States, regions with distinct climatic conditions from those of Texas consistently shows lower D max and MSE for our workflow, indicating that it selects RGCM runs that better represent the full set of GCM runs.