Convection-permitting dynamical downscaling (CPDD) allows for an explicit representation of the storm-scale generators of tornadoes, hail, severe thunderstorm winds, and locally heavy precipitation. Possible changes in such hazardous convective weather (HCW) due to human-induced climate change are therefore projected with higher confidence using CPDD than with analyses of relatively coarse global climate models (GCM). However, the computational resources necessary for CPDD are significant and therefore CPDD-based future projections of HCW have tended to be based on a single experiment, and thus absent of uncertainty measures otherwise determined with an ensemble of experiments via an ensemble of GCMs. Herein we present "environment-informed" CPDD as a means to efficiently generate a CPDD ensemble driven by different GCMs. This variant of CPDD is applied only to a subset of days and geographical domains over which the meteorological conditions potentially favor HCW; unnecessary model integrations on meteorologically unfavorable days and domains are thereby eliminated. The selection procedure also accounts for GCM biases.The temporal and geospatial occurrence of historical HCW over the United States is demonstrated from the perspective of environment-informed CPDD as applied to eight different GCMs and ERA5 reanalysis. The overall geographical distributions in HCW vary considerably from downscaled GCM to GCM, thus demonstrating the value of an ensemble. The efficiency in which HCW is realized in favorable environments also varies considerably across the eight downscaled GCMs.
Wang et al. (Fri,) studied this question.
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