Abstract
This study presents an evaluation of the skill of 12 global climate models from the CMIP6 archive in capturing convective-storm parameters over the United States. For the historical reference period 1979-2014, we compare the model-simulated 6-hourly CAPE, CIN, 0-1 km wind shear (S01) and 0-6 km wind shear (S06) to those from two independent reanalysis datasets – ERA5 and MERRA2. To obtain a comprehensive picture, we analyze the parameter distribution, climatological mean, extreme, and thresholded frequency of convective parameters. The analysis reveals significant bias in capturing both magnitude and spatial patterns, which also vary across the seasons. The spatial distribution of means and extremes of the parameters indicate that most models tend to overestimate CAPE, whereas S01, and S06 are underrepresented to varying extents. Additionally, models tend to underestimate extremes in CIN. Comparing the model profiles with rawinsonde profiles indicates that most of the high-CAPE models have warm and moist bias. We also find that the near-surface wind speed is generally underestimated by the models. The intermodel spread larger for thermodynamic parameters as compared to kinematic parameters. The models generally have a significant positive bias in CAPE over western and eastern regions of the continental US. More importantly, the bias in thresholded frequency of all four variables is considerably larger than the bias in mean, suggesting a non-uniform bias across the distribution. This likely leads to an under-representation of favorable severe thunderstorm environments, and has the potential to influence dynamical downscaling simulations via initial and boundary conditions.
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