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Abstract This study presents an evaluation of the skill of 12 global climate models from phase 6 of the Coupled Model Intercomparison Project (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 For the historical reference period 1979–2014, we compare the model-simulated 6-hourly convective available potential energy (CAPE), convective inhibition (CIN), 0–1-km wind shear (S01), and 0–6-km wind shear (S06) to those from two independent reanalysis datasets: ERA5 and Modern-Era Retrospective Analysis for Research and Applications, version 2 (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 indicates 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 a warm and moist bias. We also find that the near-surface wind speed is generally underestimated by the models. The intermodel spread is 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 United States. More importantly, the bias in the thresholded frequency of all four variables is considerably larger than the bias in the mean, suggesting a nonuniform bias across the distribution. This likely leads to an underrepresentation of favorable severe thunderstorm environments and has the potential to influence dynamical downscaling simulations via initial and boundary conditions. Significance StatementGlobal climate model projections are often used to explore future changes in severe thunderstorm activity. However, climate model outputs often have significant biases, and they can strongly impact the results. In this study, we thoroughly examined biases in convective parameters in 12 models from phase 6 of the Coupled Model Intercomparison Project with respect to two reanalysis datasets. The analysis is performed for North America, covering the period 1979–2014. The study reveals significant biases in convective parameters that differ between models and are tied to the biases in temperature, humidity, and wind profiles. These results provide valuable insight into selecting the right set of models to analyze future changes in severe thunderstorm activity across the North American continent.
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Biases in CMIP6 Historical U.S. Severe Convective Storm Environments Driven by Biases in Mean‐State Near‐Surface Moist Static Energy
Abstract This work evaluates how well Coupled Model Intercomparison Project 6 models reproduce the climatology of North American severe convective storm (SCS) environments in ERA5 reanalysis and examines what drives biases across models. Biases in spring SCS environments vary widely in magnitude and spatial pattern, though most models do well in reproducing the climatological pattern and a few (MPI and CNRM) also reproduce the overall magnitude. SCS biases are driven by biases in extreme convective available potential energy. These biases are ultimately found to be driven by biases in mean‐state near‐surface moist static energy, indicating that the SCS environments depend strongly on the near‐surface mean state. Results are similar for fall, but not summer or winter when free‐tropospheric biases are also important. Biases differ strongly across parent models but weakly across child models of the same parent. These outcomes help identify models well‐suited for studying climate effects on SCS environments.
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- PAR ID:
- 10384884
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 49
- Issue:
- 23
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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