Understanding model biases regarding the low-level jet (LLJ) is crucial to realizing areas of uncertainty in future climate scenarios, due to the role of the LLJ in the initiation and sustenance of mesoscale connective systems in the Great Plains of the United States. We have established a low-level jet metric which categorizes low-level wind maxima based on the maximum wind speed and low-level shear, consistent with previous work by Bonner (1968). This metric is applied to a matrix of dynamically downscaled regional climate models (RegCM4 and WRF-ARW) with varying horizontal grid resolutions (50km, 25km, and 12km) driven by three global climate models (GFDL-ESM2M, HadGEM2-ES, MPI-ESM-LR) and one reanalysis dataset (ERA-Interim), in conjunction with the CORDEX-North America and DOE FACETS projects. The LLJ metric is used to evaluate the performance of each combination of regional climate model, driving global climate model, and grid resolution and to diagnose model inconsistencies. Preliminary results suggest that increasing the horizontal grid resolution increases the peak frequency of the LLJ. In addition, discrepancies between the global climate model driven simulations are evident, specifically in the northward extent of the highest LLJ frequencies.

This research was sponsored in part by the U.S. Department of Energy and the USDA National Institute of Food and Agriculture.