Authors

Abstract

This study uses Weather Research and Forecast (WRF) model to evaluate the performance of six dynamical downscaled decadal historical simulations with 12-km resolution for a large domain (7200 × 6180 km) that covers most of North America. The initial and boundary conditions are from three global climate models (GCMs) and one reanalysis data. The GCMs employed in this study are the Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean Layer Dynamics component, Community Climate System Model, version 4, and the Hadley Centre Global Environment Model, version 2-Earth System. The reanalysis data is from the National Centers for Environmental Prediction-US. Department of Energy Reanalysis II. We analyze the effects of bias correcting, the lateral boundary conditions and the effects of spectral nudging. We evaluate the model performance for seven surface variables and four upper atmospheric variables based on their climatology and extremes for seven subregions across the United States. The results indicate that the simulation’s performance depends on both location and the features/variable being tested. We find that the use of bias correction and/or nudging is beneficial in many situations, but employing these when running the RCM is not always an improvement when compared to the reference data. The use of an ensemble mean and median leads to a better performance in measuring the climatology, while it is significantly biased for the extremes, showing much larger differences than individual GCM driven model simulations from the reference data. This study provides a comprehensive evaluation of these historical model runs in order to make informed decisions when making future projections.