Improving U.S. Seasonal Climate Prediction by CWRF Downscaling from NOAA Operational Forecasts with Bias Correction

The regional Climate-Weather Research and Forecasting model (CWRF) has demonstrated its exceptional modeling capabilities across the contiguous US (CONUS). Leveraging CWRF's strengths encompassing a super ensemble of physics parameterization schemes and finer spatial resolutions, we conduct dynamic downscaling of seasonal climate predictions from NCEP Coupled Forecast System model version 2 (CFSv2) operational forecasts. We found that systematic biases in the lateral boundary forcing inherited from CFSv2 circulation errors pose challenges for CWRF to achieve optimal downscaling performance. Therefore, we corrected the forcing biases by eliminating CFSv2’s long-term mean departures from the ECMWF 5^th generation reanalysis (ERA5). The proposed correction is anticipated to enhance CWRF’s ability to simulate a more faithful mean circulation state. This, in turn, will enable more accurate generation of regional climate responses to significant large-scale anomalies introduced through lateral boundary forcing. In this presentation, we will demonstrate how the bias correction affects CWRF downscaling proficiency in forecasting interannual temperature and precipitation anomalies in the CONUS for the period 2012-2023. Furthermore, we will delve into the intricate physical processes and mechanisms that underlie the resulting effects.