J9.5 Ensemble downscaling of seasonal forecasts for hydrological applications

Tuesday, 25 January 2011: 2:30 PM
609 (Washington State Convention Center)
Raymond W. Arritt, Iowa State Univ., Ames, IA

The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-model, multi-institutional project that was designed, coordinated, and successfully brought to funding by our late colleague John Roads. John's vision was that an ensemble of different regional models could add hydrologically important spatial detail to global seasonal forecasts. Unfortunately he passed away ten days before the July 1, 2008 start date of the project. This presentation gives current results from the project and is dedicated to John's memory.

MRED is producing large ensembles of downscaled seasonal forecasts from coupled atmosphere-ocean seasonal prediction models. Eight regional climate models are downscaling 15-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the new NASA seasonal forecast system based on the GEOS5 atmospheric model coupled with the MOM4 ocean model. This produces 240-member ensembles, i.e., 8 regional models x 15 global ensemble members x 2 global models, for each winter season (December-April) of 1982-2003. Results to date show that the combined global-regional downscaled forecasts have greatest skill for seasonal precipitation anomalies in strong El Niño events such as 1982-83 and 1997-98. Ensemble means of area-averaged seasonal precipitation for the regional models generally track the corresponding results for the global model, though there is considerable inter-model variability amongst the regional models. For seasons and regions where area mean precipitation is accurately simulated the regional models bring added value by extracting greater spatial detail from the global forecasts, mainly due to better resolution of terrain in the regional models. Our results also emphasize that an ensemble approach is essential to realizing the added value from the combined global-regional modeling system.

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