151. A combined dynamical and statistical downscaling technique to reduce biases in climate projections: an example for winter precipitation and snowpack in the western United States
- Author
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Robert R. Gillies, Shih-Yu Wang, and Rong Li
- Subjects
reduce biases ,Atmospheric Science ,climate projections ,010504 meteorology & atmospheric sciences ,Meteorology ,Climate ,Geopotential height ,Snowpack ,010502 geochemistry & geophysics ,Snow ,01 natural sciences ,downscaling technique ,snowpack ,Climatology ,Weather Research and Forecasting Model ,Combined Dynamical and Statistical Downscaling ,Environmental science ,Community Climate System Model ,Climate model ,western United States ,Precipitation ,winter precipitation ,0105 earth and related environmental sciences ,Downscaling - Abstract
Large biases associated with climate projections are problematic when it comes to their regional application in the assessment of water resources and ecosystems. Here, we demonstrate a method that can reduce systematic biases in regional climate projections. The global and regional climate models employed to demonstrate the technique are the Community Climate System Model (CCSM) and the Weather Research and Forecasting (WRF) model. The method first utilized a statistical regression technique and a global reanalysis dataset to correct biases in the CCSM-simulated variables (e.g., temperature, geopotential height, specific humidity, and winds) that are subsequently used to drive the WRF model. The WRF simulations were conducted for the western United States and were driven with (a) global reanalysis, (b) original CCSM, and (c) bias-corrected CCSM data. The bias-corrected CCSM data led to a more realistic regional climate simulation of precipitation and associated atmospheric dynamics, as well as snow water equivalent (SWE), in comparison to the original CCSM-driven WRF simulation. Since most climate applications rely on existing global model output as the forcing data (i.e., they cannot re-run or change the global model), which often contain large biases, this method provides an effective and economical tool to reduce biases in regional climate downscaling simulations of water resource variables.
- Published
- 2015
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