Response of stream ecosystems to climate change (II): An improved climate dynamical downscaling for the conterminous United States

Background/Question/Methods

To understand potential ecological responses to climate change, climate models are needed that can accurately predict how temperature and precipitation vary at ecologically relevant spatial scales (e.g., ~ 4 km). Global climate models predict at coarse scales (e.g., 150 km) and often exhibit significant biases. We used a combination of dynamical and statistical downscaling to produce unbiased climate predictions at a 4-km resolution for use in stream temperature, hydrologic regime, and stream species distribution models. Our approach consisted of (1) statistical correction of the global Community Climate System Model (CCSM) output (150 km resolution) to minimize climatological biases, (2) calibration of the Weather Research and Forecasting (WRF) model, forced with corrected CCSM output, to dynamically downscale climate predictions to 50-km resolution for the conterminous United States, and (3) statistical downscaling of the 50-km WRF predictions to 4-km resolution.

Results/Conclusions

Our modeling produced significant improvements in climate predictions compared with the global model output and other existing downscaled products such as those produced with the North American Regional Climate Change Assessment Program.  However, strong dry biases were still seen in our dynamically downscaled products in the southeastern United States, and further sensitivity tests indicated that these biases were related to the coarser spatial model resolution and unrealistic convection parameterizations. Through the statistical downscaling with 4-km resolution observations, these dry biases were greatly alleviated or effectively eliminated. These simulation techniques have a strong potential for reducing climate projection uncertainties at regional scales.