An Analysis Of Simulated California Climate Using Multiple Dynamical And Statistical techniques

Four dynamic regional climate models (RCMs) and one statistical downscaling approach were used to downscale 10 years of historical climate in California. To isolate possible limitations of the downscaling methods, we used initial and lateral boundary conditions from the NCEP global reanalysis. Results of this downscaling were compared to observations and to an independent, fine-resolution reanalysis (NARR). This evaluation is preparation for simulations of future-climate scenarios, the second phase of this CEC scenarios project. Each model has its own strengths and weaknesses, which are reported here. In general, the dynamic models perform as well as other state-of-the-art dynamical regional climate models, and the statistical model has comparable or superior skill, although for a very limited set of meteorological variables. As is typical, the dynamical models have the most trouble simulating clouds, precipitation, and related processes, especially snow. This suggests that the weakest aspects of the models are parameterized subgrid scale processes, the hydrological cycle, and land surface processes. However, the resulting probabilistic ensemble simulations result in reduced model uncertainty and a better understanding of model spread