An Analysis of Simulated California Climate Using Multiple Dynamical and Statistical Techniques – Final Report

CEC-500-2009-017-F“Four dynamic regional climate models (University of California, Santa Cruz” RegCM3; the University of California, San Diego’s RSM; the National Center for Atmospheric Research’s WRF-RUC; and the Lawrence Berkeley National Laboratory/University of California, Berkeley’s WRF-CLM3) and one statistical downscaling approach (the University of California, San Diego’s CANA) were used to downscale 10 years of historical climate in California. To isolate possible limitations of the downscaling methods, initial and lateral boundary conditions from the National Centers for Environmental Prediction global reanalysis were used. Results of this downscaling were compared to observations and to an independent, fine-resolution reanalysis (the North American Regional Reanalysis). This evaluation is preparation for simulations of future-climate scenarios, the second phase of this California Energy Commission climate projections project, which will lead to probabilistic scenarios. Each model has its own strengths and weaknesses, which are summarized 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 of dynamical climate models, there remain uncertainties in simulating clouds, precipitation, and snow accumulation and depletion rates. Hence, the weakest aspects of the dynamical models are parameterized processes, while the weakest aspect of the statistical downscaling procedure is the limitation in predictive variables. However, the resulting simulations yield a better understanding of model spread and bias and will be used as part of the California probabilistic scenarios and impacts.”