| dc.description.abstract | 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. | |
