Dynamical Downscaling Overcomes Deficiencies in Gridded Precipitation Products in the Sierra Nevada, California

Uncertainties in gridded and regional climate estimates of precipitation are large at high elevations where observations are sparse and spatial variability is substantial. We explore these uncertainties for a climatologically-unusual water year across California's Sierra Nevada in 10 datasets: six Weather, Research, and Forecasting (WRF) model regional climate downscalings with differing lateral boundary conditions and microphysical parameterizations, and four gauge-based, interpolation-gridded precipitation datasets. Precipitation from these 10 datasets is evaluated against 95 snow pillows and a precipitation dataset inferred from stream gauges using a Bayesian inference method. During this water year, the gridded datasets tend to underestimate frozen precipitation on the windward slope of the Sierra Nevada, particularly in vicinity of Yosemite National Park. The WRF simulations with single-moment microphysics tend to overestimate precipitation throughout much of the region, whereas the WRF simulations with double-moment microphysics tend to better agree with both the snow pillows and inferred precipitation estimates, although they somewhat overestimate windward/leeside precipitation contrast in the northern Sierra Nevada. In addition, the WRF simulations, in particular those with single-moment microphysics, better distinguish wet-versus-dry pillows and watersheds than the gridded estimates.