Warm Season Rainfall Variability over the U.S. Great Plains in Observations, NCEP and ERA-40 Reanalyses, and NCAR and NASA Atmospheric Model Simulations

Abstract Interannual variability of Great Plains precipitation in the warm season months is analyzed using gridded observations, satellite-based precipitation estimates, NCEP reanalysis data and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data, and the half-century-long NCAR Community Atmosphere Model (CAM3.0, version 3.0) and the National Aeronautics and Space Administration (NASA) Seasonal-to-Intraseasonal Prediction Project (NSIPP) atmospheric model simulations. Regional hydroclimate is the focus because of its immense societal impact and because the involved variability mechanisms are not well understood. The Great Plains precipitation variability is represented rather differently, and only quasi realistically, in the reanalyses. NCEP has larger amplitude but less traction with observations in comparison with ERA-40. Model simulations exhibit more realistic amplitudes, which are between those of NCEP and ERA-40. The simulated variability is however uncorrelated with observations in both models, with monthly correlations smaller than 0.10 in all cases. An assessment of the regional atmosphere water balance is revealing: Stationary moisture flux convergence accounts for most of the Great Plains variability in ERA-40, but not in the NCEP reanalysis and model simulations; convergent fluxes generate less than half of the precipitation in the latter, while local evaporation does the rest in models. Phenomenal evaporation in the models—up to 4 times larger than the highest observationally constrained estimate (NCEP’s)—provides the bulk of the moisture for Great Plains precipitation variability; thus, precipitation recycling is very efficient in both models, perhaps too efficient. Remote water sources contribute substantially to Great Plains hydroclimate variability in nature via fluxes. Getting the interaction pathways right is presently challenging for the models.