[1] Recent versions of the Polar Weather Research and Forecasting model are evaluated over the Antarctic to assess the impact of model improvements, resolution, large-scale circulation variability, and uncertainty in initial and lateral boundary conditions. The model skill differs more between forecasts using different sources of lateral boundary data than between forecasts from different model versions or simulated years. Using the ERA-Interim reanalysis for initial and lateral boundary conditions produces the best skill. The forecasts have a cold summer and a warm winter bias in 2 m air temperatures, with similar but smaller bias in dew point temperatures. Upper air temperature biases are small and remain less than 1 C except at the tropopause in summer. Geopotential height biases increase with height in both seasons. Deficient downward longwave radiation in all seasons and an under representation of clouds enhance radiative loss, leading to the cold summer bias. Excess summer surface incident shortwave radiation plays a secondary role, because 80% of it is reflected, leading to greater skill for clear compared with cloudy skies. The positive wind speed bias produces a warm surface bias in winter resulting from anomalously large downward flux of sensible heat toward the surface. Low temperatures on the continent limit sublimation and hence the precipitable water amounts over the ice sheet. ERA-Interim experiments with higher precipitable water showed reduced biases in downwelling shortwave and longwave radiation. Increasing horizontal resolution from 60 to 15 km improves the skill of surface wind forecasts.

Abstract. We have run two regional climate models (RCMs) forced by three sets of initial and boundary conditions to form a 2x3 suite of 10-year climate simulations for the continental United States at approximately 50 krn horizontal resolution. The three sets of driving boundary conditions are a reanalysis, an atmosphere-ocean coupled general circulation model (GCM) current climate, and a future scenario of transient climate change. Common precipitation climatology features simulated by both models included realistic orographic precipitation, east-west transcontinental gradients, and reasonable annual cycles over different geographic locations. However, both models missed heavy cool-season precipitation in the lower Mississippi River basin, a seemingly common model defect. Various simulation biases (differences) produced by the RCMs are evaluated based on the 2x3 experiment set in addition to comparisons with the GCM simulation. The RCM performance bias is smallest, whereas the GCM-RCM downscaling bias (difference between GCM and RCM) is largest. The boundary forcing bias (difference between GCM current climate driven run and reanalysis-driven run) and intermodel bias are both largest in summer, possibly due to different subgrid scale processes in individual models. The ratio of climate change to biases, which we use as one measure of confidence in projected climate changes, is substantially larger than 1 in several seasons and regions while the ratios are always less than 1 in summer. The largest ratios among all regions are in California. Spatial correlation coefficients of precipitation were computed between simulation pairs in the 2x3 set. The climate change correlation is highest and the RCM performance correlation is lowest while boundary forcing and intermodel correlations are intermediate. The high spatial correlation for climate change suggests that even though future precipitation is projected to increase, its overall continental-scale spatial pattern is expected to remain relatively constant. The low RCM performance correlation shows a modeling challenge to reproduce observed spatial precipitation patterns.

Thirteen regional climate model (RCM) simulations of June-July 1993 were compared with each other and observations. Water vapor conservation and precipitation characteristics in each RCM were examined for a 10° × 10° subregion of the upper Mississippi River basin, containing the region of maximum 60-day accumulated precipitation in all RCMs and station reports. All RCMs produced positive precipitation minus evapotranspiration (P − E > 0), though most RCMs produced P − Ebelow the observed range. RCM recycling ratios were within the range estimated from observations. No evidence of common errors of E was found. In contrast, common dry bias of P was found in the simulations. Daily cycles of terms in the water vapor conservation equation were qualitatively similar in most RCMs. Nocturnal maximums of P and C (convergence) occurred in 9 of 13 RCMs, consistent with observations. Three of the four driest simulations failed to couple P and C overnight, producing afternoon maximum P. Further, dry simulations tended to produce a larger fraction of their 60-day accumulated precipitation from low 3-h totals. In station reports, accumulation from high (low) 3-h totals had a nocturnal (early morning) maximum. This time lag occurred, in part, because many mesoscale convective systems had reached peak intensity overnight and had declined in intensity by early morning. None of the RCMs contained such a time lag. It is recommended that short-period experiments be performed to examine the ability of RCMs to simulate mesoscale convective systems prior to generating long-period simulations for hydroclimatology. Disciplines

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Precipitation from a 10-yr regional climate simulation is evaluated using three complementary analyses: selforganizing maps, bias scores, and arithmetic bias. Collectively, the three reveal a precipitation deficit in the south-central United States that emerges in September and lingers through February. Deficient precipitation for this region and time of year is also evident in other simulations, indicating a generic problem in climate simulation.Analysis of terrestrial and atmospheric water balances shows that the 10-yr average precipitation error for the region results primarily from a deficit in horizontal water vapor convergence. However, the 10-yr average for fall only suggests that the primary contributor is a deficit in evapotranspiration. Evaluation of simulated temperature and soil moisture suggests the model has insufficient terrestrial water for evaporation during fall. Results for winter are mixed; errors in both evapotranspiration and lateral moisture convergence may contribute substantially to the precipitation deficit. The model reproduces well both the time-average and time-filtered large-scale circulation, implying that the moisture convergence error arises from an error in simulating mesoscale circulation. This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/ge_at_pubs/100 ABSTRACT Precipitation from a 10-yr regional climate simulation is evaluated using three complementary analyses: selforganizing maps, bias scores, and arithmetic bias. Collectively, the three reveal a precipitation deficit in the south-central United States that emerges in September and lingers through February. Deficient precipitation for this region and time of year is also evident in other simulations, indicating a generic problem in climate simulation.Analysis of terrestrial and atmospheric water balances shows that the 10-yr average precipitation error for the region results primarily from a deficit in horizontal water vapor convergence. However, the 10-yr average for fall only suggests that the primary contributor is a deficit in evapotranspiration. Evaluation of simulated temperature and soil moisture suggests the model has insufficient terrestrial water for evaporation during fall. Results for winter are mixed; errors in both evapotranspiration and lateral moisture convergence may contribute substantially to the precipitation deficit. The model reproduces well both the time-average and time-filtered large-scale circulation, implying that the moisture convergence error arises from an error in simulating mesoscale circulation.