Abstract. The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic importance sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors.The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraftand entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e. the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some exten...

The climatological features of the winter snow depth over the Tibetan Plateau and the summer precipitation in China are diagnosed using datasets obtained from 78 snow observation stations and 160 rainfall stations during 1957 to 1998. The climatic effects of the snow anomaly over the Tibetan Plateau on the regional summer monsoon climate in China are diagnosed and numerically simulated by use of a regional climate model (RegCM2). The singular value decomposition technique is adopted to diagnose the relationships between the previous winter and spring plateau snow depth anomalies and the spring and summer regional precipitation in China. It is found that the snow depth anomaly, especially in winter, is one of the factors influencing precipitation in China; however, it is perhaps not the only one, and even not the most important one. Nevertheless, it is proved that the winter snow anomaly over the Tibetan Plateau is relatively more important than that in spring for the regional precipitation in China. Results of numerical simulations show that the snow anomaly over the plateau has effects that are evident on China's summer monsoon climate. The increase of both snow cover and snow depth can delay the onset and weaken the intensity of the summer monsoon obviously, resulting in a decrease in precipitation in southern China and an increase in the Yangtze and Huaihe River basins. The influence of the winter snow depth is more substantial than that of both the winter snow cover and the spring snow depth. The mechanism of how the plateau snow anomaly influences the regional monsoon climate is briefly analysed. It is found that snow anomalies over the Tibetan Plateau change the soil moisture and the surface temperature through the snowmelt process at first, and subsequently alter heat, moisture and radiation fluxes from the surface to the atmosphere. Abnormal circulation conditions induced by changes of surface fluxes may affect the underlying surface properties in turn. Such a long-term interaction between the wetland and the atmosphere is the key process resulting in later climatic changes.

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