CR 59:135-147 (2014)  -  DOI: https://doi.org/10.3354/cr01213

ABSTRACT: Convective parameterizations used in climate models display sensitivity to model resolution and variable skill in different climatic regimes. Although parameters in convective schemes can be calibrated using observations to reduce model errors, it is not clear if the optimal parameters calibrated based on regional data can robustly improve model skill across different model resolutions and climatic regimes. In this study, this issue is investigated using a regional modeling framework based on the Weather Research and Forecasting (WRF) model. To quantify the response and sensitivity of model performance to model parameters, we identified 5 key input parameters and specified their ranges in the Kain-Fritsch (KF) convection scheme in WRF, and calibrated them across different spatial resolutions, climatic regimes, and radiation schemes using observed precipitation data. Results show that the optimal values for 5 input parameters in the KF scheme are similar, and model sensitivity and error exhibit similar dependence on the input parameters for all experiments conducted in this study, despite differences in the precipitation climatology. We found that the model overall performances in simulating precipitation are relatively more sensitive to the coefficients of downdraft and entrainment mass flux, as well as to the starting height of downdraft. However, we found that rainfall biases—which are probably more related to structural errors—still exist over some regions in the simulation, even with the optimal parameters. This suggests that further studies are needed to identify the sources of uncertainties, as well as to reduce the model biases or structural errors, both of which are associated with missed or misrepresented physical processes and/or potential problems with the modeling.

KEY WORDS: Sensitivity · Convection scheme · Parameters · Calibration · Optimization · Regional climate model · WRF