Contributions of land model initial and boundary conditions to improving the precipitation prediction of CMA climate model

 Precipitation is an important variable of concern in weather forecast and climate prediction, its simulation and prediction performance have always been one of the indicators of the numerical models. Precipitation prediction is influenced by many factors, including not only the dynamic framework and various process parameterization schemes of the model, but also the initial conditions (i.e., initial values) and boundary conditions. Through case studies with the climate model of CMA Climate Prediction System, this study explores the impacts of initial and boundary conditions in land model on the precipitation prediction, as well as the possible land-atmosphere interactions involved.

   The AMIP-kind experiments have been conducted with the CMA climate model for participating the LS4P (Impact of initialized Land Surface temperature and Snowpack on S2S Prediction) project. By evaluating the deviation between climate model simulation results and observations, a temperature mask was created and introduced in the model to ‘correct’ the initial conditions of spring soil temperature (ST), and its impact on the temperature in May and precipitation in downstream areas in June was evaluated. The primary results indicate that adjusting the initial soil temperature of spring in key regions (such as the Tibet Plateau) can effectively improve the model predictive accuracy for summer precipitation in downstream areas. And the initial ST correction of the different large-scale terrains (Tibet Plateau and Rocky Mountains) has different impacts on the prediction of precipitation in the middle-lower reaches of the Yangtze River Valley of China. We will also introduce the impacts of boundary conditions, mainly including sea surface temperature and land cover datasets. Overall, the contributions on precipitation prediction can reach as much as 1/3. Therefore by appropriate configuring and updating initial and boundary conditions in the model, precipitation prediction can be effectively improved.