Application and Evaluation of the Gravel Parameterization Scheme in WRF‐CLM4 Model. (16th December 2022)

• Record Type: Journal Article
• Authors: You, Huiqi; Lyu, Shihua; Zhang, Shaobo; Xu, Yue; Ma, Cuili; Tao, Xingyu; Chen, Pan; Yang, Fan
• Is Part Of: Journal of advances in modeling earth systems. Volume 14:Number 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-16
• Subjects: Tibetan Plateau -- gravel parameterization -- soil temperature and moisture -- WRF‐CLM4; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2022MS003241
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: As the gravel is a vital component of soil that affects the soil hydrothermal condition, its application can help improve the numerical simulations. In this study, a soil data set containing gravel content suitable to the Weather Research and Forecasting Model‐Community Land Model version 4 (WRF‐CLM4) is firstly established, and a gravel parameterization scheme is coupled to the WRF‐CLM4. On this basis, numerical simulations are conducted over the Tibetan Plateau, whose results are further compared with the global atmospheric Re‐Analysis released by the China Meteorological Administration (CRA‐40) for evaluation. The results show that the new scheme can improve the simulation effect of soil temperature to a certain extent. The correlation coefficient ( R ) with the CRA‐40 data for soil temperature is enhanced in the shallow soil layer in the new scheme. Besides, the root mean square errors (RMSEs) of soil temperature are significantly reduced in shallow soil layer. Comparatively, the new scheme has a better simulation effect for soil moisture. The values of R increase significantly for soil moisture in the shallow soil layer. The RMSEs for soil moisture in both shallow and deep layers are greatly reduced. Regional‐averages over the three areas and time series show that gravel has large impact on soil temperature and moisture compared with the old scheme. Overall, the values of R and RMSEs for soil temperature and moisture in the new scheme have been greatly improved, while their trends in the shallow layer are closer to the comparison data. Plain Language Summary: The Tibetan Plateau (TP) as the roof of the world, has an important influence on the climate in Asia and even globally, so it is very meaningful to improve the simulation performance of the model on the TP. Gravel is an important component of soil texture, and it has a high content in the TP. However, current models neglect the role of gravels on soil thermal and hydrological processes, which is one of the important reasons for the bias in the simulation. In this study, the impact of gravel was taken into account in the regional simulation on the TP, and various approaches were applied to evaluate the effect of the new model on the simulation of soil temperature and moisture, and the results showed that the accuracy of the simulation was significantly improved. Key Points: The impact of gravel on soil thermal and hydrological processes is taken into account in the Weather Research and Forecasting Model model. Gravel parameterization scheme significantly improves the simulation of model soil temperature and soil moisture, mainly in terms of reduced bias and root mean square error, improved correlation coefficient. The shallow layer simulation is relatively better and the improvement of soil moisture is more significant The analysis of the three selected areas shows that the effect of gravel on soil temperature and moisture varies considerably in different areas, and the simulation results are poorer in the deeper layers, requiring further improvement of the model simulation performance