Impact of Soil Thermal Process on Short-Range High-Temperature Weather Forecasts by CMA-TRAMS

Precise high-temperature weather forecasts are essential, as heatwaves are increasing in frequency under the ongoing climate change. Land-surface schemes have been demonstrated to be crucial to numerical weather predictions. However, few studies have explored the impact of land surface schemes on short-range high-temperature weather forecasts via operational numerical weather prediction models. To evaluate the impact of the soil thermal process on high-temperature weather forecasts, we coupled the soil thermal process of the state-of-the-art Common Land Model (CoLM) with the South China operational numerical weather prediction model (CMA-TRAMS) and compared the coupled model with the original CMA-TRAMS, which incorporated the Simplified Model for land Surface (SMS). Contrast experiments based on two versions of CMA-TRAMS were conducted for the year 2022 when persistent extreme heatwaves were observed in Central?East China. The results are as follows: (1) Short-range high-temperature weather forecasts were sensitive to soil thermal process schemes. The original CMA-TRAMS clearly underestimated the summertime near-surface air temperature (T2m) over almost all areas of China, whereas the CoLM led to a reduction of the negative biases by approximately 0.5℃. (2) The more accurate initial soil temperatures and the deeper soil structure used in the CoLM test contributed to actual predictions of soil heat flux, soil temperature, and T2m. Nevertheless, the SMS test failed to capture upward heat transport from deeper to shallower soil layers at night due to the shallow soil structure and lower accuracy of the bottom and initial soil temperatures. (3) Higher soil temperatures resulted in increased near-surface moisture and cloud cover in the CoLM test, which led to the warmer soil and further mitigated the cold biases of T2m through reduced longwave and shortwave radiation losses at the land surface.