Improvement of algorithm for TEMCF in evaluation of daily mean temperature in mountainous districts
MTCLIM模型日平均温度调和系数获得算法的改进

This paper aims at the improvement of algorithms for the daily mean temperature in the mountainous districts. The modeling results of the daily temperature in the MTCLIM model strongly rely on the parameter (coefficient to adjust daylight average temperature).Most previous works concerning the TEMCF (coefficient to adjust daylight average temperature) used the default value 0.45 measured in the Northern America or the simple sine weighed average method, often resulting in great errors in the final model results. To evaluate the influences of heat and moisture on the TEMCF and the variant daily temperature of the mountainous districts, an improved method is proposed to evaluate the daily mean temperature under different moisture and heat gradients. In a case study in the Changbai Mountain, we find different moisture and heat conditions that have strong effects on the value of the TEMCF. TEMCF tends to decrease with daily precipitation quantity increasing and day length decreasing. And the results explain the reason of great errors in the past work concerning the MTLCIM model in China. Compared to the previous works, the method proposed here provides a possibility to improve the modeling result of the daily temperature variance in the mountainous districts by the different moisture and heat conditions. It can be an important reference to the future works in the field.