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Modelling the true monthly mean temperature from continuous measurements over global land
Author(s) -
Li Zhijun,
Wang Kaicun,
Zhou Chunlüe,
Wang Laigang
Publication year - 2015
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4445
Subject(s) - mean radiant temperature , environmental science , climatology , arid , climate change , air temperature , surface air temperature , linear regression , maximum temperature , diurnal temperature variation , atmospheric sciences , mathematics , statistics , meteorology , geography , ecology , biology , geology
The true monthly mean temperature is defined as the integral of the continuous temperature measurements in a month ( T d0 ), which is apparently different from the average ( T d1 ) of the monthly averaged maximum ( T max ) and minimum ( T min ) temperatures. Unfortunately, T d1 instead of T d0 has been widely used as the monthly mean temperature, not only as an input parameter for various models in ecology, climatology and hydrology but also as an effective factor for climate change studies. It has already been demonstrated in previous researches that the bias between T d0 and T d1 ( T bias = T d1 − T d0 ) cannot be ignored; in some places, it could even be very large. Therefore, it is with great urgency that T d0 should replace T d1 to eliminate the impact of the imperfect monthly mean temperature on related researches. However, T d0 cannot be obtained directly due to the lack of the historical observations of land surface air temperature ( T a ) at a higher temporal resolution, e.g. hourly observations. In this study, a multiple linear regression (MLR)‐based method is created to calculate T d0 with the predictors of daylength, diurnal temperature range (DTR = T max − T min ) and T d1 . The MLR method performs very well, with a mean R 2 of 0.61 over global land and 0.76 in arid or semi‐arid areas. It can be used to improve studies on regional climate change and evaluations of climate model simulations.