Variations in the probability distribution function of air temperature anomalies in winter and summer from 1961 to 2016 over China

Abstract

AbstractThe rising of near‐surface air temperature is the most obvious manifestation of global warming. The analysis of the probability distribution function (PDF) of temperature can deep our understanding of the current characteristics of climate and extreme temperature changes. In this study, the daily maximum, minimum and mean temperature observations from meteorological stations are analysed to construct the temperature anomaly PDF during 1961–2016 over China. The skewness and kurtosis of the PDF are compared between the 1961–1990 and 1991–2016 periods. The non‐Gaussian tails of the PDF are also identified to qualify the deviation of the temperature probability density curve. The results show that near‐surface air temperature in China increased during the recent decades. The daily minimum temperature increased more than the daily maximum temperature did, while the temperature increase in winter was larger than that in summer. The largest increase detected was 1.29°C for the daily minimum temperature in winter, contrasting with the smallest increase of 0.51°C recorded for the daily minimum temperature in summer between the two study periods. The temperature anomaly PDF generally presented a positive skewness in winter and a slightly negative skewness in summer. Obvious non‐Gaussian tails in the temperature anomaly PDF were present in the majority of the study sites. Most areas showed a short cold tail in winter (except for southwestern China), and long cold tails were mainly observed in summer. These results can improve our understanding of the variation of extreme temperatures, and of the responses to global warming.