Change of probability density distributions of summer temperatures in different climate zones

Abstract

Extreme events have become increasingly frequent worldwide which are reflected in diverse changes in the shape of the temperature probability density function. However, few studies have paid attention to the heterogeneity of temperature at the scale of climate zones. Here, we use the ERA5-land data set to explore interdecadal summer temperature changes and the distribution across different climate zones from 1981 to 2019. Comparing the minimum (T_min) and maximum (T_max) temperature of 1982–1991 and 2010–2019, the results imply that T_min and T_max in summer maintained a notable upward trend over the past 40 years, especially T_min. The effects of a simple shift toward a warmer climate contributed most to all climate zones, while the standard deviation, skewness and kurtosis had minor effects on extreme temperature except for tropics. Quantile analysis shows that the probability of extreme events in all climate zones is increasing in frequency and intensity, especially T_min and T_max in temperate climate zone. Understanding diverse reasons for climate change can assist us with taking different measures to address extreme climate in distinct climate zones.

Graphical abstract

Keywords

Climate change / probability density function / extreme events

Xinqiu Ouyang is a Master Student at the School of Geography and Planning, Sun Yat-sen University, Guangzhou, China. Her research interests include climate change and urban big data. E-mail: [e-mail].

Weilin Liao is currently an associate professor at the School of Geography and Planning, Sun Yat-sen University, Guangzhou, China. His research interests include geographical simulation, application of remote sensing in meteorology and hydrology, and the impacts of land use/land cover change on climate. E-mail: [e-mail].

Ming Luo is currently an associate professor at the School of Geography and Planning, Sun Yat-sen University, Guangzhou, China. His research interests include global change, climate variability, extreme climate events, urban climate, environmental health, climate disasters, spatial-temporal analysis and data mining. Email: [e-mail].

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Acknowledgments

This study was funded by the National Natural Science Foundation of China (Grant Nos. 41901327 and 42075070) and the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515010823).

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2023 Higher Education Press

审图号：GS京（2024）1246号