我国昼夜非对称性增温时空变化特征及影响因素

doi:10.7523/j.ucas.2022.042

摘要/Abstract

摘要： 近几十年来,全球变暖的一个主要特征是昼夜增温呈现非对称性,即夜间增温速率大于白天增温速率,但该现象还未引起广泛关注。为了解我国昼夜气温日温差的时空变化特征及影响因素,搜集我国838个气象站点历史观测数据,运用趋势检验等方法,对1952-2018年间气温日温差(DTR)进行分析。结果表明:空间上,全国年平均DTR最高的地区分布在西北和西南地区,其次是东北和华北地区,最低为华中、华东和华南地区;季节上,一年中月平均DTR总体呈多峰分布,其中春季、秋季各有1个波峰,夏季有1个低谷;长期来看,1952-2018年期间年平均DTR呈下降趋势(τ=-0.396;p<0.01)。DTR的长期变化与蒸发量、日照时数、纬度和海拔呈正相关,与风速、降水量、相对湿度、站点气压、总云量和经度呈负相关。

关键词: 气候变化, 非对称性增温, 时空变化, 影响因素

Abstract: Recently, a major feature of global warming is asymmetrical temperature increase during day and night, i.e., the rate of warming at night is greater than that during the day. However, effects of asymmetrical warming have not yet attracted widespread attention. In this study, by collecting historical observation data of 838 meteorological stations in China, and using methods such as trend estimation, the characteristics of the temporal and spatial variation of diurnal temperature range (DTR) and the long-term trends of DTR from 1952 to 2018 were analyzed. The results indicated that:spatially, the regions with the highest average annual DTR in the country were distributed in the northwest and southwest, followed by the northeast and north China, and the areas with lowest DTR located in central, eastern, and southern China. The average monthly DTR in the year was generally multimodal. Among four seasons, DTR in spring and autumn was higher than in summer. From 1952 to 2018, the annual average DTR decreased significantly (τ=-0.396; p<0.01). Long-term changes in DTR were positively correlated with evaporation, sunshine hours, latitude and altitude, and negatively correlated with wind speed, precipitation, relative humidity, station pressure, total cloudiness, and longitude.

Key words: climate change, asymmetric warming, temporal and spatial changes, influencing factors

中图分类号:

P423.3+1

何昶, 邓建明, 陈爽. 我国昼夜非对称性增温时空变化特征及影响因素[J]. 中国科学院大学学报, 2023, 40(5): 596-604.

HE Chang, DENG Jianming, CHEN Shuang. Temporal and spatial variation of diurnal asymmetric warming and its drivers in China[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(5): 596-604.

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