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王瑜, 钱忠华, 颜鹏程, 封国林. 非自然因素引起的增温趋势的时空分布特征[J]. 大气科学, 2020, 44(3): 565-574. DOI: 10.3878/j.issn.1006-9895.1906.19106
引用本文: 王瑜, 钱忠华, 颜鹏程, 封国林. 非自然因素引起的增温趋势的时空分布特征[J]. 大气科学, 2020, 44(3): 565-574. DOI: 10.3878/j.issn.1006-9895.1906.19106
WANG Yu, QIAN Zhonghua, YAN Pengcheng, FENG Guolin. Temporal and Spatial Distribution Characteristics of an Increasing Temperature Trend Caused by Unnatural Factors[J]. Chinese Journal of Atmospheric Sciences, 2020, 44(3): 565-574. DOI: 10.3878/j.issn.1006-9895.1906.19106
Citation: WANG Yu, QIAN Zhonghua, YAN Pengcheng, FENG Guolin. Temporal and Spatial Distribution Characteristics of an Increasing Temperature Trend Caused by Unnatural Factors[J]. Chinese Journal of Atmospheric Sciences, 2020, 44(3): 565-574. DOI: 10.3878/j.issn.1006-9895.1906.19106

非自然因素引起的增温趋势的时空分布特征

Temporal and Spatial Distribution Characteristics of an Increasing Temperature Trend Caused by Unnatural Factors

  • 摘要: 基于数据长程相关性,利用相对变化趋势,构建气温相对变化趋势的概率密度函数及超越概率,研究并计算了1951~2017年中国气温相对变化趋势基于一定置信水平下属于自然变率范畴的置信限,判别相对变化趋势是否由非自然因素引起(增温是否显著),探讨不同地区非自然因素引起的温度变化的阈值、相应的转折时间段及演变趋势。结果表明:(1)中国160站温度资料中有10%的站点趋势显著性被传统线性回归方法高估了,这些站点主要位于西北、西南和东部沿海地区。(2)从全国温度趋势的空间分布来看,除新疆中西部地区呈现降温趋势之外,其他地区均为增温趋势,其中东北、内蒙及晋北地区非自然趋势大,增温显著。(3)从不同年代际增温显著区域的空间演变来看,华北、东北地区率先增温显著,之后逐渐向南向西扩展,1966~2001年时段中国大部分区域表现为非自然增温显著;1971~2006年时段,东北地区以及内蒙东北部增温显著区域开始逐渐减少,同时中国西南地区增温显著区域开始逐渐增多;1976~2011年增温显著区域最大;1981~2016年,增温显著站点主要集中在黄河、长江流域及两大流域之间和中国南方地区。综上,中国非自然因素引起的增温显著区域在时间和空间上均存在显著的年代际转折。本研究为中国气温变化的归因及其预测研究,为加强气候变化研究成果向短期气候预测的转化及联系提供新视角、新途径。

     

    Abstract: In this study, the relative trend in temperature change in China from 1951 to 2017 was used to construct a probability density function and exceedance probability based on long-term correlation of relevant data. The confidence limit of this trend, belonging to natural variability, was studied and calculated under a certain confidence level. We sought to determine whether the relative change trend was caused by unnatural factors (whether the temperature increase was significant), and explore the threshold values of temperature change caused by unnatural factors in different regions, corresponding transition time periods, and evolutionary trends. The results showed that: (1) 10% of the site temperatures at 160 stations were overestimated when using traditional linear regression methods to calculate trend significance. These sites were mainly located in the northwest, southwest and eastern coastal areas of China. (2) From the perspective of spatial distribution of temperature trends in the country, except for the cooling trend in the central and western regions of Xinjiang, the other regions all showed warming trends. Relative temperature changes in Shanxi, Inner Mongolia, parts of Ningxia, southwestern Xinjiang, Yangtze River Delta, and southwestern Yunnan were relatively large. Unnatural trends in Northeast China, Inner Mongolia, and the northern Shanxi Province were large, and the temperature increase was significant. (3) From the spatial evolution of the significant inter-decadal warming areas, the northern and northeastern regions of China took the lead in increasing temperature, a trend that gradually expanded to the south and west. During the period of 1966–2001, most regions in China showed an increase in unnatural factors; for 1971–2006, temperatures in the northeastern and northeastern Inner Mongolia regions began to gradually decrease, while significant warming in southwestern China began to gradually expand. The number of significant warming sites was largest during 1976–2011; from 1981 to 2016, the significant warming sites were mainly concentrated in the Yellow River and Yangtze River Basins in southern China. In summary, there were prominent inter-decadal spatial and temporal transitions in significant warming areas in China caused by unnatural factors. This paper may provide new perspectives and new ways for the attribution and prediction of temperature change in China, and strengthen the linkage of climate change research and short-term climate prediction.

     

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