doi:  10.3878/j.issn.1006-9585.2019.19134
1960~2018年中国高温热浪的线性趋势分析方法与变化趋势

Linear trends in high temperature and heatwave occurrence in China for the period 1960~2018: analysis method and results
摘要点击 106  全文点击 46  投稿时间:2019-09-01  修订日期:2020-01-02
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基金:  国家重点研发计划项目(2018YFC1507701)、中国科学院青年创新促进会(2016075)
中文关键词:  高温热浪  趋势估计  显著性检验  非正态分布  自相关  非参数方法
英文关键词:  high temperature and heatwave  trend analysis  significance testing  non-Gaussian distribution  serial correlation  non-parametric method
     
作者中文名作者英文名单位
张嘉仪ZHANG Jiayi1 成都信息工程大学大气科学学院;2 中国科学院东亚区域气候-环境重点实验室,中国科学院大气物理研究所
钱诚QIAN Cheng中国科学院东亚区域气候-环境重点实验室,中国科学院大气物理研究所
引用:张嘉仪,钱诚..1960~2018年中国高温热浪的线性趋势分析方法与变化趋势[J].气候与环境研究
Citation:ZHANG Jiayi,QIAN Cheng..Linear trends in high temperature and heatwave occurrence in China for the period 1960~2018: analysis method and results[J].Climatic and Environmental Research(in Chinese)
中文摘要:
      高温热浪直接影响人体健康和作物生长。研究全球变暖背景下我国高温热浪发生率的趋势是气候变化研究的基本问题之一,可为人们的生产生活等提供重要的科学信息。目前对于高温热浪趋势的研究大都使用最小二乘(OLS)方法估计趋势,结合学生T检验判断趋势的统计显著性。本文审视了以往常用方法在研究我国高温热浪发生率的线性趋势时的适用性。首先,以2018年东北局部地区因当年高温日数异常多而形成离群值的例子展开,说明OLS方法估计趋势时对离群值非常敏感,造成虚假趋势。进一步,通过正态分布检验和自相关计算,发现1960~2018年中国至少有91.14%站点、90.06%格点的高温日数和92.18%站点、87.74%格点的热浪次数的序列不服从正态分布,而且多数存在自相关。采用一种不易受离群值影响并考虑自相关的非参数方法,本文对1960~2018年中国站点和格点、四个典型区域以及全国平均的高温日数和热浪次数的线性趋势做出了更为准确的估计。研究发现,高温日数显著增多的站点主要出现在华南和西北地区,热浪次数呈显著增多趋势的站点目前几乎仅限于华南地区和新疆的个别站点;区域平均而言,仅有华南区域和西北区域的高温日数和热浪次数是显著增多的,华北区域和东北区域趋势并不显著;全国平均的高温日数和热浪次数都是显著增多的。本文对高温热浪的趋势及其显著性估计、统计预测的方法选择上有重要参考价值。
Abstract:
      High temperature and heatwave (HT and HW) directly impact human health and crop growth. Investigating the trends in the occurrence of HT and HW is one of the fundamental questions of climate change research and can provide valuable information for living and production. Most of the previous studies on trends in the occurrence of HT and HW used ordinary least squares (OLS) method to calculate the magnitude of linear trend and then used student’s t-test to determine the statistical significance of this trend. This study examined whether traditional methods are suitable for the trend estimation of the occurrence of HT and HW in China. By showing a case of the annual count of HT days with extremely excessive occurrences in 2018 at a station in northeastern China, we illustrated that OLS method is sensitive to outliers and can give spurious trend. Further, through normality testing and autocorrelation calculation, we found at least 91.14% of stations and 90.06% of grid boxes for the annual count of HT days and 92.18% of stations and 87.74% of grid boxes for the annual count of HW in China are non-Gaussian, and the majority of them have serial correlation. Applying a nonparametric method that is insensitive to outliers and takes into account serial correlation, we gave a more accurate estimation of the linear trends in the annual count of HT days and HW for every station and grid box, four typical regions average, and China area-average for the period 1960~2018. The results show that stations with statistically significant increasing trend in HT days occurred mainly in South China and northwestern China, and those in HW occurred nearly only in South China and several stations in Xinjiang Autonomous Region. In terms of area average of the trend in annual count of HT days and HW, only South China region and northwestern China region show statistically significant increasing trend, whereas North China and northeastern China not significant; those of China average are both significant. This study provides referential information for the choice of method in the estimation of trend and its statistical significance and in statistical prediction for HT days and HW.
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