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DOI	10.1002/joc.8512
	Trends in intraseasonal temperature variability in Europe: Comparison of station data with gridded data and reanalyses
	Krauskopf, Tomas; Huth, Radan
发表日期	2024
ISSN	0899-8418
EISSN	1097-0088
英文摘要	Trends in temperature variability are often referred to have higher effect on temperature extremes than trends in the mean. We investigate trends in three complementary measures of intraseasonal temperature variability: (a) standard deviation of mean daily temperature (SD), (b) mean absolute value of day-to-day temperature change (DTD) and (c) 1-day lagged temporal autocorrelation of temperature (LAG). It is a well-established fact that different types of data (station, gridded, reanalyses) provide different temperature characteristics and particularly their trends. Moreover, we have uncovered that trends in measures of variability are considerably sensitive to data inhomogeneities. Therefore, we use five different datasets, one station based (ECA&D), one gridded (EOBS) and three reanalyses (JRA-55, NCEP/NCAR, 20CR), and compare them. The period from 1961 to 2014 where all datasets overlap is examined, and the linear regression method is utilized to calculate trends of investigated measures in summer and winter. Intraseasonal temperature variability tends to decrease in winter, especially in eastern and northern Europe, where trends below -7%decade-1 are detected for all measures. Decreases in DTD and LAG (indicating increase in persistence) prevail also in summer while summer SD tends to increase. The increase in the width of temperature distribution and the simultaneous increase in persistence indicate a tendency towards the rise in the frequency of extended extreme events in summer. Unlike previous studies, our results imply that reanalyses are not the least accurate in determining trends. JRA-55 appears to be the least diverging from other datasets, while the largest discrepancies are detected for DTD at station data. The intraseasonal temperature variability tends to decrease in Europe in winter (see the top figure). This is especially true for DTD (day-to-day temperature change) and LAG (temporal autocorrelation of temperature), while for SD (standard deviation of temperature) only three out of five datasets exhibit a decrease. Differences between data sources in determining variability trends are the main concern of this paper and are quantified in the bottom figure. image
英文关键词	climate change; day to day temperature change; gridded data; long-term series; measures of variability; reanalyses; short-term variability; standard deviation of temperature; station data; temperature trends; varying temperature variability
语种	英语
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001230291400001
来源期刊	INTERNATIONAL JOURNAL OF CLIMATOLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/291450
作者单位	Charles University Prague; Czech Academy of Sciences; Institute of Atmospheric Physics of the Czech Academy of Sciences
推荐引用方式 GB/T 7714	Krauskopf, Tomas,Huth, Radan. Trends in intraseasonal temperature variability in Europe: Comparison of station data with gridded data and reanalyses[J],2024.
APA	Krauskopf, Tomas,&Huth, Radan.(2024).Trends in intraseasonal temperature variability in Europe: Comparison of station data with gridded data and reanalyses.INTERNATIONAL JOURNAL OF CLIMATOLOGY.
MLA	Krauskopf, Tomas,et al."Trends in intraseasonal temperature variability in Europe: Comparison of station data with gridded data and reanalyses".INTERNATIONAL JOURNAL OF CLIMATOLOGY (2024).