期刊
GLOBAL AND PLANETARY CHANGE
卷 170, 期 -, 页码 146-162出版社
ELSEVIER
DOI: 10.1016/j.gloplacha.2018.08.015
关键词
Climate change; Maximum temperature; Minimum temperature; Temperature trend; Annual cycle; Europe
资金
- Czech Science Foundation [16-04676S]
Recent global warming has not been ubiquitous: there are seasons, regions, and time periods with negligible or even negative air temperature trends (frequently referred to as warming holes). This paper presents a novel method enabling a proper localization of specific trend events, such as periods of warming holes, of a particularly strong warming, and of rapid transitions of trend amplitudes during the calendar year. The method consists in analyzing trends for periods of a given length (10 to 90 days) that are sliding over the year with a one day step. This allows a detailed description of the annual cycle of trends. The analysis is conducted for daily maximum and minimum temperature at 135 stations in Europe in 1961-2000. Despite an overall warming in Europe, several warming holes are uncovered during various parts of the year, not only in autumn when a warming hole has already been reported. The autumn warming hole concentrates in Eastern Europe, but it changes its strength and spatial extent: it spreads into Western Europe in September and retreats to Eastern Europe in November when it intensifies especially north of the Black Sea. Three shorter warming holes are detected: In February and March, cooling occurs in the Eastern Mediterranean and Iceland, while in early April, cooling is detected over Central, Southern, and Southeastern Europe. Another large-scale cooling occurs in Central, Northern, and Northwestern Europe in mid-June. The periods of strongest warming occur around the middle of January in Eastern Europe, in early March over almost entire Europe, and in mid-May and early August mainly over Central and Western Europe. Cluster analysis of stations with respect to the annual cycles of trends demonstrates a spatial coherence of the trends; the lack of spatial coherence points to local peculiarities or data problems of individual stations. The method of sliding seasons proves to be much more effective in the identification and localization of notable trend events than the ordinary approach of trend detection for fixed calendar seasons and/or months.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据