4.6 Article

Annual and seasonal mean temperatures in Finland during the last 160 years based on gridded temperature data

期刊

INTERNATIONAL JOURNAL OF CLIMATOLOGY
卷 30, 期 15, 页码 2247-2256

出版社

WILEY
DOI: 10.1002/joc.2046

关键词

mean temperature; long time series; gridded data; kriging; climate change; Finland

资金

  1. Kone Foundation
  2. COST Action [ES0601]

向作者/读者索取更多资源

The annual and seasonal mean temperature of Finland was calculated for 162 years based on spatially interpolated monthly mean temperature records. The spatial interpolation method, known as kriging, was used with the following forcing parameters: the geographical coordinates, elevation of the terrain, and percentage share of lakes and sea. Homogenised data was used, and thus the most important factor affecting the accuracy of the interpolated data was the uneven distribution of the available observation stations both in time and space. The uncertainty due to the homogenisation adjustments made earlier was notably smaller. In the mid-1800s, the uncertainty in the annual and seasonal mean temperatures was large, with a maximum in winter of over +/-2.0 degrees C, but the accuracy improved quickly with time as the number of the observation stations increased. At the beginning of the 20th century, the uncertainty related to the limited station network was less than +/-0.2 degrees C, in winter less than +/-0.4 degrees C. According to the data, the rise in Finland's annual mean temperature has been statistically significant during the last 100, 50 and 30 years. During the last 100 years the increase in the mean temperature was largest during spring, but during the last 50 years winters have warmed up the most. The temperature time series obtained are compatible with grid point values picked from the global temperature data grids starting from the 1880s, though the global data sets tend to smooth the extremes. Copyright (C) 2009 Royal Meteorological Society

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。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据