文章基本信息

标题：Spatial trend analysis of gridded temperature data at varying spatial scales
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作者：Ola Haug ; Thordis L. Thorarinsdottir ; Sigrunn H. Sørbye 等
期刊名称：Advances in Statistical Climatology, Meteorology and Oceanography
印刷版ISSN：2364-3579
电子版ISSN：2364-3587
出版年度：2020
卷号：6
期号：1
页码：1-12
DOI：10.5194/ascmo-6-1-2020
出版社：Copernicus Publications
摘要：Abstract. Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space–time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modelled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case study, we assess summer season trends in 65 years of gridded temperature data over Europe. We find that while spatial smoothing generally results in larger regions where the null hypothesis of no trend is rejected, this is not the case for all subregions.