A semi-parametric regression approach to climatological quantile estimation for generating percentile-based temperature extremes indices

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• 作者：Chi Yang and Jing Xu
• 刊名：Atmospheric Science Letters
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：18
• 期：2
• 页码：60-66
• 全文大小：472K
• ISSN：1530-261X

文摘

A semi-parametric regression approach to quantile estimation for daily temperature data is proposed, in which both the biases and inhomogeneity are negligible, and is applied to the calculation of the six percentile-based Expert Team on Climate Change Detection and Indices (ETCCDI) temperature extremes indices. Comparisons of the results with those from the CLIMDEX datasets show that the three warmth indices in the latter are probably biased such that their linear trends under the RCP4.5 scenario seem to be overestimated. In order to avoid drawing misleading conclusions, it is necessary to re-examine currently adopted algorithms and available datasets, and to develop new methods for generating the percentile-based ETCCDI indices.