Using Bayesian statistics to detect trends in Alaskan precipitation
Using Bayesian statistics to detect trends in Alaskan precipitation
复制标题
DOI：
10.1002/joc.6946
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
James White;J. Walsh;R. Thoman
中科院分区：
文献类型：
--
作者：
James White;J. Walsh;R. Thoman
在过去的几十年里，包括阿拉斯加在内的整个北极地区的气温都呈现出明显的上升趋势。其他变量，如降水，由于测量的不均匀性和高内部变率，具有更不确定的趋势。使用线性回归来分析阿拉斯加的降水，结果往往相互矛盾。本文建议使用贝叶斯模型（如R包Rbeast）来进行更细致的分析。本文给出的示例显示了如何使用贝叶斯分析来检测细微的变化并更好地约束数据源之间的分歧。应用网格数据，贝叶斯分析显示了阿拉斯加各地的降水是如何随时间变化的。在过去的十年里，变化加速了，但只有北坡的降水增加可以被赋予高置信度。总的来说，这一分析突出了贝叶斯技术如何在具有异构数据源和大量内部变异性的地区的气候研究中发挥独特的作用。
Air temperature has exhibited a clear positive trend over the past several decades throughout the arctic, including Alaska. Other variables, such as precipitation, have much more uncertain trends due to inhomogeneities in measurement and high internal variability. The use of linear regression to analyse precipitation in Alaska has resulted in often contradictory results. This paper proposes the use of Bayesian models such as the R package Rbeast to allow for the more nuanced analysis. The examples given in this paper show how Bayesian analysis can be used to detect subtle changes and better constrain the disagreement between data sources. Applied to gridded data, Bayesian analysis shows how precipitation has changed overtime across Alaska. Change has accelerated over the past decade, but only precipitation increase on the North Slope can be assigned high confidence. Overall, this analysis highlights how Bayesian techniques may be uniquely useful to climate research in regions with heterogeneous data sources and substantial internal variability.