Inference for extreme spatial temperature events in a changing climate with application to Ireland
研究了1942至2020年爱尔兰极端温度事件的频率、强度和空间范围变化，开发了捕捉时空非平稳性的极值模型，并利用气候模型数据克服观测数据稀疏和偏差问题。
Abstract We investigate the changing nature of the frequency, magnitude, and spatial extent of extreme temperatures in Ireland from 1942 to 2020. We develop an extreme value model that captures spatial and temporal nonstationarity in extreme daily maximum temperature data. We model the tails of the marginal variables using the generalized Pareto distribution and the spatial dependence of extreme events by a semiparametric Brown–Resnick r-Pareto process, with parameters of each model allowed to change over time. We use weather station observations for modelling extreme events since data from climate models (not conditioned on observational data) can oversmooth these events and have trends determined by the specific climate model configuration. However, climate models do provide valuable information about the detailed physiography over Ireland and the associated climate response. We propose novel methods which exploit the climate model data to overcome issues linked to the sparse and biased sampling of the observations. Our analysis identifies a temporal change in the marginal behaviour of extreme temperature events over the study domain, which is much larger than the change in mean temperature levels over this time window. We illustrate how these characteristics result in increased spatial coverage of the events that exceed critical temperatures.