论文标题
基于极端依赖性随着事件幅度而改变非平稳温度最大值
Modeling Non-Stationary Temperature Maxima Based on Extremal Dependence Changing with Event Magnitude
论文作者
论文摘要
极端温度的时空趋势的建模可以帮助更好地理解在不断变化的气候下热浪的结构和频率。在这里，我们使用在44个监测站观察到的一个世纪跨越的数据集研究了南欧的年度最高温度最高。扩展了最大稳定过程的光谱表示，我们的建模框架依赖于最终可划分过程的新颖结构，其中包括协变量以捕获时空的非平稳性。我们的新模型在参数空间的边界上保持了流行的最大过程，同时灵活地捕获了在增加的分数水平和渐近独立性下的极端依赖性。这是通过将空间事件的整体大小与其空间相关范围联系起来来实现的，以至于更极端的事件在空间上变得更少，因此更局部。我们的模型揭示了欧洲极端温度的时空变异性的显着特征，并且显然超过了天然替代模型。结果表明，在较高高度下更严重的事件，热浪的空间范围较小，并且最近的热浪较宽。我们对2019年年度最大值的概率评估证实了2019年热浪在空间和各个地点的严重性，尤其是与1950 - 1975年盛行的气候条件相比。
The modeling of spatio-temporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate. Here, we study annual temperature maxima over Southern Europe using a century-spanning dataset observed at 44 monitoring stations. Extending the spectral representation of max-stable processes, our modeling framework relies on a novel construction of max-infinitely divisible processes, which include covariates to capture spatio-temporal non-stationarities. Our new model keeps a popular max-stable process on the boundary of the parameter space, while flexibly capturing weakening extremal dependence at increasing quantile levels and asymptotic independence. This is achieved by linking the overall magnitude of a spatial event to its spatial correlation range, in such a way that more extreme events become less spatially dependent, thus more localized. Our model reveals salient features of the spatio-temporal variability of European temperature extremes, and it clearly outperforms natural alternative models. Results show that the spatial extent of heatwaves is smaller for more severe events at higher altitudes, and that recent heatwaves are moderately wider. Our probabilistic assessment of the 2019 annual maxima confirms the severity of the 2019 heatwaves both spatially and at individual sites, especially when compared to climatic conditions prevailing in 1950-1975.
