Doshi Smit Chetan, Lohmann Gerrit, Ionita Monica
Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany.
Physics Department, University of Bremen, 28359, Bremen, Germany.
Sci Rep. 2023 Oct 23;13(1):18100. doi: 10.1038/s41598-023-45067-6.
Climate indices are often used as a climate monitoring tool, allowing us to understand how the frequency, intensity, and duration of extreme weather events are changing over time. Here, based on complex statistical analysis we identify highly correlated significant pairs of compound events at the highest spatial resolution, on a monthly temporal scale across Europe. Continental-scale monthly analysis unleashes information on compound events such as high-risk zones, hotspots, monthly shifts of hotspots and trends, risk exposure to land cover and population, and identification of maximum increasing trends. While there are many studies on single or compound climate extremes there are only a few studies that addresses the relationship between pairs of hazards, the incorporation of bioclimatic indices, the determination of a grid best-fit copula approach, and the outlining relevance of this work of compound event risks with exposures. In this respect, here, using 27-bivariate and 10-trivariate copula models, we show that the different hazard pairs have high combined risks of indices related to radiation, temperature, evapotranspiration, bioclimatic-based indices, such as the universal thermal climate index, wind chill index, and heat index, mainly over the northern and eastern European countries. Furthermore, we show that over the last 7 decades, agricultural and coastal areas are highly exposed to the risks of defined hotspots of compound events. In some of the hotspots of compound events-identified by clusters, there is no monthly shifts of hotspots, leading to higher impacts when compounded. Future work needs to integrate the framework and process to identify other compound pairs.
气候指数常被用作气候监测工具,使我们能够了解极端天气事件的频率、强度和持续时间如何随时间变化。在此,基于复杂的统计分析,我们在欧洲范围内的月度时间尺度上,以最高空间分辨率识别了复合事件的高度相关显著对。大陆尺度的月度分析揭示了诸如高风险区、热点、热点的月度变化和趋势、土地覆盖和人口的风险暴露以及最大增长趋势识别等复合事件的信息。虽然有许多关于单一或复合气候极端事件的研究,但只有少数研究涉及灾害对之间的关系、生物气候指数的纳入、网格最佳拟合Copula方法的确定以及这项复合事件风险与暴露工作的相关性概述。在这方面,在此,我们使用27个双变量和10个三变量Copula模型表明,不同的灾害对在与辐射、温度、蒸散、基于生物气候的指数(如通用热气候指数、风寒指数和热指数)相关的指数方面具有很高的综合风险,主要分布在北欧和东欧国家。此外,我们表明,在过去70年里,农业和沿海地区高度暴露于复合事件定义热点的风险之中。在一些通过聚类识别出的复合事件热点中,热点没有月度变化,这在复合时会导致更高的影响。未来的工作需要整合框架和流程以识别其他复合对。