Spatiotemporal investigation of wet–cold compound events in Greece

Q2 Earth and Planetary Sciences Advances in Science and Research Pub Date : 2023-04-21 DOI:10.5194/asr-19-145-2023
Iason Markantonis, D. Vlachogiannis, A. Sfetsos, I. Kioutsioukis, N. Politi
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Abstract

Abstract. Climate change is set to affect extreme climate and meteorological events. The combination of interacting physical processes (climate drivers) across various spatial and temporal scales resulting to an extreme event is referred to as compound event. The complex geography and topography of Greece forms a variety of regions with different local climate conditions affecting the daily minimum temperature and precipitation distributions and subsequently the distribution of compound events of low temperature and high precipitation values. The aim of our study in this work is to identify these wet–cold events based on observational data from the Hellenic National Meteorological Service (HNMS) stations, which are divided into five different geographical categories, in the period 1980–2004 and coldest months of the year (November-April) on monthly basis. Two available reanalysis products, that of ERA-Interim downscaled with the Weather Research and Forecasting (WRF) model at 5km horizontal resolution (WRF_5), and the coarser resolution (∼30 km) ERA5 Reanalysis dataset from European Centre for Medium-Range Weather Forecasts (ECMWF), are adopted to derive a gridded monthly spatial distribution of wet–cold compound events, after performing a comparison with the observations. The results yield that the monthly maximum HNMS probabilities range from 0.07 % in April to 0.85 % in February, ERA5 range from 0.4 % in April to 2.97 % in February and WRF_5 from 10.4 % in November to 25.04 % in February. The results also displayed that February, January and December, are in this order, the months with the highest WCCEs.
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希腊湿冷复合事件的时空调查
摘要气候变化势必影响极端气候和气象事件。在不同时空尺度上相互作用的物理过程(气候驱动因子)的组合导致一个极端事件被称为复合事件。希腊复杂的地理地形形成了多种区域,不同的局部气候条件影响了日最低气温和降水分布,进而影响了低温高降水复合事件的分布。本文基于希腊国家气象局(HNMS)站点1980-2004年的5个不同地理类别的观测数据,以及一年中最冷的月份(11月- 4月)的月度数据,对湿冷事件进行了识别。在与观测资料进行比较后,采用了两个可用的再分析产品,即天气研究与预报(WRF)模式5公里水平分辨率(WRF_5)的ERA-Interim再分析数据集和欧洲中期天气预报中心(ECMWF)较粗分辨率(~ 30公里)的ERA5再分析数据集,得出了冷湿复合事件的网格化月空间分布。结果表明:月最大HNMS概率在4月0.07% ~ 2月0.85%之间,ERA5在4月0.4% ~ 2月2.97%之间,WRF_5在11月10.4% ~ 2月25.04%之间。结果还显示,2月、1月和12月是WCCEs最高的月份。
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来源期刊
Advances in Science and Research
Advances in Science and Research Earth and Planetary Sciences-Geophysics
CiteScore
4.10
自引率
0.00%
发文量
13
审稿时长
22 weeks
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