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【目的】为了解山东省极端气温事件趋势变化和时空格局，【方法】基于1965—2019年93个气象站点逐日最高气温和最低气温，采用sen′s趋势、去趋势波动分析、皮尔逊相关性检验等方法及Arcgis软件分析了山东省极端气温事件的时空变化特征及影响因素。【结果】结果表明，与基准期(1965—1989年)相比，当前气候状况下(1990—2019年),山东省极端气温指数的暖指数、极值指数以及作物生长期指数大多呈现明显的增加趋势，冷极端气温指数和日气温较差则呈现减少趋势。【结论】除夏季日数外，其他极端气温指数未来变化趋势与过去相一致。当前气候状况下极端气温指数空间差异较为明显，青岛、烟台和威海等东部沿海城市以及临沂极端暖事件(冷事件)增加(减少)速率较其他地区更快。部分极端气温指数与经度和海拔呈现显著的相关性，与纬度相关性较低。山东省极端气温指数的时空变化与AMO、AO和EA/WR密切相关。欧亚大陆上空反气旋的增强加速了山东省升温。
Abstract：[Objective] In order to understand the trend change and temporal and spatial pattern of extreme temperature events in Shandong Province, [Methods]based on the daily maximum and minimum temperatures of 93 meteorological stations from 1965 to 2019, this paper analyzes the temporal and spatial change characteristics and influencing factors of extreme temperature events in Shandong Province by using sen′s trend, detrended fluctuation analysis, Pearson correlation test and ArcGIS software. [Results] The results show that compared with the reference period(1965—1989), under the current climate conditions(1990—2019), most of the warm indices, extreme indices, and crop growth period index of extreme temperature indices, in Shandong Province show an obvious increasing trend, while the cold extreme temperature indices and daily temperature range show a decreasing trend. [Conclusion] Except for the number of days in summer, the future trend of other extreme temperature indices is consistent with the past. Under the current climate conditions, the spatial difference of extreme temperature indices are obvious. The increased(decreased) rate of extreme warm events(cold events) in eastern coastal cities such as Qingdao, Yantai and Weihai and Linyi is faster than that in other regions. Some extreme temperature indices show significant correlation with longitude and altitude, but low correlation with latitude. The temporal and spatial variation of extreme temperature indices in Shandong Province are closely related to AMO, AO and EA/WR. The enhancement of anticyclone over Eurasia accelerated the warming of Shandong Province.
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基本信息:
DOI：10.13928/j.cnki.wrahe.2023.02.003
中图分类号:P423
引用信息:
[1]吴光伟,常允新,王庆兵,等.近55年来山东省极端气温事件时空分布特征[J],2023,54(02):19-32.DOI:10.13928/j.cnki.wrahe.2023.02.003.
基金信息:
国家重点研发计划项目(2016YFC0402800-05)