1961—2020年威宁县结冰日的时空分布特征及其趋势预测分析  

Spatio-temporal Distribution Characteristics and Trend Prediction Analysis of Freezing Days in Weining County During 1961-2020

作　　者：蔡军 张孝秀 吕静 杨鑫龙 林雪飞 蔡彤 CAI Jun;ZHANG Xiaoxiu;LYU Jing;YANG Xinlong;LIN Xuefei;CAI Tong(Weining Yi,Hui and Miao Autonomous County Meteorological Station of Guizhou Province,Weining 553100,China;Zunyi Meteorological Office of Guizhou Province,Zunyi 563000,China)

机构地区：[1]贵州省威宁彝族回族苗族自治县气象局,贵州威宁553100 [2]贵州省遵义市气象局,贵州遵义563000

出　　处：《山地气象学报》2024年第4期65-71,共7页Journal of Mountain Meteorology

基　　金：贵州省气象局科研业务登记项目(黔气科登[2024]4-13号)。

摘　　要：【目的】自2008年我国南方遭遇历史罕见冰冻灾害以来,结冰预报逐渐成为气象服务的焦点。1961—2020年威宁县年均结冰日达61 d,是贵州省同期年均结冰日的4.3倍,为全省之冠。为加深认识本地结冰现象的发生、发展规律,该文从气候角度分析了近60 a来威宁县结冰日时序演变特征并对其趋势进行预测。【方法】选取1961—2020年威宁县国家基准气候站逐月结冰日和气温观测资料,采用M-K突变检验、R/S分析、Morlet小波分析等数理统计方法对结冰日进行气候特征分析,得出结冰日与气温的对应关系,并进一步对年结冰日做出趋势预测分析。【结果】(1)99.1%的结冰日集中在11月—翌年3月,逐月平均结冰日呈“单峰型”分布,1月结冰日最多。不同气候背景下的年结冰日表现为冷期平均年结冰日明显多于暖期,冷期年结冰日减少率几乎与整个研究期保持一致,而暖期明显强于冷期(整个研究期)。(2)年结冰日演变过程中的主周期主要为26、12、5、3 a,分别为第1、2、3、4主周期,4种时间尺度共同影响着威宁县年结冰日的年际和年代际周期变化。(3)对第1和第2主周期下的小波系数进行拟合,重构小波系数与年结冰日具有很好的一致性,两者的阶段顶底偏差集中在1~3 a。【结论】总的来看,威宁县年结冰日与同期平均气温表现为反相关关系且总体呈减少趋势,这种趋势也会持续影响未来。预计2021—2030年平均结冰日约为50.6 d,年结冰日在2027年附近达到最多但大概率不会创历史新高。Since the rare historical freezing disaster in southern China in 2008,icing forecast has gradually become the focus of meteorological services.From 1961 to 2020,the average annual freezing days in Weining County reached 61 days,which is 4.3 times that of Guizhou Province during the same period and the highest in the province.To deepen our understanding of the occurrence and development patterns of local icing phenomena,this article studies the temporal evolution characteristics of icing in Weining County over the past 60 years from a climate perspective and predicts its variation trend.The data of monthly freezing days and temperature observation from the national benchmark climate station in Weining County from 1961 to 2020,as well as the mathematical statistical methods such as M-K mutation test,R/S analysis,and Morlet wavelet analysis are used in analyzing the climate characteristics of freezing days.The corresponding relationship between freezing days and temperature is obtained,and trend prediction of annual freezing days is further made.The results are as follows:(1) 99.1% of the freezing days are concentrated from November to the following March,and the monthly average freezing days show a "unimodal" distribution,with January having the most freezing days.The average annual freezing days under different climate backgrounds are significantly higher in the cold season than in the warm season.The reduction rate of annual freezing days during the cold season is almost consistent with that of the entire study period,while that of the warm season is significantly higher than in the cold season(the entire study period).(2) The main periods in the evolution process of annual freezing days are 26,12,5,and 3 years,which are the first,second,third,and fourth main periods,respectively.The four time scales jointly affect the interannual and interdecadal periodic changes of annual freezing days in Weining County.(3) The fitted the wavelet coefficients under the first and second main periods and the restructured wavelet

关 键 词：结冰日 R/S分析 MORLET小波分析 趋势预测 

分 类 号：P426.63[天文地球—大气科学及气象学]