1960—2020年贵州省极端气温指数时空演变特征及其影响因素分析

Spatiotemporal evolution characteristics of extreme temperature and influenced factors in Guizhou Province from 1960 to 2020

摘要

【目的】贵州省极端气候事件的频率和持续性因其独特的地形和多样气候而显著加剧，但目前关于该省极端气温方面的研究尚不完善。为掌握极端气温指数的时空变化特征并明确其主要影响因素，基于1960—2020年29个气象站气温及大尺度气候指数(PDO、ENSO、SOI、AMO)等数据，针对5个极端气温指数，【方法】运用线性趋势法、Mann-Kendall趋势检验、R/S分析了贵州省极端气温时空演变特征及未来趋势，利用相关分析及小波分析揭示了极端气温指数与大尺度气候指数等因素的联系。【结果】结果表明：(1)时间变化上，寒潮持续时间和霜冻时间显著减少，然而，月极端最低和最高气温以及夏日数均表现出上升趋势；极端气温指数的突变均集中于20世纪末至21世纪初；在未来5年内，极端气温指数将继续保持这种趋势；空间分布上，自西向东方向，低温指数的多年平均值逐渐减少，高温指数则逐渐增加；(2)寒潮持续指数发生突变很可能受到ENSO、SOI较长年际周期(2~16 a)的影响，极端高温指数则受到较短周期(2~4 a)的影响。另外，霜冻日数、月极端最低气温与海拔呈显著负相关性。【结论】极端气温指数变化的影响因素主要是气候与海拔，另外还有人类活动方面。研究结果为灾害预防与对地方气候影响等决策提供科学依据。

Abstract

[Objective] The frequency and duration of extreme climate events in Guizhou Province are intensified by its unique topography and diverse climate. However, research on extreme temperatures in the province is incomplete. This study aims to understand the spatiotemporal characteristics of extreme temperature indices and their main influencing factors using temperature data from 29 meteorological stations and large-scale climate indices(PDO, ENSO, SOI, AMO) from 1960 to 2020.[Methods] The study analyzes the spatiotemporal evolution and future trends of extreme temperatures in Guizhou Province using linear trend analysis, Mann-Kendall trend test, and R/S analysis. Correlation analysis and wavelet analysis are used to explore the relationships between extreme temperature indices and large-scale climate indices.[Results] The findings reveal a decreasing trend in the duration of cold waves and frost days, while monthly extreme minimum and maximum temperatures and summer days show an increasing trend. Abrupt changes in extreme temperature indices were concentrated in the late 20th to early 21st centuries, and this trend is expected to continue in the next five years. Spatially, low temperature indices decrease from west to east, while high temperature indices increase. The cold wave duration index is likely influenced by longer interannual cycles of ENSO and SOI, while the extreme high temperature index is affected by shorter cycles. Additionally, frost days and monthly extreme minimum temperatures are negatively correlated with altitude.[Conclusion] The main factors affecting extreme temperature indices are climate, topography, and human activities. The result provide a scientific basis for disaster prevention and climate impact decision-making.

关键词

空间差异 / R/S分析 / 相关性分析 / 极端气温指数 / 贵州省 / 气候变化 / Mann-Kendall趋势检验 / 时空变化

Key words

spatial differences / R/S analysis / correlation analysis / extreme temperature index / Guizhou Province / climate change / Mann-Kendall trend test / spatiotemporal variation

基金资助

国家自然科学基金项目(52069015)

国家自然科学基金项目(52069014)

国家自然科学基金青年基金项目(52109090)

江西省学位与研究生教育教学改革研究项目(JXYJG-2021-210)

江西省科技厅项目(20212BDH81002)