| 极点对称模态分解下陕西气候变化特征及影响因素 |
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| 引用本文： | 申雨晨,李双双,延军平,武亚群,汪成博.极点对称模态分解下陕西气候变化特征及影响因素[J].干旱区地理,2021(1):36-46. |
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| 作者姓名： | 申雨晨 李双双 延军平 武亚群 汪成博 |
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| 作者单位： | 陕西师范大学地理科学与旅游学院 |
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| 基金项目： | 国家自然科学基金(41877519,41701592)。 |
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| 摘 要： | 全球变暖背景下,受人类活动和气候系统波动共同影响,气候要素响应具有非线性、非平稳特征,如何识别气候变化多时间尺度信息,是当前研究的热点话题。基于1970-2017年气温和降水逐日数据,辅以滑动平均、趋势分析和极点对称模态分解(ESMD)等方法,对陕西3大地理单元气候时空特征进行分析,进而探讨不同海区厄尔尼诺指数与气温、降水变化的响应关系。结果表明:1970-2017年,陕北气候变化经历“暖干-冷湿-暖湿”的变化过程;关中和陕南气候在20世纪80-90年代末呈现暖干化,随后增温停滞,降水增多,近期再次呈现暖干化;利用ESMD对陕西气温和降水变化信号进行分解,发现区域气温响应变暖停滞,是受年代波动影响,周期为9.2~11.5 a左右;从趋势项分析,除陕北气温平稳波动之外,关中和陕南气温增速并未减缓;在影响因素上,不同海区海温异常与陕西气温、降水变化相关性存在差异。其中,气温影响主要在中国东部海区,且与NINO A区、黑潮区海温显著正相关;影响降水变化的关键海区在赤道太平洋,即赤道太平洋中部海温异常偏高时,关中和陕南降水呈现下降,而赤道太平洋东部海温异常偏高,陕北降水减少更为明显。
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| 关 键 词： | 极点对称模态分解 气候变化 时空分析 陕西省 |
Spatiotemporal climate variation and its influencing factors in Shaanxi Province based on extreme-point symmetric mode decomposition |
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| SHEN Yuchen,LI Shuangshuang,YAN Junping,WU Yaqun,WANG Chengbo.Spatiotemporal climate variation and its influencing factors in Shaanxi Province based on extreme-point symmetric mode decomposition[J].Arid Land Geography,2021(1):36-46. |
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| Authors: | SHEN Yuchen LI Shuangshuang YAN Junping WU Yaqun WANG Chengbo |
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| Institution: | (School of Geography and Tourism,Shaanxi Normal University,Xi’an 710119,Shaanxi,China) |
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| Abstract: | Against the background of global warming affected by human activities and climate system fluctuations,climate response is non-linear and nonsmoothed.A hot topic is how to identify multiple-time scale information about climatic change.The spatiotemporal characteristics of climate change were analyzed for three geographical sub-regions of Shaanxi Province by sliding average,trend analysis,and extreme-point symmetric mode decomposition(ESMD)methods,using daily temperature and precipitation data between 1970 and 2017 and then discussing the tele-correlation between sea surface temperature(SST)in 17 different sea areas and the regional temperature and precipitation changes.The main SST indexes included El Ni?o-Southern Oscillation,Pacific Decadal Oscillation,Atlantic Multi-decadal Oscillation,Quasi-Biennial Oscillation(QBO),and Kuroshio Current SST(KCSST).The results showed that from 1970 to 2017,the mean annual temperature in Shaanxi rose significantly,with step-change points in 1984,1999,and 2012.In detail,climate change in northern Shaanxi involved warmingdrying,cooling-wetting,and warming-wetting,whereas in Guanzhong Plain and southern Shaanxi,there was warmingdrying from 1970 to 1999.Since 1999,the regional climate of these two regions exhibited a warming hiatus,and it was not until 2012 that intensified warming and decreasing precipitation have become more obvious.The ESMD of temperature and precipitation signals showed a non-linear upward trend,which implied changes on decadal scales(9.2-11.5 a)in response to the warming hiatus.The temperature increased in Guanzhong Plain and southern Shaanxi,except for a slight decline in northern Shaanxi in 1999-2017.Spatial differences exist in the influencing factors of temperature and precipitation in Shaanxi Province.The SST of eastern China is the dominant influencing factor for annual and decadal temperature variability.Temperatures generally increase in Shaanxi with positive SST anomaly for both NINO A and KCSST.Additionally,positive SST anomalies in the central equatorial Pacific cause decreasing precipitation in both Guanzhong Plain and southern Shaanxi.Less precipitation was expected in northern Shaanxi with increasing positive SST anomalies centered in the eastern equatorial Pacific,commonly known as the East Pacific type of El Ni?o.The above results could provide insights for the regional response and risk management of extreme precipitation resulting from global climate change.However,it should be noted that the connections between the SST and climate anomalies in Shaanxi Province are not clear.Hence,future work should determine the SST-forced heat and moisture stress anomalies to explain rainfall variations. |
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| Keywords: | extreme-point symmetric mode decomposition(ESMD) climate change spatiotemporal analysis Shaanxi Province |
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