基于EOF-EEMD结合的青藏高原未来气温非平稳时空变化特征分析 

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  • 1. 三峡大学水利与环境学院,湖北 宜昌 443002
    2. 三峡库区生态环境教育部工程研究中心,湖北 宜昌 443002
    3. 中国科学院青藏高原研究所青藏高原地球系统与资源环境重点实验室地气作用与气候效应团队,北京 100101
    4. 中国科学院大学地球与行星科学学院,北京 100049
    5. 兰州大学大气科学学院,甘肃 兰州 730000
    6. 西藏珠穆朗玛特殊大气过程与环境变化国家野外科学观测研究站,西藏 定日 858200
    7. 中国科学院加德满都科教中心,北京 100101
    8. 中国科学院中国-巴基斯坦地球科学研究中心,伊斯兰堡 45320
    9. 荷兰特文特大学地球信息科学与地球观测学院(ITC),恩斯赫德,7500 AE

网络出版日期: 2025-05-08

基金资助

第二次青藏高原科学考察与研究项目(2019QZKK0103);欧洲空间局、中国国家遥感中心项目(58516

Analysis of the Characteristics of Non-stationary Spatio-temporal Variations of Future Temperature in the Qinghai-Xizang#br# Plateau Based on EOF-EEMD Combination

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  • 1. College of Hydraulic and Environmental EngineeringChina Three Gorges UniversityYichang 443002HubeiChina
    2. Engineering Research Center of Eco-environment in Three Gorges Reservoir RegionChina Three Gorges UniversityYichang 443002HubeiChina
    3. Land-Atmosphere Interaction and its Climatic Effects GroupState Key Laboratory of Tibetan Plateau Earth SystemEnvironment and ResourcesTPESER),Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China
    4. College of Earth and Planetary SciencesUniversity of Chinese Academy of SciencesBeijing 100049China
    5. College of Atmospheric ScienceLanzhou UniversityLanzhou 730000GansuChina
    6. National Observation and Research Station for Qomolongma Special Atmospheric Processes and
    Environmental Changes
    Dingri 858200XizangChina
    7. Kathmandu Center of Research and EducationChinese Academy of SciencesBeijing 100101China
    8. China-Pakistan Joint Research Center on Earth SciencesChinese Academy of SciencesIslamabad 45320Pakistan
    9. Faculty of Geo-Information Science and Earth ObservationITC),University of TwenteThe Netherlands7500 AE

Online published: 2025-05-08

摘要

使用有效的偏差订正方法以及将非平稳数据平稳化,能够提升对气温分析的科学准确性,以深入揭示其时空分布特征及演变规律。本研究使用 1970-2014ERA5_Land近地表(2 m)月平均气温观测数据集,首先利用泰勒图、泰勒指数、年际变率评估指数、秩打分法对国际耦合模式比较计划第六阶段(CMIP6)的 6 种气候模式和多模式集合(MME)平均模式进行评估及优选,然后用 Delta 偏差订正法和Normal 分布匹配法对较优模式进行订正,最后分析 SSP1-2. 6SSP2-4. 5 SSP5-8. 5 情景下青藏高原2015-2100年气温时空变化特点。结果表明:(1)本文选用的6CMIP6模式及MME平均模式中,ECEarth3模式模拟气温效果最优。(2)将EC-Earth3模式进行Delta偏差订正后的结果与观测结果对比,其确定性系数和纳什效率系数的区域平均值分别为0. 9920. 983,而用Normal分布匹配法订正后,其确定性系数和纳什效率系数的区域平均值分别为0. 9900. 978,相比之下,Delta偏差订正对模式月气温的订正效果更优。(3)通过 EOF-EEMD 结合发现,三种情景下第一典型场年气温呈现全区一致变化,且SSP1-2. 6SSP2-4. 5情景下存在共同气温变化敏感区,即藏北高原中部地区;第二典型场气温呈现以扎曲河上游区域逐渐向四周反相变化,其中 SSP1-2. 6情景下高原整体呈东部降温西部升温,SSP2-4. 5SSP5-8. 5情景下高原先东部增温西部降温,之后则东部降温、西部增温。本研究可为气候模式数据在青藏高原地区的准确应用提供偏差订正方法的参考,并为深入评估青藏高原气温变化对水资源、生态系统和环境的影响提供了关键的基础信息支持。

本文引用格式

张 雪, 董晓华, 马耀明, 龚成麒, 胡雪儿, 陈 玲, 苏中波 . 基于EOF-EEMD结合的青藏高原未来气温非平稳时空变化特征分析 [J]. 高原气象, 0 : 1 . DOI: 10.7522/j.issn.1000-0534.2025.00006

Abstract

Using effective bias correction methods and transforming non-stationary data to stationary can enhance the scientific accuracy of temperature analysisallowing for a deeper understanding of its temporal and spatial distribution characteristics and evolution patterns. This study utilizes the ERA5_Land near-surface2 mmonthly mean temperature observation dataset covering the period from 1970 to 2014. Initiallyit employs the Taylor diagramTaylor indexinterannual variability skill scoreand rank scoring method to evaluate and select among six climate models from the International Coupled Model Intercomparison Project Phase 6CMIP6and the multi-model ensembleMMEaverage models. Subsequentlythe superior models are refined using the Del‐ ta bias correction method and the Normal distribution matching method. Finallythe study analyzes the temporal and spatial temperature variation characteristics of the Qinghai-Xizang Plateau from 2015 to 2100 under the SSP1-2. 6SSP2-4. 5and SSP5-8. 5 scenarios. The results indicate that:(1Among the six CMIP6 models and the multi-model ensembleMMEaverage models analyzed in this studythe EC-Earth3 model demonstrates the most effective performance in simulating temperature.2When comparing the Delta bias correction results of the EC-Earth3 model with observational datathe regional averages of the coefficient of determinationR²and the Nash-Sutcliffe efficiency coefficientNSEare 0. 992 and 0. 983respectively. After applying the Nor‐ mal distribution matching method for correctionthe regional average values of R² and NSE are 0. 990 and 0. 978respectively. This comparison reveals that the Delta bias correction method exhibits superior correction efficacy for the model's monthly temperature.3According to the combination of EOF-EEMDthe annual temperature of the first typical field of the three scenarios changes uniformly in the whole regionand there is a common sensitive area of temperature change under SSP1-2. 6 and SSP2-4. 5 scenariosthat isthe central region of the Qiangtang Plateau. The temperature dynamics in the second typical field reveal a gradual reverse-phase change from the upper reaches of the Zhaqu River to surrounding areas. Under the SSP1-2. 6 scenariothe plateau experiences overall cooling in the east and warming in the west. Converselyunder the SSP2-4. 5 and SSP5- 8. 5 scenariosthe plateau initially warms in the east and cools in the westfollowed by a subsequent cooling in the east and warming in the west. This study provides a reference for bias correction methods that enhance the ac‐ curate application of climate model data in the Qinghai-Xizang Plateau region and offers essential foundational in‐ formation for a comprehensive assessment of the impacts of temperature changes on water resourcesecosystemsand the environment in this area.

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