基于CMIP6模式的未来气候情景下北半球多年冻土动态

doi:10.13287/j.1001-9332.202505.026

应用生态学报 ›› 2025, Vol. 36 ›› Issue (5): 1496-1506.doi: 10.13287/j.1001-9332.202505.026

基于CMIP6模式的未来气候情景下北半球多年冻土动态

田齐萱^1,2, 刘建朝³, 曾凡超^1,4, 杨茗¹, 汤钦荣^1,5, 郑洁^1,2, 左云江¹, 王楠楠¹, 尧晓晨¹, 宋艳宇^1*

¹中国科学院东北地理与农业生态研究所黑土地保护与利用重点实验室, 长春 130102;
²中国科学院大学, 北京 100049;
³吉林建筑大学测绘与勘查工程学院, 长春 130118;
⁴鲁东大学水利土木学院, 山东烟台 264025;
⁵东北师范大学地理科学学院, 长春 130024

收稿日期:2024-12-16 修回日期:2025-03-11 出版日期:2025-05-18 发布日期:2025-11-18
通讯作者: *E-mail: songyanyu@iga.ac.cn
作者简介:田齐萱, 男, 1999年生, 硕士研究生。主要从事全球变化和多年冻土研究。E-mail: tianqixuan@iga.ac.cn
基金资助:
国家自然科学基金重大项目(42494823)、国家重点研发计划项目(2024YFF0808703)、吉林省第二十批创新创业人才项目、吉林省留学人员科技创新创业项目和黑土地保护与利用重点实验室青年创新项目(2023HTDGZ-QN-03)

Permafrost dynamics in the Northern Hemisphere under future climate scenarios based on CMIP6

TIAN Qixuan^1,2, LIU Jianzhao³, ZENG Fanchao^1,4, YANG Ming¹, TANG Qinrong^1,5, ZHENG Jie^1,2, ZUO Yunjiang¹, WANG Nannan¹, YAO Xiaochen¹, SONG Yanyu^1*

¹Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;
²University of Chinese Academy of Sciences, Beijing 100049, China;
³College of Surveying and Exploration Enginee-ring, Jilin Jianzhu University, Changchun 130118, China;
⁴School of Hydraulic and Civil Engineering, Ludong University, Yantai 264025, Shandong, China;
⁵School of Geographical Sciences, Northeast Normal University, Changchun 130024, China

Received:2024-12-16 Revised:2025-03-11 Online:2025-05-18 Published:2025-11-18

摘要/Abstract

摘要： 全球变暖对北半球多年冻土的影响日益显著,多年冻土退化是现代冰冻圈中与气候变化相关的最紧迫问题之一。本研究基于15种不同地球系统模式(ACCESS-CM2、ACCESS-ESM1-5、BCC-CSM2-MR、CanESM5、CESM2、CESM2-WACCM、EC-Earth3、FGOALS-f3-L、IPSL-CM6A-LR、MIROC6、MPI-ESM1-2-HR、MPI-ESM1-2-LR、MRI-ESM2-0、NorESM2-LM、NorESM2-MM)的CMIP6土壤温度数据,分析了未来不同排放情境(SSP126、SSP245、SSP370和SSP585)下北半球多年冻土面积和活动层厚度(ALT)的时空格局,重点解析了影响ALT变化的主要环境驱动因子。结果表明: 各地球系统模式(ESM)对ALT的模拟能力差异显著。基于性能最优的4个ESM(MPI-ESM1-2-LR、ACCESS-ESM1-5、MPI-ESM1-2-HR和BCC-CSM2-MR)分析发现,2015—2100年间,高排放情境(SSP370、SSP585)下多年冻土面积减少速率显著加快,SSP585情境下冻土面积消退速率为SSP126情境的8倍;SSP126情境下多年冻土面积增加,SSP245、SSP370和SSP585情境下则持续减少。ALT在未来所有情境下均显著增加,最高排放情境SSP585的年增速是最低排放情景SSP126的22倍。每年冻土融化结束时间将逐渐从9月推移至11月,导致冻土融化持续时间逐渐增加。作为关键影响因素,空气温度、空气湿度、植被叶面积指数、积雪和风速在研究区大部分区域对冻土退化表现为明显正效应,土壤水分表现为负效应。未来通过控制温室气体排放,可以明显延缓冻土退化过程,降低北半球多年冻土面临的快速消融风险。

关键词: CMIP6, 多年冻土, 活动层厚度, 气候变化, 冻土退化

Abstract: Global warming is increasingly affecting permafrost in the Northern Hemisphere, with permafrost degradation being one of the most serious consequences of climate change on the cryosphere. Based on the CMIP6 soil temperature data from 15 different earth system models (ESMs) (ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CESM2, CESM2-WACCM, EC-Earth3, FGOALS-f3-L, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, NorESM2-MM), we analyzed the spatiotemporal variations of the permafrost area and active layer thickness (ALT) in the Northern Hemisphere under different future emission scenarios (SSP126, SSP245, SSP370, and SSP585), aiming to clarify the main environmental driving factors affecting the changes in ALT. Results showed significant discrepancies in the simulation capabilities of ALT across ESMs. Based on the analysis of the four optimal performance ESMs (MPI-ESM1-2-LR, ACCESS-ESM1-5, MPI-ESM1-2-HR, and BCC-CSM2-MR), we found that the reduction rate of permafrost area significantly accele-rated from 2015 to 2100 under high emission scenarios (SSP370, SSP585), and the rate of permafrost area decline under SSP585 scenario was eight times that of SSP126 scenario. The permafrost area would increase under SSP126 scenario, but it would continue to decrease under SSP245, SSP370, and SSP585 scenarios. ALT was projected to increase significantly under all four scenarios, with the annual increasing rate under SSP585 being 22 times higher than SSP126. Furthermore, we found that the end time of annual permafrost thawing would gradually change from September to November, leading to an extension of the thawing period. Key factors, such as air temperature, air humidity, vegetation leaf area index, snow cover, and wind speed showed positive effects on permafrost degradation in most regions, while soil moisture showed negative effect. Overall, future greenhouse gas emission controls would offer potential pathways to mitigate the risk of rapid permafrost degradation in the Northern Hemisphere.

Key words: CMIP6, permafrost, active layer thickness, climate change, permafrost degradation

田齐萱, 刘建朝, 曾凡超, 杨茗, 汤钦荣, 郑洁, 左云江, 王楠楠, 尧晓晨, 宋艳宇. 基于CMIP6模式的未来气候情景下北半球多年冻土动态[J]. 应用生态学报, 2025, 36(5): 1496-1506.

TIAN Qixuan, LIU Jianzhao, ZENG Fanchao, YANG Ming, TANG Qinrong, ZHENG Jie, ZUO Yunjiang, WANG Nannan, YAO Xiaochen, SONG Yanyu. Permafrost dynamics in the Northern Hemisphere under future climate scenarios based on CMIP6[J]. Chinese Journal of Applied Ecology, 2025, 36(5): 1496-1506.

参考文献

[1] 周幼吾. 中国冻土. 北京: 科学出版社, 2000
[2] Zhang TJ. Historical overview of permafrost studies in China. Physical Geography, 2005, 26: 279-298
[3] 彭小清, 田伟伟, 李璇佳, 等. 青藏高原和环北极冻土变化研究进展. 冰川冻土, 2023, 45(2): 521-534
[4] Chadburn SE, Burke EJ, Cox PM, et al. An observation-based constraint on permafrost loss as a function of global warming. Nature Climate Change, 2017, 7: 340-344
[5] Koven CD, Ringeval B, Friedlingstein P, et al. Permafrost carbon-climate feedbacks accelerate global warming. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108: 14769-14774
[6] 焦永亮, 李韧, 赵林, 等. 多年冻土区活动层冻融状况及土壤水分运移特征. 冰川冻土, 2014, 36(2): 237-247
[7] Smith SL, O’Neill HB, Isaksen K, et al. The changing thermal state of permafrost. Nature Reviews Earth & Environment, 2022, 3: 10-23
[8] 周天军, 邹立维, 吴波, 等. 中国地球气候系统模式研究进展: CMIP计划实施近20年回顾. 气象学报, 2014, 72(5): 892-907
[9] 周天军, 邹立维, 陈晓龙. 第六次国际耦合模式比较计划(CMIP6)评述. 气候变化研究进展, 2019, 15(5): 445-456
[10] 牟翠翠, 张国飞, 效存德, 等. IPCC第六次评估报告解读: 多年冻土变化及其影响. 冰川冻土, 2023, 45(2): 306-317
[11] Mishra U, Riley WJ. Active-layer thickness across Ala-ska: Comparing observation-based estimates with CMIP5 Earth System Model predictions. Soil Science Society of America Journal, 2014, 78: 894-902
[12] Slater AG, Lawrence DM. Diagnosing present and future permafrost from climate models. Journal of Climate, 2013, 26: 5608-5623
[13] Yokohata T, Saito K, Takata K, et al. Model improvement and future projection of permafrost processes in a global land surface model. Progress in Earth and Planetary Science, 2020, 7: 69
[14] 周鑫原, 吕世华, 罗江鑫. CMIP6 BCC等模式对青藏高原土壤冻融模拟性能分析. 高原山地气象研究, 2022, 42(2): 82-89
[15] 胡桃, 吕世华, 常燕, 等. CMIP6模式对青藏高原多年冻土变化的分析预估. 高原气象, 2022, 41(2): 363-375
[16] Soong JL, Phillips CL, Ledna C, et al. CMIP5 models predict rapid and deep soil warming over the 21st cen-tury. Journal of Geophysical Research: Biogeosciences, 2020, 125: e2019JG005266
[17] Taylor KE. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 2001, 106: 7183-7192
[18] 李锐超, 谢瑾博, 谢正辉. 不同大气强迫作用下陆面模式CAS-LSM多年冻土活动层厚度模拟与不确定性研究. 气候与环境研究, 2021, 26(1): 31-44
[19] Burke EJ, Zhang Y, Krinner G. Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change. The Cryosphere, 2020, 14: 3155-3174
[20] Magnússon RÍ, Hamm A, Karsanaev SV, et al. Extremely wet summer events enhance permafrost thaw for multiple years in Siberian tundra. Nature Communications, 2022, 13: 1556
[21] Ziehn T, Chamberlain MA, Law RM, et al. The Australian Earth System Model: ACCESS-ESM1.5. Journal of Southern Hemisphere Earth Systems Science, 2020, 70: 193-214
[22] Cai ZY, You QL, Chen HW, et al. Assessing Arctic wetting: Performances of CMIP6 models and projections of precipitation changes. Atmospheric Research, 2024, 297: 107124
[23] Zhou JZ, Zhang J, Huang YY. Evaluation of soil temperature in CMIP6 multimodel simulations. Agricultural and Forest Meteorology, 2024, 352: 110039
[24] Lawrence DM, Slater AG, Romanovsky VE, et al. Sensitivity of a model projection of near-surface permafrost degradation to soil column depth and representation of soil organic matter. Journal of Geophysical Research: Earth Surface, 2008, 113: F02011
[25] Luo DL, Wu QB, Jin HJ, et al. Recent changes in the active layer thickness across the northern hemisphere. Environmental Earth Sciences, 2016, 75: 555
[26] 范雪薇, 缪驰远, 苟娇娇, 等. 国际耦合模式比较计划及其模拟能力研究进展. 地理科学进展, 2023, 42(6): 1204-1215
[27] 张廷军. 全球多年冻土与气候变化研究进展. 第四纪研究, 2012, 32(1): 27-38
[28] Wang XJ, Ran YH, Pang GJ, et al. Contrasting characteristics, changes, and linkages of permafrost between the Arctic and the Third Pole. Earth-Science Reviews, 2022, 230: 104042
[29] Guo DL, Wang HJ. CMIP5 permafrost degradation projection: A comparison among different regions. Journal of Geophysical Research: Atmospheres, 2016, 121: 4499-4517
[30] 彭惠, 魏尧, 袁堃, 等. 多年冻土公路路基水分迁移规律与成灾机制研究. 公路交通科技, 2023, 40(3): 42-50
[31] 牟翠翠. 热喀斯特改变多年冻土区景观和地表过程. 自然杂志, 2020, 42(5): 386-392
[32] Yuan QQ, Zhong W, Yang QQ, et al. Duration of frozen days show a strong decline in the Northern Hemisphere mainly driven by autumn temperature increase. The Innovation Geoscience, 2025, 3: 100118
[33] Peng XQ, Zhang TJ, Frauenfeld OW, et al. Active layer thickness and permafrost area projections for the 21st century. Earth’s Future, 2023, 11: e2023EF0-03573
[34] 刘广岳, 谢昌卫, 杨淑华. 青藏公路沿线多年冻土区活动层起始冻融时间的时空变化特征和影响因素. 冰川冻土, 2018, 40(6): 1067-1078
[35] Hu GJ, Zhao L, Wu TH, et al. Continued warming of the permafrost regions over the Northern Hemisphere under future climate change. Earth’s Future, 2022, 10: e2022EF002835
[36] 周晓宇, 赵春雨, 李娜, 等. 东北地区冬半年积雪与气温对冻土的影响. 冰川冻土, 2021, 43(4): 1027-1039
[37] Jiang RQ, Bai XF, Wang XH, et al. Effects of freeze-thaw cycles and the prefreezing water content on the soil pore size distribution. Water, 2024, 16: 14
[38] Yuan WP, Zheng Y, Piao SL, et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Science Advances, 2019, 5: eaax1396
[39] Domine F, Fourteau K, Picard G, et al. Permafrost cooled in winter by thermal bridging through snow-covered shrub branches. Nature Geoscience, 2022, 15: 554-560
[40] Walvoord MA, Kurylyk BL. Hydrologic impacts of thawing permafrost: A review. Vadose Zone Journal, 2016, 15: vzj2016.01.0010
[41] Boike J, Roth K, Overduin PP. Thermal and hydrologic dynamics of the active layer at a continuous permafrost site (Taymyr Peninsula, Siberia). Water Resources Research, 1998, 34: 355-363
[42] Clayton LK, Schaefer K, Battaglia MJ, et al. Active layer thickness as a function of soil water content. Environmental Research Letters, 2021, 16: 055028
[43] Loranty MM, Abbott BW, Blok D, et al. Reviews and syntheses: Changing ecosystem influences on soil thermal regimes in northern high-latitude permafrost regions. Biogeosciences, 2018, 15: 5287-5313
[44] 杨堤益, 丁明虎, 邹小伟. 南极冰盖地表能量平衡的研究进展. 极地研究, 2021, 33(1): 99-114
[45] 白淑英, 史建桥, 沈渭寿, 等. 卫星遥感西藏高原积雪时空变化及影响因子分析. 遥感技术与应用, 2014, 29(6): 954-962
[46] McMahon TA, Peel MC, Lowe L, et al. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: A pragmatic synthesis. Hydrology and Earth System Sciences, 2013, 17: 1331-1363
[47] 张明礼, 周志雄, 周凤玺, 等. 夏季降雨增加对多年冻土活动层水热状态的影响研究. 岩土力学, 2022, 43(12): 3335-3346
[48] 吕久俊, 李秀珍, 胡远满, 等. 呼中自然保护区多年冻土活动层厚度的影响因子分析. 生态学杂志, 2007, 26(9): 1369-1374

基于CMIP6模式的未来气候情景下北半球多年冻土动态

Permafrost dynamics in the Northern Hemisphere under future climate scenarios based on CMIP6

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