气候变化研究进展 ›› 2022, Vol. 18 ›› Issue (1): 1-11.doi: 10.12006/j.issn.1673-1719.2021.247
所属专题: IPCC第六次评估报告WGI解读专栏
• IPCC 第六次评估报告WGI 专栏 • 上一篇 下一篇
效存德1(), 杨佼2, 张通1, 苏勃1, 王磊1, 许茜2, 闫展1, 郝海瑞1, 黄怡1
收稿日期:
2021-10-18
修回日期:
2021-11-06
出版日期:
2022-01-30
发布日期:
2021-11-10
作者简介:
效存德,男,教授, 基金资助:
XIAO Cun-De1(), YANG Jiao2, ZHANG Tong1, SU Bo1, WANG Lei1, XU Qian2, YAN Zhan1, HAO Hai-Rui1, HUANG Yi1
Received:
2021-10-18
Revised:
2021-11-06
Online:
2022-01-30
Published:
2021-11-10
摘要:
IPCC第六次评估报告(AR6)第一工作组报告对气候系统各要素的可预测性(predictability)、不可逆性(irreversibility)和深度不确定性(deep uncertainty)给出了新认识。文中基于此对全球冰冻圈变化的上述三方面加以总结和归纳。总体来看,无论何种排放情景,半球和全球尺度上冰冻圈各要素于21世纪均具有一定的可预测性,即均向融化或退化方向变化,且具有不可逆性;但在区域尺度、短时间尺度和百年以上时间尺度上,不同冰冻圈要素或因内部变率大、或因响应机制复杂而存在可逆、可预测性差乃至深度不确定性难题。
效存德, 杨佼, 张通, 苏勃, 王磊, 许茜, 闫展, 郝海瑞, 黄怡. 冰冻圈变化的可预测性、不可逆性和深度不确定性[J]. 气候变化研究进展, 2022, 18(1): 1-11.
XIAO Cun-De, YANG Jiao, ZHANG Tong, SU Bo, WANG Lei, XU Qian, YAN Zhan, HAO Hai-Rui, HUANG Yi. The predictability, irreversibility and deep uncertainty of cryospheric change[J]. Climate Change Research, 2022, 18(1): 1-11.
图1 卫星观测和CMIP6预测的北极海冰变化(a) 1979—2019年北极海冰面积相对于1979—2008年的月平均异常,(b) 3月和9月的北极海冰密集度分布 注:(b)图第一列为卫星观测的2010—2019年与1979—1988年平均海冰密集度的差值,第二列为CMIP6模式中预测在未来2045—2054年平均海冰密集度大于15%的模式数量。
Fig. 1 Arctic sea-ice historical records and CMIP6 projections (a) absolute anomaly of monthly-mean Arctic sea-ice area during 1979-2019 relative to the average monthly-mean Arctic sea-ice area during 1979-2008, (b) sea-ice concentration in the Arctic for March and September, respectively. First column: absolute change in sea-ice concentration between these two decades, with grid lines indicating non-significant differences. Second column: number of available CMIP6 models that simulate a mean sea-ice concentration above 15% for the decade 2045-2054
图2 (a) 2010—2017年间CryoSat 2雷达高度计获取的格陵兰冰盖平均高程变化,(b) RCP8.5情景下,基于MIROC5气候模式输出模拟得到的2093—2100年格陵兰冰盖高程变化的模式平均,(c) 1978—2017年间卫星高度计获取的南极冰盖高程变化,(d) RCP8.5情景下,基于NorESM1-M气候模式输出模拟得到的2061—2100年南极冰盖高程变化的模式平均
Fig. 2 (a) changes in mean elevation of the Greenland ice sheet from 2010 to 2017 as measured by CryoSat 2 radar altimeter, (b) model averages of elevation changes of the Greenland ice sheet in 2093-2100 based on MIROC5 climate model output simulations under the RCP8.5 scenario, (c) elevation changes of the Antarctic ice sheet from 1978 to 2017 as measured by satellite altimeters, (d) model averages for 2061-2100 Antarctic ice sheet elevation changes based on NorESM1-M climate model output simulations under the RCP8.5 scenario
图3 1901—2100年全球及其分区的冰川物质变化(相对于2015年) 注:括号数字代表区域编码。
Fig. 3 Global and regional glacier mass evolution between 1901 and 2100 relative to glacier mass in 2015
图4 不同变暖水平下的模拟与观测到的多年冻土范围(a)和多年冻土体积(b) 注:(a)图中黑色符号表示根据物理证据和再分析对当前多年冻土范围的估计[24,25,26],(b)图中CMIP6模拟的全球多年冻土体积变化在地表和3 m深度之间作为模拟GSAT变化的函数。
Fig. 4 Simulated versus observed permafrost extent and permafrost volume change by warming level. (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979-1998, estimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols (data from references [24,25,26]); (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated GSAT change, from the first ensemble members of a selection of scenarios, for available CMIP6 models
图5 (a) 1981—2014年间基于CMIP6模拟的和基于地表和卫星观测的积雪范围[29],(b)基于地表和卫星观测和CMIP6模拟的2050年和2090年春季北半球积雪范围变化对气温变化(相对于1995—2014年平均)的敏感性及其线性回归 注:(a)箱线图描述1981—2014年基于CMIP6多模式输出的SCE月平均值和极端值,其中蓝色线条表示多模式输出结果的中位值。该时期基于观测的SCE的年内分布用绿色菱形表示,其中黄色表示中位值。
Fig. 5 (a) Simulated CMIP6 and observed[29] snow cover extent (in millions of km2) for 1981-2014. Boxes and whiskers with outliers represent monthly mean values for the individual CMIP6 models averaged over 1981-2014, with the blue bar indicating the median of the CMIP6 multi-model ensemble for that period. The observed inter-annual distribution over the period is represented in green, with the yellow bar indicating the median. (b) Spring (March to May) Northern Hemisphere snow cover extent against GSAT (relative to the 1995-2014 average) for the CMIP6 Tier 1 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), with linear regressions. Each data point is the mean for one CMIP6 simulation (first ensemble member for each available model) in the corresponding temperature bin
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