Climate feedbacks at the Last Glacial Maximum
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摘要:
末次冰盛期（Last Glacial Maximum，简称LGM）被认为是较适合用来估算气候系统响应对辐射强迫变化的古气候区间之一。理解LGM时期气候反馈过程有助于进一步限定气候敏感度的范围。本研究利用辐射核方法和参加第三次古气候模式比较计划（Paleoclimate Modelling Intercomparison Project Phase Ⅲ，简称PMIP3）的8个耦合模式资料，对比研究了LGM时期与abrupt4xCO2（4CO2）情景下的气候反馈特征。结果表明：全球平均而言，不同情景下温度反馈、水汽反馈和反照率反馈的强度存在显著差异，然而这一关系并不存在于云反馈过程中，这可能与情景间/模式间云反馈的不确定性相联系；在不同情景下，不同反馈过程强度也存在明显空间差异。温度反馈过程的差异主要来源于LGM时期大陆冰盖强迫引起的温度变化的高度空间不均一性和海陆分布改变引起的热带对流活动的变化；水汽反馈变化可能与海陆分布变化引起的沃克环流变化以及全球降温相联系；大陆冰盖和海冰存在是导致LGM时期地表反照率反馈增加的主要原因；而云反馈的差异可能与低云云量和模式间不确定性有关。LGM时期单独强迫数值试验将有助于进一步厘清不同气候状态下气候反馈过程差异的原因。
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关键词:
- 末次冰盛期 /
- abrupt4xCO2 /
- 辐射核 /
- 气候反馈
Abstract:Assessing the impact of future global warming is impeded by the large range of Equilibrium Climate Sensitivity (ECS) to atmospheric CO2 doubling. Previous studies suggested the Last Glacial Maximum (LGM) is an ideal paleoclimate period to constrain the ECS since the abundant proxy data and coupled model simulations. However, the ECS inferred from LGM proxy data is still elusive since the climate feedback processes have not been well understood. In this study, the similarities and differences in the climate feedbacks, include temperature, water vapor, albedo and cloud feedback, for LGM and abrupt4xCO2 scenarios are investigated using the radiative kernel method and the Paleoclimate Modelling Intercomparison Project Phase Ⅲ (PMIP3) coupled model simulations. The results suggest that, in the global mean sense, the temperature, water vaper and albedo feedbacks have significant difference between LGM and abrupt4xCO2 scenarios, while the cloud feedback process is undistinguishable due to its uncertainties between scenarios and/or coupled models. The spatial distributions of feedback strengths are different in all feedback processes between scenarios. The discrepancy of temperature feedback between LGM and abrupt4xCO2 scenarios are due to highly inhomogeneous of surface temperature response due to the presence of continental ice sheets and tropical convection change because of the exposure of continental shelf. The difference in water vapor feedbacks over the equatorial eastern Pacific could be induced by the exposure of continental shelf and its induced Walker circulation change. The change of albedo feedback is consistent with the change of continental ice sheets/sea ice cover. The model uncertainty in cloud feedback strength and the change of low cloud between LGM and abrupt4xCO2 scenario could affect the total cloud feedback strength. Individual LGM forcing sensitive experiments are necessary for isolating the effect of each boundary forcing on the climate feedbacks.
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Key words:
- Last Glacial Maximum /
- abrupt4xCO2 /
- radiative kernel /
- climate feedbacks
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图 1
(a) LGM时期海陆分布(红色实线)、大陆冰盖(紫色实线)及其与现代地形的高度差(阴影，单位：m)和(b)地表温度变化(单位：℃)
Figure 1.
(a)Land-sea mask(red solid line), ice-sheet extent(purple solid line)during LGM, the change in surface elevation(shaded, unit: m)and (b) the change in surface temperature(unit: ℃)
图 2
(左)大气温度反馈的年平均和纬向平均分布(单位：W/m2/K/100 hPa)及(右)地表温度反馈的年平均特征(单位：W/m2/K)
Figure 2.
(Left)The annual-mean, zonal-mean atmospheric temperature feedback(unit: W/m2/K/100 hPa)and the annual-mean surface temperature feedback(unit: W/m2/K). (a, b)LGM; (c, d)4CO2; (e, f)Difference between LGM and 4CO2 scenario(LGM-4CO2)
图 3
水汽反馈的(左)年平均和纬向平均分布特征(单位：W/m2/K/100 hPa)及(右)年平均和垂直积分特征(单位：W/m2/K)
Figure 3.
(Left)The annual-mean, zonal-mean water vapor feedback(unit: W/m2/K/100hPa)and(Right)the annual-mean, vertical integrals of water vapor feedback(unit: W/m2/K). (a, b)LGM; (c, d)4CO2; (e, f)Difference between LGM and 4CO2 scenario(LGM-4CO2)
图 4
LGM时期与PI时期沃克环流(10°S与10°N之间经向平均)的垂直速度差(单位：10-2 Pa/s)
Figure 4.
The change in vertical velocity of Walker circulation (meridional mean from 10°S to 10°N) between the LGM and the PI scenario (unit:10-2 Pa/s)
图 5
地表反照率反馈年平均的(左)空间分布及(右)纬向平均特征(单位：W/m2/K；LGM试验不包含FGOALS-g2模式)
Figure 5.
(Left)The annual-mean surface albedo feedback and(Right)zonal, annual mean of surface albedo feedback(unit:W/m2/K; FGOALS-g2 model excluded in LGM experiment). (a, b)LGM; (c, d)4CO2; (e, f)Difference between LGM and 4CO2 scenario(LGM-4CO2)
图 6
年平均的校正云辐射强迫(单位：W/m2/K；LGM试验不包含FGOALS-g2模式)
Figure 6.
The annual-mean corrections to cloud radiative forcing.(unit:W/m2/K; FGOALS-g2 model excluded in LGM experiment). (a)LGM; (b)4CO2; (c)Difference between LGM and 4CO2 scenario(LGM-4CO2)
图 7
多模式平均的云反馈(单位：W/m2/K；LGM试验不包含FGOALS-g2模式)
Figure 7.
Multimodel ensemble-mean maps of cloud feedback(unit:W/m2/K; Except for FGOALS-g2 in LGM experiment). (a)LGM; (b)4CO2; (c)Difference between LGM and 4CO2 scenario(LGM-4CO2)
表 1
本研究中所使用的PMIP3模式和试验数据
Table 1.
PMIP 3 models and experiments used in this study
模式 国家 模式分辨率(lev，lat，lon) 模拟长度(PI，年) 模拟长度(LGM，年) 模拟长度(4CO2，年) CCSM4 美国 26×192×288 1051 101 150 CNRM-CM5 法国 31×128×256 850 200 150 FGOALS-g2 中国 26×60×128 700 100 258 GISS-E2-R 法国 40×96×96 1200 100 150 IPSL-CM5 A-LR 美国 39×90×144 1000 200 260 MIROC-ESM 日本 80×64×128 630 100 150 MPI-ESM-P 德国 47×96×192 1156 100 150 MRI-CGCM3 日本 48×160×320 500 100 150 
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表 2
PI、4CO2和LGM试验边界条件
Table 2.
Boundary conditions for the PI, 4CO2 and LGM experiments
试验 CO2浓度(×10-6) CH4浓度(×10-9) N2O浓度(×10-9) 轨道参数 冰盖 海陆分布 近日点 轨道偏心率 黄赤交角 PI 280 760 270 102.04° 0.016724 23.446° 现代 现代 4CO2 1120 760 270 102.04° 0.016724 23.446° 现代 现代 LGM 185 350 200 114.42° 0.018994 22.949° LGM LGM
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表 3
LGM和4CO2试验年平均、垂直积分及全球平均的温度和水汽反馈以及年平均、全球平均的地表反照率反馈和云反馈
Table 3.
Annual-mean, global-mean vertical integrals of the temperature feedback and water vapor feedback and annual-mean, global-mean surface albedo feedback and cloud feedback for LGM and 4CO2 experiments
模式 LGM/4CO2 温度 水汽 地表反照率 云 CCSM4 -2.63/-3.39 1.27/1.50 0.75/0.36 -0.30/0.30 CNRM-CM5 -2.88/-3.47 1.47/1.57 0.63/0.32 -0.01/0.19 FGOALS-g2 -2.66/-3.64 1.37/1.83 -/0.40 -/0.26 GISS-E2-R -2.84/-3.86 1.47/1.91 0.81/0.18 0.18/-0.23 IPSL-CM5 A-LR -3.33/-4.09 1.72/1.97 0.71/0.20 0.68/1.09 MIROC-ESM -2.69/-3.81 1.28/1.78 0.73/0.44 -0.04/0.72 MPI-ESM-P -2.89/-4.05 1.38/1.91 0.77/0.27 0.07/0.53 MRI-CGCM3 -2.90/-3.57 1.43/1.61 0.80/0.34 0.09/0.05 模式平均 -2.85/-3.74* 1.42/1.76* 0.74/0.31* 0.11/0.36 方差 0.0494/0.0674 0.0202/0.0315 0.0037/0.0084 0.0892/0.1683 单位：W/m2/K；粗体为LGM试验结果；*表示通过95 %均值差异性检验；横线表示由于缺少数据，无法计算
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[1] Thorsen T J, Kato S, Loeb N G, et al. Observation-based decomposition of radiative perturbations and radiative kernels[J]. Journal of Climate, 2018, 31(24):10039-10058. doi: 10.1175/JCLI-D-18-0045.1
[2] Shell K M, Kiehl J T, Shields C A. Using the radiative kernel technique to calculate climate feedbacks in NCAR's Community Atmospheric Model[J]. Journal of Climate, 2008, 21(10):2269-2282. doi: 10.1175/2007JCLI2044.1
[3] 周天军, 陈晓龙.气候敏感度、气候反馈过程与2℃升温阈值的不确定性问题[J].气象学报, 2015, 73(4):624-634. http://d.old.wanfangdata.com.cn/Periodical/qxxb201504002
Zhou Tianjun, Chen Xiaolong. The uncertainty in the 2℃ warming threshold issue as related to climate sensitivity and climate feedback[J]. Acta Meteorologica Sinica, 2015, 73(4):624-634. http://d.old.wanfangdata.com.cn/Periodical/qxxb201504002
[4] 巢纪平, 李耀锟.二维能量平衡模式中大气温度对二氧化碳增温效应的响应[J].气象学报, 2010, 68(2):147-152. doi: 10.3969/j.issn.1004-4965.2010.02.003
Chao Jiping, Li Yaokun. Response of atmospheric temperature to CO2 warming effect in a 2-dimensional energy balance model[J]. Acta Meteorologica Sinica, 2010, 68(2):147-152. doi: 10.3969/j.issn.1004-4965.2010.02.003
[5] Cox P M, Huntingford C, Williamson M S. Emergent constraint on equilibrium climate sensitivity from global temperature variability[J]. Nature, Nature Publishing Group, 2018, 553(7688):319-322. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=8748ee4c3c3eae9599280d7285dfabe9
[6] 李娟, 杨军.气候敏感性与极地增温放大效应[C]//第34届中国气象学会年会S9大气成分与天气、气候变化及环境影响论文集. 2017: 2.
Li Juan, Yang Jun. Climate sensitivity and polarization-warming amplification effect[C]//The 34th Annual Meeting of the Chinese Meteorological Society S9 Proceedings of Atmospheric Composition and Weather, Climate Change and Environmental Impact. 2017: 2.
[7] Capistrano V B, Nobre P, Tedeschi R G, et al. Overview of climate change in the BESM-OA2.5 climate model[J]. Geoscientific Model Development Discussions, 2018:1-29. https://doi.org/10.5194/gmd-2018-209. doi: 10.5194/gmd-2018-209
[8] Hegerl G C, Zwiers F W, Braconnot P, et al. Understanding and attributing climate change[M]//IPCC. Climate Change 2007: The Physical Science Basis: Contribution of Working Group Ⅰ to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2007: 663-745.
[9] Meehl G A, Stocker T F, Collins W D, et al. Global climate projections[M]//IPCC. Climate Change 2007: The Physical Science Basis: Contribution of Working Group Ⅰ to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2007: 747-845.
[10] 王绍武, 罗勇, 赵宗慈, 等.平衡气候敏感度[J].气候变化研究进展, 2012, 8(3):232-234. doi: 10.3969/j.issn.1673-1719.2012.03.012
Wang Shaowu, Luo Yong, Zhao Zongci, et al. Equilibrium climate sensitivity[J]. Advances in Climate Change Research, 2012, 8(3):232-234. doi: 10.3969/j.issn.1673-1719.2012.03.012
[11] Deimling T S V, Held H, Ganopolski A, et al. Climate sensitivity estimated from ensemble simulations of glacial climate[J]. Climate Dynamics, 2006, 27(2-3):149-163. doi: 10.1007/s00382-006-0126-8
[12] 周天军, 吴波, 郭准, 等.东亚夏季风变化机理的模拟和未来变化的预估:成绩和问题、机遇和挑战[J].大气科学, 2018, 42(4):902-934. http://d.old.wanfangdata.com.cn/Periodical/daqikx201804014
Zhou Tianjun, Wu Bo, Guo Zhun, et al. A review of East Asian summer monsoon simulation and projection:Achievements and problems, opportunities and challenges[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(4):902-934. http://d.old.wanfangdata.com.cn/Periodical/daqikx201804014
[13] Mitchell J F B, Manabe S, Meleshko V, et al. Equilibrium climate change and its implications for the future[M]//IPCC. Climate Change: The Scientific Assessment: Contribution of Working Group Ⅰ to First Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 1990: 137-164.
[14] Knutti R, Rugenstein M A A, Hegerl G C. Beyond equilibrium climate sensitivity[J]. Nature Geoscience, 2017, 10(10):727-736. doi: 10.1038/ngeo3017
[15] Hargreaves J C, Annan J D, Yoshimori M, et al. Can the Last Glacial Maximum constrain climate sensitivity?[J]. Geophysical Research Letters, 2013, 39(24):L24702. http://cn.bing.com/academic/profile?id=3ccd93b3c31eefe188f29a253e51ea27&encoded=0&v=paper_preview&mkt=zh-cn
[16] Hopcroft P O, Valdes P J. How well do simulated Last Glacial Maximum tropical temperatures constrain equilibrium climate sensitivity?[J]. Geophysical Research Letters, 2015, 42(13):5533-5539. doi: 10.1002/2015GL064903
[17] Crucifix M. Does the Last Glacial Maximum constrain climate sensitivity?[J]. Geophysical Research Letters, 2006, 33(18):L18701. http://cn.bing.com/academic/profile?id=5fcd1df7c7656058ae16eac7088c765a&encoded=0&v=paper_preview&mkt=zh-cn
[18] 姜大膀, 田芝平.末次冰盛期和全新世中期东亚地区水汽输送的模拟研究[J].第四纪研究, 2017, 37(5):999-1008. http://www.dsjyj.com.cn/CN/abstract/abstract11380.shtml
Jiang Dabang, Tian Zhiping. Last Glacial Maximum and mid-Holocene water vapor transport over East Asia:A modeling study[J]. Quaternary Sciences, 2017, 37(5):999-1008. http://www.dsjyj.com.cn/CN/abstract/abstract11380.shtml
[19] 姜大膀, 王会军, 郎咸梅.末次盛冰期气候模拟及青藏高原冰盖的可能影响[J].第四纪研究, 2002, 22(4):323-331. doi: 10.3321/j.issn:1001-7410.2002.04.004 http://www.dsjyj.com.cn/CN/abstract/abstract10118.shtml
Jiang Dabang, Wang Huijun, Lang Xianmei. Palaeoclimate modeling for the Last Glacial Maximum and the possible influence of the continental ice sheet over the Qinghai-Xizang Plateau[J]. Quaternary Sciences, 2002, 22(4):323-331. doi: 10.3321/j.issn:1001-7410.2002.04.004 http://www.dsjyj.com.cn/CN/abstract/abstract10118.shtml
[20] 姜大膀, 梁潇云.末次盛冰期东亚气候的成因检测[J].第四纪研究, 2008, 28(3):491-501. doi: 10.3321/j.issn:1001-7410.2008.03.014 http://www.dsjyj.com.cn/CN/abstract/abstract8913.shtml
Jiang Dabang, Liang Xiaoyun. Attribution of East Asian climate at the Last Glacial Maximum[J]. Quaternary Sciences, 2008, 28(3):491-501. doi: 10.3321/j.issn:1001-7410.2008.03.014 http://www.dsjyj.com.cn/CN/abstract/abstract8913.shtml
[21] Kutzbach J, Gallimore R, Harrison S, et al. Climate and biome simulations for the past 21000 years[J]. Quaternary Science Reviews, 1998, 17(6-7):473-506. doi: 10.1016/S0277-3791(98)00009-2
[22] 袁熹, 石正国, 周卫健.中全新世和末次盛冰期北大西洋涛动变化及其与亚洲降水的联系:基于MPI-ESM模拟试验[J].第四纪研究, 2014, 34(6):1156-1165. http://www.dsjyj.com.cn/CN/abstract/abstract10963.shtml
Yuan Xi, Shi Zhengguo, Zhou Weijian. North Atlantic Oscillation changes and its relationship with Asian precipitation in mid-Holocene and Last Glacial Maximum:Results from MPI-ESM simulations[J]. Quaternary Sciences, 2014, 34(6):1156-1165. http://www.dsjyj.com.cn/CN/abstract/abstract10963.shtml
[23] 张恩楼, 孙伟伟, 刘恩峰, 等.末次冰盛期以来洱海沉积物元素碳同位素特征与区域植被组成变化[J].第四纪研究, 2017, 37(5):1027-1036. http://www.dsjyj.com.cn/CN/abstract/abstract11383.shtml
Zhang Enlou, Sun Weiwei, Liu Enfeng, et al. Vegetation change reconstructed by a stable isotope record of elemental carbon from Lake Erhai, Southwest China since the Last Glacial Maximum[J]. Quaternary Sciences, 2017, 37(5):1027-1036. http://www.dsjyj.com.cn/CN/abstract/abstract11383.shtml
[24] 马雪云, 魏志福, 王永莉, 等.末次冰盛期以来东北地区霍拉盆地湖泊沉积物记录的C3/C4植被演化[J].第四纪研究, 2018, 38(5):1193-1202. http://www.dsjyj.com.cn/CN/abstract/abstract11533.shtml
Ma Xueyun, Wei Zhifu, Wang Yongli, et al. C3/C4 vegetation evolution recorded by lake sediments in the Huola basin, Northeast China since the Last Glacial Maximun[J]. Quaternary Sciences, 2018, 38(5):1193-1202. http://www.dsjyj.com.cn/CN/abstract/abstract11533.shtml
[25] Schmittner A, Urban N M, Shakun J D, et al. Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum[J]. Science, 2011, 334(6061):1385-1388. doi: 10.1126/science.1203513
[26] Soden B J, Held I M, Shell K M, et al. Quantifying climate feedbacks using radiative kernels[J]. Journal of Climate, 2008, 21(14):3504-3520. doi: 10.1175/2007JCLI2110.1
[27] Sanderson B M, Shell K M. Model-specific radiative kernels for calculating cloud and noncloud climate feedbacks[J]. Journal of Climate, 2012, 25(21):7607-7624. doi: 10.1175/JCLI-D-11-00726.1
[28] Wetherald R T, Manabe S. Cloud feedback processes in a general circulation model[J]. Journal of the Atmospheric Sciences, 1988, 45(8):1397-1416. doi: 10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2
[29] Colman R. A comparison of climate feedbacks in general circulation models[J]. Climate Dynamics, 2003, 20(7-8):865-873. doi: 10.1007/s00382-003-0310-z
[30] Soden B J, Broccoli A J, Hemler R S, et al. On the use of cloud forcing to estimate cloud feedback[J]. Journal of Climate, 2004, 17(19):3661-3665. doi: 10.1175/1520-0442(2004)017<3661:OTUOCF>2.0.CO;2
[31] Cess R D, Potter G L. A methodology for understanding and intercomparing atmospheric climate feedback processes in general circulation models[J]. Journal of Geophysical Research:Atmospheres, 1988, 93(8):8305-8314. http://cn.bing.com/academic/profile?id=e03e94ac6541a35f82b2ebaab2886325&encoded=0&v=paper_preview&mkt=zh-cn
[32] Cess R D, Potter G L, Blanchet J P, et al. Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models[J]. Journal of Geophysical Research:Atmospheres, 1990, 95(D10):16601-16615. doi: 10.1029/JD095iD10p16601
[33] Cess R D, Zhang M H, Ingram W J, et al. Cloud feedback in atmospheric general circulation models:An update[J]. Journal of Geophysical Research:Atmospheres, 1996, 101(D8):12791-12794. doi: 10.1029/96JD00822
[34] Zhang M H, Hack J J, Kiehl J T, et al. Diagnostic study of climate feedback processes in atmospheric general circulation models[J]. Journal of Geophysical Research, 1994, 99(D3):5525-5537. doi: 10.1029/93JD03523
[35] Soden B J, Held I M. An assessment of climate feedbacks in coupled ocean-Atmosphere models[J]. Journal of Climate, 2006, 19(14):3354-3360. doi: 10.1175/JCLI3799.1
[36] Collins W D, Rasch P J, Boville B A, et al. The formulation and atmospheric simulation of the Community Atmosphere Model Version 3(CAM3)[J]. Journal of Climate, 2006, 19(11):2144-2161. doi: 10.1175/JCLI3760.1
[37] Clark P U, Dyke A S, Shakun J D, et al. The Last Glacial Maximum[J]. Science, 2009, 325(5941):710-714. doi: 10.1126/science.1172873
[38] Waelbroeck C, Paul A, Kucera M, et al. Constraints on the magnitude and patterns of ocean cooling at the Last Glacial Maximum[J]. Nature Geoscience, 2009, 2(2):127-132. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=aeffb009386839bdd6cccdfd84dae7cf
[39] 刘煜, 何金海, 李维亮, 等. MM5对末次盛冰期中国气候的模拟研究Ⅰ:CO2和地球轨道参数的影响[J].气象学报, 2007, 65(2):139-150. doi: 10.3321/j.issn:0577-6619.2007.02.001
Liu Yu, He Jinhai, Li Weiliang, et al. MM5 simulations of the China regional climate of the LGMⅠ:Influences of CO2 and Earth orbit changes[J]. Acta Meteorologica Sinica, 2007, 65(2):139-150. doi: 10.3321/j.issn:0577-6619.2007.02.001
[40] 刘煜, 何金海, 李维亮, 等. MM5对末次盛冰期中国气候的模拟研究Ⅱ:海陆分布、植被和大尺度环流背景变化的影响[J].气象学报, 2007, 65(2):151-159. doi: 10.3321/j.issn:0577-6619.2007.02.002
Liu Yu, He Jinhai, Li Weiliang, et al. MM5 simulations of the China regional climate of the LGMⅡ:Influences of changes of land area, vegetation, and large-scale circulation background[J]. Acta Meteorologica Sinica, 2007, 65(2):151-159. doi: 10.3321/j.issn:0577-6619.2007.02.002
[41] 吴乃琴, 刘秀平, 顾兆炎, 等.末次盛冰期黄土高原蜗牛化石记录的气候快速变化及其影响机制[J].第四纪研究, 2002, 22(3):283-291. doi: 10.3321/j.issn:1001-7410.2002.03.012
Wu Naiqin, Liu Xiuping, Gu Zhaoyan, et al. Rapid climate change and its influence mechanism recorded by snail fossils in the Loess Plateau during the Last Glacial Maximum[J]. Quaternary Sciences, 2002, 22(3):283-291. doi: 10.3321/j.issn:1001-7410.2002.03.012
[42] 何薇, 汪亘, 王永莉, 等.四川邛海湖泊沉积物记录的过去30 cal.ka B. P.以来的古气候环境特征[J].第四纪研究, 2018, 38(5):1179-1192. http://www.dsjyj.com.cn/CN/abstract/abstract11532.shtml
He Wei, Wang Gen, Wang Yongli, et al. Characteristics of climate and environment over the past 30 cal.ka B. P. recorded in lacustrine deposits of the Qionghai Lake, Sichuan Province[J]. Quaternary Sciences, 2018, 38(5):1179-1192. http://www.dsjyj.com.cn/CN/abstract/abstract11532.shtml
[43] 曹剑. NUIST地球系统模式研发及对末次盛冰期全球季风活动的模拟[D].南京: 南京信息工程大学博士论文, 2015: 1-131.
http://cdmd.cnki.com.cn/Article/CDMD-10300-1016063925.htm Cao Jian. The Development of NUIST Earth System Model and Simulation of Global Monsoon Activities during Last Glacial Maximum[D]. Nanjing: The Doctoral Dissertation of Nanjing University of Information Science & Technology, 2015: 1-131.
[44] Caldwell P M, Zelinka M D, Taylor K E, et al. Quantifying the sources of inter-model spread in equilibrium climate sensitivity[J]. Journal of Climate, 2016, 29(2):513-524. doi: 10.1175/JCLI-D-15-0352.1
[45] Cao J, Wang B, Liu J. Attribution of the Last Glacial Maximum climate formation[J]. Climate Dynamics, 2019:1-19.https://doi.org/10.1007/s00382-019-04711-6. doi: 10.1007/s00382-019-04711-6
[46] Solomon S, Qin D, Manning M, et al. Global climate projections[M]//IPCC. Climate Change 2007: The Physical Science Basis: Contribution of Working Group Ⅰ to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2007: 589-662.
[47] Held I M, Soden B J. Water vapor feedback and global warming[J]. Annual Review of Energy and the Environment, 2000, 25(1):441-475. doi: 10.1146/annurev.energy.25.1.441
[48] Mitchell J F B, Ingram W J. Carbon dioxide and climate:Mechanisms of changes in cloud[J]. Journal of Climate, 1992, 5(1):5-21. doi: 10.1175/1520-0442(1992)005<0005:CDACMO>2.0.CO;2
[49] 姜大膀, 刘叶一.温室效应会使地球温度上升多高?——关于平衡气候敏感度[J].科学通报, 2016, 61(7):691-694. http://www.cnki.com.cn/Article/CJFDTotal-KXTB201607005.htm
Jiang Dabang, Liu Yeyi. How hot will the greenhouse world be?-A brief review of equilibrium climate sensitivity[J]. Chinese Science Bulletin, 2016, 61(7):691-694. http://www.cnki.com.cn/Article/CJFDTotal-KXTB201607005.htm
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