高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

对流层增暖热带放大现象:基于FGOALS-g3的模拟研究

刘士嘉 周天军 江洁 左萌 巫明娜

刘士嘉, 周天军, 江洁, 等. 2023. 对流层增暖热带放大现象:基于FGOALS-g3的模拟研究[J]. 大气科学, 47(1): 86−100 doi: 10.3878/j.issn.1006-9895.2110.21083
引用本文: 刘士嘉, 周天军, 江洁, 等. 2023. 对流层增暖热带放大现象:基于FGOALS-g3的模拟研究[J]. 大气科学, 47(1): 86−100 doi: 10.3878/j.issn.1006-9895.2110.21083
LIU Shijia, ZHOU Tianjun, JIANG Jie, et al. 2023. Tropical Amplification in Tropospheric Warming Simulated using the Flexible Global Ocean–Atmosphere–Land System Version 3 Climate System Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(1): 86−100 doi: 10.3878/j.issn.1006-9895.2110.21083
Citation: LIU Shijia, ZHOU Tianjun, JIANG Jie, et al. 2023. Tropical Amplification in Tropospheric Warming Simulated using the Flexible Global Ocean–Atmosphere–Land System Version 3 Climate System Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(1): 86−100 doi: 10.3878/j.issn.1006-9895.2110.21083

对流层增暖热带放大现象:基于FGOALS-g3的模拟研究

doi: 10.3878/j.issn.1006-9895.2110.21083
基金项目: 国家自然科学基金项目41775091,中国科学院“国际伙伴计划—国际大科学计划培育专项”项目“全球季风模拟研究国际计划”134111KYSB20160031
详细信息
    作者简介:

    刘士嘉,男,1998年出生,硕士研究生,主要从事气候模拟研究。E-mail: liusj@lasg.iap.ac.cn

    通讯作者:

    周天军,E-mail: zhoutj@lasg.iap.ac.cn

  • 中图分类号: P467

Tropical Amplification in Tropospheric Warming Simulated using the Flexible Global Ocean–Atmosphere–Land System Version 3 Climate System Model

Funds: National Natural Science Foundation of China (NSFC) (Grant 41775091),International Partnership Program of Chinese Academy of Sciences (Grant 134111KYSB20160031)
  • 摘要: 热带地区的湿绝热过程会放大地表的增暖幅度,在约200 hPa高度上产生增暖峰值,该现象被称为“热带对流层放大”。热带对流层放大是气候变化的显著特征之一,是检验气候模式性能的重要指标。本文基于RSS4.0卫星数据和ERA5.1再分析资料,系统分析了FGOALS-g3模式对气温变化特别是热带对流层放大的模拟能力,并通过新旧版本模式(FGOALS-g3与FGOALS-g2)的比较指出了新版本模式模拟技巧的提升;通过比较FGOALS-g3历史模拟试验与GAMIL3单独大气模式AMIP试验结果,研究了海气耦合过程对模拟结果的影响。结果表明,FGOALS-g3能够合理再现观测中的全球对流层显著增温趋势,但模拟的增温趋势偏强,这与气候系统内部变率以及两代气候系统模式所使用的历史气候外强迫差异有关。其对于观测中热带平均增温廓线以及热带对流层放大的空间分布均表现出良好的模拟性能,模拟的热带对流层放大现象的量值大小存在正偏差,与模拟的对流层低层温度变化偏强有关。FGOALS-g3较FGOALS-g2在性能上有一定提升,主要表现为增加了对于火山气溶胶强迫的响应,并在热带对流层放大的空间分布及平均气温趋势廓线的刻画方面更加合理。由于缺少海气耦合过程,GAMIL3 AMIP试验无法有效体现外强迫变化对于对流层增温趋势的作用,故在长期趋势的模拟上存在结构性偏差,但由于受观测海温驱动其在年际变率方面表现合理。
  • 图  1  1979~2015年全球平均温度距平序列(单位:°C):(a)平流层低层(TLS);(b)对流层中高层(TMT);(c)对流层低层(TLT)

    Figure  1.  Global mean temperature anomalies series (units: °C) from 1979 to 2015: (a) Lower stratosphere temperature; (b) mid-to-upper troposphere temperature; (c) lower troposphere temperature

    图  2  全球平均气温10年(左列)和20年(右列)滑动趋势 [单位:°C (10 a)−1]:(a,b)平流层低层;(c,d)对流层中高层;(e,f)对流层低层。横坐标为起始时间

    Figure  2.  10 years’ (left) and 20 years’ (right) sliding trend of the global mean temperature [units: °C (10 a)−1]: (a, b) Lower stratosphere; (c, d) mid-to-upper troposphere; (e, f) lower troposphere. The abscissa is the starting time

    图  3  1979~2015年对流层中高层(左列)和对流层低层(右列)气温变化趋势 [单位:°C (10 a)−1]:(a,b)RSS4.0卫星资料;(c,d)ERA5.1再分析资料;(e,f)GAMIL3;(g,h)FGOALS-g2;(i,j)FGOALS-g3。打点区域表示通过95%的信度水平检验

    Figure  3.  Temperature variation trends in the mid-to-upper troposphere (left) and lower troposphere (right) in 1979–2015 [units: °C (10 a)−1]: (a, b) RSS4.0; (c, d) ERA5.1; (e, f) GAMIL3; (g, h) FGOALS-g2; (i, j) FGOALS-g3. The dotted area indicates the significant values at the 95% confidence level

    图  4  1979~2015观测和模式模拟的全球对流层中高层温度(TMT)与对流层低层温度(TLT)逐点回归系数的空间分布(单位:1):(a)RSS4.0;(b)ERA5.1;(c)FGOALS-g3,(d)GAMIL3,(e)FGOALS-g2。打点区域表示通过95%的信度水平

    Figure  4.  Spatial distribution of the regression coefficients of the TMT and TLT series observed and simulated from 1979 to 2015 (units: 1): (a) RSS4.0; (b) ERA5.1; (c) FGOALS-g3; (d) GAMIL3; (e) FGOALS-g2. The dotted area indicates the significant values at the 95% confidence level

    图  5  热带地区(a,c)对流层中高层和(b,d)对流层低层温度距平序列(单位:°C):(a,b)RSS4.0、ERA5.1和GAMIL3试验;(c,d)FGOALS-g2和FGOALS-g3历史模拟试验

    Figure  5.  Temperature anomalies series in the (a, c) mid-to-upper troposphere and (b, d) lower troposphere over the tropics (units: °C): (a, b) RSS4.0, ERA5.1, and GAMIL3; (c, d) FGOALS-g2 and FGOALS-g3

    图  6  (a)1979~2015年热带地区(20°N~20°S)平均对流层中高层和对流层低层温度距平序列的最小二乘线性趋势 [单位:°C (10 a)−1],所有趋势均通过99%的信度水平。(b)同(a),但为标准差(单位:°C)

    Figure  6.  (a) Least-squares linear trend of mean temperature anomalies in the mid-to-upper and lower troposphere over the tropical region (20°N–20°S) from 1979 to 2015 [units: °C (10 a)−1]; all the linear trends are significant at the 99% confidence level. (b) Same as (a), except for the standard deviation (units: °C)

    图  7  5套数据的热带海洋平均放大系数(b)随选取时段的变化情况。横坐标为选取时段长度(10年至37年)

    Figure  7.  Variation of the mean amplification factor (b) of the tropical oceans with the selected period, and the abscissa is the selected period length (10 years to 37 years)

    图  8  (a)1979~2015年热带地区(20°N~20°S)平均各层温度趋势与1000 hPa温度趋势之比(单位:1),黑线为根据假绝热过程计算的理论值。(b)同图(a),但为温度趋势 [单位:°C (10 a)−1]。(c)同图(a),但为温度的年际变率之比)

    Figure  8.  (a) Ratio of the average temperature trend of each layer in the tropical region (20°N–20°S) to the temperature trend of 1000 hPa in 1979–2015 (units: 1). The black line is the theoretical value calculated according to the pseudo-adiabatic process. (b) The same as figure (a), except for the temperature trend [units: °C (10 a)−1]. (c) Same as fig. (a), except for the ratio of interannual variability

    表  1  GAMIL2和GAMIL3模式的主要差异

    Table  1.   Main difference points between GAMIL2 model and GAMIL3 model

    GAMIL2GAMIL3
    水平分辨率128×60180×80
    水汽方案两步保形平流方案(TSPAS)修正后的TSPAS,保证极区水汽守恒
    并行方式一维剖分二维剖分
    对流动量输送
    边界层方案K-廓线方案TKE方案
    层积云方案传统低对流层稳定度“估算逆温”(EIS)方案
    气溶胶模块气溶胶活化参数化和瞬变云微物理过程MACv2-SP,提供人为气溶胶光学性质和云滴数浓度的相对变化
    外强迫CMIP5推荐CMIP6推荐
    下载: 导出CSV

    表  2  5套数据1979~2015年全球平均平流层低层(TLS)、对流层中高层(TMT)和对流层低层(TLT)线性趋势 [单位:°C (10 a)−1],所有趋势均通过99%的信度水平检验

    Table  2.   Linear trend of the global averages of Temperature of Lower Stratosphere (TLS), Temperature of Mid-to-upper Troposphere (TMT), and Temperature of Lower Troposphere (TLT) from 1979 to 2015 [units: °C (10 a)−1], all the linear trends are significant at the 99% confidence level

    温度线性趋势/°C (10 a)−1
    RSS4.0ERA5.1GAMIL3FGOALS-g3FGOALS-g2
    TLS−0.19−0.30−0.21−0.19−0.15
    TMT0.170.120.180.250.22
    TLT0.200.140.180.260.21
    下载: 导出CSV
  • [1] Aquila V, Swartz W H, Waugh D W, et al. 2016. Isolating the roles of different forcing agents in global stratospheric temperature changes using model integrations with incrementally added single forcings [J]. J. Geophys. Res.: Atmos., 121(13): 8067−8082. doi: 10.1002/2015JD023841
    [2] Fu Q, Johanson C M. 2004. Stratospheric influences on MSU-derived tropospheric temperature trends: A direct error analysis [J]. J. Climate, 17(24): 4636−4640. doi: 10.1175/JCLI-3267.1
    [3] Fu Q, Johanson C M. 2005. Satellite-derived vertical dependence of tropical tropospheric temperature trends [J]. Geophys. Res. Lett., 32(10): L10703. doi: 10.1029/2004GL022266
    [4] Fu Q, Manabe S, Johanson C M. 2011. On the warming in the tropical upper troposphere: Models versus observations [J]. Geophys. Res. Lett., 38(15): L15704. doi: 10.1029/2011GL048101
    [5] Fu Q, Johanson C M, Warren S G, et al. 2004. Contribution of stratospheric cooling to satellite-inferred tropospheric temperature trends [J]. Nature, 429(6987): 55−58. doi: 10.1038/nature02524
    [6] Gillett N P, Santer B D, Weaver A J. 2004. Stratospheric cooling and the troposphere [J]. Nature, 432(7017): 1. doi: 10.1038/nature03209
    [7] Hegerl G C, Wallace J M. 2002. Influence of patterns of climate variability on the difference between satellite and surface temperature trends [J]. J. Climate, 15(17): 2412−2428. doi: 10.1175/1520-0442(2002)015<2412:IOPOCV>2.0.CO;2
    [8] Hersbach H, Bell B, Berrisford P, et al. 2020. The ERA5 global reanalysis [J]. Quart. J. Roy. Meteor. Soc., 146(730): 1999−2049. doi: 10.1002/qj.3803
    [9] Johanson C M, Fu Q. 2006. Robustness of tropospheric temperature trends from MSU channels 2 and 4 [J]. J. Climate, 19(17): 4234−4242. doi: 10.1175/JCLI3866.1
    [10] Kalnay E, Kanamitsu M, Kistler R, et al. 1996. The NCEP/NCAR 40-year reanalysis project [J]. Bull. Amer. Meteor. Soc., 77(3): 437−472. doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
    [11] Li L J, Dong L, Xie J B, et al. 2020a. The GAMIL3: Model description and evaluation [J]. J. Geophys. Res.: Atmos., 125(15): e2020JD032574. doi: 10.1029/2020JD032574
    [12] Li L J, Yu Y Q, Tang Y L, et al. 2020b. The Flexible Global Ocean–Atmosphere–Land System Model Grid-point version 3 (FGOALS-g3): Description and evaluation [J]. Journal of Advances in Modeling Earth Systems, 12(9): e2019MS002012. doi: 10.1029/2019MS002012
    [13] Lin P F, Liu H L, Xue W, et al. 2016. A coupled experiment with LICOM2 as the ocean component of CESM1 [J]. J. Meteor. Res., 30(1): 76−92. doi: 10.1007/s13351-015-5045-3
    [14] Lin P F, Yu Z P, Liu H L, et al. 2020. LICOM model datasets for the CMIP6 Ocean Model Intercomparison Project [J]. Advances in Atmospheric Sciences, 37(3): 239−249. doi: 10.1007/s00376-019-9208-5
    [15] Lindzen R S, Giannitsis C. 2002. Reconciling observations of global temperature change [J]. Geophys. Res. Lett., 29(12): 24-1–24-3. doi:10.1029/2001GL014074
    [16] Maycock A C, Randel W J, Steiner A K, et al. 2018. Revisiting the mystery of recent stratospheric temperature trends [J]. Geophys. Res. Lett., 45(18): 9919−9933. doi: 10.1029/2018GL078035
    [17] Mears C A, Wentz F J. 2016. Sensitivity of satellite-derived tropospheric temperature trends to the diurnal cycle adjustment [J]. J. Climate, 29(10): 3629−3646. doi: 10.1175/JCLI-D-15-0744.1
    [18] Mears C A, Wentz F J. 2017. A satellite-derived lower-tropospheric atmospheric temperature dataset using an optimized adjustment for diurnal effects [J]. J. Climate, 30(19): 7695−7718. doi: 10.1175/JCLI-D-16-0768.1
    [19] Nie Y, Li L J, Tang Y L, et al. 2019. Impacts of changes of external forcings from CMIP5 to CMIP6 on surface temperature in FGOALS-g2 [J]. SOLA, 15: 211−215. doi: 10.2151/sola.2019-038
    [20] Ohlson J A, Kim S. 2015. Linear valuation without OLS: The Theil-Sen estimation approach [J]. Review of Accounting Studies, 20(1): 395−435. doi: 10.1007/s11142-014-9300-0
    [21] Po-Chedley S, Fu Q. 2012. Discrepancies in tropical upper tropospheric warming between atmospheric circulation models and satellites [J]. Environmental Research Letters, 7(4): 044018. doi: 10.1088/1748-9326/7/4/044018
    [22] Po-Chedley S, Thorsen T J, Fu Q. 2015. Removing diurnal cycle contamination in satellite-derived tropospheric temperatures: Understanding tropical tropospheric trend discrepancies [J]. J. Climate, 28(6): 2274−2290. doi: 10.1175/JCLI-D-13-00767.1
    [23] Po-Chedley S, Santer B D, Fueglistaler S, et al. 2021. Natural variability contributes to model–satellite differences in tropical tropospheric warming [J]. Proceedings of the National Academy of Sciences of the United States of America, 118(13): e2020962118. doi: 10.1073/pnas.2020962118
    [24] Rieger L A, Cole J N S, Fyfe J C, et al. 2020. Quantifying CanESM5 and EAMv1 sensitivities to Mt. Pinatubo volcanic forcing for the CMIP6 historical experiment [J]. Geoscientific Model Development, 13: 4831−4843. doi: 10.5194/gmd-13-4831-2020
    [25] Robock A. 2000. Volcanic eruptions and climate [J]. Rev. Geophys., 38(2): 191−219. doi: 10.1029/1998RG000054
    [26] Santer B D, Wigley T M L, Mears C, et al. 2005. Amplification of surface temperature trends and variability in the tropical atmosphere [J]. Science, 309(5740): 1551−1556. doi: 10.1126/science.1114867
    [27] Santer B D, Thorne P W, Haimberger L, et al. 2008. Consistency of modelled and observed temperature trends in the tropical troposphere [J]. International Journal of Climatology, 28(13): 1703−1722. doi: 10.1002/joc.1756
    [28] Santer B D, Mears C, Doutriaux C, et al. 2011. Separating signal and noise in atmospheric temperature changes: The importance of timescale [J]. J. Geophys. Res.: Atmos., 116(D22): D22105. doi: 10.1029/2011JD016263
    [29] Santer B D, Bonfils C, Painter J F, et al. 2014. Volcanic contribution to decadal changes in tropospheric temperature [J]. Nature Geoscience, 7(3): 185−189. doi: 10.1038/ngeo2098
    [30] Santer B D, Fyfe J C, Pallotta G, et al. 2017a. Causes of differences in model and satellite tropospheric warming rates [J]. Nature Geoscience, 10(7): 478−485. doi: 10.1038/ngeo2973
    [31] Santer B D, Solomon S, Pallotta G, et al. 2017b. Comparing tropospheric warming in climate models and satellite data [J]. J. Climate, 30(1): 373−392. doi: 10.1175/JCLI-D-16-0333.1
    [32] Santer B D, Fyfe J C, Solomon S, et al. 2019. Quantifying stochastic uncertainty in detection time of human-caused climate signals [J]. Proceedings of the National Academy of Sciences of the United States of America, 116(40): 19821−19827. doi: 10.1073/pnas.1904586116
    [33] Simmons A, Soci C, Nicolas J P, et al. 2020. Global stratospheric temperature bias and other stratospheric aspects of ERA5 and ERA5.1 [R]. Report Number 859.
    [34] Solomon S, Daniel J S, Neely R R III, et al. 2011. The persistently variable ‘background’ stratospheric aerosol layer and global climate change [J]. Science, 333(6044): 866−870. doi: 10.1126/science.1206027
    [35] Steiner A K, Ladstädter F, Randel W J, et al. 2020. Observed temperature changes in the troposphere and stratosphere from 1979 to 2018 [J]. J. Climate, 33(19): 8165−8194. doi: 10.1175/JCLI-D-19-0998.1
    [36] Stone P H, Carlson J H. 1979. Atmospheric lapse rate regimes and their parameterization [J]. J. Atmos. Sci., 36(3): 415−423. doi: 10.1175/1520-0469(1979)036<0415:ALRRAT>2.0.CO;2
    [37] Tuel A. 2019. Explaining differences between recent model and satellite tropospheric warming rates with tropical SSTs [J]. Geophys. Res. Lett., 46(15): 9023−9030. doi: 10.1029/2019GL083994
    [38] Xie Z H, Wang L H, Wang Y, et al. 2020. Land surface model CAS-LSM: Model description and evaluation [J]. Journal of Advances in Modeling Earth Systems, 12(12): e2020MS002339. doi: 10.1029/2020MS002339
    [39] Zhou T J, Song F F. 2014. The twentieth century historical climate simulation of FGOALS [M]//Zhou T J, Yu Y Q, Liu Y M, et al. Flexible Global Ocean-Atmosphere-Land System Model. Berlin, Heidelberg: Springer, 199–206. doi:10.1007/978-3-642-41801-3_24
    [40] 周天军, 邹立维, 陈晓龙. 2019. 第六次国际耦合模式比较计划(CMIP6)评述 [J]. 气候变化研究进展, 15(5): 445−456. doi: 10.12006/j.issn.1673-1719.2019.193

    Zhou T J, Zou L W, Chen X L. 2019. Commentary on the Coupled Model Intercomparison Project Phase 6 (CMIP6) [J]. Climate Change Research (in Chinese), 15(5): 445−456. doi: 10.12006/j.issn.1673-1719.2019.193
    [41] 周天军, 邹立维, 吴波, 等. 2014. 中国地球气候系统模式研究进展: CMIP计划实施近20年回顾 [J]. 气象学报, 72(5): 892−907. doi: 10.11676/qxxb2014.083

    Zhou T J, Zou L W, Wu B, et al. 2014. Development of earth/climate system models in China: A review from the Coupled Model Intercomparison Project perspective [J]. Acta Meteorologica Sinica (in Chinese), 72(5): 892−907. doi: 10.11676/qxxb2014.083
    [42] 周天军, 陈梓明, 邹立维, 等. 2020. 中国地球气候系统模式的发展及其模拟和预估 [J]. 气象学报, 78(3): 332−350. doi: 10.11676/qxxb2020.029

    Zhou T J, Chen Z M, Zou L W, et al. 2020. Development of climate and earth system models in China: Past achievements and new CMIP6 results [J]. Acta Meteorologica Sinica (in Chinese), 78(3): 332−350. doi: 10.11676/qxxb2020.029
    [43] Zou C Z, Goldberg M D, Hao X J. 2018. New generation of U. S. satellite microwave sounder achieves high radiometric stability performance for reliable climate change detection [J]. Science Advances, 4(10): eaau0049. doi: 10.1126/sciadv.aau0049
  • 加载中
图(8) / 表(2)
计量
  • 文章访问数:  210
  • HTML全文浏览量:  25
  • PDF下载量:  47
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-05-18
  • 录用日期:  2021-11-03
  • 网络出版日期:  2021-12-09
  • 刊出日期:  2023-01-15

目录

    /

    返回文章
    返回