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CAS FGOALS-g3 Model Datasets for the CMIP6 Scenario Model Intercomparison Project (ScenarioMIP)

Fund Project:

This study was supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0603903, 2017YFA0603901, and 2017YFA0603902), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB42010404) and the National Basic Research (973) Program of China (Grant Nos. 2015CB954102)


doi: 10.1007/s00376-020-2032-0

  • This paper describes the datasets from the Scenario Model Intercomparison Project (ScenarioMIP) simulation experiments run with the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Model, GridPoint version 3 (CAS FGOALS-g3). FGOALS-g3 is driven by eight shared socioeconomic pathways (SSPs) with different sets of future emission, concentration, and land-use scenarios. All Tier 1 and 2 experiments were carried out and were initialized using historical runs. A branch run method was used for the ensemble simulations. Model outputs were three-hourly, six-hourly, daily, and/or monthly mean values for the primary variables of the four component models. An evaluation and analysis of the simulations is also presented. The present results are expected to aid research into future climate change and socio-economic development.
    摘要: 本文介绍了中国科学院大气物理研究所研发的CAS FGOALS-g3模式在第六次国际耦合模式比较计划(CMIP6)的情景模式比较计划(ScenarioMIP)试验数据集。FGOALS-g3模式由8个共享社会经济路径(SSPs)驱动,它们分别具有不同的未来温室气体排放、浓度和土地利用情景。通过使用历史试验模拟结果进行初始化,模式完成了所有的第1层和第2层试验。模式输出数据包含四个分量模式的3小时、6小时、每日和/或每月平均的主要变量。文章对各组试验的模拟结果进行了初步的评估和分析。本文所涉及的试验结果将有助于对未来气候变化评估以及为社会经济发展制定相关政策提供数据支撑。
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  • Figure 1.  Global mean (a) surface air temperature anomaly (units: °C) and (b) precipitation anomaly (units: mm d−1) time series from observations (black and deep red lines), historical runs (red line) for 1980–2014, and eight SSP scenario experiments for 2015–2100. The base period is 1980–2009.

    Figure 2.  Annual mean global surface temperature difference (units: °C; 2070–2099 minus 1980–2009) between the eight ScenarioMIP experiments (2070–2099) and historical runs (1980–2009) for scenarios (a) SSP1-1.9, (b) SSP1-2.6, (c) SSP4-3.4, (d) SSP5-3.4-over, (e) SSP2-4.5, (f) SSP4-6.0, (g) SSP3-7.0, and (h) SSP5-8.5. Black dots denote the results significant at the 95% confidence level (similarly for Figs. 3 and 4).

    Figure 3.  As in Fig. 2, but for annual mean global precipitation (units: mm d−1).

    Figure 4.  As in Fig. 2, but for the spatial distribution of winter snow cover fraction over the NH.

    Figure 5.  AMOC (units: Sv) time series from historical runs (red line) for 1980–2014 and eight SSP scenario experiments for 2015–2100.

    Figure 6.  SIA anomaly (units: 106 km2) time series for the (a) NH and (b) SH from NSIDC observations (1980–2019), historical runs (1980–2014), and eight ScenarioMIP runs (2015–2100). The base period is 1980–2009.

    Table 1.  ScenarioMIP experiment descriptions.

    Experiment IDVariant LabelDescription
    Tier 1SSP1-2.6
    doi:10.22033/ESGF/CMIP6.3465
    r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP1-2.6 scenario.
    r2i1p1f1Initialized from the historical r2i1p1f1 branch run.
    r3i1p1f1Initialized from the historical r3i1p1f1 branch run.
    r4i1p1f1Initialized from the historical r4i1p1f1 branch run.
    SSP2-4.5
    doi:10.22033/ESGF/CMIP6.3469
    r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP2-4.5 scenario.
    r2i1p1f1Initialized from the historical r2i1p1f1 branch run.
    r3i1p1f1Initialized from the historical r3i1p1f1 branch run.
    r4i1p1f1Initialized from the historical r4i1p1f1 branch run.
    SSP3-7.0
    doi:10.22033/ESGF/CMIP6.3480
    r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP3-7.0 scenario.
    r2i1p1f1Initialized from the historical r2i1p1f1 branch run.
    r3i1p1f1Initialized from the historical r3i1p1f1 branch run.
    r4i1p1f1Initialized from the historical r4i1p1f1 branch run.
    r5i1p1f1Initialized from the historical r5i1p1f1 branch run.
    SSP5-8.5
    doi:10.22033/ESGF/CMIP6.3503
    r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP5-8.5 scenario.
    r2i1p1f1Initialized from the historical r2i1p1f1 branch run.
    r3i1p1f1Initialized from the historical r3i1p1f1 branch run.
    r4i1p1f1Initialized from the historical r4i1p1f1 branch run.
    Tier 2SSP1-1.9
    doi:10.22033/ESGF/CMIP6.3462
    r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP1-1.9 scenario.
    SSP4-3.4
    doi:10.22033/ESGF/CMIP6.3493
    r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP4-3.4 scenario.
    SSP5-3.4-over
    doi:10.22033/ESGF/CMIP6.3499
    r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP5-3.4-over scenario.
    SSP4-6.0
    doi:10.22033/ESGF/CMIP6.3496
    r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP4-6.0 scenario.
    DownLoad: CSV

    Table 2.  AGCM output variables from FGOALS-g3 for the ScenarioMIP experiments. TOA means top of atmosphere; * represents additional high-frequency output variables.

    Variable NameDescriptionOutput Frequency
    clPercentage Cloud CoverMonthly
    cliMass Fraction of Cloud IceMonthly
    cliviIce Water PathMonthly
    cltTotal Cloud Cover Percentage3-h*, Daily, Monthly
    clwMass Fraction of Cloud Liquid WaterMonthly
    clwviCondensed Water PathMonthly
    evspsblEvaporation Including Sublimation and TranspirationMonthly
    hflsSurface Upward Latent Heat Flux3-h*, Daily, Monthly
    hfssSurface Upward Sensible Heat Flux3-h*, Daily, Monthly
    hurRelative HumidityDaily, Monthly
    hursNear-Surface Relative Humidity6-h*, Daily, Monthly
    hursmaxDaily Maximum Near-Surface Relative HumidityDaily
    hursminDaily Minimum Near-Surface Relative HumidityDaily
    husSpecific Humidity6-h*, Daily, Monthly
    hussNear-Surface Specific Humidity3-h*, Daily, Monthly
    mcConvective Mass FluxMonthly
    o3Mole Fraction of O3Monthly
    pfullPressure at Model Full-Levels6-h*, Monthly
    phalfPressure on Model Half-LevelsMonthly
    prPrecipitation3-h*, 6-h*, Daily, Monthly
    prcConvective Precipitation3-h*, Daily, Monthly
    prhmaxMaximum Hourly Precipitation Rate6-h*
    prsnSnowfall Flux3-h*, Daily, Monthly
    prwWater Vapor PathMonthly
    psSurface Air Pressure3-h*, 6-h*, Monthly
    pslSea Level Pressure6-h*, Daily, Monthly
    rldsSurface Downwelling Longwave Radiation3-h*, Daily, Monthly
    rldscsSurface Downwelling Clear-Sky Longwave Radiation3-h*, Monthly
    rlsNet Longwave Surface RadiationDaily
    rlusSurface Upwelling Longwave Radiation3-h*, Daily, Monthly
    rlutTOA Outgoing Longwave RadiationDaily, Monthly
    rlutcsTOA Outgoing Clear-Sky Longwave RadiationMonthly
    rsdsSurface Downwelling Shortwave Radiation3-h*, Daily, Monthly
    rsdscsSurface Downwelling Clear-Sky Shortwave Radiation3-h*, Monthly
    rsdsdiffSurface Diffuse Downwelling Shortwave Radiation3-h*
    rsdtTOA Incident Shortwave RadiationMonthly
    rssNet Shortwave Surface RadiationDaily
    rsusSurface Upwelling Shortwave Radiation3-h*, Daily, Monthly
    rsuscsSurface Upwelling Clear-Sky Shortwave Radiation3-h*, Monthly
    rsutTOA Outgoing Shortwave RadiationMonthly
    rsutcsTOA Outgoing Clear-Sky Shortwave RadiationMonthly
    rtmtNet Downward Radiative Flux at Top of ModelMonthly
    sfcWindNear-Surface Wind Speed6-h*, Daily, Monthly
    sfcWindmaxDaily Maximum Near-Surface Wind SpeedDaily
    taAir Temperature6-h*, Daily, Monthly
    tasNear-Surface Air Temperature3-h*, 6-h*, Daily, Monthly
    tasmaxDaily Maximum Near-Surface Air TemperatureDaily, Monthly
    tasminDaily Minimum Near-Surface Air TemperatureDaily, Monthly
    tauuSurface Downward Eastward Wind StressMonthly
    tauvSurface Downward Northward Wind StressMonthly
    tsSurface TemperatureMonthly
    uaEastward Wind6-h*, Daily, Monthly
    vaNorthward Wind6-h*, Daily, Monthly
    wapOmega (= dp/ dt)6-h*, Daily, Monthly
    zgGeopotential HeightDaily, Monthly
    DownLoad: CSV

    Table 3.  OGCM output variables from FGOALS-g3 for the ScenarioMIP experiments.

    Variable NameDescriptionOutput Frequency
    friverWater Flux into Sea Water from RiversMonthly
    hfbasinNorthward Ocean Heat TransportMonthly
    hfdsDownward Heat Flux at Sea Water SurfaceMonthly
    hflsoSurface Downward Latent Heat FluxMonthly
    hfssoSurface Downward Sensible Heat FluxMonthly
    mlotstOcean Mixed Layer Thickness Defined by Sigma TMonthly
    msftbarotOcean Barotropic Mass Stream FunctionMonthly
    msftmzOcean Meridional Overturning Mass Stream FunctionMonthly
    msftmzmpaOcean Meridional Overturning Mass Stream Function Due to Parameterized Mesoscale AdvectionMonthly
    rlntdsSurface Net Downward Longwave RadiationMonthly
    rsntdsNet Downward Shortwave Radiation at Sea Water SurfaceMonthly
    soSea Water SalinityMonthly
    sogaGlobal Mean Sea Water SalinityMonthly
    sosSea Surface SalinityMonthly
    thetaoSea Water Potential TemperatureMonthly
    thetaogaGlobal Average Sea Water Potential TemperatureMonthly
    tosSea Surface TemperatureMonthly
    tossqSquare of Sea Surface TemperatureMonthly
    umoOcean Mass X TransportMonthly
    uoSea Water X VelocityMonthly
    vmoOcean Mass Y TransportMonthly
    voSea Water Y VelocityMonthly
    vsfVirtual Salt Flux into Sea WaterMonthly
    wfoWater Flux into Sea WaterMonthly
    wmoUpward Ocean Mass TransportMonthly
    woSea Water Vertical VelocityMonthly
    zosSea Surface Height Above GeoidMonthly
    zossqSquare of Sea Surface Height Above GeoidMonthly
    DownLoad: CSV

    Table 4.  Land model output variables from FGOALS-g3 for the ScenarioMIP experiments.

    Variable NameDescriptionOutput Frequency
    evspsblsoiWater Evaporation from SoilMonthly
    evspsblvegEvaporation from CanopyMonthly
    gwtGroundwater IntakeMonthly
    mrfsoSoil Frozen Water ContentMonthly
    mrroTotal RunoffMonthly
    mrrosSurface RunoffMonthly
    mrsoTotal Soil Moisture ContentMonthly
    mrsosMoisture in Upper Portion of Soil ColumnMonthly
    prvegPrecipitation onto CanopyMonthly
    tslTemperature of SoilMonthly
    frostdpFrost DeepMonthly
    sncSnow Area PercentageMonthly
    sndSnow DepthMonthly
    thawdpThaw DepthMonthly
    DownLoad: CSV

    Table 5.  Sea ice model output variables from FGOALS-g3 for the ScenarioMIP experiments.

    Variable NameDescriptionOutput Frequency
    sfdsiDownward Sea Ice Basal Salt FluxMonthly
    siconcSea-Ice Area Percentage (Ocean Grid)Monthly
    sidconcdynSea-Ice Area Percentage Tendency Due to DynamicsMonthly
    sidconcthSea-Ice Area Percentage Tendency Due to ThermodynamicsMonthly
    sidivvelDivergence of the Sea-Ice Velocity FieldMonthly
    sidmassdynSea-Ice Mass Change from DynamicsMonthly
    sidmassgrowthbotSea-Ice Mass Change Through Basal GrowthMonthly
    sidmassgrowthwatSea-Ice Mass Change Through Growth in Supercooled Open Water (Frazil)Monthly
    sidmasslatLateral Sea-Ice Melt RateMonthly
    sidmassmeltbotSea-Ice Mass Change Through Bottom MeltingMonthly
    sidmassmelttopSea-Ice Mass Change Through Surface MeltingMonthly
    sidmasssiSea-Ice Mass Change Through Snow-to-Ice ConversionMonthly
    sidmassthSea-Ice Mass Change from ThermodynamicsMonthly
    siflcondtopNet Conductive Heat Flux in Ice at the SurfaceMonthly
    sifllatstopNet Latent Heat Flux over Sea IceMonthly
    sifllwdtopDownwelling Longwave Flux over Sea IceMonthly
    sifllwutopUpwelling Longwave Flux over Sea IceMonthly
    siflsenstopNet Upward Sensible Heat Flux over Sea IceMonthly
    siflsensupbotNet Upward Sensible Heat Flux Under Sea IceMonthly
    siflswdbotDownwelling Shortwave Flux Under Sea IceMonthly
    siflswdtopDownwelling Shortwave Flux over Sea IceMonthly
    siflswutopUpwelling Shortwave Flux over Sea IceMonthly
    siforcecoriolxCoriolis Force Term in Force Balance (X-Component)Monthly
    siforcecoriolyCoriolis Force Term in Force Balance (Y-Component)Monthly
    siforceintstrxInternal Stress Term in Force Balance (X-Component)Monthly
    siforceintstryInternal Stress Term in Force Balance (Y-Component)Monthly
    siprRainfall Rate over Sea IceMonthly
    sishevelMaximum Shear of Sea-Ice Velocity FieldMonthly
    sisnconcSnow Area PercentageMonthly
    sistrxdtopX-Component of Atmospheric Stress on Sea IceMonthly
    sistrxubotX-Component of Ocean Stress on Sea IceMonthly
    sistrydtopY-Component of Atmospheric Stress on Sea IceMonthly
    sistryubotY-Component of Ocean Stress on Sea IceMonthly
    sitemptopSurface Temperature of Sea IceMonthly
    sitimefracFraction of Time Steps with Sea IceMonthly
    siuX-Component of Sea-Ice VelocityMonthly
    sivY-Component of Sea-Ice VelocityMonthly
    sndmassmeltSnow Mass Rate of Change Through MeltMonthly
    sndmasssiSnow Mass Rate of Change Through Snow-to-Ice ConversionMonthly
    sndmasssnfSnow Mass Change Through SnowfallMonthly
    DownLoad: CSV
  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979−present). Journal of Hydrometeorology, 4, 1147−1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
    Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, https://doi.org/10.1029/2005JD006290.
    Calvin, K., and Coauthors, 2017: The SSP4: a world of deepening inequality. Global Environmental Change, 42, 284−296, https://doi.org/10.1016/j.gloenvcha.2016.06.010.
    Craig, A. P., M. Vertenstein, and R. Jacob, 2012: A new flexible coupler for earth system modeling developed for CCSM4 and CESM1. The International Journal of High Performance Computing Applications, 26, 31−42, https://doi.org/10.1177/1094342011428141.
    Fricko, O., and Coauthors, 2017: The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century. Global Environmental Change, 42, 251−267, https://doi.org/10.1016/j.gloenvcha.2016.06.004.
    Fujimori, S., and Coauthors, 2017: SSP3: AIM implementation of shared socioeconomic pathways. Global Environmental Change, 42, 268−283, https://doi.org/10.1016/j.gloenvcha.2016.06.009.
    Gervais, M., J. Shaman, and Y. Kushnir, 2019: Impacts of the North Atlantic warming hole in future climate projections: mean atmospheric circulation and the North Atlantic jet. J. Climate, 32, 2673−2689, https://doi.org/10.1175/JCLI-D-18-0647.1.
    Houghton, J. T., and Coauthors, 1996: Climate Change 1995: The Science of Climate Change. Cambridge University Press, 572 pp, https://doi.org/10.1177/095968369700700115.
    Houghton, J. T., and Coauthors, 2001: Climate Change 2001: The Scientific Basis. Cambridge University Press, 944 pp.
    Huang, W. Y., B. Wang, L. J. Li, W. B. Dong, and Y. Y. Shi, 2014: Response of Atlantic meridional overturning circulation in FGOALS-g2 model to three representation concentration pathways. Climatic and Environmental Research, 19, 670−682, https://doi.org/10.3878/j.issn.1006-9585.2013.13130. (in Chinese with English abstract)
    IPCC, 1990: Climate Change: The IPCC Scientific Assessment. Cambridge University Press, 289−310.
    IPCC, 1992: Climate Change 1992: The Supplementary Report to the IPCC Scientific Assessment. Cambridge University Press, 60−95.
    IPCC, 2000: Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press 59−293.
    IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, R. K. Pachauri and L. A. Meyer, Eds., IPCC, Geneva, Switzerland, 151 pp.
    Kriegler, E., and Coauthors, 2017: Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century. Global Environmental Change, 42, 297−315, https://doi.org/10.1016/j.gloenvcha.2016.05.015.
    Li, L. J., and Coauthors, 2013: Evaluation of Grid-point Atmospheric Model of IAP LASG version 2 (GAMIL2). Adv. Atmos. Sci., 30, 855−867, https://doi.org/10.1007/s00376-013-2157-5.
    Li, L. J., and Coauthors, 2020a: The flexible global ocean−atmosphere−land system model grid-point version 3 (FGOALS-g3): description and evaluation. Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2019MS002012.
    Li, L. J., and Coauthors, 2020b: The Grid-point Atmospheric Model of the IAP LASG version 3 (GAMIL3): Model Description and Evaluation. J. Geophys. Res, https://doi.org/10.1029/2020JD032574.
    Lin, P. F., and Coauthors, 2016: A coupled experiment with LICOM2 as the ocean component of CESM1. Journal of Meteorological Research, 30, 76−92, https://doi.org/10.1007/s13351-015-5045-3.
    Lin, P. F., and Coauthors, 2020: LICOM model datasets for the CMIP6 Ocean Model Intercomparison Project. Adv. Atmos. Sci., 37, 239−249, https://doi.org/10.1007/s00376-019-9208-5.
    Liu, H. L., P. F. Lin, Y. Q. Yu, and X. H. Zhang, 2012: The baseline evaluation of LASG/IAP Climate system Ocean Model (LICOM) version 2. Acta Meteorologica Sinica, 26, 318−329, https://doi.org/10.1007/s13351-012-0305-y.
    Liu, L., C. Zhang, R. Z. Li, B. Wang, and G. W. Yang, 2018: C-Coupler2: a flexible and user-friendly community coupler for model coupling and nesting. Geoscientific Model Development, 11, 3557−3586, https://doi.org/10.5194/gmd-11-3557-2018.
    Martin, J., and Coauthors, 2020: The CMIP6 Data request (DREQ, version 01.00.31). Geoscientific Model Development, 13, 201−224, https://doi.org/10.5194/gmd-13-201-2020.
    Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. J. Geophys. Res., 117, D08101, https://doi.org/10.1029/2011JD017187.
    Mudersbach, C., and Coauthors, 2013: Trends in high sea levels of German North Sea gauges compared to regional mean sea level changes. Cont. Shelf Res., 65, 111−120, https://doi.org/10.1016/j.csr.2013.06.016.
    O’Neill, B. C., and Coauthors, 2016: The scenario model Intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9, 3461−3482, https://doi.org/10.5194/gmd-9-3461-2016.
    van Vuuren, D., and Coauthors, 2011: A proposal for a new scenario framework to support research and assessment in different climate research communities. Global Environmental Change, 22, 21−35, https://doi.org/10.1016/j.gloenvcha.2011.08.002.
    van Vuuren, D., and Coauthors, 2017: Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Global Environmental Change, 42, 237−250, https://doi.org/10.1016/j.gloenvcha.2016.05.008.
    Wang, A.-H., and J.-J. Fu, 2013: Changes in daily climate extremes of Observed temperature and precipitation in China. Atmos. Oceanic Sci. Lett., 6, 312−319, https://doi.org/10.3878/j.issn.1674-2834.12.0106.
    Ye, D. Z., C. B. Fu, W. J. Dong, G. Wen, and X. D. Yan, 2003: Some advance in global change science study. Chinese Journal of Atmospheric Sciences, 27, 435−450, https://doi.org/10.3878/j.issn.1006-9895.2003.04.02. (in Chinese with English abstract)
    Yun, X., B. Y. Huang, J. Y. Cheng, W. H. Xu, S. B. Qiao, and Q. X. Li, 2019: A new merge of global surface temperature datasets since the start of the 20th Century. Earth System Science Data, 11, 1629−1643, https://doi.org/10.5194/essd-11-1629-2019.
    Zhang, L. X., X. L. Chen, and X. G. Xin, 2019: Short commentary on CMIP6 scenario model Intercomparison project (ScenarioMIP). Climate Change Research, 15, 519−525, https://doi.org/10.12006/j.issn.1673-1719.2019.082. (in Chinese with English abstract)
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    [18] Yajuan SONG, Xinfang LI, Ying BAO, Zhenya SONG, Meng WEI, Qi SHU, Xiaodan YANG, 2020: FIO-ESM v2.0 Outputs for the CMIP6 Global Monsoons Model Intercomparison Project Experiments, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1045-1056.  doi: 10.1007/s00376-020-9288-2
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    [20] Xinyao RONG, Jian LI, Haoming CHEN, Jingzhi SU, Lijuan HUA, Zhengqiu ZHANG, Yufei XIN, 2021: The CMIP6 Historical Simulation Datasets Produced by the Climate System Model CAMS-CSM, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 285-295.  doi: 10.1007/s00376-020-0171-y

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Manuscript received: 07 February 2020
Manuscript revised: 28 May 2020
Manuscript accepted: 18 June 2020
通讯作者: 陈斌, bchen63@163.com
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CAS FGOALS-g3 Model Datasets for the CMIP6 Scenario Model Intercomparison Project (ScenarioMIP)

    Corresponding author: Hongbo LIU, hongboliu@mail.iap.ac.cn
  • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. Department of Atmospheric Sciences, Yunnan University, Kunming 650504, China

Abstract: This paper describes the datasets from the Scenario Model Intercomparison Project (ScenarioMIP) simulation experiments run with the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Model, GridPoint version 3 (CAS FGOALS-g3). FGOALS-g3 is driven by eight shared socioeconomic pathways (SSPs) with different sets of future emission, concentration, and land-use scenarios. All Tier 1 and 2 experiments were carried out and were initialized using historical runs. A branch run method was used for the ensemble simulations. Model outputs were three-hourly, six-hourly, daily, and/or monthly mean values for the primary variables of the four component models. An evaluation and analysis of the simulations is also presented. The present results are expected to aid research into future climate change and socio-economic development.

摘要: 本文介绍了中国科学院大气物理研究所研发的CAS FGOALS-g3模式在第六次国际耦合模式比较计划(CMIP6)的情景模式比较计划(ScenarioMIP)试验数据集。FGOALS-g3模式由8个共享社会经济路径(SSPs)驱动,它们分别具有不同的未来温室气体排放、浓度和土地利用情景。通过使用历史试验模拟结果进行初始化,模式完成了所有的第1层和第2层试验。模式输出数据包含四个分量模式的3小时、6小时、每日和/或每月平均的主要变量。文章对各组试验的模拟结果进行了初步的评估和分析。本文所涉及的试验结果将有助于对未来气候变化评估以及为社会经济发展制定相关政策提供数据支撑。

    • Climate change and sustainable development are at the frontier of international geoscience research in the 21st century. Their global impacts have made them two of the most important challenges facing human society today (Houghton et al., 1996, 2001; Ye et al., 2003). According to the Fifth Intergovernmental Panel on Climate Change (IPCC) Assessment Report, it is clear that human activity affects the climate system and recent anthropogenic emissions of greenhouse gases are the highest in history. Recent climatic changes have had a wide range of impacts on human and natural systems. Since 1950, many changes in extreme weather events and the climate have been observed, such as a decrease in extreme low temperatures, an increase in extreme high temperatures, extremely high sea levels, and heavy precipitation events in some regions (Alexander et al., 2006; Mudersbach et al., 2013; Wang and Fu, 2013). Continued emissions of greenhouse gases will lead to further warming and long-term changes in all components of the climate system, increasing the likelihood of serious, widespread, and irreversible impacts on human society and Earth’s ecosystems (AR5; IPCC, 2014).

      Measurements of economic risk are science-based tools used by governments to make important decisions related to climate change. They are also core components of previous IPCC scientific assessment reports. To better measure the relationship between different socioeconomic development models and climate change risks, the IPCC developed scenario A (SA90) for the first assessment report (FAR) in 1990 (IPCC, 1990), IS92 for the third assessment report (TAR) in 1992 (IPCC, 1992), the SRE scenario for the TAR’s special report on emissions and the fourth assessment report (AR4; IPCC, 2000), and the Representative Concentration Pathway (RCP) for the fifth assessment report (van Vuuren et al., 2011). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) uses six integrated assessment models (IAMs), various shared socioeconomic paths (SSPs), and the latest trends in anthropogenic emissions and land-use changes to generate new prediction scenarios. These scenarios form part of CMIP6 and are referred to as the Scenario Model Intercomparison Project (ScenarioMIP; O’Neill et al., 2016).

      ScenarioMIP is a matrix combination of different SSPs and radiative forcing. An SSP describes possible future social development without the effects of climate change or climate policy (Zhang et al., 2019). O’Neill et al. (2016) gave a complete description of ScenarioMIP for CMIP6. For the analysis presented here, we briefly describe each SSP. A total of five pathways (i.e., SSP1, SSP2, SSP3, SSP4, and SSP5) are included in CMIP6, which consider the effects of population changes, economic growth, and urbanization (Calvin et al., 2017; Kriegler et al., 2017; Fricko et al., 2017; Fujimori et al., 2017; van Vuuren et al., 2017). Among the pathways, SSP1 is the most optimistic scenario and maintains sustainable development. In contrast, SSP5 assumes an energy intensive, fossil-fuel-based economy, although it also assumes relatively optimistic development. SSP2 is a middle pathway, which assumes current development trends continue in the future. SSP3 and SSP4 are the most undesirable pathways and assume unsustainable development trends, involving less investment in education and health, fast-growing populations, and increasing inequality. ScenarioMIP uses the IAMs to generate quantitative predictions of greenhouse gas emissions, atmospheric component concentrations, and land-use changes that may occur under different SSP energy scenarios. ScenarioMIP divides the experiments into two groups: Tier 1 and Tier 2. Tier 1 includes new SSP-based scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) as continuations of the RCP2.6, RCP4.5, and RCP8.5 forcing levels, and an additional unmitigated forcing scenario (SSP3-7.0) with particularly high aerosol emissions and land-use change. Tier 2 includes additional scenarios of interest as well as additional ensemble members and long-term extensions (SSP1-1.9, SSP4-3.4, SSP4-6.0, SSP5-3.4-over) (O’Neill et al., 2016).

      The Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Model, GridPoint version 3 (CAS FGOALS-g3), developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), has completed the Tier 1 and 2 experiments of ScenarioMIP (Li et al., 2020a). Simulation results have been submitted to the Earth System Grid (ESG) data server (https://esgf-nodes.llnl.gov/projects/cmip6/). Section 2 provides detailed descriptions of the experimental design, model configuration, and output variables of the ScenarioMIP Tier 1 and 2 experiments performed using the CAS FGOALS-g3 model. Section 3 presents a preliminary model verification and future projections for each scenario. A brief usage note is provided in section 4.

    2.   Model and experiments
    • CAS FGOALS-g3 comprises the following five components:

      (1) Atmospheric general circulation model (AGCM). The Gridpoint Atmospheric Model of IAP LASG, version 3 (GAMIL3) (Li et al. 2020b), is an updated version of GAMIL2 (Li et al., 2013).

      (2) Oceanic general circulation model (OGCM). The LASG/IAP Climate Ocean Model (LICOM3) has been updated from LICOM2 (Liu et al., 2012; Lin et al., 2016). LICOM3 has performed the OMIP simulations and a detailed description of the results is given by Lin et al. (2020).

      (3) Land model. The Land Surface Model of the Chinese Academy of Sciences (CAS-LSM), the land component of FGOALS-g3 with the same horizontal resolution as the atmospheric model, is based on the Community Land Model, version 4.5 (CLM4.5).

      (4) Sea ice model. The sea ice model is the improved Los Alamos sea ice model, version 4.0, which uses the same grid as the oceanic model.

      (5) Coupler. In FGOALS-g3, there are two optional couplers: CPL7, developed by the National Center for Atmospheric Research (NCAR) (Craig et al., 2012), and the Community Coupler, version 2 (C-Coupler2), developed by Tsinghua University (Liu et al., 2018).

      A detailed description of CAS FGOALS-g3 is given in Li et al. (2020a).

    • Following the requirements for ScenarioMIP experiments (O’Neill et al., 2016), we carried out simulations for eight scenarios (Experiment ID in Table 1). In these experiments, the external forcings, including greenhouse gas concentrations, ozone concentrations, anthropogenic aerosol optical properties and an associated Twomey effect, land-use changes, and solar irradiance, are all based on the SSP scenario. All experiments were initialized from 1 January 2015 (branch run from the end of the historical runs, which ended on 31 December 2014) and share the same physical scheme settings, which are exactly same as those of the historical run. Experiment variants are labelled; e.g., r1i1p1f1, indicating the realization, initialization, physical, and forcing indices. We used the branch run method for the Tier 1 and 2 SSP scenario simulations. For example, the label r1i1p1f1 indicates that the initial conditions are the outputs from the historical r1i1p1f1 branch run. Table 1 gives detailed descriptions of each experiment.

      Experiment IDVariant LabelDescription
      Tier 1SSP1-2.6
      doi:10.22033/ESGF/CMIP6.3465
      r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP1-2.6 scenario.
      r2i1p1f1Initialized from the historical r2i1p1f1 branch run.
      r3i1p1f1Initialized from the historical r3i1p1f1 branch run.
      r4i1p1f1Initialized from the historical r4i1p1f1 branch run.
      SSP2-4.5
      doi:10.22033/ESGF/CMIP6.3469
      r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP2-4.5 scenario.
      r2i1p1f1Initialized from the historical r2i1p1f1 branch run.
      r3i1p1f1Initialized from the historical r3i1p1f1 branch run.
      r4i1p1f1Initialized from the historical r4i1p1f1 branch run.
      SSP3-7.0
      doi:10.22033/ESGF/CMIP6.3480
      r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP3-7.0 scenario.
      r2i1p1f1Initialized from the historical r2i1p1f1 branch run.
      r3i1p1f1Initialized from the historical r3i1p1f1 branch run.
      r4i1p1f1Initialized from the historical r4i1p1f1 branch run.
      r5i1p1f1Initialized from the historical r5i1p1f1 branch run.
      SSP5-8.5
      doi:10.22033/ESGF/CMIP6.3503
      r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP5-8.5 scenario.
      r2i1p1f1Initialized from the historical r2i1p1f1 branch run.
      r3i1p1f1Initialized from the historical r3i1p1f1 branch run.
      r4i1p1f1Initialized from the historical r4i1p1f1 branch run.
      Tier 2SSP1-1.9
      doi:10.22033/ESGF/CMIP6.3462
      r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP1-1.9 scenario.
      SSP4-3.4
      doi:10.22033/ESGF/CMIP6.3493
      r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP4-3.4 scenario.
      SSP5-3.4-over
      doi:10.22033/ESGF/CMIP6.3499
      r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP5-3.4-over scenario.
      SSP4-6.0
      doi:10.22033/ESGF/CMIP6.3496
      r1i1p1f1Initialized from the historical r1i1p1f1 branch run. All external forcings were from the SSP4-6.0 scenario.

      Table 1.  ScenarioMIP experiment descriptions.

      We used the model outputs for the period 2015–2100 in our analysis. Following the requirements of CMIP6 (Martin et al., 2020), monthly mean values for the primary variables of each component model were output. To investigate predicted extreme weather events in each scenario, the atmospheric component also provides additional 6-h and 3-h high-frequency outputs for some variables, including precipitation, specific humidity, and near-surface air temperature, for both future predictions and the historical runs. Details of the primary outputs and diagnostic variables for each component model are given in Tables 25.

      Variable NameDescriptionOutput Frequency
      clPercentage Cloud CoverMonthly
      cliMass Fraction of Cloud IceMonthly
      cliviIce Water PathMonthly
      cltTotal Cloud Cover Percentage3-h*, Daily, Monthly
      clwMass Fraction of Cloud Liquid WaterMonthly
      clwviCondensed Water PathMonthly
      evspsblEvaporation Including Sublimation and TranspirationMonthly
      hflsSurface Upward Latent Heat Flux3-h*, Daily, Monthly
      hfssSurface Upward Sensible Heat Flux3-h*, Daily, Monthly
      hurRelative HumidityDaily, Monthly
      hursNear-Surface Relative Humidity6-h*, Daily, Monthly
      hursmaxDaily Maximum Near-Surface Relative HumidityDaily
      hursminDaily Minimum Near-Surface Relative HumidityDaily
      husSpecific Humidity6-h*, Daily, Monthly
      hussNear-Surface Specific Humidity3-h*, Daily, Monthly
      mcConvective Mass FluxMonthly
      o3Mole Fraction of O3Monthly
      pfullPressure at Model Full-Levels6-h*, Monthly
      phalfPressure on Model Half-LevelsMonthly
      prPrecipitation3-h*, 6-h*, Daily, Monthly
      prcConvective Precipitation3-h*, Daily, Monthly
      prhmaxMaximum Hourly Precipitation Rate6-h*
      prsnSnowfall Flux3-h*, Daily, Monthly
      prwWater Vapor PathMonthly
      psSurface Air Pressure3-h*, 6-h*, Monthly
      pslSea Level Pressure6-h*, Daily, Monthly
      rldsSurface Downwelling Longwave Radiation3-h*, Daily, Monthly
      rldscsSurface Downwelling Clear-Sky Longwave Radiation3-h*, Monthly
      rlsNet Longwave Surface RadiationDaily
      rlusSurface Upwelling Longwave Radiation3-h*, Daily, Monthly
      rlutTOA Outgoing Longwave RadiationDaily, Monthly
      rlutcsTOA Outgoing Clear-Sky Longwave RadiationMonthly
      rsdsSurface Downwelling Shortwave Radiation3-h*, Daily, Monthly
      rsdscsSurface Downwelling Clear-Sky Shortwave Radiation3-h*, Monthly
      rsdsdiffSurface Diffuse Downwelling Shortwave Radiation3-h*
      rsdtTOA Incident Shortwave RadiationMonthly
      rssNet Shortwave Surface RadiationDaily
      rsusSurface Upwelling Shortwave Radiation3-h*, Daily, Monthly
      rsuscsSurface Upwelling Clear-Sky Shortwave Radiation3-h*, Monthly
      rsutTOA Outgoing Shortwave RadiationMonthly
      rsutcsTOA Outgoing Clear-Sky Shortwave RadiationMonthly
      rtmtNet Downward Radiative Flux at Top of ModelMonthly
      sfcWindNear-Surface Wind Speed6-h*, Daily, Monthly
      sfcWindmaxDaily Maximum Near-Surface Wind SpeedDaily
      taAir Temperature6-h*, Daily, Monthly
      tasNear-Surface Air Temperature3-h*, 6-h*, Daily, Monthly
      tasmaxDaily Maximum Near-Surface Air TemperatureDaily, Monthly
      tasminDaily Minimum Near-Surface Air TemperatureDaily, Monthly
      tauuSurface Downward Eastward Wind StressMonthly
      tauvSurface Downward Northward Wind StressMonthly
      tsSurface TemperatureMonthly
      uaEastward Wind6-h*, Daily, Monthly
      vaNorthward Wind6-h*, Daily, Monthly
      wapOmega (= dp/ dt)6-h*, Daily, Monthly
      zgGeopotential HeightDaily, Monthly

      Table 2.  AGCM output variables from FGOALS-g3 for the ScenarioMIP experiments. TOA means top of atmosphere; * represents additional high-frequency output variables.

      Variable NameDescriptionOutput Frequency
      friverWater Flux into Sea Water from RiversMonthly
      hfbasinNorthward Ocean Heat TransportMonthly
      hfdsDownward Heat Flux at Sea Water SurfaceMonthly
      hflsoSurface Downward Latent Heat FluxMonthly
      hfssoSurface Downward Sensible Heat FluxMonthly
      mlotstOcean Mixed Layer Thickness Defined by Sigma TMonthly
      msftbarotOcean Barotropic Mass Stream FunctionMonthly
      msftmzOcean Meridional Overturning Mass Stream FunctionMonthly
      msftmzmpaOcean Meridional Overturning Mass Stream Function Due to Parameterized Mesoscale AdvectionMonthly
      rlntdsSurface Net Downward Longwave RadiationMonthly
      rsntdsNet Downward Shortwave Radiation at Sea Water SurfaceMonthly
      soSea Water SalinityMonthly
      sogaGlobal Mean Sea Water SalinityMonthly
      sosSea Surface SalinityMonthly
      thetaoSea Water Potential TemperatureMonthly
      thetaogaGlobal Average Sea Water Potential TemperatureMonthly
      tosSea Surface TemperatureMonthly
      tossqSquare of Sea Surface TemperatureMonthly
      umoOcean Mass X TransportMonthly
      uoSea Water X VelocityMonthly
      vmoOcean Mass Y TransportMonthly
      voSea Water Y VelocityMonthly
      vsfVirtual Salt Flux into Sea WaterMonthly
      wfoWater Flux into Sea WaterMonthly
      wmoUpward Ocean Mass TransportMonthly
      woSea Water Vertical VelocityMonthly
      zosSea Surface Height Above GeoidMonthly
      zossqSquare of Sea Surface Height Above GeoidMonthly

      Table 3.  OGCM output variables from FGOALS-g3 for the ScenarioMIP experiments.

      Variable NameDescriptionOutput Frequency
      evspsblsoiWater Evaporation from SoilMonthly
      evspsblvegEvaporation from CanopyMonthly
      gwtGroundwater IntakeMonthly
      mrfsoSoil Frozen Water ContentMonthly
      mrroTotal RunoffMonthly
      mrrosSurface RunoffMonthly
      mrsoTotal Soil Moisture ContentMonthly
      mrsosMoisture in Upper Portion of Soil ColumnMonthly
      prvegPrecipitation onto CanopyMonthly
      tslTemperature of SoilMonthly
      frostdpFrost DeepMonthly
      sncSnow Area PercentageMonthly
      sndSnow DepthMonthly
      thawdpThaw DepthMonthly

      Table 4.  Land model output variables from FGOALS-g3 for the ScenarioMIP experiments.

      Variable NameDescriptionOutput Frequency
      sfdsiDownward Sea Ice Basal Salt FluxMonthly
      siconcSea-Ice Area Percentage (Ocean Grid)Monthly
      sidconcdynSea-Ice Area Percentage Tendency Due to DynamicsMonthly
      sidconcthSea-Ice Area Percentage Tendency Due to ThermodynamicsMonthly
      sidivvelDivergence of the Sea-Ice Velocity FieldMonthly
      sidmassdynSea-Ice Mass Change from DynamicsMonthly
      sidmassgrowthbotSea-Ice Mass Change Through Basal GrowthMonthly
      sidmassgrowthwatSea-Ice Mass Change Through Growth in Supercooled Open Water (Frazil)Monthly
      sidmasslatLateral Sea-Ice Melt RateMonthly
      sidmassmeltbotSea-Ice Mass Change Through Bottom MeltingMonthly
      sidmassmelttopSea-Ice Mass Change Through Surface MeltingMonthly
      sidmasssiSea-Ice Mass Change Through Snow-to-Ice ConversionMonthly
      sidmassthSea-Ice Mass Change from ThermodynamicsMonthly
      siflcondtopNet Conductive Heat Flux in Ice at the SurfaceMonthly
      sifllatstopNet Latent Heat Flux over Sea IceMonthly
      sifllwdtopDownwelling Longwave Flux over Sea IceMonthly
      sifllwutopUpwelling Longwave Flux over Sea IceMonthly
      siflsenstopNet Upward Sensible Heat Flux over Sea IceMonthly
      siflsensupbotNet Upward Sensible Heat Flux Under Sea IceMonthly
      siflswdbotDownwelling Shortwave Flux Under Sea IceMonthly
      siflswdtopDownwelling Shortwave Flux over Sea IceMonthly
      siflswutopUpwelling Shortwave Flux over Sea IceMonthly
      siforcecoriolxCoriolis Force Term in Force Balance (X-Component)Monthly
      siforcecoriolyCoriolis Force Term in Force Balance (Y-Component)Monthly
      siforceintstrxInternal Stress Term in Force Balance (X-Component)Monthly
      siforceintstryInternal Stress Term in Force Balance (Y-Component)Monthly
      siprRainfall Rate over Sea IceMonthly
      sishevelMaximum Shear of Sea-Ice Velocity FieldMonthly
      sisnconcSnow Area PercentageMonthly
      sistrxdtopX-Component of Atmospheric Stress on Sea IceMonthly
      sistrxubotX-Component of Ocean Stress on Sea IceMonthly
      sistrydtopY-Component of Atmospheric Stress on Sea IceMonthly
      sistryubotY-Component of Ocean Stress on Sea IceMonthly
      sitemptopSurface Temperature of Sea IceMonthly
      sitimefracFraction of Time Steps with Sea IceMonthly
      siuX-Component of Sea-Ice VelocityMonthly
      sivY-Component of Sea-Ice VelocityMonthly
      sndmassmeltSnow Mass Rate of Change Through MeltMonthly
      sndmasssiSnow Mass Rate of Change Through Snow-to-Ice ConversionMonthly
      sndmasssnfSnow Mass Change Through SnowfallMonthly

      Table 5.  Sea ice model output variables from FGOALS-g3 for the ScenarioMIP experiments.

      We used the following observational datasets for the model validation: Global Precipitation Climatology Project (GPCP, version 2.3) monthly data (Adler et al., 2003), HadCRUT4 monthly mean near-surface temperatures (Morice et al., 2012), China Merged Surface Temperature data (Yun et al., 2019), and the Arctic and Antarctic sea ice area records provided by the National Snow and Ice Data Center (NSIDC; http://nsidc.org/arcticseaicenews/sea-ice-tools/). The ensemble means from the historical runs (six members) and Tier 1 SSP experiments (see Table 1 for ensemble sizes) were used in our analysis. The base period for each anomaly analysis was 1980–2009.

    3.   Model validation and future projections
    • Reasonable reproductions of the past climate form the basis of the future projections generated by most climate models. In our historical runs, the trend of increasing surface temperature (i.e., global warming; 1980–2009 base period is adopted) since 1980 is well reproduced, and the fluctuation around 1990–1995 (related to volcano activities) is also well captured (Fig. 1a). This warming trend remains for all ScenarioMIP experiments until the 2030s when the projections diverge. The surface temperature increase remains roughly linear for high-emission scenarios with large radiative forcings, especially for SSP5-8.5, but also for SSP3-7.0, SSP4-6.0, and SSP2-4.5. By 2100, the positive anomaly is projected to reach 3.2°C (SSP5-8.5), 2.8°C (SSP3-7.0), 1.8°C (SSP4-6.0), or 1.4°C (SSP2-4.5). In contrast, there is no significant temperature increase projected for SSP5-3.4-over, SSP4-3.4, or SSP1-2.6, and a decreasing trend is even projected for SSP1-1.9. The positive anomaly in 2018 is 0.6°C but decreases to 0.4°C after 2050 for SSP1-1.9.

      Figure 1.  Global mean (a) surface air temperature anomaly (units: °C) and (b) precipitation anomaly (units: mm d−1) time series from observations (black and deep red lines), historical runs (red line) for 1980–2014, and eight SSP scenario experiments for 2015–2100. The base period is 1980–2009.

      During the period 1980–2016, the observed global precipitation (GPCP) follows an increasing trend but with large annual fluctuations (Fig. 1b). The FGOALS-g3 model captures this increasing trend, but with less pronounced annual fluctuations. This is reasonable because the result of FGOALS-g3 is the ensemble mean, which smooths some model internal variability. Under all scenarios, the precipitation increases until 2050 when precipitation variability increases and results diverge among the scenarios, as was the case for the surface temperature trends (Fig. 1a). For SSP1-1.9, the precipitation decreases after 2050, and eventually returns to the values of the 2000s and 2010s. For SSP4-3.4 and SSP5-3.4-over, the increasing tends are not significant. By 2100, the anomaly reaches 0.06 mm d−1 for scenarios SSP2-4.5 and SSP4-6.0, and exceeds 0.10 mm d−1 for scenarios SSP3-7.0 and SSP5-8.5.

      Consistent with the results shown in Fig. 1a, the annual mean global surface temperature follows an overall increasing trend over the period 2070–99 relative to the base period, as radiative forcing increases. However, large spatial discrepancies for the same emission and land-use scenarios exist in the simulations from each experiment (Fig. 2). In general, the surface temperature over the Arctic and high-latitude regions of the NH presents the strongest warming signals, with amplitudes of 1.0°C to >5.0°C for the SSP1-1.9 to SSP5-8.5 scenarios. The surface warming over the continents is generally higher than over the oceans, particularly for the Tibetan and Brazilian plateaus. Although ocean surface warming remains relatively weak, the equatorial East–Central Pacific shows an El Niño-like warm tongue for SSP4-3.4 and the last four scenario simulations (Figs. 2c and eh). The differences in regional patterns of warming are consistent with expectations and previous results. Note that although a global warming trend exists under each scenario, the North Atlantic Ocean is an exception. The northwest–southeast belt-shaped “warm hole” (i.e., cooling anomaly) in this region even strengthens with increased radiative forcing and reaches −5.0°C for SSP4-6.0 (Fig. 2f). This phenomenon has been observed in other simulation studies (Gervais et al., 2019), and may be related to Arctic sea ice melting and resulting changes to ocean circulation patterns, such as the Atlantic Meridional Overturning Circulation (AMOC). We will describe them in the following contents.

      Figure 2.  Annual mean global surface temperature difference (units: °C; 2070–2099 minus 1980–2009) between the eight ScenarioMIP experiments (2070–2099) and historical runs (1980–2009) for scenarios (a) SSP1-1.9, (b) SSP1-2.6, (c) SSP4-3.4, (d) SSP5-3.4-over, (e) SSP2-4.5, (f) SSP4-6.0, (g) SSP3-7.0, and (h) SSP5-8.5. Black dots denote the results significant at the 95% confidence level (similarly for Figs. 3 and 4).

      The projected annual mean precipitation shows large variations in the tropical and subtropical regions of both the NH and SH (Fig. 3). Between 2070 and 2099, there is a narrow quasi-east–west belt of increased rainfall over the equatorial Pacific with large amounts of precipitation to the east of the Maritime Continent. The positive rainfall anomalies increase from 0.5 mm d−1 to >3.0 mm d−1 as the scenario varies from lower to higher future forcing. In contrast, the tropical Indian Ocean, subtropical southwestern Pacific, tropical western Atlantic, and northern South America all show decreases in rainfall of −0.5 to −1.5 mm d−1. Rainfall anomalies present an obvious dipole feature in the tropical Indian ocean in all scenarios. Lower rainfall intensities in these regions are associated with greater radiative forcing.

      Figure 3.  As in Fig. 2, but for annual mean global precipitation (units: mm d−1).

      The spatial distributions of winter snow cover over the NH for the period 2070–2099 relative to the base period for eight scenarios are shown in Fig. 4. Clear negative anomalies are evident in the NH under the various emission scenarios. Western Europe and southern North America experience the most significant decrease. With increasing carbon dioxide concentrations and anthropogenic radiative forcing, these negative anomalies grow. For SSP3-7.0 and SSP5-8.5, most areas north of 30°N show negative anomalies, with values less than −0.2 over Eurasia and North America (Figs. 4g and h). Results suggest that to maintain snow cover over the NH, it will be important to control greenhouse gas emissions in the future.

      Figure 4.  As in Fig. 2, but for the spatial distribution of winter snow cover fraction over the NH.

      The AMOC plays an important role in regulating the climate by transporting heat northward in the Atlantic and thus maintaining the warmth of the NH. The annual mean maximum volume transport stream function at 26.5°N [units: Sverdrups (Sv)] in the Atlantic is used to measure the intensity of the AMOC. The historical and eight scenario simulations of the AMOC are shown in Fig. 5. From 1980 to 2015, the simulated AMOC from historical runs maintains an intensity of approximately 27.0 Sv, with a weak increase during the 1980s and a decrease in the early 1990s. Similar to surface temperature, the projected AMOC shows an overall weakening trend from 2015 to 2100 for SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5. By 2100, the AMOC shows a decrease in intensity of 26% (37%) for SSP2-4.5 and SSP4-6.0 (SSP3-7.0 and SSP5-8.5). Due to the internal variability of AMOC, the regulation of deep water formation in the Greenland–Iceland–Norwegian Seas and Arctic sea ice melting, the projected AMOC shows large fluctuations under small-to-medium radiative forcing scenarios. This is similar to the results of FGOALS-g2 (Huang et al., 2014). For example, in the 2090s, the AMOC exhibits a strong rebound with a 4% intensity increase relative to 2014 values for SSP1-1.9. For SSP1-2.6, SSP4-3.4, and SSP5-3.4-over, the AMOC also rebounds, but to a lesser extent than for SSP1-1.9.

      Figure 5.  AMOC (units: Sv) time series from historical runs (red line) for 1980–2014 and eight SSP scenario experiments for 2015–2100.

      Figure 6 presents the sea ice area (SIA) anomaly time series over both hemispheres for the NSIDC observations, the historical runs, and the eight ScenarioMIP experiments. Overall, the variation of the SIA over the SH is greater than in the NH in both observations and simulations. The observed SIA over the NH first rises at the end of the 1980s and then gradually decreases over the subsequent 30 years (Fig. 6a). In contrast, the SIA over the SH has continuously increased, with relatively large annual variations, since 1980, and reached a peak around 2014 before decreasing sharply to its lowest point in 2017. The SIA anomalies associated with the historical runs over the NH are more consistent with the observations (i.e., they follow a decreasing trend), but show large discrepancies over the SH, especially between 2008 and 2014 (Fig. 6b). The rate of projected SIA decay over the NH is the largest for SSP5-8.5, followed by SSP3-7.0. The projected SIA over the NH is constant for SSP1-1.9, SSP1-2.6, SSP4-3.4, and SSP5-3.4-over, and even increases in the mid-21st century for SSP1-1.9. Although the SIA over the SH exhibits similar variations to those over the NH for each SSP, the decay rate decreases (e.g., for SSP5-8.5 and SSP3-7.0) and the amplitude of the annual fluctuations increases for all ScenarioMIP experiments.

      Figure 6.  SIA anomaly (units: 106 km2) time series for the (a) NH and (b) SH from NSIDC observations (1980–2019), historical runs (1980–2014), and eight ScenarioMIP runs (2015–2100). The base period is 1980–2009.

    4.   Usage notes
    • The AGCM and land surface model use the same horizontal resolution; i.e., an equal area-weighted 180 × 80 horizontal grid in the zonal and meridional directions. The OGCM and sea ice model use the same tripolar 360 × 218 grid. The model outputs on their native grids have been saved and transformed to the Climate Model Output Rewriter (CMOR) file structure as required by CMIP6. According to the standard of CMOR, each variable is stored in a separate file. The dataset format is Network Common Data Form (NetCDF), version 4. The data can be downloaded from CMIP6 website.

      Acknowledgements. This study was supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0603903, 2017YFA0603901, and 2017YFA0603902), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB42010404) and the National Basic Research (973) Program of China (Grant Nos. 2015CB954102).

    Data availability statement and sharing policy.
    • The data that support the findings of this study are available at https://esgf-node.llnl.gov/projects/cmip6/. The citation for ScenarioMIP is “CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. Doi :10.22033/ESGF/CMIP6.2056”. Users are encouraged to download and share the data mentioned in this paper. All the datasets are free.

    Disclosure statement.
    • No potential conflicts of interest are reported by the authors.

      Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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