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LICOM Model Datasets for the CMIP6 Ocean Model Intercomparison Project

Funds:

National Key R&D Program for Developing Basic Sciences (Grant Nos. 2016YFC1401401 and 2016YFC1401601), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDC01000000), and the National Natural Science Foundation of China (Grants Nos. 41576026, 41576025, 41776030, 41931183 and 41976026)

  • The datasets of two Ocean Model Intercomparison Project (OMIP) simulation experiments from the LASG/IAP Climate Ocean Model, version 3 (LICOM3), forced by two different sets of atmospheric surface data, are described in this paper. The experiment forced by CORE-II (Co-ordinated Ocean–Ice Reference Experiments, Phase II) data (1948–2009) is called OMIP1, and that forced by JRA55-do (surface dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis) data (1958–2018) is called OMIP2. First, the improvement of LICOM from CMIP5 to CMIP6 and the configurations of the two experiments are described. Second, the basic performances of the two experiments are validated using the climatological-mean and interannual time scales from observation. We find that the mean states, interannual variabilities, and long-term linear trends can be reproduced well by the two experiments. The differences between the two datasets are also discussed. Finally, the usage of these data is described. These datasets are helpful toward understanding the origin system bias of the fully coupled model.
    摘要: 本文描述了大气所大气科学和地球流体力学数值模拟国家重点实验室(LASG/IAP) 气候系统海洋模式第三版(LICOM3)参与海洋模式比较计划的两个模拟试验数据集。两个模拟试验采用不同大气表面强迫数据,采用规范的海洋-海冰参考试验第二阶段 (Co-ordinated Ocean–Ice Reference Experiments, Phase II, CORE-II) 作为外强迫的试验称为海洋模式比较计划1阶段(OMIP1,1948-2009),采用来自日本55年大气再分析表面数据驱动海洋-海冰模式 (JRA55-do)强迫试验称为海洋模式比较计划第二阶段(OMIP2,1958-2018)。首先,文章描述了LICOM从CMIP5到CMIP6的改进和试验基本设置。其次,利用来自观测气侯态和年际变化数据,对两个试验的模拟性能进行检验。结果表明模式很好再现了两个试验中的平均态、年际变化和长期趋势。同时,讨论了两个模拟试验数据间的差异。最后,介绍了数据集的使用情况。这两个数据集有助于了解全耦合模式的系统偏差来源。
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  • Figure FIG. 170.. 

    Figure 1.  Annual global mean (a) SST (units: °C), (b) volume ocean temperature (VOT; units: °C), (c) SSS (units: psu), and (d) volume ocean salinity (VOS; units: psu) for OMIP1 (cyan) and OMIP2 (purple) during all the six cycles. The black lines in the figure indicate the reference value calculated from WOA13 observation.

    Figure 2.  (a) Annual global mean SST (units: °C), (b) annual mean SIC in the NH and SH, (c) steric sea level, and (d) AMOC, for OMIP1 (cyan), OMIP2 (purple) and ERSST.v5/NSIDC/Argo/RAPID (black) during the sixth cycles. The STDs (removing linear trend), linear trends and correlation coefficients (SST during 1958–2009, SIC for 1980–2009, 2005–2018 for Argo, 2004–16 for RAPID) are provided in the figures.

    Figure 3.  Simulations (contours) and biases (shaded) of SST (units: °C) for (a) OMIP1 and (b) OMIP2, and (c) the difference betweeen OMIP2 and OMIP1 (OMIP2 minus OMIP1). (d–f) As in (a–c), respectively, but for SSS (units: psu). The annual SST and SSS distributions employ the data from 1980–2009 of the sixth cycle. The RMSEs, mean values, and minimim and maximal values are provided in the figures.

    Figure 4.  The (a) observed and (b, c) OMIP1- and OMIP2-simulated SSH (units: m) during 1993–2009 of the sixth cycle. The spatial correlations between the observation and simulation are noted in the top right.

    Figure 5.  The simulated AMOC (units: Sv; 1S v = 1 × 106 m3 s−1) for (a) OMIP1 and (b) OMIP2 during the last 30 years (1980–2009) of the sixth cycle. (c) AMOC values at the latitude of 26.5°N for OMIP1, OMIP2 and RAPID (2005–09). The maximal values and STD (+/-) between 2005–09 are noted. The y-axises represent ocean depth (units: km).

    Figure 6.  The STDs (units °C) of observed SST anomalies from (a) ERSST.v5 and (b, c) OMIP1- and OMIP2-simulated SST anomalies during 1960–2009. Before calculating the STD, both the linear trend and annual cycle were removed from the SST. The spatial correlation coefficients (R) with the observation are denoted in the top middle. The STDs in the Niño3 region (5°S–5°N, 150°–90°W) are denoted in the top right.

    Table 1.  Comparison of model configurations between two versions of LICOM (LICOM2.0 and LICOM3).

    ConfigurationLICOM2.0LICOM3
    GridGridLongitude/LatitudeTripolar
    Resolution~1°, 30 levels~1°, 30 or 80 levels
    Dynamic coreTracer advectionCentral differential schemePreserved shape scheme (Yu, 1994)
    Momentum time integrationExplicitImplicit
    PhysicsDiapycnal mixingMixing in the mixed layer (Canuto et al., 2001, 2002)Mixing in the mixed layer (Canuto et al., 2001) and internal tide mixing (St. Laurent et al., 2002)
    Isopycnal mixingIsopycnal mixing (Redi, 1982) and advection (Gent and McWilliams, 1990)Isopycnal mixing (Redi, 1982) and advection (Gent and McWilliams, 1990) with N2 thickness diffusivity (Ferreira et al., 2005)
    Computing technicsCoupler interfaceNCAR Flux Coupler 6NCAR Flux Coupler 7
    Parallel1D MPI and OMP2D MPI and OMP
    DataInitial conditionWOA01 (Conkright et al., 2002)PHC3.0 (Steele et al., 2001)
    BathymetryDBDB5 (https://www.bodc.ac.uk/resources/inventories/edmed/report/356/)ETOPO2 (https://ngdc.noaa.gov/mgg/global/etopo2.html)
    DownLoad: CSV

    Table 2.  Depths of model levels for the T- and W-grid. Positive is upward.

    LevelDepth for TDepths for W
    1−50
    2−15−10
    3−25−20
    4−35−30
    5−45−40
    6−55−50
    7−65−60
    8−75−70
    9−85−80
    10−95−90
    11−105−100
    12−115−110
    13−125−120
    14−135−130
    15−145−140
    16−156.9303−150
    17−178.4277−163.8606
    18−222.5018−192.9948
    19−303.1057−252.0088
    20−432.5961−354.2027
    21−621.1931−510.9896
    22−876.5334−731.3966
    23−1203.337−1021.67
    24−1603.2−1385.003
    25−2074.526−1821.396
    26−2612.596−2327.656
    27−3209.772−2897.536
    28−3855.835−3522.009
    29−4538.428−4189.662
    30−5243.597−4887.194
    31−5600
    DownLoad: CSV

    Table 3.  Descriptions of the experiments of LICOM3.

    Experiment_idModelInitial condition (TS/currents)Forcing dataPeriodFrequency
    OMIP1LICOM3/CICE4.0PHC3.0/ZeroCORE-II1948–2009 (6 cycles)6 h
    OMIP2LICOM3/CICE4.0PHC3.0/ZeroJRA55-do1958–2018 (6 cycles)6 h
    DownLoad: CSV

    Table 4.  Descriptions of dataset variables of Priority 1.

    NameDescriptionFrequency
    hfbasinNorthward ocean heat transportMonthly
    hfbasinpmadvNorthward ocean heat transport due to parameterized mesoscale advectionMonthly
    hfbasinpmdiffNorthward ocean heat transport due to parameterized mesoscale diffusionMonthly
    hfdsDownward heat flux at sea water surfaceMonthly
    masscelloOcean grid-cell mass per areaMonthly
    mlotstOcean mixed-layer thickness defined by sigma TMonthly
    msftbarotOcean barotropic mass streamfunctionMonthly
    msftmzOcean meridional overturning mass streamfunctionMonthly
    msftmzmpaOcean meridional overturning mass streamfunction due to parameterized mesoscale advectionMonthly
    obvfsqSquare of Brunt–Vaisala frequency in sea waterMonthly
    pboSea water pressure at sea floorMonthly
    psoSea water pressure at sea water surfaceMonthly
    soSea water salinityMonthly
    sobSea water salinity at sea floorMonthly
    sogaGlobal mean sea water salinityMonthly
    sosSea surface salinityMonthly
    sosgaGlobal average sea surface salinityMonthly
    thetaoSea water potential temperatureMonthly
    thetaogaGlobal average sea water potential temperatureMonthly
    tobSea water potential temperature at sea floorMonthly
    tosSea surface temperatureMonthly
    tosgaGlobal average sea surface temperatureMonthly
    uoSea water X velocityMonthly
    umoOcean mass X transportMonthly
    voSea water Y velocityMonthly
    vmoOcean mass Y transportMonthly
    wfoWater flux into sea waterMonthly
    woSea water vertical velocityMonthly
    wmoUpward ocean mass transportMonthly
    zosSea surface height above geoidMonthly
    omldamaxMean daily maximum ocean mixed-layer thickness defined by mixing schemeDaily
    areacelloGrid-cell area for ocean variablesFixed
    depthoSea floor depth below geoidFixed
    thkcelloOcean model cell thicknessFixed
    ugridoUGRID grid specificationFixed
    volcelloOcean grid-cell volumeFixed
    DownLoad: CSV
  • Bao Q., and Coauthors, 2013: The flexible global ocean-atmosphere-land system model, spectral version 2: FGOALS-s2. Adv. Atmos. Sci., 30, 561−576, https://doi.org/10.1007/s00376-012-2113-9.
    Canuto, V. M., A. Howard, Y. Cheng, and M. S. Dubovikov, 2001: Ocean turbulence. Part I: One-point closure model—Momentum and heat vertical diffusivities. J. Phys. Oceanogr., 31, 1413−1426, https://doi.org/10.1175/1520-0485(2001)031<1413:OTPIOP>2.0.CO;2.
    Canuto, V. M., A. Howard, Y. Cheng, and M. S. Dubovikov, 2002: Ocean turbulence. Part II: Vertical diffusivities of momentum, heat, salt, mass, and passive scalars. J. Phys. Oceanogr., 32, 240−264, https://doi.org/10.1175/1520-0485(2002)032<0240:otpivd>2.0.co;2.
    Conkright, M. E., R. A. Locarnini, H. E. Garcia, T. D. O’Brien, T. P. Boyer, C. Stephens, and J. I. Antonov, 2002: World ocean atlas 2001: Objective analyses, data statistics, and figures, CD-ROM documentation. National Oceanographic Data Center, Silver Spring, MD, 17 pp.
    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.
    Cunningham, S. A., and Coauthors, 2007: Temporal variability of the Atlantic meridional overturning circulation at 26.5°N. Science, 317, 935−938, https://doi.org/10.1126/science.1141304.
    Danabasoglu, G., and Coauthors, 2014: North Atlantic simulations in coordinated ocean-ice reference experiments phase II (CORE-II). Part I: Mean states. Ocean Modelling, 73, 76−107, https://doi.org/10.1016/j.ocemod.2013.10.005.
    Danabasoglu, G., and Coauthors, 2016: North Atlantic simulations in coordinated ocean-ice reference experiments phase II (CORE-II). Part II: Inter-annual to decadal variability. Ocean Modelling, 97, 65−90, https://doi.org/10.1016/j.ocemod.2015.11.007.
    Ferreira, D., J. Marshall, and P. Heimbach, 2005: Estimating eddy stresses by fitting dynamics to observations using a residual-mean ocean circulation model and its adjoint. J. Phys. Oceanogr., 35, 1891−1910, https://doi.org/10.1175/JPO2785.1.
    Fetterer, F., K. Knowles, W. Meier, M. Savoie, and A. Windnagel, 2017: Updated daily. Sea Ice Index, Version 3. [Indicate subset used]. NSIDC, Boulder, Colorado, USA, https://doi.org/10.7265/N5K072F8.
    Gent, P. R., and J. C. McWilliams, 1990: Isopycnal mixing in ocean circulation models. J. Phys. Oceanogr., 20, 150−155, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2.
    Griffies, S. M., and Coauthors, 2009: Coordinated ocean-ice reference experiments (COREs). Ocean Modelling, 26, 1−46, https://doi.org/10.1016/j.ocemod.2008.08.007.
    Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30(20), 8179−8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
    Large, W. G., and S. G. Yeager, 2004: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. NCAR/TN-460+STR, CGD Division of the National Center for Atmospheric Research, https://doi.org/10.5065/D6KK98Q6.
    Lee, S. K., R. Lumpkin, M. O. Baringer, C. S. Meinen, M. Goes, S. F. Dong, H. Lopez, and S. G. Yeager, 2019: Global meridional overturning circulation inferred from a data-constrained ocean & sea-ice model. Geophys. Res. Lett., 46, 1521−1530, https://doi.org/10.1029/2018GL080940.
    Li, L. J., and Coauthors, 2013: The flexible global ocean-atmosphere-land system model, grid-point version 2: FGOALS-g2. Adv. Atmos. Sci., 30, 543−560, https://doi.org/10.1007/s00376-012-2140-6.
    Li, Y. W., H. L. Liu, and P. F. Lin, 2019: The role of thickness diffusivity coefficients in a climate ocean model. PhD dissertation, 142 pp.
    Lin, P. F., Y. Q. Yu, and H. L. Liu, 2013a: Long-term stability and oceanic mean state simulated by the coupled model FGOALS-s2. Adv. Atmos. Sci., 30, 175−192, https://doi.org/10.1007/s00376-012-2042-7.
    Lin, P. F., Y. Q. Yu, and H. L. Liu, 2013b: Oceanic climatology in the coupled model FGOALS-g2: Improvements and biases. Adv. Atmos. Sci., 30(3), 819−840, https://doi.org/10.1007/s00376-012-2137-1.
    Lin, P. F., and Coauthors, 2016: A coupled experiment with LICOM2 as the ocean component of CESM1. Journal of Meteorological Research, 30(1), 76−92, https://doi.org/10.1007/s13351-015-5045-3.
    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 2.0. Acta Meteorologica Sinica, 26(3), 318−329, https://doi.org/10.1007/s13351-012-0305-y.
    Lumpkin, R., and K. Speer, 2007: Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550−2562, https://doi.org/10.1175/JPO3130.1.
    Madec, G., and M. Imbard, 1996: A global ocean mesh to overcome the north pole singularity. Climate Dyn., 12(6), 381−388, https://doi.org/10.1007/BF00211684.
    Murray, R. J., 1996: Explicit generation of orthogonal grids for ocean models. J. Comput. Phys., 126(2), 251−273, https://doi.org/10.1006/jcph.1996.0136.
    Ohlmann, J. C., 2003: Ocean radiant heating in climate models. J. Climate, 16, 1337−1351, https://doi.org/10.1175/1520-0442-16.9.1337.
    Redi, M. H., 1982: Oceanic isopycnal mixing by coordinate rotation. J. Phys. Oceanogr., 12, 1154−1158, https://doi.org/10.1175/1520-0485(1982)012<1154:OIMBCR>2.0.CO;2.
    St. Laurent, L. C., H. L. Simmons, and S. R. Jayne, 2002: Estimating tidally driven mixing in the deep ocean. Geophys. Res. Lett., 29, 2106, https://doi.org/10.1029/2002GL015633.
    Steele, M., R. Morley, and W. Ermold, 2001: PHC: A global ocean hydrography with a high-quality Arctic Ocean. J. Climate, 14, 2079−2087, https://doi.org/10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2.
    Tsujino, H., and Coauthors, 2018: JRA-55 based surface dataset for driving ocean—sea-ice models (JRA55-do). Ocean Modelling, 130, 79−139, https://doi.org/10.1016/j.ocemod.2018.07.002.
    Xiao, C., 2006: Adoption of a two-step shape-preserving advection scheme in an OGCM and its coupled experiment. M.S. thesis, Institute of Atmospheric Physics, Chinese Academy of Sciences, 89 pp. (in Chinese).
    Yu, R. C., 1994: A two-step shape-preserving advection scheme. Adv. Atmos. Sci., 11(4), 479−490, https://doi.org/10.1007/BF02658169.
    Yu, Y. Q., S. L. Tang, H. L. Liu, P. F. Lin, and X. L. Li., 2018: Development and evaluation of the dynamic framework of an ocean general circulation model with arbitrary orthogonal curvilinear coordinate. Chinese Journal of Atmospheric Sciences, 42(4), 877−889, https://doi.org/10.3878/j.issn.1006-9895.1805.17284. (in Chinese)
    Yu, Z. P., H. L. Liu, and P. F. Lin, 2017: A numerical study of the influence of tidal mixing on Atlantic meridional overturning circulation (AMOC) Simulation. Chinese Journal of Atmospheric Sciences, 41(5), 1087−1100, https://doi.org/10.3878/j.issn.1006-9895.1702.16263. (in Chinese)
    Zhang, X. H., and X. Z. Liang, 1989: A numerical world ocean general circulation model. Adv. Atmos. Sci., 6(1), 43−61, https://doi.org/10.1007/BF02656917.
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    [3] YANG Yang, REN Rongcai, Ming CAI, RAO Jian, 2015: Attributing Analysis on the Model Bias in Surface Temperature in the Climate System Model FGOALS-s2 through a Process-Based Decomposition Method, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 457-469.  doi: 10.1007/s00376-014-4061-z
    [4] Banglin ZHANG, Vijay TALLAPRAGADA, Fuzhong WENG, Jason SIPPEL, Zaizhong MA, 2016: Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System: Sequential Implementation, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 659-672.  doi: 10.1007/ s00376-015-5155-y
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    [6] Yu Rucong, Jin Xiangze, Zhang Xuehong, 1995: Design and Numerical Simulation of an Arctic Ocean Circulation and Thermodynamic Sea-Ice Model, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 289-310.  doi: 10.1007/BF02656978
    [7] Xinrong WU, Shaoqing ZHANG, Zhengyu LIU, 2016: Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 193-207.  doi: 10.1007/s00376-015-5099-2
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    [10] Jin Xiangze, Huang Ruixin, Yang Jiayan, 1999: Centennial Oscillations in an Ocean-ice Coupled Model, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 323-342.  doi: 10.1007/s00376-999-0012-5
    [11] Yueliang CHEN, Changxiang YAN, Jiang ZHU, 2018: Assimilation of Sea Surface Temperature in a Global Hybrid Coordinate Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1291-1304.  doi: 10.1007/s00376-018-7284-6
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    [20] YAN Qing*, WANG Huijun, Ola M. JOHANNESSEN, and ZHANG Zhongshi, 2014: Greenland Ice Sheet Contribution to Future Global Sea Level Rise based on CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 8-16.  doi: 10.1007/s00376-013-3002-6

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Manuscript History

Manuscript received: 29 September 2019
Manuscript revised: 19 November 2019
Manuscript accepted: 05 December 2019
通讯作者: 陈斌, bchen63@163.com
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LICOM Model Datasets for the CMIP6 Ocean Model Intercomparison Project

    Corresponding author: Hailong LIU, lhl@lasg.iap.ac.cn
  • 1. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
  • 3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • 4. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: The datasets of two Ocean Model Intercomparison Project (OMIP) simulation experiments from the LASG/IAP Climate Ocean Model, version 3 (LICOM3), forced by two different sets of atmospheric surface data, are described in this paper. The experiment forced by CORE-II (Co-ordinated Ocean–Ice Reference Experiments, Phase II) data (1948–2009) is called OMIP1, and that forced by JRA55-do (surface dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis) data (1958–2018) is called OMIP2. First, the improvement of LICOM from CMIP5 to CMIP6 and the configurations of the two experiments are described. Second, the basic performances of the two experiments are validated using the climatological-mean and interannual time scales from observation. We find that the mean states, interannual variabilities, and long-term linear trends can be reproduced well by the two experiments. The differences between the two datasets are also discussed. Finally, the usage of these data is described. These datasets are helpful toward understanding the origin system bias of the fully coupled model.

摘要: 本文描述了大气所大气科学和地球流体力学数值模拟国家重点实验室(LASG/IAP) 气候系统海洋模式第三版(LICOM3)参与海洋模式比较计划的两个模拟试验数据集。两个模拟试验采用不同大气表面强迫数据,采用规范的海洋-海冰参考试验第二阶段 (Co-ordinated Ocean–Ice Reference Experiments, Phase II, CORE-II) 作为外强迫的试验称为海洋模式比较计划1阶段(OMIP1,1948-2009),采用来自日本55年大气再分析表面数据驱动海洋-海冰模式 (JRA55-do)强迫试验称为海洋模式比较计划第二阶段(OMIP2,1958-2018)。首先,文章描述了LICOM从CMIP5到CMIP6的改进和试验基本设置。其次,利用来自观测气侯态和年际变化数据,对两个试验的模拟性能进行检验。结果表明模式很好再现了两个试验中的平均态、年际变化和长期趋势。同时,讨论了两个模拟试验数据间的差异。最后,介绍了数据集的使用情况。这两个数据集有助于了解全耦合模式的系统偏差来源。

    • The Ocean Model Intercomparison Project (OMIP) is one of the endorsed Model Intercomparison Projects in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The purpose of OMIP is to understand the origin of systematic model biases. The subject also belongs to one of the scientific questions of CMIP6. The primary contribution of OMIP to the World Climate Research Programme’s Grand Challenges in Climate Science is in regional sea level change and near-term (climate/decadal) prediction. The OMIP experiment is a hindcast experiment for global coupled ocean–sea-ice models, inheriting the protocol from the Co-ordinated Ocean–Ice Reference Experiments, Phase II (CORE-II; Griffies et al., 2009). The experiments are driven by the prescribed atmospheric forcings from modified reanalysis and observational datasets. The turbulence momentum and heat fluxes are computed through the method of Large and Yeager (2004).

      The LASG/IAP Climate Ocean Model (LICOM) was developed at 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) around 30 years in the late 1980s (Zhang et al., 1989). The latest version of LICOM is LICOM3, preparing for CMIP6. Currently, LICOM3 is the ocean component of two climate system models of CMIP6: the CAS Flexible Global Ocean–Atmosphere–Land System model, finite-volume version 3 (CAS FGOALS-f3) and the CAS Flexible Global Ocean–Atmosphere–Land System model, grid-point version 3 (CAS FGOALS-g3), which in CMIP5 were FGOALS-s2 (Bao et al., 2013, Lin et al., 2013a) and FGOALS-g2 (Li et al., 2013; Lin et al., 2013b), respectively. In addition, the previous version of LICOM, LICOM2.0 (Liu et al., 2012), is also employed as the ocean component in CAS-ESM (the CAS Earth System Model), version 1.0 (Dr. Minghua ZHANG, personal communication, 2016).

      The OMIP simulations of LICOM3 were finished in June 2019 and the data have submitted to the Earth System Grid (ESG) data server (https://esgf-nodes.llnl.gov/projects/cmip6/). The purpose of the present paper is to provide a comprehensive description of the OMIP datasets of LICOM3 for a variety of users. We document detailed descriptions of the model configurations and experiments, as well as the algorithm of the diagnostic variables. Section 2 presents the model descriptions and experiment designs. Section 3 presents a basic technical validation of the LICOM3 experiments. Section 4 describes the datasets. Section 5 provides usage notes.

    2.   Model and experiments
    • Since LICOM2.0 (Liu et al., 2012), LICOM has been substantially upgraded in the interface with the flux coupler, the dynamic core, and the physical packages (Table 1). First, we upgraded the LICOM interface from the NCAR flux coupler 6 to coupler 7 (Lin et al., 2016), because the new version has been optimized for high-resolution modeling (Craig et al., 2012). Here, LICOM3 coupled with the Community Ice Code, version 4 (CICE4), i.e., the ocean–ice coupled model, is used to conduct the OMIP experiments. The prescribed atmospheric data have been input from the atmospheric data model and then passed to the coupler to drive the ocean–sea-ice coupled model.

      ConfigurationLICOM2.0LICOM3
      GridGridLongitude/LatitudeTripolar
      Resolution~1°, 30 levels~1°, 30 or 80 levels
      Dynamic coreTracer advectionCentral differential schemePreserved shape scheme (Yu, 1994)
      Momentum time integrationExplicitImplicit
      PhysicsDiapycnal mixingMixing in the mixed layer (Canuto et al., 2001, 2002)Mixing in the mixed layer (Canuto et al., 2001) and internal tide mixing (St. Laurent et al., 2002)
      Isopycnal mixingIsopycnal mixing (Redi, 1982) and advection (Gent and McWilliams, 1990)Isopycnal mixing (Redi, 1982) and advection (