Development of Climate and Earth System Models in China: Past Achievements and New CMIP6 Results

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  • Corresponding author: Tianjun ZHOU, zhoutj@lasg.iap.ac.cn
  • Funds:

    Supported by the International Partnership Program of Chinese Academy of Sciences (134111KYSB20160031) and National Natural Science Foundation of China (41875132)

  • doi: 10.1007/s13351-020-9164-0

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  • The Earth–Climate System Model (ECSM) is an important platform for multi-disciplinary and multi-sphere integration research, and its development is at the frontier of international geosciences, especially in the field of global change. The research and development (R&D) of ECSM in China began in the 1980s and have achieved great progress. In China, ECSMs are now mainly developed at the Chinese Academy of Sciences, ministries, and universities. Following a brief review of the development history of Chinese ECSMs, this paper summarized the technical characteristics of nine Chinese ECSMs participating in the Coupled Model Intercomparison Project Phase 6 and preliminarily assessed the basic performances of four Chinese models in simulating the global climate and the climate in East Asia. The projected changes of global precipitation and surface air temperature and the associated relationship with the equilibrium climate sensitivity under four shared socioeconomic path scenarios were also discussed. Finally, combined with the international situation, from the perspective of further improvement, eight directions were proposed for the future development of Chinese ECSMs.
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  • Fig. 1.  1995–2014 (a) DJF and (c) JJA mean surface air temperature (°C) derived from the observation (Obs, shading) and the multi-model ensemble mean (MME, contour) of four Chinese models that participated in CMIP6. (b) DJF and JJA mean biases of the MME, in which the dotted areas denote values passing the 95% confidence level. The observation is the average of HadCRUT4 (Hadley Centre and Climate Research Unit Temperature) and BEST (Berkeley Earth Surface Temperatures) data.

    Fig. 2.  As in Fig. 1, but for precipitation (mm day−1). The observed precipitation is derived from GPCP data.

    Fig. 3.  (a) DJF and (b) JJA mean root mean square error (RMSE) of simulated global surface air temperature (°C) and precipitation (mm day−1) from four Chinese models (red dot) and other 16 models (gray dot) that participated in CMIP6. The RMSE of the four Chinese models ensemble mean (red triangle) and 20 CMIP6 models ensemble mean (black triangle) are also presented.

    Fig. 4.  Spatial patterns of the June–July–August–September (JJAS) mean low-level wind field (m s−1) at 850 hPa averaged for 1995–2014 derived from (a) JRA55 and (c) multi-model ensemble mean of historical simulations from the four Chinese models that participated in CMIP6. (b) The differences between ERA-Interim and JRA55. (d) The simulated bias of the Chinese models, relative to the mean of JRA55 and ERA-Interim data.

    Fig. 5.  Anomalies of (a) annual mean surface air temperature (MST, °C) between 60°S and 60°N and (b) global land averaged rainfall (%, divided by the 1900–1950 mean) from the observation and the multi-model ensemble mean of historical simulations from four Chinese models. A 20-yr running average was applied to the precipitation anomaly. The surface air temperature (global land averaged rainfall) anomalies were calculated as departures from the annual mean of 1850–1900 (1900–1950).

    Fig. 6.  Projected changes of (a) global mean surface air temperature (GMST, °C) and (b) percentage changes of global land averaged rainfall (%) with respect to the period 1995–2014 under Shared Socioeconomic Pathway (SSP) 1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios derived from four Chinese models. A 20-yr running average was applied to the anomalies. The data before 2014 are from the historical simulations. Solid lines denote the multi-model ensemble mean of four models, while shadings denote the uncertainty range of 5th to 95th percentile.

    Fig. 7.  Spatial patterns of the projected changes of surface air temperature (°C) in (a, b) DJF and (c, d) JJA under (a, c) SSP1-2.6 and (b, d) SSP5-8.5 scenarios for the period 2081–2100 relative to the period 1995–2014 by multi-model ensemble mean of four Chinese models that participated in CMIP6. The areas with dots indicate that the projected changes are with the same sign in at least three models.

    Fig. 8.  Spatial patterns of the percentage changes of projected rainfall ( mm day−1) in (a, b) DJF and (c, d) JJA under (a, c) SSP1-2.6 and (b, d) SSP5-8.5 scenarios for the period 2081–2100 relative to climatological rainfall averaged for the period 1995–2014 by multi-model ensemble mean of four Chinese models that participated in CMIP6. The areas with dots indicate that the projected changes are with the same sign in four models.

    Fig. 9.  The Equilibrium Climate Sensitivity (ECS) versus the projected changes in global mean surface temperature (GMST) for the pe-riod 2081–2100 relative to 1995–2014 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios by four Chinese models that participated in CMIP6.

    Fig. 10.  (a) Spatial pattern of the temporal correlation skill of the annual mean SSTs derived from the decadal prediction experiments averaged over the hindcast years 2–5. White dots denote values passing the 5% significance level. (c) As in (a), but for detrended SSTAs. (b, d) As in (a, c), but for the hindcast years 6–9. Adopted from Wu et al. (2018).

    Table 1.  The models from China used for IPCC AR1–4

    IPCCCMIPModelHorizontal resolution of atmospheric modelHorizontal resolution of oceanic model
    FARIAP4° × 5°, L2Mixed layer ocean model
    SARCMIP1GOALS24° × 5°, L24° × 5°, L20
    TARCMIP2GOALS4R15 (4.5° × 7.5°), L94° × 5°, L20
    AR4CMIP3FGOALS-g1.02.8° × 2.8°, L261.0° × 1.0°, L30
    *FAR: First Assessment Report; SAR: Second Assessment Report; TAR: Third Assessment Report; and AR4: Fourth Assessment Report
    Download: Download as CSV

    Table 2.  The models from China that participated in CMIP5 and used in IPCC AR5

    ModelInstituteHorizontal resolution of atmospheric modelHorizontal resolution of oceanic model
    BCC_CSM1.1BCC2.8° × 2.8°0.8° × 1.0°
    BCC_CSM1.1(m)BCC1.1° × 1.1°0.8° × 1.0°
    BNU-ESMBNU2.8° × 2.8°0.9° × 1.0°
    FGOALS-g2LASG-CESS3.0° × 2.8°0.9° × 1.0°
    FGOALS-s2LASG1.7° × 2.8°0.9° × 1.0°
    FIO-ESMFIO2.8° × 2.8°0.9° × 1.0°
    Download: Download as CSV

    Table 3.  Metrics of current Earth/Climate System Models that have participated in CMIP6 from China

    ModelInstituteAtmospheric modelOceanic modelLand surface modelSea ice modelCoupler
    1BCC-CSM2-MR
    BCC-CSM2-HR
    BCC-ESM1.0
    (Wu et al., 2019a, b;
    Xin et al., 2019)
    BCCBCC-AGCM3-Chem
     (T42, ~280 km, L26)
    BCC-AGCM3-MR
     (T106, ~120 km, L46)
    BCC-AGCM3-HR
     (T266, 45 km, L56)
    MOM4-L40 gx1v1
    MOM4-L40 gx1v1
    MOM5-L50 (0.25°)
    BCC-AVIM2SISCPL5
    2BNU-ESM-1-1
    (Ji et al., 2014)
    BNUCAM3.5
    (2.8°[~280 km], L26)
    MOM4p1 (1.0° at high lati-
    tudes, close to 0.3° in the tropics, L40)
    CoLMCICE4CPL
    3CAMS-CSM
    (Rong et al., 2019)
    CAMSECHAM5
    (T106, ~120 km, L31)
    MOM4 (1.0° at high lati-
    tudes, close to 0.3° in the tropics, L50)
    CoLMSISFMS-coupler
    4CAS-ESMICCES, IAP, CASIAP AGCM5
    (1.4°[~140 km], L35)
    LICOM2 (1.0°, 0.5° in the meridional directions over the tropics, L30)CoLMCICE4CPL7
    5CAS FGOALS-f3
    (Bao et al., 2019)
    LASG, IAP, CASFAMIL2.2
    (100 km, 25 km, L32)
    LICOM3 (1°/L30, 0.1°/L55)CLM4CICE4CPL7
    6CAS FGOALS-g3
    (Tang et al., 2019)
    LASG, IAP, CASGAMIL3
    (2°[~200 km], L26)
    LICOM3 (1.0°, 0.5° in the meridional directions over the tropics, L30)CAS-LSMCICE4CPL7
    7CIESM
    (Lin et al., 2019)
    CESS, THUModified CAM5
    (~100 km, L30)
    POP2 (1°, L60)CLM4CICE4C-coupler2
    8FIO-ESM2.0
    (Song et al., 2019)
    FIO, NMRCAM5
    (~100 km, L30)
    POP2 (1.1° at high lati-
    tudes, 0.3°–0.5° in the tropics, L61)
    CLM4CICE4CPL7
    9NESM v3
    (Cao et al., 2019)
    NUISTECHAM6.3
    (T63, ~200 km, L47)
    NEMO v3.4 (1.0° at high latitudes, close to 0.3° in the tropics, L46)JABACHCICE4OASIS3-MCT
    10TaiESMRCEC, Taiwan, ChinaCAM5.3
    (~100 km, ~200 km, L30)
    POP2 (1.0°, L70)CLM4CICE4CPL7
    Note: Most acronyms can be found in the main text. ICCES: International Center for climate and Environment Sciences; CESS: Center for Earth System Science. Overview of Chinese contribution to CMIP6: Status and challenges, Presentation by T. J. Zhou at Pan-WCRP modelling Meeting, Met Office, Exeter, 9–13 October 2017.
    Download: Download as CSV

    Table 4.  Pattern correlation coefficients (PCCs) and root mean square errors (RMSEs) of surface air temperature (°C) and precipitation (mm day−1) between the observation and the historical simulations derived from four Chinese models and the multi-model ensemble mean

    ModelTemperaturePrecipitation
    PCCRMSEPCCRMSE
    JJADJFJJADJFJJADJFJJADJF
    MME0.990.991.652.110.840.851.441.42
    BCC-CSM20.990.992.832.470.820.821.661.74
    CAMS-CSM0.990.982.103.180.740.761.931.74
    FGOALS-g30.990.992.172.960.710.812.101.69
    NESM30.990.991.922.350.800.761.751.89
    Download: Download as CSV

    Table 5.  CMIP6 annual mean surface air temperature anomalies (°C) from the 1995–2014 reference period for selected time periods and Shared Socioeconomic Pathways (SSPs). The multi-model mean ± 1 standard deviation ranges across the individual models are listed and the 5%–95% ranges from the models’ distribution (based on a Gaussian assumption and obtained by multiplying the CMIP6 ensemble standard deviation by 1.64) are given in brackets. The values tabulated here are for single simulations from the four models that have thus far contributed to the CMIP6 exercise. The models are BCC-CSM2-MR, CAMS-CSM1.0, FGOALS-g3, and NESM3

    Model and periodSSP1-2.6 (°C)SSP2-4.5 (°C)SSP3-7.0 (°C)SSP5-8.5 (°C)
    MME 2021–20400.55 ± 0.22 (0.18, 0.87)0.59 ± 0.21 (0.24, 0.93)0.58 ± 0.09 (0.42, 0.73)0.71 ± 0.23 (0.33, 1.08)
       2041–20600.75 ± 0.33 (0.21, 1.29)1.05 ± 0.26 (0.62, 1.47)1.14 ± 0.19 (0.82, 1.45)1.46 ± 0.43 (0.76, 2.16)
       2081–21000.76 ± 0.30 (0.28, 1.25)1.61 ± 0.37 (1.00, 2.22)2.43 ± 0.38 (1.81, 3.04)3.17 ± 0.78 (1.89, 4.44)
    BCC-CSM2-MR: 2081–21000.72 ± 0.09 (0.57, 0.87)1.63 ± 0.13 (1.42,1.84)2.84 ± 0.23 (2.46, 3.21)3.13 ± 0.23 (2.75, 3.51)
    CAMS-CSM1.0: 2081–21000.67 ± 0.16 (0.42, 0.93)1.31 ± 0.12 (1.12, 1.51)2.10 ± 0.21 (1.75, 2.44)2.52 ± 0.20 (2.20, 2.84)
    FGOALS-g3: 2081–21000.48 ± 0.10 (0.32, 0.64)1.36 ± 0.10 (1.19, 1.53)2.34 ± 0.26 (1.92, 2.77)2.75 ± 0.23 (2.38, 3.12)
    NESM3: 2081–21001.18 ± 0.13 (0.97, 1.39)2.13 ± 0.11 (1.94, 2.32)4.27 ± 0.37 (3.67, 4.87)
    Note: NESM3 does not provide the projection under SSP3-7.0 scenario.
    Download: Download as CSV
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