Based on several domestic operationally-run climate models and internationally imported prediction data, the NCC/CMA has established CMMEv1.0 for monthly–seasonal forecasting. Figure 1 shows its technical framework. As shown, the CMMEv1.0 consists of several modules, such as data collection, model initialization, time integration, post-processing, and the product output. Currently, CMMEv1.0 includes four domestic climate models and two imported prediction datasets. The domestic climate models are BCC-CSM1.1m from NCC/CMA (Wu et al., 2014), FGOALS-f2 from the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) of Institute of Atmospheric Physics (IAP) of Chinese Academy of Sciences (CAS) (Bao et al., 2019), FGOALS-s2 from LASG/IAP/CAS (Bao et al., 2013), and NZC-PCCSM4 from the Nansen–Zhu International Research Centre (NZC) of IAP/CAS (Ma and Wang, 2014). The imported prediction data are from ECMWF-SYSTEM4 (ECMWF-S4) (Molteni et al., 2011) and the NCEP Climate Forecast System version 2 (NCEP-CFSv2) (Saha et al., 2014).
Table 1 lists detailed information on these models in CMMEv1.0. The atmospheric component of BCC-CSM1.1m has a T106 horizontal resolution and 26 vertical levels; the horizontal resolution of the land component is the same as the atmospheric component; the oceanic component has a resolution of 1.0° latitude × 1.0° longitude × 40 vertical levels and has the same resolution as the sea ice component. The atmospheric component of FGOALS-f2 is FAMIL with a 1.0° × 1.0° horizontal resolution and 32 vertical levels; the land component is CLM4.0 (Oleson et al., 2010); the oceanic component of FGOALS-f2 is POP2, which uses two displaced-pole grids centered at Greenland, at a nominal 1° (gx1v6) horizontal resolution and 60 vertical levels; the horizontal resolution of the sea ice component CICE4 (Holland et al., 2012), is the same as the oceanic component. The atmospheric component of FGOALS-s2 is SAMIL2 version 2.4.7, which is a spectral model with a R42 horizontal resolution (approximately 1.66° × 2.81°) and 26 vertical levels; the land component is CLM3.0 (Oleson et al., 2004); the horizontal resolution of the oceanic component (LICOM2, Liu et al., 2004) is increased in the tropics (from 1.0° × 1.0° to 0.5° × 0.5°) with 30 vertical levels; the sea ice component is CSIM5 (Briegleb et al., 2004) and has the same horizontal resolution as the oceanic component. The atmospheric component (CAM4) of NZC-PCCSM4 has a 2.5° × 1.9° horizontal resolution and 26 vertical levels; the horizontal resolution of the land component (CLM4, Lawrence et al., 2011) is the same as that of CAM4; the oceanic component is a mixed-layer model (SOM) with a resolution of 1.0° × 1.0°; the horizontal resolution of the sea ice component CICE4 used is the same as the oceanic component.
Model Institute Atmospheric resolution Oceanic resolution Ensemble member Forecast lead month M1 FGOALS-f2 IAP 1.0°×1.0°, L32 1.0°×1.0°, L60 35 6 M2 FGOALS-s2 IAP R42, L26 0.5°×0.5°–1.0°×1.0°, L30 4 6 M3 BCC-CSM1.1m BCC T106, L26 1.0°×1.0°, L40 24 13 M4 NZC-PCCSM4 IAP 2.5°×1.9°, L26 1.0°×1.0° 8 6 M5 ECMWF-S4 ECMWF TL255, L91 1.0°×1.0°, L42 15 7 M6 NCEP-CFSv2 NCEP T126, L64 1.0°×1.0°, L40 4 9 Note: The initial condition perturbation is used to generate ensemble members for each model.
Table 1. Descriptions of the climate prediction models in CMMEv1.0
These models use the nudging method to assimilate atmospheric and oceanic reanalysis data for initialization. The atmospheric assimilation data include the meridional and zonal winds, air temperature, and geopotential height from the Japanese 55-yr reanalysis (JRA55, Kobayashi et al., 2015) for FGOALS-f2, FGOALS-s2, and NZC-PCCSM4, and from the NCEP-I reanalysis for BCC-CSM1.1m. The oceanic assimilation data of the four models are the 3D ocean temperature from the NCEP Global Ocean Data Assimilation System (GODAS, Behringer and Xue, 2004). The initial condition perturbation is used to generate ensemble members for each mo-del. The number of ensemble members of BCC-CSM1.1m, FGOALS-f2, FGOALS-s2, and NZC-PCCSM4 are 24, 35, 4, and 8, respectively. The real-time forecasts begin on the 20th and 21st days of each month for FGOALS-f2, FGOALS-s2, and NZC-PCCSM4 and on the 1st day of each month for BCC-CSM1.1m. Recently, CMMEv1.0 has generated a monthly mean hindcast dataset for the period of 1991–2016 and a real-time forecast dataset since 2017. All of the model outputs were interpolated into a unified horizontal resolution of 1.0° × 1.0°.
The prediction skill (temporal correlation coefficient, TCC) of CMMEv1.0 is calculated based on the monthly mean hindcast for the period of 1991–2016. To verify the prediction performance, the anomalies of prediction are derived by subtracting the hindcast climatology from the original prediction data for each model in CMMEv1.0, where the model climatology is calculated by using the monthly hindcast data during 1991–2010 as a function of the initial calendar month and lead month, and observational climatology is obtained for the same period. All of the observational SST indices are calculated by using the Optimum Interpolation SST version 2 (OISSTv2) (Reynolds et al., 2007). The atmospheric verification data are computed by using NCEP–I reanalysis data for air temperature, 500-hPa geopotential height, and 850-hPa zonal wind (Kalnay et al., 1996) and the NOAA Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP, Xie and Arkin, 1997). The summer (June–July–August, JJA) average surface (2 m) air temperature and precipitation for China are from the daily climate data of Chinese stations for global exchange (V3.0).
CMMEv1.0 has carried out the real-time forecasts for the 2018 flood season (June–August) based on the outputs of FGOALS-f2, FGOALS-s2, and NZC-PCCSM4 starting on 20 and 21 February, BCC-CSM1.1m starting on 1 March, ECMWF-S4 starting on 10 March, and NCEP-CFSv2 starting on 18 March 2018. In this paper, we show the monthly mean predictions during March–August and the JJA average predictions in 2018, including the global SST, ENSO, Indian Ocean Dipole (IOD), North Atlantic SST triple (NAST), WPSH, East Asian summer monsoon (EASM), surface air temperature (SAT), and precipitation. Table 2 shows definitions of the indices used for ENSO, IOD, NAST, WPSH, and EASM predictions.
Index Definition Reference Niño3.4 (5°S–5°N, 170°E–120°W) regional mean SSTA Internationally used Niño4 (5°S–5°N, 160°E–150°W) regional mean SSTA Internationally used Niño3 (5°S–5°N, 150°–90°W) regional mean SSTA Internationally used NCP Niño4 － α×Niño3, when Niño3×Niño4 > 0，α = 0.4; otherwise α = 0 Ren and Jin (2011) NEP Niño3 － α×Niño4, when Niño3×Niño4 > 0，α = 0.4; otherwise α = 0 Ren and Jin (2011) IOD Difference between (10°S–10°N, 50°–70°E) and (10°S–0°, 90°–110°E) regional mean SSTA Saji et al. (1999) NAST [SSTA]A － ([SSTA]S + [SSTA]T), [SSTA]A: (34°–44°N, 72°–62°W) mean SSTA; [SSTA]S:
(44°–56°N, 40°–24°W) mean SSTA; [SSTA]T: (0°–18°N, 46°–24°W) mean SSTA
Zuo et al. (2013) Area of WPSH Over 110°E–180° and north of 10°N, the total area of all ≥ 588-dagpm contours in 500-hPa
geopotential height field
Liu et al. (2012) Intensity of WPSH Over 110°E–180° and north of 10°N, sum of the product of the area of ≥ 588-dagpm contours in
500-hPa geopotential height field and the grid point height minus 587 dagpm
Liu et al. (2012) Western ridge point
Over 90°E–180°, the longitude of the 588-dagpm westernmost point. If there is no 588-dagpm
contour in a certain month, it is replaced by the historical maximum value of the month for
Liu et al. (2012) EASM Difference between (10°–20°N, 100°–150°E) and (25°–35°N, 100°–150°E) regional mean
850-hPa zonal wind
Zhang et al. (2003)
Table 2. Definitions of the different indices used in the prediction
|Model||Institute||Atmospheric resolution||Oceanic resolution||Ensemble member||Forecast lead month|
|M1||FGOALS-f2||IAP||1.0°×1.0°, L32||1.0°×1.0°, L60||35||6|
|M2||FGOALS-s2||IAP||R42, L26||0.5°×0.5°–1.0°×1.0°, L30||4||6|
|M3||BCC-CSM1.1m||BCC||T106, L26||1.0°×1.0°, L40||24||13|
|M5||ECMWF-S4||ECMWF||TL255, L91||1.0°×1.0°, L42||15||7|
|M6||NCEP-CFSv2||NCEP||T126, L64||1.0°×1.0°, L40||4||9|
|Note: The initial condition perturbation is used to generate ensemble members for each model.|