Allan, R., and T. Ansell, 2006: A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850–2004. J. Climate, 19(22), 5816−5842, https://doi.org/10.1175/jcli3937.1.
Anderson, J. L., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D: Nonlinear Phenomena, 230(1−2), 99−111, https://doi.org/10.1016/j.physd.2006.02.011.
Bao, Q., X. F. Wu, J. X. Li, L. Wang, B. He, X. C. Wang, Y. M. Liu, and G. X. Wu, 2019: Outlook for El Niño and the Indian Ocean Dipole in autumn-winter 2018−2019. Chinese Science Bulletin, 64(1), 73−78, https://doi.org/10.1360/n972018-00913. (in Chinese with English abstract
Bethke, I., and Coauthors, 2021: NorCPM1 and its contribution to CMIP6 DCPP. Geoscientific Model Development, 14(11), 7073−7116, https://doi.org/10.5194/gmd-14-7073-2021.
Bilbao, R., and Coauthors, 2021: Assessment of a full-field initialized decadal climate prediction system with the CMIP6 version of EC-Earth. Earth System Dynamics, 12(1), 173−196, https://doi.org/10.5194/esd-12-173-2021.
Bloom, S. C., L. L. Takacs, A. M. Da Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124(6), 1256−1271, https://doi.org/10.1175/1520-0493(1996)124<1256:Dauiau>2.0.Co;2.
Boer, G. J., and Coauthors, 2016: The decadal climate prediction project (DCPP) contribution to CMIP6. Geoscientific Model Development, 9(10), 3751−3777, https://doi.org/10.5194/gmd-9-3751-2016.
Borchert, L. F., M. B. Menary, D. Swingedouw, G. Sgubin, L. Hermanson, and J. Mignot, 2021: Improved decadal predictions of North Atlantic subpolar gyre SST in CMIP6. Geophys. Res. Lett., 48(3), e2020GL091307, https://doi.org/10.1029/2020gl091307.
Carmo-Costa, T., R. Bilbao, P. Ortega, A. Teles-Machado, and E. Dutra, 2022: Trends, variability and predictive skill of the ocean heat content in North Atlantic: An analysis with the EC-Earth3 model. Climate Dyn., 58, 1311−1328, https://doi.org/10.1007/s00382-021-05962-y.
Doblas-Reyes, F. J., and Coauthors, 2013: Initialized near-term regional climate change prediction. Nature Communications, 4, 1715, https://doi.org/10.1038/ncomms2704.
Goddard, L., and Coauthors, 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dyn., 40(1−2), 245−272, https://doi.org/10.1007/s00382-012-1481-2.
Good, S. A., M. J. Martin, and N. A. Rayner, 2013: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res.: Oceans, 118(12), 6704−6716, https://doi.org/10.1002/2013jc009067.
Guemas, V., S. Corti, J. Garcia-Serrano, F. J. Doblas-Reyes, M. Balmaseda, and L. Magnusson, 2013: The Indian ocean: The region of highest skill worldwide in decadal climate prediction. J. Climate, 26(3), 726−739, https://doi.org/10.1175/Jcli-D-12-00049.1.
Guo, Y. Y., Y. Q. Yu, P. F. Lin, H. L. Liu, B. He, Q. Bao, S. W. Zhao, and X. W. Wang, 2020a: Overview of the CMIP6 historical experiment datasets with the climate system model CAS FGOALS-f3-L. Adv. Atmos. Sci., 37(10), 1057−1066, https://doi.org/10.1007/s00376-020-2004-4.
Guo, Y. Y., and Coauthors, 2020b: Simulation and improvements of oceanic circulation and sea ice by the coupled climate system model FGOALS-f3-L. Adv. Atmos. Sci., 37(10), 1133−1148, https://doi.org/10.1007/s00376-020-0006-x.
He, B., and Coauthors, 2019: CAS FGOALS-f3-L model datasets for CMIP6 historical atmospheric model intercomparison project simulation. Adv. Atmos. Sci., 36(8), 771−778, https://doi.org/10.1007/s00376-019-9027-8.
He, B., and Coauthors, 2020: CAS FGOALS-f3-L model dataset descriptions for CMIP6 DECK experiments. Atmos. Ocean. Sci. Lett., 13(6), 582−588, https://doi.org/10.1080/16742834.2020.1778419.
Hu, S., and T. J. Zhou, 2021: Skillful prediction of summer rainfall in the Tibetan Plateau on multiyear time scales. Science Advances, 7(24), eabf9395, https://doi.org/10.1126/sciadv.abf9395.
Hu, S., B. Wu, T. J. Zhou, and Z. Guo, 2019: A comparison of full-field and anomaly initialization for seasonal prediction of Indian Ocean basin mode. Climate Dyn., 53(9−10), 6089−6104, https://doi.org/10.1007/s00382-019-04916-9.
Hu, S., T. J. Zhou, and B. Wu, 2020: Improved ENSO prediction skill resulting from reduced climate drift in IAP-DecPreS: A comparison of full-field and anomaly initializations. Journal of Advances in Modeling Earth Systems, 12(2), e2019MS001759, https://doi.org/10.1029/2019ms001759.
Huang, B. Y., 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.
Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The los alamos sea ice model documentation and software user’s manual. version 4.1, LA-CC-06-012, 675 pp.
Kataoka, T., and Coauthors, 2020: Seasonal to decadal predictions with MIROC6: Description and basic evaluation. Journal of Advances in Modeling Earth Systems, 12(12), e2019MS002035, https://doi.org/10.1029/2019ms002035.
Kirtman, B., and Coauthors, 2013: Near-term climate change: projections and predictability. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds., Cambridge University Press, 953−1028.
Kushnir, Y., and Coauthors, 2019: Towards operational predictions of the near-term climate. Nature Climate Change, 9(2), 94−101, https://doi.org/10.1038/s41558-018-0359-7.
Li, J. X., Q. Bao, Y. M. Liu, G. X. Wu, L. Wang, B. He, X. C. Wang, and J. D. Li, 2019: Evaluation of FAMIL2 in simulating the climatology and seasonal-to-interannual variability of tropical cyclone characteristics. Journal of Advances in Modeling Earth Systems, 11(4), 1117−1136, https://doi.org/10.1029/2018ms001506.
Lin, P. F., and Coauthors, 2020: LICOM model datasets for the CMIP6 ocean model intercomparison project. Adv. Atmos. Sci., 37(3), 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(3), 318−329, https://doi.org/10.1007/s13351-012-0305-y.
Meehl, G. A., and Coauthors, 2009: Decadal prediction. Bull. Amer. Meteor. Soc., 90(10), 1467−1486, https://doi.org/10.1175/2009bams2778.1.
Meehl, G. A., A. X. Hu, and H. Y. Teng, 2016: Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation. Nature Communications, 7, 11718, https://doi.org/10.1038/ncomms11718.
Meehl, G. A., and Coauthors, 2021: Initialized Earth System prediction from subseasonal to decadal timescales. Nature Reviews Earth & Environment, 2(5), 340−357, https://doi.org/10.1038/s43017-021-00155-x.
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. Geophy. Res., 117(D8), D08101, https://doi.org/10.1029/2011jd017187.
Oke, P. R., J. S. Allen, R. N. Miller, G. D. Egbert, and P. M. Kosro, 2002: Assimilation of surface velocity data into a primitive equation coastal ocean model. J. Geophys. Res., 107(C9), 3122, https://doi.org/10.1029/2000jc000511.
Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR/TN-478+STR, 173 pp.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108(D14), 4407, https://doi.org/10.1029/2002jd002670.
Saurral, R. I., J. García-Serrano, F. J. Doblas-Reyes, L. B. Díaz, and C. S. Vera, 2020: Decadal predictability and prediction skill of sea surface temperatures in the South Pacific region. Climate Dyn., 54(9−10), 3945−3958, https://doi.org/10.1007/s00382-020-05208-3.
Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, M. Ziese, and B. Rudolf, 2014: GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol., 115(1−2), 15−40, https://doi.org/10.1007/s00704-013-0860-x.
Sheen, K. L., D. M. Smith, N. J. Dunstone, R. Eade, D. P. Rowell, and M. Vellinga, 2017: Skilful prediction of Sahel summer rainfall on inter-annual and multi-year timescales. Nature Communications, 8, 14966, https://doi.org/10.1038/ncomms14966.
Smith, D. M., R. Eade, and H. Pohlmann, 2013: A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Climate Dyn., 41(11−12), 3325−3338, https://doi.org/10.1007/s00382-013-1683-2.
Smith, D. M., and Coauthors, 2019: Robust skill of decadal climate predictions. npj Climate and Atmospheric Science, 2(1), 13, https://doi.org/10.1038/s41612-019-0071-y.
Smith, D. M., and Coauthors, 2020: North Atlantic climate far more predictable than models imply. Nature, 583(7818), 796−800, https://doi.org/10.1038/s41586-020-2525-0.
Sospedra-Alfonso, R., W. J. Merryfield, G. J. Boer, V. V. Kharin, W.-S. Lee, C. Seiler, and J. R. Christian, 2021: Decadal climate predictions with the Canadian Earth System Model version 5 (CanESM5). Geoscientific Model Development, 14(11), 6863−6891, https://doi.org/10.5194/gmd-14-6863-2021.
Sun, Q., B. Wu, T.-J. Zhou, and Z.-X. Yan, 2018: ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: Comparison of full-field and anomaly initialization. Atmos. Ocean. Sci. Lett., 11(1), 54−62, https://doi.org/10.1080/16742834.2018.1411753.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2009: A summary of the CMIP5 experiment design. [Available from https://pcmdi.llnl.gov/mips/cmip5/docs/Taylor_CMIP5_design.pdf]
WCRP Joint Scientific Committee (JSC), 2019: World Climate Research Programme Strategic Plan 2019−2028. WCRP Publication No. 1/2019.
Wu, B., and T. J. Zhou, 2012: Prediction of decadal variability of sea surface temperature by a coupled global climate model FGOALS_gl developed in LASG/IAP. Chinese Science Bulletin, 57(19), 2453−2459, https://doi.org/10.1007/s11434-012-5134-y.
Wu, B., X. L. Chen, F. F. Song, Y. Sun, and T. J. Zhou, 2015: Initialized decadal predictions by LASG/IAP climate system model FGOALS-s2: Evaluations of strengths and weaknesses. Advances in Meteorology, 2015, 904826, https://doi.org/10.1155/2015/904826.
Wu, B., T. J. Zhou, and F. Zheng, 2018: EnOI-IAU initialization scheme designed for decadal climate prediction system IAP-DecPreS. Journal of Advances in Modeling Earth Systems, 10(2), 342−356, https://doi.org/10.1002/2017ms001132.
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), 877−899, https://doi.org/10.3878/j.issn.1006-9895.1805.17284. (in Chinese with English abstract
Zhou, L. J., Y. M. Liu, Q. Bao, H. Y. Yu, and G. X. Wu, 2012: Computational performance of the high-resolution atmospheric model FAMIL. Atmos. Ocean. Sci. Lett., 5(5), 355−359, https://doi.org/10.1080/16742834.2012.11447024.
Zhou, T. J., and Coauthors, 2020: Development of climate and earth system models in China: Past achievements and new CMIP6 results. Journal of Meteorological Research, 34(1), 1−19, https://doi.org/10.1007/s13351-020-9164-0.