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Seasonal climate predictability with Tier-one and Tier-two prediction systems

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Abstract

In this study seasonal predictability of Tier-one and Tier-two predictions are evaluated and compared. Through the comparison of these two predictions, it is demonstrated that the air–sea coupled process is an important factor not only for climatological simulation but also for seasonal predictability. In particular, the air–sea coupling plays a crucial role over the warm pool region, as the atmosphere tends to lead the ocean in anomalous variability. In this region, the Tier-one prediction has better climatology compared to the Tier-two prediction despite the presence of a climatological SST bias. Furthermore, the Tier-one has a relatively higher seasonal predictive skill than that of the Tier-two although its SST prediction skill is relatively poor. It is suggested that the air–sea coupled process plays a role to reduce both the climatological and anomalous biases in the uncoupled AGCM by means of the negative feedback of the SST-heat flux-precipitation loop. Using the CliPAS and DEMETER seasonal prediction data, the robustness of these results are demonstrated in the multi-model frame works.

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Notes

  1. The terminology “air–sea coupled process” contains various physical processes between atmosphere and ocean, but it will be specified in this study as a feedback from atmosphere to ocean, which is not embedded in the T2 system unlike the T1 system.

References

  • Barnett TP, Arpe K, Bengtsson L, Ji M, Kumar A (1997) Potential predictability and AMIP implications of midlatitude climate variability in two general circulation models. J Clim 10:2321–2329

    Article  Google Scholar 

  • Bengtsson L, Schlese U, Roeckner E, Latif M, Barnett TP, Graham N (1993) A two-tiered approach to long-range climate forecasting. Science 261:1026–1029

    Article  Google Scholar 

  • Davey MK, Huddleston M, Sperber KR, Braconnot P, Bryan F, Chen D, Colman RA, Cooper C, Cubasch U, Delecluse P, DeWitt D, Fairhead L, Flato G, Gordon C, Hogan T, Ji M, Kimoto M, Kitoh A, Knutson TR, Latif M, Le Treut H, Li T, Manabe S, Mechoso CR, Meehl GA, Power SB, Roeckner E, Terray L, Vintzileos A, Voss R, Wang B, Washington WM, Yoshikawa I, Yu JY, Yukimoto S, Zebiak SE (2002) STOIC: a study of coupled model climatology and variability in tropical ocean regions. Clim Dyn 18:403–420

    Article  Google Scholar 

  • Fu XH, Wang B, Li T (2002) Impacts of air–sea coupling on the simulation of mean Asian summer monsoon in the ECHAM4 model. Mon Weather Rev 130:2889–2904

    Article  Google Scholar 

  • Gadgil S, Sajani S (1998) Monsoon precipitation in the AMIP runs. Clim Dyn 14:659–689

    Article  Google Scholar 

  • Graham RJ, Gordon M, Mclean PJ, Ineson S, Huddleston MR, Davey MK, Brookshaw A, Barnes RTH (2005) A performance comparison of coupled and uncoupled versions of the Met Office seasonal prediction general circulation model. Tellus Dyn Meteorol Oceanogr 57:320–339

    Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471

    Article  Google Scholar 

  • Kang IS, Kug JS (2000) An El-Nino prediction system using an intermediate ocean and a statistical atmosphere. Geophy Res Lett 27:1167–1170

    Article  Google Scholar 

  • Kang IS, Ho CH, Lim YK, Lau KM (1999) Principal modes of climatological seasonal and intraseasonal variations of the Asian summer monsoon. Mon Weather Rev 127:322–340

    Article  Google Scholar 

  • Kang IS, Jin K, Lau KM, Shukla J, Krishnamurthy V, Schubert SD, Waliser DE, Stern WF, Satyan V, Kitoh A, Meehl GA, Kanamitsu M, Galin VY, Sumi A, Wu G, Liu Y, Kim JK (2002) Intercomparison of atmospheric GCM simulated anomalies associated with the 1997/98 El Nino. J Clim 15:2791–2805

    Article  Google Scholar 

  • Kim J-K (1999) Parameterization of land surface processes in an atmospheric general circulation model. PhD thesis, Seoul National University, Korea

  • Kitoh A, Arakawa O (1999) On overestimation of tropical precipitation by an atmospheric GCM with prescribed SST. Geophys Res Lett 26:2965–2968

    Article  Google Scholar 

  • Kug JS, Kang IS, Zebiak SE (2001) The impact of the model assimilated wind stress data in the initialization of an intermediate ocean model and the ENSO predictability. Geophy Res Lett 28:3713–3717

    Article  Google Scholar 

  • Kug JS, Kang IS, Lee JY, Jhun JG (2004) A statistical approach to Indian Ocean sea surface temperature prediction using a dynamical ENSO prediction. Geophys Res Lett 31:09212. doi:10.1029

    Google Scholar 

  • Kug JS, Kang IS, Jhun JG (2005) Western Pacific SST Prediction with an intermediate coupled model. Mon Weather Rev 133:1343–1352

    Article  Google Scholar 

  • Kug JS, Lee JY, Kang IS (2007) Global sea surface temperature prediction using a multi-model ensemble. Mon Weather Rev (accepted)

  • Kumar A, Hoerling MP (1998) Annual cycle of Pacific-North American seasonal predictability associated with different phases of ENSO. J Clim 11:3295–3308

    Article  Google Scholar 

  • Linzen RS, Nigam S (1987) On the role of sea surface temperature gradients in forcing low level winds and convergence in the tropics. J Atmos Sci 44:2418–2436

    Article  Google Scholar 

  • Livezey RE, Masutani M, Ji M (1996) SST-forced seasonal simulation and prediction skill for versions of the NCEP/MRF model. Bull Am Meteorol Soc 77:507–517

    Article  Google Scholar 

  • Moorthi S, Suarez MJ (1992) Relaxed Arakawa-Schubert: a parametrization of moist convection for general circulation models. Mon Weather Rev 120:978–1002

    Article  Google Scholar 

  • Nakajima T, Tsukamoto M, Tsushima Y, Numaguti A (1995) Modelling of the radiative process in a AGCM. In: Matsuno T (ed) Climate system dynamics and modelling, vol 1–3, pp 104–123

  • Noh Y, Kim HJ (1999) Simulations of temperature and turbulence structure of the oceanic boundary layer with the improved near-surface process. J Geophys Res Oceans 104:15621–15634

    Article  Google Scholar 

  • Palmer TN, Alessandri A, Andersen U, Cantelaube P, Davey M, Delecluse P, Deque M, Diez E, Doblas-Reyes FJ, Feddersen H, Graham R, Gualdi S, Gueremy JF, Hagedorn R, Hoshen M, Keenlyside N, Latif M, Lazar A, Maisonnave E, Marletto V, Morse AP, Orfila B, Rogel P, Terres JM, Thomson MC (2004) Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull Am Meteorol Soc 85:853–872

    Article  Google Scholar 

  • Shukla J (1998) Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282:728–731

    Article  Google Scholar 

  • Shukla J, Wallace JM (1983) Numerical simulation of the atmospheric response to equatorial Pacific sea surface temperature anomalies. J Atmos Sci 40:1613–1630

    Article  Google Scholar 

  • Shukla J, Anderson J, Baumhefner D, Brankovic C, Chang Y, Kalnay E, Marx L, Palmer T, Paolino D, Ploshay J, Schubert S, Straus D, Suarez M, Tribbia J (2000) Dynamical seasonal prediction. Bull Am Meteorol Soc 81:2593–2606

    Article  Google Scholar 

  • Smith TM, Reynolds RW (2004) Improved extended reconstruction of SST (1854–1997). J Clim 17:2466–2477

    Article  Google Scholar 

  • Stockdale TN, Anderson DLT, Alves JOS, Balmaseda MA (1998) Global seasonal rainfall forecasts using a coupled ocean-atmosphere model. Nature 392:370–373

    Article  Google Scholar 

  • Wang B, Wu RG, Lau KM (2001) Interannual variability of the Asian summer monsoon: Contrasts between the Indian and the western North Pacific-east Asian monsoons. J Clim 14:4073–4090

    Article  Google Scholar 

  • Wang B, Kang IS, Lee JY (2004) Ensemble simulations of Asian-Australian monsoon variability by 11 AGCMs. J Clim 17:803–818

    Article  Google Scholar 

  • Wang B, Ding QH, Fu XH, Kang IS, Jin K, Shukla J, Doblas-Reyes F (2005) Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophys Res Lett 32:L15711

    Article  Google Scholar 

  • Xie PP, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539–2558

    Article  Google Scholar 

  • Yu JY, Mechoso CR (1999) A discussion on the errors in the surface heat fluxes simulated by a coupled GCM. J Clim 12:416–426

    Article  Google Scholar 

  • Zebiak S (1986) Atmospheric convergence feedback in a simple model for El Nino. Mon Weather Rev 114:1263–1271

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER_2007-4206. D.H. Choi was supported by the second stage of the Brain Korea 21 Project in 2007.

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Correspondence to In-Sik Kang.

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This paper is a contribution to the AMIP-CMIP Diagnostic Sub-project on General Circulation Model Simulation of the East Asian Climate, coordinated by W.-C. Wang.

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Kug, JS., Kang, IS. & Choi, DH. Seasonal climate predictability with Tier-one and Tier-two prediction systems. Clim Dyn 31, 403–416 (2008). https://doi.org/10.1007/s00382-007-0264-7

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  • DOI: https://doi.org/10.1007/s00382-007-0264-7

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