PNU CGCM V1.1을 이용한 12개월 앙상블 예측 시스템의 개발
Development of 12-month Ensemble Prediction System Using PNU CGCM V1.1
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초록
This study investigates a 12 month-lead predictability of PNU Coupled General Circulation Model (CGCM) V1.1 hindcast, for which an oceanic data assimilated initialization is used to generate ocean initial condition. The CGCM, a participant model of APEC Climate Center (APCC) long-lead multi-model ensemble system, has been initialized at each and every month and performed 12-month-lead hindcast for each month during 1980 to 2011. The 12-month-lead hindcast consisted of 2-5 ensembles and this study verified the ensemble averaged hindcast. As for the sea-surface temperature concerns, it remained high level of confidence especially over the tropical Pacific and the mid-latitude central Pacific with slight declining of temporal correlation coefficients (TCC) as lead month increased. The CGCM revealed trustworthy ENSO prediction skills in most of hindcasts, in particular. For atmospheric variables,like air temperature, precipitation, and geopotential height at 500hPa, reliable prediction results have been shown during entire lead time in most of domain, particularly over the equatorial region. Though the TCCs of hindcasted precipitation are lower than other variables, a skillful precipitation forecasts is also shown over highly variable regions such as ITCZ. This study also revealed that there are seasonal and regional dependencies on predictability for each variable and lead.
Abstract
This study investigates a 12 month-lead predictability of PNU Coupled General Circulation Model (CGCM) V1.1 hindcast, for which an oceanic data assimilated initialization is used to generate ocean initial condition. The CGCM, a participant model of APEC Climate Center (APCC) long-lead multi-model ensemble system, has been initialized at each and every month and performed 12-month-lead hindcast for each month during 1980 to 2011. The 12-month-lead hindcast consisted of 2-5 ensembles and this study verified the ensemble averaged hindcast. As for the sea-surface temperature concerns, it remained high level of confidence especially over the tropical Pacific and the mid-latitude central Pacific with slight declining of temporal correlation coefficients (TCC) as lead month increased. The CGCM revealed trustworthy ENSO prediction skills in most of hindcasts, in particular. For atmospheric variables,like air temperature, precipitation, and geopotential height at 500hPa, reliable prediction results have been shown during entire lead time in most of domain, particularly over the equatorial region. Though the TCCs of hindcasted precipitation are lower than other variables, a skillful precipitation forecasts is also shown over highly variable regions such as ITCZ. This study also revealed that there are seasonal and regional dependencies on predictability for each variable and lead.
참고문헌 (22)
- 안중배, "VAF변분법을 이용한 전구 해양자료 동화 연구", 바다, 10(1), pp.69~78 (2005), 한국해양학회
- 안중배, "중규모 해양모형을 이용한 한반도 주변해역 해양순환 재현", 한국해양학회지 바다, 5(3), pp.186~194 (2000)
- 안중배, "해양대순환모형을 이용한 해빙의 역할에 관한 수치실험 연구", 한국해양학회지 바다, 6(4), pp.225~233 (2001)
- 정혜인, "PNU", CME CGCM을 이용한 엘니뇨, 바다, pp.2007, 12(3)[DOI]
- The ECMWF System 3 Ocean Analysis System, "Balmaseda, M. A", ~, pp.ECMWF Technical Memorandum 408. (2007)
- Bonan, G. B., "The land surface climatology of the NCAR Land Surface Model (LSM 1.0) coupled to the NCAR Community Climate Model (CCM3)", J. Climate, 11, pp.1307~1326 (1998)
- Briegleb, B. P., "Delta-Eddington approximation for solar radiation in the NCAR Community Climate Model", J. Geophys. Res, 97, pp.7603~7612 (1992)
- Carval, T., "Argo data management: User's manual", pp.6~7 (2002), Ifremer
- Hack, J. J., "Parameterization of moist convection in the National Center for Atmospheric Research Community Climate Model (CCM2)", J. Geophys. Res, 99, pp.5551~5568 (1994)
- Holtslag, A. A. M, "Local versus nonlocal boundary-layer diffusion in a global climate model", J. Climate, 6, pp.1825~1842 (1993)
- Huang, X. Y, "Variational Analysis Using Spatial Filters", Mon. Wea. Rev., 128(7), pp.2588~2600 (2000)
- Hunke, E. C, "An Elastic-Viscous- Plastic Model for Sea Ice Dynamics", J. Phys. Oceanogr, 27, pp.1849~1867 (1997)
- IPCC, "Climate Change 2007: The Physical Science Basis . Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change" (2007), Cambridge University Press
- Ji, M, "An ocean analysis system for seasonal to interannual climate studies", Mon. Wea. Rev, 123, pp.460~481 (1995)
- Description of the NCAR Community Climate Model(CCM3), "Kiehl, J. T", 152~, pp.NCAR Tech. Note. NCAR (1996), TN- 420+STR,
- The NEMOVAR ocean data assimilation system as implemented in the ECMWF ocean analysis for System 4, "Mogensen, K.", ~, pp.ECMWF Tech Memo 668. (2012)
- Pacanowski, R. C, "MOM 3.0 Manual", NOAA, 1998
- Semtner, A. J., Jr., "A model for the thermodynamic growth of sea ice in numerical investigations of climate", J. Phys. Oceanogr., 6, pp.379~389 (1976)
- Sun, J. Q, "A GCM-based forecasting model for the landfall of tropical cyclones in China", Adv. Atmos. Sci., 28(5), pp.1049~1055 (2011)
- Usui, N., "Meteorological Research Institute multivariate ocean variational estimation (MOVE) system: Some early results", Advances in Space Res., 37, pp.806~822 (2006)
- Yin, Y., "An Ensemble Ocean Data Assimilation System for Seasonal Prediction", Mon. Wea. Rev, 139, pp.786~808 (2011)
- Zhang, G. J, "Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model", Atmosphere-Ocean, 33, pp.407~446 (1995)