한국지구과학회지 (Journal of the Korean earth science society)

  • 제36권1호
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  • Pages.82-89
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  • 2015
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  • 1225-6692(pISSN)
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  • 2287-4518(eISSN)
한국지구과학회 (The Korean Earth Science Society)
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전지구 해양·해빙예측시스템 NEMO-CICE/NEMOVAR의 북극 영역 해빙초기조건 특성 분석

Analyzing the Characteristics of Sea Ice Initial Conditions for a Global Ocean and Sea Ice Prediction System, the NEMO-CICE/NEMOVAR over the Arctic Region

  • 안중배 (부산대학교 지구환경시스템학부) ;
  • 이수봉 (부산대학교 지구환경시스템학부)
  • Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University) ;
  • Lee, Su-Bong (Division of Earth Environmental System, Pusan National University)
  • 투고 : 2015.01.20
  • 심사 : 2015.02.16
  • 발행 : 2015.02.28
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초록

전지구 해양 해빙 예측시스템인 NEMO-CICE/NEMOVAR의 해빙 초기조건의 특성을 2013년 6월부터 2014년 5월까지 북극영역에 대하여 분석하였다. 이를 위하여 관측 자료와 재분석 자료를 모델의 초기조건과 비교하였다. 모델 초기조건은 관측에서 나타나는 해빙 면적과 해빙 두께의 월 변동을 잘 보이는 반면, 분석 기간 동안 관측과 재분석 자료보다 북극의 해빙 면적을 좁게, 해빙 두께를 얇게 나타내었다. 모델 초기조건의 북극 해빙 면적이 좁은 것은 해빙의 경계 지역에서 해빙 농도 초기조건이 약 20% 정도 재분석자료보다 낮기 때문이다. 또한 북극 평균 해빙 두께가 얇게 나타나는 이유는 연중 두꺼운 해빙이 유지되는 그린란드 및 북극 군도와 인접한 북극해 영역에서 모델의 초기조건이 약 60 cm 정도 얇기 때문이다.

In this study, the characteristics of sea ice initial conditions generated from a global ocean and sea ice prediction system, the Nucleus for European Modeling of the Ocean (NEMO) - Los Alamos Sea Ice Model (CICE)/NEMOVAR were analyzed for the period June 2013 to May 2014 over the Arctic region. For the purpose, the observed and reanalyzed data were used to compare with the sea ice initial conditions. Results indicated that the variability of the monthly sea ice extent and thickness in model initial conditions were well represented as compared to the observation, while it was found that the extent and thickness of Arctic sea ice in initial data were narrower and thinner than those in reanalysis and observation for the period. The reason for the narrower sea ice extent in model initial conditions seems to be due to the fact that the initial sea ice concentration at the boundary area of sea ice was about 20 percent less than the reanalysis data. Also, the reason for the thinner sea-ice thickness in the Arctic region is due to the underestimation of Arctic sea ice thickness (about 60 cm) of the model initial conditions in the Arctic Ocean area adjacent to Greenland and Arctic archipelago where thick sea ice appears all the year round.

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참고문헌

  1. NIMR, 2013, Study on development and application of earthquake monitoring techniques. Korea meteorological administration, 669 p. (in Korean)
  2. Balmaseda, M.A. and D. Anderson, 2009, Impact of initialization strategies and observations on seasonal forecast skill. Geophysical research letters, 36, L0170, doi:10.1029/2008GL035561.
  3. Balmaseda, M.A., K. Mogensen, and A. Weaver, 2013, Evaluation of the ECMWF ocean reanalysis system ORAS4. Quarterly journal of the royal meteorological society, 139, 1132-1161. https://doi.org/10.1002/qj.2063
  4. Francis, J.A. and S.J. Vavrus, 2012, Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophysical research letters, 39, L06801, doi: 10.1029/2012GL051000.
  5. Guemas, V., F.J. Doblas-Reyes, K. Mogensen, S. Keeley, and Y. Tang, 2014, Ensemble of sea ice initial conditions for interannual climate predictions. Climate dynamics, 43, 2813-2829. https://doi.org/10.1007/s00382-014-2095-7
  6. Kalnay, E., 2003, Atmospheric modelling, data assimilation and predictability. Cambridge University Press, New York, USA, 341 p.
  7. Kim, B.-M., S.-W. Son, S.-K. Min, J.-H. Jeong, S.-J. Kim, X. Zhang, T. Shim, and J.-H. Yoon, 2014, Weakening of the stratospheric polar vortex by Arctic sea-ice loss. Nature communications, 5, doi:10.1038/ncomms5646.
  8. Kim, J.G., E.C. Hunke, and W.H. Lipscomb, 2006, Sensitivity analysis and parameter tuning scheme for global sea-ice modeling. Ocean modelling, 14, 61-80. https://doi.org/10.1016/j.ocemod.2006.03.003
  9. Lee, S.-H., 2012, Analysis of the Impact of QuikSCAT and ASCAT Sea Wind Data Assimilation on the Prediction of Regional Wind Field near Coastal Area. Journal of the Korean earth science society, 33, 309-319. (in Korean) https://doi.org/10.5467/JKESS.2012.33.4.309
  10. Lim, Y.-K., S.-K. Song, and S.-O. Han, 2014, Data assimilation effect of mobile rawinsonde observation using unified model observing system experiment during the summer intensive observation period in 2013. Journal of Korean earth science society, 35, 215- 224. (in Korean) https://doi.org/10.5467/JKESS.2014.35.4.215
  11. Lindsay, R., C. Haas, S. Hendricks, P. Hunkeler, N. Kurtz, J. Paden, B. Panzer, J. Sonntag, J. Yungel, and J. Zhang, 2012, Seasonal forecasts of Arctic sea ice initialized with observations of ice thickness. Geophysical research letters, 39, L21502, doi:10.1029/2012GL053576.
  12. Madec, G., 2008, NEMO ocean engine. Note du Pole de modelisation, Institut Pierre-Simon Laplace (IPSL), France, 217 p.
  13. Marshall, A.G., D. Hudson, M.C. Wheeler, O. Alves, H.H. Hendon, M.J. Pook, and J.S. Risbey, 2014, Intraseasonal drivers of extreme heat over Australia in observations and POAMA-2. Climate dynamics, 43, 1915-1937. https://doi.org/10.1007/s00382-013-2016-1
  14. Merryfield, W. J., W.-S. Lee, G.J. Boer, V.V. Kharin, J.F. Scinocca, G.M. Flato, R.S. Ajayamohan, J.C. Fyfe, Y. Tang, and S. Polavarapu, 2013, The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization. Monthly weather review, 141, 2910-2945. https://doi.org/10.1175/MWR-D-12-00216.1
  15. Mogensen, K., M.A. Balmaseda, and A. Weaver, 2012, The NEMOVAR ocean data assimilation system as implemented in the ECMWF ocean analysis for System 4, ECMWF Tech Memo 668. European centre for medium-range weather forecasts, 59 p.
  16. Pringle, D. J., H. Eicken, H.J. Trodahl, and L.G.E. Backstrom, 2007, Thermal conductivity of landfast Antarctic and Arctic sea ice. Journal of geophysical research, 112, C04017, doi:10.1029/2006JC003641.
  17. Ryu, C.-S, H.-S. Won, and S.-H. Lee, 2005, A study on the influence of aerological observation data assimilation at Honam area on numerical weather prediction. Journal of the Korean earth science society, 26, 66-77. (in Korean)
  18. Saha, S., S. Moorthi, X. Wu, J. Wang, S. Nadiga, P. Tripp, D. Behringer, Y.-T. Hou, H.-Y. Chuang, M. Iredell, M. Ek, J. Meng, R. Yang, M. P. Mendez, H.v.d. Dool, Q. Zhang, W. Wang, M. Chen, and E. Becker, 2014, The NCEP Climate forecast system version 2. Journal of climate, 27, 2185-2208. https://doi.org/10.1175/JCLI-D-12-00823.1
  19. Tang, Q., X. Zhang, X. Yang, and J.A. Francis, 2013, Cold winter extremes in northern continents linked to Arctic sea ice loss. Environmental research letters, 8, 014036, doi:10.1088/1748-9326/8/1/014036.
  20. Tietsche, S., D. Notz, J.H. Jungclaus, and J. Marotzke, 2013, Assimilation of sea-ice concentration in a global climate model-physical and statistical aspects. Ocean science, 9, 19-36. https://doi.org/10.5194/os-9-19-2013
  21. Vancoppenolle, M., T. Fichefet, and C.M. Bits, 2006, Modeling the salinity profile of undeformed Arctic sea ice. Geophysical research letters, 33, L21501, doi:10.1029/2006GL028342.
  22. Vancoppenolle, M., T. Fichefet, H. Goosse, S. Bouillon, G. Madec, and M.A.M. Maqueda, 2009, Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1. Model description and validation. Ocean Modelling, 27, 33-53. https://doi.org/10.1016/j.ocemod.2008.10.005