Ocean and Polar Research

  • 제43권2호
  • /
  • Pages.53-63
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  • 2021
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  • 1598-141X(pISSN)
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  • 2234-7313(eISSN)
한국해양과학기술원 (Korea Institute of Ocean Science & Technology)
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북서태평양 중기해양예측모형(OMIDAS) 해면수온 예측성능: 계절적인 차이

Predictability of Sea Surface Temperature in the Northwestern Pacific simulated by an Ocean Mid-range Prediction System (OMIDAS): Seasonal Difference

  • 정희석 (한국해양과학기술원 해양순환.기후연구센터) ;
  • 김용선 (한국해양과학기술원 해양순환.기후연구센터) ;
  • 신호정 (연세대학교 비가역적기후변화연구센터) ;
  • 장찬주 (한국해양과학기술원 해양순환.기후연구센터)
  • Jung, Heeseok (Ocean Circulation and Climate Research Center, Korea Institute of Ocean Science and Technology) ;
  • Kim, Yong Sun (Ocean Circulation and Climate Research Center, Korea Institute of Ocean Science and Technology) ;
  • Shin, Ho-Jeong (Irreversible Climate Change Research Center, Yonsei University) ;
  • Jang, Chan Joo (Ocean Circulation and Climate Research Center, Korea Institute of Ocean Science and Technology)
  • 투고 : 2020.08.08
  • 심사 : 2021.06.01
  • 발행 : 2021.06.30
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초록

Changes in a marine environment have a broad socioeconomic implication on fisheries and their relevant industries so that there has been a growing demand for the medium-range (months to years) prediction of the marine environment Using a medium-range ocean prediction model (Ocean Mid-range prediction System, OMIDAS) for the northwest Pacific, this study attempted to assess seasonal difference in the mid-range predictability of the sea surface temperature (SST), focusing on the Korea seas characterized as a complex marine system. A three-month re-forecast experiment was conducted for each of the four seasons in 2016 starting from January, forced with Climate Forecast System version 2 (CFSv2) forecast data. The assessment using relative root-mean-square-error was taken for the last month SST of each experiment. Compared to the CFSv2, the OMIDAS revealed a better prediction skill for the Korea seas SST, particularly in the Yellow sea mainly due to a more realistic representation of the topography and current systems. Seasonally, the OMIDAS showed better predictability in the warm seasons (spring and summer) than in the cold seasons (fall and winter), suggesting seasonal dependency in predictability of the Korea seas. In addition, the mid-range predictability for the Korea seas significantly varies depending on regions: the predictability was higher in the East Sea than in the Yellow Sea. The improvement in the seasonal predictability for the Korea seas by OMIDAS highlights the importance of a regional ocean modeling system for a medium-range marine prediction.

키워드

과제정보

이 연구는 2021년 해양수산부의 재원으로 해양수산과학기술진흥원(해양수치모델링과 지능정보기술을 활용한 해양예측 정확도 향상 연구)과 한국연구재단 기초연구사업(한국해 해양열파 특성 및 변동성, NRF-2020R1F1A1072447), 그리고 한중해양과학기술협력 공동위원회 협력사업(20082002)의 지원을 받아 수행되었습니다.

참고문헌

  1. Kim J-Y, Seo K-H (2014) The development of ensemble statistical prediction model for changma precipitation. atmosphere. J Korean Meteor Soc 24(4):533-540
  2. Shin H-J, Jang CJ (2016) Regional characteristics of global warming: linear projection for the timing of unprecedented climate. J Korean Soc Oceanogr 21(2):49-57
  3. Jung H, Jang CJ, Kim YS, Kang S (2018) Development of Ocean Mid-range Prediction System (OMIDAS) for Korea water: a preliminary prediction. J Coast Disa Prev 5:17-23 https://doi.org/10.20481/kscdp.2018.5.1.17
  4. Barnston, AG, Li S, Mason SJ, DeWitt DG, Goddard L, Gong X (2010) Verification of the first 11 years of IRI's seasonal climate forecasts. J Appl Meteorol Clim 49:493-520 https://doi.org/10.1175/2009JAMC2325.1
  5. Belkin IM (2009) Rapid warming of large marine ecosystems. Prog Oceanogr 81:207-213 https://doi.org/10.1016/j.pocean.2009.04.011
  6. Cai R, Tan H, Kontoyiannis H (2017) Robust surface warming in offshore China Seas and its relationship to the East Asian Monsoon wind field and ocean forcing on interdecadal time scales. J Climate 30:8987-9005 https://doi.org/10.1175/JCLI-D-16-0016.1
  7. Chassignet EP, Hurlburt HE, Smedstad OM, Halliwell GR, Hogan PJ, Wallcraft AJ, Barailled R, Blecke R (2007) The HYCOM (hybrid coordinate ocean model) data assimilative system. J Marine Syst 65(1):60-83 https://doi.org/10.1016/j.jmarsys.2005.09.016
  8. Choi BH, Kim D-H, Kim J-W (2002) Regional responses of climate in the northwestern Pacific Ocean to gradual global warming for a CO2 quadrupling. J Meteorol Soc Jpn 80:1427-1442 https://doi.org/10.2151/jmsj.80.1427
  9. Choi BJ, Cho SH, Jung HS, Lee SH, Byun DS, Kwon K (2018) Interannual variation of surface circulation in the Japan/East Sea due to external forcings and intrinsic variability. Ocean Sci 53(1):1-16 https://doi.org/10.1007/s12601-017-0058-8
  10. Chu PC, Fang C-L, Kim CS (2005) Japan/East Sea model predictability. Cont Shelf Res 25(17):2107-2121 https://doi.org/10.1016/j.csr.2005.03.006
  11. Fairall, CW, Bradley EF, Rogers DP, Edson JB, Young GS (1996) Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere response experiment. J Geophys Res-Oceans 101(C2):3747-3764 https://doi.org/10.1029/95JC03205
  12. Kim YS, Jang CJ, Yeh S-W (2018) Recent surface cooling in the Yellow and East China Seas and the associated North Pacific climate regime shift. Cont Shelf Res 156:43-54 https://doi.org/10.1016/j.csr.2018.01.009
  13. Kimura F, Kitoh A (2007) Downscaling by pseudo global warming method. Research Institute for Humanity and Nature (RIHN), Final Report. ICCAP, Kyoto, pp 43-46
  14. Kobayashi M, Hofmann EE, Powell EN, Klinck JM, Kusaka K (1997) A population dynamics model for the Japanese oyster, Crassostrea gigas. Aquaculture 149:285-321 https://doi.org/10.1016/S0044-8486(96)01456-1
  15. Large WG, McWilliams JC, Doney SC (1994) Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev Geophys 32:363-403 https://doi.org/10.1029/94RG01872
  16. Lee HT, Laszlo I, Gruber A (2010) ABI earth radiation budget upward longwave radiation: surface (ULR), NOAA NESIDS CENTER for SATELLITE APPLICATIONS and RESEARCH algorithm theoretical basis document version 2.0. NOAA, Washington DC, 39 p
  17. Park T, Jang CJ (2012) Seasonal variation of freshwater budget in the Yellow and East China Seas simulated from an ocean general circulation model. Ocean Sci J 47:51-59 https://doi.org/10.1007/s12601-012-0005-7
  18. Park T, Jang CJ, Jungclaus J-H, Haak H, Park W, Oh I-S (2011) Effects of the Changjiang river discharge on sea surface warming in the Yellow and East China Seas in summer. Cont Shelf Res 31:15-22 https://doi.org/10.1016/j.csr.2010.10.012
  19. Park T, Jang CJ, Kwon M, Na H, Kim K-Y (2015) An effect of ENSO on summer surface salinity in the Yellow and East. J Marine Syst 141:122-127 https://doi.org/10.1016/j.jmarsys.2014.03.017
  20. Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Climate 15:1609-1625 https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2
  21. Saha S, Moorthi S, Pan HL, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D, Liu H, Stokes D, Grumbine R, Gayno G, Hou YT, Chuang HY, Juang H-MH, Sela J, Iredell M, Treadon R, Kleist D, van Delst P, Keyser D, Derber J, Ek M, Meng J, Wei H, Yang R, Lord S, van d en Dool H, Kumar A, Wang W, Long C, Chelliah M, Xue Y, Huang B, Schemm JK, Ebisuzaki W, Lin R, Xie P, Chen M, Zhou S, Higgins W, Zou C Z, Liu Q, Chen Y, Han Y, Cucurull L, Reynolds RW, Rutledge G, Goldberg M (2010) The NCEP climate forecast system reanalysis. B Am Meteorol Soc 91(8):1015-1057 https://doi.org/10.1175/2010BAMS3001.1
  22. Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Tripp P, Behringer D, Hou YT, Chuang HY, Iredell M, Ek M, Meng J, Yang R, Mendez MP, van den Dool H, Zhang Q, Wang W, Chen M, Becker E (2014) The NCEP climate forecast system version 2. J Climate 27:2185-2208 https://doi.org/10.1175/JCLI-D-12-00823.1
  23. Sakamoto TT, Hasumi H, Ishii M, Emori S, Suzuki T, Nishimura T, Sumi A (2005) Responses of the Kuroshio and the Kuroshio Extension to global warming in a high-resolution climate model. Geophys Res Lett 32(14):L14617. doi:10.1029/2005GL023384
  24. Shchepetkin AF, McWilliams JC (2005) The Regional Ocean Modeling System (ROMS): a split-explicit, free-surface, topography-following coordinates ocean model. Ocean Model 9(4):347-404 https://doi.org/10.1016/j.ocemod.2004.08.002
  25. Siedlecki SA, Kaplan IC, Hermann AJ, Nguyen TT, Bond NA, Newton JA, Williams GD, Peterson WT, Alin SR, Feely RA (2016) Experiments with Seasonal forecasts of ocean conditions for the northern region of the California Current upwelling system. Sci Rep 6:27203 https://doi.org/10.1038/srep27203
  26. Sohn SJ, Tam CJ, Ahn JB (2013) Development of a multimodel-based seasonal prediction system for extreme droughts and floods: a case study for South Korea. Int J Climate 33(4):793-805 https://doi.org/10.1002/joc.3464
  27. Stocker T, Dahe Q, Plattner, GKE (2013) Working group I contribution to the IPCC fifth assessment report climate change 2013, the physical science basis. Final draft underlying scientific-technical assessment IPCC, IPCC, Stockholm, pp 255-315
  28. Tang Q, Mu L, Sidorenko D, Goessling H, Semmler T, Nerger L (2020) Improving the ocean and atmosphere in a coupled ocean-atmosphere model by assimilating satellites ea-surface temperature and subsurface profile data. Q J Roy Meteor Soc 2020:1-16
  29. Yeh S-W, Kim C-H (2010) Recent warming in the Yellow/East China Sea during winter and the associated atmospher ic circulation. Cont Shelf Res 30:1428-1434 https://doi.org/10.1016/j.csr.2010.05.002
  30. Yeh S-W, Won YJ, Hong JS, Lee KJ, Kwon M, Seo KH, Ham YG (2018) The record-breaking heat wave in 2016 over South Korea and its physical mechanism. Mon Weather Rev 146(5):1463-1474 https://doi.org/10.1175/MWR-D-17-0205.1
  31. Yu L, Jin X, Weller RA (2006) Role of net surface heat flux in the seasonal evolution of sea surface temperature in the Atlantic Ocean. J Climate 19:6153-6169 https://doi.org/10.1175/JCLI3970.1
  32. Yu L, Jin X, Weller RA (2007) Annual, seasonal, and interannual variability of air-sea heat fluxes in the Indian Ocean. J Climate 20(13):3190-3209 https://doi.org/10.1175/JCLI4163.1