Ocean and Polar Research

Korea Institute of Ocean Science & Technology (한국해양과학기술원)
권호 표지
DOI QR코드

DOI QR Code

Assessment of Ocean Surface Current Forecasts from High Resolution Global Seasonal Forecast System version 5

고해상도 기후예측시스템의 표층해류 예측성능 평가

  • Lee, Hyomee (Earth System Research Division, National Institute of Meteorological Sciences) ;
  • Chang, Pil-Hun (Earth System Research Division, National Institute of Meteorological Sciences) ;
  • Kang, KiRyong (Earth System Research Division, National Institute of Meteorological Sciences) ;
  • Kang, Hyun-Suk (Numerical Model Development Division, Korea Meteorological Administration) ;
  • Kim, Yoonjae (Earth System Research Division, National Institute of Meteorological Sciences)
  • 이효미 (국립기상과학원 지구시스템연구과) ;
  • 장필훈 (국립기상과학원 지구시스템연구과) ;
  • 강기룡 (국립기상과학원 지구시스템연구과) ;
  • 강현석 (기상청 수치모델개발과) ;
  • 김윤재 (국립기상과학원 지구시스템연구과)
  • Received : 2018.05.02
  • Accepted : 2018.09.14
  • Published : 2018.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

In the present study, we assess the GloSea5 (Global Seasonal Forecasting System version 5) near-surface ocean current forecasts using globally observed surface drifter dataset. Annual mean surface current fields at 0-day forecast lead time are quite consistent with drifter-derived velocity fields, and low values of root mean square (RMS) errors distributes in global oceans, except for regions of high variability, such as the Antarctic Circumpolar Current, Kuroshio, and Gulf Stream. Moreover a comparison with the global high-resolution forecasting system, HYCOM (Hybrid Coordinate Ocean Model), signifies that GloSea5 performs well in terms of short-range surface-current forecasts. Predictions from 0-day to 4-week lead time are also validated for the global ocean and regions covering the main ocean basins. In general, the Indian Ocean and tropical regions yield relatively high RMS errors against all forecast lead times, whilst the Pacific and Atlantic Oceans show low values. RMS errors against forecast lead time ranging from 0-day to 4-week reveal the largest increase rate between 0-day and 1-week lead time in all regions. Correlation against forecast lead time also reveals similar results. In addition, a strong westward bias of about $0.2m\;s^{-1}$ is found along the Equator in the western Pacific on the initial forecast day, and it extends toward the Equator of the eastern Pacific as the lead time increases.

Keywords

References

  1. Korea Meteorological Administration (2014) Development of the diagnostic system for the Korea-UK joint climate prediction system (III). Korea Meteorological Administration, Seoul, 329 p
  2. Ko EB, Moon I-J, Jeong YY, Chang P-H (2018) A comparison of accuracy of the ocean thermal environments using the daily analysis data of the KMA NEMO/NEMOVAR and the US Navy HYCOM/NCODA. Atmosphere 28(1):1-14 https://doi.org/10.14191/ATMOS.2018.28.1.001
  3. Kim YH, Choi B-J, Lee J-S, Byun D-S, Kang K, Kim, Y-G, Cho Y-K (2013) Korean ocean forecasting system: present and future. The Sea 18(2):89-103 https://doi.org/10.7850/jkso.2013.18.2.89
  4. Lee S-M, Kang H-S, Kim Y-H, Byun Y-H, Cho CH (2016) Verification and comparison of forecast skill between global seasonal forecasting system version 5 and unified model during 2014. Atmosphere 26(1):59-72 https://doi.org/10.14191/Atmos.2016.26.1.059
  5. Jung M-I, Son S-W, Choi J, Kang H-S (2015) Assessment of 6-month lead prediction skill of the GloSea5 hindcast experiment. Atmosphere 25(2):323-337 https://doi.org/10.14191/Atmos.2015.25.2.323
  6. Jeong YY, Moon I-J, Chang P-H (2016) Accuracy of shortterm ocean prediction and the effect of atmosphere-ocean coupling on KMA Global Seasonal forecast system (GloSea5) during the development of ocean stratification. Atmosphere 26(4):599-615 https://doi.org/10.14191/Atmos.2016.26.4.599
  7. Ham H, Won D, Lee Y-S (2017) Performance assessment of weekly ensemble prediction data at Seasonal forecast system with high resolution. Atmosphere 27(3):261-276 https://doi.org/10.14191/ATMOS.2017.27.3.261
  8. Adcroft A, Hallberg R, Dunne JP, Samuels BL, Galt JA, Barker CH, Payton D (2010) Simulations of underwater plumes of dissolved oil in the Gulf of Mexico. Geophys Res Lett 37(18). doi:10.1029/2010GL044689
  9. Best MJ, Pryor M, Clark DB, Rooney GG, Essery RLH, Menard CB, Edwards JM, Hendry MA, Porson A, Gedney N, Mercado LM, Sitch S, Blyth E, Boucher O, Cox PM, Grimmond CSB, Harding RJ (2011) The Joint UK Land Environment Simulator (JULES), model description-part 1: energy and water fluxes. Geosci Model Dev 4(3):677-699. doi: 10.5194/gmd-4-677-2011
  10. Blockley EW, Martin MJ, Hyder P (2012) Validation of FOAM near-surface ocean current forecasts using Lagrangian drifting buoys. Ocean Sci 8(4):551-565 https://doi.org/10.5194/os-8-551-2012
  11. Blockley EW, Martin MJ, McLaren AJ, Ryan AG, Waters J, Lea DJ, Mirouze I, Peterson KA, Sellar A, Storkey D (2014) Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new global FOAM forecasts. Geoscientific Model Dev 7(6):2613-2638 https://doi.org/10.5194/gmd-7-2613-2014
  12. Bonjean F, Lagerloef GS (2002) Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean. J Phys Oceanogr 32(10):2938-2954 https://doi.org/10.1175/1520-0485(2002)032<2938:DMAAOT>2.0.CO;2
  13. Bowler N, Arribas A, Beare S, Mylne KE, Shutts G (2009) The local ETKF and SKEB: upgrades to the MOGREPS short-range ensemble prediction system. Q J Roy Meteor Soc 135(640):767-776 https://doi.org/10.1002/qj.394
  14. Davidson FJM, Allen A, Brassington GB, Breivik O, Daniel P, Kamachi M, Sato S, King B, Lefevre F, Sutton M, Kaneko H (2009) Applications of GODAE ocean current forecasts to search and rescue and ship routing. Oceanography 22(3):176-181 https://doi.org/10.5670/oceanog.2009.76
  15. Huckerby J (2011) Marine energy: resources, technologies, research and policies. In: Schiller A, Brassington GB (eds) Operational oceanography in the 21st century. Springer, Dordrecht, pp 695-720
  16. Hunke EC, Lipscomb WH, Turner AK, Jeffery N, Elliott S (2010) CICE: the Los Alamos sea ice model documentation and software user's manual version 4.1 LA-CC-06-012. T-3 fluid dynamics group. Los Alamos National Laboratory Technical Report 675, 76 p
  17. Kawamura H, Kobayashi T, Furuno A, In T, Ishikawa Y, Nakayama T, Shima S, Awaji T (2011) Preliminary numerical experiments on oceanic dispersion of $^{131}I$ and $^{137}Cs$ discharged into the ocean because of the Fukushima Daiichi nuclear power plant disaster. J Nucl Sci Technol 48(11):1349-1356. doi:10.1080/18811248.2011.9711826
  18. Lee T, Waliser DE, Li J-L F, Landerer FW, Gierach MM (2013) Evaluation of CMIP3 and CMIP5 wind stress climatology using satellite measurements and atmospheric reanalysis products. J Climate 26:5810-5826. doi:10.1175/JCLI-D-12-00591.1
  19. Lumpkin R, Grodsky SA, Centurioni L, Rio MH, Carton JA, Lee D (2013) Removing spurious low-frequency variability in drifter velocities. J Atmos Ocean Tech 30(2):353-360 https://doi.org/10.1175/JTECH-D-12-00139.1
  20. MacLachlan C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon M, Vellinga M, Williams A, Corner RE, Camp J, Xavier P, Madec G (2015) Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q J Roy Meteor Soc 141(689):1072-1084 https://doi.org/10.1002/qj.2396
  21. Madec G (2008) NEMO ocean engine, Note du Pole de modelisation. Institut Pierre-Simon Laplace, Paris, 386 p
  22. Maltrud M, Peacock S, Visbeck M (2010) On the possible long-term fate of oil released in the Deepwater Horizon incident, estimated using ensembles of dye release simulations. Environ Res Lett 5(3):035301. doi:10.1088/1748-9326/5/3/035301
  23. Mulholland DP, Laloyaux P, Haines K, Balmaseda MA (2015) Origin and impact of initialization shocks in coupled atmosphere-ocean forecasts. Mon Weather Rev 143(11):4631-4644 https://doi.org/10.1175/MWR-D-15-0076.1
  24. Nakano M, Povinec PP (2012) Long-term simulations of the $^{137}Cs$ dispersion from the Fukushima accident in the world ocean. J Environ Radioactiv 111:109-115 https://doi.org/10.1016/j.jenvrad.2011.12.001
  25. National Academies of Sciences, Engineering, and Medicine (2016) Next generation Earth system prediction: strategies for subseasonal to seasonal forecasts. The National Academies Press, Washington DC, 350 p
  26. Park S, Kim DJ, Lee SW, Lee KW, Kim J, Song EJ, Seo KH (2017) Comparison of extended medium-range forecast skill between KMA ensemble, ocean coupled ensemble, and GloSea5. Asia-Pac J Atmos Sci 53(3):393-401 https://doi.org/10.1007/s13143-017-0035-2
  27. Rossi V, Van Sebille E, Gupta AS, Garcon V, England MH (2013) Multi-decadal projection of surface and interior pathways of the Fukushima Cesium-137 radioactive plume. Deep-Sea Res Part I 80:37-46 https://doi.org/10.1016/j.dsr.2013.05.015
  28. Walters DN, Best MJ, Bushell AC, Copsey D, Edwards JM, Falloon PD, Harris CM, Lock AP, Manners JC, Morcrette CJ, Roberts MJ, Stratton RA, Webster S, Wilkinson JM, Willett MR, Boutle IA, Earnshaw PD, Hill PG, MacLachlan C, Martin GM, Moufouma-Okia W, Palmer MD, Petch JC, Rooney GG, Scaife AA, Williams KD (2011) The Met Office unified model global atmosphere 3.0/3.1 and JULES global land 3.0/3.1 configurations. Geosci Model Dev 4(4):919-941. doi: 10.5194/gmd-4-919-2011
  29. Waters J, Lea DJ, Martin MJ, Mirouze I, Weaver A, While J (2014) Implementing a variational data assimilation system in an operational 1/4 degree global ocean model. Q J Roy Meteor Soc 141(687):333-349
  30. Williams KD, Harris CM, Bodas-Salcedo A, Camp J, Comer RE, Copsey D, Fereday D, Graham T, Hill R, Hinton T, Hyder P, Ineson S, Masato G, Milton SF, Roberts MJ, Rowell DP, Sanchez C, Shelly A, Sinha B, Walters DN, West A, Woollings T, Xavier PK (2015) The Met Office global coupled model 2.0 (GC2) configuration. Geosci Model Dev 8:1509-1524. doi:10.5194/gmd-8-1509-2015
  31. Xu H, Tokinaga H, Xie S-P (2010) Atmospheric effects of the Kuroshio large meander during 2004-05. J Climate 23:4704-4715. doi:10.1175/2010JCLI3267.1