DOI QR코드

DOI QR Code

기상청 전지구 해양자료동화시스템 2(GODAPS2): 운영체계 및 개선사항

Global Ocean Data Assimilation and Prediction System 2 in KMA: Operational System and Improvements

  • 박형식 (국립기상과학원 기후연구부) ;
  • 이조한 (국립기상과학원 기후연구부) ;
  • 이상민 (국립기상과학원 기후연구부) ;
  • 황승언 (국립기상과학원 기후연구부) ;
  • 부경온 (국립기상과학원 기후연구부)
  • Hyeong-Sik Park (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Johan Lee (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Sang-Min Lee (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Seung-On Hwang (Climate Research Division, National Institute of Meteorological Sciences) ;
  • Kyung-On Boo (Climate Research Division, National Institute of Meteorological Sciences)
  • 투고 : 2023.03.31
  • 심사 : 2023.06.13
  • 발행 : 2023.08.31

초록

The updated version of Global Ocean Data Assimilation and Prediction System (GODAPS) in the NIMS/KMA (National Institute of Meteorological Sciences/Korea Meteorological Administration), which has been in operation since December 2021, is being introduced. This technical note on GODAPS2 describes main progress and updates to the previous version of GODAPS, a software tool for the operating system, and its improvements. GODAPS2 is based on Forecasting Ocean Assimilation Model (FOAM) vn14.1, instead of previous version, FOAM vn13. The southern limit of the model domain has been extended from 77°S to 85°S, allowing the modelling of the circulation under ice shelves in Antarctica. The adoption of non-linear free surface and variable volume layers, the update of vertical mixing parameterization, and the adjustment of isopycnal diffusion coefficient for the ocean model decrease the model biases. For the sea-ice model, four vertical ice layers and an additional snow layer on top of the ice layers are being used instead of previous single ice and snow layers. The changes for data assimilation include the updated treatment for background error covariance, a newly added bias scheme combined with observation bias, the application of a new bias correction for sea level anomaly, an extension of the assimilation window from 1 day to 2 days, and separate assimilations for ocean and sea-ice. For comparison, we present the difference between GODAPS and GODAPS2. The verification results show that GODAPS2 yields an overall improved simulation compared to GODAPS.

키워드

과제정보

이 연구는 기상청 국립기상과학원 「기후예측 현업시스템 운영 및 개발」(KMA2018-00322)의 지원으로 수행되었습니다. 검증을 위한 SST 및 MLD 자료는 ECMWF의 Copernicus Climate Data Store 포털 (CDS)에서, CryoSat-2 해빙 두께 자료는 www.cpom.ucl.ac.uk/csopr에서 입수하였다.

참고문헌

  1. Amante, C., and B. W. Eakins, 2009: ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis, NOAA Technical Memorandum NESDIS NGDC-24, 19 pp, doi:10.7289/V5C8276M.
  2. Balmaseda, M., and D. Anderson, 2009: Impact of initialization strategies and observations on seasonal forecast skill. Geophys. Res. Lett., 36, L01701, doi: 10.1029/2008GL035561.
  3. Barnier, B., and Coauthors, 2006: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution. Ocean Dyn., 56, 543-567, doi:10.1007/s10236-006-0082-1.
  4. Bigg, G. R., M. R. Wadley, D. P. Stevens, and J. A. Johnson, 1997: Modelling dynamics and thermodynamics of icebergs. Cold Reg. Sci. Technol., 26, 113-135, doi:10.1016/S0165-232X(97)00012-8.
  5. Bitz, C. M., and W. H. Lipscomb, 1999: An energy-conserving thermodynamic model of sea ice. J. Geophys. Res.-Oceans, 104, 15669-15677, doi:10.1029/1999JC900100.
  6. Blockley, E. W., and Coauthors, 2014: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts. Geosci. Model Dev., 7, 2613-2638, doi:10.5194/gmd-7-2613-2014.
  7. C3S CDS, 2021: ORAS5 global ocean reanalysis monthly data from 1958 to present. Copernicus Climate Change Service (C3S), Climate Data Store (CDS). doi: 10.24381/cds.67s.67e8eeb7.
  8. Chang, P.-H., S.-O. Hwang, S.-H. Choo, J. Lee, S.-M. Lee, and K.-O. Boo, 2021: Global Ocean Data Assimilation and Prediction System in KMA: Description and assessment. Atmosphere, 31, 229-240, doi:10.14191/Atmos.2021.31.2.229 (in Korean with English abstract).
  9. Ducousso, N., J. L. Sommer, J.-M. Molines, and M. Bell, 2017: Impact of the "Symmetric Instability of the Computational Kind" at mesoscale-and submesosacle-permitting resoulutions. Ocean Modelling, 120, 18-26, doi:10.1016/j.oceamod.2017.10.006.
  10. Fiedler, E., C. Mao, S. Good, J. Waters, and M. Martin, 2019: Improvements to feature resolution in the OSTIA sea surface temperature analysis using the NEMOVAR assimilation scheme. Quart. J. Roy. Meteor. Soc., 145, 3609-3625, doi:10.1002/qj.3644.
  11. Flocco, D., D. L. Feltham, and A. K. Turner, 2010: Incorporation of a physically based melt pond scheme into the sea ice component of a climate model. J. Geophys. Res.-Oceans, 115, C08012, doi:10.1029/2009JC005568.
  12. Flocco, D., D. Schroeder, D. L. Feltham, and E. C. Hunke, 2012: Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007. J. Geophys. Res. Oceans, 117, C09032, doi:10.1029/2012JC008195.
  13. Gaspar, P., Y. Gregoris, and J.-M. Lefevre, 1990: A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at station Papa and long-term upper ocean study site. J. Geophys. Res. Oceans, 95, 16179-16193, doi:10.1029/JC095iC09p16179.
  14. Griffies, S. M., and Coauthors, 2015: Impacts on ocean heat from transient mesoscale eddies in a hierarchy of climate models. J. Climate, 28, 952-977, doi:10.1175/JCLI-D-14-00353.1.
  15. Hollingsworth, A., P. Kallberg, V. Renner, and D. M. Burridge, 1983: An internal symmetric computational instability. Quart. J. Roy. Meteor. Soc., 109, 417-428, doi:10.1002/qj.49710946012.
  16. Hunke, E. C., W. H. Lipscomb, A. K. Turner, N. Jeffery, and S. Elliot, 2015: CICE: the Los Alamos sea ice model, documentation and user's manual version 5.1, Los Alamos National Laboratory, USA, LA-CC-06-012, [Available online at https://svn-ccsm-models.cgd. ucar.edu/cesm1/alphas/branches/cesm1_5_alpha04c_-timers/components/cice/src/doc/cicedoc.pdf].
  17. IOC, IHO and BODC, 2003: Centenary Edition of the GEBCO Digital Atlas, published on CD-ROM on behalf of the Intergovernmental Oceanographic Commission and the International Hydrographic Organization as part of the General Bathymetric Chart of the Oceans, British Oceanographic Data Centre, Liverpool, UK.
  18. Kim, H., J. Lee, Y.-K. Hyun, and S.-O. Hwang, 2021: The KMA Global Seasonal forecasting system (GloSea6) - Part 1: Operational system and improvements. Atmosphere, 31, 341-359, doi:10.14191/Atmos.2021.31.3. 341 (in Korean with English abstract).
  19. Madec, G., 2016: NEMO ocean engine. Note du Pole de modelisation de l'Institut Pierre-Simon Laplace No 27, [Available online at https://www.nemo-ocean.eu/doc/].
  20. Marsh, R., and Coauthors, 2015: NEMO-ICB (v1.0): interactive icebergs in the NEMO ocean model globally configured at eddy-permitting resolution. Geosci. Model Dev., 8, 1547-1562, doi:10.5194/gmd-8-1547-2015.
  21. Martin, M. J., and Coauthors, 2017: Recent developments in global oean data assimilation using NEMOVAR at the Met Office. GOV DA-TT meeting, La Spezia. [Available online at https://www.godae.org/~godae-data/OceanView/Events/DA-OSEval-TT-2017/2.2-FOAMDA_MM_Oct2017.pdf].
  22. Mathiot, P., A. Jenkins, C. Harris, and G. Madec, 2017: Explicit representation and parametrised impacts of under ice shelf seas in the z* coordinate ocean model NEMO 3.6. Geosci. Model Dev., 10, 2849-2874, doi:10.5194/gmd-10-2849-2017.
  23. Merchant, C. J., and Coauthors, 2019: Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci. Data, 6, 223, doi:10.1038/s41597-019-0236-x.
  24. Mirouze, I., and A. T. Weaver, 2010: Representation of correlation functions in variational assimilation using and implicit diffusion operator. Quart. J. Roy. Meteor. Soc., 136, 1421-1443, doi:10.1002/qj.643.
  25. Ridley, J. K., E. W. Blockley, A. B. Keen, J. G. L. Rae, A. E. West, and D. Schroeder, 2018: The sea ice model component of HadGEM3-GC3.1. Geosci. Model Dev., 11, 713-723, doi:10.5194/gmd-11-713-2018.
  26. Rodgers, K. B., and Coauthors, 2014: Strong sensitivity of Southern Ocean carbon uptake and nutrient cycling to wind stirring. Biogeosciences, 11, 4077-4098, doi: 10.5194/bg-11-4077-2014.
  27. Semtner Jr, A. J., 1976: A model for the thermodynamic growth of sea ice in numerical investigations of climate. J. Phys. Oceanogr., 6, 379-389, doi:10.1175/1520-0485(1976)006<0379:AMFTTG>2.0.CO;2.
  28. Storkey, D., and Coauthors, 2018: UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions. Geosci. Model Dev., 11, 3187-3213, doi:10.5194/gmd-11-3187-2018.
  29. Thorndike, A. S., D. A. Rothrock, G. A. Maykut, and R. Colony, 1975: The thickness distribution of sea ice. J. Geophys. Res., 80, 4501-4513, doi:10.1029/JC080i033p04501.
  30. Tilling, R. L., A. Ridout, and A. Shepherd, 2016: Near-real-time Arctic sea ice thickness and volume from CryoSat-2, The Cr yospher e, 10, 2003-2012, doi: 10.5194/tc-10-2003-2016.
  31. Walters, D. N., and Coauthors, 2019: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geosci. Model Dev., 12, 1909-1963, doi:10.5194/gmd-12-1909-2019.
  32. Waters, J., M. J. Bell, M. J. Martin, and D. J. Lea, 2017: Reducing ocean model imbalances in the equatorial region caused by data assimilation. Quart. J. Roy. Meteor. Soc., 143, 195-208, doi:10.1002/qj.2912.
  33. Weaver. A. T., C. Deltel, E. Machu, S. Ricc, and N. Daget, 2005: A multivariate balance operator for variational ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3605-3625, doi:10.1256/qj.05.119.
  34. While, J., and M. J. Martin, 2019: Variational bias correction of satellite sea-surface temperature data incorporating observations of the bias. Quart. J. Roy. Meteor. Soc., 145, 2733-2754, doi:10.1002/qj.3590.
  35. Williams, K. D., and Coauthors, 2017: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. J. Adv. Model. Earth Sys., 10, 357-380, doi:10.1002/2017MS001115.