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Internal variability of a 3-D ocean model Cover

Internal variability of a 3-D ocean model

Tellus
Volume 68 (2016): Issue 1
By:  and    
Open Access
|Dec 2016
References

References

  1. Brankart J. M., Candille G., Garnier F., Calone C., Melet A., co-authors. A generic approach to explicit simulation of uncertainty in the NEMO ocean model. Geosci. Model Dev. 2015; 8: 1285–1297. http://dx.doi.org/10.5194/gmd-8-1285-2015.
    Search in Google ScholarBack to article
  2. Broström G., Carrasco A., Hole L. R., Dick S., Janssen F., co-authors. Usefulness of high resolution coastal models for operational oil spill forecast: the full city accident. Ocean Sci. 2011; 7(6): 805–820. http://dx.doi.org/10.5194/os-7-805-2011.
    Search in Google ScholarBack to article
  3. Büchmann B., Hansen C., Söderkvist J. Improvement of hydrodynamic forecasting of Danish waters: impact of low-frequency North Atlantic barotropic variations. Ocean Dynam. 2011; 61: 1611–1617. http://dx.doi.org/10.1007/s10236-011-0451-2.
    Search in Google ScholarBack to article
  4. Burchard H., Bolding K. GETM A General Estuarine Transport Model. Scientific Documentation. 2002. Technical Report EUR 20253 EN. European Commission, Ispra (VA), Italy. Online at: http://bookshop.europa.eu/en/getm-a-general-estuarine-transport-model-pbEUNA20253/.
    Search in Google ScholarBack to article
  5. Chu P. C., Miller S. E., Hansen J. A. Fuel-saving ship route using the Navy's ensemble meteorological and oceanic forecasts. J. Def. Model Simulation Appl. Methodol. Technol. 2015; 12(1): 41–56. http://dx.doi.org/10.1177/1548512913516552.
    Search in Google ScholarBack to article
  6. COAS. OSU Tidal Data Inversion. 2015. Online at: http://volkov.oce.orst.edu/tides/.
    Search in Google ScholarBack to article
  7. Egbert G. D., Erofeeva S. Y. Efficient inverse modeling of barotropic ocean tides. J. Atmos. Ocean. Technol. 2002; 19(2): 183–204. http://dx.doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2.
    Search in Google ScholarBack to article
  8. Fu W., She J., Zhuang S. Application of an ensemble optimal interpolation in a North/Baltic Sea model: assimilating temperature and salinity profiles. Ocean Model. 2011; 40(3–4): 227–245. http://dx.doi.org/10.1016/j.ocemod.2011.09.004.
    Search in Google ScholarBack to article
  9. Golbeck I., Li X., Janssen F., Brüning T., Nielsen J. W., co-authors. Uncertainty estimation for operational ocean forecast products – a multi-model ensemble for the North Sea and the Baltic Sea. Ocean Dynam. 2015; 65(12): 1603–1631. http://dx.doi.org/10.1007/s10236-015-0897-8.
    Search in Google ScholarBack to article
  10. Gustafsson B. High frequency variability of the surface layers in the Skagerrak during SKAGEX. Cont. Shelf Res. 1999; 19(8): 1021–1047. http://dx.doi.org/10.1016/S0278-4343(99)00008-4.
    Search in Google ScholarBack to article
  11. Holt T. R., Cummings J. A., Bishop C. H., Doyle J. D., Hong X., co-authors. Development and testing of a coupled ocean atmosphere mesoscale ensemble prediction system. Ocean Dynam. 2011; 61(11): 1937–1954. http://dx.doi.org/10.1007/s10236-011-0449-9.
    Search in Google ScholarBack to article
  12. Holtermann P. L., Burchard H., Gräwe U., Klingbeil K., Umlauf L. Deep-water dynamics and boundary mixing in a nontidal stratified basin: a modeling study of the Baltic Sea. J. Geophys. Res. Oceans. 2014; 119(2): 1465–1487. http://dx.doi.org/10.1002/2013JC009483.
    Search in Google ScholarBack to article
  13. Lamb H . Hydrodynamics. 1932; 6th ed, Cambridge, UK: Cambridge University Press.
    Search in Google ScholarBack to article
  14. Lindström G., Johansson B., Persson M., Gardelin M., Bergström S. Development and test of the distributed HBV-96 hydrological model. J. Hydrol. 1997; 201: 272–288. http://dx.doi.org/10.1016/S0022-1694(97)00041-3.
    Search in Google ScholarBack to article
  15. Lorenz E. N. Deterministic nonperiodic flow. J. Atmos. Sci. 1963; 20: 130–141. http://dx.doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.
    Search in Google ScholarBack to article
  16. Losa S. N., Danilov S., Schröter J., Nerger L., Maßmann S., co-authors. Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: inference about the data. J. Marine Syst. 2012; 122: 105–108. http://dx.doi.org/10.1016/j.jmarsys.2012.07.008.
    Search in Google ScholarBack to article
  17. Melsom A. Mesoscale activity in the North Sea as seen in ensemble simulations. Ocean Dynam. 2005; 55(3): 338–350. http://dx.doi.org/10.1007/s10236-005-0016-3.
    Search in Google ScholarBack to article
  18. Melsom A., Counillon F., LaCasce J. H., Bertino L. Forecasting search areas using ensemble ocean circulation modeling. Ocean Dynam. 2012; 62(8): 1245–1257. http://dx.doi.org/10.1007/s10236-012-0561-5.
    Search in Google ScholarBack to article
  19. Molteni F., Buizza R., Palmer T. N., Petroliagis T. The ECMWF ensemble prediction system: methodology and validation. Q. J. Roy. Meteorol. Soc. 1996; 122(529): 73–119. http://dx.doi.org/10.1002/qj.49712252905.
    Search in Google ScholarBack to article
  20. Nielsen M. H. The baroclinic surface currents in the Kattegat. J. Marine Syst. 2005; 55: 97–121. http://dx.doi.org/10.1016/j.jmarsys.2004.08.004.
    Search in Google ScholarBack to article
  21. O'Dea E. J., Arnold A. K., Edwards K. P., Furner R., Hyder P., co-authors. An operational ocean forecast system incorporating NEMO and SST data assimilation for the tidally driven European North-West shelf. J. Oper. Oceanogr. 2012; 5(1): 3–17. http://dx.doi.org/10.1080/1755876X.2012.11020128.
    Search in Google ScholarBack to article
  22. Oke P. R., Brassington G. B., Griffin D. A., Schiller A. The Bluelink ocean data assimilation system (BODAS). Ocean Model. 2008; 21(1–2): 3301–3311. http://dx.doi.org/10.1016/j.ocemod.2007.11.002.
    Search in Google ScholarBack to article
  23. Sakov P., Counillon F., Bertino L., Lisæter K. A., Oke P. R., co-authors. TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Sci. 2012; 8(4): 633–656. http://dx.doi.org/10.5194/os-8-633-2012.
    Search in Google ScholarBack to article
  24. Shchepetkin A. F., McWilliams J. C. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model. 2005; 9(4): 347–404. DOI: http://dx.doi.org/10.1016/j.ocemod.2004.08.002.
    Search in Google ScholarBack to article
  25. Toth Z., Kalnay E. Ensemble forecasting at NMC: the generation of perturbations. Bull. Am. Meteorol. Soc. 1993; 74(12): 2317–2330. http://dx.doi.org/10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.
    Search in Google ScholarBack to article
  26. Umlauf L., Burchard H. Second-order turbulence closure models for geophysical boundary layers. A review of recent work. Cont. Shelf Res. 2005; 25: 795–827. http://dx.doi.org/10.1016/j.csr.2004.08.004.
    Search in Google ScholarBack to article
  27. Undén P., Rontu L., Järvinen H., Lynch P., Calvo J., co-authors. HIRLAM-5 scientific documentation. 2002. Online at: http://hirlam.org/index.php/hirlam-documentation.
    Search in Google ScholarBack to article
  28. van Leeuwen P. J. Nonlinear data assimilation in geosciences: an extremely efficient particle filter. Q. J. Roy. Meteorol. Soc. 2010; 136: 1991–1999. http://dx.doi.org/10.1002/qj.699.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.3402/tellusa.v68.30417 | Journal eISSN: 3035-9554
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Language: English
Page range: 30417 - 30417
Submitted on: Nov 16, 2015
Accepted on: Oct 6, 2016
Published on: Dec 1, 2016
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
Baroclinic,
ensemble modelling,
salinity,
temperature,
elevation,
Skagerrak,
Baltic Sea

© 2016 Bjarne Büchmann, Johan Söderkvist, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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