Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter

Journal of Geophysical Research: Oceans

Volumn 119, Issue 10, 2014, Pages 6680-6692

  Yang, Qinghua ^a,b   Losa, Svetlana N ^b   Losch, Martin ^b   Tian Kunze, Xiangshan ^c   Nerger, Lars ^b   Liu, Jiping ^d   Kaleschke, Lars ^c   Zhang, Zhanhai ^e  

^a National Marine Environmental Forecasting Center   (China)

^b ALFRED WEGENER INSTITUTE FOR POLAR AND MARINE RESEARCH   (Germany)

^c UNIVERSITY OF HAMBURG   (Germany)

^d STATE UNIVERSITY OF NEW YORK   (United States)

^e Ministry of Natural Resources of the People's Republic of China   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SET; FORECASTING METHOD; ICE THICKNESS; KALMAN FILTER; NUMERICAL MODEL; SATELLITE IMAGERY; SEA ICE; SMOS;

MASSACHUSETTS; UNITED STATES;

EID: 84912108568     PISSN: 21699275     EISSN: 21699291     Source Type: Journal    
DOI: 10.1002/2014JC009963     Document Type: Article

References (39)

1. Cavalieri, D. J., C. L. Parkinson, N. DiGirolamo, and A. Ivanoff (2012), Intersensor calibration between F13 SSMI and F17 SSMIS for global sea ice data records, IEEE Trans. Geosci. Remote Sens., 9(2), 233-236.
2. Eastwood, S., K. R. Larsen, T. Lavergne, E. Neilsen, and R. Tonboe (2011), OSI SAF global sea ice concentration reprocessing: Product user manual, Version 1.3, report, SAF/OSI/CDOP/met.no/TEC/MA/138.
3. Eicken, H. (2013), Ocean science: Arctic sea ice needs better forecasts, Nature, 497(7450), 431-433.
4. Gaspari, G., and S. E. Cohn (1999), Construction of correlation functions in two and three dimensions, Q. J. R. Meteorol. Soc., 125(554), 723-757.
5. Huntemann, M., G. Heygster, L. Kaleschke, T. Krumpen, M. Makynen, and M. Drusch (2014), Empir ical sea ice thickness retrieval during the freeze up period from SMOS high incident angle observations, Cryosphere, 8(2), 439-451, doi:10.5194/tc-8-439-2014.
6. Janjić , T., L. Nerger, A. Albertella, J. Schroter, and S. Skachko (2011), On domain localization in ensemble based Kalman filter algorithms, Mon. Weather Rev., 139, 2046-2060.
7. Kaleschke, L., N. Maaß , C. Haas, S. Heygster, and R. Tonboe (2010), A se a-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice, Cryosphere, 4, 583-592, doi:10.5194/tc-4-583-2010.
8. Kaleschke, L., X. Tian-Kunze, N. Maaß, M. Makynen, and M. Drusch (2012), Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period, Geophys. Res. Lett., 39, L05501, doi:10.1029/2012GL050916.
9. Kivman, G. A., A . L. Kurapov, and A. Guessen (2001), An entropy approach to tuning weights and smoothing in the generalized inversion, J. Atmos. Oceanic Technol., 18, 266-276.
10. Kwok, R., and D. Rothrock (2009), Dec line in Arctic sea ice thickness from submarine and ICESat records: 1958- 2008, Geophys. Res. Lett., 36, L15501, doi:10.1029/2009GL039035.
11. Kwok, R., and D. Sulsky (2010), Arctic Ocean sea ice thickness an d kinematics: Satellite retrievals and modeling, Oceanography, 23(4), 134-143.
12. Lindsay, R. W., and J. Zhang (2006), Assimilation of ice concentration in an ice-ocean model, J. Atmos. Oceanic Technol., 23(5), 742-749.
13. Lisæ ter, K., G. Evensen, and S. Laxon (2007), Assimilating synthetic CryoSat sea ice thickness in a coupled ice-ocean model, J. Geophys. Res., 112, C07023, doi:10.1029/2006JC003786.
14. Lisæ ter, K. A., J. Rosanova, and G. Evensen (2003), Assimilation of ice concentration in a coupled ice-ocean model, using the Ensemble Kalman filter, Ocean Dyn., 53(4), 368-388.
15. Losa, S. N., S. Danilov, J. Schroter, L. Nerger, S. Mabmann, and F. Janssen (2012), Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data, J. Mar. Sy st., 105, 152-162.
16. Losa, S. N., S. Danilov, J. Schroter, T. Janjić, L. Nerger, and F. Janssen (2014), Assimilating NOAA SST data into BSH operational circulation model for the North and Baltic Seas: Pa rt 2. Sensitivity of the forecast's skill to the prior model error statistics, J. Mar. Syst., 129, 259-270.
17. Losch, M., D. Menemenlis, J.-M. Campin, P. Heimbach, and C. Hill (2010), On the formulation of sea ice models. Part 1: Effects of different solver implementations and parameterizations, Ocean Modell., 33(1), 129-144.
18. Marshall, J., A. Adcroft, C. Hill, L. Perelman, and C. Heisey (1997), A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers, J. Geophys. Res., 102(C3)
19. Mecklenburg, S., M. Drusch, Y. H. Kerr, J. Font, M. Martin-Neira, S. Delwart, G. Buenadicha, N. Reul, E. Daganzo-Eusebio, and R. Oliva (2012), ESA's soil moisture and ocean salinity mission: Mission performance and operations, IEEE Trans. Geosci. Remote Sens., 50(5), 1354-1366.
20. Mel ling, H., P. H. Johnston, and D. A. Riedel (1995), Measurements of the underside topography of sea ice by moored subsea sonar, J. Atmos. Oceanic Technol., 12(3), 589-602.
21. Menemenlis, D., J.-M. Campin, P. Heimbach, C. Hill, T. Lee, A. Nguyen, M. Schodlok, and H. Zhang (2008), ECCO2: High resolution global ocean and sea ice data synthesis, Mercator Ocean Q. Newsl., 31, 13-21.
22. Nerger, L., and W. Hiller (2013), Software for ensemble-based data assimilation systems-Implementation strategies and scalability, Comput. Geosci., 55, 110-118.
23. Nerger, L., S. Danilov, W. Hiller, and J. Schroter (2006 ), Using sea-level data to constrain a finite-element primitive-equation ocean model with a local SEIK filter, Ocean Dyn., 56(5- 6), 634-649.
24. Nguyen, A. T., D. Menemenlis, and R. Kwok (2011), Arc tic ice-ocean simulation with optimized model parameters: Approach and assessment, J. Geophys. Res., 116, C04025, doi:10.1029/2010JC006573.
25. Nguyen, A. T., R. Kwok, and D. Menemenlis (2012), Source and pathway o f the Western Arctic upper halocline in a data-constrained coupled ocean and sea ice model, J. Phys. Oceanogr., 42(5), 802-823.
26. Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori, and K. Takahashi (2007), The JRA-25 reanalysis, J. Meteorol. Soc. Jpn., 85(3), 369-432.
27. Perovich, D. K., J. A. Richter-Menge, B. Elder, K. Claffey, and C. Polashenski (2009), Observing and understanding climate change: Monitoring the mass balance, motion, and thickness of Arctic sea ice. [Available at http://imb.crrel.usace.army.mil/.]
28. Pham, D. T. (2001), Stochastic metho ds for sequential data assimilation in strongly nonlinear systems, Mon. Weather Rev., 129(5), 1194-1207.
29. Richter-Menge, J. A., D. K. Perovich, B. C. Elder, K. Claffey, I. Rigor, and M. Ortmeyer (2006), Ice mass-balance buoys: A tool for measuring and attributing changes in the thickness of the Arctic sea ice cover, Ann. Glaciol., 44(1), 205-210.
30. Rollenhagen, K., R. Timmermann, T. Janjić, J. Schroter, and S. Danilov (2009), Assimilation of sea ice motion in a finite-element sea ice model, J. Geophys. Res., 114, C05007, doi:10.1029/2008JC005067.
31. Schweiger, A., R. Linds ay, J. Zhang, M. Steele, H. Stern, and R. Kwok (2011), Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06, doi:10.1029/2011JC007084.
32. Smith, W. H., and D. T. Sandwell (1997), Global sea floor topography from satellite altimetry and ship depth soundings, Science, 277(5334), 1956-1962.
33. Stark, J., J. Ridley, M. Martin, and A. Hines (2008), Sea ice concentration and motion assimilation in a sea ice2ocean model, J. Geophys. Res., 113, C05S91
34. Stroeve, J. C., M. C. Serreze, M. M. Holland, J. E. Kay, J. Malanik, and A. P. Barrett (2012), The Arctic's rapidly shrinking sea ice cover: A research synthesis, Clim. Change, 110(3 -4), 1005-1027.
35. Tian-Kunze, X., L. Kaleschke, N. Maaß, M. Makynen, N. Serra, M. Drusch, and T. Krumpen ( 2014), SMOS-derived thin sea ice thickness: Algorithm baseline, product specifications and initial verification, Cryosphere, 8, 997-1018, doi:10.5194/tc-8-997-2014.
36. Tietsche, S., D. Notz, J. Jungclaus, and J. Marotzke (2013), Assimilation of sea ice concentration in a global climate model-physical and statistical aspects, Ocean Sci., 9(1), 19-36.
37. Tonboe R.,E. Nielsen, (2010), Global sea ice concentration reprocessing validation report, Product OSI-409, Version 1, report, SAF/OSI/CDOP/met.no/TEC/RP/150.
38. Yang, Q., N. S. Losa, M. Losch, J. Liu, Z. Zhang, L. Nerger, and H. Yang (2014), Assimilating summer sea ice concentra tion into a coupled iceocean model using a localized SEIK filter, Ann. Glaciol., 56(69), 38-44, doi:10.3189/2015AoG69A740.
39. Zhang, J., and W. Hibler III (1997), On an efficient numerical method for modeling sea ice dynamics, J. Geophys. Res., 102(C4), 8691-8702.