Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter

Journal of Geophysical Research: Oceans

Volumn 119, Issue 7, 2014, Pages 4168-4184

  Massonnet, F ^a   Goosse, H ^a   Fichefet, T ^a   Counillon, F ^b  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^b NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER   (Norway)

Author keywords

data assimilation; parameter estimation; sea ice dynamics

Indexed keywords

CALIBRATION; COMPUTER SIMULATION; CURRICULA; DRAG; HEURISTIC METHODS; KALMAN FILTERS; OCEAN CURRENTS; PARAMETER ESTIMATION;

CHOICE OF PARAMETERS; DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; ESTIMATION OF PARAMETERS; EXPONENTIAL INCREASE; PARAMETER CALIBRATION; SEA ICE DYNAMICS; SEA ICE THICKNESS DISTRIBUTIONS;

SEA ICE;

AUTOMATION; CALIBRATION; ESTIMATION METHOD; HEURISTICS; ICE MOVEMENT; ICE THICKNESS; KALMAN FILTER; PARAMETERIZATION; SEA ICE; SIMULATION;

ARCTIC OCEAN;

EID: 84906084131     PISSN: None     EISSN: 21699291     Source Type: Journal    
DOI: 10.1002/2013JC009705     Document Type: Article

References (67)

1. Anderson, J. L., (2001), An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129 (12), 2884-2903, doi: 10.1175/1520- 0493(2001)129 <2884:AEAKFF>2.0.CO;2. (Pubitemid 34004796)
2. Annan, J. D., D. J. Lunt, J. C. Hargreaves, and, P. J. Valdes, (2005), Parameter estimation in an atmospheric GCM using the Ensemble Kalman Filter, Nonlinear Processes Geophys., 12 (3), 363-371, doi:10.5194/npg-12-363-2005. (Pubitemid 40518138)
3. Baumgartner, A., and, E. Reichel, (1976), The world water balance mean annual global, continental and maritime precipitation, evaporation and run-off, Agric. Water Manage., 1 (1), 100-101. [Available at http://ideas.repec.org/a/ eee/agiwat/v1y1976i1p100-101.html.]
4. Beckmann, A., and, R. Döscher, (1997), A method for improved representation of dense water spreading over topography in geopotential- coordinate models, J. Phys. Oceanogr., 27 (4), 581-591, doi: 10.1175/1520-0485(1997)027 <0581:AMFIRO>2.0.CO;2.
5. Berliand, M. E., and, T. G. Strokina, (1980), Global Distribution of the Total Amount of Clouds, 71 pp., Hydrometeorological, Leningrad, Russia.
6. Blanke, B., and, P. Delecluse, (1993), Variability of the tropical Atlantic Ocean simulated by a general circulation model with two different mixed-layer physics, J. Phys. Oceanogr., 23 (7), 1363-1388, doi: 10.1175/1520-0485(1993)023 <1363:VOTTAO>2.0.CO;2. (Pubitemid 24429521)
7. Bocquet, M., (2011), Ensemble Kalman filtering without the intrinsic need for inflation, Nonlinear Processes Geophys., 18 (5), 735-750, doi: 10.5194/npg-18-735-2011.
8. Bocquet, M., and, P. Sakov, (2012), Combining inflation-free and iterative ensemble kalman filters for strongly nonlinear systems, Nonlinear Processes Geophys., 19, 383-399, doi: 10.5194/npg-19-383-2012.
9. Bougeault, P., and, P. Lacarrere, (1989), Parameterization of orography-induced turbulence in a mesobeta-scale model, Mon. Weather Rev., 117 (8), 1872-1890, doi: 10.1175/1520-0493(1989)117 <1872:POOITI>2.0.CO;2.
10. Bouillon, S., M. A. Morales Maqueda, V. Legat, and, T. Fichefet, (2009), An elastic-viscous-plastic sea ice model formulated on Arakawa B and C grids, Ocean Modell., 27, 174-184.
11. Burgers, G., P. van Leeuwen, and, G. Evensen, (1998), Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev., 126 (6), 1719-1724, doi: 10.1175/1520-0493(1998)126 <1719:ASITEK>2.0.CO;2. (Pubitemid 128597660)
12. Chevallier, M., and, D. Salas Mélia, (2012), The role of sea ice thickness distribution in the Arctic sea ice potential predictability: A diagnostic approach with a coupled GCM, J. Clim., 25, 3025-3038.
13. Comiso, J. C., and, F. Nishio, (2008), Trends in the sea ice cover using enhanced and compatible AMSR-E, SSM/I and SMMR data, J. Geophys. Res., 113, C02S07, doi: 10.1029/2007JC004257.
14. Dorn, W., K. Dethloff, A. Rinke, S. Frickenhaus, R. Gerdes, M. Karcher, and, F. Kauker, (2007), Sensitivities and uncertainties in a coupled regional atmosphere-ocean-ice model with respect to the simulation of Arctic sea ice, J. Geophys. Res., 112, D10118, doi: 10.1029/2006JD007814.
15. Evensen, G., (2003), The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dyn., 53, 343-367.
16. Evensen, G., (2007), Data Assimilation-The Ensemble Kalman Filter, Springer, N. Y.
17. Feltham, D. L., (2008), Sea ice rheology, Annu. Rev. Fluid Mech., 40 (1), 91-112, doi: 10.1146/annurev.fluid.40.111406.102151. (Pubitemid 351234694)
18. Fichefet, T., and, M. A. Morales Maqueda, (1997), Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics, J. Geophys. Res., 102, 12,609-12,646.
19. Fischer, H., and, P. Lemke, (2013), On the required accuracy of atmospheric forcing fields for driving dynamic-thermodynamic sea ice models, in The Polar Oceans and Their Role in Shaping the Global Environment, pp. 373-381, AGU, Washington, D. C., doi: 10.1029/GM085p0373.
20. Girard, L., J. Weiss, J. M. Molines, B. Barnier, and, S. Bouillon, (2009), Evaluation of high-resolution sea ice models on the basis of statistical and scaling properties of Arctic sea ice drift and deformation, J. Geophys. Res., 114, C08015, doi: 10.1029/2008JC005182.
21. Girard, L., S. Bouillon, J. Weiss, D. Amitrano, T. Fichefet, and, V. Legat, (2011), A new modeling framework for sea-ice mechanics based on elasto-brittle rheology, Ann. Glaciol., 52 (57), 123-132.
22. Goosse, H., (1997), Modelling the large-scale behaviour of the coupled ocean-sea-ice system, PhD thesis, Université catholique de Louvain.
23. Goosse, H., and, T. Fichefet, (1999), Importance of ice-ocean interactions for the global ocean circulation: A model study, J. Geophys. Res., 104, 23,337-23,355.
24. GRDC (2000), Observed historical discharge data from major rivers for climate model validation, Tech. Rep. 307, Max Planck Inst., Hamburg, Germany. [Available at https://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max- scirep-307.pdf.]
25. Harder, M., and, H. Fischer, (1999), Sea ice dynamics in the Weddell Sea simulated with an optimized model, J. Geophys. Res., 104, 11,151-11,162, doi: 10.1029/1999JC900047.
26. Hibler, W. D., (1979), A dynamic thermodynamic sea ice model, J. Phys. Oceanogr., 9, 815-846.
27. Hunke, E. C., and, J. K. Dukowicz, (1997), An elastic-viscous-plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849-1867. (Pubitemid 127607138)
28. Hunke, E. C., W. H. Lipscomb, and, A. K. Turner, (2010), Sea ice models for climate study: Retrospective and new directions, J. Glaciol., 56, 1162-1172.
29. Jazwinski, A. H., (1970), Stochastic Processes and Filtering Theory, Academic, N. Y.
30. Juricke, S., P. Lemke, R. Timmermann, and, T. Rackow, (2012), Effects of stochastic ice strength perturbation on Arctic finite element sea ice modeling, J. Clim., 26 (11), 3785-3802, doi: 10.1175/JCLI-D-12-00388.1.
31. Kalnay, E., et al. (1996), The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77, 437-471.
32. Keghouche, I., (2010), Modeling the dynamics and drift of icebergs in the Barents Sea, PhD thesis, Univ. of Bergen, Bergen, Norway.
33. Kim, J. G., E. C. Hunke, and, W. H. Lipscomb, (2006), Sensitivity analysis and parameter tuning scheme for global sea-ice modeling, Ocean Modell., 14, 61-80. (Pubitemid 43905992)
34. Kloster, K., and, S. Sandven, (2011), Ice motion and ice area flux in Fram Strait at 79N using ASAR and passive microwave for Feb. 2004 Jul. 2010, Tech. Rep. 322, Nansen Environ. and Remote Sens. Cent., Bergen, Norway. [Available at http://arctic-roos.org/Members/nguest/monitoring-sea-ice-area- flux-in-the-fram-strait-using-satellite-sar-data-nersc.]
35. Kurtz, N. T., S. L. Farrell, M. Studinger, N. Galin, J. P. Harbeck, R. Lindsay, V. D. Onana, B. Panzer, and, J. G. Sonntag, (2013), Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data, Cryosphere, 7 (4), 1035-1056, doi:10.5194/tc-7-1035-2013.
36. Large, W. G., and, S. G. Yeager, (2004), Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies, technical report NCAR (National Center for Atmospheric Research)/TN-460+STR, Boulder, Colo.
37. Lavergne, T., S. Eastwood, Z. Teffah, H. Schyberg, and, L.-A. Breivik, (2010), Sea ice motion from low-resolution satellite sensors: An alternative method and its evaluation in the Arctic, J. Geophys. Res., 115, C10032, doi: 10.1029/2009JC005958.
38. Leppäranta, M., (2005), The Drift of Sea Ice, Springer, N. Y.
39. Levitus, S., (1998), NODC World Ocean Atlas. [Available at http://www.esrl.noaa.gov/psd/.], National Oceanic and Atmospheric Administration (NOAA), Washington, D. C.
40. Lisæter, 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, 368-388, doi: 10.1007/s10236-003-0049-4.
41. Lisæter, K. A., G. Evensen, and, S. Laxon, (2007), Assimilating synthetic CryoSat sea ice thickness in a coupled ice-ocean, J. Geophys. Res., 112, C07023, doi: 10.1029/2006JC003786.
42. Madec, G., (2008), NEMO Ocean Engine: Note du Pôle de Modélisation, vol. 27, Inst. Pierre-Simon Laplace, France. [Available at http://www.nemo-ocean.eu/About-NEMO/Reference-manuals.]
43. Maslanik, J., T. Agnew, M. Drinkwater, W. Emery, C. Fowler, R. Kwok, and, A. Liu, (1998), Summary of ice-motion mapping using passive microwave data, technical report number 8, National Snow and Ice Data Center (NSIDC), Boulder, Colo. [Available at http://nsidc.org/pubs/special/.]
44. Massonnet, F., T. Fichefet, H. Goosse, M. Vancoppenolle, P. Mathiot, and, C. König Beatty, (2011), On the influence of model physics on simulations of Arctic and Antarctic sea ice, The Cryosphere, 5 (3), 687-699, doi: 10.5194/tc-5-687-2011. [Available at http://www.the-cryosphere.net/5/687/2011/.]
45. Massonnet, F., P. Mathiot, T. Fichefet, H. Goosse, C. König Beatty, M. Vancoppenolle, and, T. Lavergne, (2013), A model reconstruction of the Antarctic sea ice thickness and volume changes over 1980-2008 using data assimilation, Ocean Modell., 64, 67-75, doi: 10.1016/j.ocemod.2013.01.003.
46. Mathiot, P., C. König Beatty, T. Fichefet, H. Goosse, F. Massonnet, and, M. Vancoppenolle, (2012), Better constraints on the sea-ice state using global sea-ice data assimilation, Geosci. Model Dev., 5 (6), 1501-1515, doi: 10.5194/gmd-5-1501-2012.
47. Mauritsen, T., et al. (2012), Tuning the climate of a global model, J. Adv. Model. Earth Syst., 4, M00A01, doi: 10.1029/2012MS000154.
48. McPhee, M., (1980), An analysis of pack ice drift in summer, in Sea Ice Processes and Models, edited by, R. Pritchard, pp. 62-75, Univ. of Wash., Seattle.
49. Miller, P. A., S. W. Laxon, D. L. Feltham, and, D. J. Cresswell, (2006), Optimization of a sea ice model using basinwide observations of Arctic sea ice thickness, extent and velocity, J. Clim., 19, 1090-1108.
50. Nguyen, A. T., D. Menemenlis, and, R. Kwok, (2011), Arctic ice-ocean simulation with optimized model parameters: Approach and assessment, J. Geophys. Res., 116, C04025, doi: 10.1029/2010JC006573.
51. Notz, D., (2012), Challenges in simulating sea ice in earth system models, WIREs Clim. Change, 3 (6), 509-526, doi: 10.1002/wcc.189.
52. Owens, W. B., and, P. Lemke, (1990), Sensitivity studies with a sea ice-mixed layer-pycnocline model in the Weddell Sea, J. Geophys. Res., 95, 9527-9538, doi: 10.1029/JC095iC06p09527.
53. Rae, J., H. Hewitt, A. Keen, J. Ridley, J. Edwards, and, C. Harris, (2014), A sensitivity study of the sea ice simulation in the global coupled climate model, hadgem3, Ocean Modell., 74, 60-76, doi: 10.1016/j.ocemod.2013.12. 003.
54. Rampal, P., J. Weiss, D. Marsan, and, M. Bourgoin, (2009), Arctic sea ice velocity field: General circulation and turbulent-like fluctuations, J. Geophys. Res., 114, C10014, doi: 10.1029/2008JC005227.
55. Sakov, P., and, P. R. Oke, (2008), A deterministic formulation of the ensemble Kalman filter: An alternative to ensemble square root filters, Tellus, Ser. A, 60, 361-371.
56. Shine, K. P., and, A. Henderson-Sellers, (1985), The sensitivity of a thermodynamic sea ice model to changes in surface albedo parameterization, J. Geophys. Res., 90, 2243-2250.
57. Steele, M., J. Zhang, D. Rothrock, and, H. Stern, (1997), The force balance of sea ice in a numerical model of the arctic ocean, J. Geophys. Res., 102, 21,061-21,079, doi: 10.1029/97JC01454.
58. Stössel, A., (1992), Sensitivity of Southern Ocean sea-ice simulations to different atmospheric forcing algorithms, Tellus, Ser. A, 44 (5), 395-413, doi: 10.1034/j.1600-0870.1992.t01-3-00004.x. (Pubitemid 23376575)
59. Stössel, A., P. Lemke, and, W. B. Owens, (1990), Coupled sea ice-mixed layer simulations for the Southern Ocean, J. Geophys. Res., 95, 9539-9555, doi: 10.1029/JC095iC06p09539.
60. Sumata, H., F. Kauker, R. Gerdes, C. Köberle, and, M. Karcher, (2013), A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model, Ocean Sci., 9 (4), 609-630, doi: 10.5194/os-9-609-2013.
61. Thomas, D., and, G. Dieckmann, (2010), Sea Ice, Wiley, Oxford, U. K.
62. Thorndike, A. S., (1986), Diffusion of sea ice, J. Geophys. Res., 91, 7691-7696, doi: 10.1029/JC091iC06p07691.
63. 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.
64. Trenberth, K. E., W. Large, and European Centre for Medium Range Weather Forecasts (1989), A global ocean wind stress climatology based on the ECMWF analyses. [Available at http://dss.ucar.edu/datasets/ds110.1/.], National Center for Atmospheric Research (NCAR), Boulder, Colo.
65. Uotila, P., S. OFarrell, S. Marsland, and, D. Bi, (2012), A sea-ice sensitivity study with a global ocean-ice model, Ocean Modell., 51, 1-18, 10.1016/j.ocemod.2012.04.002.
66. Vancoppenolle, M., T. Fichefet, H. Goosse, S. Bouillon, G. Madec, and, M. A. Morales Maqueda, (2009), Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1. Model description and validation, Ocean Modell., 27, 33-53.
67. Vancoppenolle, M., et al. (2013), Role of sea ice in global biogeochemical cycles: Emerging views and challenges, Quat. Sci. Rev., 79, 207-230, doi: 10.1016/j.quascirev.2013.04.011.