## REFERENCES

Andersen, S, Tonboe, R, Kern, S and Schyberg, H (2006) Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms. Remote Sens. Environ., 104, 374–392.

Barnes, S (1964) A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3(4), 396–409 (doi: 10.1175/1520-0450(1964)003¡0396:ATFMDI¿2.0.CO;2)

Bouttier, F, Derber, J and Fisher, M (1997) The 1997 revision of the jb term in 3d/4d-var. ECMWF Tech. Memo., 238.

Burgers, G, van Leeuwen, P and Evensen, G (1998) Analysis scheme in the ensemble Kalman filter. Mon. Weather Rev., 126, 1719–1791 (doi: 10.1175/1520-0493(1998)126¡1719:ASITEK¿2.0.CO;2)

Caya, A, Buehner, M and Carrieres, T (2010) Analysis and forecasting of sea ice conditions with three-dimensional variational data assimilation and a coupled ice-ocean model. J. Atmos. Oceanic Technol., 27(2), 353–369 (doi: 10.1175/2009JTECHO701.1)

Dee, DP, 35 others (2011) The era-interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137(656), 553–597 (doi: 10.1002/qj.828)

Dimet, FXL and Talagrand, O (1986) Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus, 38A(2), 97–110 (doi: 10.3402/tellusa.v38i2.11706)

Dumont, D, Gratton, Y and Arbetter, TE (2009) Modeling the dynamics of the north water polynya ice bridge. J. Phys. Oceanogr., 39(6), 1448–1461 (doi: 10.1175/2008JPO3965.1)

Evensen, G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10143–10162 (doi: 10.1029/94JC00572)

Evensen, G (2003) The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343–367 (doi: 10.1007/s10236-003-0036-9)

Evensen, G (2009) The ensemble Kalman filter for combined state and parameter estimation. IEEE Control Syst. Mag., 29(3), 83–104 (doi: 10.1109/MCS.2009.932223)

Gaspari, G and Cohn, SE (1999) Construction of correlation functions in two and three dimensions. Q. J. R. Meteorol. Soc., 125(554), 723–757 (doi: 10.1002/qj.49712555417)

Gauthier, P, Tanguay, M, Laroche, S, Pellerin, S and Morneau, J (2007) Extension of 3dvar to 4dvar: Implementation of 4dvar at the meteorological service of Canada. Mon. Weather Rev., 135(6), 2339–2354 (doi: 10.1175/MWR3394.1)

Hibler, WD III (1979) A dynamic thermodynamic sea ice model. J. Phys. Oceanogr., 9(4), 815–846 (doi: 10.1175/1520-0485(1979)009¡0815:ADTSIM¿2.0.CO;2)

Hoke, JE and Anthes, RA (1976) The initialization of numerical models by a dynamic-initialization technique. Mon. Weather Rev., 104(12), 1551–1556 (doi: 10.1175/1520-0493(1976)104¡1551:TIONMB¿2.0.CO;2)

Houtekamer, PL and Zhang, F (2016) Review of the ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev., 144(12), 4489–4532 (doi: 10.1175/MWR-D-15-0440.1)

Hunke, E and Dukowicz, J (1997) An elastic-viscous-plastic model for sea ice dynamics. J. Phys. Oceanogr, 27, 1849–1867.

Hunke, E and Dukowicz, J (2002) The elastic-viscous-plastic sea ice dynamics model in general orthogonal curvilinear coordinates on a sphere-incorporation of metric terms. Mon. Weather Rev., 130, 1848–1865 (doi: 10.1175/1520-0493(2002)130<1848:TEVPSI>2.0.CO;2)

Hurrell, JW, 22 others (2013) The community earth system model: A framework for collaborative research. Bull. Am. Meteorol. Soc., 94(9), 1339–1360 (doi: 10.1175/BAMS-D-12-00121.1)

Jazwinski, A (1970) Stochastic processes and filtering theory. Academic, Sand Diego, California.

Lindsay, RW and Zhang, J (2006) Assimilation of ice concentration in an ice-ocean model. J. Atmos. Oceanic Technol., 23(5), 742–749 (doi: 10.1175/JTECH1871.1)

Lisæter, KA, Rosanova, J and Evensen, G (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)

Lisæter, KA, Evensen, G and Laxon, S (2007) Assimilating synthetic cryosat sea ice thickness in a coupled ice-ocean model. J. Geophys. Res., 112(C7) (doi: 10.1029/2006JC003786)

Losch, M, Menemenlis, D, Campin, JM, Heimbach, P and Hill, C (2010) On the formulation of sea-ice models. part 1: Effects of different solver implementations and parameterizations. Ocean Modelling, 33(12), 129–144 (doi: http://dx.doi.org/10.1016/j.ocemod.2009.12.008) Marshall, J, Adcroft, A, Hill, C, Perelman, L and Heisey, C (1997) A finite-volume, incompressible navier stokes model for studies of the ocean on parallel computers. J. Geophys. Res., 102(C3), 5753–5766 (doi: 10.1029/96JC02775)

Massonnet, F, Fichefet, T and Goosse, H (2015) Prospects for improved seasonal Arctic sea ice predictions from multivariate data assimilation. Ocean Modelling, 88, 16–25 (doi: 10.1016/j.ocemod.2014.12.013)

Panofsky, R (1949) Objective weather-map analysis. J. Meteor., 6(6), 386–392 (doi: 10.1175/1520-0469(1949)006¡0386:OWMA¿2.0.CO;2)

Rayner, N, Horton, E, Parker, D, Folland, C and Hacket, R (1996) Version 2.2 of the global sea-ice and sea surface temperature data set, 1903–1994. Hadley Centre for Clim. Prediction Res. Clim. Res. Tech., 74(21)

Sakov, P and Bertino, L (2011) Relation between two common localisation methods for the enkf. Comput. Geosci., 15(2), 225–237 (doi: 10.1007/s10596-010-9202-6)

Sakov, P and Oke, P (2008) A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters. Tellus, 60A(2), 361–371 (doi: 10.1111/j.1600-0870.2007.00299.x)

Sakov, P, 5 others (2012) Topaz4: an ocean-sea ice data assimilation system for the north atlantic and arctic. Ocean Sci., 8(4), 633–656 (doi: 10.5194/os-8-633-2012)

Sasaki, Y (1970) Some basic formalisms in numerical variational analysis. Mon. Weather Rev., 98(12), 875–883 (doi: 10.1175/1520-0493(1970)098¡0875:SBFINV¿2.3.CO;2)

Shchepetkin, A and McWilliams, J (2005) The regional oceanic modeling system (roms): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9(4), 347–404 (doi: 10.1016/j.ocemod.2004.08.002)

Spreen, G, Kaleschke, L and Heygster, G (2008) Sea ice remote sensing using amsr-e 89-ghz channels. J. Geophys. Res., 113(C2) (doi: 10.1029/2005JC003384)

Stroeve, J, Holland, MM, Meier, W, Scambos, T and Serreze, M (2007) Arctic sea ice decline: Faster than forecast. Geophys. Res. Lett., 34(9) (doi: 10.1029/2007GL029703)

Tian-Kunze, X, 6 others (2014) Smos-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification. Cryosphere, 8(3), 997–1018 (doi: 10.5194/tc-8-997-2014)

Wang, K, Debernard, J, Sperrevik, A, Isachsen, P and Lavergne, T (2013) A combined optimal interpolation and nudging scheme to assimilate osisaf sea-ice concentration into roms. Ann. Glaciol, 54(62), 8–12 (doi: 10.3189/2013AoG62A138)

Xie, J, Counillon, F, Bertino, L, Tian-Kunze, X and Kaleschke, L (2016) Benefits of assimilating thin sea ice thickness from smos into the topaz system. Cryosphere, 10(6), 2745–2761 (doi: 10.5194/tc-10-2745-2016)

Yao, T, Tang, C and Peterson, I (2000) Modeling the seasonal variation of sea ice in the labrador sea with a coupled multicategory ice model and the princeton ocean model. J. Geophys. Res., 105(C1), 1153–1165 (doi: 10.1029/1999JC900264).