Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment

References

• Aagaard, K. and Carmack, E. C. 1989. The role of sea ice and other fresh water in the Arctic circulation. J. Geophys. Res. 94, 14485–14498. doi:10.1029/JC094iC10p14485
   Google Scholar
• Bentsen, M., Evensen, G., Drange, H. and Jenkins, A. D. 1999. Coordinate transformation on a sphere using conformal mapping. Mon. Weather Rev. 127, 2733–2740. doi:10.1175/1520-0493(1999)127<2733:CTOASU>2.0.CO;2
   Google Scholar
• Bleck, R. 2002. An oceanic general circulation model framed in hybrid isopycnic-cartesian coordinates. Ocean Model. 4, 55–88. doi:10.1016/S1463-5003(01)00012-9
   Google Scholar
• Borup, M., Grum, M., Madsen, H. and Mikkelsen, P. S. 2015. A partial ensemble Kalman filtering approach to enable use of range limited observations. Stoch. Environ. Res. Risk Assess. 29, 119–129. Online at: doi:10.1007/s00477-014-0908-1
   Google Scholar
• Bouillon, S., Fichefet, T., Legat, V. and Madec, G. 2013. The elastic–viscous–plastic method revisited. Ocean Model. 71, 2–12. Part of special issue: Arctic Ocean. doi:10.1016/j.ocemod.2013.05.013
   Google Scholar
• Burgers, G., Jan van Leeuwen, P. and Evensen, G. 1998. Analysis scheme in the ensemble Kalman filter. Mon. Weather Rev. 126, 1719–1724. doi:10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2
   Google Scholar
• Chassignet, E. P., Smith, L. T., Halliwell, G. R. and Bleck, R. 2003. North Atlantic simulations with the hybrid coordinate ocean model (HYCOM): Impact of the vertical coordinate choice, reference pressure, and thermobaricity. J. Phys. Oceanogr. 33, 2504–2526. doi:10.1175/1520-0485(2003)033<2504:NASWTH>2.0.CO;2
   Google Scholar
• Collow, T. W., Wang, W., Kumar, A. and Zhang, J. 2015. Improving Arctic sea ice prediction using PIOMAS initial sea ice thickness in a coupled ocean-atmosphere model. Mon. Weather Rev. 143, 4618–4630. doi:10.1175/MWR-D-15-0097.1
   Google Scholar
• Connor, L. N., Laxon, S. W., Ridout, A. L., Krabill, W. B. and McAdoo, D. C. 2009. Comparison of Envisat radar and airborne laser altimeter measurements over Arctic sea ice. Remote Sens. Environ. 113, 563–570. doi:10.1016/j.rse.2008.10.015
   Google Scholar
• Day, J. J., Hawkins, E. and Tietsche, S. 2014. Will Arctic sea ice thickness initialization improve seasonal forecast skill? Geophys. Res. Lett. 41, 7566–7575. doi:10.1002/2014GL061694
   Google Scholar
• Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P. and co-authors. 2011. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597. doi:10.1002/qj.828
   Google Scholar
• Drange, H. and Simonsen, K. 1996. Formulation of Air-Sea Fluxes in the ESOP2 Version of MICOM. Technical Report 125, Nansen Environmental and Remote Sensing Center, Bergen.
   Google Scholar
• Evensen, G. 2003. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn. 53, 343–367. doi:10.1007/s10236-003-0036-9
   Google Scholar
• Evensen, G. 2004. Sampling strategies and square root analysis schemes for the EnKF. Ocean Dyn. 54, 539–560. doi:10.1007/s10236-004-0099-2
   Google Scholar
• Forsberg, R. and Skourup, H. 2005. Arctic Ocean gravity, geoid and sea-ice freeboard heights from ICESat and GRACE. Geophys. Res. Lett. 32, L21502. doi:10.1029/2005GL023711
   Google Scholar
• Fritzner, S., Graversen, R., Christensen, K. H., Rostosky, P. and Wang, K. 2019. Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system. Cryosphere 13, 491–509. doi:10.5194/tc-13-491-2019
   Google Scholar
• Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J. J., Déqué, M. and co-authors. 2016. A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales. Q. J. R. Meteorol. Soc. 142, 546–561. doi:10.1002/qj.2401
   Google Scholar
• Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Weather Rev. 126, 796–811. doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2
   Google Scholar
• Hunke, E. C. and Dukowicz, J. K. 1997. An elastic–viscous–plastic model for sea ice dynamics. J. Phys. Oceanogr. 27, 1849–1867. doi:10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2
   Google Scholar
• Huntemann, M., Heygster, G., Kaleschke, L., Krumpen, T., Mäkynen, M. and co-authors. 2014. Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations. Cryosphere 8, 439–451. doi:10.5194/tc-8-439-2014
   Google Scholar
• Kaleschke, L., Maaß, N., Haas, C., Hendricks, S., Heygster, G. and co-authors. 2010. A sea-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
   Google Scholar
• Kaleschke, L., Tian-Kunze, X., Maas, N., Ricker, R., Hendricks, S. and Drusch, M.2015. Improved retrieval of sea ice thickness from SMOS and CryoSat-2. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, 2015, pp 5232–5235. doi:10.1109/IGARSS.2015.7327014
   Google Scholar
• Kimmritz, M., Counillon, F., Bitz, C., Massonnet, F., Bethke, I. and co-authors. 2018. Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model. Tellus A: Dynamic Meteorology and Oceanography 70, 1–23. doi:10.1080/16000870.2018.1435945
   Google Scholar
• Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R. and co-authors. 2013. CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophys. Res. Lett. 40, 732–737. doi:10.1002/grl.50193
   Google Scholar
• Mu, L., Losch, M., Yang, Q., Ricker, R., Losa, S. N. and co-authors. 2018. Arctic-wide sea ice thickness estimates from combining satellite remote sensing data and a dynamic ice-ocean model with data assimilation during the CryoSat-2 period. J. Geophys. Res. Oceans 123, 7763–7780. doi:10.1029/2018JC014316
   Google Scholar
• Ricker, R., Hendricks, S., Helm, V., Skourup, H. and Davidson, M. 2014. Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation. The Cryosphere 8, 1607–1622. doi:10.5194/tc-8-1607-2014
   Google Scholar
• Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J. and co-authors. 2017. A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data. The Cryosphere 11, 1607–1623. doi:10.5194/tc-11-1607-2017
   Google Scholar
• Sakov, P., Counillon, F., Bertino, L., Lisaeter, K., Oke, P. and co-authors. 2012. TOPAZ4: An ocean-sea ice data assimilation system for the north Atlantic and Arctic. Ocean Sci. 8, 633. doi:10.5194/os-8-633-2012
   Google Scholar
• Screen, J. and Simmonds, I. 2010. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 464, 1334–1337. doi:10.1038/nature09051
   Google Scholar
• Shah, A., Gharamti, M. E. and Bertino, L. 2018. Assimilation of semi-qualitative observations with a stochastic ensemble kalman filter. Q. J. R. Meteorol. Soc.144, 1882–1894. doi:10.1002/qj.3381
   Google Scholar
• Tian-Kunze, X., Kaleschke, L., Maaß, N., Mäkynen, M., Serra, N. and co-authors. 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
   Google Scholar
• Tilling, R. L., Ridout, A. and Shepherd, A. 2016. Near-real-time Arctic sea ice thickness and volume from CryoSat-2. Cryosphere 10, 2003–2012. doi:10.5194/tc-10-2003-2016
   Google Scholar
• Wang, X., Key, J., Kwok, R. and Zhang, J. 2016. Comparison of Arctic sea ice thickness from satellites, aircraft, and PIOMAS data. Remote Sens. 8, 713. doi:10.3390/rs8090713
   Google Scholar
• Xie, J., Bertino, L., Counillon, F., Lisaeter, K. A. and Sakov, P. 2017. Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013. Ocean Sci. 13, 123–144. doi:10.5194/os-13-123-2017
   Google Scholar
• Xie, J., Counillon, F. and Bertino, L. 2018. Impact of assimilating a merged sea-ice thickness from cryosat-2 and smos in the arctic reanalysis. Cryosphere 12, 3671–3691. doi:10.5194/tc-12-3671-2018
   Google Scholar
• 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, 2745–2761. doi:10.5194/tc-10-2745-2016
   Google Scholar
• Yang, Q., Losa, S. N., Losch, M., Tian-Kunze, X., Nerger, L. and co-authors. 2014. Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter. J. Geophys. Res. Oceans 119, 6680–6692. doi:10.1002/2014JC009963
   Google Scholar