Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5365-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-5365-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Nov 2024
Research article |
| 21 Nov 2024
| 21 Nov 2024
Bounded and categorized: targeting data assimilation for sea ice fractional coverage and nonnegative quantities in a single-column multi-category sea ice model
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Christopher Riedel
Data Assimilation Research Section, NSF National Center for Atmospheric Research, Boulder, CO, USA
Advanced Study Program, NSF National Center for Atmospheric Research, Boulder, CO, USA
Jeffrey L. Anderson
Data Assimilation Research Section, NSF National Center for Atmospheric Research, Boulder, CO, USA
Cecilia M. Bitz
Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
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Cited articles
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz, N. T., Phelps, M. W., Posey, P. G., Tilling, R., and Wallcraft, A. J.: Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system, Adv. Space Res., 62, 1265–1280, https://doi.org/10.1016/J.ASR.2017.12.030, 2018.
Anderson, J. L.: An ensemble adjustment kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001. a, b
Anderson, J. L.: A non-Gaussian ensemble filter update for data assimilation, Mon. Weather Rev., 138, 4186–4198, 2010. a
Anderson, J. L.: A marginal adjustment rank histogram filter for non-Gaussian ensemble data assimilation, Mon. Weather Rev., 148, 3361–3378, 2020.
Anderson, J. L.: A quantile-conserving ensemble filter framework. Part I: Updating an observed variable, Mon. Weather Rev., 150, 1061–1074, https://doi.org/10.1175/MWR-D-21-0229.1, 2022. a, b, c, d
Anderson, J. L.: A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space, Mon. Weather Rev., https://doi.org/10.1175/MWR-D-23-0065.1, 2023. a, b
Anderson, J. L., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Arellano A.: The Data Assimilation Research Testbed: A community facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009. a
Balan-Sarojini, B., Tietsche, S., Mayer, M., Balmaseda, M., Zuo, H., de Rosnay, P., Stockdale, T., and Vitart, F.: Year-round impact of winter sea ice thickness observations on seasonal forecasts, The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, 2021.
Bitz, C. M. and Roe, G. H.: A mechanism for the high rate of sea ice thinning in the Arctic Ocean, J. Climate, 17, 3623–3632, 2004. a
Blockley, E. W. and Peterson, K. A.: Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness, The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, 2018. a
Brennan, M. K., and Hakim, G. J.: Reconstructing Arctic Sea Ice over the Common Era Using Data Assimilation, Journal of Climate, 35, 1231–1247, https://doi.org/10.1175/JCLI-D-21-0099.1, 2022. a
Chen, Z., Liu, J., Song, M., Yang, Q., and Xu, S.: Impacts of assimilating satellite sea ice concentration and thickness on Arctic sea ice prediction in the NCEP Climate Forecast System, J. Climate, 30, 8429–8446, https://doi.org/10.1175/JCLI-D-17-0093.1, 2017.
Chevallier, M. and Salas-Mélia, D.: The role of sea ice thickness distribution in the Arctic sea ice potential predictability: A diagnostic approach with a coupled GCM, J. Climate, 25, 3025–3038, https://doi.org/10.1175/JCLI-D-11-00209.1, 2012. a
El Gharamti, M., Raeder, K., Anderson, J. L., and Wang, X.: Comparing adaptive prior and posterior inflation for ensemble filters using an atmospheric general circulation model, Mon. Weather Rev., 147, 2535–2553, https://doi.org/10.1175/MWR-D-18-0389.1, 2019. a, b
Fiedler, E. K., Martin, M. J., Blockley, E., Mignac, D., Fournier, N., Ridout, A., Shepherd, A., and Tilling, R.: Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM), The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, 2022. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, R., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Houtekamer, P. L. and Zhang, F.: Review of the ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 144, 4489–4532, 2016. a
Hunke, E., Allard, R., Bailey, D. A., Blain, P., Craig, A., Dupont, F., DuVivier, A., Grumbine, R., Hebert, D., Holland, M., Jeffery, N., Lemieux, J.-F., Osinski, R., Rasmussen, T., Ribergaard, M., and Roberts, A.: CICE-Consortium/Icepack: Icepack 1.3.1 (1.3.1), Zenodo [code], https://doi.org/10.5281/zenodo.6314133, 2022. a, b
Kalman, R. E.: A new approach to linear filtering and prediction problems, Trans. ASME, 82, 35–45, 1960. a
Kimmritz, M., Counillon, F., Bitz, C. M., Massonnet, F., Bethke, I., and Gao Y.: Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model, Tellus A, 70, 1–23, https://doi.org/10.1080/16000870.2018.1435945, 2018. a
Korosov, A., Rampal, P., Ying, Y., Ólason, E., and Williams, T.: Towards improving short-term sea ice predictability using deformation observations, The Cryosphere, 17, 4223–4240, https://doi.org/10.5194/tc-17-4223-2023, 2023. a
Lindsay, R. W.: Ice deformation near SHEBA, J. Geophys. Res., 107, 8042, https://doi.org/10.1029/2000JC000445, 2002. a
Lipscomb, W. H.: Remapping the thickness distribution in sea ice models, J. Geophys. Res.-Oceans, 106, 13 989-14 000, 2001.
Lisæter, K. A., Evensen, G., and Laxon, S. W.: Assimilating synthetic CryoSat sea ice thickness in a coupled ice-ocean model, J. Geophys. Res.-Oceans, 112, 7023, https://doi.org/10.1029/2006JC003786, 2007.
Massonnet, F., Fichefet, T., and Goosse, H.: Prospects for improved seasonal Arctic sea ice predictions from multivariate data assimilation, Ocean Model., 88, 16–25, https://doi.org/10.1016/J.OCEMOD.2014.12.013, 2015.
Mu, L., Yang, Q., Losch, M., Losa, S. N., Ricker, R., Nerger, L., and Liang, X.: Improving sea ice thickness estimates by assimilating CryoSat-2 and SMOS sea ice thickness data simultaneously, Q. J. Roy. Meteor. Soc., 144, 529–38, https://doi.org/10.1002/QJ.3225, 2018a. a
Mu, L., Losch, M., Yang, Q., Ricker, R., Losa, S. N., and Nerger, L.: 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, 7763–80, https://doi.org/10.1029/2018JC014316, 2018b. a
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models, Part I: a discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Raeder, K., Hoar, T. J., El Gharamti, M., Johnson, B. K., Collins, N., Anderson, J. L., Steward, J., and Coady, M.: A new CAM6 + DART reanalysis with surface forcing from CAM6 to other CESM models, Sci. Rep., 11, 16384, https://doi.org/10.1038/s41598-021-92927-0, 2021. a
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.: Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res., 116, C00D06, https://doi.org/10.1029/2011JC007084, 2011. a
Thorndike, A., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, https://doi.org/10.1029/JC080i033p04501, 1975. a
UCAR/NSF NCAR/CISL/DAReS: The Data Assimilation Research Testbed (Version 11.8.5) [computer software], https://doi.org/10.5065/D6WQ0202, 2024. a
Wieringa, M. Code for CICE-SCM-DART non-Gaussian data assimilation experiments (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.8310112, 2023. a
Williams, N., Byrne, N., Feltham, D., Van Leeuwen, P. J., Bannister, R., Schroeder, D., Ridout, A., and Nerger, L.: The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system, The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, 2023. a, b, c, d
Xie, J., Counillon, F., and Bertino, L.: Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis, The Cryosphere, 12, 3671–3691, https://doi.org/10.5194/tc-12-3671-2018, 2018.
Yang, Q., Losa, S. N., Losch, M., Tian-Kunze, X., Nerger, X., Liu, J., Kaleschke, L., and Zhang, Z.: Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter, J. Geophys. Res.-Oceans, 119, 6680–92, https://doi.org/10.1002/2014JC009963, 2014.
Zhang, Y. F., Bitz, C. M., Anderson, J. L., Collins, N. S., Hendricks, J., Hoar, T. J., Raeder, K. D., and Massonnet, F.: Insights on sea ice data assimilation from perfect model Observing System Simulation Experiments, J. Climate, 31, 5911–26, https://doi.org/10.1175/JCLI-D-17-0904.1, 2018. a, b, c, d
Zhang, Y.-F., Bitz, C. M., Anderson, J. L., Collins, N. S., Hoar, T. J., Raeder, K. D., and Blanchard-Wrigglesworth, E.: Estimating parameters in a sea ice model using an ensemble Kalman filter, The Cryosphere, 15, 1277–1284, https://doi.org/10.5194/tc-15-1277-2021, 2021. a, b
Short summary
Statistically combining models and observations with data assimilation (DA) can improve sea ice forecasts but must address several challenges, including irregularity in ice thickness and coverage over the ocean. Using a sea ice column model, we show that novel, bounds-aware DA methods outperform traditional methods for sea ice. Additionally, thickness observations at sub-grid scales improve modeled ice estimates of both thick and thin ice, a finding relevant for forecasting applications.
Statistically combining models and observations with data assimilation (DA) can improve sea ice...
