Articles | Volume 18, issue 6
https://doi.org/10.5194/tc-18-2875-2024
© 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-18-2875-2024
© 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 Jun 2024
Research article |
| 21 Jun 2024
| 21 Jun 2024
Exploring non-Gaussian sea ice characteristics via observing system simulation experiments
Christopher Riedel
CORRESPONDING AUTHOR
Advanced Study Program, National Center for Atmospheric Research, Boulder, Colorado, USA
Jeffrey Anderson
Data Assimilation Research Section, National Center for Atmospheric Research, Boulder, Colorado, USA
Related authors
Molly M. Wieringa, Christopher Riedel, Jeffrey L. Anderson, and Cecilia M. Bitz
The Cryosphere, 18, 5365–5382, https://doi.org/10.5194/tc-18-5365-2024, https://doi.org/10.5194/tc-18-5365-2024, 2024
Short summary
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.
Carla Cardinali, Giovanni Conti, Marcelo Guatura, Sami Saarinen, Luis Gustavo Gonçalves De Gonçalves, Jeffrey Anderson, and Kevin Raeder
EGUsphere, https://doi.org/10.5194/egusphere-2025-4294, https://doi.org/10.5194/egusphere-2025-4294, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Scientists have developed research systems to test new ideas in data assimilation, but these often lack the efficiency and robustness needed for operational use. We addressed this gap with key innovations: a flexible observation database, first guess at the appropriate time, and modular, parallelised software enabling the assimilation of millions of observations.
Molly M. Wieringa, Christopher Riedel, Jeffrey L. Anderson, and Cecilia M. Bitz
The Cryosphere, 18, 5365–5382, https://doi.org/10.5194/tc-18-5365-2024, https://doi.org/10.5194/tc-18-5365-2024, 2024
Short summary
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.
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden
Atmos. Meas. Tech., 17, 1941–1963, https://doi.org/10.5194/amt-17-1941-2024, https://doi.org/10.5194/amt-17-1941-2024, 2024
Short summary
Short summary
We assimilate different MOPITT CO products to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately.
Elia Gorokhovsky and Jeffrey L. Anderson
Nonlin. Processes Geophys., 30, 37–47, https://doi.org/10.5194/npg-30-37-2023, https://doi.org/10.5194/npg-30-37-2023, 2023
Short summary
Short summary
Older observations of the Earth system sometimes lack information about the time they were taken, posing problems for analyses of past climate. To begin to ameliorate this problem, we propose new methods of varying complexity, including methods to estimate the distribution of the offsets between true and reported observation times. The most successful method accounts for the nonlinearity in the system, but even the less expensive ones can improve data assimilation in the presence of time error.
Xueling Liu, Arthur P. Mizzi, Jeffrey L. Anderson, Inez Fung, and Ronald C. Cohen
Atmos. Chem. Phys., 21, 9573–9583, https://doi.org/10.5194/acp-21-9573-2021, https://doi.org/10.5194/acp-21-9573-2021, 2021
Short summary
Short summary
Observations of winds in the planetary boundary layer remain sparse, making it challenging to simulate and predict the atmospheric conditions that are most important for describing and predicting urban air quality. Here we investigate the application of data assimilation of NO2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of wind fields in the boundary layer.
Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, and Edward Blanchard-Wrigglesworth
The Cryosphere, 15, 1277–1284, https://doi.org/10.5194/tc-15-1277-2021, https://doi.org/10.5194/tc-15-1277-2021, 2021
Short summary
Short summary
Sea ice models suffer from large uncertainties arising from multiple sources, among which parametric uncertainty is highly under-investigated. We select a key ice albedo parameter and update it by assimilating either sea ice concentration or thickness observations. We found that the sea ice albedo parameter is improved by data assimilation, especially by assimilating sea ice thickness observations. The improved parameter can further benefit the forecast of sea ice after data assimilation stops.
Andrew Tangborn, Belay Demoz, Brian J. Carroll, Joseph Santanello, and Jeffrey L. Anderson
Atmos. Meas. Tech., 14, 1099–1110, https://doi.org/10.5194/amt-14-1099-2021, https://doi.org/10.5194/amt-14-1099-2021, 2021
Short summary
Short summary
Accurate prediction of the planetary boundary layer is essential to both numerical weather prediction (NWP) and pollution forecasting. This paper presents a methodology to combine these measurements with the models through a statistical data assimilation approach that calculates the correlation between the PBLH and variables like temperature and moisture in the model. The model estimates of these variables can be improved via this method, and this will enable increased forecast accuracy.
Benjamin Gaubert, Louisa K. Emmons, Kevin Raeder, Simone Tilmes, Kazuyuki Miyazaki, Avelino F. Arellano Jr., Nellie Elguindi, Claire Granier, Wenfu Tang, Jérôme Barré, Helen M. Worden, Rebecca R. Buchholz, David P. Edwards, Philipp Franke, Jeffrey L. Anderson, Marielle Saunois, Jason Schroeder, Jung-Hun Woo, Isobel J. Simpson, Donald R. Blake, Simone Meinardi, Paul O. Wennberg, John Crounse, Alex Teng, Michelle Kim, Russell R. Dickerson, Hao He, Xinrong Ren, Sally E. Pusede, and Glenn S. Diskin
Atmos. Chem. Phys., 20, 14617–14647, https://doi.org/10.5194/acp-20-14617-2020, https://doi.org/10.5194/acp-20-14617-2020, 2020
Short summary
Short summary
This study investigates carbon monoxide pollution in East Asia during spring using a numerical model, satellite remote sensing, and aircraft measurements. We found an underestimation of emission sources. Correcting the emission bias can improve air quality forecasting of carbon monoxide and other species including ozone. Results also suggest that controlling VOC and CO emissions, in addition to widespread NOx controls, can improve ozone pollution over East Asia.
Cited articles
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A community facility, B. Am. Meteorol. Soc., 90, 1283–1296, 2009. a
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. a
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, 59A, 210–224, 2007. a
Anderson, J. L.: A marginal adjustment rank histogram filter for non-Gaussian ensemble data assimilation, Mon. Weather Rev., 148, 3361–3378, 2020. a
Briegleb, B. and Light, B.: A delta-Eddington multiple scattering parameterization for solar radiation in the sea ice component of the 625 Community Climate System Model, Tech. rep., NCAR Technical Note NCAR/TN-472+ STR, https://doi.org/10.5065/D6B27S71, 2007. a
Burgers, G., Van Leeuwen, P. J., and Evensen, G.: Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724, 1998. 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, 2017. a
Cheng, S., Chen, Y., Aydoğdu, A., Bertino, L., Carrassi, A., Rampal, P., and Jones, C. K. R. T.: Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020, The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, 2023. a
Christensen, H., Moroz, I., and Palmer, T.: Simulating weather regimes: Impact of stochastic and perturbed parameter schemes in a simple atmospheric model, Clim. Dynam., 44, 2195–2214, 2015. a
Chung, E.-S., Ha, K.-J., Timmermann, A., Stuecker, M. F., Bodai, T., and Lee, S.-K.: Cold-season Arctic amplification driven by Arctic ocean-mediated seasonal energy transfer, Earth's Future, 9, e2020EF001898, https://doi.org/10.1029/2020EF001898, 2021. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The community earth system model version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020 (software available at: https://www.cesm.ucar.edu/models/cesm2, last access: August 2020). a, b
Day, J., Hawkins, E., and Tietsche, S.: Will Arctic sea ice thickness initialization improve seasonal forecast skill?, Geophys. Res. Lett., 41, 7566–7575, 2014. a
Dirkson, A., Merryfield, W. J., and Monahan, A.: Impacts of sea ice thickness initialization on seasonal Arctic sea ice predictions, J. Climate, 30, 1001–1017, 2017. a
England, M. R., Eisenman, I., Lutsko, N. J., and Wagner, T. J.: The recent emergence of Arctic Amplification, Geophys. Res. Lett., 48, e2021GL094086, https://doi.org/10.1029/2021GL094086, 2021. a
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003. a
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
Fowler, A. and Jan Van Leeuwen, P.: Observation impact in data assimilation: the effect of non-Gaussian observation error, Tellus A, 65, 20035, https://doi.org/10.3402/tellusa.v65i0.20035, 2013. a
Fritzner, S., Graversen, R., Christensen, K. H., Rostosky, P., and Wang, K.: Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system, The Cryosphere, 13, 491–509, https://doi.org/10.5194/tc-13-491-2019, 2019. a, b
Fritzner, S. M., Graversen, R. G., Wang, K., and Christensen, K. H.: Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation, J. Glaciol., 64, 387–396, 2018. a
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999. a
Goessling, H. F., Tietsche, S., Day, J. J., Hawkins, E., and Jung, T.: Predictability of the Arctic sea ice edge, Geophys. Res. Lett., 43, 1642–1650, 2016. a
Hall, D. K., Nghiem, S. V., Rigor, I. G., and Miller, J. A.: Uncertainties of temperature measurements on snow-covered land and sea ice from in situ and MODIS data during BROMEX, J. Climate Appl. Meteor., 54, 966–978, 2015. a
Holland, M. M. and Bitz, C. M.: Polar amplification of climate change in coupled models, Clim. Dynam., 21, 221–232, 2003. a
Holland, M. M., Clemens-Sewall, D., Landrum, L., Light, B., Perovich, D., Polashenski, C., Smith, M., and Webster, M.: The influence of snow on sea ice as assessed from simulations of CESM2, The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, 2021. a
Houtekamer, P. L. and Zhang, F.: Review of the ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 144, 4489–4532, 2016. a
Jansen, E., Christensen, J. H., Dokken, T., Nisancioglu, K. H., Vinther, B. M., Capron, E., Guo, C., Jensen, M. F., Langen, P. L., Pedersen, R. A., and Yang, S.: Past perspectives on the present era of abrupt Arctic climate change, Nat. Clim. Change, 10, 714–721, 2020. a
Jenkins, M. and Dai, A.: The Impact of Sea-Ice Loss on Arctic Climate Feedbacks and Their Role for Arctic Amplification, Geophys. Res. Lett., 48, e2021GL094599, https://doi.org/10.1029/2021GL094599, 2021. a
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., 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, 70, 1–23, 2018. a
Kwok, R. and Cunningham, G.: ICESat over Arctic sea ice: Estimation of snow depth and ice thickness, J. Geophys. Res.-Oceans, 113, https://doi.org/10.1029/2008JC004753, 2008. a
Lawson, W. G. and Hansen, J. A.: Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth, Mon. Weather Rev., 132, 1966–1981, 2004. a
Lei, J., Bickel, P., and Snyder, C.: Comparison of ensemble Kalman filters under non-Gaussianity, Mon. Weather Rev., 138, 1293–1306, 2010. a
Lindsay, R. and Zhang, J.: Assimilation of ice concentration in an ice–ocean model, J. Atmos. Ocean. Tech., 23, 742–749, 2006. a
Lipscomb, W. H.: Remapping the thickness distribution in sea ice models, J. Geophys. Res.-Oceans, 106, 13989–14000, 2001. a
Lisæter, K. A., Rosanova, J., and Evensen, G.: Assimilation of ice concentration in a coupled ice–ocean model, using the Ensemble Kalman filter, Ocean Dynam., 53, 368–388, 2003. a
Lu, P., Li, Z., Cheng, B., and Leppäranta, M.: A parameterization of the ice-ocean drag coefficient, J. Geophys. Res.-Oceans, 116, https://doi.org/10.1029/2010JC006878, 2011. a
Maaß, N., Kaleschke, L., Tian-Kunze, X., and Drusch, M.: Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data, The Cryosphere, 7, 1971–1989, https://doi.org/10.5194/tc-7-1971-2013, 2013. a
Mathiot, P., König Beatty, C., Fichefet, T., Goosse, H., Massonnet, F., and Vancoppenolle, M.: Better constraints on the sea-ice state using global sea-ice data assimilation, Geosci. Model Dev., 5, 1501–1515, https://doi.org/10.5194/gmd-5-1501-2012, 2012. a
Meier, W. N. and Maslanik, J. A.: Effect of environmental conditions on observed, modeled, and assimilated sea ice motion errors, J. Geophys. Res.-Oceans, 108, https://doi.org/10.1029/2002JC001333, 2003. a
Meier, W. N., Fetterer, F., Windnagel, A. K., and Stewart, J. S.: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4, Boulder, Colorado USA, National Snow and Ice Data Center [data set], https://doi.org/10.7265/efmz-2t65, 2021. a
Metref, S., Cosme, E., Snyder, C., and Brasseur, P.: A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation, Nonlin. Processes Geophys., 21, 869–885, https://doi.org/10.5194/npg-21-869-2014, 2014. a
Msadek, R., Vecchi, G. A., Winton, M., and Gudgel, R. G.: Importance of initial conditions in seasonal predictions of Arctic sea ice extent, Geophys. Res. Lett., 41, 5208–5215, 2014. a
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, 529–538, 2018. a
Murphy, J., Sexton, D., Barnett, D., Jones, G., Webb, M., Collins, M., and Stainforth, D.: Quantifying uncertainties in climate change from a large ensemble of general circulation model predictions, Nature, 430, 768–772, 2004. a
NCAR: The Data Assimilation Research Testbed (Version9.9.0), Boulder, Colorado, UCAR/NSF NCAR/CISL/DAReS [software], https://doi.org/10.5065/D6WQ0202, 2020. a
Orth, R., Dutra, E., and Pappenberger, F.: Improving weather predictability by including land surface model parameter uncertainty, Mon. Weather Rev., 144, 1551–1569, 2016. a
Pham, D. T.: Stochastic methods for sequential data assimilation in strongly nonlinear systems, Mon. Weather Rev., 129, 1194–1207, 2001. a
Pires, C. A., Talagrand, O., and Bocquet, M.: Diagnosis and impacts of non-Gaussianity of innovations in data assimilation, Physica D, 239, 1701–1717, 2010. a
Posey, P. G., Metzger, E. J., Wallcraft, A. J., Hebert, D. A., Allard, R. A., Smedstad, O. M., Phelps, M. W., Fetterer, F., Stewart, J. S., Meier, W. N., and Helfrich, S. R.: Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems, The Cryosphere, 9, 1735–1745, https://doi.org/10.5194/tc-9-1735-2015, 2015. 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, 1–24, 2021. a
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Commun. Earth Environ., 3, 1–10, 2022. a
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017. a
Riedel, C.: Perturbed Parameters for CICE-DART Study Titled “Exploring Non-Gaussian Sea Ice Characteristics via Observing System Simulation Experiments” (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8164431, 2023. a
Rostosky, P., Spreen, G., Gerland, S., Huntemann, M., and Mech, M.: Modeling the microwave emission of snow on Arctic sea ice for estimating the uncertainty of satellite retrievals, J. Geophys. Res.-Oceans, 125, e2019JC015465, https://doi.org/10.1029/2019JC015465, 2020. 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, 2012a. a, b, c
Sakov, P., Oliver, D. S., and Bertino, L.: An iterative EnKF for strongly nonlinear systems, Mon. Weather Rev., 140, 1988–2004, 2012b. a
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in recent Arctic temperature amplification, Nature, 464, 1334–1337, 2010. a
Serreze, M. C., Barrett, A. P., Stroeve, J. C., Kindig, D. N., and Holland, M. M.: The emergence of surface-based Arctic amplification, The Cryosphere, 3, 11–19, https://doi.org/10.5194/tc-3-11-2009, 2009. a
Serreze, M. C. and Francis, J. A.: The Arctic amplification debate, Clim. Change, 76, 241–264, 2006. a
Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame, D. J., Kettleborough, J. A., Knight, S., Martin, A., Murphy, J., and Piani, C.: Uncertainty in predictions of the climate response to rising levels of greenhouse gases, Nature, 433, 403–406, 2005. a
Stark, J. D., Ridley, J., Martin, M., and Hines, A.: Sea ice concentration and motion assimilation in a sea ice- ocean model, J. Geophys. Res.-Oceans, 113, https://doi.org/10.1029/2007JC004224, 2008. a
Sturm, M. and Massom, R. A.: Snow in the sea ice system: friend or foe?, Sea ice, 3rd edn., Wiley and Blackwell, Oxford, United Kingdom, 65–109, 2017. a
Tietsche, S., Day, J. J., Guemas, V., Hurlin, W., Keeley, S., Matei, D., Msadek, R., Collins, M., and Hawkins, E.: Seasonal to interannual Arctic sea ice predictability in current global climate models, Geophys. Res. Lett., 41, 1035–1043, 2014. a
Tilling, R. L., Ridout, A., and Shepherd, A.: Near-real-time Arctic sea ice thickness and volume from CryoSat-2, The Cryosphere, 10, 2003–2012, https://doi.org/10.5194/tc-10-2003-2016, 2016. a
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J. S.: Ensemble Square Root Filters, Mon. Weather Rev., 131, 1485–1490, https://doi.org/10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2, 2003. a
Urrego-Blanco, J. R., Urban, N. M., Hunke, E. C., Turner, A. K., and Jeffery, N.: Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model, J. Geophys. Res.-Oceans, 121, 2709–2732, 2016. a
Van Woert, M. L., Zou, C.-Z., Meier, W. N., Hovey, P. D., Preller, R. H., and Posey, P. G.: Forecast verification of the Polar Ice Prediction System (PIPS) sea ice concentration fields, J. Atmos. Ocean. Tech., 21, 944–957, 2004. a
Walsh, J. E.: Intensified warming of the Arctic: Causes and impacts on middle latitudes, Glob. Planet. Change, 117, 52–63, 2014. a
Welch, B. L.: The generalization of “STUDENT'S” problem when several different population variances are involved, Biometrika, 34, 28–35, 1947. a
Xie, J., Counillon, F., Bertino, L., Tian-Kunze, X., and Kaleschke, L.: Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system, The Cryosphere, 10, 2745–2761, https://doi.org/10.5194/tc-10-2745-2016, 2016. a
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. a
Yu, L., Zhong, S., Vihma, T., and Sun, B.: Attribution of late summer early autumn Arctic sea ice decline in recent decades, npj Clim. Atmos. Sci., 4, 1–14, 2021. a
Zampieri, L., Goessling, H. F., and Jung, T.: Bright prospects for Arctic sea ice prediction on subseasonal time scales, Geophys. Res. Lett., 45, 9731–9738, 2018. a
Zygmuntowska, M., Rampal, P., Ivanova, N., and Smedsrud, L. H.: Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8, 705–720, https://doi.org/10.5194/tc-8-705-2014, 2014. a, b
Short summary
Accurate sea ice conditions are crucial for quality sea ice projections, which have been connected to rapid warming over the Arctic. Knowing which observations to assimilate into models will help produce more accurate sea ice conditions. We found that not assimilating sea ice concentration led to more accurate sea ice states. The methods typically used to assimilate observations in our models apply assumptions to variables that are not well suited for sea ice because they are bounded variables.
Accurate sea ice conditions are crucial for quality sea ice projections, which have been...
