Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-3721-2023
© Author(s) 2023. 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-17-3721-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Sep 2023
Research article |
| 01 Sep 2023
| 01 Sep 2023
Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates
Nationalt Center for Klimaforskning, Danish Meteorological Institute, Lyngbyvej 100, 2100 Copenhagen, Denmark
Electronic Systems, Aalborg University, A. C. Meyers Vænge 15, 2450 Copenhagen, Denmark
Till A. S. Rasmussen
Nationalt Center for Klimaforskning, Danish Meteorological Institute, Lyngbyvej 100, 2100 Copenhagen, Denmark
Lars Stenseng
DTU Space, Technical University of Denmark, Elektrovej Bygning 328, 2800 Kongens Lyngby, Denmark
Related authors
Imke Sievers, Henriette Skourup, and Till A. S. Rasmussen
The Cryosphere, 18, 5985–6004, https://doi.org/10.5194/tc-18-5985-2024, https://doi.org/10.5194/tc-18-5985-2024, 2024
Short summary
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To derive sea ice thickness (SIT) from satellite freeboard (FB) observations, assumptions about snow thickness, snow density, sea ice density and water density are needed. These parameters are impossible to observe alongside FB, so many existing products use empirical values. In this study, modeled values are used instead. The modeled values and otherwise commonly used empirical values are evaluated against in situ observations. In a further analysis, the influence on SIT is quantified.
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Our study introduces a new method to compare CMIP6 models' sea ice and snow simulations with in-situ (MOSAiC) measurements. We assessed models for their accuracy in replicating Arctic sea ice and snow thicknesses, using two sea-ice and atmosphere-based methods to select "proxy years." We show that the models often overestimate snow thickness and mistime sea ice cycles. Despite limitations, this approach provides a valuable tool for evaluating climate models in localized time and space.
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Preprint withdrawn
Short summary
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To predict Arctic sea ice models are used. Many ice models exists. They all are skill full, but give different results. Often this differences result from forcing as for example air temperature. Other differences result from the way the physical equations are solved in the model. In this study two commonly used models are compared under equal forcing, to find out how much the models differ under similar external forcing. The results are compared to observations and to eachother.
Imke Sievers, Henriette Skourup, and Till A. S. Rasmussen
The Cryosphere, 18, 5985–6004, https://doi.org/10.5194/tc-18-5985-2024, https://doi.org/10.5194/tc-18-5985-2024, 2024
Short summary
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To derive sea ice thickness (SIT) from satellite freeboard (FB) observations, assumptions about snow thickness, snow density, sea ice density and water density are needed. These parameters are impossible to observe alongside FB, so many existing products use empirical values. In this study, modeled values are used instead. The modeled values and otherwise commonly used empirical values are evaluated against in situ observations. In a further analysis, the influence on SIT is quantified.
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
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Preprint archived
Short summary
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Our study introduces a new method to compare CMIP6 models' sea ice and snow simulations with in-situ (MOSAiC) measurements. We assessed models for their accuracy in replicating Arctic sea ice and snow thicknesses, using two sea-ice and atmosphere-based methods to select "proxy years." We show that the models often overestimate snow thickness and mistime sea ice cycles. Despite limitations, this approach provides a valuable tool for evaluating climate models in localized time and space.
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Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
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Preprint withdrawn
Short summary
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To predict Arctic sea ice models are used. Many ice models exists. They all are skill full, but give different results. Often this differences result from forcing as for example air temperature. Other differences result from the way the physical equations are solved in the model. In this study two commonly used models are compared under equal forcing, to find out how much the models differ under similar external forcing. The results are compared to observations and to eachother.
Cited articles
Aaboe, S., Down, E. J., and Eastwood, S.: Product User Manual for the Global
sea-ice edge and type Product, Norwegian Meteorological Institute: Oslo,
Norway, https://osisaf-hl.met.no/sites/osisaf-hl/files/user_manuals/osisaf_cdop3_ss2_pum_sea-ice-edge-type_v3p1.pdf (last access: 30 August 2023), 2021. a
BGEP (Beaufort Gyre Exploration Program): https://www2.whoi.edu/site/beaufortgyre/, Woods Hole Oceanographic Institution last access: 29 June 2022. a
Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, 2014. a
Bloom, S., Takacs, L., Da Silva, A., and Ledvina, D.: Data assimilation using
incremental analysis updates, Mon. Weather Rev., 124, 1256–1271, 1996. a
Cao, Y., Liang, S., Sun, L., Liu, J., Cheng, X., Wang, D., Chen, Y., Yu, M.,
and Feng, K.: Trans-Arctic shipping routes expanding faster than the model
projections, Global Environ. Chang., 73, 102488, https://doi.org/10.1016/j.gloenvcha.2022.102488, 2022. a
Cox, G. F. and Weeks, W. F.: Salinity variations in sea ice, J.
Glaciol., 13, 109–120, 1974. a
Dai, A. and Trenberth, K. E.: Estimates of freshwater discharge from
continents: Latitudinal and seasonal variations, J. Hydrometeorol.,
3, 660–687, 2002. a
Drinkwater, M. R., Francis, R., Ratier, G., and Wingham, D. J.: The European
Space Agency’s earth explorer mission CryoSat: measuring variability in the
cryosphere, Ann. Glaciol., 39, 313–320, 2004. a
Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic
ocean tides, J. Atmos. Ocean. Tech., 19, 183–204,
2002. a
Fetterer, F. and Stewart, J. S.: U.S. National Ice Center Arctic and Antarctic Sea Ice Concentration and Climatologies in Gridded Format, Version 1, Boulder, Colorado USA, National Snow and Ice Data Center [data set],
https://doi.org/10.7265/46cc-3952, 2020. 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, b, c
Garnier, F., Fleury, S., Garric, G., Bouffard, J., Tsamados, M., Laforge, A., Bocquet, M., Fredensborg Hansen, R. M., and Remy, F.: Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements, The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, 2021. 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, F., Villaume, S., and Thépaut, J.-N.: Complete ERA5:
Fifth generation of ECMWF atmospheric reanalyses of the global climate,
Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.1002/qj.3803,
2017. a
Hordoir, R., Skagseth, Ø., Ingvaldsen, R. B., Sandø, A. B., Löptien,
U., Dietze, H., Gierisch, A. M., Assmann, K. M., Lundesgaard, Ø., and
Lind, S.: Changes in Arctic Stratification and Mixed Layer Depth Cycle: A
Modeling Analysis, J. Geophys. Res.-Oceans, 127,
e2021JC017270, https://doi.org/10.1029/2021jc017270, 2022. 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., Roberts, A., Turner, M., Winton, M., and Rethmeier, S.: CICE-Consortium/CICE: CICE Version 6.2.0 (6.2.0), Zenodo [code], https://doi.org/10.5281/zenodo.4671172, 2021. 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., Roberts, A., Turner, M.,
and Winton, M.: CICE-Consortium/Icepack: Icepack 1.2.5, Zenodo,
https://doi.org/10.5281/zenodo.4671132, 2021b. a, b
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., Roberts, A., Turner, M.,
Winton, M., and Rethmeier, S.: CICE Version 6.2.0,
https://github.com/CICE-Consortium/CICE/tree/CICE6.2.0 (last access: 12 April 2021),
2021a. a, b, c
Ivanova, N., Tonboe, R., and Pedersen, L.: SICCI Product Validation and
Algorithm Selection Report (PVASR)–Sea Ice Concentration, Technical Report, https://doi.org/10.13140/2.1.2204.0649,
2013. a
Ivanova, N., Johannessen, O. M., Pedersen, L. T., and Tonboe, R. T.: Retrieval
of Arctic Sea Ice Parameters by Satellite Passive Microwave Sensors: A
Comparison of Eleven Sea Ice Concentration Algorithms, IEEE T.
Geosci. Remote, 52, 7233–7246,
https://doi.org/10.1109/TGRS.2014.2310136, 2014. a
Jackson, K., Wilkinson, J., Maksym, T., Meldrum, D., Beckers, J., Haas, C., and
Mackenzie, D.: A novel and low-cost sea ice mass balance buoy, J.
Atmos. Ocean. Tech., 30, 2676–2688, 2013. a
Kaminski, T., Kauker, F., Toudal Pedersen, L., Voßbeck, M., Haak, H., Niederdrenk, L., Hendricks, S., Ricker, R., Karcher, M., Eicken, H., and Gråbak, O.: Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance, The Cryosphere, 12, 2569–2594, https://doi.org/10.5194/tc-12-2569-2018, 2018. a, b
Kern, S., Khvorostovsky, K., Skourup, H., Rinne, E., Parsakhoo, Z. S., Djepa, V., Wadhams, P., and Sandven, S.: The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise, The Cryosphere, 9, 37–52, https://doi.org/10.5194/tc-9-37-2015, 2015. a, b
Kern, S., Rösel, A., Pedersen, L. T., Ivanova, N., Saldo, R., and Tonboe, R. T.: The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations, The Cryosphere, 10, 2217–2239, https://doi.org/10.5194/tc-10-2217-2016, 2016. a
King, J., Skourup, H., Hvidegaard, S. M., Rösel, A., Gerland, S., Spreen,
G., Polashenski, C., Helm, V., and Liston, G. E.: Comparison of freeboard
retrieval and ice thickness calculation from ALS, ASIRAS, and CryoSat-2 in
the Norwegian Arctic to field measurements made during the N-ICE2015
expedition, J. Geophys. Res.-Oceans, 123, 1123–1141, 2018. a, b
Kurtz, N. T., Farrell, S. L., Studinger, M., Galin, N., Harbeck, J. P., Lindsay, R., Onana, V. D., Panzer, B., and Sonntag, J. G.: Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data, The Cryosphere, 7, 1035–1056, https://doi.org/10.5194/tc-7-1035-2013, 2013. a
Kurtz, N. T. and Farrell, S. L.: Large-scale surveys of snow depth on Arctic
sea ice from Operation IceBridge, Geophys. Res. Lett., 38, L20505, https://doi.org/10.1029/2011GL049216, 2011. a, b
Kwok, R. and Cunningham, G.: Variability of Arctic sea ice thickness and volume
from CryoSat-2, Philos. T. Roy. Soc. A, 373, 20140157, https://doi.org/10.1098/rsta.2014.0157, 2015. a
Kwok, R., Panzer, B., Leuschen, C., Pang, S., Markus, T., Holt, B., and
Gogineni, S.: Airborne surveys of snow depth over Arctic sea ice, J.
Geophys. Res.-Oceans, 116, C11018, https://doi.org/10.1029/2011JC007371, 2011. a
Landy, J. C., Tsamados, M., and Scharien, R. K.: A facet-based numerical model
for simulating SAR altimeter echoes from heterogeneous sea ice surfaces, IEEE
T. Geosci. Remote, 57, 4164–4180, 2019. a
Landy, J. C., Dawson, G. J., Tsamados, M., Bushuk, M., Stroeve, J. C., Howell,
S. E., Krumpen, T., Babb, D. G., Komarov, A. S., Heorton, H. D., Belter, B. S., Jakob, H., and Yevgeny, A.: A
year-round satellite sea-ice thickness record from CryoSat-2, Nature, 609,
517–522, 2022. a
Laxon, S., Peacock, N., and Smith, D.: High interannual variability of sea ice
thickness in the Arctic region, Nature, 425, 947–950, 2003. a
Laxon, S. W., Giles, K. A., Ridout, A. L., Wingham, D. J., Willatt, R., Cullen,
R., Kwok, R., Schweiger, A., Zhang, J., Haas, C., Hendricks, S., Krishfield, R., Kurtz, N., Farrell, S., and Davidson, M.: CryoSat-2 estimates
of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40,
732–737, 2013. a, b, c, d
Lei, R., Cheng, B., Hoppmann, M., and Zuo, G.: Snow depth and sea ice thickness
derived from the measurements of SIMBA buoys deployed in the Arctic Ocean
during the Legs 1a, 1, and 3 of the MOSAiC campaign in 2019–2020, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.938244, 2021. a, b
Lellouche, J.-M., Greiner, E., Bourdallé-Badie, R., Garric, G., Melet, A.,
Drévillon, M., Bricaud, C., Hamon, M., Le Galloudec, O., Regnier, C.,
Candela, T., Testut, C.-E., Gasparin, F., Ruggiero, G., Benkiran, M., Drillet, Y., and Le Traon, P.-Y.: The Copernicus global 1/12° oceanic and sea ice GLORYS12 reanalysis,
Front. Earth Sci., 9, 585, https://doi.org/10.3389/feart.2021.698876, 2021. a
Liston, G. E., Itkin, P., Stroeve, J., Tschudi, M., Stewart, J. S., Pedersen,
S. H., Reinking, A. K., and Elder, K.: A Lagrangian snow-evolution system for
sea-ice applications (SnowModel-LG): Part I–Model description, J.
Geophys. Res.-Oceans, 125, e2019JC015913, https://doi.org/10.1029/2019JC015913, 2020. a
Madec, G., Bourdallé-Badie, R., Chanut, J., Clementi, E., Coward, A., Ethé, C., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Rousset, C., Storkey, D., Vancoppenolle, M., Müeller, S., Nurser, G., Bell, M., and Samson, G.:
NEMO ocean engine, Zenodo [code], https://doi.org/10.5281/zenodo.3878122, 2017. a, b
Mallett, R. D. C., Lawrence, I. R., Stroeve, J. C., Landy, J. C., and Tsamados, M.: Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates, The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, 2020. a, b, c, d, e
Martino, A. J., Neumann, T. A., Kurtz, N. T., and McLennan, D.: ICESat-2
mission overview and early performance, in: Sensors, systems, and
next-generation satellites XXIII, 11151, 68–77, SPIE, 2019. 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
Nerger, L., Janjić, T., Schröter, J., and Hiller, W.: A regulated
localization scheme for ensemble-based Kalman filters, Q. J. Roy. Meteor. Soc., 138, 802–812, 2012. a
Nord, A., Kärnä, T., Lindenthal, A., Ljungemyr, P., Maljutenko, I.,
Falahat, S., Ringgaard, I. M., Korabel, V., Kanarik, H., Verjovkina, S.,
Jandt, S., with support of the whole
BAL MFC team: New coupled forecasting system for the baltic sea area, in: 9th
EuroGOOS International conference, Ifremer; EuroGOOS AISBL, May 2021, Brest, France, 238–244, hal-03328374v2f, 2021. a
OSI SAF: Global Sea Ice Concentration Climate Data Record v2.0 – Multimission,
EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_0008, 2017. a
OSI SAF: OSISAF: Global Sea Ice Concentration Climate Data Record v3.0 – Multimission, EUMETSAT SAF on Ocean and Sea Ice [data set], https://doi.org/10.15770/EUM_SAF_OSI_0014, 2022. a
Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.: Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation, The Cryosphere, 8, 1607–1622, https://doi.org/10.5194/tc-8-1607-2014, 2014. a, b, c, d
Rösel, A. and Kaleschke, L.: Influence of melt ponds on microwave sensors'
sea ice concentration retrieval algorithms, in: 2012 IEEE International
Geoscience and Remote Sensing Symposium, July 2012,
Munich, Germany,
3261–3264, https://doi.org/10.1109/IGARSS.2012.6350608, 2012. a
Sallila, H., Farrell, S. L., McCurry, J., and Rinne, E.: Assessment of contemporary satellite sea ice thickness products for Arctic sea ice, The Cryosphere, 13, 1187–1213, https://doi.org/10.5194/tc-13-1187-2019, 2019. a, b, c, d
Smith, G. C., Liu, Y., Benkiran, M., Chikhar, K., Surcel Colan, D., Gauthier, A.-A., Testut, C.-E., Dupont, F., Lei, J., Roy, F., Lemieux, J.-F., and Davidson, F.: The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system using an online tidal harmonic analysis, Geosci. Model Dev., 14, 1445–1467, https://doi.org/10.5194/gmd-14-1445-2021, 2021. a
Tranchant, B., Testut, C.-E., Ferry, N., and Brasseur, P.: SAM2: The second
generation of Mercator assimilation system, European Operational
Oceanography: Present and Future, p. 650, ISBN 92-894-9788-2, 2006. a
Tsamados, M., Feltham, D. L., Schroeder, D., Flocco, D., Farrell, S. L., Kurtz,
N., Laxon, S. W., and Bacon, S.: Impact of variable atmospheric and oceanic
form drag on simulations of Arctic sea ice, J. Phys. Oceanogr.,
44, 1329–1353, 2014. a
Tschudi, M. A., Meier, W. N., and Stewart, J. S.: An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC), The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, 2020. a
Vernieres, G., Zhao, B., Cullather, R. I., Akella, S., Vikhliaev, Y. V., Kurtz,
N. T., and Kovach, R. M.: Assimilation of Cryosat 2 Arctic Sea-Ice Freeboard
in an Ensemble of Coupled GEOS5, American Geophysical Union, 2016, HE13A–06,
2016. a
Wingham, D., Francis, C., Baker, S., Bouzinac, C., Brockley, D., Cullen, R.,
de Chateau-Thierry, P., Laxon, S., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P., and Wallis, D.:
CryoSat: A mission to determine the fluctuations in Earth’s land and marine
ice fields, Adv. Space Res., 37, 841–871, 2006. a
Ye, Y., Luo, Y., Sun, Y., Shokr, M., Aaboe, S., Girard-Ardhuin, F., Hui, F., Cheng, X., and Chen, Z.: Inter-comparison and evaluation of Arctic sea ice type products, The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023, 2023. a
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019. a
Short summary
The satellite CryoSat-2 measures freeboard (FB), which is used to derive sea ice thickness (SIT) under the assumption of hydrostatic balance. This SIT comes with large uncertainties due to errors in the observed FB, sea ice density, snow density and snow thickness. This study presents a new method to derive SIT by assimilating the FB into the sea ice model, evaluates the resulting SIT against in situ observations and compares the results to the CryoSat-2-derived SIT without FB assimilation.
The satellite CryoSat-2 measures freeboard (FB), which is used to derive sea ice thickness (SIT)...
