Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-5445-2025
© Author(s) 2025. 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-19-5445-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Nov 2025
Research article |
| 06 Nov 2025
| 06 Nov 2025
Sensitivity of iceberg drift and deterioration simulations to input data from different ocean, sea ice and atmosphere models in the Barents Sea
Lia Herrmannsdörfer
CORRESPONDING AUTHOR
Norwegian University of Science and Technology, Trondheim, Norway
Raed Khalil Lubbad
Norwegian University of Science and Technology, Trondheim, Norway
Knut Vilhelm Høyland
Norwegian University of Science and Technology, Trondheim, Norway
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Disagreement between models describing the Arctic raises the question of suitability of those models for individual use-cases. We compared the ocean-sea ice models Topaz and Barents-2.5, and the atmospheric reanalyses ERA5 and CARRA in the Barents Sea. The results are later used to explain differences caused in iceberg simulations. We highlight spatial differences e.g. at the sea ice edge and coastlines, that are caused by different horizontal resolution and physical variable description.
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Cited articles
Abramov, V. and Tunik, A.: Atlas of Arctic Icebergs: The Greenland, Barents, East-Siberian and Chukchi Seas in the Arctic Basin, Backbone Publ. Co., ISBN 9780964431140, 1996. a
Bigg, G. R., Wadley, M. R., Stevens, D. P., and Johnson, J. A.: Modelling the dynamics and thermodynamics of icebergs, Cold Regions Science and Technology, 26, 113–135, https://doi.org/10.1016/S0165-232X(97)00012-8, 1997. a
Dezecot, C. and Eik, K.: Barents East blocks Metocean Design Basis, Statoil Report, document no.: ME2015-005, 2015. a
Eik, K.: Iceberg drift modelling and validation of applied metocean hindcast data, Cold Regions Science and Technology, 57, 67–90, https://doi.org/10.1016/j.coldregions.2009.02.009, 2009b. a, b, c, d
El-Tahan, M., Venkatesh, S., and El-Tahan, H.: Validation and Quantitative Assessment of the Deterioration Mechanisms of Arctic Icebergs, Journal of Offshore Mechanics and Arctic Engineering, 109, 102–108, https://doi.org/10.1115/1.3256983, 1987. a, b
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
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.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47,, 2023. a, b, c, d, e
Idžanović, M., Rikardsen, E. S. U., and Röhrs, J.: Forecast uncertainty and ensemble spread in surface currents from a regional ocean model, Frontiers in Marine Science, 10, https://doi.org/10.3389/fmars.2023.1177337, 2023. a, b
Jakobsson, M., Mayer, L., Coakley, B., Dowdeswell, J. A., Forbes, S., Fridman, B., Hodnesdal, H., Noormets, R., Pedersen, R., Rebesco, M., Schenke, H. W., Zarayskaya, Y., Accettella, D., Armstrong, A., Anderson, R. M., Bienhoff, P., Camerlenghi, A., Church, I., Edwards, M., Gardner, J. V., Hall, J. K., Hell, B., Hestvik, O., Kristoffersen, Y., Marcussen, C., Mohammad, R., Mosher, D., Nghiem, S. V., Pedrosa, M. T., Travaglini, P. G., and Weatherall, P.: The International Bathymetric Chart of the Arctic Ocean (IBCAO) Version 3.0, Geophysical Research Letters, 39, https://doi.org/10.1029/2012GL052219, 2012. a
Keghouche, I., Bertino, L., and Lisæter, K. A.: Parameterization of an Iceberg Drift Model in the Barents Sea, Journal of Atmospheric and Oceanic Technology, 26, 2216–2227, https://doi.org/10.1175/2009JTECHO678.1, 2009. a, b, c
Kubat, I., Savage, S., Carrieres, T., and Crocker, G.: An Operational Iceberg Deterioration Model, Proceedings of the International Offshore and Polar Engineering Conference, 652–657, https://publications-cnrc.canada.ca/eng/view/object/?id=a0990ffa-7621-4167-99e5-b61a6415959e (last access: 3 November 2025), 2007. a
Køltzow, M., Casati, B., Bazile, E., Haiden, T., and Valkonen, T.: An NWP Model Intercomparison of Surface Weather Parameters in the European Arctic during the Year of Polar Prediction Special Observing Period Northern Hemisphere, Weather and Forecasting, 34, 959–983, https://doi.org/10.1175/WAF-D-19-0003.1, 2019. a, b
Køltzow, M., Schyberg, H., Støylen, E., and Yang, X.: Value of the Copernicus Arctic Regional Reanalysis (CARRA) in representing near-surface temperature and wind speed in the north-east European Arctic, Polar Research, 41, https://doi.org/10.33265/polar.v41.8002, 2022. a, b, c
Lichey, C. and Hellmer, H.: Modeling giant iceberg drift under the influence of sea ice in the Weddell Sea, Journal of Glaciology, 158, 452–460, 2001. a
MET-Norway: Barents-2.5 ocean and ice forecast archive (ROMS-EPS), Norwegian Meteorological Institute [data set], https://thredds.met.no/thredds/fou-hi/barents_eps.html, last access: 30 August 2023b. a
Monteban, D., Lubbad, R., Samardzija, I., and Løset, S.: Enhanced iceberg drift modelling in the Barents Sea with estimates of the release rates and size characteristics at the major glacial sources using Sentinel-1 and Sentinel-2, Cold Regions Science and Technology, 175, 103084, https://doi.org/10.1016/j.coldregions.2020.103084, 2020. a, b, c, d, e, f, g, h, i, j
Röhrs, J., Gusdal, Y., Rikardsen, E. S. U., Durán Moro, M., Brændshøi, J., Kristensen, N. M., Fritzner, S., Wang, K., Sperrevik, A. K., Idžanović, M., Lavergne, T., Debernard, J. B., and Christensen, K. H.: Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard, Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, 2023. a, b, c, d, e, f
Röhrs, J., Sutherland, G., Jeans, G., Bedington, M., Sperrevik, A., Dagestad, K.-F., Gusdal, Y., Mauritzen, C., Dale, A., and LaCasce, J.: Surface currents in operational oceanography: Key applications, mechanisms, and methods, Journal of Operational Oceanography, 16, 60–88, https://doi.org/10.1080/1755876X.2021.1903221, 2023b. a, b
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
Savage, S.: Aspects of Iceberg Deterioration and Drift, Springer Berlin Heidelberg, Berlin, Heidelberg, 279–318, ISBN 978-3-540-45670-4, https://doi.org/10.1007/3-540-45670-8_12, 2001. a, b
Schyberg, H., Yang, X., Køltzow, M., Amstrup, B., Bakketun, A., Bazile, E., Bojarova, J., Box, J. E., Dahlgren, P., Hagelin, S., Homleid, M., Horányi, A., Høyer, J., Johansson, A., Killie, M., Körnich, H., Le Moigne, P., Lindskog, M., Manninen, T., Nielsen Englyst, P., Nielsen, K., Olsson, E., Palmason, B., Peralta Aros, C., Randriamampianina, R., Samuelsson, P., Stappers, R., Støylen, E., Thorsteinsson, S., Valkonen, T., and Wang, Z.: Arctic regional reanalysis on single levels from 1991 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.713858f6, 2023. a, b, c, d, e
Slagstad, D., Støle-Hansen, K., and Loeng, H.: Density driven currents in the Barents Sea calculated by a numerical model, Modeling, Identification and Control (MIC), 11, 181–190, https://doi.org/10.4173/mic.1990.4.1, 1990. a
Xie, J. and Bertino, L.: Arctic Ocean Physics Reanalysis, Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00007, 2022. a, b, c, d
Yang, X., Schyberg, H., Palmason, B., Bojarova, J., Pagh, N. K., Dahlborn, M., Peralta, C., Homleid, M., Køltzow, M., Randriamampianina, R., Dahlgren, P., Vignes, O., Støylen, E., Valkonen, T., Lindskog, M., Hagelin, S., Körnich, H., and Thorsteinsson, S.: Complete test and verification report on fully configured reanalysis and monitoring system, Climate Data Store, https://doi.org/10.24381/cds.713858f6, 2020. a
Short summary
Numerical simulations of iceberg drift and deterioration are a useful tool to fill the gap of otherwise scarce iceberg observations in the Barents Sea. We create statistics of iceberg simulations with input from different combinations of ocean, sea ice and atmosphere models to study their impact on the simulation results. We find that especially using different sea ice models Topaz and Barents-2.5 influences the iceberg drift, deterioration and occurrence in the domain.
Numerical simulations of iceberg drift and deterioration are a useful tool to fill the gap of...
