Challenges in the Description of Sea Ice for a Kilometer-Scale Weather Forecasting System

Malte Müller aDevelopment Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway
bDepartment of Geosciences, University of Oslo, Oslo, Norway

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Yurii Batrak aDevelopment Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway

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Frode Dinessen cResearch and Development Department, Norwegian Meteorological Institute, Oslo, Norway

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Rafael Grote aDevelopment Centre for Weather Forecasting, Norwegian Meteorological Institute, Oslo, Norway

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Keguang Wang cResearch and Development Department, Norwegian Meteorological Institute, Oslo, Norway

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Abstract

Simulation of atmosphere–ocean–ice interactions in coupled Earth modeling systems with kilometer-scale resolution is a new challenge in operational numerical weather prediction. This study presents an assessment of sensitivity experiments performed with different sea ice products in a convective-scale weather forecasting system for the European Arctic. On kilometer-scale resolution sea ice products are challenged by the large footprint of passive microwave satellite observations and issues with spurious sea ice detection of the higher-resolution retrievals based on synthetic aperture radar instruments. We perform sensitivity experiments with sea ice concentration fields of 1) the global ECMWF-IFS forecast system, 2) a newly developed multisensor product processed through a coupled sea ice–ocean forecasting system, and 3) the AMSR2 product based on passive microwave observations. There are significant differences between the products on O(100) km scales in the northern Barents Sea and along the Marginal Ice Zone north of the Svalbard archipelago and toward the Fram Strait. These differences have a direct impact on the modeled surface skin temperature over ocean and sea ice, the turbulent heat flux, and 2-m air temperature (T2M). An assessment of Arctic weather stations shows a significant improvement of forecasted T2M in the north and east of Svalbard when using the new multisensor product; however, south of Svalbard this product has a negative impact. The different sea ice products are resulting in changes of the surface turbulent heat flux of up to 400 W m−2, which in turn results in T2M variations of up to 5°C. Over a 2-day forecast lead time this can lead to uncertainties in weather forecasts of about 1°C even hundreds of kilometers away from the sea ice.

Significance Statement

Weather forecasting in polar regions requires an accurate description of sea ice properties due to the very important atmosphere–ocean–ice interactions. With the increasing resolution of weather forecasting systems, there is also a need to advance the resolution of the sea ice characteristics in the models. This is, however, not straightforward due to various issues in the sea ice satellite products. This study explores new products and approaches to integrate high-resolution sea ice in a weather prediction system. We find that the model is sensitive to the choice of the sea ice product and that it is still challenging to provide an accurate sea ice field on a kilometer-scale resolution.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Malte Müller, maltem@met.no

Publisher's Note: This article was revised on 25 July 2023 to fix incorrect paneling labeling in Figs. 5 and 6 that were present when originally published.

Abstract

Simulation of atmosphere–ocean–ice interactions in coupled Earth modeling systems with kilometer-scale resolution is a new challenge in operational numerical weather prediction. This study presents an assessment of sensitivity experiments performed with different sea ice products in a convective-scale weather forecasting system for the European Arctic. On kilometer-scale resolution sea ice products are challenged by the large footprint of passive microwave satellite observations and issues with spurious sea ice detection of the higher-resolution retrievals based on synthetic aperture radar instruments. We perform sensitivity experiments with sea ice concentration fields of 1) the global ECMWF-IFS forecast system, 2) a newly developed multisensor product processed through a coupled sea ice–ocean forecasting system, and 3) the AMSR2 product based on passive microwave observations. There are significant differences between the products on O(100) km scales in the northern Barents Sea and along the Marginal Ice Zone north of the Svalbard archipelago and toward the Fram Strait. These differences have a direct impact on the modeled surface skin temperature over ocean and sea ice, the turbulent heat flux, and 2-m air temperature (T2M). An assessment of Arctic weather stations shows a significant improvement of forecasted T2M in the north and east of Svalbard when using the new multisensor product; however, south of Svalbard this product has a negative impact. The different sea ice products are resulting in changes of the surface turbulent heat flux of up to 400 W m−2, which in turn results in T2M variations of up to 5°C. Over a 2-day forecast lead time this can lead to uncertainties in weather forecasts of about 1°C even hundreds of kilometers away from the sea ice.

Significance Statement

Weather forecasting in polar regions requires an accurate description of sea ice properties due to the very important atmosphere–ocean–ice interactions. With the increasing resolution of weather forecasting systems, there is also a need to advance the resolution of the sea ice characteristics in the models. This is, however, not straightforward due to various issues in the sea ice satellite products. This study explores new products and approaches to integrate high-resolution sea ice in a weather prediction system. We find that the model is sensitive to the choice of the sea ice product and that it is still challenging to provide an accurate sea ice field on a kilometer-scale resolution.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Malte Müller, maltem@met.no

Publisher's Note: This article was revised on 25 July 2023 to fix incorrect paneling labeling in Figs. 5 and 6 that were present when originally published.

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  • Andreas, E. L, 1980: Estimation of heat and mass fluxes over Arctic leads. Mon. Wea. Rev., 108, 20572063, https://doi.org/10.1175/1520-0493(1980)108<2057:EOHAMF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Batrak, Y., and M. Müller, 2018: Atmospheric response to kilometer-scale changes in sea ice concentration within the Marginal Ice Zone. Geophys. Res. Lett., 45, 67026709, https://doi.org/10.1029/2018GL078295.

    • Search Google Scholar
    • Export Citation
  • Batrak, Y., and M. Müller, 2019: On the warm bias in atmospheric reanalyses induced by the missing snow over arctic sea-ice. Nat. Commun., 10, 4170, https://doi.org/10.1038/s41467-019-11975-3.

    • Search Google Scholar
    • Export Citation
  • Batrak, Y., E. Kourzeneva, and M. Homleid, 2018: Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1. Geosci. Model Dev., 11, 33473368, https://doi.org/10.5194/gmd-11-3347-2018.

    • Search Google Scholar
    • Export Citation
  • Bengtsson, L., and Coauthors, 2017: The HARMONIE-AROME model configuration in the ALADIN-HIRLAM NWP system. Mon. Wea. Rev., 145, 19191935, https://doi.org/10.1175/MWR-D-16-0417.1.

    • Search Google Scholar
    • Export Citation
  • Blair, B., M. Müller, C. Palerme, R. Blair, D. Crookall, M. Knol-Kauffman, and M. Lamers, 2022: Coproducing sea ice predictions with stakeholders using simulation. Wea. Climate Soc., 14, 399413, https://doi.org/10.1175/WCAS-D-21-0048.1.

    • Search Google Scholar
    • Export Citation
  • Bourassa, M. A., and Coauthors, 2013: High-latitude ocean and sea ice surface fluxes: Challenges for climate research. Bull. Amer. Meteor. Soc., 94, 403423, https://doi.org/10.1175/BAMS-D-11-00244.1.

    • Search Google Scholar
    • Export Citation
  • Day, J. J., S. Keeley, G. Arduini, L. Magnusson, K. Mogensen, M. Rodwell, I. Sandu, and S. Tietsche, 2022: Benefits and challenges of dynamic sea ice for weather forecasts. Wea. Climate Dyn., 3, 713731, https://doi.org/10.5194/wcd-3-713-2022.

    • Search Google Scholar
    • Export Citation
  • Debernard, J., N. M. Kristensen, S. Maartensson, K. Wang, K. Hedstrom, J. Brændshøi, and N. Szapiro, 2021: metno/metroms: Version 0.4.1. Zenodo, accessed 3 Jul 2023, https://doi.org/10.5281/zenodo.5067164.

  • Dierking, W., 2013: Sea ice monitoring by synthetic aperture radar. Oceanography, 26, 100111, https://doi.org/10.5670/oceanog.2013.33.

    • Search Google Scholar
    • Export Citation
  • Dinessen, F., 2017: Operational multisensor sea ice concentration algorithm utilizing Sentinel-1 and AMSR2 data. Geophysical Research Abstracts, Vol. 19, Abstract EGU2017-19037, https://meetingorganizer.copernicus.org/EGU2017/EGU2017-19037.pdfEGU.

  • Dinessen, F., A. Korosov, C. Wettre, and T. Lavergne, 2021: Product user manual: Arctic sea ice concentration and Arctic sea ice type—Greenland sea ice concentration. Tech. Rep. SEAICE_ARC_PHY_AUTO_L4_NRT_011_015, Norwegian Meteorological Institute/Nansen Environmental and Remote Sensing Center, 30 pp., https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-SI-PUM-011-015.pdf.

  • Donlon, C. J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sens. Environ., 116, 140158, https://doi.org/10.1016/j.rse.2010.10.017.

    • Search Google Scholar
    • Export Citation
  • ECMWF, 2018a: IFS documentation CY45R1—Part II: Data assimilation. ECMWF Newsletter, No. 2, ECMWF, Reading, United Kingdom, 103 pp., https://www.ecmwf.int/node/18712.

  • ECMWF, 2018b: IFS documentation CY45R1—Part IV: Physical processes. ECMWF Newsletter, No. 4, ECMWF, Reading, United Kingdom, 223 pp., https://www.ecmwf.int/node/18714.

  • ECMWF, 2018c: IFS documentation CY45R1—Part V: Ensemble prediction system. ECMWF Newsletter, No. 5, ECMWF, Reading, United Kingdom, 23 pp., https://www.ecmwf.int/node/18715.

  • Emmerson, C., and G. Lahn, 2012: Arctic opening: Opportunity and risk in the high north. Chatham House-Lloyd’s Risk Insight Rep., 60 pp., https://assets.lloyds.com/assets/pdf-risk-reports-arctic-risk-report-webview/1/pdf-risk-reports-Arctic-Risk-Report-webview.pdf.

  • Fritzner, S., R. Graversen, and K. H. Christensen, 2020: Assessment of high-resolution dynamical and machine learning models for prediction of sea ice concentration in a regional application. J. Geophys. Res. Oceans, 125, e2020JC016277, https://doi.org/10.1029/2020JC016277.

    • Search Google Scholar
    • Export Citation
  • Gryschka, M., C. Drüe, D. Etling, and S. Raasch, 2008: On the influence of sea-ice inhomogeneities onto roll convection in cold-air outbreaks. Geophys. Res. Lett., 35, L23804, https://doi.org/10.1029/2008GL035845.

    • Search Google Scholar
    • Export Citation
  • Hall, D., J. Key, K. Casey, G. Riggs, and D. Cavalieri, 2004: Sea ice surface temperature product from MODIS. IEEE Trans. Geosci. Remote Sens., 42, 10761087, https://doi.org/10.1109/TGRS.2004.825587.

    • Search Google Scholar
    • Export Citation
  • Herrmannsdörfer, L., M. Müller, M. D. Shupe, and P. Rostosky, 2023: Surface temperature comparison of the Arctic winter MOSAiC observations, ERA5 reanalysis, and MODIS satellite retrieval. Elementa, 11, 00085, https://doi.org/10.1525/elementa.2022.00085.

    • Search Google Scholar
    • Export Citation
  • Isaksen, K., O. Nordli, E. J. Førland, E. Lupikasza, S. Eastwood, and T. Niedźwiedź, 2016: Recent warming on Spitsbergen—Influence of atmospheric circulation and sea ice cover. J. Geophys. Res. Atmos., 121, 11 91311 931, https://doi.org/10.1002/2016JD025606.

    • Search Google Scholar
    • Export Citation
  • Jeuring, J., M. Knol-Kauffman, and A. Sivle, 2020: Toward valuable weather and sea-ice services for the marine Arctic: Exploring user-producer interfaces of the Norwegian Meteorological Institute. Polar Geogr., 43, 139159, https://doi.org/10.1080/1088937X.2019.1679270.

    • Search Google Scholar
    • Export Citation
  • Kern, S., T. Lavergne, D. Notz, L. T. Pedersen, R. T. Tonboe, R. Saldo, and A. M. Sørensen, 2019: Satellite passive microwave sea-ice concentration data set intercomparison: Closed ice and ship-based observations. Cryosphere, 13, 32613307, https://doi.org/10.5194/tc-13-3261-2019.

    • Search Google Scholar
    • Export Citation
  • Khachatrian, E., W. Dierking, S. Chlaily, T. Eltoft, F. Dinessen, N. Hughes, and A. Marinoni, 2023: SAR and passive microwave fusion scheme: A test case on Sentinel-1/AMSR-2 for sea ice classification. Geophys. Res. Lett., 50, e2022GL102083, https://doi.org/10.1029/2022GL102083.

    • Search Google Scholar
    • Export Citation
  • Lavergne, T., and Coauthors, 2019: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records. Cryosphere, 13, 4978, https://doi.org/10.5194/tc-13-49-2019.

    • Search Google Scholar
    • Export Citation
  • Liu, Q., R. Zhang, Y. Wang, H. Yan, and M. Hong, 2021: Short-term daily prediction of sea ice concentration based on deep learning of gradient loss function. Front. Mar. Sci., 8, 736429, https://doi.org/10.3389/fmars.2021.736429.

    • Search Google Scholar
    • Export Citation
  • Malmgren-Hansen, D., and Coauthors, 2021: A convolutional neural network architecture for Sentinel-1 and AMSR2 data fusion. IEEE Trans. Geosci. Remote Sens., 59, 18901902, https://doi.org/10.1109/TGRS.2020.3004539.

    • Search Google Scholar
    • Export Citation
  • Masson, V., and Coauthors, 2013: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of Earth surface variables and fluxes. Geosci. Model Dev., 6, 929960, https://doi.org/10.5194/gmd-6-929-2013.

    • Search Google Scholar
    • Export Citation
  • Melsom, A., C. Palerme, and M. Müller, 2019: Validation metrics for ice edge position forecasts. Ocean Sci., 15, 615630, https://doi.org/10.5194/os-15-615-2019.

    • Search Google Scholar
    • Export Citation
  • Müller, M., Y. Batrak, J. Kristiansen, M. A. O. Køltzow, G. Noer, and A. Korosov, 2017a: Characteristics of a convective-scale weather forecasting system for the European Arctic. Mon. Wea. Rev., 145, 47714787, https://doi.org/10.1175/MWR-D-17-0194.1.

    • Search Google Scholar
    • Export Citation
  • Müller, M., and Coauthors, 2017b: AROME-MetCoOp: A Nordic convective-scale operational weather prediction model. Wea. Forecasting, 32, 609627, https://doi.org/10.1175/WAF-D-16-0099.1.

    • Search Google Scholar
    • Export Citation
  • Ólason, E., P. Rampal, and V. Dansereau, 2021: On the statistical properties of sea-ice lead fraction and heat fluxes in the Arctic. Cryosphere, 15, 10531064, https://doi.org/10.5194/tc-15-1053-2021.

    • Search Google Scholar
    • Export Citation
  • Palerme, C., and M. Müller, 2021: Calibration of sea ice drift forecasts using random forest algorithms. Cryosphere, 15, 39894004, https://doi.org/10.5194/tc-15-3989-2021.

    • Search Google Scholar
    • Export Citation
  • Ricker, R., S. Hendricks, L. Kaleschke, X. Tian-Kunze, J. King, and C. Haas, 2017: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data. Cryosphere, 11, 16071623, https://doi.org/10.5194/tc-11-1607-2017.

    • Search Google Scholar
    • Export Citation
  • Röhrs, J., and L. Kaleschke, 2012: An algorithm to detect sea ice leads by using AMSR-E passive microwave imagery. Cryosphere, 6, 343352, https://doi.org/10.5194/tc-6-343-2012.

    • Search Google Scholar
    • Export Citation
  • Sakov, P., F. Counillon, L. Bertino, K. A. Lisæter, P. R. Oke, and A. Korablev, 2012: TOPAZ4: An ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Sci., 8, 633656, https://doi.org/10.5194/os-8-633-2012.

    • Search Google Scholar
    • Export Citation
  • Smith, G. C., and Coauthors, 2016: Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System. Quart. J. Roy. Meteor. Soc., 142, 659671, https://doi.org/10.1002/qj.2555.

    • Search Google Scholar
    • Export Citation
  • Spensberger, C., and T. Spengler, 2021: Sensitivity of air-sea heat exchange in cold-air outbreaks to model resolution and sea-ice distribution. J. Geophys. Res. Atmos., 126, e2020JD033610, https://doi.org/10.1029/2020JD033610.

    • Search Google Scholar
    • Export Citation
  • Spreen, G., L. Kaleschke, and G. Heygster, 2008: Sea ice remote sensing using AMSR-E 89-GHz channels. J. Geophys. Res., 113, C02S03, https://doi.org/10.1029/2005JC003384.

    • Search Google Scholar
    • Export Citation
  • Stocker, A. N., A. H. H. Renner, and M. Knol-Kauffman, 2020: Sea ice variability and maritime activity around Svalbard in the period 2012–2019. Sci. Rep., 10, 17043, https://doi.org/10.1038/s41598-020-74064-2.

    • Search Google Scholar
    • Export Citation
  • Thomas, E. E., M. Müller, P. Bohlinger, Y. Batrak, and N. Szapiro, 2022: A kilometer-scale coupled atmosphere-wave forecasting system for the European Arctic. Wea. Forecasting, 36, 20872099, https://doi.org/10.1175/WAF-D-21-0065.1.

    • Search Google Scholar
    • Export Citation
  • Tian-Kunze, X., L. Kaleschke, N. Maaß, M. Mäkynen, N. Serra, M. Drusch, and T. Krumpen, 2014: SMOS-derived thin sea ice thickness: Algorithm baseline, product specifications and initial verification. Cryosphere, 8, 9971018, https://doi.org/10.5194/tc-8-997-2014.

    • Search Google Scholar
    • Export Citation
  • von Schuckmann, K., and Coauthors, 2021: Copernicus marine service ocean state report, issue 5. J. Oper. Oceanogr., 14, 1185, https://doi.org/10.1080/1755876X.2021.1946240.

    • Search Google Scholar
    • Export Citation
  • Wang, K., J. Debernard, A. K. Sperrevik, P. E. Isachsen, and T. Lavergne, 2013: A combined optimal interpolation and nudging scheme to assimilate OSISAF sea-ice concentration into ROMS. Ann. Glaciol., 54, 812, https://doi.org/10.3189/2013AoG62A138.

    • Search Google Scholar
    • Export Citation
  • Willmes, S., and G. Heinemann, 2015: Pan-Arctic lead detection from MODIS thermal infrared imagery. Ann. Glaciol., 56, 2937, https://doi.org/10.3189/2015AoG69A615.

    • Search Google Scholar
    • Export Citation
  • Willmes, S., and G. Heinemann, 2016: Sea-ice wintertime lead frequencies and regional characteristics in the Arctic, 2003–2015. Remote Sens., 8, 4, https://doi.org/10.3390/rs8010004.

    • Search Google Scholar
    • Export Citation
  • Zakhvatkina, N., V. Smirnov, and I. Bychkova, 2019: Satellite SAR data-based sea ice classification: An overview. Geosciences, 9, 152, https://doi.org/10.3390/geosciences9040152.

    • Search Google Scholar
    • Export Citation
  • Zuo, H., M. A. Alonso-Balmaseda, K. Mogensen, and S. Tietsche, 2018: OCEAN5: The ECMWF ocean reanalysis system and its real-time analysis component. ECMWF Tech. Memo. 823, 46 pp., https://www.ecmwf.int/sites/default/files/elibrary/2018/18519-ocean5-ecmwf-ocean-renalysis-system-and-its-real-time-analysis-component.pdf.

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