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Sensitivity of the Arctic sea ice concentration forecasts to different atmospheric forcing: a case study

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Abstract

A regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) is used as the coupled ice-ocean model for forecasting sea ice conditions in the Arctic Ocean at the National Marine Environmental Forecasting Center of China (NMEFC), and the numerical weather prediction from the National Center for Environmental Prediction Global Forecast System (NCEP GFS) is used as the atmospheric forcing. To improve the sea ice forecasting, a recently developed Polar Weather Research and Forecasting model (Polar WRF) model prediction is also tested as the atmospheric forcing. Their forecasting performances are evaluated with two different satellite-derived sea ice concentration products as initializations: (1) the Special Sensor Microwave Imager/Sounder (SSMIS) and (2) the Advanced Microwave Scanning Radiometer for EOS (AMSR-E). Three synoptic cases, which represent the typical atmospheric circulations over the Arctic Ocean in summer 2010, are selected to carry out the Arctic sea ice numerical forecasting experiments. The evaluations suggest that the forecasts of sea ice concentrations using the Polar WRF atmospheric forcing show some improvements as compared with that of the NCEP GFS.

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References

  • Bertino L, Lisæter K A. 2008. The TOPAZ monitoring and prediction system for the Atlantic and Arctic Oceans. J Oper Oceanogr, 1(2): 15–18

    Google Scholar 

  • Bougeault P, Toth Z, Bishop C, et al. 2010. The THORPEX interactive grand global ensemble. Bull Amer Meteor Soc, 91(8): 1059–1072

    Article  Google Scholar 

  • Bromwich D H, Hines K M, Bai L S. 2009. Development and testing of Polar Weather Research and Forecasting model: 2. Arctic Ocean. J Geophys Res, 114: D08122, doi: 10.1029/2008JD010300

    Google Scholar 

  • Cavalieri D J, Parkinson C L, DiGirolamo N, et al. 2012. Intersensor calibration between F13 SSMI and F17 SSMIS for global sea ice data records. IEEE Trans Geosci Remote Sens Lett, 9(2): 233–236

    Article  Google Scholar 

  • Cressmann G P. 1959. An operational objective analysis system. Mon Wea Rev, 87(10): 367–374

    Article  Google Scholar 

  • Eicken H. 2013. Ocean science: Arctic sea ice needs better forecasts. Nature, 497(7450): 431–433

    Article  Google Scholar 

  • Hines K M, Bromwich D H. 2008. Development and testing of Polar WRF. Part I: Greenland Ice Sheet meteorology. Mon Wea Rev, 136(6): 1971–1989

    Article  Google Scholar 

  • IPCC. 2013. Climate Change 2013: The Physical Science Basis. In: Stocker T F, Qin D, Plattner G K, et al., eds. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 1535

    Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, et al. 1996. The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteor Soc, 77(3): 437–471

    Article  Google Scholar 

  • Kwok R, Cunningham G, Wensnahan M, et al. 2009. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008. J Geophys Res, 114(C7): C07005, doi:10.1029/2009JC005312.

    Google Scholar 

  • Lisæter K A, Rosanova J, Evensen G. 2003. Assimilation of ice concentration in a coupled ice-ocean model using the Ensemble Kalman filter. Ocean Dyn, 53(4): 368–388

    Article  Google Scholar 

  • Liu Jiping, Song Mirong, Horton R M, et al. 2013. Reducing spread in climate model projections of a September ice-free Arctic. Proc Natl Acad Sci USA, 110(31): 12571–12576

    Article  Google Scholar 

  • Losch M, Menemenlis D, Campin J-M, et al. 2010. On the formulation of sea-ice models. Part 1: Effects of different solver implementations and parameterizations. Ocean Modell, 33(1): 129–144

    Article  Google Scholar 

  • Marshall J, Adcroft A, Hill C, et al. 1997. A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers. J Geophys Res, 102(C3): 5753–5766

    Article  Google Scholar 

  • Menemenlis D, Campin J-M, Heimbach P, et al. 2008. ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, 31: 13–21

    Google Scholar 

  • Nguyen A T, Kwok R, Menemenlis D. 2012. Source and pathway of the Western Arctic upper halocline in a data-constrained coupled ocean and sea ice model. J Phys Oceanogr, 42(5): 802–823

    Article  Google Scholar 

  • Nguyen A T, Menemenlis D, Kwok R. 2011. Arctic ice-ocean simulation with optimized model parameters: Approach and assessment. J Geophys Res, 116(C4): C04025, doi: 10.1029/2010JC006573

    Google Scholar 

  • Pham D T, Verron J, Gourdeau L. 1998. Singular evolutive Kalman filters for data assimilation in oceanography. Comptes Rendus-Academie des Sciences Paris, Earth Planet Sci, 326: 255–260

    Google Scholar 

  • Posey P G, Smedstad L F, Preller R H, et al. 2008. User’s Manual for the Polar Ice Prediction System (PIPS) Version 3.0. Naval Research Laboratory Technical Report, NRL/MR/7320-08-9154. Mississippi: Stennis Space Center

    Google Scholar 

  • Preller R H, Posey P G, Maslowski W, et al. 2002. Navy sea ice prediction systems. Oceanography, 15(1): 44–56

    Article  Google Scholar 

  • Sakov P, Counillon F, Bertino L, et al. 2012. TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Sci, 8(4): 633–656

    Article  Google Scholar 

  • Serreze M, Holland M, Stroeve J. 2007. Perspectives on the Arctic’s shrinking sea-ice cover. Science, 315(5818): 1533–1536

    Article  Google Scholar 

  • Spreen G, Kaleschke L, Heygster G. 2008. Sea ice remote sensing using Amsr-e 89 GHz channels. J Geophys Res, 113: C02S03, doi: 10.1029/2005JC003384

    Google Scholar 

  • Skamarock W C, Klemp J B, Dudhia J, et al. 2005. A description of the advanced research WRF version 2. NCAR Technical Notes NCAR/TN-468-STR. Colorado: National Center for Atmospheric Research

    Google Scholar 

  • Smith W H, Sandwell D T. 1997. Global sea floor topography from satellite altimetry and ship depth soundings. Science, 277(5334): 1956–1962

    Article  Google Scholar 

  • Wang Jia, Zhang Jinlun, Watanabe E, et al. 2009. Is the Dipole Anomaly a major driver to record lows in Arctic summer sea ice extent? Geophys Res Lett, 36(5): L05706, doi: 10.1029/2008GL036706

    Article  Google Scholar 

  • Yang Qinghua, Li Chunhua, Xing Jianyong, et al. 2012. Arctic sea ice forecasting experiments in the summer of 2010. Chinese Journal of Polar Research (in Chinese), 24(1): 87–94

    Article  Google Scholar 

  • Yang Qinghua, Liu Jiping, Zhang Zhanhai, et al. 2011. A preliminary study of the Arctic sea ice numerical forecasting: Coupled sea ice-ocean modelling experiments based on MITgcm. Chinese Journal of Atmospheric Sciences (in Chinese), 35(3): 473–482

    Google Scholar 

  • Yang Qinghua, Losa N S, Losch M, et al. 2014. Assimilating summer sea ice concentration into a coupled ice-ocean model using a localized SEIK filter. Ann Glaciol, 56(69): doi: 10.3189/2015Ao-G69A740

    Google Scholar 

  • Zhang Jinlun, Hibler III W D. 1997. On an efficient numerical method for modeling sea ice dynamics. J Geophys Res, 102(C4): 8691–8702

    Article  Google Scholar 

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Correspondence to Qinghua Yang.

Additional information

Foundation item: The Ocean Public Welfare Project of China under contract No. 201205007; the National Natural Science Foundation of China under contract Nos 41176169, 41376005, 41376188 and 41106165.

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Yang, Q., Liu, J., Zhang, Z. et al. Sensitivity of the Arctic sea ice concentration forecasts to different atmospheric forcing: a case study. Acta Oceanol. Sin. 33, 15–23 (2014). https://doi.org/10.1007/s13131-014-0566-7

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  • DOI: https://doi.org/10.1007/s13131-014-0566-7

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