Climate Dynamics

, Volume 51, Issue 4, pp 1585–1603 | Cite as

On the sensitivity of Antarctic sea ice model biases to atmospheric forcing uncertainties

  • Antoine BarthélemyEmail author
  • Hugues Goosse
  • Thierry Fichefet
  • Olivier Lecomte


Although atmospheric reanalyses are an extremely valuable tool to study the climate of polar regions, they suffer from large uncertainties in these data-poor areas. In this work, we examine how Antarctic sea ice biases in an ocean-sea ice model are related to these forcing uncertainties. Three experiments are conducted in which the NEMO-LIM model is driven by different atmospheric forcing sets. The minimum ice extent, the ice motion and the ice thickness are sensitive to the reanalysis chosen to drive the model, while the wintertime ice extent and inner pack concentrations are barely affected. The analysis of sea ice concentration budgets allows identifying the processes leading to differences between the experiments, and also indicates that large and similar errors compared to observations are present in all three cases. Our assessment of the influence of forcing inaccuracies on the simulated Antarctic sea ice allows disentangling two types of model biases: the ones that can be reduced thanks to better atmospheric forcings, and those that would require improvements of the physics of the ice or ocean model.


Sea ice Antarctic Model Atmospheric forcing Uncertainties 



We thank two anonymous reviewers for their valuable comments on the original manuscript. H. G. and O. L. are respectively Research Director and Postdoctoral Researcher with the Fonds de la Recherche Scientifique (F.R.S.-FNRS/Belgium). This work was supported by the F.R.S.-FNRS research project “Amélioration de la représentation de la glace de mer antarctique dans les modèles climatiques grâce à? une meilleure compréhension des processus gouvernant son état moyen et sa variabilité”, under grant agreement T.0007.14. Computational resources have been provided by the supercomputing facilities of the Université catholique de Louvain (CISM/UCL) and the Consortium des Equipements de Calcul Intensif en Fédération Wallonie Bruxelles (CECI) funded by the F.R.S.-FNRS under convention 2.5020.11.


  1. Barthélemy A, Fichefet T, Goosse H (2016) Spatial heterogeneity of ocean surface boundary conditions under sea ice. Ocean Modell 102:82–98. doi: 10.1016/j.ocemod.2016.05.003 CrossRefGoogle Scholar
  2. Barthélemy A, Fichefet T, Goosse H, Madec G (2016) A multi-column vertical mixing scheme to parameterize the heterogeneity of oceanic conditions under sea ice. Ocean Modell 104:28–44. doi: 10.1016/j.ocemod.2016.05.005 CrossRefGoogle Scholar
  3. Berliand ME, Strokina TG (1980) Global distribution of the total amount of clouds (in Russian). Tech. rep., Hydrometeorological Publishing House, LeningradGoogle Scholar
  4. Blanke B, Delecluse P (1993) Variability of the tropical Atlantic ocean simulated by a general circulation model with two different mixed-layer physics. J Phys Oceanogr 23(7):1363–1388. doi:10.1175/1520-0485(1993)023<1363:VOTTAO>2.0.CO;2CrossRefGoogle Scholar
  5. Bouillon S, Fichefet T, Legat V, Madec G (2013) The elastic-viscous-plastic method revisited. Ocean Modell 71:2–12. doi: 10.1016/j.ocemod.2013.05.013 CrossRefGoogle Scholar
  6. Bracegirdle TJ, Stephenson DB, Turner J, Phillips T (2015) The importance of sea ice area biases in 21st century multimodel projections of Antarctic temperature and precipitation. Geophys Res Lett 42(24):10,832–10,839. doi: 10.1002/2015GL067055 CrossRefGoogle Scholar
  7. Brodeau L, Barnier B, Treguier AM, Penduff T, Gulev S (2010) An ERA40-based atmospheric forcing for global ocean circulation models. Ocean Modell 31(3–4):88–104. doi: 10.1016/j.ocemod.2009.10.005 CrossRefGoogle Scholar
  8. Bromwich DH, Fogt RL, Hodges KI, Walsh JE (2007) A tropospheric assessment of the ERA-40, NCEP, and JRA-25 global reanalyses in the polar regions. J Geophys Res 112(D10):D10,111. doi: 10.1029/2006JD007859 CrossRefGoogle Scholar
  9. Cavalieri DJ, Parkinson CL, Gloersen P, Zwally H (1996) Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. [1979–2010], NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, doi: 10.5067/8GQ8LZQVL0VL
  10. Chaudhuri AH, Ponte RM, Forget G (2016) Impact of uncertainties in atmospheric boundary conditions on ocean model solutions. Ocean Modell 100:96–108. doi: 10.1016/j.ocemod.2016.02.003 CrossRefGoogle Scholar
  11. Dai A, Trenberth KE (2002) Estimates of freshwater discharge from continents: latitudinal and seasonal variations. J Hydrometeor 3(6):660–687. doi: 10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2
  12. Dansereau V, Weiss J, Saramito P, Lattes P (2016) A Maxwell elasto-brittle rheology for sea ice modelling. Cryosphere 10(3):1339–1359. doi: 10.5194/tc-10-1339-2016 CrossRefGoogle Scholar
  13. Depoorter MA, Bamber JL, Griggs JA, Lenaerts JTM, Ligtenberg SRM, van den Broeke MR, Moholdt G (2013) Calving fluxes and basal melt rates of Antarctic ice shelves. Nature 502(7469):89–92. doi: 10.1038/nature12567 CrossRefGoogle Scholar
  14. Dussin R, Barnier B, Brodeau L, Molines JM (2016) The making of the DRAKKAR Forcing Set DFS5. Drakkar/myocean report 01-04-16, Laboratoire de Glaciologie et de Géophysique de l’Environnement, Université de Grenoble, Grenoble, FranceGoogle Scholar
  15. EUMETSAT (2015) Global sea ice concentration reprocessing dataset 1978-2015 (v1.2)., Ocean and Sea Ice Satelitte Application Facility, Norwegian and Danish Meteorological Institutes
  16. Fetterer F, Knowles K, Meier W, Savoie M (2016) Sea ice index, version 2. Updated daily. National Snow and Ice Data Center, Boulder, Colorado. doi: 10.7265/N5736NV7
  17. Fowler C, Maslanik J, Emery W, Tschudi M (2013) Polar Pathfinder Daily 25 km EASE-grid sea ice motion vectors. version 2. [2003–2010]. National Snow and Ice Data Center, Boulder. doi: 10.5067/O57VAIT2AYYY
  18. Gent PR, Mcwilliams JC (1990) isopycnal mixing in ocean circulation models. J Phys Oceanogr 20(1):150–155. doi: 10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2
  19. Goosse H (1997) Modelling the large-scale behaviour of the coupled ocean-sea-ice system. PhD thesis, Université catholique de LouvainGoogle Scholar
  20. Haid V, Timmermann R, Ebner L, Heinemann G (2015) Atmospheric forcing of coastal polynyas in the south-western Weddell Sea. Antarct Sci 27(4):388–402. doi: 10.1017/S0954102014000893 CrossRefGoogle Scholar
  21. Heil P, Fowler CW, Maslanik JA, Emery WJ, Allison I (2001) A comparison of East Antarctic sea-ice motion derived using drifting buoys and remote sensing. Ann Glaciol 33(1):139–144. doi: 10.3189/172756401781818374 CrossRefGoogle Scholar
  22. Hobbs WR, Massom R, Stammerjohn S, Reid P, Williams G, Meier W (2016) A review of recent changes in Southern Ocean sea ice, their drivers and forcings. Glob Planet Change 143:228–250. doi: 10.1016/j.gloplacha.2016.06.008 CrossRefGoogle Scholar
  23. Holland PR, Kimura N (2016) Observed concentration budgets of Arctic and Antarctic sea ice. J Clim 29(14):5241–5249. doi: 10.1175/JCLI-D-16-0121.1 CrossRefGoogle Scholar
  24. Holland PR, Kwok R (2012) Wind-driven trends in Antarctic sea-ice drift. Nat Geosci 5(12):872–875. doi: 10.1038/ngeo1627 CrossRefGoogle Scholar
  25. Hunke EC, Holland MM (2007) Global atmospheric forcing data for Arctic ice-ocean modeling. J Geophys Res 112(C4):1–13. doi: 10.1029/2006JC003640 CrossRefGoogle Scholar
  26. Iovino D, Masina S, Storto A, Cipollone A, Stepanov VN (2016) A 1/16\(^\circ\) eddying simulation of the global NEMO sea-ice-ocean system. Geosci Model Dev 9(8):2665–2684. doi: 10.5194/gmd-9-2665-2016 CrossRefGoogle Scholar
  27. Ivanova N, Pedersen LT, Tonboe RT, Kern S, Heygster G, Lavergne T, Sørensen A, Saldo R, Dybkjær G, Brucker L, Shokr M (2015) Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations. Cryosphere 9(5):1797–1817. doi: 10.5194/tc-9-1797-2015 CrossRefGoogle Scholar
  28. Jones RW, Renfrew IA, Orr A, Webber BGM, Holland DM, Lazzara MA (2016) Evaluation of four global reanalysis products using in situ observations in the Amundsen Sea Embayment, Antarctica. J Geophys Res Atmos 121(11):6240–6257. doi: 10.1002/2015JD024680 CrossRefGoogle Scholar
  29. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bull Am Meteor Soc 77(3):437–471. doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 CrossRefGoogle Scholar
  30. Kimura N, Nishimura A, Tanaka Y, Yamaguchi H (2013) Influence of winter sea ice motion on summer ice cover in the Arctic. Polar Res 32(20):193. doi: 10.3402/polar.v32i0.20193 Google Scholar
  31. Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Endo H, Miyaoka K, Takahashi K (2015) The JRA-55 Reanalysis: General Specifications and Basic Characteristics. J Meteorol Soc Jpn 93(1):5–48. doi: 10.2151/jmsj.2015-001 CrossRefGoogle Scholar
  32. Kurtz NT, Markus T (2012) Satellite observations of Antarctic sea ice thickness and volume. J Geophys Res 117(C8):C08,025. doi: 10.1029/2012JC008141 CrossRefGoogle Scholar
  33. Large WG, Yeager SG (2004) Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. Tech. rep., National Center for Atmospheric Research, BoulderGoogle Scholar
  34. Lecomte O, Goosse H, Fichefet T, Holland P, Uotila P, Zunz V, Kimura N (2016) Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models. Ocean Modell 105:60–70. doi: 10.1016/j.ocemod.2016.08.001 CrossRefGoogle Scholar
  35. Lefebvre W, Goosse H (2005) Influence of the Southern Annular Mode on the sea ice-ocean system: the role of the thermal and mechanical forcing. Ocean Sci 1(3):145–157. doi: 10.5194/os-1-145-2005 CrossRefGoogle Scholar
  36. Lefebvre W, Goosse H, Timmermann R, Fichefet T (2004) Influence of the Southern Annular Mode on the sea ice-ocean system. J Geophys Res 109(C9):1–12. doi: 10.1029/2004JC002403 CrossRefGoogle Scholar
  37. Lindsay R, Wensnahan M, Schweiger A, Zhang J (2014) Evaluation of seven different atmospheric reanalysis products in the Arctic. J Clim 27(7):2588–2606. doi: 10.1175/JCLI-D-13-00014.1 CrossRefGoogle Scholar
  38. Locarnini RA, Mishonov AV, Antonov JI, Boyer TP, Garcia HE, Baranova OK, Zweng MM, Paver CR, Reagan JR, Johnson DR, Hamilton M, Seidov D (2013) World Ocean Atlas 2013 Volume 1: Temperature. In: Levitus S (ed) mishonov technical ed., noaa atlas nesdis. National Centers for Environmental Information, p 73, 40Google Scholar
  39. Lüpkes C, Gryanik VM, Hartmann J, Andreas EL (2012) A parametrization, based on sea ice morphology, of the neutral atmospheric drag coefficients for weather prediction and climate models. J Geophys Res 117(D13):D13,112. doi: 10.1029/2012JD017630 CrossRefGoogle Scholar
  40. Madec G (2008) NEMO ocean engine. Note du Pôle de modélisation 27. Institut Pierre-Simon Laplace, France, iSSN No 1288-1619Google Scholar
  41. Madec G, Imbard M (1996) A global ocean mesh to overcome the North Pole singularity. Clim Dyn 12(6):381–388. doi: 10.1007/BF00211684 CrossRefGoogle Scholar
  42. Massonnet F, Fichefet T, Goosse H, Vancoppenolle M, Mathiot P, König Beatty C (2011) On the influence of model physics on simulations of Arctic and Antarctic sea ice. Cryosphere 5(3):687–699. doi: 10.5194/tc-5-687-2011 CrossRefGoogle Scholar
  43. Massonnet F, Mathiot P, Fichefet T, Goosse H, König Beatty C, Vancoppenolle M, Lavergne T (2013) A model reconstruction of the Antarctic sea ice thickness and volume changes over 1980–2008 using data assimilation. Ocean Modell 64:67–75. doi: 10.1016/j.ocemod.2013.01.003 CrossRefGoogle Scholar
  44. Merino N, Le Sommer J, Durand G, Jourdain NC, Madec G, Mathiot P, Tournadre J (2016) Antarctic icebergs melt over the Southern Ocean: climatology and impact on sea ice. Ocean Modell 104:99–110. doi: 10.1016/j.ocemod.2016.05.001 CrossRefGoogle Scholar
  45. Nihashi S, Ohshima KI (2015) Circumpolar mapping of antarctic coastal polynyas and landfast sea ice: relationship and variability. J Clim 28(9):3650–3670. doi: 10.1175/JCLI-D-14-00369.1 CrossRefGoogle Scholar
  46. Pellichero V, Sallée JB, Schmidtko S, Roquet F, Charrassin JB (2017) The ocean mixed layer under Southern Ocean sea-ice: seasonal cycle and forcing. J Geophys Res Oceans 122(2):1608–1633. doi: 10.1002/2016JC011970 CrossRefGoogle Scholar
  47. Rousset C, Vancoppenolle M, Madec G, Fichefet T, Flavoni S, Barthélemy A, Benshila R, Chanut J, Levy C, Masson S, Vivier F (2015) The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities. Geosci Model Dev 8(10):2991–3005. doi: 10.5194/gmd-8-2991-2015 CrossRefGoogle Scholar
  48. Schroeter S, Hobbs W, Bindoff NL (2017) Interactions between Antarctic sea ice and large-scale atmospheric modes in CMIP5 models. Cryosphere 11(2):789–803. doi: 10.5194/tc-11-789-2017 CrossRefGoogle Scholar
  49. Shu Q, Song Z, Qiao F (2015) Assessment of sea ice simulations in the CMIP5 models. Cryosphere 9(1):399–409. doi: 10.5194/tc-9-399-2015 CrossRefGoogle Scholar
  50. Stössel A, Zhang Z, Vihma T (2011) The effect of alternative real-time wind forcing on Southern Ocean sea ice simulations. J Geophys Res 116(C11):1–19. doi: 10.1029/2011JC007328 CrossRefGoogle Scholar
  51. Timmermann R, Worby A, Goosse H, Fichefet T (2004) Utilizing the ASPeCt sea ice thickness data set to evaluate a global coupled sea ice-ocean model. J Geophys Res 109(C7):1–10. doi: 10.1029/2003JC002242 CrossRefGoogle Scholar
  52. Timmermann R, Goosse H, Madec G, Fichefet T, Ethe C, Valérie D (2005) On the representation of high latitude processes in the ORCA-LIM global coupled sea ice-cean model. Ocean Modell 8(1–2):175–201. doi: 10.1016/j.ocemod.2003.12.009 CrossRefGoogle Scholar
  53. Tonboe RT, Eastwood S, Lavergne T, Sørensen AM, Rathmann N, Dybkjær G, Pedersen LT, Høyer JL, Kern S (2016) The EUMETSAT sea ice concentration climate data record. Cryosphere 10(5):2275–2290. doi: 10.5194/tc-10-2275-2016 CrossRefGoogle Scholar
  54. Trenberth KE, Large WG, Olson JG (1989) Global Ocean Wind Stress, climatology and monthly, by Trenberth et al. Digital media. National Center for Atmospheric Research, BoulderGoogle Scholar
  55. Tsamados M, Feltham DL, Wilchinsky AV (2013) Impact of a new anisotropic rheology on simulations of Arctic sea ice. J Geophys Res Oceans 118(1):91–107. doi: 10.1029/2012JC007990 CrossRefGoogle Scholar
  56. Tsamados M, Feltham DL, Schroeder D, Flocco D, Farrell SL, Kurtz N, Laxon SW, Bacon S (2014) Impact of variable atmospheric and oceanic form drag on simulations of Arctic sea ice. J Phys Oceanogr 44(5):1329–1353. doi: 10.1175/JPO-D-13-0215.1 CrossRefGoogle Scholar
  57. Turner J, Bracegirdle TJ, Phillips T, Marshall GJ, Hosking JS (2013) An initial assessment of antarctic sea ice extent in the CMIP5 models. J Clim 26(5):1473–1484. doi: 10.1175/JCLI-D-12-00068.1 CrossRefGoogle Scholar
  58. Turner J, Hosking JS, Marshall GJ, Phillips T, Bracegirdle TJ (2016) Antarctic sea ice increase consistent with intrinsic variability of the Amundsen Sea Low. Clim Dyn 46(7):2391–2402. doi: 10.1007/s00382-015-2708-9 CrossRefGoogle Scholar
  59. Uotila P, O’Farrell S, Marsland S, Bi D (2012) A sea-ice sensitivity study with a global ocean-ice model. Ocean Modell 51:1–18. doi: 10.1016/j.ocemod.2012.04.002 CrossRefGoogle Scholar
  60. Uotila P, Holland P, Vihma T, Marsland S, Kimura N (2014) Is realistic Antarctic sea-ice extent in climate models the result of excessive ice drift? Ocean Modell 79:33–42. doi: 10.1016/j.ocemod.2014.04.004 CrossRefGoogle Scholar
  61. Uotila P, Iovino D, Vancoppenolle M, Lensu M, Rousset C (2017) Comparing sea ice, hydrography and circulation between NEMO3.6 LIM3 and LIM2. Geosci Model Dev 10(2):1009–1031. doi: 10.5194/gmd-10-1009-2017 CrossRefGoogle Scholar
  62. Vancoppenolle M, Fichefet T, Goosse H, Bouillon S, Madec G, Morales Maqueda MA (2009) Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1. Model description and validation. Ocean Modell 27(1–2):33–53. doi: 10.1016/j.ocemod.2008.10.005 CrossRefGoogle Scholar
  63. Vancoppenolle M, Timmermann R, Ackley SF, Fichefet T, Goosse H, Heil P, Leonard KC, Lieser J, Nicolaus M, Papakyriakou T, Tison JL (2011) Assessment of radiation forcing data sets for large-scale sea ice models in the Southern Ocean. Deep-Sea Res Pt II 58(9–10):1237–1249. doi: 10.1016/j.dsr2.2010.10.039 CrossRefGoogle Scholar
  64. Vihma T, Uotila J, Cheng B, Launiainen J (2002) Surface heat budget over the Weddell Sea: Buoy results and model comparisons. J Geophys Res 107(C2):5–1–5–15. doi: 10.1029/2000JC000372 CrossRefGoogle Scholar
  65. Wang Y, Zhou D, Bunde A, Havlin S (2016) Testing reanalysis data sets in Antarctica: Trends, persistence properties, and trend significance. J Geophys Res Atmos 121(21):12,839–12,855. doi: 10.1002/2016JD024864 CrossRefGoogle Scholar
  66. Wilchinsky AV, Heorton HDBS, Feltham DL, Holland PR (2015) Study of the impact of ice formation in leads upon the sea ice pack mass balance using a new frazil and grease ice parameterization. J Phys Oceanogr 45(8):2025–2047. doi: 10.1175/JPO-D-14-0184.1 CrossRefGoogle Scholar
  67. Worby AP, Geiger CA, Paget MJ, Van Woert ML, Ackley SF, DeLiberty TL (2008) Thickness distribution of Antarctic sea ice. J Geophys Res 113(C5):C05S92. doi: 10.1029/2007JC004254 Google Scholar
  68. Zunz V, Goosse H, Massonnet F (2013) How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent? Cryosphere 7(2):451–468. doi: 10.5194/tc-7-451-2013 CrossRefGoogle Scholar
  69. Zweng M, Reagan J, Antonov J, Locarnini R, Mishonov A, Boyer T, Garcia H, Baranova O, Johnson D, DSeidov, Biddle M (2013) World Ocean Atlas 2013, Volume 2: Salinity. In: Levitus S (ed) mishonov technical ed., noaa atlas nesdis a. National Centers for Environmental Information, p 74, 39Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Georges Lemaître Centre for Earth and Climate Research (TECLIM), Earth and Life Institute (ELI)Université catholique de Louvain (UCL)Louvain-la-NeuveBelgium

Personalised recommendations