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Arctic sea-ice change tied to its mean state through thermodynamic processes

Nature Climate Change volume 8, pages 599–603 (2018)Cite this article

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Abstract

One of the clearest manifestations of ongoing global climate change is the dramatic retreat and thinning of the Arctic sea-ice cover1. While all state-of-the-art climate models consistently reproduce the sign of these changes, they largely disagree on their magnitude1,2,3,4, the reasons for which remain contentious3,5,6,7. As such, consensual methods to reduce uncertainty in projections are lacking7. Here, using the CMIP5 ensemble, we propose a process-oriented approach to revisit this issue. We show that intermodel differences in sea-ice loss and, more generally, in simulated sea-ice variability, can be traced to differences in the simulation of seasonal growth and melt. The way these processes are simulated is relatively independent of the complexity of the sea-ice model used, but rather a strong function of the background thickness. The larger role played by thermodynamic processes as sea ice thins8,9 further suggests that the recent10 and projected11 reductions in sea-ice thickness induce a transition of the Arctic towards a state with enhanced volume seasonality but reduced interannual volume variability and persistence, before summer ice-free conditions eventually occur. These results prompt modelling groups to focus their priorities on the reduction of sea-ice thickness biases.

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Fig. 1: Changing seasonality of Arctic sea-ice cover.
Fig. 2: Efficiency of growth and melt processes as a function of the mean state.
Fig. 3: Influence of mean state on sea-ice volume variability.
Fig. 4: Challenges in reducing uncertainties of sea-ice volume projections.

References

  1. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  2. Stroeve, J. et al. Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett. 39, L16502 (2012).

    Article  Google Scholar 

  3. Massonnet, F. et al. Constraining projections of summer Arctic sea ice. Cryosphere 6, 1383–1394 (2012).

    Article  Google Scholar 

  4. Wang, M. & Overland, J. E. A sea ice free summer Arctic within 30 years: An update from CMIP5 models. Geophys. Res. Lett. 39, L1850 (2012).

    Google Scholar 

  5. Rosenblum, E. & Eisenman, I. Sea ice trends in climate models only accurate in runs with biased global warming. J. Clim. 30, 6265–6278 (2017).

    Article  Google Scholar 

  6. Mahlstein, I. & Knutti, R. Ocean heat transport as a cause for model uncertainty in projected arctic warming. J. Clim. 24, 1451–1460 (2011).

    Article  Google Scholar 

  7. Notz, D. How well must climate models agree with observations? Phil. Trans. R. Soc. A 373, 2052 (2014).

    Google Scholar 

  8. Holland, M. M., Bitz, C. M. & Tremblay, B. Future abrupt reductions in the summer Arctic sea ice. Geophys. Res. Lett. 33, L23503 (2006).

    Article  Google Scholar 

  9. Bitz, C. M. & Roe, G. H. A mechanism for the high rate of sea ice thinning in the Arctic Ocean. J. Clim. 17, 3623–3632 (2004).

    Article  Google Scholar 

  10. Kwok, R. et al. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008. J. Geophys. Res. 114, C07005 (2009).

    Article  Google Scholar 

  11. Melia, N., Haines, K. & Hawkins, E. Improved Arctic sea ice thickness projections using bias-corrected CMIP5 simulations. Cryosphere 9, 2237–2251 (2015).

    Article  Google Scholar 

  12. Swart, N., Fyfe, J. C., Hawkins, E., Kay, J. E. & Jahn, A. Influence of internal variability on Arctic sea-ice trends. Nat. Clim. Change 5, 86–89 (2015).

    Article  Google Scholar 

  13. Bintanja, R. & van der Linden, E. C. The changing seasonal climate in the Arctic. Sci. Rep. 3, 1556 (2013).

    Article  CAS  Google Scholar 

  14. Holland, M. M., Serreze, M. C. & Stroeve, J. The sea ice mass budget of the Arctic and its future change as simulated by coupled climate models. Clim. Dynam. 34, 185 (2010).

    Article  Google Scholar 

  15. Notz, D. & Bitz, C. M. in Sea Ice (ed. Thomas, D. N.) (John Wiley & Sons, Chichester, 2017).

  16. Curry, J. A., Schramm, J. L. & Ebert, E. E. Sea ice–albedo climate feedback mechanism. J. Clim. 8, 240–247 (1995).

    Article  Google Scholar 

  17. Kay, J. E. et al. The Community Earth System Model (CESM) Large Ensemble Project: A community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Article  Google Scholar 

  18. Barnhart, K. R., Miller, C. R., Overeem, I. & Kay, J. E. Mapping the future expansion of Arctic open water. Nat. Clim. Change 6, 280–285 (2016).

    Article  Google Scholar 

  19. Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).

    Article  CAS  Google Scholar 

  20. Hunke, E. Thickness sensitivities in the CICE sea ice model. Ocean Model. 34, 137–149 (2010).

    Article  Google Scholar 

  21. Notz, D. et al. Sea Ice Model Intercomparison Project (SIMIP): Understanding sea ice through climate-model simulations. Geosci. Model Dev. 9, 3427–3446 (2016).

    Article  Google Scholar 

  22. Schweiger, A., Lindsay, R., Zhang, J., Steele, M. & Stern, H. Uncertainty in modeled Arctic sea ice volume. J. Geophys. Res. 116, C00D06 (2011).

    Article  Google Scholar 

  23. Cheng, W., Blanchard-Wrigglesworth, E., Bitz, C. M., Ladd, C. & Stabeno, P. J. Diagnostic sea ice predictability in the pan-Arctic and U.S. Arctic regional seas. Geophys. Res. Lett. 43, 11688–11696 (2016).

    Article  Google Scholar 

  24. Goosse, H., Arzel, O., Bitz, C. M., de Montety, A. & Vancoppenolle, M. Increased variability of the Arctic summer ice extent in a warmer climate. Geophys. Res. Lett. 36, L23702 (2009).

    Article  Google Scholar 

  25. Blanchard-Wrigglesworth, E. & Bitz, C. M. Characteristics of Arctic sea-ice thickness variability in GCMs. J. Clim. 27, 8244–8258 (2014).

    Article  Google Scholar 

  26. Fučkar, N. S., Volpi, D., Guemas, V. & Doblas-Reyes, F. J. A posteriori adjustment of near-term climate predictions: Accounting for the drift dependence on the initial conditions. Geophys. Res. Lett. 41, 5200–5207 (2014).

    Article  Google Scholar 

  27. Knutti, R. et al. A climate model projection weighting scheme accounting for performance and interdependence. Geophys. Res. Lett. 44, 1909–1918 (2017).

    Google Scholar 

  28. Stroeve, J., Barrett, A., Serreze, M. & Schweiger, A. Using records from submarine, aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness. Cryosphere 8, 1839–1854 (2014).

    Article  Google Scholar 

  29. Zhang, X. Sensitivity of Arctic summer sea ice coverage to global warming forcing: toward reducing uncertainty in Arctic climate change projections. Tellus A 62, 220–227 (2010).

    Article  Google Scholar 

  30. Zygmuntowksa, M., Rampal, P., Ivanova, N. & Smedsrud, L. H. Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends. Cryosphere 8, 705–720 (2014).

    Article  Google Scholar 

  31. Fetterer, F., Knowles, K., Meier, W., Savoie, M. & Windnagel, A. K. Sea Ice Index Version 2 G02135 (National Snow and Ice Data Center, 2017); https://doi.org/10.7265/N5736NV7

  32. van Vuuren, D. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Article  Google Scholar 

  33. Global Sea Ice Concentration Reprocessing Dataset 1978–2015 Version 1.2 (EUMETSAT Ocean and Sea Ice Satelite Application Facility, Norwegian and Danish Meteorological Institutes, 2015); http://osisaf.met.no

  34. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  35. Fichefet, T. & Morales Maqueda, M. M. Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J. Geophys. Res. 102, 12609–12646 (1997).

    Article  Google Scholar 

  36. Tsamados, M., Feltham, D., Petty, A., Schroeder, D. & Flocco, D. Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model. Phil. Trans. R. Soc. A 373, 2052 (2015).

    Article  Google Scholar 

  37. Goosse, H. & Fichefet, T. Importance of ice–ocean interactions for the global ocean circulation: A model study. J. Geophys. Res. 104, 23337–23335 (1999).

    Article  Google Scholar 

  38. Notz, D. Thermodynamic and Fluid-Dynamical Processes in Sea Ice. PhD thesis, Univ. Cambridge (2005).

  39. Maykut, G. A. & Untersteiner, N. Some results from a time-dependent, thermodynamic model of sea ice. J. Geophys. Res. 76, 1550–1575 (1971).

    Article  Google Scholar 

  40. Perovich, D. K. et al. SHEBA: Snow and Ice Studies CD-ROM (Cold Regions Research and Engineering Laboratory, 1999).

  41. Hendricks, S., Ricker, R. and Helm, V. AWI CryoSat-2 Sea Ice Thickness Data Product Version 1.2 (Alfred Wegener Institute, 2016); http://www.meereisportal.de/fileadmin/user_upload/www.meereisportal.de/MeereisBeobachtung/Beobachtungsergebnisse_aus_Satellitenmessungen/CryoSat-2_Meereisprodukt/AWI_cryosat2_user_guide_v1.2_july2016.pdf

  42. Lindsay, R. & Schweiger, A. Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations. Cryosphere 9, 269–283 (2015).

    Article  Google Scholar 

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Acknowledgements

The research leading to these results has received funding from the Belgian Fonds National de la Recherche Scientifique (F.R.S.-FNRS), and the European Commission’s Horizon 2020 projects APPLICATE (GA 727862) and PRIMAVERA (GA 641727). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in the Supplementary Information) for producing and making available their model output. We acknowledge the CESM Large Ensemble Community Project and supercomputing resources provided by NSF/CISL/Yellowstone for access to the CESM-LE data. The authors thank C. M. Bitz and D. Notz for useful discussions, and F. Kauker for providing the ITRP data. The authors thank M. M. Holland and E. C. Hunke for the review of this manuscript.

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Authors and Affiliations

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

    François Massonnet, Hugues Goosse, David Docquier & Thierry Fichefet

  2. Earth Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain

    François Massonnet

  3. Sorbonne Universités (UPMC Paris 6), LOCEAN-IPSL, CNRS/IRD/MNHN, Paris, France

    Martin Vancoppenolle

  4. Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA

    Edward Blanchard-Wrigglesworth

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  1. François Massonnet
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Contributions

F.M., M.V. and H.G. designed the science plan. All authors contributed to the design of the study. F.M. assembled the data and wrote the manuscript.

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Correspondence to François Massonnet.

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Massonnet, F., Vancoppenolle, M., Goosse, H. et al. Arctic sea-ice change tied to its mean state through thermodynamic processes. Nature Clim Change 8, 599–603 (2018). https://doi.org/10.1038/s41558-018-0204-z

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