Letter | Published:

September Arctic sea-ice minimum predicted by spring melt-pond fraction

Nature Climate Change volume 4, pages 353357 (2014) | Download Citation

Abstract

The area of Arctic September sea ice has diminished from about 7 million km2 in the 1990s to less than 5 million km2 in five of the past seven years, with a record minimum of 3.6 million km2 in 2012 (ref. 1). The strength of this decrease is greater than expected by the scientific community, the reasons for this are not fully understood, and its simulation is an on-going challenge for existing climate models2,3. With growing Arctic marine activity there is an urgent demand for forecasting Arctic summer sea ice4. Previous attempts at seasonal forecasts of ice extent were of limited skill5,6,7,8,9. However, here we show that the Arctic sea-ice minimum can be accurately forecasted from melt-pond area in spring. We find a strong correlation between the spring pond fraction and September sea-ice extent. This is explained by a positive feedback mechanism: more ponds reduce the albedo; a lower albedo causes more melting; more melting increases pond fraction. Our results help explain the acceleration of Arctic sea-ice decrease during the past decade. The inclusion of our new melt-pond model10 promises to improve the skill of future forecast and climate models in Arctic regions and beyond.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , , & Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data. [1979–2012]. (NASA DAAC at the National Snow and Ice Data Center, 1996, updated 2013).

  2. 2.

    & Loss of sea ice in the Arctic. Ann. Rev. Mar. Sci. 1, 417–441 (2009).

  3. 3.

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

  4. 4.

    Arctic sea ice needs better forecasts. Nature 497, 431–433 (2013).

  5. 5.

    , , & Seasonal predictions of ice extent in the Arctic Ocean. J. Geophys. Res. 113, C02023 (2008).

  6. 6.

    , , , & Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system. Geophys. Res. Lett. 40, 529–534 (2013).

  7. 7.

    , , , & Seasonal forecasts of the pan-Arctic sea ice extent using a GCM-based seasonal prediction system. J. Clim. 26, 6092–6104 (2013).

  8. 8.

    , & Seasonal prediction of Arctic sea ice extent from a coupled dynamical forecast system. Mon. Weat. Rev. 141, 1375–1394 (2013).

  9. 9.

    , , & Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J. Clim. 24, 231–250 (2011).

  10. 10.

    , , & Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007. J. Geophys. Res. 117, C09032 (2012).

  11. 11.

    et al. Transpolar observations of the morphological properties of Arctic sea ice-albedo. J. Geophys. Res. 114, C00A04 (2009).

  12. 12.

    , , , & CICE: The Los Alamos Sea Ice Model, Documentation and Software User’s Manual, Version 5.0. Tech. Rep. LA-CC-06-012, Los Alamos National Laboratory. Available at: (2013).

  13. 13.

    et al. NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Am. Meteorol. Soc.1631–1643 (2002, updated 2013).

  14. 14.

    & Observations of melt ponds on Arctic sea ice. J. Geophys. Res. 103, 24821–24835 (1998).

  15. 15.

    , , , & Hydraulic controls of summer Arctic pack ice albedo. J. Geophys. Res. 109, C08007 (2004).

  16. 16.

    & Exceptional melt pond occurrence in the years 2007 and 2011 on the Arctic sea ice revealed from MODIS satellite data. J. Geophys. Res. 117, C05018 (2012).

  17. 17.

    , , & Seasonal evolution and interannual variability of the local solar energy absorbed by the Arctic sea ice–ocean system. J. Geophys. Res. 112, C03005 (2007).

  18. 18.

    , , , & On the Arctic climate paradox and the continuing role of atmospheric circulation in affecting sea ice conditions. Geophys. Res. Lett. 34, L03711 (2007).

  19. 19.

    & On the 2012 record low Arctic sea ice cover: Combined impact of preconditioning and an August storm . Geophys. Res. Lett 40, 1356–1361 (2013).

  20. 20.

    & A model of the three-dimensional evolution of Arctic melt ponds on first-year and multiyear sea ice. J. Geophys. Res. 115 , C12064 (2010).

  21. 21.

    , & (2013).

  22. 22.

    , & Observed changes in the albedo of the Arctic sea-ice zone for the period 1982–2009. Nature Clim. Change 3, 895–898 (2013).

  23. 23.

    & An elastic viscous plastic model for sea ice dynamics. J. Phys. Oceanogr. 27, 1849–1868 (1997).

  24. 24.

    , & Impact of a new anisotropic rheology on simulations of Arctic sea ice. J. Geophys. Res. 118, 91–107 (2013).

  25. 25.

    & Modelling the rheology of sea ice as a collection of diamond-shaped floes. J. Non-Newtonian Fluid Mech. 138, 22–32 (2006).

  26. 26.

    & A continuum model of melt pond evolution on Arctic sea ice . J. Geophys. Res. 112, C08016 (2007).

  27. 27.

    , & Incorporation of a physically based melt pond scheme into the sea ice component of a climate model. J. Geophys. Res. 115, C08012 (2010).

  28. 28.

    et al. Product User Manual GLOBAL-REANALYSIS-PHYS-001-004-a and b (MyOcean, Eur. Comm., Brussels 2011).

Download references

Acknowledgements

NCEP_Reanalysis 2 data were provided by the NOAA National Weather Service, USA, from their website at http://nomads.ncep.noaa.gov/txt_descriptions/servers.shtml.

We would like to thank A. Turner and E. Hunke for their contributions to the melt-pond model and E. Hawkins for proofreading our manuscript and his advice on how to verify predictions.

Author information

Affiliations

  1. CPOM, Department of Meteorology, University of Reading, PO Box 243 Reading, RG6 6BB, UK

    • David Schröder
    • , Daniel L. Feltham
    • , Daniela Flocco
    •  & Michel Tsamados

Authors

  1. Search for David Schröder in:

  2. Search for Daniel L. Feltham in:

  3. Search for Daniela Flocco in:

  4. Search for Michel Tsamados in:

Contributions

D.F., D.L.F. and D.S. developed the melt-pond model. M.T. and D.L.F. developed the EAP model. D.S. performed the CICE simulations and the statistical calculations. All authors discussed the results.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to David Schröder.

Supplementary information

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nclimate2203

Further reading