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Cloud microphysics and circulation anomalies control differences in future Greenland melt


Recently, the Greenland Ice Sheet (GrIS) has become the main source of barystatic sea-level rise1,2. The increase in the GrIS melt is linked to anticyclonic circulation anomalies, a reduction in cloud cover and enhanced warm-air advection3,4,5,6,7. The Climate Model Intercomparison Project fifth phase (CMIP5) General Circulation Models (GCMs) do not capture recent circulation dynamics; therefore, regional climate models (RCMs) driven by GCMs still show significant uncertainties in future GrIS sea-level contribution, even within one emission scenario5,8,9,10. Here, we use the RCM Modèle Atmosphèrique Règional to show that the modelled cloud water phase is the main source of disagreement among future GrIS melt projections. We show that, in the current climate, anticyclonic circulation results in more melting than under a neutral-circulation regime. However, we find that the GrIS longwave cloud radiative effect is extremely sensitive to the modelled cloud liquid-water path, which explains melt anomalies of +378 Gt yr–1 (+1.04 mm yr–1 global sea level equivalent) in a +2 °C-warmer climate with a neutral-circulation regime (equivalent to 21% more melt than under anticyclonic circulation). The discrepancies between modelled cloud properties within a high-emission scenario introduce larger uncertainties in projected melt volumes than the difference in melt between low- and high-emission scenarios11.

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Fig. 1: Cumulative summer melt and radiation anomalies expressed as melt potential.
Fig. 2: Cloud optical depth, LWD and their connection.
Fig. 3: Evolution of LWP and IWP and comparison to in-situ and satellite observations.
Fig. 4: Impact of anticyclonic circulation anomalies and cloud liquid-water fraction anomalies on melt and the SEB.

Data availability

The monthly means from 1980 to 2100 of all three MAR RCP 8.5 simulations used in this study are available via If daily outputs are required, they can be requested from X.F. ( and S.H.

Code availability

All the code used for the analysis in this study is available upon request from the corresponding author.


  1. Van Den Broeke, M. R. et al. On the recent contribution of the Greenland ice sheet to sea level change. Cryosphere 10, 1933–1946 (2016).

    Article  Google Scholar 

  2. van den Broeke, M. et al. Greenland ice sheet surface mass loss: recent developments in observation and modeling. Curr. Clim. Change Rep. 3, 345–356 (2017).

    Article  Google Scholar 

  3. Box, J. E. et al. Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers. Cryosphere 6, 821–839 (2012).

    Article  Google Scholar 

  4. Hofer, S., Tedstone, A. J., Fettweis, X. & Bamber, J. L. Decreasing cloud cover drives the recent mass loss on the Greenland Ice Sheet. Sci. Adv. 3, e1700584 (2017).

    Article  Google Scholar 

  5. Delhasse, A., Fettweis, X., Kittel, C., Amory, C. & Agosta, C. Brief communication: impact of the recent atmospheric circulation change in summer on the future surface mass balance of the Greenland Ice Sheet. Cryosphere 12, 3409–3418 (2018).

    Article  Google Scholar 

  6. Fettweis, X. et al. Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model. Cryosphere 11, 1015–1033 (2017).

    Article  Google Scholar 

  7. Hanna, E., Cropper, T. E., Hall, J. & Cappelen, J. Greenland blocking index 1851–2015: a regional climate change signal. Int. J. Climatol. 4861, 4847–4861 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Knutti, R. & Sedláček, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Clim. Change 3, 369–373 (2013).

    Article  Google Scholar 

  10. Hanna, E., Fettweis, X. & Hall, R. J. Brief communication: recent changes in summer Greenland blocking captured by none of the CMIP5 models. Cryosphere 12, 3287–3292 (2018).

    Article  Google Scholar 

  11. Church, J. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1137–1216 (IPCC, Cambridge Univ. Press, 2013).

  12. Bintanja, R. & Van Den Broeke, M. R. The influence of clouds on the radiation budget of ice and snow surfaces in Antarctica and Greenland in summer. Int. J. Climatol. 16, 1281–1296 (1996).

    Article  Google Scholar 

  13. Warren, S. G. Optical properties of snow. Rev. Geophys. 20, 67 (1982).

    Article  Google Scholar 

  14. Shupe, M. D. & Intrieri, J. M. Cloud radiative forcing of the Arctic surface: the influence of cloud properties, surface albedo, and solar zenith angle. J. Climate 17, 616–628 (2004).

    Article  Google Scholar 

  15. Van Tricht, K. et al. Clouds enhance Greenland ice sheet meltwater runoff. Nat. Commun. 7, 10266 (2016).

    Article  Google Scholar 

  16. Bennartz, R. et al. July 2012 Greenland melt extent enhanced by low-level liquid clouds. Nature 496, 83–86 (2013).

    CAS  Article  Google Scholar 

  17. Bony, S. et al. CFMIP: Towards a better evaluation and understanding of clouds and cloud feedbacks in CMIP5 models. Clivar Exch. 56, 20–22 (2011).

    Google Scholar 

  18. Tsay, S. et al. Radiative energy budget in the cloudy and hazy Arctic. J. Atmos. Sci. 46 , 1002–1018 (1989).

    Article  Google Scholar 

  19. Fettweis, X. et al. Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR. Cryosphere 7, 469–489 (2013).

    Article  Google Scholar 

  20. Fettweis, X., Tedesco, M., Van Den Broeke, M. & Ettema, J. Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models. Cryosphere 5, 359–375 (2011).

    Article  Google Scholar 

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

  22. Franco, B., Fettweis, X. & Erpicum, M. Future projections of the Greenland ice sheet energy balance driving the surface melt. Cryosphere 7, 1–18 (2013).

    Article  Google Scholar 

  23. Tedesco, M. et al. The darkening of the Greenland ice sheet: trends, drivers, and projections (1981–2100). Cryosphere 10, 477–496 (2016).

    Article  Google Scholar 

  24. Tedstone, A. J. et al. Dark ice dynamics of the south-west Greenland Ice Sheet. Cryosphere 11, 2491–2506 (2017).

    Article  Google Scholar 

  25. Alexander, P. M. et al. Assessing spatio-temporal variability and trends in modelled and measured Greenland Ice Sheet albedo (2000–2013). Cryosphere 8, 2293–2312 (2014).

    Article  Google Scholar 

  26. Cook, J. M. et al. Quantifying bioalbedo: a new physically based model and discussions of empirical methods for characterising biological influence on ice and snow albedo. Cryosphere 11, 2611–2632 (2017).

    Article  Google Scholar 

  27. Curry, J. A. & Ebert, E. E. Annual cycle of radiation fluxes over the Arctic Ocean: sensitivity to cloud optical properties. J. Climate 5, 1267–1280 (1992).

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Trenberth, K. E., Fasullo, J. T., Branstator, G. & Phillips, A. S. Seasonal aspects of the recent pause in surface warming. Nat. Clim. Change 4, 911–916 (2014).

    Article  Google Scholar 

  30. Ding, Q. et al. Tropical forcing of the recent rapid Arctic warming in northeastern Canada and Greenland. Nature 509, 209–12 (2014).

    CAS  Article  Google Scholar 

  31. Miller, N. B. et al. Cloud radiative forcing at Summit Greenland. J. Clim. 28, 6267–6280 (2015).

    Article  Google Scholar 

  32. Miller, N. B. et al. Surface energy budget responses to radiative forcing at Summit, Greenland. Cryosphere 11, 497–516 (2017).

    Article  Google Scholar 

  33. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. Roy. Meteor. Soc. 137, 553–597 (2011).

    Article  Google Scholar 

  34. Hanna, E. et al. Atmospheric and oceanic climate forcing of the exceptional Greenland ice sheet surface melt in summer 2012. Int. J. Climatol. 34, 1022–1037 (2014).

    Article  Google Scholar 

  35. Fettweis, X. et al. Brief communication: important role of the mid-tropospheric atmospheric circulation in the recent surface melt increase over the Greenland ice sheet. Cryosphere 7, 241–248 (2013).

    Article  Google Scholar 

  36. Wang, W., Zender, C. S. & van As, D. Temporal characteristics of cloud radiative effects on the Greenland ice sheet: discoveries from multiyear automatic weather station measurements. J. Geophys. Res.-Atmos. 123, 11,348–11,361 (2018).

    Article  Google Scholar 

  37. Wang, W., Zender, C. S., van As, D. & Miller, N. B. Spatial distribution of melt‐season cloud radiative effects over Greenland: evaluating satellite observations, reanalyses, and model simulations against in-situ measurements. J. Geophys. Res.-Atmos. 124, 57–71 (2018).

    Article  Google Scholar 

  38. Fettweis, X. Reconstruction of the 1979–2006 Greenland ice sheet surface mass balance using the regional climate model MAR. Cryosphere 1, 21–40 (2007).

    Article  Google Scholar 

  39. Gallee, H. & Schayes, G. Development of a three-dimensional meso-γ primitive equation model: katabatic winds simulation in the area of Terra Nova Bay, Antarctica. Mon. Weather Rev. 122, 671–685 (1994).

    Article  Google Scholar 

  40. De Ridder, K. & Gallée, H. Land surface–induced regional climate change in Southern Israel. J. Appl. Meteorol. 37, 1470–1485 (1998).

    Article  Google Scholar 

  41. Gallée, H., Guyomarc’h, G. & Brun, E. Impact of snow drift on the Antarctic ice sheet surface mass balance: possible sensitivity to snow-surface properties. Bound.-Lay. Meteorol. 99, 1–19 (2001).

    Article  Google Scholar 

  42. Meyers, M. P., DeMott, P. J. & Cotton, W. R. New primary ice-nucleation parameterizations in an explicit cloud model. J. Appl. Meteorol. 31, 708–721 (1992).

    Article  Google Scholar 

  43. Fridlind, A. M. et al. Ice properties of single‐layer stratocumulus during the mixed‐phase Arctic cloud experiment: 2. model results. J. Geophys. Res. 112, D24201 (2007).

    Article  Google Scholar 

  44. van den Broeke, M. et al. Partitioning recent Greenland mass loss. Science 326, 984–986 (2009).

    Article  Google Scholar 

  45. Uppala, S. M. et al. The ERA‐40 re-analysis. Quart. J. R. Meteorolog. Soc. 131, 2961–3012 (2005).

    Article  Google Scholar 

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This work was supported by the National Environment Research Council (grant no. ME/M021025/1) and received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 694188. This work was also supported by the Fonds de la Recherche Scientifique (FNRS) and the Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO) under the EOS Project n° O0100718F. For the MAR simulations, computational resources were provided by the Consortium des Equipements de Calcul Intensif, funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under grant no. 2.5020.11, and the Tier-1 supercomputer (Zenobel) of the Fèdèration Wallonie-Buxelles, and infrastructure was funded by the Walloon Region under grant agreement no. 1117545. X.F. is a research associate of the F.R.S.-FNRS. S.H. would like to thank M. McCrystall for valuable discussions on the topic.

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Authors and Affiliations



S.H. analysed the data and wrote the manuscript. S.H., J.B. and A.T. designed the study and methods. J.B. and A.T. supervised the project. X.F. developed and provided the daily climate model outputs as well as additional analyses. All authors discussed the results and commented on the manuscript.

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Correspondence to Stefan Hofer.

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The authors declare no competing interests.

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Peer review information: Nature Climate Change thanks Ruth Mottram, Matthew Shupe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Tables 1 and 2, and Supplementary Figs. 1 and 2.

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Hofer, S., Tedstone, A.J., Fettweis, X. et al. Cloud microphysics and circulation anomalies control differences in future Greenland melt. Nat. Clim. Chang. 9, 523–528 (2019).

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