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Decreasing surface albedo signifies a growing importance of clouds for Greenland Ice Sheet meltwater production

Author

Listed:
  • J. C. Ryan

    (University of Oregon)

  • L. C. Smith

    (Brown University
    Brown University)

  • S. W. Cooley

    (University of Oregon)

  • B. Pearson

    (Oregon State University)

  • N. Wever

    (University of Colorado)

  • E. Keenan

    (University of Colorado)

  • J. T. M. Lenaerts

    (University of Colorado)

Abstract

Clouds regulate the Greenland Ice Sheet’s surface energy balance through the competing effects of shortwave radiation shading and longwave radiation trapping. However, the relative importance of these effects within Greenland’s narrow ablation zone, where nearly all meltwater runoff is produced, remains poorly quantified. Here we use machine learning to merge MODIS, CloudSat, and CALIPSO satellite observations to produce a high-resolution cloud radiative effect product. For the period 2003–2020, we find that a 1% change in cloudiness has little effect (±0.16 W m−2) on summer net radiative fluxes in the ablation zone because the warming and cooling effects of clouds compensate. However, by 2100 (SSP5-8.5 scenario), radiative fluxes in the ablation zone will become more than twice as sensitive (±0.39 W m−2) to changes in cloudiness due to reduced surface albedo. Accurate representation of clouds will therefore become increasingly important for forecasting the Greenland Ice Sheet’s contribution to global sea-level rise.

Suggested Citation

  • J. C. Ryan & L. C. Smith & S. W. Cooley & B. Pearson & N. Wever & E. Keenan & J. T. M. Lenaerts, 2022. "Decreasing surface albedo signifies a growing importance of clouds for Greenland Ice Sheet meltwater production," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31434-w
    DOI: 10.1038/s41467-022-31434-w
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    References listed on IDEAS

    as
    1. Stefan Hofer & Andrew J. Tedstone & Xavier Fettweis & Jonathan L. Bamber, 2019. "Cloud microphysics and circulation anomalies control differences in future Greenland melt," Nature Climate Change, Nature, vol. 9(7), pages 523-528, July.
    2. Xianyao Chen & Xuebin Zhang & John A. Church & Christopher S. Watson & Matt A. King & Didier Monselesan & Benoit Legresy & Christopher Harig, 2017. "The increasing rate of global mean sea-level rise during 1993–2014," Nature Climate Change, Nature, vol. 7(7), pages 492-495, July.
    3. K. Van Tricht & S. Lhermitte & J. T. M. Lenaerts & I. V. Gorodetskaya & T. S. L’Ecuyer & B. Noël & M. R. van den Broeke & D. D. Turner & N. P. M. van Lipzig, 2016. "Clouds enhance Greenland ice sheet meltwater runoff," Nature Communications, Nature, vol. 7(1), pages 1-9, April.
    4. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    5. Martin Jung & Markus Reichstein & Philippe Ciais & Sonia I. Seneviratne & Justin Sheffield & Michael L. Goulden & Gordon Bonan & Alessandro Cescatti & Jiquan Chen & Richard de Jeu & A. Johannes Dolman, 2010. "Recent decline in the global land evapotranspiration trend due to limited moisture supply," Nature, Nature, vol. 467(7318), pages 951-954, October.
    6. Jonathan C. Ryan & Alun Hubbard & Marek Stibal & Tristram D. Irvine-Fynn & Joseph Cook & Laurence C. Smith & Karen Cameron & Jason Box, 2018. "Dark zone of the Greenland Ice Sheet controlled by distributed biologically-active impurities," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
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