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Ubiquitous acceleration in Greenland Ice Sheet calving from 1985 to 2022

Nature volume 625, pages 523–528 (2024)Cite this article

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Abstract

Nearly every glacier in Greenland has thinned or retreated over the past few decades1,2,3,4, leading to glacier acceleration, increased rates of sea-level rise and climate impacts around the globe5,6,7,8,9. To understand how calving-front retreat has affected the ice-mass balance of Greenland, we combine 236,328 manually derived and AI-derived observations of glacier terminus positions collected from 1985 to 2022 and generate a 120-m-resolution mask defining the ice-sheet extent every month for nearly four decades. Here we show that, since 1985, the Greenland Ice Sheet (GrIS) has lost 5,091 ± 72 km2 of area, corresponding to 1,034 ± 120 Gt of ice lost to retreat. Our results indicate that, by neglecting calving-front retreat, current consensus estimates of ice-sheet mass balance4,9 have underestimated recent mass loss from Greenland by as much as 20%. The mass loss we report has had minimal direct impact on global sea level but is sufficient to affect ocean circulation and the distribution of heat energy around the globe10,11,12. On seasonal timescales, Greenland loses 193 ± 25 km2 (63 ± 6 Gt) of ice to retreat each year from a maximum extent in May to a minimum between September and October. We find that multidecadal retreat is highly correlated with the magnitude of seasonal advance and retreat of each glacier, meaning that terminus-position variability on seasonal timescales can serve as an indicator of glacier sensitivity to longer-term climate change.

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Fig. 1: Greenland glacier terminus observations since 1985.
Fig. 2: Cumulative mass change resulting from glacier retreat since 1985.
Fig. 3: Relationship between seasonality and decadal retreat.
Fig. 4: Predictors of glacier sensitivity.

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Data availability

The monthly ice masks described in this work were developed for the NASA MEaSUREs ITS_LIVE project and are available through the National Snow and Ice Data Center at https://doi.org/10.5067/579TO87M7IZB.

Code availability

An archived version of the code used to create the monthly ice masks, analyse the data and create the figures in this manuscript is available at https://doi.org/10.5281/zenodo.8388136. Any updates to the code will be available at https://github.com/chadagreene/greenland-icemask.

References

  1. Mouginot, J. et al. Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018. Proc. Natl Acad. Sci. 116, 9239–9244 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wood, M. et al. Ocean forcing drives glacier retreat in Greenland. Sci. Adv. 7, eaba7282 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  3. Moon, T. A., Gardner, A. S., Csatho, B., Parmuzin, I. & Fahnestock, M. A. Rapid reconfiguration of the Greenland Ice Sheet coastal margin. J. Geophys. Res. Earth Surf. 125, e2020JF005585 (2020).

    Article  ADS  Google Scholar 

  4. Otosaka, I. N. et al. Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020. Earth Syst. Sci. Data 15, 1597–1616 (2023).

    Article  ADS  Google Scholar 

  5. Nick, F. M., Vieli, A., Howat, I. M. & Joughin, I. Large-scale changes in Greenland outlet glacier dynamics triggered at the terminus. Nat. Geosci. 2, 110–114 (2009).

    Article  ADS  CAS  Google Scholar 

  6. King, M. D. et al. Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat. Commun. Earth Environ. 1, 1 (2020).

    Article  ADS  Google Scholar 

  7. Moon, T., Joughin, I. & Smith, B. Seasonal to multiyear variability of glacier surface velocity, terminus position, and sea ice/ice mélange in northwest Greenland. J. Geophys. Res. Earth Surf. 120, 818–833 (2015).

    Article  ADS  Google Scholar 

  8. Vijay, S. et al. Resolving seasonal ice velocity of 45 Greenlandic glaciers with very high temporal details. Geophys. Res. Lett. 46, 1485–1495 (2019).

    Article  ADS  Google Scholar 

  9. Fox-Kemper, B. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 1211–1362 (Cambridge Univ. Press, 2021).

  10. Enderlin, E. M., Hamilto, G. S., Straneo, F. & Sutherland, D. A. Iceberg meltwater fluxes dominate the freshwater budget in Greenland’s iceberg‐congested glacial fjords. Geophys. Res. Lett. 43, 11,287–11,294 (2016).

    Article  Google Scholar 

  11. Marsh, R. et al. Short-term impacts of enhanced Greenland freshwater fluxes in an eddy-permitting ocean model. Ocean Sci. 6, 749–760 (2010).

    Article  ADS  Google Scholar 

  12. Böning, C. W., Behrens, E., Biastoch, A., Getzlaff, K. & Bamber, J. L. Emerging impact of Greenland meltwater on deepwater formation in the North Atlantic Ocean. Nat. Geosci. 9, 523–527 (2016).

    Article  ADS  Google Scholar 

  13. Choi, Y., Morlighem, M., Rignot, E. & Wood, M. Ice dynamics will remain a primary driver of Greenland ice sheet mass loss over the next century. Commun. Earth Environ. 2, 26 (2021).

    Article  ADS  Google Scholar 

  14. Rückamp, M., Goelzer, H. & Humbert, A. Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the Alfred Wegener Institute (AWI) contribution to ISMIP6 Greenland using the Ice-sheet and Sea-level System Model (ISSM). Cryosphere 14, 3309–3327 (2020).

    Article  ADS  Google Scholar 

  15. Robel, A. A., Roe, G. H. & Haseloff, M. Response of marine‐terminating glaciers to forcing: time scales, sensitivities, instabilities, and stochastic dynamics. J. Geophys. Res. Earth Surf. 123, 2205–2227 (2018).

    Article  ADS  Google Scholar 

  16. Felikson, D., Nowicki, S., Nias, I., Morlighem, M. & Seroussi, H. Seasonal tidewater glacier terminus oscillations bias multi‐decadal projections of ice mass change. J. Geophys. Res. Earth Surf. 127, e2021JF006249 (2022).

    Article  ADS  Google Scholar 

  17. Schild, K. M. & Hamilton, G. S. Seasonal variations of outlet glacier terminus position in Greenland. J. Glaciol. 59, 759–770 (2013).

    Article  ADS  Google Scholar 

  18. Black, T. E. & Joughin, I. Weekly to monthly terminus variability of Greenland’s marine-terminating outlet glaciers. Cryosphere 17, 1–13 (2023).

    Article  ADS  Google Scholar 

  19. Felikson, D. et al. Steep glacier bed knickpoints mitigate inland thinning in Greenland. Geophys. Res. Lett. 48, e2020GL090112 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  20. Schoof, C. Ice sheet grounding line dynamics: steady states, stability, and hysteresis. J. Geophys. Res. Earth Surf. 112, F03S28 (2007).

    Article  ADS  Google Scholar 

  21. Ultee, L., Felikson, D., Minchew, B., Stearns, L. A. & Riel, B. Helheim Glacier ice velocity variability responds to runoff and terminus position change at different timescales. Nat. Commun. 13, 6022 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fried, M. J. et al. Reconciling drivers of seasonal terminus advance and retreat at 13 Central West Greenland tidewater glaciers. J. Geophys. Res. Earth Surf. 123, 1590–1607 (2018).

    Article  ADS  Google Scholar 

  23. Xu, Y., Rignot, E., Menemenlis, D. & Koppes, M. Numerical experiments on subaqueous melting of Greenland tidewater glaciers in response to ocean warming and enhanced subglacial discharge. Ann. Glaciol. 53, 229–234 (2012).

    Article  ADS  Google Scholar 

  24. Morlighem, M., Wood, M., Seroussi, H., Choi, Y. & Rignot, E. Modeling the response of northwest Greenland to enhanced ocean thermal forcing and subglacial discharge. Cryosphere 13, 723–734 (2019).

    Article  ADS  Google Scholar 

  25. Simonsen, S. B., Barletta, V. R., Colgan, W. T. & Sørensen, L. S. Greenland Ice Sheet mass balance (1992–2020) from calibrated radar altimetry. Geophys. Res. Lett. 48, e2020GL091216 (2021).

    Article  ADS  Google Scholar 

  26. Smith, B. et al. Pervasive ice sheet mass loss reflects competing ocean and atmosphere processes. Science 368, 1239–1242 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  27. Mankoff, K. D. et al. Greenland ice sheet mass balance from 1840 through next week. Earth Syst. Sci. Data 13, 5001–5025 (2021).

    Article  ADS  Google Scholar 

  28. Velicogna, I. & Wahr, J. Time-variable gravity observations of ice sheet mass balance: precision and limitations of the GRACE satellite data. Geophys. Res. Lett. 40, 3055–3063 (2013).

    Article  ADS  Google Scholar 

  29. The IMBIE Team. Mass balance of the Greenland Ice Sheet from 1992 to 2018. Nature 579, 233–239 (2020).

    Article  Google Scholar 

  30. Khan, S. A. et al. Greenland mass trends from airborne and satellite altimetry during 2011–2020. J. Geophys. Res. Earth Surf. 127, e2021JF006505 (2022).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  31. Bamber, J. L. et al. Land ice freshwater budget of the Arctic and North Atlantic Oceans: 1. Data, methods, and results. J. Geophys. Res. Oceans 123, 1827–1837 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sutherland, D. A. & Pickart, R. S. The East Greenland coastal current: structure, variability, and forcing. Prog. Oceanogr. 78, 58–77 (2008).

    Article  ADS  Google Scholar 

  33. Gou, R., Pennelly, C. & Myers, P. G. The changing behavior of the West Greenland current system in a very high‐resolution model. J. Geophys. Res. Oceans 127, e2022JC018404 (2022).

    Article  ADS  Google Scholar 

  34. Davison, B. J., Cowton, T. R., Cottier, F. R. & Sole, A. J. Iceberg melting substantially modifies oceanic heat flux towards a major Greenlandic tidewater glacier. Nat. Commun. 11, 5983 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Castro De La Guardia, L., Hu, X. & Myers, P. G. Potential positive feedback between Greenland Ice Sheet melt and Baffin Bay heat content on the west Greenland shelf. Geophys. Res. Lett. 42, 4922–4930 (2015).

    Article  ADS  Google Scholar 

  36. Rahmstorf, S. et al. Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nat. Clim. Change 5, 475–480 (2015).

    Article  ADS  Google Scholar 

  37. Swingedouw, D. et al. AMOC recent and future trends: a crucial role for oceanic resolution and Greenland melting? Front. Clim. 4, 838310 (2022).

    Article  Google Scholar 

  38. Bakker, P. et al. Fate of the Atlantic Meridional Overturning Circulation: strong decline under continued warming and Greenland melting. Geophys. Res. Lett. 43, 12,252–12,260 (2016).

    Article  Google Scholar 

  39. Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. 105, 1786–1793 (2008).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Liu, W., Xie, S.-P., Liu, Z. & Zhu, J. Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in warming climate. Sci. Adv. 3, e1601666 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  41. Ditlevsen, P. & Ditlevsen, S. Warning of a forthcoming collapse of the Atlantic meridional overturning circulation. Nat. Commun. 14, 4254 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hansen, J. et al. Ice melt, sea level rise and superstorms: evidence from paleoclimate data, climate modeling, and modern observations that 2 °C global warming could be dangerous. Atmos. Chem. Phys. 16, 3761–3812 (2016).

    Article  ADS  CAS  Google Scholar 

  43. Ciemer, C., Winkelmann, R., Kurths, J. & Boers, N. Impact of an AMOC weakening on the stability of the southern Amazon rainforest. Eur. Phys. J. Spec. Top. 230, 3065–3073 (2021).

    Article  Google Scholar 

  44. Velasco, J. A. et al. Synergistic impacts of global warming and thermohaline circulation collapse on amphibians. Commun. Biol. 4, 141 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Osman, M. B. et al. Industrial-era decline in subarctic Atlantic productivity. Nature 569, 551–555 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  46. Ritchie, P. D. L. et al. Shifts in national land use and food production in Great Britain after a climate tipping point. Nat. Food 1, 76–83 (2020).

    Article  Google Scholar 

  47. Defrance, D. et al. Consequences of rapid ice sheet melting on the Sahelian population vulnerability. Proc. Natl Acad. Sci. 114, 6533–6538 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lique, C., Holland, M. M., Dibike, Y. B., Lawrence, D. M. & Screen, J. A. Modeling the Arctic freshwater system and its integration in the global system: lessons learned and future challenges. J. Geophys. Res. Biogeosci. 121, 540–566 (2016).

    Article  Google Scholar 

  49. Fox-Kemper, B. et al. Challenges and prospects in ocean circulation models. Front. Mar. Sci. 6, 65 (2019).

    Article  Google Scholar 

  50. Forget, G. et al. ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geosci. Model Dev. 8, 3071–3104 (2015).

    Article  ADS  Google Scholar 

  51. von Schuckmann, K. et al. Heat stored in the Earth system: where does the energy go? Earth Syst. Sci. Data 12, 2013–2041 (2020).

    Article  ADS  Google Scholar 

  52. Aschwanden, A., Bartholomaus, T. C., Brinkerhoff, D. J. & Truffer, M. Brief communication: A roadmap towards credible projections of ice sheet contribution to sea level. Cryosphere 15, 5705–5715 (2021).

    Article  ADS  Google Scholar 

  53. Goldberg, D. N., Heimbach, P., Joughin, I. & Smith, B. Committed retreat of Smith, Pope, and Kohler Glaciers over the next 30 years inferred by transient model calibration. Cryosphere 9, 2429–2446 (2015).

    Article  ADS  Google Scholar 

  54. Greene, C. A., Gwyther, D. E. & Blankenship, D. D. Antarctic mapping tools for MATLAB. Comput. Geosci. 104, 151–157 (2017).

    Article  ADS  Google Scholar 

  55. Zhang, E., Catania, G. & Trugman, D. AutoTerm: a “big data” repository of Greenland glacier termini delineated using deep learning. https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1095/ (2022).

  56. Enze, Z. AutoTerm: a “big data” repository of glacier termini delineated using deep learning. Zenodo https://doi.org/10.5281/ZENODO.7782039 (2022).

  57. Black, T. MEaSUREs weekly to monthly Greenland outlet glacier terminus positions from Sentinel-1 mosaics, version 1. National Snow and Ice Data Center (NSIDC) https://doi.org/10.5067/DGBOSSIULSTD (2022).

  58. Joughin, I. & University Of Washington. MEaSUREs annual Greenland outlet glacier terminus positions from SAR mosaics, version 2. National Snow and Ice Data Center (NSIDC) https://doi.org/10.5067/ESFWE11AVFKW (2021).

  59. Cheng, D. et al. Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019. Cryosphere 15, 1663–1675 (2021).

    Article  ADS  Google Scholar 

  60. Cheng, D., Hayes, W. & Larour, E. CALFIN subseasonal Greenland glacial terminus positions, version 1. National Snow and Ice Data Center (NSIDC) https://doi.org/10.5067/7FILV218JZA2 (2021).

  61. Goliber, S. et al. TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications. Cryosphere 16, 3215–3233 (2022).

    Article  ADS  Google Scholar 

  62. Goliber, S. & Black, T. TermPicks: a century of Greenland glacier terminus data for use inmachine learning applications. Zenodo https://doi.org/10.5281/ZENODO.5117931 (2021).

  63. Gardner, A., Fahnestock, M. & Scambos, T. MEaSUREs ITS_LIVE regional glacier and ice sheet surface velocities, version 1. National Snow and Ice Data Center (NSIDC) https://doi.org/10.5067/6II6VW8LLWJ7 (2022).

  64. Gardner, A. S. et al. Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. Cryosphere 12, 521–547 (2018).

    Article  ADS  Google Scholar 

  65. Joughin, I. MEaSUREs Greenland ice velocity annual mosaics from SAR and Landsat, version 1. National Snow and Ice Data Center (NSIDC) https://doi.org/10.5067/OBXCG75U7540 (2017).

  66. Larour, E., Seroussi, H., Morlighem, M. & Rignot, E. Continental scale, high order, high spatial resolution, ice sheet modeling using the Ice Sheet System Model (ISSM). J. Geophys. Res. Earth Surf. 117, F01022 (2012).

    Article  ADS  Google Scholar 

  67. Briner, J. P. et al. Rate of mass loss from the Greenland Ice Sheet will exceed Holocene values this century. Nature 586, 70–74 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  68. Cuzzone, J. K. et al. The impact of model resolution on the simulated Holocene retreat of the southwestern Greenland ice sheet using the Ice Sheet System Model (ISSM). Cryosphere 13, 879–893 (2019).

    Article  ADS  Google Scholar 

  69. Cuzzone, J. K., Young, N. E., Morlighem, M., Briner, J. P. & Schlegel, N.-J. Simulating the Holocene deglaciation across a marine-terminating portion of southwestern Greenland in response to marine and atmospheric forcings. Cryosphere 16, 2355–2372 (2022).

    Article  ADS  Google Scholar 

  70. Goelzer, H. et al. The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6. Cryosphere 14, 3071–3096 (2020).

    Article  ADS  Google Scholar 

  71. Dias Dos Santos, T., Morlighem, M. & Brinkerhoff, D. A new vertically integrated MOno-Layer Higher-Order (MOLHO) ice flow model. Cryosphere 16, 179–195 (2022).

    Article  ADS  Google Scholar 

  72. Cuzzone, J. K., Morlighem, M., Larour, E., Schlegel, N. & Seroussi, H. Implementation of higher-order vertical finite elements in ISSM v4.13 for improved ice sheet flow modeling over paleoclimate timescales. Geosci. Model Dev. 11, 1683–1694 (2018).

    Article  ADS  CAS  Google Scholar 

  73. Howat, I., Ohio State University & Byrd Polar Research Center. MEaSUREs Greenland Ice Mapping Project (GIMP) land ice and ocean classification mask, version 1. National Snow and Ice Data Center (NSIDC) https://doi.org/10.5067/B8X58MQBFUPA (2017).

  74. Greene, C. A. et al. The Climate Data Toolbox for MATLAB. Geochem. Geophys. Geosyst. 20, 3774–3781 (2019).

    Article  ADS  Google Scholar 

  75. Morlighem, M. et al. BedMachine v3: complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo sounding combined with mass conservation. Geophys. Res. Lett. 44, 11,051–11,061 (2017).

    Article  CAS  Google Scholar 

  76. Morlighem, M. IceBridge BedMachine Greenland, version 5. National Snow and Ice Data Center (NSIDC) https://doi.org/10.5067/GMEVBWFLWA7X (2022).

  77. Korsgaard, N. J. et al. Digital elevation model and orthophotographs of Greenland based on aerial photographs from 1978–1987. Sci. Data 3, 160032 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Mouginot, J. & Rignot, E. Glacier catchments/basins for the Greenland Ice Sheet. Dryad https://doi.org/10.7280/D1WT11 (2019).

  79. Greene, C. A., Gardner, A. S., Schlegel, N.-J. & Fraser, A. D. Antarctic calving loss rivals ice-shelf thinning. Nature 609, 948–953 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  80. Medley, B., Neumann, T. A., Zwally, H. J. & Smith, B. E. Forty-year simulations of firn processes over the Greenland and Antarctic ice sheets. https://tc.copernicus.org/preprints/tc-2020-266/tc-2020-266.pdf (2020).

  81. Schwanghart, W. & Scherler, D. Short Communication: TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences. Earth Surf. Dyn. 2, 1–7 (2014).

    Article  ADS  Google Scholar 

  82. Oceans Melting Greenland (OMG). OMG CTD Conductivity Temperature Depth (CTD) profiles. Jet Propulsion Laboratory https://doi.org/10.5067/OMGEV-CTDS1 (2020).

  83. Fenty, I. et al. Oceans Melting Greenland: early results from NASA’s ocean-ice mission in Greenland. Oceanography 29, 72–83 (2016).

    Article  Google Scholar 

  84. Willis, J. et al. Ocean-ice interactions in Inglefield Gulf: early results from NASA’s Oceans Melting Greenland mission. Oceanography 31, 100–108 (2018).

    Article  Google Scholar 

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Acknowledgements

We thank T. Black, D. Cheng, S. Goliber, I. Joughin and E. Zhang for making their terminus-position data available to the public and for many helpful discussions about their data. This research was supported by the NASA Cryospheric Science and MEaSUREs programmes and was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration ©2023. All rights reserved.

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Authors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Chad A. Greene & Alex S. Gardner

  2. Moss Landing Marine Laboratories, San José State University, San José, CA, USA

    Michael Wood

  3. Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, CA, USA

    Joshua K. Cuzzone

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  1. Chad A. Greene
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Contributions

C.A.G. and A.S.G. conceived the study. C.A.G. generated the ice masks, analysed the ice time series data, created all figures and wrote the first draft of the manuscript. M.W. provided oceanographic data and assisted in the oceanographic data analysis. J.K.C. provided ice-sheet-model data and assisted in their application to this work. All authors contributed to revisions and the final draft of this manuscript.

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Correspondence to Chad A. Greene.

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Extended data figures and tables

Extended Data Fig. 1 GrIS area and mass variability.

Pan-Greenland totals show that the ice sheet has lost 5,091 ± 72 km2 of its area (a) or 1,034 ± 120 Gt of mass (b) to glacier terminus retreat since 1985. Seasonal cycles of area (c) and mass (d) are characterized by the median of residuals for the years 2014–2020, after subtracting a 12-month moving average from the full monthly time series. Shaded blue regions in all panels indicate measurement uncertainty, estimated from the root sum square of uncertainties related to terminus position and ice thickness for each glacier (Methods).

Extended Data Fig. 2 Observation data density.

The total length of terminus observation data within each glacier catchment is summed for each month of the time series, as a proxy for which glaciers are best observed and when. Colour is presented on a log scale, in which dark purple indicates a high density of data. Histograms along the top and side of the matrix show totals, indicating when observations are available and which glaciers are best sampled. We use data collected as early as 1972 to constrain our ice masks, but the data analysis presented in this paper begins in 1985.

Extended Data Fig. 3 Distributions of seasonal amplitudes and phases.

178 marine-terminating glaciers in Greenland exhibit substantial, consistent seasonal variability in terminus position each year. The median range of minimum-to-maximum glacier extent within a given year is about 0.8 km2 (a) or 0.1 Gt (b) per glacier, but glacier sizes roughly follow a Pareto distribution, meaning that a few glaciers have seasonal ranges that are 10 to 20 times larger than the GIS median. Glaciers tend to reach their maximum area (c) and mass (d) in May or June, then retreat to a minimum that occurs around October. Most glaciers exhibit a maximum and minimum extent that occurs slightly after the overall ice-sheet average, largely because of the influence that the early cycle of Jakobshavn Isbræ has on the areal extents and total mass of the entire ice sheet.

Extended Data Fig. 4 Correlations between retreat and local environmental factors.

A matrix of correlation coefficients (r) compares relationships between glacier mass change owing to calving, the range of seasonal mass variability owing to calving, the timing of seasonal maximum mass, bed slope and surface slope within 5 km of the glacier terminus, bed elevation, thickness, velocity and ice flux at the terminus, mean surface runoff from each catchment, oceanographic sill depth and mean ocean-temperature anomalies measured within 10 km of each glacier terminus, compared for 95 glaciers for which observations of all variables are available. The top row of the mass variability correlation matrix distils the results shown in Fig. 4, and negative values indicate that glaciers tend to lose mass where the dependent variable is higher. To account for relationships between mass and glacier terminus thickness and width, we normalize mass values by glacier terminus face area, providing a measure of effective length variability that reveals that seasonal variability is the strongest simple predictor of long-term retreat.

Extended Data Fig. 5 Ocean-temperature observations.

We use 2,828 oceanographic temperature profiles collected by NASA’s Oceans Melting Greenland project. Each profile on the left is colour-scaled by a scalar local thermal anomaly value shown in the map on the right. The heavy black profile on the left represents the mean of all 2,828 profiles. By subtracting the mean profile from each individual profile and then calculating a mean anomaly value within the top 1,000 m of each profile, we obtain a measure of ocean temperature that reflects the spatial distribution of available heat energy, with minimal influence from the depth of the available observations. Map created with Arctic Mapping Tools for MATLAB54 using geographic outlines developed in this work.

Extended Data Fig. 6 Terminus observation timing by dataset.

We use terminus-position observations from five sources (Methods) to analyse changes in the extent of the GrIS since 1985 and we use observations from 2014 to 2020 to characterize the seasonal cycles of growth and retreat of the ice sheet.

Extended Data Fig. 7 Gridded velocity and thickness data.

This work required knowledge of ice velocity and thickness beyond the current measurable extents of the ice sheet. We combine data from several sources (Methods) to generate complete gridded velocity and thickness fields that cover the entire domain of interest. Ice velocity and thickness values are unrealistic in the open ocean but are reasonable and well constrained within fjords and close to the current extents of the ice sheet, for which the values are used in this work. a–d, The entire GrIS. e–h, Detailed views of the region surrounding the terminus of Jakobshavn Isbræ. Maps created with Arctic Mapping Tools for MATLAB54 using geographic outlines developed in this work.

Extended Data Fig. 8 Terminus-position data densification.

An example of the terminus-position data we use is shown as 260 blue-to-yellow coloured dots. Raw terminus-position data are not necessarily distributed as continuous line segments from one side of a glacier terminus to the other, as seen by the blue dots that start near the top and then continue at the bottom of the image above. We sort and densify all terminus-position data and then use a flow model to determine whether any given point lies upstream or downstream of the observed terminus position (Methods). Map created with Arctic Mapping Tools for MATLAB54 with terminus position from the TermPicks dataset61.

Extended Data Fig. 9 Masking process example.

An ice mask representing a previous assumption (Methods) is shown in white. Blue lines show all terminus observations taken within 30 days after 15 August 2015 and advected upstream to their expected location on 15 August 2015. Yellow lines show terminus observations taken within 30 days before 15 August 2015 and advected downstream to their expected location on 15 August 2015. a, All pixels upstream of the blue lines are in the ‘Fill region’ and are set as true in the ice mask. b, Pixels downstream of the yellow line defined as being in the ‘Carve region’ and are set as false in the ice mask. c, No adjustments are made where the Prior mask terminus falls between the Carve region and the Fill region. This can occur when ice is lost to calving in the time between terminus observations or it can be because of a mismatch in terminus-position picks. d, We set ice pixels to true wherever downstream-advected and upstream-advected terminus positions overlap. Map created with Arctic Mapping Tools for MATLAB54 using geographic outlines developed in this work.

Extended Data Fig. 10 Ice-thickness uncertainty.

Errors in ice thickness are estimated on the basis of the source of the thickness data (Methods). The inset in the right panel matches the inset in Extended Data Fig. 7. Maps created with Arctic Mapping Tools for MATLAB54 using geographic outlines developed in this work.

Extended Data Fig. 11 Extrapolated glacier catchments.

To properly account for terminus activity that occurred beyond the present-day extents of the ice sheet, we extrapolate 260 glacier catchment regions downstream along our extrapolated flowlines (Extended Data Fig. 7) and then dilate each catchment area by up to 5 km to fill any gaps near fjord walls. The inset in the right panel matches the inset in Extended Data Fig. 7. Maps created with Arctic Mapping Tools for MATLAB54 using geographic outlines developed in this work.

Supplementary information

Supplementary Table 1

Time series of glacier area and mass.

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Greene, C.A., Gardner, A.S., Wood, M. et al. Ubiquitous acceleration in Greenland Ice Sheet calving from 1985 to 2022. Nature 625, 523–528 (2024). https://doi.org/10.1038/s41586-023-06863-2

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