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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An archived version of the code used to create the monthly ice masks, analyse the data and create the figures in this manuscript is available at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.

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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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