The Greenland ice sheet (GrIS) is a growing contributor to global sea-level rise1, with recent ice mass loss dominated by surface meltwater runoff2,3. Satellite observations reveal positive trends in GrIS surface melt extent4, but melt variability, intensity and runoff remain uncertain before the satellite era. Here we present the first continuous, multi-century and observationally constrained record of GrIS surface melt intensity and runoff, revealing that the magnitude of recent GrIS melting is exceptional over at least the last 350 years. We develop this record through stratigraphic analysis of central west Greenland ice cores, and demonstrate that measurements of refrozen melt layers in percolation zone ice cores can be used to quantifiably, and reproducibly, reconstruct past melt rates. We show significant (P < 0.01) and spatially extensive correlations between these ice-core-derived melt records and modelled melt rates5,6 and satellite-derived melt duration4 across Greenland more broadly, enabling the reconstruction of past ice-sheet-scale surface melt intensity and runoff. We find that the initiation of increases in GrIS melting closely follow the onset of industrial-era Arctic warming in the mid-1800s, but that the magnitude of GrIS melting has only recently emerged beyond the range of natural variability. Owing to a nonlinear response of surface melting to increasing summer air temperatures, continued atmospheric warming will lead to rapid increases in GrIS runoff and sea-level contributions.
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Ice-core melt records, the derived runoff reconstructions, and other records from cores NU, GC and GW are available via the NSF Arctic Data Center (http://arcticdata.io) and from the corresponding author upon request. Additionally, source data for Figs. 2, 4 are provided in the online version of this paper. RACMO2 model outputs5 as well as downscaled 1-km surface mass balance data are available from B.P.Y.N. and M.R.v.d.B. upon request. MAR model outputs6 are available from X.F. upon request. Greenland air-temperature data51 are available from http://www.dmi.dk/laer-om/generelt/dmi-publikationer/tekniske-rapporter/. Sea-ice data29,30 are available from https://nsidc.org/data/g10010and https://www.nature.com/articles/nature10581. Arctic air-temperature reconstruction data21 are available from https://www.nature.com/articles/nature19082. Satellite melt data4,41 are available from http://www.cryocity.org.
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Funding was provided by US National Science Foundation (NSF) awards OPP-1205196 and PLR-1418256 to S.B.D., ARC-1205062 to B.E.S. and OPP-1205008 to M.J.E. L.D.T. acknowledges institutional support from Rowan University and the Doherty Postdoctoral Scholarship at Woods Hole Oceanographic Institution. M.B.O. acknowledges support from the Department of Defense Office of Naval Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a. Collection, analysis and interpretation of core D5 was supported by NSF grant 0352511 to J.R.McC. B.P.Y.N. and M.R.v.d.B. acknowledge support from the Polar Program of the Netherlands Organization for Scientific Research (NWO/NPP) and the Netherlands Earth System Science Centre (NESSC). For running the MAR model, computational resources have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (FRS–FNRS) under grant number 2.5020.11 and the Tier-1 supercomputer (Zenobe) of the Fédération Wallonie Bruxelles infrastructure funded by the Walloon Region under grant agreement number 1117545. We thank M. Waszkiewicz and IDPO/IDDO for ice core drilling support. We thank the NSF Ice Core Facility (formerly NICL), A. York, M. Bingham, M. Hatch, S. Zarfos, Z. Li, and Milton Academy students for ice core sampling and processing support. We thank R. Banta for help with the D5 core, and A. Arienzo and N. Chellman for help in analysing the NU core. We thank M. Tedesco for providing the satellite melt duration data used in Fig. 3d. Maps in Figs. 1a, 3 and Extended Data Figs. 3, 4 were created with the NCAR Command Language (https://www.ncl.ucar.edu), and maps in Fig. 1b and Extended Data Fig. 6 were created with Esri ArcGIS. We acknowledge the use of Rapid Response imagery in Fig. 1b from the Land, Atmosphere Near real-time Capability for EOS (LANCE) system operated by the NASA/GSFC/Earth Science Data and Information System (ESDIS) with funding provided by NASA/HQ.
Nature thanks J. Briner and B. Vinther for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Example core scan image (top) and resulting digitized melt layers in blue (bottom). b, Annual melt per cent time series for cores in the CWG stack. Bold lines show 5-year moving averages. See Fig. 1 for locations. c, The top panel shows the presence (blue) or absence (grey) of any amount of refrozen surface melt within a particular year in our three longest ice cores, showing regular annual occurrence of melt at each location. The bottom panel shows that, when filtered to show only years with melt percentages greater than the eighteenth-century mean melt at each site, a pattern towards recently more frequent, thicker (and thus more intense) melt emerges. As core GC does not span the eighteenth century, the mean eighteenth-century melt from the nearby D5 core was used as a baseline for GC as well.
a–d, Multi-taper method spectral power plots for our ice cores, the Greenland Blocking Index59 and the North Atlantic Oscillation58. e, Cross-wavelet transform plot between NU melt and southwest Greenland temperatures51. In the spectral plots (a–d), years corresponding to specific peaks in spectral power are indicated by numbers, although many peaks are surrounded by a range of years with elevated spectral power. Shaded areas represent 95% confidence bounds, and we note that many peaks do not show up significantly (5% confidence) above a white-noise threshold. In the cross-wavelet plot in e, areas of significant (P < 0.05) coherence are surrounded by a black line. The white-shaded areas represent regions where coherence cannot be confidently established owing to edge effects. Arrows indicate phase relationships: rightward (leftward) arrows indicate in-phase (out-of-phase) relationships, while downward (upward) arrows indicate melt leading (lagging) temperature. Our analyses found the strongest, and generally in-phase, coherence between NU and air temperature, as opposed to a single climate index, suggestive of the combined influence of multiple climate modes on GrIS melt that are well represented by air temperature (Methods).
Extended Data Fig. 3 Further evidence of spatially broad representation of melt processes in CWG cores.
Spearman rank order correlations between the CWG melt stack and MARv3.7-modelled annual snowmelt (a) and snowmelt runoff (b) over the period 1978–2013. As in correlations against RACMO2 and satellite melt (Fig. 3), correlations here are shown only for areas where MAR-simulated melt or runoff in at least 50% of the years of common overlap between the core and modelled datasets (18 years). Areas of significant correlation (P < 0.01) are denoted by a stipple pattern. Locations of cores used in the CWG stack are denoted by yellow (a) or black (b) points.
Pearson correlation coefficients (r) between GrIS-integrated surface melt and surface melt in each grid cell (a), GrIS-integrated runoff and runoff in each grid cell (b), and GrIS-integrated runoff and surface melt in each grid cell (c). Correlations calculated for RACMO2.3p2 over 1958–2013 (left-hand plots) and MARv3.7 over 1979–2013 (right-hand plots). Interannual variability in GrIS-integrated melt/runoff is well represented by local-scale interannual variability in melt/runoff. In situ melt records (for example, from well sited percolation-zone ice cores that are able to capture interannual melt variability) can therefore be used to quantify GrIS-integrated melt and runoff.
Reconstructed runoff using the NU and CWG ice-core melt records calibrated to RACMO2.3p2 for 19 surface drainage basins47 and for the full GrIS by summing each basin (lower right; as in Fig. 4). Note unique vertical axes. Reconstruction statistics shown are the maximum values achieved for each metric across 20 stepwise calibration/validation intervals. Shaded regions around runoff reconstruction represent 95% confidence bounds. Modelled datasets smoothed using a 5-year Lowess filter. For details on reconstruction see Methods and see Extended Data Fig. 6 for basin location and further statistical assessment.
Median values of runoff reconstruction skill statistics (r2, CE, RE) over 20 stepwise calibration/validation intervals, calculated for each surface drainage basin (basin numbers in left-hand plot), and for the GrIS as a whole (inset plots). Hatching denotes areas where at least half of the calibration/validation intervals were found to be statistically significant at the P < 0.1 level, determined using 10,000 Monte Carlo simulations (see Methods). All basins had at least one calibration/validation interval where at least one of the three validation statistics was significant. See Methods for details on reconstruction methods.
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Trusel, L.D., Das, S.B., Osman, M.B. et al. Nonlinear rise in Greenland runoff in response to post-industrial Arctic warming. Nature 564, 104–108 (2018). https://doi.org/10.1038/s41586-018-0752-4
- GrIS Melt
- Record Melting
- Greenland Ice Sheet (GrIS)
- Meltwater Runoff
- Uneven Melting
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