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Nonlinear rise in Greenland runoff in response to post-industrial Arctic warming

Nature volume 564, pages 104–108 (2018)Cite this article

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Abstract

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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Fig. 1: Positive trends in Greenland surface melt and locations of ice cores used to reconstruct past surface melt variability.
Fig. 2: Multi-century evolution of CWG surface melt from ice cores.
Fig. 3: Spatially broad representation of melt processes captured by CWG ice cores.
Fig. 4: Exceptional rise in Greenland ice-sheet runoff and climate warming context.

Data availability

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.

References

  1. Hanna, E. et al. Ice-sheet mass balance and climate change. Nature 498, 51–59 (2013).

    Article  CAS  ADS  Google Scholar 

  2. Enderlin, E. M. et al. An improved mass budget for the Greenland ice sheet. Geophys. Res. Lett. 41, 2013GL059010 (2014).

    Article  Google Scholar 

  3. 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  ADS  Google Scholar 

  4. Tedesco, M. et al. Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data. Cryosphere 7, 615–630 (2013).

    Article  ADS  Google Scholar 

  5. Noël, B. et al. Modelling the climate and surface mass balance of polar ice sheets using RACMO2—Part 1: Greenland (1958–2016). Cryosphere 12, 811–831 (2018).

    Article  ADS  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  ADS  Google Scholar 

  7. Humphrey, N. F., Harper, J. T. & Pfeffer, W. T. Thermal tracking of meltwater retention in Greenland’s accumulation area. J. Geophys. Res. 117, F01010 (2012).

    Article  ADS  Google Scholar 

  8. Machguth, H. et al. Greenland meltwater storage in firn limited by near-surface ice formation. Nat. Clim. Chang. 6, 390–393 (2016).

    Article  ADS  Google Scholar 

  9. Thornalley, D. J. R. et al. Anomalously weak Labrador Sea convection and Atlantic overturning during the past 150 years. Nature 556, 227–230 (2018).

    Article  CAS  ADS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

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

    Article  Google Scholar 

  12. 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  ADS  Google Scholar 

  13. Fausto, R. S. et al. The implication of nonradiative energy fluxes dominating Greenland ice sheet exceptional ablation area surface melt in 2012. Geophys. Res. Lett. 43, 2649–2658 (2016).

    Article  ADS  Google Scholar 

  14. 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 

  15. Keegan, K. M., Albert, M. R., McConnell, J. R. & Baker, I. Climate change and forest fires synergistically drive widespread melt events of the Greenland Ice Sheet. Proc. Natl Acad. Sci. USA 111, 7964–7967 (2014).

    Article  CAS  ADS  Google Scholar 

  16. Graeter, K. A. et al. Ice core records of West Greenland melt and climate forcing. Geophys. Res. Lett. 45, 3164–3172 (2018).

  17. Herron, M. M., Herron, S. L. & Langway, C. C. Climatic signal of ice melt features in southern Greenland. Nature 293, 389–391 (1981).

    Article  ADS  Google Scholar 

  18. Kameda, T. et al. Melt features in ice cores from Site J, southern Greenland: some implications for summer climate since AD 1550. Ann. Glaciol. 21, 51–58 (1995).

    Article  ADS  Google Scholar 

  19. 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 

  20. Ahlstrøm, A. P., Petersen, D., Langen, P. L., Citterio, M. & Box, J. E. Abrupt shift in the observed runoff from the southwestern Greenland ice sheet. Sci. Adv. 3, e1701169 (2017).

    Article  ADS  Google Scholar 

  21. Abram, N. J. et al. Early onset of industrial-era warming across the oceans and continents. Nature 536, 411–418 (2016).

    Article  CAS  Google Scholar 

  22. Liu, J. et al. Has Arctic sea-ice loss contributed to increased surface melting of the Greenland ice sheet? J. Clim. 29, 3373–3386 (2016).

  23. 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  ADS  Google Scholar 

  24. Ding, Q. et al. Tropical forcing of the recent rapid Arctic warming in northeastern Canada and Greenland. Nature 509, 209–212 (2014).

    Article  CAS  ADS  Google Scholar 

  25. Ding, Q. et al. Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Clim. Chang. 7, 289–295 (2017).

    Article  ADS  Google Scholar 

  26. Abram, N. J. et al. Acceleration of snow melt in an Antarctic Peninsula ice core during the twentieth century. Nat. Geosci. 6, 404–411 (2013).

    Article  CAS  ADS  Google Scholar 

  27. Trusel, L. D. et al. Divergent trajectories of Antarctic surface melt under two twenty-first-century climate scenarios. Nat. Geosci. 8, 927–932 (2015).

    Article  CAS  ADS  Google Scholar 

  28. Lecavalier, B. S. et al. High Arctic Holocene temperature record from the Agassiz ice cap and Greenland ice sheet evolution. Proc. Natl Acad. Sci. USA 114, 5952–5957 (2017).

    Article  CAS  ADS  Google Scholar 

  29. Walsh, J. E., Fetterer, F., Scott Stewart, J. & Chapman, W. L. A database for depicting Arctic sea ice variations back to 1850. Geogr. Rev. 107, 89–107 (2017).

    Article  Google Scholar 

  30. Kinnard, C. et al. Reconstructed changes in Arctic sea ice over the past 1,450 years. Nature 479, 509–512 (2011).

    Article  CAS  ADS  Google Scholar 

  31. Curran, M. A. & Palmer, A. S. Suppressed ion chromatography methods for the routine determination of ultra low level anions and cations in ice cores. J. Chromatogr. A 919, 107–113 (2001).

    Article  CAS  Google Scholar 

  32. Sigl, M. et al. Timing and climate forcing of volcanic eruptions for the past 2,500 years. Nature 523, 543–549 (2015).

    Article  CAS  ADS  Google Scholar 

  33. McConnell, J. R., Lamorey, G. W., Lambert, S. W. & Taylor, K. C. Continuous ice-core chemical analyses using inductively coupled plasma mass spectrometry. Environ. Sci. Technol. 36, 7–11 (2002).

    Article  CAS  ADS  Google Scholar 

  34. McConnell, J. R. et al. 20th-century industrial black carbon emissions altered Arctic climate forcing. Science 317, 1381–1384 (2007).

    Article  CAS  ADS  Google Scholar 

  35. Gfeller, G. et al. Representativeness and seasonality of major ion records derived from NEEM firn cores. Cryosphere 8, 1855–1870 (2014).

    Article  ADS  Google Scholar 

  36. Arienzo, M. M. et al. A method for continuous 239Pu determinations in Arctic and Antarctic ice cores. Environ. Sci. Technol. 50, 7066–7073 (2016).

    Article  CAS  ADS  Google Scholar 

  37. McGwire, K. C. et al. An integrated system for optical imaging of ice cores. Cold Reg. Sci. Technol. 53, 216–228 (2008).

    Article  Google Scholar 

  38. Das, S. B. & Alley, R. B. Characterization and formation of melt layers in polar snow: observations and experiments from West Antarctica. J. Glaciol. 51, 307–312 (2005).

    Article  ADS  Google Scholar 

  39. Das, S. B. & Alley, R. B. Rise in frequency of surface melting at Siple Dome through the Holocene: evidence for increasing marine influence on the climate of West Antarctica. J. Geophys. Res. 113, D02112 (2008).

    ADS  Google Scholar 

  40. Noël, B. et al. A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958–2015). Cryosphere 10, 2361–2377 (2016).

    Article  ADS  Google Scholar 

  41. Tedesco, M. Greenland Daily Surface Melt 25km EASE-Grid [1988-2013] http://www.cryocity.org/data.html (City University of New York, New York, 2014).

  42. Ebisuzaki, W. A method to estimate the statistical significance of a correlation when the data are serially correlated. J. Clim. 10, 2147–2153 (1997).

    Article  ADS  Google Scholar 

  43. Macias-Fauria, M., Grinsted, A., Helama, S. & Holopainen, J. Persistence matters: estimation of the statistical significance of paleoclimatic reconstruction statistics from autocorrelated time series. Dendrochronologia 30, 179–187 (2012).

    Article  Google Scholar 

  44. Cook, E. R., Briffa, K. R. & Jones, P. D. Spatial regression methods in dendroclimatology: a review and comparison of two techniques. Int. J. Climatol. 14, 379–402 (1994).

    Article  Google Scholar 

  45. Tierney, J. E. et al. Tropical sea surface temperatures for the past four centuries reconstructed from coral archives. Paleoceanography 30, 2014PA002717 (2015).

    Article  Google Scholar 

  46. Anchukaitis, K. J. et al. Last millennium Northern Hemisphere summer temperatures from tree rings. Part II, spatially resolved reconstructions. Quat. Sci. Rev. 163, 1–22 (2017).

    Article  ADS  Google Scholar 

  47. Zwally, H. J., Giovinetto, M. B., Beckley, M. A. & Saba, J. L. Antarctic and Greenland Drainage Systems http://icesat4.gsfc.nasa.gov/cryo_data/ant_grn_drainage_systems.php (GSFC Cryospheric Sciences Laboratory, NASA 2012).

  48. Fisher, D. et al. Recent melt rates of Canadian arctic ice caps are the highest in four millennia. Glob. Planet. Change 84, 3–7 (2012).

    Article  ADS  Google Scholar 

  49. Vernon, C. L. et al. Surface mass balance model intercomparison for the Greenland ice sheet. Cryosphere 7, 599–614 (2013).

    Article  ADS  Google Scholar 

  50. Vinther, B. M., Andersen, K. K., Jones, P. D., Briffa, K. R. & Cappelen, J. Extending Greenland temperature records into the late eighteenth century. J. Geophys. Res. 111, D11105 (2006).

  51. Cappelen, J. (ed) Greenland—DMI Historical Climate Data Collection 1784-2017 DMI Report 18-04 (DMI, Copenhagen, 2018).

  52. Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M. & Bladé, I. The effective number of spatial degrees of freedom of a time-varying field. J. Clim. 12, 1990–2009 (1999).

    Article  ADS  Google Scholar 

  53. Hannig, J. & Marron, J. S. Advanced distribution theory for SiZer. J. Am. Stat. Assoc. 101, 484–499 (2006).

    Article  CAS  MathSciNet  Google Scholar 

  54. Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. 39, L01702 (2012).

    Article  ADS  Google Scholar 

  55. 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  ADS  Google Scholar 

  56. de la Peña, S. et al. Changes in the firn structure of the western Greenland Ice Sheet caused by recent warming. Cryosphere 9, 1203–1211 (2015).

    Article  ADS  Google Scholar 

  57. Noël, B. et al. A tipping point in refreezing accelerates mass loss of Greenland’s glaciers and ice caps. Nature Commun. 8, 14730 (2017).

    Article  ADS  Google Scholar 

  58. Hurrell, J. & National Center for Atmospheric Research Staff (eds) The Climate Data Guide: Hurrell North Atlantic Oscillation (NAO) Index (station-based). https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-station-based (NCAR, Boulder, 2003).

  59. Hanna, E., Cropper, T. E., Hall, R. J. & Cappelen, J. Greenland Blocking Index 1851–2015: a regional climate change signal. Int. J. Climatol. 36, 4847–4861 (2016).

    Article  Google Scholar 

  60. Mann, M. E. & Lees, J. M. Robust estimation of background noise and signal detection in climatic time series. Clim. Change 33, 409–445 (1996).

    Article  ADS  Google Scholar 

  61. Schlesinger, M. E. & Ramankutty, N. An oscillation in the global climate system of period 65–70 years. Nature 367, 723–726 (1994).

    Article  ADS  Google Scholar 

  62. Trenberth, K. E. & Shea, D. J. Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett. 33, L12704 (2006).

  63. Grinsted, A., Moore, J. C. & Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11, 561–566 (2004).

    Article  ADS  Google Scholar 

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Acknowledgements

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.

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Nature thanks J. Briner and B. Vinther for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Department of Geology, Rowan University, Glassboro, NJ, USA

    Luke D. Trusel

  2. Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Luke D. Trusel & Sarah B. Das

  3. Joint Program in Oceanography, Massachusetts Institute of Technology/Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Matthew B. Osman

  4. Department of Chemistry, Wheaton College, Norton, MA, USA

    Matthew J. Evans

  5. Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA, USA

    Ben E. Smith

  6. Department of Geography, University of Liège, Liège, Belgium

    Xavier Fettweis

  7. Division of Hydrologic Sciences, Desert Research Institute, Reno, NV, USA

    Joseph R. McConnell

  8. Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, Netherlands

    Brice P. Y. Noël & Michiel R. van den Broeke

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  1. Luke D. Trusel
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Contributions

L.D.T. and S.B.D. conceived of and designed the study with input from M.B.O. B.E.S., S.B.D., M.J.E. and L.D.T. determined the ice core siting. S.B.D., L.D.T., M.B.O. and M.J.E. collected the ice cores. L.D.T. analysed melt stratigraphy for cores NU, GC and GW. S.B.D., J.R.McC. and L.D.T. analysed core D5. M.J.E. and J.R.McC. analysed ice core chemistry. Ice core chronology was led by M.B.O. with input from B.E.S., S.B.D., J.R.McC. and L.D.T. M.B.O. developed melt reconstruction code with input from L.D.T. B.P.Y.N. and M.R.v.d.B. provided RACMO2 model output and expertise. X.F. provided MAR model output and expertise. L.D.T. led the data analyses and interpretation, and wrote the manuscript, with input from S.B.D. and M.B.O. All authors read and commented on the manuscript.

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Correspondence to Luke D. Trusel.

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Extended data figures and tables

Extended Data Fig. 1 Example core section and additional annual melt records.

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.

Extended Data Fig. 2 Analysis of spectral signatures in melt and climate records.

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.

Extended Data Fig. 4 Relationships between local and ice-sheet-integrated melt processes.

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.

Extended Data Fig. 5 Basin-specific runoff reconstructions.

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.

Extended Data Fig. 6 Basin-specific and GrIS-integrated reconstruction statistics.

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.

Extended Data Table 1 Details of firn and ice cores used in this study
Full size table
Extended Data Table 2 Statistical relationships among ice cores and CWG air temperature
Full size table
Extended Data Table 3 Statistical relationships among ice cores, reconstructed runoff and pan-Greenland air temperatures
Full size table
Extended Data Table 4 Timing of trend initiation and climate emergence
Full size table

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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

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Keywords

  • GrIS Melt
  • Record Melting
  • Greenland Ice Sheet (GrIS)
  • Meltwater Runoff
  • Uneven Melting

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