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Global environmental consequences of twenty-first-century ice-sheet melt


Government policies currently commit us to surface warming of three to four degrees Celsius above pre-industrial levels by 2100, which will lead to enhanced ice-sheet melt. Ice-sheet discharge was not explicitly included in Coupled Model Intercomparison Project phase 5, so effects on climate from this melt are not currently captured in the simulations most commonly used to inform governmental policy. Here we show, using simulations of the Greenland and Antarctic ice sheets constrained by satellite-based measurements of recent changes in ice mass, that increasing meltwater from Greenland will lead to substantial slowing of the Atlantic overturning circulation, and that meltwater from Antarctica will trap warm water below the sea surface, creating a positive feedback that increases Antarctic ice loss. In our simulations, future ice-sheet melt enhances global temperature variability and contributes up to 25 centimetres to sea level by 2100. However, uncertainties in the way in which future changes in ice dynamics are modelled remain, underlining the need for continued observations and comprehensive multi-model assessments.

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Data availability

CMIP5 data were downloaded from Antarctic bedrock topography and ice thickness data are from the BEDMAP2 compilation, available at Greenland topography and ice thickness data are from BedMachine v3, available at Greenland mass balance and geothermal heat flux data are available from the seaRISE website: Information on Antarctic surface mass balance data are available at Antarctic geothermal heat flux data are available at Drainage basin outlines as shown in Fig. 3 are based on ICESat data96. Antarctic grounding lines and calving lines shown in Fig. 3a are from the MODIS-MOA 2009 dataset97,98. The datasets generated and analysed during this study are also available from the corresponding author on reasonable request.

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We acknowledge K. Buckley (Victoria University high-performance compute cluster), the Parallel Ice Sheet Model groups at University of Alaska, Fairbanks, the Potsdam Institute for Climate Impact Research and the CMIP community for making their data openly available. PISM is supported by NASA grants NNX13AM16G and NNX13AK27G. This work was funded by contract VUW1501 to N.R.G. from the Royal Society Te Aparangi, with support from the Antarctic Research Centre, Victoria University of Wellington, and GNS Science through the Ministry for Business, Innovation and Employment contract CO5X1001. N.G. was supported by the Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs programme. J.B. was supported by the MAGIC-DML project through DFG SPP 1158 (RO 4262/1-6). L.D.T. acknowledges support from the NSF Antarctic Glaciology Program (award 1643733).

Reviewer information

Nature thanks F. Pattyn, H. Seroussi and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

N.R.G. devised and carried out the ice-sheet modelling experiments, E.D.K. undertook climate model simulations and N.G. performed the sea-level calculations. K.A.N., J.B. and L.D.T. provided Antarctic basal and surface melt simulations from regional models. All authors contributed to the development of ideas and writing of the manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Nicholas R. Golledge.

Extended data figures and tables

Extended Data Fig. 1 High-latitude air temperature and sea-level anomalies.

a, b, Air (surface) temperature anomalies at 2100 arising from meltwater perturbations from ice sheets simulated under an RCP8.5 climate scenario. Arctic landmasses experience slight cool or warm anomalies, but temperatures over the Arctic ocean warm substantially in the region to the northeast of Greenland (around Svalbard), as far north as the North Pole (a). In the Southern Hemisphere, cooling of up to 3–4 °C occurs across the Southern Ocean and around the margins of Antarctica (b). Temperature anomalies are 30-year means to avoid aliasing short-term variability. c, d, Sea-level changes in the Southern Ocean and around Antarctica computed from the sea-level model (c), and with the addition of sea surface height changes due to ocean temperature changes (d). The thermosteric anomalies are from a 30-year mean to avoid aliasing short-term variability.

Extended Data Fig. 2 Global and regional surface temperature anomalies.

a, b, Surface air (a) and sea surface (b) temperature anomalies at 2100 arising solely from imposed meltwater fluxes, as a percentage of CMIP5 predictions based on emissions forcing but not including meltwater fluxes. c, Zonally and meridionally averaged surface air temperature anomalies for the globe, the Southern Ocean (40–85° S) and over the four largest ice shelves in Antarctica. d, Same as c, but adjusted to give changes relative to 2018.

Extended Data Fig. 3 Antarctic ice-sheet extent under Pliocene conditions.

Shown are results of 5-km-resolution simulations of the Antarctic ice sheet under peak-warmth Pliocene conditions, based on proxy-constrained climate and ocean fields from regional climate modelling54 but using an ice-sheet parameterization identical to that used for the RCP simulations presented in the main paper. The total sea-level-equivalent (SLE) mass loss after 5,000 years is 10.4 m, close to the 11.3 m simulated by a previous study that used ice-shelf hydrofracture and marine ice-cliff instability20, neither of which are used here.

Extended Data Fig. 4 Committed response of West Antarctica.

The extent of grounded ice in West Antarctica at 2100, 2300 and 2500 is illustrated for two emissions pathways (RCP4.5 and RCP8.5) and for experiments in which the climate forcing is held constant from 2020, 2050 or 2100, but without the inclusion of ice–ocean–atmosphere feedbacks. Mass loss in these scenarios illustrates long-term commitments locked in by cumulative forcing up to the point of stabilization. Thwaites Glacier basin retreats in all scenarios, suggesting that the threshold for its stability has already been passed. Contour intervals are 250 m. Black lines show modern coast, for context.

Extended Data Fig. 5 Grounding-line sensitivity and basal-melt parameterization

. Control run (constant year-2000 climatology) and RCP8.5-forced experiments (including ice–ocean–atmosphere feedbacks) for Antarctica (a) and Greenland (b), with and without the incorporation of the sub-grid grounding-line melt scheme. Without the scheme, Antarctic ice volumes are higher in the forced run than with sub-grid melt enabled, but the control run also increases in volume, which suggests that other aspects of model parameterization would need to be optimized to ensure agreement with observational constraints (Extended Data Tables 1 and 2). Greenland simulations are far less affected by the sub-grid melt scheme. The Greenland runs shown all incorporate the evolving surface mass balance and basal traction parameterization (Methods), for clearer comparison between control and perturbed experiments. c, Change in grounded ice volume in Antarctica, compared to control runs, simulated by our ice-sheet model using a range of horizontal grid resolutions (see legend) but otherwise identical parameterization and including the sub-grid grounding-line basal melt scheme. d, Rate of Greenland Ice Sheet mass loss for the best-fitting simulation (dark blue line) compared to simulations in which either a steeper increase in sliding is applied (light blue line) or sliding is maintained at a constant value for the entire run (orange line). Numbers in brackets quantify the change in till friction angle in the piecewise-linear basal traction parameter below −200 m and above 500 m, relative to the ‘No taper’ experiment. Gold boxes show the time span (x axis) and uncertainty (y axis) of empirical data values used as targets during parameter optimization, from sources detailed in Extended Data Tables 1 and 2. e, f, Target melt rates from an empirically constrained99,100 ice-sheet simulation25 (e) are used as inputs to an inverse scheme that solves for a spatially distributed melt factor to translate CMIP5 sea surface temperatures into realistic melt fields (f). This approach greatly improves the representation of ice-shelf basal melting in our simulation compared to previous studies18,20.

Extended Data Fig. 6 Ice-sheet influence on subsurface ocean temperature.

ac, Ocean temperature anomalies by 2100 at 415-m depth from Greenland meltwater flux only (a), Antarctic meltwater flux only (b) and combined meltwater flux from both ice sheets (c). Anomalies are 30-year means to avoid aliasing short-term variability.

Extended Data Fig. 7 Modelled versus measured surface elevation.

ad, Measured values of surface elevation of the Greenland63 (a) and Antarctic62 (b) ice sheets compared to modelled values (c, d) at year 2000. e, f, Differences between the two (modelled minus observed).

Extended Data Fig. 8 Modelled versus measured surface velocity.

ad, Measured values of surface velocity of the Greenland101 (a) and Antarctic102 (b) ice sheets compared to modelled values (c, d) at year 2000. e, f, Differences between the two (modelled minus observed).

Extended Data Table 1 Empirical constraints used to guide Antarctic Ice Sheet parameterization
Extended Data Table 2 Empirical constraints used to guide Greenland Ice Sheet parameterization

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Further reading

Fig. 1: Simulated and observed ice-sheet mass balance.
Fig. 2: Sea-level contributions from Greenland and Antarctica.
Fig. 3: Causes of changes in ice-sheet thickness by 2100.
Fig. 4: Environmental consequences of twenty-first-century ice-sheet meltwater flux.
Fig. 5: Effect of ice-sheet melt on the AMOC.
Extended Data Fig. 1: High-latitude air temperature and sea-level anomalies.
Extended Data Fig. 2: Global and regional surface temperature anomalies.
Extended Data Fig. 3: Antarctic ice-sheet extent under Pliocene conditions.
Extended Data Fig. 4: Committed response of West Antarctica.
Extended Data Fig. 5: Grounding-line sensitivity and basal-melt parameterization
Extended Data Fig. 6: Ice-sheet influence on subsurface ocean temperature.
Extended Data Fig. 7: Modelled versus measured surface elevation.
Extended Data Fig. 8: Modelled versus measured surface velocity.


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