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Change in future climate due to Antarctic meltwater

Naturevolume 564pages5358 (2018) | Download Citation

Abstract

Meltwater from the Antarctic Ice Sheet is projected to cause up to one metre of sea-level rise by 2100 under the highest greenhouse gas concentration trajectory (RCP8.5) considered by the Intergovernmental Panel on Climate Change (IPCC). However, the effects of meltwater from the ice sheets and ice shelves of Antarctica are not included in the widely used CMIP5 climate models, which introduces bias into IPCC climate projections. Here we assess a large ensemble simulation of the CMIP5 model ‘GFDL ESM2M’ that accounts for RCP8.5-projected Antarctic Ice Sheet meltwater. We find that, relative to the standard RCP8.5 scenario, accounting for meltwater delays the exceedance of the maximum global-mean atmospheric warming targets of 1.5 and 2 degrees Celsius by more than a decade, enhances drying of the Southern Hemisphere and reduces drying of the Northern Hemisphere, increases the formation of Antarctic sea ice (consistent with recent observations of increasing Antarctic sea-ice area) and warms the subsurface ocean around the Antarctic coast. Moreover, the meltwater-induced subsurface ocean warming could lead to further ice-sheet and ice-shelf melting through a positive feedback mechanism, highlighting the importance of including meltwater effects in simulations of future climate.

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Data availability

The GFDL ESM2M model code is publicly available from https://github.com/mom-ocean. The results from the standard- and meltwater-ensemble simulations are available from the corresponding author. The prescribed RCP8.5 freshwater flux used here is available from ref. 4. Antarctic sea-ice extent from satellite measurements is available from the NSIDC at https://nsidc.org/data. The Southern Ocean state-estimate data used for model evaluation is available from http://sose.ucsd.edu/bsose_solution_Iter105.html. The topographical data used in Figs. 1, 2, 4 and Extended Data Figs. 1, 9, 10 are available in MATLAB and provided by NOAA73.

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Acknowledgements

This work was supported by the NSF’s Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project under the NSF award PLR-1425989, the NOAA and NASA. O.V.S. is supported by NSF OPP-1246151 and by NOAA awards NA14OAR4320106 and NA13OAR431009 from the US Department of Commerce. Support for K.B.R. was provided by NASA award NNX17AI75G. The statements, findings, conclusions and recommendations presented here are those of the authors and do not necessarily reflect the views of the NOAA or the US Department of Commerce. We thank J. Sarmiento, J. Yin and A. Haumann for their insight.

Author information

Affiliations

  1. Department of Geosciences, University of Arizona, Tucson, AZ, USA

    • Ben Bronselaer
    • , Ronald J. Stouffer
    •  & Joellen L. Russell
  2. Geophysical Fluid Dynamics Laboratory, Princeton University Forrestal Campus, Princeton, NJ, USA

    • Ben Bronselaer
    • , Michael Winton
    • , Stephen M. Griffies
    • , William J. Hurlin
    • , Olga V. Sergienko
    •  & Ronald J. Stouffer
  3. Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA

    • Ben Bronselaer
    • , Stephen M. Griffies
    • , Keith B. Rodgers
    •  & Olga V. Sergienko

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Contributions

B.B. performed the simulations with help from W.J.H., M.W., R.J.S. and K.B.R.; B.B., M.W. and J.L.R. analysed the data with help from R.J.S. (climate response to ocean meltwater input), S.M.G. (ocean dynamics and heat budget) and O.V.S. (ice–ocean interactions and feedbacks); M.W. and J.L.R. supervised the project. All authors wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Ben Bronselaer.

Extended data figures and tables

  1. Extended Data Fig. 1 Applied RCP8.5 freshwater flux.

    The orange line shows the digitized data applied to ESM2M and the blue line shows the original data4 (1 Sv = 106 m3 s−1).

  2. Extended Data Fig. 2 Regional-area mean precipitation–evaporation anomalies.

    ad, Anomalies are shown as a function of time for the Sahel (a), Central America (b), Australia (c) and South America (d). Orange shows the standard RCP8.5 30-member ESM2M ensemble (‘standard ensemble’) and blue shows the 10-member RCP8.5 with added time-varying freshwater melt around Antarctica (‘meltwater ensemble’). The solid lines show the ensemble means and the shading shows the 95% uncertainty in the mean. The data points on the right of each panel show the 2080–2100 mean anomalies, expressed as a percentage relative to the pre-industrial mean state (note the different vertical axes), with the error bars showing the 95% uncertainty in the means. Here, the anomalies are calculated with respect to the pre-industrial control simulation. The maps in the insets indicate the area over which the respective anomalies (colour-coded) are calculated. All time series are smoothed with a 5-year filter.

  3. Extended Data Fig. 3 Seasonal sea-ice anomalies.

    a, b, Time series of the February (a) and September (b) SHI anomalies relative to the 1950–1970 mean. Orange shows the standard ensemble and blue shows the meltwater ensemble. Solid lines show ensemble means, the dark shading shows the uncertainty in the mean and the light shading shows the full ensemble spread of 20-year SHI means. The solid black line shows the difference between the orange and blue lines, and the applied meltwater flux is shown in grey (scaled to the final 5-year mean of the meltwater-induced SHI anomaly). The green bar indicates the period when the full ensembles have diverged. The insets show the period 1980–2020, with the double black line showing the observed monthly mean sea-ice area from the NSIDC, relative to the 1980–2000 mean. The thin grey line shows the unsmoothed observations. c, d, Distribution of linear trends in SHI over the period 1979–2017, calculated for each ensemble member, for February (c) and September (d) means. The red bars show the standard ensemble and blue bars show the meltwater ensemble, with different x axes. The solid lines show Gaussian fits to the distributions and the dashed black line shows the pre-industrial distribution. The observations are shown as black diamonds.

  4. Extended Data Fig. 4 Heat-budget analysis.

    a, b, 1980–2030 freshwater-induced heat-flux anomalies (a) and temperature anomaly (b), averaged along the Antarctic coast, due to a 0.1-Sv freshwater perturbation, as a function of depth. c, 1980–2030-mean freshwater-induced heat-flux anomalies, averaged over 400–700-m depth along the Antarctic coast. All anomalies shown here are calculated relative to the mean of standard ensemble.

  5. Extended Data Fig. 5 Ice-melt feedback.

    The ice-melt freshwater input, in equivalent global-mean sea level (ΔGMSL), due to the RCP8.5 prescribed meltwater is shown in black. The dark and light grey shading show the components of the prescribed flux from ice-shelf and ice-sheet melt, respectively. Only the ice-sheet melt contributes to sea level. The blue line shows the total freshwater flux, including the prescribed flux and the estimated feedback associated with ice-shelf melt from the freshwater-induced ocean warming. The blue shading shows the 95% uncertainty range.

  6. Extended Data Fig. 6 Selection of ensemble members for meltwater experiments.

    ad, Time series of the global-mean SAT (a), PRE (b), annual-mean SHI (c) and ACT (d) anomalies in the standard ensemble relative to the pre-industrial control. The black lines show all 30 ensemble members and the red lines show those used for the freshwater-forced simulations.

  7. Extended Data Fig. 7 Sensitivity to base state.

    ad, Time series of the global-mean SAT (a), PRE (b), SHI (c) and ACT (d) anomalies relative to the pre-industrial control. Orange shows the yearly standard ensemble and blue shows the 5-year means of the meltwater ensemble. The meltwater ensemble in these experiments is hosed with 0.1 Sv for 50 years in two separate periods, 1980–2030 and 2050–2100, initialized from the standard ensemble. Solid lines show ensemble means and the dark shading shows the 90% uncertainty in the mean. The solid black lines show the difference between the meltwater and standard ensembles.

  8. Extended Data Fig. 8 Range of 400-m ocean warming.

    a, Range of freshwater flux. The solid grey line shows the projected flux4; the dashed lines and shading show the same flux, but multiplied by factor of 0.5 and 2. b, ACT anomalies in the standard (orange) and meltwater (blue) ensembles. The solid blue line shows the response to the projected flux4; the dashed blue lines show the temperature range covered by the experiments with half and double the projected flux, which have three ensemble members each. The shading shows the full ensemble spread of 20-year means.

  9. Extended Data Fig. 9 Warming response of polynya and non-polynya members.

    a, b, 2080–2100 ACT anomaly in the meltwater-ensemble members branched from the eight non-polynya (a) and two (open-ocean Weddell Sea) polynya members (b) from the standard ensemble. Anomalies are calculated pair-wise, relative to the standard-ensemble members. Hatching indicates where the anomalies are not significant at the 95% level.

  10. Extended Data Fig. 10 ESM2M–SOSE comparison.

    a, ESM2M pre-industrial annual-mean depth of the mixed layer. b, Area-mean density profiles for ESM2M (black) and SOSE (red) for each of the numbered boxes in a. c, ESM2M 2005–2100-mean meltwater-induced temperature anomaly (zonal mean; colour scale), and zonal-mean ESM2M (solid) and SOSE (dashed) isopycnal surfaces, as functions of depth and latitude. The insets illustrate these quantities for the numbered regions from a, showing the upper 2,000 m of the ocean, between 80° S and 60° S (regions (1) and (2)) or 70° S and 50° S (region (3)).

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https://doi.org/10.1038/s41586-018-0712-z

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