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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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.
Paolo, F. S., Fricker, H. A. & Padman, L. Volume loss from Antarctic ice shelves is accelerating. Science 348, 327–331 (2015).
Wouters, B. et al. Dynamic thinning of glaciers on the southern Antarctic peninsula. Science 348, 899–903 (2015).
Konrad, H. et al. Net retreat of Antarctic glacier grounding lines. Nat. Geosci. 11, 258–262 (2018).
DeConto, R. M. & Pollard, D. Contribution of Antarctica to past and future sea-level rise. Nature 531, 591–597 (2016).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Rignot, E., Velicogna, I., van den Broeke, M. R., Monaghan, A. & Lenaerts, J. Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise. Geophys. Res. Lett. 38, L05503 (2011).
Stouffer, R. J., Seidov, D. & Haupt, B. J. Climate response to external sources of freshwater: North Atlantic versus the Southern Ocean. J. Clim. 20, 436–448 (2007).
Fogwill, C. J., Phipps, S. J., Turney, C. S. M. & Golledge, N. R. Sensitivity of the Southern Ocean to enhanced regional Antarctic ice sheet meltwater input. Earths Futur. 3, 317–329 (2015).
Park, W. & Latif, M. Ensemble global warming simulations with idealized Antarctic meltwater. Clim. Dyn. https://doi.org/10.1007/s00382-018-4319-8 (2018).
Bintanja, R., van Oldenborgh, G. J., Drijfhout, S. S., Wouters, B. & Katsman, C. A. Important role for ocean warming and increased ice-shelf melt in Antarctic sea-ice expansion. Nat. Geosci. 6, 376–379 (2013).
Pauling, A. G., Smith, I. J., Langhorne, P. J. & Bitz, C. M. Time-dependent freshwater input from ice shelves: impacts on Antarctic sea ice and the Southern Ocean in an Earth system model. Geophys. Res. Lett. 44, 10454–10461 (2017).
Rhodes, C. J. The 2015 Paris climate change conference: COP21. Sci. Prog. 99, 97–104 (2016).
Oppenheimer, M. Global warming and the stability of the West Antarctic Ice Sheet. Nature 393, 325–332 (1998).
Rignot, E. & Jacobs, S. Rapid bottom melting widespread near Antarctic ice sheet grounding lines. Science 296, 2020–2023 (2002).
Shepherd, A., Wingham, D. & Rignot, E. Warm ocean is eroding West Antarctic Ice Sheet. Geophys. Res. Lett. 31, L23402 (2004).
Obase, T., Abe-Ouchi, A., Kusahara, K., Hasumi, H. & Ohgaito, R. Responses of basal melting of Antarctic ice shelves to the climatic forcing of the Last Glacial Maximum and CO2 doubling. J. Clim. 30, 3473–3497 (2017).
Aiken, C. M. & England, M. H. Sensitivity of the present-day climate to freshwater forcing associated with Antarctic sea ice loss. J. Clim. 21, 3936–3946 (2008).
Bakker, P., Clark, P. U., Golledge, N. R., Schmittner, A. & Weber, M. E. Centennial-scale Holocene climate variations amplified by Antarctic Ice Sheet discharge. Nature 541, 72–76 (2017).
Swart, N. C. & Fyfe, J. C. The influence of recent Antarctic ice sheet retreat on simulated sea ice area trends. Geophys. Res. Lett. 40, 4328–4332 (2013).
Zhang, R. & Delworth, T. Simulated tropical response to a substantial weakening of the Atlantic thermohaline circulation. J. Clim. 18, 1853–1860 (2005).
Cabré, A., Marinov, I. & Gnanadesikan, A. Global atmospheric teleconnections and multidecadal climate oscillations driven by Southern Ocean convection. J. Clim. 30, 8107–8126 (2017).
Purich, A., Cai, W., England, M. H. & Cowan, T. Evidence for link between modelled trends in Antarctic sea ice and underestimated westerly wind changes. Nat. Commun. 7, 10409 (2016).
Polvani, L. M. & Smith, K. L. Can natural variability explain observed Antarctic sea ice trends? New modeling evidence from CMIP5. Geophys. Res. Lett. 40, 3195–3199 (2013).
Haumann, F. A., Notz, D. & Schmidt, H. Anthropogenic influence on recent circulation-driven Antarctic sea ice changes. Geophys. Res. Lett. 41, 8429–8437 (2014).
Merino, N. et al. Impact of increasing antarctic glacial freshwater release on regional sea-ice cover in the Southern Ocean. Ocean Model. 121, 76–89 (2018).
Bintanja, R., Van Oldenborgh, G. J. & Katsman, C. A. The effect of increased fresh water from Antarctic ice shelves on future trends in Antarctic sea ice. Ann. Glaciol. 56, 120–126 (2015).
Shepherd, A. et al. A reconciled estimate of ice-sheet mass balance. Science 338, 1183–1189 (2012).
Sutterley, T. C. et al. Mass loss of the Amundsen sea embayment of West Antarctica from four independent techniques. Geophys. Res. Lett. 41, 8421–8428 (2014).
Pauling, A. G., Bitz, C. M., Smith, I. J. & Langhorne, P. J. The response of the Southern Ocean and Antarctic sea ice to freshwater from ice shelves in an Earth system model. J. Clim. 29, 1655–1672 (2016).
Goddard, P. B., Dufour, C. O., Yin, J., Griffies, S. M. & Winton, M. CO2-induced ocean warming of the Antarctic continental shelf in an eddying global climate model. J. Geophys. Res. Oceans 122, 8079–8101 (2017).
Stewart, A. L. & Thompson, A. F. Eddy-mediated transport of warm circumpolar deep water across the Antarctic shelf break. Geophys. Res. Lett. 42, 432–440 (2015).
Silvano, A. et al. Freshening by glacial meltwater enhances melting of ice shelves and reduces formation of Antarctic bottom water. Sci. Adv. 4, eaap9467 (2018).
Spence, P. et al. Localized rapid warming of West Antarctic subsurface waters by remote winds. Nat. Clim. Chang. 7, 595–603 (2017).
Massom, R. A. et al. Antarctic ice shelf disintegration triggered by sea ice loss and ocean swell. Nature 558, 383–389 (2018).
Vizcaino, M. et al. Coupled simulations of Greenland Ice Sheet and climate change up to AD 2300. Geophys. Res. Lett. 42, 3927–3935 (2015).
Sangiorgi, F. et al. Southern Ocean warming and Wilkes Land ice sheet retreat during the mid-Miocene. Nat. Commun. 9, 317 (2018).
Fyke, J., Sergeinko, O., Loftverstorm, M., Price, S. & Lenaerts, J. T. M. An Overview of Interactions and Feedbacks Between Ice Sheets and the Earth System. Rev. Geophys. 56, 361–408 (2018).
Stern, A. A., Adcroft, A. & Sergienko, O. The effects of Antarctic iceberg calving-size distribution in a global climate model. J. Geophys. Res. Oceans 121, 5773–5788 (2016).
Rignot, E., Jacobs, S., Mouginot, J. & Scheuchl, B. Ice-shelf melting around Antarctica. Science 341, 266–270 (2013).
Stammer, D. Response of the global ocean to Greenland and Antarctic ice melting. J. Geophys. Res. Oceans 113, C06022 (2008).
Haid, V., Iovino, D. & Masina, S. Impacts of freshwater changes on Antarctic sea ice in an eddy-permitting sea-ice-ocean model. Cryosphere 11, 1387–1402 (2017).
He, J., Winton, M., Vecchi, G., Jia, L. & Rugenstein, M. Transient climate sensitivity depends on base climate ocean circulation. J. Clim. 30, 1493–1504 (2017).
Swingedouw, D., Fichefet, T., Goosse, H. & Loutre, M. F. Impact of transient freshwater releases in the Southern Ocean on the AMOC and climate. Clim. Dyn. 33, 365–381 (2009).
Gregory, J. M. et al. The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) contribution to CMIP6: investigation of sea-level and ocean climate change in response to CO2 forcing. Geosci. Model Dev. 9, 3993–4017 (2016).
Fetterer, F., Knowles, K., Meier, W., Savoie, M. & Windnagel, A. K. Sea ice index, version 3: sea ice extent. National Snow and Ice Data Center https://nsidc.org/data/G02135/versions/3 (2017).
NOAA. Data Announcement 88-MGG-02, Digital Relief of the Surface of the Earth https://www.ngdc.noaa.gov/mgg/global/etopo5.HTML (National Geophysical Data Center, Boulder, 1988).
Gent, P. R. et al. The community climate system model version 4. J. Clim. 24, 4973–4991 (2011).
Dunne, J. P. et al. GFDL’s ESM2 global coupled climate-carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).
Dunne, J. P. et al. GFDL’s ESM2 global coupled climate-carbon earth system models. Part II: Carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).
Griffies, S. The Gent-McWilliams skew flux. J. Phys. Oceanogr. 28, 831–841 (1998).
Stocker, T. et al. in Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) 33–115 (Cambridge Univ. Press, Cambridge, 2013).
Sallée, J. B. et al. Assessment of Southern Ocean water mass circulation and characteristics in CMIP5 models: historical bias and forcing response. J. Geophys. Res. Oceans 118, 1830–1844 (2013).
Shu, Q., Song, Z. & Qiao, F. Assessment of sea ice simulations in the CMIP5 models. Cryosphere 9, 399–409 (2015).
Reintges, A., Martin, T., Latif, M. & Park, W. Physical controls of Southern Ocean deep-convection variability in CMIP5 models and the Kiel climate model. Geophys. Res. Lett. 44, 6951–6958 (2017).
Gordon, A. Deep Antarctic convection west of Maud rise. J. Phys. Oceanogr. 8, 600–612 (1978).
de Lavergne, C., Palter, J. B., Galbraith, E. D., Bernardello, R. & Marinov, I. Cessation of deep convection in the open Southern Ocean under anthropogenic climate change. Nat. Clim. Chang. 4, 278–282 (2014).
Pellichero, V., Sallee, J.-B., Schmidtko, S., Roquet, F. & Charrassin, J.-B. The ocean mixed layer under Southern Ocean sea-ice: seasonal cycle and forcing. J. Geophys. Res. Oceans 122, 1608–1633 (2017).
Swart, N. C. & Fyfe, J. C. Observed and simulated changes in the southern hemisphere surface westerly wind-stress. Geophys. Res. Lett. 39, L16711 (2012).
Downes, S. M. & Hogg, A. M. Southern Ocean circulation and eddy compensation in CMIP5 models. J. Clim. 26, 7198–7220 (2013).
Frölicher, T. L. et al. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J. Clim. 28, 862–886 (2015).
Verdy, A. & Mazloff, M. R. A data assimilating model for estimating Southern Ocean biogeochemistry. J. Geophys. Res. Oceans 122, 6968–6988 (2017).
Rodgers, K. B., Lin, J. & Froelicher, T. L. Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model. Biogeosciences 12, 3301–3320 (2015).
Wang, Z. et al. An atmospheric origin of the multi-decadal bipolar seesaw. Sci. Rep. 5, 8909 (2015).
Meehl, G. A., Arblaster, J. M., Bitz, C. M., Chung, C. T. Y. & Teng, H. Antarctic sea-ice expansion between 2000 and 2014 driven by tropical Pacific decadal climate variability. Nat. Geosci. 9, 590–595 (2016).
Depoorter, M. A. et al. Calving fluxes and basal melt rates of Antarctic ice shelves. Nature 502, 89–92 (2013).
Dupont, T. & Alley, R. Assessment of the importance of ice-shelf buttressing to ice-sheet flow. Geophys. Res. Lett. 32, L04503 (2005).
Lazeroms, W. M. J., Jenkins, A., Gudmundsson, G. H. & van de Wal, R. S. W. Modelling present-day basal melt rates for Antarctic ice shelves using a parametrization of buoyant meltwater plumes. Cryosphere 12, 49–70 (2018).
MacAyeal, D. R. in Oceanology of the Antarctic Continental Shelf (ed. Jacobs, S.) 133–143 (American Geophysical Union, Washington, 1985).
Holland, P. R., Jenkins, A. & Holland, D. M. The response of ice shelf basal melting to variations in ocean temperature. J. Clim. 21, 2558–2572 (2008).
Little, C. M., Gnanadesikan, A. & Oppenheimer, M. How ice shelf morphology controls basal melting. J. Geophys. Res. Oceans 114, C12007 (2009).
Goldberg, D. N. et al. Investigation of land ice-ocean interaction with a fully coupled ice-ocean model: 2. Sensitivity to external forcings. J. Geophys. Res. Earth Surf. 117, F02038 (2012).
Griffies, S. M. Elements of the modular ocean model (MOM). Report No. 7, https://github.com/mom-ocean/mom-ocean.github.io/blob/master/assets/pdfs/MOM5_elements.pdf (NOAA GFDL Ocean Group, 2012).
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.
The authors declare no competing interests.
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Extended data figures and tables
The orange line shows the digitized data applied to ESM2M and the blue line shows the original data4 (1 Sv = 106 m3 s−1).
a–d, 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.
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.
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.
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.
a–d, 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.
a–d, 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.
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.
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.
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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Bronselaer, B., Winton, M., Griffies, S.M. et al. Change in future climate due to Antarctic meltwater. Nature 564, 53–58 (2018). https://doi.org/10.1038/s41586-018-0712-z
- Coupled Model Intercomparison Project Phase 5 (CMIP5)
- CMIP5 Model
- Antarctic Coast
- Large Ensemble Simulations
- Intergovernmental Panel On Climate Change (IPCC)
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