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Ensemble global warming simulations with idealized Antarctic meltwater input

  • W. Park
  • M. Latif
Article

Abstract

The Earth will exhibit continued global surface warming in response to a sustained increase of atmospheric carbon dioxide (CO2) levels. Massive meltwater input from the Antarctic ice sheet into the Southern Ocean could be one consequence of this warming. Here we investigate the impacts which this meltwater input may have on Earth’s surface climate and ocean circulation in a warming world. To this end a set of ensemble experiments has been conducted with a global climate model forced by increasing atmospheric CO2-concentration and an idealized Antarctic meltwater input to the Southern Ocean with varying amplitude and spatial pattern. As long as the atmospheric CO2-concentration stays moderate, i.e. below approximately twice the preindustrial concentration, and if a strong meltwater forcing of either 0.05 or 0.1 Sv is applied, enhanced Antarctic sea–ice cover and surface air temperature cooling over most parts of the Southern Ocean is observed. When the atmospheric CO2-concentration becomes larger than twice the preindustrial concentration, the meltwater only plays a minor role. The Antarctic meltwater drives significant slowing of the Southern Ocean meridional overturning circulation (MOC). Again, the meltwater influence only is detectable as long as the CO2-forcing is moderate. Much larger MOC changes develop in response to highly elevated atmospheric CO2-levels independent of whether or not a meltwater forcing is applied. The response of the Antarctic circumpolar current (ACC) is nonlinear. Substantial and persistent ACC slowing is simulated when solely the meltwater forcing of 0.1 Sv is applied, which is due to the halt of Weddell Sea deep convection and subsequent collapse of the Southern Ocean MOC. When the increasing atmospheric CO2-concentration additionally drives the model the ACC partly recovers in the long run. The partial recovery is due to strengthening westerly wind stress over the Southern Ocean, which intensifies the Ekman Cell. This study suggests that Southern Hemisphere climate projections for the twenty-first century could benefit from incorporating interactive Antarctic ice sheet.

Notes

Acknowledgements

This study was supported by the InterDec project “The potential of seasonal-to-decadal-scale inter-regional linkages to advance climate predictions” of the JPI CLIM Belmont-Forum, and by the PalMod Project of BMBF (http://www.palmod.de). The climate model integrations were performed at the Computer Centre at the Kiel University and at the High Performance Computing Centre (HRLN).

Supplementary material

382_2018_4319_MOESM1_ESM.eps (8 mb)
Fig. S1. Ensemble-mean response (relative to CTRL) of selected variables in MW10 (MW10 – CTRL, first column) and the signal-to-noise ratio (second column), all averaged over the first 50 years. Ensemble-mean response: (a) SAT, (c) SLP, and (e) precipitation. Signal-to-noise ratio: (b) SAT, (d) SLP, and (f) precipitation. Units: SAT (°C), SLP (hPa), precipitation (mm/d). MW10 is the ensemble only forced by Antarctic meltwater input with magnitude 0.1 Sv. The atmospheric CO2-concentration is constant at preindustrial levels (EPS 8171 KB)
382_2018_4319_MOESM2_ESM.eps (15.7 mb)
Fig. S2. Ensemble-mean response (relative to CTRL) of selected variables in GW (first column) and GMWM05 (second column), their differences (GMWM05–GW, third column) and the signal-to-noise ratio (fourth column), all averaged over the first 50 years. Ensemble-mean response in GW: (a) SAT, (e) SLP, and (i) precipitation. Ensemble-mean response in GMWM05: (b) SAT, (f) SLP, and (j) precipitation. Ensemble-mean difference (GMWM05 ̶ GW): (c) SAT, (g) SLP, and (k) precipitation. Signal-to-noise ratio defined as the ratio between the ensemble-mean difference and the corresponding spread (not shown): (d) SAT, (h) SLP, and (l) precipitation. Units: SAT (°C), SLP (hPa), precipitation (mm/d) (EPS 16122 KB)
382_2018_4319_MOESM3_ESM.eps (14.5 mb)
Fig. S3. Ensemble-mean response (relative to CTRL) of selected variables in GW (first column) and GMWM0wa (second column), their differences (GMWMwa–GW, third column) and the signal-to-noise ratio (fourth column), all averaged over the first 50 years. Ensemble-mean response in GW: (a) SAT, (e) SLP, and (i) precipitation. Ensemble-mean response in GMWMwa: (b) SAT, (f) SLP, and (j) precipitation. Ensemble-mean difference (GMWMwa ̶ GW): (c) SAT, (g) SLP, and (k) precipitation. Signal-to-noise ratio defined as the ratio between the ensemble-mean difference and the corresponding spread (not shown): (d) SAT, (h) SLP, and (l) precipitation. Units: SAT (°C), SLP (hPa), precipitation (mm/d) (EPS 14896 KB)
382_2018_4319_MOESM4_ESM.eps (335 kb)
Fig. S4. Evolution (full lines) of the Antarctic Circumpolar Circulation (ACC) strength as measured by the Drake Passage transport [Sv = 106m3/sec] in CTRLpd (black), GWpd (red) and in GWMW10pd (blue). GWpd and GWMW10pd are the ensembles in which the realizations start from a “present-day” control run (CTRLpd) employing atmospheric CO2-concentration of 348 ppm. The dashed lines represent one standard deviation of the “present-day” control run or of the ensemble spread. The annual-mean data were smoothed with an 11-year running-mean filter (EPS 334 KB)
382_2018_4319_MOESM5_ESM.eps (9.6 mb)
Fig. S5. Ensemble-mean response (relative to CTRL) of ocean temperature [° C] at 512 m in GW (first column) and GMWM10 (second column), their differences (GMWM10–GW, third column) and the signal-to-noise ratio (fourth column), all averaged over the first 50 years (a-d) and over the last 50 years (e-h). Signal-to-noise ratio is defined as the ratio between the ensemble-mean difference and the corresponding spread. Note the nonlinear color scale of temperature change, with 0.2° C interval between -1° C and 1° C, and 1° C interval for changes with larger magnitud (EPS 9847 KB)
382_2018_4319_MOESM6_ESM.eps (8.3 mb)
Fig. S6. Ensemble-mean response (relative to CTRL) of sea level [m] in GW (first column) and GMWM10 (second column), their differences (GMWM10–GW, third column) and the signal-to-noise ratio (fourth column), all averaged over the first 50 years (a-d) and over the last 50 years (e-h). Signal-to-noise ratio is defined as the ratio between the ensemble-mean difference and the corresponding spread. Note that signal-to-noise ratios are very large and off scale (the scale was kept to enable comparison with other variables). Sea level change includes both steric and mass changes (EPS 8524 KB)

References

  1. Andrews T et al (2012) Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophys Res Lett 39:L09712. doi. https://doi.org/10.1029/2012GL051607 Google Scholar
  2. Armour KC et al (2016) Southern Ocean warming delayed by circumpolar upwelling and equatorward transport. Nat Geosci 9:549–554.  https://doi.org/10.1038/ngeo2731 CrossRefGoogle Scholar
  3. Bamber JL, Westaway RM, Marzeion B, Wouters B (2018) The land ice contribution to sea level during the satellite era. Environ Res Lett 13:063008.  https://doi.org/10.1088/1748-9326/aac2f0 CrossRefGoogle Scholar
  4. Bindoff NL et al (2013) Detection and attribution of climate change: from global to regional. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  5. Bintanja R et al (2013) Important role for ocean warming and increased ice-shelf melt in Antarctic sea–ice expansion. Nat Geosci 6:376–379CrossRefGoogle Scholar
  6. Bintanja R, van Oldenborgh GJ, Katsman CA (2015) The effect of increased fresh water from Antarctic ice shelves on future trends in Antarctic sea ice. Ann Glaciol.  https://doi.org/10.3189/2015AoG69A001 Google Scholar
  7. Böning CW, Dispert A, Visbeck M, Rintoul S, Schwarzkopf F (2008) The response of the Antarctic circumpolar current to recent climate change. Nat Geosci 1:864–869.  https://doi.org/10.1038/ngeo362 CrossRefGoogle Scholar
  8. Cobb KM (2014) A southern misfit. Nat Clim Change 4:328–329CrossRefGoogle Scholar
  9. Collins M et al (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  10. de Lavergne C, Palter JB, Galbraith ED, Bernardello R, Marinov I (2014) Cessation of deep convection in the open Southern Ocean under anthropogenic climate change. Nat Clim Change 4(4):278–282.  https://doi.org/10.1038/nclimate2132 CrossRefGoogle Scholar
  11. DeConto RM, Pollard D (2016) Contribution of Antarctica to past and future sea-level rise. Nature 531:591–597.  https://doi.org/10.1038/nature17145 CrossRefGoogle Scholar
  12. Downes SM, Hogg AM (2013) Southern Ocean circulation and eddy compensation in CMIP5 models. J Clim 26:7198–7220.  https://doi.org/10.1175/JCLI-D-12-00504.1 CrossRefGoogle Scholar
  13. Farneti R et al (2015) An assessment of Antarctic Circumpolar Current and Southern Ocean meridional overturning circulation during 1958–2007 in a suite of interannual CORE-II simulations. Ocean Model 93:84–120CrossRefGoogle Scholar
  14. Flato G et al (2013) Evaluation of climate models. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  15. Fogwill CJ et al (2015) Sensitivity of the Southern Ocean to enhanced regional Antarctic ice sheet meltwater input. Earth’s Future.  https://doi.org/10.1002/2015EF000306 Google Scholar
  16. Gent PR, Large WG, Bryan FO (2001) What sets the mean transport through Drake Passage? J Geophys Res 106:2693–2712CrossRefGoogle Scholar
  17. Gillett NP, Thompson DWJ (2003) Simulation of recent Southern Hemisphere climate change. Science 302:273–275.  https://doi.org/10.1126/science.108744 CrossRefGoogle Scholar
  18. Griesel A, Mazloff MR, Gille ST (2012) Mean dynamic topography in the Southern Ocean: evaluating Antarctic circumpolar current transport. J Geophys Res Oceans 117:C01020.  https://doi.org/10.1029/2011JC007573 CrossRefGoogle Scholar
  19. Hansen J et al (2016) Ice melt, sea level rise and superstorms: evidence from paleoclimate data, climate modeling, and modern observations that 2 °C global warming could be dangerous. Atmos Chem Phys 16:3761–3812.  https://doi.org/10.5194/acp-16-3761-2016 CrossRefGoogle Scholar
  20. Hartmann DL et al (2013) Observations: atmosphere and surface. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  21. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107.  https://doi.org/10.1175/2009BAMS2607.1 CrossRefGoogle Scholar
  22. The IMBIE team (2018) Mass balance of the Antarctic Ice Sheet from 1992 to 2017. Nature.  https://doi.org/10.1038/s41586-018-0179-y Google Scholar
  23. IPCC (2013) The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013. Cambridge University Press, CambridgeGoogle Scholar
  24. Jones JM et al (2016) Assessing recent trends in high-latitude Southern Hemisphere surface climate. Nat Clim Change 6(10):917–926.  https://doi.org/10.1038/NCLIMATE3103 CrossRefGoogle Scholar
  25. Jourdain NC et al (2017) Ocean circulation and sea–ice thinning induced by melting ice shelves in the Amundsen Sea. J Geophys Res Oceans 112:2550–2573.  https://doi.org/10.1002/2016JC012509 CrossRefGoogle Scholar
  26. Kirtman B et al (2013) Near-term climate change: projections and predictability. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  27. Latif M, Martin T, Park W (2013) Southern Ocean sector centennial climate variability and recent decadal trends. J Clim 26(19):7767–7782.  https://doi.org/10.1175/JCLI-D-12-00281.1 CrossRefGoogle Scholar
  28. Latif M, To. Martin A, Reintges, Park W (2017) Southern ocean decadal variability and predictability. Curr Clim Change Rep.  https://doi.org/10.1007/s40641-017-0068-8 Google Scholar
  29. Le Bars D, Drijfhout S, de Vries H (2017) A high-end sea level rise probabilistic projection including rapid Antarctic ice sheet mass loss. Environ Res Lett 12(4):044013CrossRefGoogle Scholar
  30. Marshall GJ (2003) Trends in the Southern Annular Mode from observations and reanalyses. J Clim 16:4134–4143.  https://doi.org/10.1175/1520-0442(2003)016%3C4134:titsam%3E2.0.co;2 CrossRefGoogle Scholar
  31. Martin T, Park W, Latif M (2013) Multi-centennial variability controlled by southern ocean convection in the Kiel climate model. Clim Dyn 40:7, 2005–2022.  https://doi.org/10.1007/s00382-012-1586-7 CrossRefGoogle Scholar
  32. Meijers AJS (2014) The Southern ocean in the coupled model intercomparison project phase 5. Philos Trans A Math Phys Eng Sci 372(2019):20130296.  https://doi.org/10.1098/rsta.2013.0296 CrossRefGoogle Scholar
  33. Meijers AJS et al (2012) Representation of the Antarctic circumpolar current in the CMIP5 climate models and future changes under warming scenarios. J Geophys Res Oceans 117:C12008.  https://doi.org/10.1029/2012JC008412 CrossRefGoogle Scholar
  34. Park W, Keenlyside N, Latif M et al (2009) Tropical Pacific climate and its response to global warming in the Kiel Climate Model. J Clim 22:71–92.  https://doi.org/10.1175/2008JCLI2261.1 CrossRefGoogle Scholar
  35. Parkinson CL, Cavalieri DJ (2012) Antarctic sea ice variability and trends, 1979–2010. Cryosphere 6:871–878CrossRefGoogle Scholar
  36. Pauling AG et al (2016) The response of the Southern Ocean and Antarctic Sea Ice to freshwater from ice shelves in an earth system model. J Climate 29:1655–1672CrossRefGoogle Scholar
  37. Purkey SG, Johnson GC (2013) Antarctic bottom water warming and freshening: contributions to sea level rise, ocean freshwater budgets, and global heat gain. J Clim 26:6105–6122CrossRefGoogle Scholar
  38. Reintges A, Martin T, Latif M, Keenlyside NS (2017a) Uncertainty in twenty-first century projections of the Atlantic Meridional Overturning Circulation in CMIP3 and CMIP5 models. Clim Dyn.  https://doi.org/10.1007/s00382-016-3180-x80 Google Scholar
  39. Reintges A, Martin T, Latif M, Park W (2017b) Physical controls of Southern Ocean deep-convection variability in CMIP5 models and in the Kiel climate model. Geophys Res Lett.  https://doi.org/10.1002/2017GL074087 Google Scholar
  40. Rignot E, Velicogna I, van den Broeke MR, Monaghan A, Lenaerts J (2011) Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise. Geophys Res Lett 38:L0550CrossRefGoogle Scholar
  41. Rignot E, Jacobs S, Mouginot J, Scheuchl B (2013) Ice-shelf melting around Antarctica. Science 341:266–270CrossRefGoogle Scholar
  42. Stroeve J et al (2007) Arctic sea ice decline: faster than forecast. Geophys Res Lett 34:L09501CrossRefGoogle Scholar
  43. Swart NC, Fyfe JC (2013) The influence of recent Antarctic ice sheet retreat on simulated sea ice area trends. Geophys Res Lett 40:4328–4332.  https://doi.org/10.1002/grl.50820 CrossRefGoogle Scholar
  44. Thompson DWJ, Solomon S (2002) Interpretation of recent Southern Hemisphere climate change. Science 296:895–899CrossRefGoogle Scholar
  45. Velicogna I (2009) Increasing rates of ice mass loss from the Greenland and Antarctic ice sheets revealed by GRACE. Geophys Res Lett 36:L19503CrossRefGoogle Scholar
  46. Visbeck M (2009) A station-based Southern Annular Mode index from 1884 to 2005. J Clim 22:940–950.  https://doi.org/10.1175/2008JCLI2260.1 CrossRefGoogle Scholar
  47. Wang G, Dommenget D (2015) The leading modes of decadal SST variability in the Southern Ocean in CMIP5 simulations. Clim Dyn 47:1775–1792.  https://doi.org/10.1007/s00382-015-2932-3 CrossRefGoogle Scholar
  48. Wang Z, Kuhlbrodt T, Meredith MP (2011) On the response of the Antarctic circumpolar current transport to climate change in coupled climate models. J Geophys Res 116:C08011.  https://doi.org/10.1029/2010JC006757 Google Scholar
  49. Zunz V, Goosse H, Massonnet F (2013) How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent? Cryosphere 7(2):451–468.  https://doi.org/10.5194/tc-7-451-2013 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.GEOMAR Helmholtz Centre for Ocean Research KielKielGermany
  2. 2.Cluster of Excellence “The Future Ocean”University of KielKielGermany

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