Rapid CommunicationThis is the author accepted manuscript. The final version is available from Nature Publishing Group via the DOI in this record

D Notz

F Massonnet

I Mahlstein

J Boe

J Cohen

J Liu

J Rogelj

M Wang

Nature Climate Change

Screen, JA

Williamson, D

Open Research Exeter

Correspondence to Nature Climate Change 	 1	Ice-free at 1.5°C? James A. Screen1 and Daniel Williamson1 1Department of Mathematics, University of Exeter, Exeter, EX4 4QE, UK  To the Editor − The Paris Agreement1 to combat climate change includes an aspirational goal to limit global warming to 1.5°C above the pre-industrial level, substantially more ambitious than the previous target of 2°C. One of the most visible and iconic aspects of recent climate change is the dramatic loss of Arctic sea-ice2, which is having profound implications on the environment, ecosystems and human inhabitants of this region and beyond3.   The concept of an ‘ice-free Arctic’ has captured scientific attention4-9 and public imagination. Scientists commonly define this as when the Arctic first becomes ice-free at the end of summer; specifically, the first year when the average September sea-ice extent falls below 1 million km2. This threshold is considered appropriate, rather than zero, because some land-fast ice is expected to remain along the northern coasts of Greenland and Ellesmere Island for many years after the bulk of the Arctic Ocean first becomes open water4. Without efforts to slow manmade global warming, an ice-free Arctic would likely occur in summer by the middle of this century2,4-9. But would limiting warming to 1.5°C, or even 2°C, prevent the Arctic ever going ice-free?  Different climate models give vastly different projections of the lowest sea-ice extent for global warming of less than 1.5°C (Fig. 1a) or less than 2°C (Fig. 1b). Models that over-estimate (or under-estimate) sea-ice extent in the last ten years are also those that project more ice (or less ice) remaining into the future. This can be summarised mathematically by Correspondence to Nature Climate Change 	 2	the strong log-linear relationship (we use logarithms because sea-ice extent cannot physically be less than zero) between model-simulated sea-ice extent averaged for years 2007 to 2016 and the minimum sea-ice extent for global warming of up to 1.5°C (Fig. 1a) or up to 2°C (Fig. 1b). Using the observed sea-ice extent for the last decade (4.8 million km2) as a predictor in our simple regression models yields an observationally-constrained prediction of 2.9 million km2 (90% credible interval: 2.5 to 3.4 million km2) for the minimum sea-ice extent if global warming is limited to 1.5°C (Fig. 1a), or 1.2 (0.4 to 4.0) million km2 if global warming remains below 2°C (Fig. 1b).   Using Bayesian statistics allows us to compare estimates of the probability of an ice-free Arctic for the 1.5°C or 2°C target (Fig. 1c). We estimate there is less than a 1-in-100000 (exceptionally unlikely in IPCC parlance2) chance of an ice-free Arctic if global warming stays below 1.5 °C, and around a 1-in-3 chance (39%; about as likely as not) if global warming is limited to 2.0°C. We suppose then that a summer ice-free Arctic is virtually certain to be avoided if the 1.5°C target of the Paris Agreement is met. However, the 2°C target may be insufficient to prevent an ice-free Arctic. Furthermore, our analysis suggests that the Intended Nationally Determined Contributions submitted by countries to support the Paris Agreement (which imply warming of around 3°C10) would likely (73%) lead to the Arctic becoming ice-free (Fig. 1c).  1. UNFCCC. http://unfccc.int/paris_agreement/items/9485.php (2015) 2. IPCC. Climate Change 2013: The Physical Science Basis (2013) 3. Cohen, J. et al. Nature Geosci. 7, 627-637 (2014) 4. Wang, M. & Overland, J. E. Geophys. Res. Lett. 36, L07502 (2009) 5. Boe, J., Hall, A. & Qu, X. Nature Geosci. 2, 341–343 (2009)Correspondence to Nature Climate Change 	 3	6. Liu, J., Song, M., Horton, R. M. & Hu, Y. Proc. Natl. Acad. Sci. USA 110, 12571–12576 (2013) 7. Massonnet, F., et al. Cryosphere 6, 1383–1394 (2012) 8. Mahlstein, I. & Knutti, R. J. Geophys. Res. 117, D06104 (2012) 9. Notz, D. & Stroeve, J. Science 354, 747-750 (2016) 10. Rogelj, J., et al. Nature 534, 631-639 (2016)  Acknowledgements The authors are grateful to the climate modeling groups for producing and making available their model output and to the following data providers: National Snow and Ice Data Center (observed sea-ice extent) and Earth System Grid (CMIP5 multi-model ensemble).   Correspondence to Nature Climate Change 	 4	 Figure 1. Predicting the likelihood of an ice-free Arctic for the 1.5°C and 2°C targets. a, Model projections of the minimum September-mean sea-ice extent (SSIE) for global warming of less than 1.5°C plotted against the 2007-2016 average SSIE. Each symbol corresponds to output from a different CMIP5 model forced by historical emissions from 1850 to 2005 and then either a strong (RCP8.5; crosses) or medium (RCP4.5; squares) greenhouse gas emissions scenario through to 2100. In each case we identified the first year when the annual- and global-mean surface temperature exceeded 1.5°C above the pre-industrial level (1862-1900) and found the lowest (non-zero) SSIE prior to that year. Simulations that do not reach 1.5°C by 2100 are excluded from the analysis. For reference, in the analysed simulations the average year of exceeding 1.5°C is 2026 and 2°C is 2043. The black line shows the log-linear regression model. To ensure positive predictions, we take the natural logarithm of SSIE and fit a standard Bayesian linear regression (with reference prior distributions so that the fit coincides with that obtained by ordinary least squares). The observed 2007-2016 average SSIE (blue) is used as a predictor in the regression model to provide an observationally-constrained prediction 𝑦 (green) of the minimum SSIE for global warming below 1.5°C. b, As a but for projections of the minimum SSIE for global warming of less than 2°C. c, Cumulative probability distribution of 𝑦 for global warming below 1.5°C (yellow), below 2°C (red) and below 3°C (blue) obtained by Monte Carlo sampling (n=100000) from the posterior predictive 2 4 6 8 102007-16 average SSIE (106 km2)0246810Low SSIE under 1.5o C (106  km2 ) y^ =2.9 million km2 (2.5-3.4)2 4 6 8 102007-16 average SSIE (106 km2)0246810Low SSIE under 2o C (106  km2 ) y^ =1.2 million km2 (0.4-4.0)0 1 2 3 4Lowest SSIE (106 km2)020406080100Cumulative probability (%)Ice-free2oC 1.5oC3oCVery unlikelyUnlikelyLikelyVery likelya b cCorrespondence to Nature Climate Change 	 5	distribution for 𝑦. The 1 million km2 SSIE threshold and IPCC likelihood ranges are indicated.  

Ice-free at 1.5°C?

Abstract

Similar works

Full text

Available Versions

Open Research Exeter

Crossref