On the basis of projected losses of their essential sea-ice habitats, a United States Geological Survey research team concluded in 2007 that two-thirds of the world’s polar bears (Ursus maritimus) could disappear by mid-century if business-as-usual greenhouse gas emissions continue1,2,3. That projection, however, did not consider the possible benefits of greenhouse gas mitigation. A key question is whether temperature increases lead to proportional losses of sea-ice habitat, or whether sea-ice cover crosses a tipping point and irreversibly collapses when temperature reaches a critical threshold4,5,6. Such a tipping point would mean future greenhouse gas mitigation would confer no conservation benefits to polar bears. Here we show, using a general circulation model7, that substantially more sea-ice habitat would be retained if greenhouse gas rise is mitigated. We also show, with Bayesian network model outcomes, that increased habitat retention under greenhouse gas mitigation means that polar bears could persist throughout the century in greater numbers and more areas than in the business-as-usual case3. Our general circulation model outcomes did not reveal thresholds leading to irreversible loss of ice6; instead, a linear relationship between global mean surface air temperature and sea-ice habitat substantiated the hypothesis that sea-ice thermodynamics can overcome albedo feedbacks proposed to cause sea-ice tipping points5,6,8. Our outcomes indicate that rapid summer ice losses in models9 and observations6,10 represent increased volatility of a thinning sea-ice cover, rather than tipping-point behaviour. Mitigation-driven Bayesian network outcomes show that previously predicted declines in polar bear distribution and numbers3 are not unavoidable. Because polar bears are sentinels of the Arctic marine ecosystem11 and trends in their sea-ice habitats foreshadow future global changes, mitigating greenhouse gas emissions to improve polar bear status would have conservation benefits throughout and beyond the Arctic12.
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Principal funding for this project was provided by the USGS. B.G.M. acknowledges support from the USDA Forest Service, Pacific Northwest Research Station. E.T.D. acknowledges the support of the Office of Science (BER), US Department of Energy, under grant ER64735 to the University of Maryland. E.D.’s work also was supported by the National Science Foundation (NSF) during his employment there. The findings reported here, however, are not endorsed by and do not necessarily reflect the views of the NSF. CCSM3 simulations were performed using computing resources provided by the National Center for Atmospheric Research and the Earth Simulator in Japan. D.A.B. was supported under a grant from the NSF Office of Polar Programs, award number 0908675. M. Holland provided comments regarding model design and analysis. We acknowledge the Program for Climate Model Diagnosis and Intercomparison and the World Climate Research Programme’s Working Group on Coupled Modeling for their roles in making available the Coupled Model Intercomparison Project phase 3 multi-model data set (support of this data set is provided by the Office of Science, US Department of Energy). We thank W. Washington and L. Buja for running the AS and providing us with the output from the CCSP integrations. We thank D. Vongraven and S. Vavrus for comments on earlier versions of this manuscript, and we thank N. Lunn and L. Peacock for providing the peer reviews necessary for the beta version of our Bayesian network model. Any use of trade names is for descriptive purposes only and does not represent endorsement by the US government.
The file contains Supplementary Methods, a Supplementary Discussion, Supplementary Figures 1-8 with legends, Supplementary Table 1 and additional references.
About this article
Nature Communications (2013)