Greenhouse gas mitigation can reduce sea-ice loss and increase polar bear persistence

Journal name:
Nature
Volume:
468,
Pages:
955–958
Date published:
DOI:
doi:10.1038/nature09653
Received
Accepted
Published online

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.

At a glance

Figures

  1. Changes from the present in polar bear habitat features varied greatly among greenhouse gas scenarios.
    Figure 1: Changes from the present in polar bear habitat features varied greatly among greenhouse gas scenarios.

    ad, The DIV is illustrated here. Shown are changes in optimal polar bear foraging habitat (a), extent of sea ice over continental shelves (b), number of months continental shelves are ice free (c) and the distance from the shelf edge to the edge of the perennial pack ice as projected by CCSM3 with four greenhouse gas scenarios (defined in text) (d). Thin lines plot annual averages of the model runs under each greenhouse gas scenario, witherror bars showing data ±1 s.d. Bold lines are 10-year centred running averages of the annual mean values. OBS is observed passive microwave satellite data, black dots are the annual satellite observed values.

  2. Relationship between GMAT change and change in polar bear habitat features is essentially linear.
    Figure 2: Relationship between GMAT change and change in polar bear habitat features is essentially linear.

    ad, The DIV is illustrated here. The optimal polar bear foraging habitat (a), extent of sea ice over continental shelves (b), number of months continental shelves are ice free (c) and the distance from the shelf edge to the edge of the perennial pack ice (d). Linear relationship between habitat and GMAT changes does not support the tipping-point hypothesis. Projections are from CCSM3 running four different greenhouse gas scenarios (defined in text).

  3. September sea-ice extent (50% concentration) recovers from a RILE in a 2020 greenhouse gas commitment realization.
    Figure 3: September sea-ice extent (50% concentration) recovers from a RILE in a 2020 greenhouse gas commitment realization.

    In the 2020 commitment realization, which was integrated from the same initial state as the A1B reference realization, greenhouse gas concentrations followed the A1B scenario until 2020, and were fixed thereafter. RILEs occurred in both realizations during the decade of the 2020s. In contrast to the reference run (red line), the substantial sea-ice recovery in the 2020 commitment scenario (purple line) supports the concept that RILEs represent natural sea-ice variability superimposed on a secular warming-induced sea-ice decline, rather than tipping points. All lines represent 10-year running averages compiled from the annual data.

  4. Future polar bear persistence varies among ecoregions and greenhouse gas scenarios.
    Figure 4: Future polar bear persistence varies among ecoregions and greenhouse gas scenarios.

    Bayesian network model projected outcomes (coloured bars) are shown for each of four greenhouse gas scenarios, four future decades, and four ecoregions. Although substantial risk of extirpation continues for the SEA and DIV even with mitigation, increased levels of greenhouse gas mitigation improve the probability of future polar bear persistence in all ecoregions. In the x-axis legend, we refer to the decades of 2020–2029, 2045–2054, 2070–2079 and 2090–2099 as years 25, 50, 75 and 95.

  5. Greenhouse gas mitigation and best possible wildlife management could allow polar bears to persist throughout current range.
    Figure 5: Greenhouse gas mitigation and best possible wildlife management could allow polar bears to persist throughout current range.

    Bayesian network outcomes with habitat inputs from the MIT scenario are shown for the last decade of the twenty-first century. When temperature rise is kept at or below the MIT scenario and when on-the-ground management of harvest, bear–human interactions, oil and gas activities etc. is maximized (influence run no. 2), extinction is not the most probable outcome in any of the four ecoregions.

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Author information

Affiliations

  1. US Geological Survey, Alaska Science Center, 4210 University Drive, Anchorage, Alaska 99508, USA

    • Steven C. Amstrup &
    • George M. Durner
  2. National Science Foundation, 4201 Wilson Blvd., Arlington, Virginia 22230, USA

    • Eric T. DeWeaver
  3. US Geological Survey, Alaska Science Center, 3100 National Park Road, Juneau, Alaska 99801, USA

    • David C. Douglas
  4. USDA Forest Service, PNW Research Station, 620 SW Main St., Suite 400, Portland, Oregon 97205, USA

    • Bruce G. Marcot
  5. Atmospheric Sciences, University of Washington, Seattle, Washington 98195, USA

    • Cecilia M. Bitz
  6. National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, Colorado 80305, USA

    • David A. Bailey
  7. Present address: Polar Bears International, 810 N. Wallace, Suite E, P. O. Box 3008, Bozeman, Montana 59772, USA.

    • Steven C. Amstrup

Contributions

S.C.A. conceived the project, assembled the team, and led writing. E.T.D. helped refine the project and analysed habitat/GMAT and RIGEs. D.C.D. staged sea-ice data and did the spatial analysis related to sea-ice metrics. B.G.M. conducted Bayesian network model runs and compiled outcomes. G.M.D. led development of the resource selection function approach to habitat analysis. C.M.B. proposed and helped interpret the 2020 CO2 stabilization experiments. D.A.B. set up and ran the climate model simulations. E.T.D., C.M.B. and D.A.B. led interpretation of GCM outcomes. S.C.A., B.G.M. and D.C.D. interpreted biological outcomes. E.T.D. and D.C.D. developed all graphics. All authors contributed to writing and responding to review comments.

Competing financial interests

The authors declare no competing financial interests.

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    The file contains Supplementary Methods, a Supplementary Discussion, Supplementary Figures 1-8 with legends, Supplementary Table 1 and additional references.

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