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Partitioning uncertainty in projections of Arctic sea ice
  • David Bonan,
  • Flavio Lehner,
  • Marika M Holland
David Bonan
California Institute of Technology, California Institute of Technology

Corresponding Author:dbonan@caltech.edu

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Flavio Lehner
Cornell University, Cornell University
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Marika M Holland
National Center for Atmospheric Research, National Center for Atmospheric Research
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Improved knowledge of the contributing sources of uncertainty in projections of Arctic sea ice over the 21st century is essential for evaluating impacts of a changing Arctic ecosystem. Here, we consider the role of internal variability, model structure and emissions scenario in projections of Arctic sea-ice extent (SIE) by using six single model initial-condition large ensembles and a suite of models participating in Phase 5 of the Coupled Model Intercomparison Project. For projections of September Arctic SIE, internal variability accounts for as much as 60% of the total uncertainty in the next few decades, while emissions scenario dominates uncertainty toward the end of the century. Model structure accounts for approximately 70% of the total uncertainty by mid-century and declines to 20% at the end of the 21st century. For projections of wintertime Arctic SIE, internal variability contributes as much as 60% of the total uncertainty in the first few decades and impacts total uncertainty at longer lead times when compared to summer SIE. Model structure contributes the rest of the uncertainty with emissions scenario contributing little to the total uncertainty. At regional scales, the contribution of internal variability can vary widely and strongly depends on the month and region. For wintertime SIE in the GIN and Barents Seas, internal variability contributes approximately 70% to the total uncertainty over the coming decades and remains important much longer than in other regions. We further find that the relative contribution of internal variability to total uncertainty is state-dependent and increases as sea ice volume declines. These results demonstrate the need to improve the representation of internal variability of Arctic SIE in models, which is a significant source of uncertainty in future projections.
01 Apr 2021Published in Environmental Research Letters volume 16 issue 4 on pages 044002. 10.1088/1748-9326/abe0ec