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Published April 2021 | Submitted + Published
Journal Article Open

Partitioning uncertainty in projections of Arctic sea ice

Abstract

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 environment. Here, we consider the role of internal variability, model structure and emissions scenario in projections of Arctic sea-ice area (SIA) 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 SIA change, internal variability accounts for as much as 40%–60% of the total uncertainty in the next decade, while emissions scenario dominates uncertainty toward the end of the century. Model structure accounts for 60%–70% of the total uncertainty by mid-century and declines to 30% at the end of the 21st century in the summer months. For projections of wintertime Arctic SIA change, internal variability contributes as much as 50%–60% of the total uncertainty in the next decade and impacts total uncertainty at longer lead times when compared to the summertime. In winter, there exists a considerable scenario dependence of model uncertainty with relatively larger model uncertainty under strong forcing compared to weak forcing. At regional scales, the contribution of internal variability can vary widely and strongly depends on the calendar month and region. For wintertime SIA change in the Greenland-Iceland-Norwegian and Barents Seas, internal variability contributes 60%–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 that internal variability is a significant source of uncertainty in projections of Arctic sea ice.

Additional Information

© 2021 The Author(s). Published by IOP Publishing Ltd. Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 30 September 2020; Accepted 28 January 2021; Published 10 March 2021. The authors thank the US CLIVAR Working Group on Large Ensembles for making the output publicly available. The authors also thank the climate modeling groups for producing and making available their output. This work was greatly improved through discussions with Mitch Bushuk and comments from Tapio Schneider and Katie Brennan. The authors are grateful for helpful comments from Dirk Notz and one anonymous reviewer, as well as the Editor. D B B was supported by an American Meteorological Society (AMS) Graduate Fellowship. F L was supported by the Swiss National Science Foundation Ambizione Fellowship (Project PZ00P2_174128) and the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy's Office of Biological & Environmental Research (BER) via NSF IA 1844590. Contributions from M M H were supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the NSF under Cooperative Agreement No. 1852977. Data availability statement: The data that support the findings of this study are openly available at the following URLs: https://esgf-node.llnl.gov/search/cmip5/ and www.cesm.ucar.edu/projects/community-projects/MMLEA/.

Attached Files

Published - Bonan_2021_Environ._Res._Lett._16_044002.pdf

Submitted - essoar.10504570.1.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 20, 2023