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Published July 16, 2020 | Supplemental Material + Published
Journal Article Open

A mechanism for the Arctic sea ice spring predictability barrier

Abstract

The decline of Arctic sea ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically driven SIM export and negative feedbacks from sea ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice thickness observations collected after melt onset are particularly critical for summer Arctic sea ice predictions.

Additional Information

© 2020 American Geophysical Union. Received 8 APR 2020; Accepted 21 MAY 2020; Accepted article online 9 JUN 2020. We thank two anonymous reviewers for constructive comments. We thank Elisa Mantelli and Gan Zhang for comments on a preliminary version of this manuscript. This research from the Geophysical Fluid Dynamics Laboratory is supported by NOAA's Science Collaboration Program and administered by UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS) under Awards NA16NWS4620043 and NA18NWS4620043B. D. B. B. gratefully acknowledges support from the AMS Graduate Fellowship. E. B. W. gratefully acknowledges support from NSF Grant 1751363. We acknowledge the CESM Large Ensemble Community Project, which is supported by the National Science Foundation (NSF). Five of the CESM‐LE simulations were produced at the University of Toronto under the supervision of Paul Kushner. We acknowledge computing resources from GFDL's Seasonal‐to‐Decadal Variability and Predictability Division that were used to produce the FLOR‐LE. Data Availability Statement: The daily NASA team sea ice concentration observations used in this study are available from the National Snow and Ice Data Center website (http://nsidc.org/data/NSIDC-0051/versions/1). The daily PIOMAS sea ice thickness data are available from the Polar Science Center at the University of Washington (http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/model_grid). The CESM‐LE data are available for download from the Earth System Grid Federation data portal (http://www.cesm.ucar.edu/projects/community-projects/LENS/data-sets.html). The FLOR‐LE sea ice data and data analysis code are available online via Zenodo (https://doi.org/10.5281/zenodo.3862947).

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Published - 2020GL088335.pdf

Supplemental Material - grl60744-sup-0001-text_si-s01.pdf

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023