Articles | Volume 13, issue 4
https://doi.org/10.5194/tc-13-1073-2019
https://doi.org/10.5194/tc-13-1073-2019
Research article
 | 
03 Apr 2019
Research article |  | 03 Apr 2019

Benchmark seasonal prediction skill estimates based on regional indices

John E. Walsh, J. Scott Stewart, and Florence Fetterer

Related authors

Sea ice breakup and freeze-up indicators for users of the Arctic coastal environment
John E. Walsh, Hajo Eicken, Kyle Redilla, and Mark Johnson
The Cryosphere, 16, 4617–4635, https://doi.org/10.5194/tc-16-4617-2022,https://doi.org/10.5194/tc-16-4617-2022, 2022
Short summary
Impacts of a lengthening open water season on Alaskan coastal communities: deriving locally relevant indices from large-scale datasets and community observations
Rebecca J. Rolph, Andrew R. Mahoney, John Walsh, and Philip A. Loring
The Cryosphere, 12, 1779–1790, https://doi.org/10.5194/tc-12-1779-2018,https://doi.org/10.5194/tc-12-1779-2018, 2018
Short summary
Past, present and future biomes in Beringia: Comparison between simulations and pollen analysis
Kazuyuki Saito, Amy Hendricks, John Walsh, and Nancy Bigelow
Clim. Past Discuss., https://doi.org/10.5194/cp-2018-29,https://doi.org/10.5194/cp-2018-29, 2018
Preprint withdrawn
Short summary
Northern Hemisphere storminess in the Norwegian Earth System Model (NorESM1-M)
Erlend M. Knudsen and John E. Walsh
Geosci. Model Dev., 9, 2335–2355, https://doi.org/10.5194/gmd-9-2335-2016,https://doi.org/10.5194/gmd-9-2335-2016, 2016
Short summary

Related subject area

Discipline: Sea ice | Subject: Arctic (e.g. Greenland)
Causes and evolution of winter polynyas north of Greenland
Younjoo J. Lee, Wieslaw Maslowski, John J. Cassano, Jaclyn Clement Kinney, Anthony P. Craig, Samy Kamal, Robert Osinski, Mark W. Seefeldt, Julienne Stroeve, and Hailong Wang
The Cryosphere, 17, 233–253, https://doi.org/10.5194/tc-17-233-2023,https://doi.org/10.5194/tc-17-233-2023, 2023
Short summary
Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023,https://doi.org/10.5194/tc-17-127-2023, 2023
Short summary
Sea ice breakup and freeze-up indicators for users of the Arctic coastal environment
John E. Walsh, Hajo Eicken, Kyle Redilla, and Mark Johnson
The Cryosphere, 16, 4617–4635, https://doi.org/10.5194/tc-16-4617-2022,https://doi.org/10.5194/tc-16-4617-2022, 2022
Short summary
Improving model-satellite comparisons of sea ice melt onset with a satellite simulator
Abigail Smith, Alexandra Jahn, Clara Burgard, and Dirk Notz
The Cryosphere, 16, 3235–3248, https://doi.org/10.5194/tc-16-3235-2022,https://doi.org/10.5194/tc-16-3235-2022, 2022
Short summary
Rapid Sea Ice Changes in the Future Barents Sea
Ole Rieke, Marius Årthun, and Jakob Simon Dörr
EGUsphere, https://doi.org/10.5194/egusphere-2022-324,https://doi.org/10.5194/egusphere-2022-324, 2022
Short summary

Cited articles

Agnew, T. A. and Howell, S.: Comparison of digitized Canadian ice charts and passive microwave sea-ice concentrations, Geoscience and Remote Sensing Symposium, 24–28 June 2002, Toronto, Ontario, Canada, IGARSS '02. 2002 IEEE International, 1, 231–233, https://doi.org/10.1109/IGARSS.2002.1024996, 2002. 
AMAP: Snow, Water, Ice and Permafrost in the Arctic: 2017 Update. Arctic Monitoring and Assessment Programme, Oslo, Norway, xiv + 269 pp., 2017. 
Barnett, D. G.: A long-range ice forecasting method for the north coast of Alaska, Sea Ice Processes and Models, edited by: Pritchard, R., University of Washington Press, Seattle, WA, USA, 402–409, 1980. 
Blanchard-Wrigglesworth, E., Armour, K. C., and Bitz, C. M.: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations, J. Climate, 24, 231–250, 2011. 
Download
Short summary
Persistence-based statistical forecasts of a Beaufort Sea ice severity index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the trends are removed from the data. This finding is consistent with the notion of a springtime “predictability barrier” that has been found in sea ice forecasts based on more sophisticated methods.