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Abstract This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.
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1,000 Predictions: What's New and What's Old in a Retrospective Analysis of the Sea Ice Outlook, 2008-2020
Each Arctic summer since 2008, the Sea Ice Outlook (SIO) has invited researchers and the engaged public to contribute predictions regarding the September extent of Arctic sea ice. Then, each September, we see the accuracy or inaccuracy of those predictions. More than 1,000 individual predictions, based on many different methods, were contributed from 2008 to 2020. Earlier papers analyzed the ensemble skill of the first few hundred SIO contributions through 2013 ( Stroeve et al. 2014) and through 2015 ( Hamilton & Stroeve 2016). Here, I bring those analyses up to date with data through 2020. The main conclusions from earlier papers have proven to be robust, but unexpected new insights emerged as well. The long term downward trend in ice extent is reasonably well described as linear (R2 = 0.79) or quadratic (R2 = 0.81). Very large changes from the previous year’s extent in 2012 and 2013 resulted in the largest prediction errors. Both errors reflect one 2012 cyclone. For reasons not yet understood, SIO predictions especially those from dynamic modeling predict the previous year’s extent rather than the current year.
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- Award ID(s):
- 1748325
- PAR ID:
- 10207507
- Date Published:
- Journal Name:
- American Geophysical Union
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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