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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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Precursors of September Arctic Sea-Ice Extent Based on Causal Effect Networks
Although standard statistical methods and climate models can simulate and predict sea-ice changes well, it is still very hard to distinguish some direct and robust factors associated with sea-ice changes from its internal variability and other noises. Here, with long-term observations (38 years from 1980 to 2017), we apply the causal effect networks algorithm to explore the direct precursors of September Arctic sea-ice extent by adjusting the maximal lead time from one to eight months. For lead time of more than three months, June downward longwave radiation flux in the Canadian Arctic Archipelago is the only one precursor. However, for lead time of 1–3 months, August sea-ice concentration in Western Arctic represents the strongest positive correlation with September sea-ice extent, while August sea-ice concentration factors in other regions have weaker influences on the marginal seas. Other precursors include August wind anomalies in the lower latitudes accompanied with an Arctic high pressure anomaly, which induces the sea-ice loss along the Eurasian coast. These robust precursors can be used to improve the seasonal predictions of Arctic sea ice and evaluate the climate models.
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- Award ID(s):
- 1751363
- PAR ID:
- 10082519
- Date Published:
- Journal Name:
- Atmosphere
- Volume:
- 9
- Issue:
- 11
- ISSN:
- 2073-4433
- Page Range / eLocation ID:
- 437
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/atmos9110437
