Articles | Volume 10, issue 1
https://doi.org/10.5194/esd-10-189-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-10-189-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Mar 2019
Research article |
| 21 Mar 2019
| 21 Mar 2019
September Arctic sea ice minimum prediction – a skillful new statistical approach
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
MARUM – Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
Klaus Grosfeld
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Patrick Scholz
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Renate Treffeisen
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Gerrit Lohmann
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
MARUM – Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
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- A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover S. Horvath et al. 10.1029/2020EA001176
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- Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes W. Gregory et al. 10.1175/WAF-D-19-0107.1
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- Forecasting low flow conditions months in advance through teleconnection patterns, with a special focus on summer 2018 M. Ionita & V. Nagavciuc 10.1038/s41598-020-70060-8
- Interplay of anthropogenic and natural drivers of observed coupled sea surface temperature - Arctic sea ice variability P. Vaideanu et al. 10.1007/s00382-025-07623-w
- Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections F. Diebold & G. Rudebusch 10.2139/ssrn.3513025
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- PERFORMANCE ASSESSMENT OF DATA-DRIVEN MODELS IN ARCTIC SEA ICE PREDICTION R. PANG 10.1142/S2630534824500025
- A linear mixed effects model for seasonal forecasts of Arctic sea ice retreat S. Horvath et al. 10.1080/1088937X.2021.1987999
- Large-scale drivers of the exceptionally low winter Antarctic sea ice extent in 2023 M. Ionita 10.3389/feart.2024.1333706
- Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula F. Hillebrand et al. 10.1007/s10661-021-08843-3
- A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover S. Horvath et al. 10.1029/2020EA001176
- The first tree-ring reconstrruction of streamflow variability over the last ∼250 years in the Lower Danube N. Viorica et al. 10.1016/j.jhydrol.2023.129150
- Early predictors of seasonal Arctic sea-ice volume loss: the impact of spring and early-summer cloud radiative conditions M. King et al. 10.1017/aog.2020.60
- Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes W. Gregory et al. 10.1175/WAF-D-19-0107.1
- Seasonal Influence of the Atmosphere and Ocean on the Fall Sea Ice Extent in the Barents‐Kara Seas R. Chen et al. 10.1029/2021JD035144
- Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections F. Diebold & G. Rudebusch 10.1016/j.jeconom.2020.12.007
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Discussed (final revised paper)
Latest update: 26 Jul 2025
Short summary
Based on a simple statistical model we show that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' oceanic and atmospheric conditions. Our statistical model skillfully captures the interannual variability of the September sea ice extent and could provide a valuable tool for identifying relevant regions and oceanic and atmospheric parameters that are important for the sea ice development in the Arctic.
Based on a simple statistical model we show that the September sea ice extent has a high...
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