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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Cited
19 citations as recorded by crossref.
- Multivariate deep learning approach to forecast of an ice-free Arctic summer under the SSP2-4.5 and SSP5-8.5 scenarios M. Bilgili
- Forecasting low flow conditions months in advance through teleconnection patterns, with a special focus on summer 2018 M. Ionita & V. Nagavciuc
- Interplay of anthropogenic and natural drivers of observed coupled sea surface temperature - Arctic sea ice variability P. Vaideanu et al.
- Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections F. Diebold & G. Rudebusch
- Hotspots for warm and dry summers in Romania V. Nagavciuc et al.
- The Climatic Response of Tree Ring Width Components of Ash (Fraxinus excelsior L.) and Common Oak (Quercus robur L.) from Eastern Europe C. Roibu et al.
- DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration L. Tan et al.
- A tree ring-based hydroclimate reconstruction for eastern Europe reveals large-scale teleconnection patterns C. Roibu et al.
- PERFORMANCE ASSESSMENT OF DATA-DRIVEN MODELS IN ARCTIC SEA ICE PREDICTION R. PANG
- Subseasonal-to-Seasonal Prediction of Arctic Sea Ice Concentration and Thickness Using a Multivariate Linear Markov Model J. Yang et al.
- A linear mixed effects model for seasonal forecasts of Arctic sea ice retreat S. Horvath et al.
- Large-scale drivers of the exceptionally low winter Antarctic sea ice extent in 2023 M. Ionita
- Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula F. Hillebrand et al.
- A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover S. Horvath et al.
- The first tree-ring reconstrruction of streamflow variability over the last ∼250 years in the Lower Danube N. Viorica et al.
- Early predictors of seasonal Arctic sea-ice volume loss: the impact of spring and early-summer cloud radiative conditions M. King et al.
- Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes W. Gregory et al.
- Seasonal Influence of the Atmosphere and Ocean on the Fall Sea Ice Extent in the Barents‐Kara Seas R. Chen et al.
- Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections F. Diebold & G. Rudebusch
19 citations as recorded by crossref.
- Multivariate deep learning approach to forecast of an ice-free Arctic summer under the SSP2-4.5 and SSP5-8.5 scenarios M. Bilgili
- Forecasting low flow conditions months in advance through teleconnection patterns, with a special focus on summer 2018 M. Ionita & V. Nagavciuc
- Interplay of anthropogenic and natural drivers of observed coupled sea surface temperature - Arctic sea ice variability P. Vaideanu et al.
- Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections F. Diebold & G. Rudebusch
- Hotspots for warm and dry summers in Romania V. Nagavciuc et al.
- The Climatic Response of Tree Ring Width Components of Ash (Fraxinus excelsior L.) and Common Oak (Quercus robur L.) from Eastern Europe C. Roibu et al.
- DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration L. Tan et al.
- A tree ring-based hydroclimate reconstruction for eastern Europe reveals large-scale teleconnection patterns C. Roibu et al.
- PERFORMANCE ASSESSMENT OF DATA-DRIVEN MODELS IN ARCTIC SEA ICE PREDICTION R. PANG
- Subseasonal-to-Seasonal Prediction of Arctic Sea Ice Concentration and Thickness Using a Multivariate Linear Markov Model J. Yang et al.
- A linear mixed effects model for seasonal forecasts of Arctic sea ice retreat S. Horvath et al.
- Large-scale drivers of the exceptionally low winter Antarctic sea ice extent in 2023 M. Ionita
- Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula F. Hillebrand et al.
- A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover S. Horvath et al.
- The first tree-ring reconstrruction of streamflow variability over the last ∼250 years in the Lower Danube N. Viorica et al.
- Early predictors of seasonal Arctic sea-ice volume loss: the impact of spring and early-summer cloud radiative conditions M. King et al.
- Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes W. Gregory et al.
- Seasonal Influence of the Atmosphere and Ocean on the Fall Sea Ice Extent in the Barents‐Kara Seas R. Chen et al.
- Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections F. Diebold & G. Rudebusch
Saved (final revised paper)
Latest update: 30 Apr 2026
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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