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Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes
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| Zeitschriftentitel: | Weather and Forecasting |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Weather and Forecasting, 35, 2020, 3, S. 793-806 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Meteorological Society
|
| Schlagwörter: |
| author_facet |
Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter |
|---|---|
| author |
Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter |
| spellingShingle |
Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter Weather and Forecasting Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes Atmospheric Science |
| author_sort |
gregory, william |
| spelling |
Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter 0882-8156 1520-0434 American Meteorological Society Atmospheric Science http://dx.doi.org/10.1175/waf-d-19-0107.1 <jats:title>Abstract</jats:title> <jats:p>Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.</jats:p> Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes Weather and Forecasting |
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Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_unstemmed |
Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_full |
Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_fullStr |
Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_full_unstemmed |
Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_short |
Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_sort |
regional september sea ice forecasting with complex networks and gaussian processes |
| topic |
Atmospheric Science |
| url |
http://dx.doi.org/10.1175/waf-d-19-0107.1 |
| publishDate |
2020 |
| physical |
793-806 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.</jats:p> |
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| author | Gregory, William, Tsamados, Michel, Stroeve, Julienne, Sollich, Peter |
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| description | <jats:title>Abstract</jats:title> <jats:p>Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.</jats:p> |
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| spelling | Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter 0882-8156 1520-0434 American Meteorological Society Atmospheric Science http://dx.doi.org/10.1175/waf-d-19-0107.1 <jats:title>Abstract</jats:title> <jats:p>Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.</jats:p> Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes Weather and Forecasting |
| spellingShingle | Gregory, William, Tsamados, Michel, Stroeve, Julienne, Sollich, Peter, Weather and Forecasting, Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes, Atmospheric Science |
| title | Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_full | Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_fullStr | Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_full_unstemmed | Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_short | Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| title_sort | regional september sea ice forecasting with complex networks and gaussian processes |
| title_unstemmed | Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
| topic | Atmospheric Science |
| url | http://dx.doi.org/10.1175/waf-d-19-0107.1 |