Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes
Abstract 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 app...
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Language: | English |
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ftsubgoettingen:oai:publications.goettingen-research-online.de:2/136517 2023-11-05T03:38:29+01:00 Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter 2020 https://resolver.sub.uni-goettingen.de/purl?gro-2/136517 https://doi.org/10.1175/WAF-D-19-0107.1 en eng https://resolver.sub.uni-goettingen.de/purl?gro-2/136517 doi:10.1175/WAF-D-19-0107.1 info:eu-repo/semantics/openAccess info:eu-repo/semantics/article journal_article yes 2020 ftsubgoettingen https://doi.org/10.1175/WAF-D-19-0107.1 2023-10-08T16:58:30Z Abstract 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. Article in Journal/Newspaper Arctic Canadian Archipelago Chukchi laptev Sea ice Georg-August-Universität Göttingen: GoeScholar Weather and Forecasting 35 3 793 806 |
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Open Polar |
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Georg-August-Universität Göttingen: GoeScholar |
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ftsubgoettingen |
language |
English |
description |
Abstract 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. |
author2 |
Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter |
format |
Article in Journal/Newspaper |
author |
Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter |
spellingShingle |
Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
author_facet |
Gregory, William Tsamados, Michel Stroeve, Julienne Sollich, Peter |
author_sort |
Gregory, William |
title |
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_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_sort |
regional september sea ice forecasting with complex networks and gaussian processes |
publishDate |
2020 |
url |
https://resolver.sub.uni-goettingen.de/purl?gro-2/136517 https://doi.org/10.1175/WAF-D-19-0107.1 |
genre |
Arctic Canadian Archipelago Chukchi laptev Sea ice |
genre_facet |
Arctic Canadian Archipelago Chukchi laptev Sea ice |
op_relation |
https://resolver.sub.uni-goettingen.de/purl?gro-2/136517 doi:10.1175/WAF-D-19-0107.1 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1175/WAF-D-19-0107.1 |
container_title |
Weather and Forecasting |
container_volume |
35 |
container_issue |
3 |
container_start_page |
793 |
op_container_end_page |
806 |
_version_ |
1781694193562812416 |