Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes
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. Th...
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ftucl:oai:eprints.ucl.ac.uk.OAI2:10091542 2023-12-24T10:12:57+01:00 Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes Gregory, W Tsamados, M Stroeve, J Sollich, P 2020-06 text https://discovery.ucl.ac.uk/id/eprint/10091542/1/Gregory_wafd190107.pdf https://discovery.ucl.ac.uk/id/eprint/10091542/ eng eng American Meteorological Society https://discovery.ucl.ac.uk/id/eprint/10091542/1/Gregory_wafd190107.pdf https://discovery.ucl.ac.uk/id/eprint/10091542/ open Weather and Forecasting , 35 (3) pp. 793-806. (2020) Arctic Sea ice Bayesian methods Forecasting Seasonal forecasting Statistical forecasting Article 2020 ftucl 2023-11-27T13:07:28Z 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 University College London: UCL Discovery Arctic |
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Open Polar |
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University College London: UCL Discovery |
op_collection_id |
ftucl |
language |
English |
topic |
Arctic Sea ice Bayesian methods Forecasting Seasonal forecasting Statistical forecasting |
spellingShingle |
Arctic Sea ice Bayesian methods Forecasting Seasonal forecasting Statistical forecasting Gregory, W Tsamados, M Stroeve, J Sollich, P Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes |
topic_facet |
Arctic Sea ice Bayesian methods Forecasting Seasonal forecasting Statistical forecasting |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Gregory, W Tsamados, M Stroeve, J Sollich, P |
author_facet |
Gregory, W Tsamados, M Stroeve, J Sollich, P |
author_sort |
Gregory, W |
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 |
publisher |
American Meteorological Society |
publishDate |
2020 |
url |
https://discovery.ucl.ac.uk/id/eprint/10091542/1/Gregory_wafd190107.pdf https://discovery.ucl.ac.uk/id/eprint/10091542/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Canadian Archipelago Chukchi laptev Sea ice |
genre_facet |
Arctic Canadian Archipelago Chukchi laptev Sea ice |
op_source |
Weather and Forecasting , 35 (3) pp. 793-806. (2020) |
op_relation |
https://discovery.ucl.ac.uk/id/eprint/10091542/1/Gregory_wafd190107.pdf https://discovery.ucl.ac.uk/id/eprint/10091542/ |
op_rights |
open |
_version_ |
1786178105400885248 |