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Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

Gregory, W; Tsamados, M; Stroeve, J; Sollich, P; (2020) Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes. Weather and Forecasting , 35 (3) pp. 793-806. 10.1175/WAF-D-19-0107.1. Green open access

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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.

Type: Article
Title: Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes
Open access status: An open access version is available from UCL Discovery
DOI: 10.1175/WAF-D-19-0107.1
Publisher version: https://doi.org/10.1175/WAF-D-19-0107.1
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Arctic, Sea ice, Bayesian methods, Forecasting, Seasonal forecasting, Statistical forecasting
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Earth Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10091542
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