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 spatio-temporal homogeneity – 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 utilised 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 de-trended predictive skills of 0.53, 0.62 and 0.81 at 3, 2, and 1
month(s) lead times respectively. Regional forecasts are also performed for 9
Arctic regions. On average, 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.
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 spatio-temporal homogeneity – 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 utilised 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 de-trended predictive skills of 0.53, 0.62 and 0.81 at 3, 2, and 1
month(s) lead times respectively. Regional forecasts are also performed for 9
Arctic regions. On average, 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.
| Original language | English |
|---|---|
| Journal | JOURNAL OF CLIMATE |
| Publication status | Accepted/In press - 13 Feb 2020 |
