The Arctic's sea ice cover is retreating as the region continues to warm at nearly four times the global average rate (Rantanen et al., 2022). Alongside a decrease in extent and age (Stroeve & Notz, 2018), the sea ice is thinning (Kwok, 2018;Mallett et al., 2021) and snow depth is declining (Stroeve et al., 2020;Webster et al., 2014). A thinning ice pack affects the thermodynamic processes that govern seasonal ice melt and growth, as well as the dynamic processes that control ice mobility (e.g., Rampal et al., 2009). Assimilation of accurate sea ice thickness and snow depth data into models offers an opportunity to improve the prediction of future sea ice state (e.g., Holland et al., 2021;Mignac et al., 2022). Despite the importance of sea ice thickness, the most accurate estimates come from highly localized in situ observations from autonomous ice-buoys or upward-looking sonar instruments. While airborne-or submarine-based campaigns offer greater spatial coverage, they too remain temporally and spatially constrained. Satellite-mounted laser and radar altimeters offer a potential solution by providing year-round, pan-Arctic monitoring.Several studies have demonstrated an approach to convert Ku-band satellite radar altimeter freeboards from CryoSat-2 (CS2) and Sentinel-3 (S3) to sea ice thickness (Lawrence et al., 2019;Laxon et al., 2013). Sea ice freeboard, the height of the snow-ice interface relative to the surrounding ocean surface, is estimated from the return-time of a radar pulse. Thickness can be derived from the freeboard by applying the assumption of hydrostatic equilibrium together with assumptions concerning the snow, ice and water densities and the snow depth.
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.
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