Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations

Abstract

Abstract. This work evaluates the statistical predictability of the Arctic sea ice volume (SIV) anomaly – here defined as the detrended and deseasonalized SIV – on the interannual timescale. To do so, we made use of six datasets, from three different atmosphere–ocean general circulation models, with two different horizontal grid resolutions each. Based on these datasets, we have developed a statistical empirical model which in turn was used to test the performance of different predictor variables, as well as to identify optimal locations from where the SIV anomaly could be better reconstructed and/or predicted. We tested the hypothesis that an ideal sampling strategy characterized by only a few optimal sampling locations can provide in situ data for statistically reproducing and/or predicting the SIV interannual variability. The results showed that, apart from the SIV itself, the sea ice thickness is the best predictor variable, although total sea ice area, sea ice concentration, sea surface temperature, and sea ice drift can also contribute to improving the prediction skill. The prediction skill can be enhanced further by combining several predictors into the statistical model. Applying the statistical model with predictor data from four well-placed locations is sufficient for reconstructing about 70 % of the SIV anomaly variance. As suggested by the results, the four first best locations are placed at the transition Chukchi Sea–central Arctic–Beaufort Sea (79.5∘ N, 158.0∘ W), near the North Pole (88.5∘ N, 40.0∘ E), at the transition central Arctic–Laptev Sea (81.5∘ N, 107.0∘ E), and offshore the Canadian Archipelago (82.5∘ N, 109.0∘ W), in this respective order. Adding further to six well-placed locations, which explain about 80 % of the SIV anomaly variance, the statistical predictability does not substantially improve taking into account that 10 locations explain about 84 % of that variance. An improved model horizontal resolution allows a better trained statistical model so that the reconstructed values better approach the original SIV anomaly. On the other hand, if we inspect the interannual variability, the predictors provided by numerical models with lower horizontal resolution perform better when reconstructing the original SIV variability. We believe that this study provides recommendations for the ongoing and upcoming observational initiatives, in terms of an Arctic optimal observing design, for studying and predicting not only the SIV values but also its interannual variability.

Brief communication: Arctic sea ice thickness internal variability and its changes under historical and anthropogenic forcing

“…Figure 3a, b show the mean Arctic sea ice circulation over the pre-industrial period by compositing the low (a) and high (b) indices for the first mode of SIT variability. The sea ice drift anomaly associated with the pos-itive and negative phases of the first SIT mode shares similar features with the Arctic Oscillation: a cyclonic anomaly in the Beaufort Gyre, impacting the Transpolar Drift Stream, the Laptev Sea Gyre and the East Siberian circulation, as described by Rigor et al (2002). Furthermore, applying wavelet analysis to the associated time series of the first spatial mode of variability indicates that the main periodicity of this mode is centred on 8 years and spans from 5 to 10 years (not shown).…”

“…However, to validate and improve our predictions, observational data are crucial. In this sense, our variability analysis of internal SIV and SIT variability might help the development of an optimal sampling strategy, taking into account the selection of well-placed sampling locations for monitoring the SIT and, therefore, the pan-Arctic SIV that are not as well documented as the sea ice extent and area (Ponsoni et al, 2020).…”

Cryosphere Sciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

“…Regarding cross-disciplinary integration, many cryosphere studies have a history of international collaborations not only across traditional disciplines but also across spatial and/or temporal scales (I-integrated). For example, the recent Year of Polar Prediction (YOPP) and the European Union Horizon 2020 project APPLICATE brought together the efforts from several international partners to, among other goals, improve the prediction capability of polar regions including their cryosphere components (sea ice and snow), from weather to climate scales (Jung et al, 2016), by making better use of observational (in situ and satellite) data sets and model outputs (C-coordinated) (Ponsoni et al, 2020). Furthermore, the YOPP-endorsed activities are making data openly accessible (O-open).…”

The potential of numerical prediction systems to support the design of Arctic observing systems: Insights from the APPLICATE and YOPP projects

“…This finding suggests that a limited number of well-placed in situ monitoring stations could be sufficient to estimate the large-scale changes in sea-ice thickness and volume on interannual time-scales (Ponsoni et al, 2020), especially if the in situ data records are complemented and cross-validated by large-scale satellite retrievals, for which large uncertainties remain (Zygmuntowska et al, 2014). A proposed list of 10 sampling sites is given in Ponsoni et al (2020) and is illustrated in Figure 1 (middle panel). According to their study, sea-ice thickness measurements sampled from as little as six well-placed stations are sufficient to reconstruct most (80%) of the actual sea-ice volume variability.…”

“…An analysis of the simulated sea-ice thicknesses in state-of-the-art climate models participating in the High Resolution Model Intercomparison Project (High-ResMIP: Haarsma et al, 2016) of CMIP6 has revealed that the variability of this field exhibits significant spatial auto-correlation if grid-cell averages and monthly means are considered. This finding suggests that a limited number of well-placed in situ monitoring stations could be sufficient to estimate the large-scale changes in sea-ice thickness and volume on interannual time-scales (Ponsoni et al, 2020), especially if the in situ data records are complemented and cross-validated by large-scale satellite retrievals, for which large uncertainties remain (Zygmuntowska et al, 2014). A proposed list of 10 sampling sites is given in Ponsoni et al (2020) and is illustrated in Figure 1 (middle panel).…”