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

Leandro Ponsoni,François Massonnet,Thierry Fichefet,Guillian Van Achter,David Docquier

doi:10.5194/tc-14-2409-2020

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.

Highlights

To fully address the three overall directions and the hypothesis described above, this study is guided by the following open questions. (i) What is the performance of different pan-Arctic predictors for predicting pan-Arctic sea ice volume (SIV) anomalies? (ii) What are the best locations for in situ sampling of predictor variables to optimize the statistical predictability of SIV anomalies in terms of reproducibility and variability? (iii) How many optimal sites are needed for explaining a substantial amount (e.g. 70 % – an arbitrarily chosen threshold) of the original SIV anomaly variance? (iv) Are the results model dependent, in particular, and/or are they sensitive to horizontal resolution?
As per 3 leading months, from December to February, the predictive capability substantially improves by 43 % (Sc = 0.57 ×103 km3), 59 % (Sc = 0.41 ×103 km3), and 77 % (Sc = 0.23 ×103 km3), respectively (Fig. 4a)
sea ice concentration (SIC) (Fig. 4e), sea surface temperature (SST) (Fig. 4f), and Drift (Fig. 4g) have poorer performance compared to sea ice thickness (SIT) but similar behaviour, with the score slightly improving over time until one leading month

Summary

Introduction

The ongoing melting of the Arctic sea ice observed in the last decades (e.g. Chapman and Walsh, 1993; Parkinson et al, 1999; Rothrock et al, 1999; Parkinson and Cavalieri, 2002; Zhang and Walsh, 2006; Stroeve et al, 2007, 2012; Notz and Stroeve, 2016; Petty et al, 2018), associated with the respective reductions in total sea ice area (SIA) and volume (SIV), had significant impact on climate processes at global and regional scales. Chapman and Walsh, 1993; Parkinson et al, 1999; Rothrock et al, 1999; Parkinson and Cavalieri, 2002; Zhang and Walsh, 2006; Stroeve et al, 2007, 2012; Notz and Stroeve, 2016; Petty et al, 2018), associated with the respective reductions in total sea ice area (SIA) and volume (SIV), had significant impact on climate processes at global and regional scales. The sea ice depletion is reported to impact aspects of the weather at low- and mid-latitude regions, by means of both oceanographic (Drijfhout, 2015; Sévellec et al, 2017) and atmospheric teleconnections (Serreze et al, 2007; Overland and Wang, 2010), including the increased occurrence of extreme events Other pressing local issues are showing important implications for the Arctic countries such as the opening of new ship routes (Lindstad et al, 2016), the development of the tourism industry (Handorf, 2011), and mineral resource extraction (Gleick, 1989)

Results

Discussion

Conclusion