Statistical predictability of the Arctic sea ice volume anomaly:identifying predictors and optimal sampling locations
This work evaluates the statistical predictabilityof the Arctic sea ice volume (SIV) anomaly – here definedas the detrended and deseasonalized SIV – on the interan-nual timescale. To do so, we made use of six datasets, fromthree different atmosphere–ocean general circulation models,with two differen...
| Published in: | The Cryosphere |
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| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://zenodo.org/record/3977951 https://doi.org/10.5194/tc-14-2409-2020 |
| Summary: | This work evaluates the statistical predictabilityof the Arctic sea ice volume (SIV) anomaly – here definedas the detrended and deseasonalized SIV – on the interan-nual timescale. To do so, we made use of six datasets, fromthree different atmosphere–ocean general circulation models,with two different horizontal grid resolutions each. Basedon these datasets, we have developed a statistical empiricalmodel which in turn was used to test the performance of dif-ferent predictor variables, as well as to identify optimal lo-cations from where the SIV anomaly could be better recon-structed and/or predicted. We tested the hypothesis that anideal sampling strategy characterized by only a few optimalsampling locations can provide in situ data for statisticallyreproducing and/or predicting the SIV interannual variabil-ity. The results showed that, apart from the SIV itself, thesea ice thickness is the best predictor variable, although totalsea ice area, sea ice concentration, sea surface temperature,and sea ice drift can also contribute to improving the pre-diction skill. The prediction skill can be enhanced furtherby combining several predictors into the statistical model.Applying the statistical model with predictor data from fourwell-placed locations is sufficient for reconstructing about70 % of the SIV anomaly variance. As suggested by theresults, the four first best locations are placed at the tran-sition Chukchi Sea–central Arctic–Beaufort Sea (79.5◦N,158.0◦W), near the North Pole (88.5◦N, 40.0◦E), at the tran-sition central Arctic–Laptev Sea (81.5◦N, 107.0◦E), and off-shore the Canadian Archipelago (82.5◦N, 109.0◦W), in thisrespective order. Adding further to six well-placed locations,which explain about 80 % of the SIV anomaly variance, thestatistical predictability does not substantially improve tak-ing into account that 10 locations explain about 84 % of thatvariance. An improved model horizontal resolution allows abetter trained statistical model so that the reconstructed val-ues better approach the original SIV ... |
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