Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations
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 time scale. To do so, we made use of 6 datasets, from 3 different atmosphere-ocean general circulation models, with 2 different ho...
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ftcopernicus:oai:publications.copernicus.org:tcd81369 2023-05-15T14:52:26+02:00 Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations Ponsoni, Leandro Massonnet, François Docquier, David Achter, Guillian Fichefet, Thierry 2019-11-22 application/pdf https://doi.org/10.5194/tc-2019-257 https://tc.copernicus.org/preprints/tc-2019-257/ eng eng doi:10.5194/tc-2019-257 https://tc.copernicus.org/preprints/tc-2019-257/ eISSN: 1994-0424 Text 2019 ftcopernicus https://doi.org/10.5194/tc-2019-257 2020-07-20T16:22:33Z 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 time scale. To do so, we made use of 6 datasets, from 3 different atmosphere-ocean general circulation models, with 2 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. Feeding the statistical model with predictor data from 4 well-placed locations is enough for reconstructing about 70 % of the SIV anomaly variance. An improved model horizontal resolution allows a better trained statistical model so that the reconstructed values approach better to the original SIV anomaly. On the other hand, if we look at the interannual variability, the predictors provided by numerical models with lower horizontal resolution perform better for reconstructing the original SIV variability. As per 6 well-placed locations, the statistical predictability does not substantially improve by adding new sites. As suggested by the results, the 4 first best locations are placed at the transition Chukchi Sea–Central Arctic–Beaufort Sea (158.0° W, 79.5° N), near the North Pole (40° E, 88.5° N), at the transition Central Arctic–Laptev Sea (107° E, 81.5° N), and offshore the Canadian Archipelago (109.0° W, 82.5° N), in this respective order. 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. Text Arctic Beaufort Sea Canadian Archipelago Chukchi Chukchi Sea laptev Laptev Sea North Pole Sea ice Copernicus Publications: E-Journals Arctic Chukchi Sea Laptev Sea North Pole |
institution |
Open Polar |
collection |
Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
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 time scale. To do so, we made use of 6 datasets, from 3 different atmosphere-ocean general circulation models, with 2 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. Feeding the statistical model with predictor data from 4 well-placed locations is enough for reconstructing about 70 % of the SIV anomaly variance. An improved model horizontal resolution allows a better trained statistical model so that the reconstructed values approach better to the original SIV anomaly. On the other hand, if we look at the interannual variability, the predictors provided by numerical models with lower horizontal resolution perform better for reconstructing the original SIV variability. As per 6 well-placed locations, the statistical predictability does not substantially improve by adding new sites. As suggested by the results, the 4 first best locations are placed at the transition Chukchi Sea–Central Arctic–Beaufort Sea (158.0° W, 79.5° N), near the North Pole (40° E, 88.5° N), at the transition Central Arctic–Laptev Sea (107° E, 81.5° N), and offshore the Canadian Archipelago (109.0° W, 82.5° N), in this respective order. 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. |
format |
Text |
author |
Ponsoni, Leandro Massonnet, François Docquier, David Achter, Guillian Fichefet, Thierry |
spellingShingle |
Ponsoni, Leandro Massonnet, François Docquier, David Achter, Guillian Fichefet, Thierry Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations |
author_facet |
Ponsoni, Leandro Massonnet, François Docquier, David Achter, Guillian Fichefet, Thierry |
author_sort |
Ponsoni, Leandro |
title |
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations |
title_short |
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations |
title_full |
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations |
title_fullStr |
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations |
title_full_unstemmed |
Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations |
title_sort |
statistical predictability of the arctic sea ice volume anomaly: identifying predictors and optimal sampling locations |
publishDate |
2019 |
url |
https://doi.org/10.5194/tc-2019-257 https://tc.copernicus.org/preprints/tc-2019-257/ |
geographic |
Arctic Chukchi Sea Laptev Sea North Pole |
geographic_facet |
Arctic Chukchi Sea Laptev Sea North Pole |
genre |
Arctic Beaufort Sea Canadian Archipelago Chukchi Chukchi Sea laptev Laptev Sea North Pole Sea ice |
genre_facet |
Arctic Beaufort Sea Canadian Archipelago Chukchi Chukchi Sea laptev Laptev Sea North Pole Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2019-257 https://tc.copernicus.org/preprints/tc-2019-257/ |
op_doi |
https://doi.org/10.5194/tc-2019-257 |
_version_ |
1766323680162349056 |