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 timescale. To do so, we made use of six datasets, from three different atmosphere–ocean general circulation models, with two diffe...
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ftdoajarticles:oai:doaj.org/article:48ea35016ce84bb39d5ca2171c098859 2023-05-15T14:53:06+02:00 Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations L. Ponsoni F. Massonnet D. Docquier G. Van Achter T. Fichefet 2020-07-01T00:00:00Z https://doi.org/10.5194/tc-14-2409-2020 https://doaj.org/article/48ea35016ce84bb39d5ca2171c098859 EN eng Copernicus Publications https://tc.copernicus.org/articles/14/2409/2020/tc-14-2409-2020.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-14-2409-2020 1994-0416 1994-0424 https://doaj.org/article/48ea35016ce84bb39d5ca2171c098859 The Cryosphere, Vol 14, Pp 2409-2428 (2020) Environmental sciences GE1-350 Geology QE1-996.5 article 2020 ftdoajarticles https://doi.org/10.5194/tc-14-2409-2020 2022-12-31T13:36:58Z 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 ... Article in Journal/Newspaper Arctic Beaufort Sea Canadian Archipelago Chukchi Chukchi Sea laptev Laptev Sea North Pole Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles Arctic Laptev Sea Chukchi Sea North Pole The Cryosphere 14 7 2409 2428 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 L. Ponsoni F. Massonnet D. Docquier G. Van Achter T. Fichefet Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
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 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 ... |
format |
Article in Journal/Newspaper |
author |
L. Ponsoni F. Massonnet D. Docquier G. Van Achter T. Fichefet |
author_facet |
L. Ponsoni F. Massonnet D. Docquier G. Van Achter T. Fichefet |
author_sort |
L. Ponsoni |
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 |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-14-2409-2020 https://doaj.org/article/48ea35016ce84bb39d5ca2171c098859 |
geographic |
Arctic Laptev Sea Chukchi Sea North Pole |
geographic_facet |
Arctic Laptev Sea Chukchi Sea North Pole |
genre |
Arctic Beaufort Sea Canadian Archipelago Chukchi Chukchi Sea laptev Laptev Sea North Pole Sea ice The Cryosphere |
genre_facet |
Arctic Beaufort Sea Canadian Archipelago Chukchi Chukchi Sea laptev Laptev Sea North Pole Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 14, Pp 2409-2428 (2020) |
op_relation |
https://tc.copernicus.org/articles/14/2409/2020/tc-14-2409-2020.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-14-2409-2020 1994-0416 1994-0424 https://doaj.org/article/48ea35016ce84bb39d5ca2171c098859 |
op_doi |
https://doi.org/10.5194/tc-14-2409-2020 |
container_title |
The Cryosphere |
container_volume |
14 |
container_issue |
7 |
container_start_page |
2409 |
op_container_end_page |
2428 |
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1766324523074846720 |