Journal article Open Access

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

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

### DataCite XML Export

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<identifier identifierType="URL">https://zenodo.org/record/3566808</identifier>
<creators>
<creator>
<creatorName>Ponsoni, Leandro</creatorName>
<givenName>Leandro</givenName>
<familyName>Ponsoni</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2218-271X</nameIdentifier>
<affiliation>Université catholique de Louvain</affiliation>
</creator>
<creator>
<creatorName>Massonnet, François</creatorName>
<givenName>François</givenName>
<familyName>Massonnet</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4697-5781</nameIdentifier>
<affiliation>Université catholique de Louvain</affiliation>
</creator>
<creator>
<creatorName>Docquier, David</creatorName>
<givenName>David</givenName>
<familyName>Docquier</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5720-4253</nameIdentifier>
<affiliation>Université catholique de Louvain</affiliation>
</creator>
<creator>
<creatorName>Van Achter, Guillian</creatorName>
<givenName>Guillian</givenName>
<familyName>Van Achter</familyName>
<affiliation>Université catholique de Louvain</affiliation>
</creator>
<creator>
<creatorName>Fichefet, Thierry</creatorName>
<givenName>Thierry</givenName>
<familyName>Fichefet</familyName>
<affiliation>Université catholique de Louvain</affiliation>
</creator>
</creators>
<titles>
<title>Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2019</publicationYear>
<dates>
<date dateType="Issued">2019-11-22</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="Text">Journal article</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3566808</alternateIdentifier>
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<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="Cites" resourceTypeGeneral="Dataset">10.22033/ESGF/CMIP6.2461</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="Cites" resourceTypeGeneral="Dataset">10.22033/ESGF/CMIP6.2463</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="Cites" resourceTypeGeneral="Dataset">10.22033/ESGF/CMIP6.1901</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="Cites" resourceTypeGeneral="Dataset">10.22033/ESGF/CMIP6.1902</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="Cites" resourceTypeGeneral="Dataset">10.22033/ESGF/CMIP6.1202</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="Cites" resourceTypeGeneral="Dataset">10.22033/ESGF/CMIP6.1209</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsNewVersionOf" resourceTypeGeneral="Text">10.5194/tc-2019</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.5194/tc-2019-257</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/applicate</relatedIdentifier>
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<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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<descriptions>
<description descriptionType="Abstract">&lt;p&gt;This work evaluates the statistical predictability of the Arctic sea ice volume (SIV) anomaly &amp;ndash; here defined as the detrended and deseasonalized SIV &amp;ndash; 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 when 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&amp;ndash;Central Arctic&amp;ndash;Beaufort Sea (158.0◦W, 79.5◦N), near the North Pole (40◦ E, 88.5◦ N), at the transition Central Arctic&amp;ndash;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.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/641727/">641727</awardNumber>
<awardTitle>PRocess-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment</awardTitle>
</fundingReference>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/727862/">727862</awardNumber>
<awardTitle>Advanced Prediction in Polar regions and beyond: Modelling, observing system design and LInkages associated with ArctiC ClimATE change</awardTitle>
</fundingReference>
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