Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model
| dc.contributor.author | Moreno Chamarro, Eduardo |
| dc.contributor.author | Ortega Montilla, Pablo |
| dc.contributor.author | Massonnet, François |
| dc.contributor.other | Barcelona Supercomputing Center |
| dc.date.accessioned | 2020-10-06T16:06:21Z |
| dc.date.available | 2020-10-06T16:06:21Z |
| dc.date.issued | 2020-10-05 |
| dc.description.abstract | This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K-means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC variability is obtained from increasing the number of sea ice thickness categories beyond the current standard with five categories in NEMO3.6–LIM3. |
| dc.description.peerreviewed | Peer Reviewed |
| dc.description.sponsorship | This research has been supported by the European Commission's Horizon 2020 APPLICATE project (grant no. GA 727862) and the European Commission's Horizon 2020 PRIMAVERA project (grant no. GA 641727). |
| dc.description.version | Postprint (published version) |
| dc.format.extent | 15 p. |
| dc.identifier.citation | Moreno Chamarro, E.; Ortega Montilla, P.; Massonnet, F. Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model. "Geoscientific Model Development (GMD)", 5 Octubre 2020, vol. 13, núm. 10, p. 4773-4787. |
| dc.identifier.doi | 10.5194/gmd-13-4773-2020 |
| dc.identifier.issn | 1991959X |
| dc.identifier.uri | https://hdl.handle.net/2117/329910 |
| dc.language.iso | eng |
| dc.publisher | Copernicus Publications |
| dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/727862/EU/Advanced Prediction in Polar regions and beyond: Modelling, observing system design and LInkages associated with ArctiC ClimATE change/APPLICATE |
| dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/641727/EU/PRocess-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment/PRIMAVERA |
| dc.relation.publisherversion | https://gmd.copernicus.org/articles/13/4773/2020/ |
| dc.relation.uri | https://gmd.copernicus.org/articles/13/4773/2020/gmd-13-4773-2020-supplement.pdf |
| dc.rights.access | Open Access |
| dc.rights.licensename | Attribution 3.0 Spain |
| dc.rights.licensename | Attribution 4.0 International (CC BY 4.0) |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ |
| dc.subject | Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia |
| dc.subject.lcsh | Computer simulation |
| dc.subject.lcsh | Sea ice--Arctic regions |
| dc.subject.lcsh | Artificial satellites in earth sciences |
| dc.subject.lemac | Simulació per ordinador |
| dc.subject.lemac | Satèl·lits artificials en ciències de la terra |
| dc.subject.other | Sea ice model |
| dc.subject.other | NEMO3.6–LIM3 |
| dc.subject.other | Ice thickness distribution |
| dc.subject.other | Climate models |
| dc.subject.other | Satellite observations |
| dc.subject.other | Simulations |
| dc.title | Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model |
| dc.type | Article |
| dspace.entity.type | Publication |
| local.citation.endingPage | 4787 |
| local.citation.number | 10 |
| local.citation.publicationName | Geoscientific Model Development (GMD) |
| local.citation.startingPage | 4773 |
| local.citation.volume | 13 |
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