Modelling carbon exchange in the air, sea, and ice of the Arctic Ocean
Notice bibliographique
Résumé
The purpose of this study is to investigate the evolution of the Arctic Ocean’s carbon \nuptake capacity and impacts on ocean acidification with the changing sea-ice scape. In \nparticular, I study the influence on air-ice-sea fluxes of carbon with two major updates to \ncommonly-used carbon cycle models I have included. One, incorporation of sea ice algae \nto the ecosystem, and two, modification of the sea-ice carbon pump, to transport brineassociated \nDissolved Inorganic Carbon (DIC) and Total Alkalinity (TA) to the depth of \nthe bottom of the mixed layer (as opposed to releasing it in the surface model layer). I \ndeveloped the ice algal ecosystem model by adding a sympagic (ice-associated) ecosystem \ninto a 1D coupled sea ice-ocean model. The 1D model was applied to Resolute Passage in \nthe Canadian Arctic Archipelago and evaluated with observations from a field campaign \nduring the spring of 2010. I then implemented an inorganic carbon system into the model. \nThe carbon system includes effects on both DIC and TA due to the coupled ice-ocean \necosystem, ikaite precipitation and dissolution, ice-air and air-sea carbon exchange, and \nice-sea DIC and TA exchange through a formulation for brine rejection to depth and \nfreshwater dilution associated with ice growth and melt. The 1D simulated ecosystem was \nfound to compare reasonably well with observations in terms of bloom onset and seasonal \nprogression for both the sympagic and pelagic algae. In addition, the inorganic carbon \nsystem showed reasonable agreement between observations of upper water column DIC \nand TA content. The simulated average ocean carbon uptake during the period of open \nwater was 10.2 mmol C m−2 day−1 ( 11 g C m−2 over the entire open-water season). \nUsing the developments from the 1D model, a 3D biogeochemical model of the Arctic Ocean \nincorporating both sea ice and the water column was developed and tested, with a focus \non the pan-Arctic oceanic uptake of carbon in the recent era of Arctic sea ice decline (1980 \n– 2015). The model suggests the total uptake of carbon for the Arctic Ocean (north of \n66.5 N) increases from 110 Tg C yr−1 in the early eighties (1980 – 1985) to 140 Tg C yr−1 \nfor 2010 – 2015, an increase of 30%. The rise in SST accounts for 10% of the increase \nin simulated pan-Arctic sea surface pCO2. A regional analysis indicated large variability \nbetween regions, with the Laptev Sea exhibiting low sea surface pH relative to the pan- \nArctic domain mean and seasonal undersaturation of \narag by the end of the standard run. Two sensitivity studies were performed to assess the effects of sea-ice algae and the sea-ice carbon pump in the pan-Arctic, with a focus on sea surface inorganic carbon properties. Excluding the sea ice-carbon-pump showed a marked decrease in seasonal variability of sea-surface DIC and TA averaged over the Arctic Ocean compared to the standard run, but only a small change in the net total carbon uptake (of 1% by the end of the no icecarbon-pump run). Neglecting the sea ice algae, on the other hand, exhibits only a small change in sea-surface DIC and TA averaged over the pan-Arctic Ocean, but a cumulative effect on the net total carbon uptake of the Arctic Ocean (reaching 5% less than that of the standard run by the end of the no-ice-algae run).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».