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 uptake capacity and impacts on ocean acidification with the changing sea-ice scape. In particular, I study the influence on air-ice-sea fluxes of carbon with two major updates to commonly-used carbon cycle models I have included. One, incorporation of sea ice algae to the ecosystem, and two, modification of the sea-ice carbon pump, to transport brineassociated Dissolved Inorganic Carbon (DIC) and Total Alkalinity (TA) to the depth of the bottom of the mixed layer (as opposed to releasing it in the surface model layer). I developed the ice algal ecosystem model by adding a sympagic (ice-associated) ecosystem into a 1D coupled sea ice-ocean model. The 1D model was applied to Resolute Passage in the Canadian Arctic Archipelago and evaluated with observations from a field campaign during the spring of 2010. I then implemented an inorganic carbon system into the model. The carbon system includes effects on both DIC and TA due to the coupled ice-ocean ecosystem, ikaite precipitation and dissolution, ice-air and air-sea carbon exchange, and ice-sea DIC and TA exchange through a formulation for brine rejection to depth and freshwater dilution associated with ice growth and melt. The 1D simulated ecosystem was found to compare reasonably well with observations in terms of bloom onset and seasonal progression for both the sympagic and pelagic algae. In addition, the inorganic carbon system showed reasonable agreement between observations of upper water column DIC and TA content. The simulated average ocean carbon uptake during the period of open water was 10.2 mmol C m−2 day−1 ( 11 g C m−2 over the entire open-water season). Using the developments from the 1D model, a 3D biogeochemical model of the Arctic Ocean incorporating both sea ice and the water column was developed and tested, with a focus on the pan-Arctic oceanic uptake of carbon in the recent era of Arctic sea ice decline (1980 – 2015). The model suggests the total uptake of carbon for the Arctic Ocean (north of 66.5 N) increases from 110 Tg C yr−1 in the early eighties (1980 – 1985) to 140 Tg C yr−1 for 2010 – 2015, an increase of 30%. The rise in SST accounts for 10% of the increase in simulated pan-Arctic sea surface pCO2. A regional analysis indicated large variability between regions, with the Laptev Sea exhibiting low sea surface pH relative to the pan- Arctic domain mean and seasonal undersaturation of arag 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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».