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Enregistrement W7076144767 · doi:10.26092/elib/4387

Transparent Exopolymer Particles in the Surface Arctic Ocean by Ocean Biogeochemistry Modeling

2025· article· en· W7076144767 sur OpenAlexaboutno aff

Notice bibliographique

RevueMedia (https://www.suub.uni-bremen.de/) · 2025
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueTheoretical and Computational Physics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhytoplanktonExopolymerBiogeochemistryArcticMarine ecosystemEcosystemSea icePrimary producersCarbon cycleBiogeochemical cycle

Résumé

récupéré en direct d'OpenAlex

In light of the Arctic Amplification of global warming, it is fundamental to enhance our comprehension of ecosystem dynamics in the Arctic Ocean. This will facilitate predictions about how alterations in phytoplankton and broader ecological processes may evolve under future warming scenarios. The primary production of the Arctic Ocean is principally based on phytoplankton building up organic carbon. The growth and distribution of phytoplankton are strongly shaped by the seasonality of polar night and day, sea ice cover and nutrient availability. Focusing on one essential component of the organic carbon cycle, I simulate transparent exopolymer particles (TEP) in the upper ocean. As in situ observations are scarce, modeling can extend our knowledge on their spatial and temporal occurrence patterns and trends. Additionally, these particles have recently been reported to act themselves as biogenic aerosol precursors, or as precursor for other organic compounds. These may be an important source of primary marine organic aerosols in the Arctic atmosphere. In the first part of my dissertation, I present a coupled ocean sea-ice biogeochemistry model where I integrate dissolved acidic polysaccharides (PCHO) and TEP. Phytoplankton exude organic carbon into the surrounding ocean, particularly under nutrient-depleted conditions. PCHO are defined as one part of the exuded organic carbon, which can then aggregate to form larger particles, such as TEP. There is a strong seasonal cycle of TEP in the upper ocean, as the occurrence of TEP follows the phytoplankton blooms both temporally and spatially. The simulation provides an initial estimate of TEP concentration with the highest levels reaching 200-400 µg C/L in the upper ocean (0-30 m depth) simulated in the Fram Strait and on the continental shelves under conditions of nutrient depletion for June to August. In the central basins, TEP concentration range from 10 to 50 µg C/L. When considered in the context of observation datasets, this simulation performs well in terms of Total Chlorophyll a (TChla) and particulate organic carbon. There is reasonable agreement for TEP compared with the few in situ datasets available. It would be recommended to gather more observational data on TEP in conjunction with data on TChla and other relevant biogeochemical parameters. This would allow deeper insights into the ecosystem dynamics and time series of TEP in the Arctic Ocean. As a consequence of the simulation analysis, the regions of interest for in situ measurements should be the marginal ice zones, and especially the high Arctic due to the seasonal variations of sea ice and its overall declining trend. Moreover, the simulation for the period 1990 to 2019 indicates a significant negative trend of TEP concentration in summer in regions affected by the inflow of Atlantic water, such as the eastern Fram Strait, the Barents Sea, and parts of the Eurasian Basin. Regions of the Arctic Ocean influenced by Pacific water exhibit a significant positive trend in TEP concentration, including the Amerasian Basin, the Canadian Arctic Archipelago and the Kara Sea. In the second part of my thesis, I re-analyze the simulated environmental variables in order to ascertain their role as TEP drivers in three exemplary regions. The analysis demonstrates that TChla is an important predictor for TEP occurrence in general, but also physical factors such as photosynthetically active radiation and sea ice concentration exert a significant influence on the distribution of TEP in the Laptev Sea, while nutrient availability plays an important role in shaping the time series observed in the Fram Strait. In the third part, I asses the long-term trend of TEP in the Arctic Ocean following a high-emission scenario proposed by the Intergovernmental Panel on Climate Change. The Arctic-wide TEP concentration is projected to increase significantly from 80 to 105 µg C/L in the upper ocean until 2100. This increase is driven mostly by the retreat of the sea ice cover, which triggers increases in phytoplankton carbon concentration and a subsequent increase in phytoplankton nutrient limitations. However, there are regional differences as, e.g., in the western Fram Strait, the trends seem to be shaped by the sea ice retreat and light availability, whereas in the eastern Fram Strait, nutrient availability and remineralization of TEP are important factors. The final part of my dissertation revisits the role of biogenic aerosol precursors in the upper ocean. I briefly discuss one application of the knowledge gained about the organic carbon cycle in the upper Arctic Ocean. By serving as an ocean-atmosphere boundary condition, the simulation enables the determination of primary marine organic aerosol emissions in an aerosol-climate model. I conclude with a discussion on two perspectives for future research, namely the simulation of organic matter enrichment at the ocean surface and the distribution of TEP in the water column, especially with respect to sinking and degradation processes.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,097
Score d'incertitude au seuil0,193

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,242
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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