Evaluating the Arabian Sea as a regional source of atmospheric CO <sub>2</sub> : seasonal variability and drivers
Notice bibliographique
Résumé
Abstract. The Arabian Sea (AS) was confirmed to be a net emitter of CO2 to the atmosphere during the international Joint Global Ocean Flux Study program of the 1990s, but since then little in situ data has been collected, leaving data-based methods to calculate air-sea exchange with fewer data and potentially out-of-date. Additionally, coarse-resolution models underestimate CO2 flux compared to other approaches. To address these shortcomings, we employ a high-resolution (1/24°) regional model to quantify the seasonal cycle of air-sea CO2 exchange in the AS by focusing on two main contributing factors, pCO2 and winds. We compare the model to available in situ pCO2 data and find that uncertainties in dissolved inorganic carbon (DIC) and total alkalinity (TA) lead to the greatest discrepancies. Nevertheless, the model is more successful than neural network approaches in replicating the large variability in summertime pCO2 because it captures the AS's intense monsoon dynamics. In the seasonal pCO2 cycle, temperature plays the major role in determining surface pCO2, except where DIC delivery is important in summer upwelling areas. Since seasonal temperature forcing is relatively uniform, pCO2 differences between the AS's sub-regions are mostly caused by geographic DIC gradients. We find that primary productivity during both summer and winter monsoon blooms, but also generally, is insufficient to off-set the physical delivery of DIC to the surface, resulting in limited biological control of CO2 release. The most intense air-sea CO2 exchange occurs during the summer monsoon where outgassing rates reach ~6 molCm−2 yr−1 in the upwelling regions of Oman and Somalia, but the entire AS contributes CO2 to the atmosphere. Despite a regional spring maximum of pCO2 driven by surface heating, CO2 exchange rates peak in summer due to winds, which account for ~90 % of the summer CO2 flux variability versus 6 % for pCO2 in a Reynolds decomposition. In comparison with other estimates, we find that the AS emits ~160 TgCyr−1, slightly higher than previously reported. Altogether, there is 2x variability in annual flux magnitude across methodologies considered. Future attempts to reduce the variability in estimates will likely require more in situ carbon data. Since summer monsoon winds are critical in determining flux both directly and indirectly through temperature, DIC, TA, mixing, and primary production effects on pCO2, studies looking to predict CO2 emissions in the AS with ongoing climate change will need to correctly resolve their timing, strength, and upwelling dynamics.
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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 | 0,002 | 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 | 0,000 |
| 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 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 ».