Evaluating the Arabian Sea as a regional source of atmospheric CO <sub>2</sub> : seasonal variability and drivers
Bibliographic record
Abstract
The Arabian Sea (AS) was confirmed to be a net emitter of CO 2 to the atmosphere during the international Joint Global Ocean Flux Study program of the 1990s, but since then few in situ data have been collected, leaving data-based methods to calculate air–sea exchange with fewer and potentially out-of-date data. Additionally, coarse-resolution models underestimate CO 2 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 CO 2 exchange in the AS by focusing on two main contributing factors, p CO 2 and winds. We compare the model to available in situ p CO 2 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 p CO 2 because it captures the AS's intense monsoon dynamics. In the seasonal p CO 2 cycle, temperature plays the major role in determining surface p CO 2 except where DIC delivery is important in summer upwelling areas. Since seasonal temperature forcing is relatively uniform, p CO 2 differences between the AS's subregions 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 offset the physical delivery of DIC to the surface, resulting in limited biological control of CO 2 release. The most intense air–sea CO 2 exchange occurs during the summer monsoon when outgassing rates reach ∼ 6 molCm-2yr-1 in the upwelling regions of Oman and Somalia, but the entire AS contributes CO 2 to the atmosphere. Despite a regional spring maximum of p CO 2 driven by surface heating, CO 2 exchange rates peak in summer due to winds, which account for ∼ 90 % of the summer CO 2 flux variability vs. 6 % for p CO 2 . In comparison with other estimates, we find that the AS emits ∼ 160 Tg C yr −1 , slightly higher than previously reported. Altogether, there is 2× 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 p CO 2 , studies looking to predict CO 2 emissions in the AS with ongoing climate change will need to correctly resolve their timing, strength, and upwelling dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".