Evaluating the Arabian Sea as a regional source of atmospheric CO <sub>2</sub> : seasonal variability and drivers
Bibliographic record
Abstract
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situ data has been collected, leaving data-based methods to calculate air-sea exchange with fewer data and potentially out-of-date.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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".