Examining the Impact of Tropical Cyclones on Air‐Sea CO<sub>2</sub> Exchanges in the Bay of Bengal Based on Satellite Data and In Situ Observations
Bibliographic record
Abstract
Abstract The impact of tropical cyclones (TCs) on the CO2 partial pressure at the sea surface (pCO2sea) and air‐sea CO2 flux (FCO2) in the Bay of Bengal (BoB) was quantified based on satellite data and in situ observations between November 2013 and January 2017. The in situ observations were made at the BoB Ocean Acidification mooring buoy. A weak time‐mean net source of 55.78 ± 11.16 mmol CO2 m−2 year−1 at the BoB Ocean Acidification site was estimated during this period. A wide range in increases of pCO2sea (1.0–14.8 μatm) induced by TCs occurred in postmonsoon (October–December), and large decreases of pCO2sea (−14.0 μatm) occurred in premonsoon (March–May). Large vertical differences in the ratio of dissolved inorganic carbon (DIC) to total alkalinity (TA) in the upper layer (ΔDIC/TA) were responsible for increasing pCO2sea in postmonsoon. Relatively small values of ΔDIC/TA were responsible for decreasing pCO2sea in premonsoon. Five TCs (Hudhud, Five, Kyant, Vardah, and Roanu) were considered. Hudhud significantly enhanced CO2 efflux (18.49 ± 3.70 mmol CO2/m2) in oversaturated areas due to the wind effect during the storm and wind‐pump effects after the storm. Vardah insignificantly changed FCO2 (1.22 ± 0.24 mmol CO2/m2) in undersaturated areas because of the counteraction of these two effects. Roanu significantly enhanced CO2 efflux (19.08 ± 3.82 mmol CO2/m2) in highly oversaturated conditions (ΔpCO2 > 20 μatm) since the wind effect greatly exceeded the wind‐pump effects. These five TCs were estimated to account for 55 ± 23% of the annual‐mean CO2 annual efflux, suggesting that TCs have significant impacts on the carbon cycle in the BoB.
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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 | 0.000 | 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 | 0.000 |
| 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 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".