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Record W4212987229 · doi:10.5194/bg-19-907-2022

Evaluating the Arabian Sea as a regional source of atmospheric CO <sub>2</sub> : seasonal variability and drivers

2022· article· en· W4212987229 on OpenAlexfundno aff
Alain de Verneil, Zouhair Lachkar, K. Shafer Smith, Marina Lévy

Bibliographic record

VenueBiogeosciences · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
FundersTamkeenYork UniversityNew York University Abu Dhabi
KeywordsUpwellingEnvironmental scienceAlkalinityOceanographyClimatologyCarbon cycleMonsoonAtmospheric sciencesBathythermographGeologyChemistryEcosystemBiology

Abstract

fetched live from OpenAlex

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in situ data have been collected, leaving data-based methods to calculate air–sea exchange with fewer and potentially out-of-date data.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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.237
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2022
Admission routes1
Has abstractyes

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