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Record W4414612817 · doi:10.5194/bg-22-5103-2025

From small-scale variability to mesoscale stability in surface ocean pH: implications for air–sea CO <sub>2</sub> equilibration

2025· article· en· W4414612817 on OpenAlexaff
Louise Delaigue, Gert‐Jan Reichart, Li Qiu, Eric P. Achterberg, Yasmina Ourradi, Chris Galley, André Mutzberg, Matthew Humphreys

Bibliographic record

VenueBiogeosciences · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of OttawaMemorial University of Newfoundland
FundersBundesministerium für Bildung und ForschungSorbonne UniversitéUniversiteit Utrecht
KeywordsSeawaterAbiotic componentMesoscale meteorologyCarbonateOptodeSea surface temperatureOcean acidificationSurface water

Abstract

fetched live from OpenAlex

Abstract. One important aspect of understanding ocean acidification is the nature and drivers of pH variability in surface waters on smaller spatial (i.e. areas up to 100 km2) and temporal (i.e. days) scales. However, there has been a lack of high-quality pH data at sufficiently high resolution. Here, we describe a simple optical system for continuous high-resolution surface seawater pH measurements. The system includes a PyroScience pH optode placed in a flow-through cell directly connected to the underway supply of a ship through which near-surface seawater is constantly pumped. Seawater pH is measured at a rate of 2 to 4 measurements min−1 and is cross-calibrated using discrete carbonate system observations (total alkalinity, dissolved inorganic carbon, and nutrients). This setup was used during two research cruises in different oceanographic conditions: the North Atlantic Ocean (December 2020–January 2021) and the South Pacific Ocean (February–April 2022). By leveraging this novel high-frequency measurement approach, our findings reveal fine-scale fluctuations in surface seawater pH across the North Atlantic and South Pacific oceans. While temperature is a significant abiotic factor driving these variations, it does not account for all observed changes. Instead, our results highlight the interplay between temperature, biological activity, and waters with distinct temperature–salinity properties and their impact on pH. Notably, the variability differed between the two regions, suggesting differences in the dominant factors influencing pH. In the South Pacific, biological processes appeared to be mostly responsible for pH variability, while in the North Atlantic, additional abiotic and biotic factors complicated the correlation between expected and observed pH changes. While our findings indicate that broader ocean-basin-scale analyses based on lower-resolution datasets can effectively capture surface ocean CO2 variability at a global scale, they also highlight the necessity of fine-scale observations for resolving regional processes and their drivers, which is essential for improving predictive models of ocean acidification and air–sea CO2 exchange.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.032
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.257
Teacher spread0.235 · 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 teacher head, 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

Citations1
Published2025
Admission routes1
Has abstractyes

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