From small-scale variability to mesoscale stability in surface ocean pH: implications for air–sea CO <sub>2</sub> equilibration
Bibliographic record
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 km 2 ) 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 CO 2 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 CO 2 exchange.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".