Temporal evolution of anthropogenic carbon in the subpolar North Atlantic gyre between 2011 - 2021
Bibliographic record
Abstract
The ocean plays a major role in the moderation of anthropogenically-induced climate change by absorbing roughly a quarter of anthropogenic CO2 (Cant). This absorption of Cant by the ocean leads to ocean acidification, threatening marine’s life. The North Atlantic Ocean encompasses the highest ocean storage capacity of Cant per unit area. The subpolar North Atlantic gyre is subject to a large seasonal to decadal variability that might impact Cant storage. To investigate Cant evolution over the 2011-2021 period and its relationship with ocean dynamics in this region, we use the Argo-O2 array combined with neural networks and a back-calculation method (φCTO method). We compute monthly time-series of Cant in the Labrador and Irminger Seas. We show that Cant concentrations in the first 2000 dbar of the Labrador and Irminger Seas are strongly affected by winter deep convection, especially between winter 2015 and winter 2018. The Cant inventories in the top 2000 dbar of the Labrador and Irminger Seas increase through time, at rates of 1.63±0.32% yr-1 and 1.49±0.30% yr-1, respectively. Our monthly Argo-based Cant estimates complement high-quality ship-based measurements acquired at a biennial or lower frequency. Additionally, this study shows that Cant concentrations and Cant inventories in deep convection areas may depend on the method employed to calculate Cant. As a consequence, we take over the model ensemble idea and propose to use several methods to compute Cant, which would give its methodological uncertainty.
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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".