Seasonal and interannual inorganic carbon dynamics in the Northeast Pacific
Bibliographic record
Abstract
The Northeast Pacific is an important net sink of atmospheric CO2. However, substantial natural variability modulates the long-term increase of seawater partial pressure of CO2 (pCO2), potentially influencing the magnitude of the CO2 sink. In addition to the seasonal cycle, the Pacific Decadal Oscillation (PDO) is known to play a role in driving a large fraction of the non-seasonal variability in the region. Yet, the magnitude of this natural variability, especially periods of high surface dissolved inorganic carbon (DIC), are not well constrained. Here we quantify the seasonal and non-seasonal variability in DIC and pCO2 using observations from the Line P program, the longest marine carbonate system time-series transect in the NE Pacific (1990-2019), as well as an ensemble of historical simulations with an Earth system model (EC-Earth-CC). Preliminary results show that the mean amplitude of the DIC seasonal cycle is similar across our NE Pacific transect (23-30 µmol kg-1) and decreases with depth to less than 5 µmol kg-1 at 60 to 70 m. In contrast, the non-seasonal variability remains approximately constant with depth, ranging between 10 – 20 µmol kg-1. We quantify the role of the PDO in driving this residual non-seasonal variability, and analyse the contrasting impact of temperature and DIC changes in controlling surface pCO2 during opposite phases of the PDO.
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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 | 0.000 | 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".