North Pacific carbon cycle response to climate variability on seasonal to decadal timescales
Bibliographic record
Abstract
Climate variability drives significant changes in the physical state of the North Pacific, and there may be important impacts of this variability on the upper ocean carbon balance across the basin. We address this issue by considering the response of seven biogeochemical ocean models to climate variability in the North Pacific. The models' upper ocean pCO2 and air‐sea CO2 flux respond similarly to climate variability on seasonal to decadal timescales. Modeled seasonal cycles of pCO2 and its temperature‐ and non‐temperature‐driven components at three contrasting oceanographic sites capture the basic features found in observations (Takahashi et al., 2002, 2006; Keeling et al., 2004; Brix et al., 2004). However, particularly in the Western Subarctic Gyre, the models have difficulty representing the temporal structure of the total pCO2 seasonal cycle because it results from the difference of these two large and opposing components. In all but one model, the air‐sea CO2 flux interannual variability (1σ) in the North Pacific is smaller (ranges across models from 0.03 to 0.11 PgC/yr) than in the Tropical Pacific (ranges across models from 0.08 to 0.19 PgC/yr), and the time series of the first or second EOF of the air‐sea CO2 flux has a significant correlation with the Pacific Decadal Oscillation (PDO). Though air‐sea CO2 flux anomalies are correlated with the PDO, their magnitudes are small (up to ±0.025 PgC/yr (1σ)). Flux anomalies are damped because anomalies in the key drivers of pCO2 (temperature, dissolved inorganic carbon (DIC), and alkalinity) are all of similar magnitude and have strongly opposing effects that damp total pCO2 anomalies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".