A Synthesis of Global Coastal Ocean Greenhouse Gas Fluxes
Bibliographic record
Abstract
Abstract The coastal ocean contributes to regulating atmospheric greenhouse gas concentrations by taking up carbon dioxide (CO 2 ) and releasing nitrous oxide (N 2 O) and methane (CH 4 ). In this second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP2), we quantify global coastal ocean fluxes of CO 2 , N 2 O and CH 4 using an ensemble of global gap‐filled observation‐based products and ocean biogeochemical models. The global coastal ocean is a net sink of CO 2 in both observational products and models, but the magnitude of the median net global coastal uptake is ∼60% larger in models (−0.72 vs. −0.44 PgC year −1 , 1998–2018, coastal ocean extending to 300 km offshore or 1,000 m isobath with area of 77 million km 2 ). We attribute most of this model‐product difference to the seasonality in sea surface CO 2 partial pressure at mid‐ and high‐latitudes, where models simulate stronger winter CO 2 uptake. The coastal ocean CO 2 sink has increased in the past decades but the available time‐resolving observation‐based products and models show large discrepancies in the magnitude of this increase. The global coastal ocean is a major source of N 2 O (+0.70 PgCO 2 ‐e year −1 in observational product and +0.54 PgCO 2 ‐e year −1 in model median) and CH 4 (+0.21 PgCO 2 ‐e year −1 in observational product), which offsets a substantial proportion of the coastal CO 2 uptake in the net radiative balance (30%–60% in CO 2 ‐equivalents), highlighting the importance of considering the three greenhouse gases when examining the influence of the coastal ocean on climate.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".