Parameterising CO2 air-sea gas transfer with wave breaking energy dissipation rate, sea state, and wind speed
Bibliographic record
Abstract
Air-sea gas exchange has up-scale ramifications for global climate and ocean biogeochemistry that are of paramount relevance. Gas transfer velocity (k) measurements or appropriate parameterizations for them are required to quantify the fluxes and budgets of the important trace gases (e.g., CO2, DMS, and CH4). Where gas flux and concentration gradients are not explicitly measured, k is subdivided into diffusive and bubble-mediated components – each parameterized. Although diffusive transfer velocity, ks , has been well-described by power-law relationships involving the Schmidt number Sc, large variability exists in parameterizations for bubble-mediated gas transfer velocity, kb. Since kb is driven primarily by entrainment of gases through wave breaking, the uncertainty is acutely problematic at high winds where gas flux measurements are scarce. To address the paucity of such data, the High Wind Gas Exchange Study (HiWinGS) directly calculated gas transfer velocity of CO2 (kCO2) from flux and concentration gradient measurements taken in the Labrador Sea from October 9 – November 13, 2013, where 10-meter neutral wind speeds ranged between 1.8 – 25.2 m s-1. We use these data to validate a novel gas transfer velocity parameterization constructed using output from a wave hindcast obtained with the spectral wave model (ecWAM) forced with the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Generation Reanalysis (ERA5). Our parameterisation combines a diffusive term based on wind speed and Sc, and a bubble-mediated term based on gas solubility, wave age, and wave breaking energy dissipation rate to capture gas transfer velocity. We compare our results to common wind-speed-only parameterisations and more recent sea-state based relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".