The Ocean’s Memory of the Atmosphere: Residence-Time and Ventilation-Rate Distributions of Water Masses
Bibliographic record
Abstract
Abstract A conceptually new approach to diagnosing tracer-independent ventilation rates is developed. Tracer Green functions are exploited to partition ventilation rates according to the ventilated fluid’s residence time in the ocean interior and according to where this fluid enters and exits the interior. In the presence of mixing by mesoscale eddies, which are reasonably represented by diffusion, ventilation rates for overlapping entry and exit regions cannot meaningfully be characterized by a single rate. It is a physical consequence of diffusive transport that fluid elements that spend an infinitesimally short time in the interior cause singularly large ventilation rates for overlapping entry and exit regions. Therefore, ventilation must generally be characterized by a ventilation-rate distribution, ϕ, partitioned according to the time that the ventilated fluid spends in the interior between successive surface contacts. An offline forward and adjoint time-averaged OGCM is used to illustrate the rich detail that ϕ and the closely related probability density function of residence times ℛ provide on the way the ocean communicates with the surface. These diagnostics quantify the relative importance of various surface regions for ventilating the interior ocean by either exposing old water masses to the atmosphere or by forming newly ventilated ones. The model results suggest that the Southern Ocean plays a dominant role in ventilating the ocean, both as a region where new waters are ventilated into the interior and where old waters are first reexposed to the atmosphere.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".