Using tracer observations to reduce the uncertainty of ocean diapycnal mixing and climate–carbon cycle projections
Bibliographic record
Abstract
What is the uncertainty of climate–carbon cycle projections in response to anthropogenic greenhouse gas emissions, and how can we reduce this uncertainty? We address this question by quantifying the ability of available ocean tracer observations to constrain the values of diapycnal diffusivity in the pelagic ocean (Kv), a key uncertain parameter representing sub‐grid‐scale diapycnal (vertical) mixing in physical circulation models. We show that model versions with weak mixing (i.e., low Kv) lead to higher projections of atmospheric CO2 and larger global warming than do models with vigorous mixing. Slower heat uptake and slower carbon uptake by the oceans contribute about equally to the accelerated warming in the low‐mixing models. A Bayesian data‐model fusion method is developed to quantify the likelihood of different structural and parametric model choices given an array of observed 20th century ocean tracer distributions. These spatially resolved observations provide strong limits on the upper value of Kv, whereas global metrics used in previous studies, such as the historical evolution of global average surface air temperature, global ocean heat uptake, or atmospheric CO2 concentration, provide only poor constraints. We compare different methods to quantify the probability of a particular diffusivity value given the observational constraints. One‐dimensional, globally horizontally averaged data result in sharper probability density functions compared with the full 3‐D fields. This perhaps unexpected result opens up an avenue to objectively determine the optimal degree of aggregation at which model predictions have skill, and at which observations are most helpful in constraining model parameters. Our best estimate for Kv in the pelagic pycnocline is around 0.05–0.2 cm2/s, in agreement with earlier independent estimates based on tracer dispersion experiments and turbulence microstructure measurements.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".