The devil's in the disequilibrium: multi-component analysis of dissolved carbon and oxygen changes under a broad range of forcings in a general circulation model
Bibliographic record
Abstract
Abstract. The complexity of dissolved gas cycling in the ocean presents a challenge for mechanistic understanding and can hinder model intercomparison. One helpful approach is the conceptualization of dissolved gases as the sum of multiple, strictly defined components. Here we decompose dissolved inorganic carbon (DIC) into four components: saturation (DICsat), disequilibrium (DICdis), carbonate (DICcarb), and soft tissue (DICsoft). The cycling of dissolved oxygen is simpler, but can still be aided by considering O2, O2sat, and O2dis. We explore changes in these components within a large suite of simulations with a complex coupled climate–biogeochemical model, driven by changes in astronomical parameters, ice sheets, and radiative forcing, in order to explore the potential importance of the different components to ocean carbon storage on long timescales. We find that both DICsoft and DICdis vary over a range of 40 µmol kg−1 in response to the climate forcing, equivalent to changes in atmospheric pCO2 on the order of 50 ppm for each. The most extreme values occur at the coldest and intermediate climate states. We also find significant changes in O2 disequilibrium, with large increases under cold climate states. We find that, despite the broad range of climate states represented, changes in global DICsoft can be quantitatively approximated by the product of deep ocean ideal age and the global export production flux. In contrast, global DICdis is dominantly controlled by the fraction of the ocean filled by Antarctic Bottom Water (AABW). Because the AABW fraction and ideal age are inversely correlated among the simulations, DICdis and DICsoft are also inversely correlated, dampening the overall changes in DIC. This inverse correlation could be decoupled if changes in deep ocean mixing were to alter ideal age independently of AABW fraction, or if independent ecosystem changes were to alter export and remineralization, thereby modifying DICsoft. As an example of the latter, we show that iron fertilization causes both DICsoft and DICdis to increase and that the relationship between these two components depends on the climate state. We propose a simple framework to consider the global contribution of DICsoft+DICdis to ocean carbon storage as a function of the surface preformed nitrate and DICdis of dense water formation regions, the global volume fractions ventilated by these regions, and the global nitrate inventory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".