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
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 (DIC sat ), disequilibrium (DIC dis ), carbonate (DIC carb ), and soft tissue (DIC soft ). The cycling of dissolved oxygen is simpler, but can still be aided by considering O 2 , 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 DIC soft and DIC dis vary over a range of 40 µmol kg −1 in response to the climate forcing, equivalent to changes in atmospheric p CO 2 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 O 2 disequilibrium, with large increases under cold climate states. We find that, despite the broad range of climate states represented, changes in global DIC soft can be quantitatively approximated by the product of deep ocean ideal age and the global export production flux. In contrast, global DIC dis 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, DIC dis and DIC soft 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 DIC soft . As an example of the latter, we show that iron fertilization causes both DIC soft and DIC dis 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 DIC soft +DIC dis to ocean carbon storage as a function of the surface preformed nitrate and DIC dis 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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".