Calcium carbonate production response to future ocean warming and acidification
Bibliographic record
Abstract
Abstract. Anthropogenic carbon dioxide (CO2) emissions are acidifying the ocean, affecting calcification rates in pelagic organisms and thereby modifying the oceanic alkalinity cycle. However, the responses of pelagic calcifying organisms to acidification vary widely between species, contributing uncertainty to predictions of atmospheric CO2 and the resulting climate change. Meanwhile, ocean warming caused by rising CO2 is expected to drive increased growth rates of all pelagic organisms, including calcifiers. It thus remains unclear whether anthropogenic CO2 will ultimately increase or decrease the globally-integrated pelagic calcification rate. Here, we assess the importance of this uncertainty by introducing a variable dependence of calcium carbonate (CaCO3) production on calcite saturation state (ΩCaCO3) in the University of Victoria Earth System Climate Model, an intermediate complexity coupled carbon-climate model. In a series of model simulations, we examine the impact of this parameterization on global ocean carbon cycling under two CO2 emissions scenarios, both integrated to the year 3500. The simulations show a significant sensitivity of the vertical and surface horizontal alkalinity gradients to the parameterization, as well as the removal of alkalinity from the ocean through CaCO3 burial. These sensitivities result in an additional oceanic uptake of carbon when calcification depends on ΩCaCO3 (of up to 13 % of total carbon emissions), compared to the case where calcification is insensitive to acidification. In turn, this response causes a reduction of global surface air temperature of up to 0.4 °C in year 3500, a 13 % reduction in the amplitude of warming. Narrowing these uncertainties will require better understanding of both temperature and acidification effects on pelagic calcifiers. Preliminary examination suggests that alkalinity observations can be used to constrain the range of uncertainties and may exclude large sensitivities of CaCO3 production on ΩCaCO3.
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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 | 0.000 |
| 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".