Irreversible ocean thermal expansion under negative CO <sub>2</sub> emissions
Bibliographic record
Abstract
Abstract. In the Paris Agreement in 2015 countries agreed on holding global mean surface air warming well below 2 °C but the emission reduction pledges under that agreement are not ambitious enough to meet this target. Therefore, the question arises whether restoring temperature to this target after exceeding it by artificially removing CO2 from the atmosphere is possible. One important aspect regards the reversibility of ocean heat uptake and the associated sea level rise, which have very long (centennial to millennial) response time scales. In this study the response of sea level rise due to thermal expansion (TSLR) to a 1 % yearly increase of atmospheric CO2 up to a quadrupling of the pre-industrial concentration followed by a 1 % yearly decline back to the pre-industrial CO2 concentration is examined using the University of Victoria Earth System Climate Model (UVic ESCM). We find that TSLR continues for several decades after atmospheric CO2 starts to decline and that sea level does not return to pre-industrial levels for over thousand years after atmospheric CO2 is restored to pre-industrial concentrations. This finding is independent of the strength of vertical sub-grid scale ocean mixing implemented in the model. Furthermore, TSLR rises faster than it declines in response to a symmetric rise and decline in atmospheric CO2 concentration partly because the deep ocean continues to warm for centuries after atmospheric CO2 returns to the pre-industrial concentration. Both TSLR rise and decline rate increase with increasing vertical ocean mixing. Exceptions from this behaviour arise if the overturning circulations in the North Atlantic and Southern Ocean intensify beyond pre-industrial levels in model versions with lower vertical mixing, which leads to rapid cooling of the deep ocean.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".