Abrupt shift of El Niño periodicity under CO <sub>2</sub> mitigation
Bibliographic record
Abstract
Removing CO 2 from the atmosphere is emerging as a viable strategy to mitigate global warming, yet the responses of the climate system to CO 2 reduction remain uncertain. One of the most uncertain aspects of El Niño behavior is the change in periodicity in response to CO 2 forcing [O. Alizadeh, Earth-Sci. Rev. 235 , 104246 (2022)]. In this study, we show that climate models consistently project an abrupt shortening of El Niño periodicity once CO 2 reductions commence in ramp-up and ramp-down CO 2 experiments. Besides the contribution of slow mean state changes, this phenomenon is shown to be driven by a southward shift of the Intertropical Convergence Zone (ITCZ) [J.-S. Kug, et al. , Nat. Clim. Chang. 12 , 47–53 (2022)] and the consequent narrowing of El Niño’s spatial pattern, which enhances the effectiveness of ocean heat recharge/discharge processes, thereby shortening its periodicity. This suggests that the abrupt shift in El Niño periodicity results from a cascading reaction involving ITCZ dynamics and El Niño’s spatial configuration. These findings highlight the critical role of the global energy balance in shaping El Niño characteristics.
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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 | 0.000 |
| 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) | 0.001 | 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".