The Effect of Greenhouse Gas–Induced Warming on the Impact of El Niño and La Niña Events on Daily Precipitation Extremes in the Boreal Cold Season
Bibliographic record
Abstract
Abstract El Niño–Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large-ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3–7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes.” We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces a strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".