A multimodel examination of climate extremes in an idealized geoengineering experiment
Bibliographic record
Abstract
Temperature and precipitation extremes are examined in the Geoengineering Model Intercomparison Project experiment G1, wherein an instantaneous quadrupling of CO2 from its preindustrial control value is offset by a commensurate reduction in solar irradiance. Compared to the preindustrial climate, changes in climate extremes under G1 are generally much smaller than under 4 × CO2 alone. However, it is also the case that extremes of temperature and precipitation in G1 differ significantly from those under preindustrial conditions. Probability density functions of standardized anomalies of monthly surface temperature and precipitation in G1 exhibit an extension of the high‐ tail over land, of the low‐ tail over ocean, and a shift of to drier conditions. Using daily model output, we analyzed the frequency of extreme events, such as the coldest night (TNn), warmest day (TXx), and maximum 5 day precipitation amount, and also duration indicators such as cold and warm spells and consecutive dry days. The strong heating at northern high latitudes simulated under 4 × CO2 is much alleviated in G1, but significant warming remains, particularly for TNn compared to TXx. Internal feedbacks lead to regional increases in absorbed solar radiation at the surface, increasing temperatures over Northern Hemisphere land in summer. Conversely, significant cooling occurs over the tropical oceans, increasing cold spell duration there. Globally, G1 is more effective in reducing changes in temperature extremes compared to precipitation extremes and for reducing changes in precipitation extremes versus means but somewhat less effective at reducing changes in temperature extremes compared to means.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".