On the Connection Between Global Hydrologic Sensitivity and Regional Wet Extremes
Bibliographic record
Abstract
Abstract A highly uncertain aspect of anthropogenic climate change is the rate at which the global hydrologic cycle intensifies. The future change in global‐mean precipitation per degree warming, or hydrologic sensitivity, exhibits a threefold spread (1–3%/K) in current global climate models. In this study, we find that the intermodel spread in this value is associated with a significant portion of variability in future projections of extreme precipitation in the tropics, extending also into subtropical atmospheric river corridors. Additionally, there is a very tight intermodel relationship between changes in extreme and nonextreme precipitation, whereby models compensate for increasing extreme precipitation events by decreasing weak‐moderate events. Another factor linked to changes in precipitation extremes is model resolution, with higher resolution models showing a larger increase in heavy extremes. These results highlight ways various aspects of hydrologic cycle intensification are linked in models and shed new light on the task of constraining precipitation extremes.
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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 | 0.002 |
| 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 | 0.001 |
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".