North Pacific response to hemispheric warming forces Holocene drought
Bibliographic record
Abstract
Abstract The Southwest United States is prone to severe and persistent drought1, but the influence of anthropogenic forcing on current and future precipitation remains uncertain2-7. To improve our understanding of the drivers of Southwest drought, we quantified precipitation and temperature changes in the southern Rockies and combined these with a multi-model ensemble of climate simulations for the mid-Holocene, a past interval when the region experienced exceptional and persistent drought. Reconstructed mid-Holocene warming in the Rockies is consistent with existing proxy reconstructions. In most models, this warming only occurs in simulations with prescribed mid-Holocene vegetation, including a “greening” of the Sahara, supporting the hypothesis that expanded vegetation was critical for producing warming consistent with proxy data15. In response to this hemispheric warming, a distinct pattern of higher temperatures resembling the negative phase of the Pacific Decadal Oscillation emerges in the models, increasing the magnitude of wintertime precipitation declines across the western US, in better agreement with proxy reconstructions. A similar forced response could be excited by anthropogenic forcings, enhancing future drought across the Southwest US. However, reductions in winter precipitation associated with the development of this pattern of North Pacific warming are underestimated in simulations of both the mid-Holocene and the instrumental period, suggesting that current projections may underestimate the magnitude and the risk of persistent human-made drought in the future.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".