Observed and Modeled Amplification of the Frequency, Duration, and Extreme Heat Impacts of the Pacific Trough Regime
Bibliographic record
Abstract
Abstract The large‐scale atmospheric circulation is a key driver for regional climate extremes, yet its response to anthropogenic forcing remains uncertain. The Pacific trough (PT) regime is a persistent circulation pattern modulating temperature, precipitation, and fires over North America. We show that the observed boreal winter‐spring (December to May) PT frequency and duration have increased significantly over the past 76 years, contributing to amplified extreme anomalous heat over western and central Canada. These observed changes are not well represented in the climate simulations analyzed herein. However, our results indicate that rising greenhouse gas concentrations likely contribute to increased winter‐spring PT frequency, which is further modulated by sea surface temperatures (SSTs). While the recent La Niña‐like and negative Pacific Decadal Oscillation‐like SST trends have dampened this increase, our results suggest that if an eventual emergence of the modeled El Niño‐like response to elevated were to occur in reality, it would reinstate the increase in PT frequency, duration, and downstream amplification of regional extreme heat. However, the occurrence, timing, and magnitude of this shift remain uncertain, given the complex, interlaced role of external forcings and internal variability in modulating historical trends, as well as models' inability to reproduce them. Additionally, modeling decisions regarding future trajectories for anthropogenic emissions, including aerosols and greenhouse gases, play a critical role in projecting future changes in PT frequency. Our findings underscore the need for a better understanding and modeled representation of long‐term changes in the atmospheric circulation to inform climate adaptation and risk assessment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".