Contribution of Atmospheric Rivers to Annual, Seasonal, and Extreme Precipitation Across British Columbia and Southeastern Alaska
Bibliographic record
Abstract
Abstract Lying in the frontline of the prevailing midlatitude westerlies, British Columbia and southeastern Alaska (BCSAK) often receive copious amounts of precipitation through atmospheric rivers (ARs). This study quantifies the contribution of ARs to annual, seasonal, and extreme precipitation across BCSAK from 1979 to 2012 using a recently developed high‐resolution gridded precipitation data set, a regional AR catalog, and integrated vapor transport fields calculated from a reanalysis data set. On average, ARs contribute 13% of total annual precipitation with the higher contribution along the coastal regions (up to 33%), parts of which are one of the wettest locations on Earth, followed by the Columbia and Rocky Mountains (~9%–15%). The highest contributions occur during September (up to 57%) and October (up to 49%). The contribution of ARs to extreme precipitation attains >90% along the western arc of the Coast Mountains and the coastal regions of BCSAK. ARs act as the main synoptic‐scale mechanism that brings rainfall to the Rocky Mountains in winter. The probability of observing AR‐related precipitation increases over the study period; however, no change occurs in the average AR‐related precipitation amount for most of BCSAK during 1979–2012. This work provides insights on the critical role ARs play on the water resources of northwestern North America and has broader implications on community water supply and management, hydropower operations, and flood risk assessment and mitigation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".