Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5 global climate models
Bibliographic record
Abstract
Abstract Atmospheric rivers (ARs) often trigger extreme precipitation events in British Columbia (BC), Canada. Here we analyze how well the autumn AR events with the highest probability for extreme precipitation over BC, henceforth called AR‐extreme events, are simulated in five Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and how these AR‐extreme events are projected to change by the end of the century. We examine the daily synoptic patterns of integrated water vapor transport (IVT) over the Pacific Ocean that favor the formation of AR‐extreme events. Our analysis and comparison with AR‐extreme events in four reanalysis products for the period 1979–2010 reveal that the GCMs more successfully resolve their seasonality and interannual variability than their frequencies and amount of precipitation brought to BC. For the CMIP5 scenario's Representative Concentration Pathway (RCP) 4.5 and RCP8.5, the frequency of AR‐extreme events will increase for the period 2070–2100 with the largest increase in December. All models project an increase in total precipitation over BC, due to the increase in frequency and intensity of the AR‐extreme events; however, the dominant factor is the increase in frequency, especially of those events with precipitation exceeding 20 mmd−1. The path of the ARs during the AR‐extreme events is projected to move northward, bringing stronger IVT and more precipitation to the north coast of BC, while the south coast may become drier than at the present day. The shift in the ARs is driven by the northward shift in the Aleutian Low pressure system, especially in RCP8.5.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".