Atmospheric Rivers as Triggers of Compound Flooding: Quantifying Extreme Joint Events in Western North America Under Climate Change
Bibliographic record
Abstract
Abstract. Atmospheric Rivers (ARs) are narrow bands of concentrated moisture that transport water vapor from the tropics to higher latitudes. They are responsible for ~90 % of poleward water vapor transport and play a vital role in water resource management along the North American west coast. While ARs significantly contribute to regional water supplies, they are also major drivers of flooding. This study investigates the extent to which ARs contribute to compound inland flooding (CIF) events where multiple drivers intensify flood risks, namely Rain on Snow (ROS) and Saturation Excess Flooding (SEF) events. Furthermore, the influence of internal climate variability is investigated relative to anthropogenic climate change. Using the CanRCM4 Large Ensemble simulations, we analyze the frequency and seasonality of AR-driven CIF events in Western North American coastal areas, with a focus on understanding how ARs interact with additional factors such as snowpack and soil moisture. ARs are shown to be dominant drivers of CIF events by contributing to the development and intensification of these events. These conditions also shape the seasonality and intensity of AR-driven CIFs. Projections suggest that internal climate variability can significantly contribute to future uncertainty in CIF frequency and intensity, complicating efforts to predict and mitigate these events. The findings underscore the importance of integrating AR-related flooding risks into flood management strategies and infrastructure design to adapt to a changing climate.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".