Global atmospheric moisture transport associated with precipitation extremes: Mechanisms and climate change impacts
Bibliographic record
Abstract
Abstract The atmospheric moisture transport processes are of great importance to the occurrence and intensity of precipitation extremes. In this paper, we review the linkage between processes, including the large‐scale atmospheric circulation, atmospheric moisture transport, and extreme precipitation events. We first summarize the thermodynamic and dynamic processes and moisture transport trackings for historical precipitation extremes. We then focus on the contribution of three major atmospheric moisture transport pathways, that is, atmospheric rivers, low‐level jets, and tropical cyclones, to the occurrence and intensity of regional precipitation extremes. Studies on large‐scale atmospheric circulation driving water vapor transport for precipitation extremes over East Asia and North America were specifically reviewed for the understanding of physical mechanisms and predictability of moisture transport and extreme precipitation events. We then pay more attention to the effects of global warming on atmospheric moisture transport, and thus regional precipitation extremes from the perspectives of thermodynamic and dynamic changes of the atmosphere. In the end, we summarize future research challenges on the physical mechanisms of atmospheric moisture transport that are associated with regional precipitation extremes, especially under a warming climate. This article is categorized under: Science of Water > Water Extremes Science of Water > Hydrological Processes
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".