Trends in Persistent Seasonal-Scale Atmospheric Circulation Patterns Responsible for Seasonal Precipitation Totals and Occurrences of Precipitation Extremes over Canada
Bibliographic record
Abstract
Abstract Both large-scale atmospheric circulation and moisture content in the atmosphere govern regional precipitation. We partition recent changes in mean, heavy, and extreme precipitation for all seasons over Canada to changes in synoptic circulation patterns (dynamic changes) and in atmospheric moisture conditions (thermodynamic changes) using 500-hPa geopotential height and precipitation data over 1979–2014. Using the self-organizing map (SOM) cluster analysis, we identify statistically significant trends in occurrences of certain synoptic circulation patterns over the Canadian landmass, which have dynamically contributed to observed changes in precipitation totals and occurrence of heavy and extreme precipitation events over Canada. Occurrences of circulation patterns such as westerlies and ridges over western North America and the North Pacific have considerably affected regional precipitation over Canada. Precipitation intensity and occurrences of precipitation extremes associated with each SOM circulation pattern also showed statistically significant trends resulting from thermodynamic changes in the atmospheric moisture supply for precipitation events. A partition analysis based on the thermodynamic–dynamic partition method indicates that most (~90%) changes in mean and extreme precipitation over Canada resulted from changes in precipitation regimes occurring under each synoptic circulation pattern (thermodynamic changes). Other regional precipitation changes resulted from changes in occurrences of synoptic circulation patterns (dynamic changes). Because of the high spatial variability of precipitation response to changes in thermodynamic and dynamic conditions, dynamic contributions could offset thermodynamic contributions to precipitation changes over some regions if thermodynamic and dynamic contributions are in opposition to each other (negative or positive), which would result in minimal changes in precipitation intensity and occurrences of heavy and extreme precipitation events.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".