Predicting Climate Change Impacts on Precipitation for Western North America. M.Sc. Thesis
Bibliographic record
Abstract
Global Circulation Models (GCMs) are used to create projections of possible future climate characteristics under global climate change scenarios. Future local and regional precipitation scenarios can be developed by downscaling synoptic CGM data. Daily 500-mb geopotential heights from the Canadian Centre for Climate Modelling and Analysis's CGCM2 are used to represent future (2020-2050) synoptics and are compared to daily historical (1960-1990) 500-mb geopotential height reanalysis data. The comparisons are made based on manually classified synoptic patterns identified by Changnon et al. (1993. Mon. Weather Rev. 121: 633-647). Multiple linear regression models are used to link the historical synoptic pattern frequencies and precipitation amounts for 372 weather stations across western North America,. The station-specific models are then used to forecast future precipitation amounts per weather station based on synoptic pattern frequencies forecast by the CGCM2 climate change forcing scenario. Spatial and temporal variations in precipitation are explored to determine monthly, seasonal and annual trends in climate change impacts on precipitation in western North America. The resulting precipitation scenarios demonstrate a decrease in precipitation from 10 to 30 % on an annual basis for much of the south and western regions of the study area. Seasonal forecasts show variations of the same regions with decreases in precipitation and select regions with increases in future precipitation. A major advancement of this analysis was the application of synoptic pattern downscaling to summer precipitation scenarios for western North America. iii
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".