Climate model downscaling of Vancouver Island precipitation using a synoptic typing approach
Bibliographic record
Abstract
A statistical downscaling technique is employed to link atmospheric circulation produced by climate models at the large-scale to precipitation recorded at individual weather stations on Vancouver Island. Relationships between the different spatial scales are established with synoptic typing, coupled with non-homogeneous Markov models to simulate precipitation intensity and occurrence in historical and future periods. Types are generated through a clustering algorithm which processes daily precipitation observations recorded by Environment Canada weather stations spanning 1971 to 2000. Large-scale atmospheric circulation data is taken from an ensemble of climate model projections made under the IPCC AR4 SRES A2 scenario through the end of the 21st century. Atmospheric predictors used to influence the Markov model are derived from two versions of the data: Averages of model grid cells selected by correlation maps of circulation and precipitation data; a new approach involving Common Empirical Orthogonal Functions (EOFs) calculated from model output over the Northeast Pacific Ocean. Circulation-based predictors capture the role of sea level pressure (SLP), and winds in influencing coastal precipitation over Vancouver Island. The magnitude and spatial distribution of the projected differences are dependent on the predictors used. Projections for 2081 to 2100 made using common EOFs result in most stations reporting no statistically significant change compared to the baseline period (1971 to 2000) in both seasons. Projections using averaged grid cells find winter season (Nov-Feb) precipitation anomalies produce values that are modestly positive, with typical gains of 6.5% in average precipitation, typical increases of 7.5% rising up to 15% in extreme precipitation, and little spatial dependence. In contrast, average and extreme summer precipitation intensity (Jun-Sep) declines negligibly at most island weather stations with the exception of those on the southern and western sections, which experience reductions of up to 20% relative to the latter thirty years of the twentieth century. Precipitation occurrence decreases slightly in both seasons at all stations with declines in the total days with measurable precipitation ranging from 2% to 8% with reductions also seen in the length of extended periods of precipitation in both seasons.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".