An evaluation of statistical downscaling methods in central Canada for climate change impact studies
Bibliographic record
Abstract
This study tested two popuìar statistical downscaling models, the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical DownScaling Model (SDSM), for their ability to simulate daily time series of local precipitation and temperature for sites in central Canada.The two models were specifically evaluated for their ability to accurately reproduce observed local daily precipitation and temperature means and variability (extremes).Results of the evaluation using available data in central Canada indicated that both models were able to describe the basic statistical properties of daily minimum and maximum temperatures at local sites.However, SDSM could not simulate the extremes of precipitation well while LARS-WG demonstrated more skill at simulating the means and extremes of precipitation-The skill of SDSM with predictors fi'om CGCM3 was degraded lelative to NCEP predictors suggesting the presence of bias in the CGCM3 predictors.A trend analysis in the study area showed an increasing temperature trend.Future downscaled climate change scenarios using SDSM and LARS-WG for the 2050s and 2090s were generated.using three future emission scenarios for two future decadal periods.1.4 Thesis Organization Chapter 2 presents background information relevant to this research.It introduces common terminology used in climate impact studies as well as provides a general introduction to downscaling, highlighting the best practices to statistical downscaling.Detailed descriptions of SDSM and LARS-WG along with an overview of the various studies where these models have been tested and applied are also presented.Chapter 3 provides a description of the data sources and study area.It describes the current climate at the meteorological stations in the study area.Chapter 4 describes the evaluation of the solar radiation model, Climatic Data Generator.This model was utilized to simulate historical solar radiation at the meteorological stations where solar radiation was not available.Chapter 5 summarizes the methodology used to determine historical climate trends, select predictors, evaluate SDSM and LARS-WG, and develop future climate change scenarios.Chapter 6 provides details regarding historical climate trends, the evaluations of SDSM and LARS-WG, and climate change scenarios at the meteorological stations.Chapter 7 provides conclusions that were drawn from the research and recommendations for future study.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".