Assessing the Performance of Regional Climate Model Wind Speeds Over Canada
Bibliographic record
Abstract
ABSTRACT Human‐induced climate change is reshaping wind patterns across Canada, posing significant challenges for sectors such as wind energy and infrastructure planning. This study assesses the capability of regional climate models (RCMs) in simulating near‐surface wind speed (WS) across Canada by analysing outputs from various RCM ensembles, which downscale CMIP5 global climate model (GCM) output, including the NA‐CORDEX multi‐model ensemble (at 0.22° resolution) and the CanRCM4 single‐model large ensemble (at 0.44° resolution). These RCM outputs are compared against observational data, two reanalysis data sets (ERA5 and AgERA5), and GCM ensembles from CMIP5 and CMIP6. The evaluation examines the models' ability to replicate historical WS distributions, biases in mean and extreme WS, trends and temporal variability. The findings reveal that, despite the higher spatial resolution of RCMs, their added value over the GCM ensembles is limited, raising concerns about the reliability of RCM‐derived WS projections for climate services without further bias adjustment or statistical downscaling. The inability of both RCMs and GCMs to accurately simulate WS trends diminishes confidence in future WS projections, potentially leading to inadequate risk assessments and insufficient preparation for the impacts of climate change on vital sectors like energy and infrastructure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".