Regional Climate Projection for Atlantic Canada under SSP245 and SSP585
Bibliographic record
Abstract
We use the Weather Research and Forecasting (WRF4.4) model for a regional climate simulation in Atlantic Canada. We seek to establish a robust repository of future climate projections for the region, that include the influence of northern ice coverage from the Labrador Sea and Ungava Bay, and sea surface temperatures (SST). The simulation is bounded by a Bias-Corrected ensemble of 18 CMIP6 General Circulation Models (GCMs) that offer better quality boundary conditions than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events.The simulation extends within the historical period from 1980 to 2014 and two future scenarios (SSP245 and SSP585) from 2015 to 2100. The configuration includes three domains with progressively increasing resolution from 30km to 9km and 3km. The finest resolution of 3 km by 3 km covers an area of approximately 561 kilometers by 462 kilometers around the province of Nova Scotia, Canada. The temporal resolution in WRF is set at 180 seconds, with boundary conditions updated every 6 hours, yielding output at a 6-hour time step for all WRF variables.To validate the historical simulation, we use the reanalysis from ECMWF (ERA5) and Station-Level Inputs and Cross-Validation for North America from The Oak Ridge National Laboratory (DAYMET). Preliminary statistical metrics reveal that our historical simulation underestimates the daily maximum temperature by 13%, overestimates daily minimum temperature by 2.7%, and underestimates the daily total precipitation by 16%. These findings provide valuable insights into the model performance and variability, and highlight areas for potential refinement for our projection scenarios. Analyses of the future (2015-2100) simulations are focused on estimating future precipitation (convective permitting), and surface air temperature (T2) extreme 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".