Canada’s Fourth Generation of Homogenized Surface Air Temperature and its Trends for 1948–2023
Bibliographic record
Abstract
This study presents a newly developed homogenized temperature dataset aimed at enhancing the reliability of the observed temperature records for computing long-term trends. The dataset includes two main improvements over its previous versions: an adjustment applied to correct a cold bias affecting daily minimum temperatures recorded after 1 July 1961, at all stations with daily and hourly observations, and a missing values and gaps filling procedure based on ANUSPLIN surface temperature estimates. The procedures can be summarized as follows. Daily minimum temperatures were first adjusted for the cold bias arising from changes in the timing of the observation window. Data from closely located stations were joined into single records to ensure observations for long periods. Comprehensive data quality assurance procedures were then applied to make sure that the data was of good quality. Daily and monthly ANUSPLIN surface estimates were used to fill missing values and gaps in both daily and monthly series. Finally, changepoints due to non-climatic changes were identified in monthly time series using a semi-automatic data homogenization procedure, and a quantile-matching procedure was applied to adjust the daily and monthly data for the changepoints identified by the procedure. The dataset contains homogenized daily maximum and minimum temperatures, as well as their monthly means, for 651 sites across the country, with all sites active and updated to 2023. Gap fillings start from 1948 for ensuring complete temporal coverage at each site and consistency when evaluating trends at regional and national levels. An ordinary kriging method was used to interpolate the daily and monthly temperature anomalies onto 10 km grids. This new homogenized temperature dataset indicates that warming continues across Canada, with an increase of 2.08°C in the annual mean of the daily maximum temperature and of 2.46 °C in the annual mean of the daily minimum temperature over the past 76 years (1948–2023); the findings also show that the warming continues to be more pronounced during the wintertime.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".