Underestimated warming of northern Canada in the Berkeley Earth temperature product
Bibliographic record
Abstract
ABSTRACT The Berkeley Earth surface temperature ( BETP ) project provides gridded global temperature anomaly products using an automated geostatistical approach to adjust station data for systematic biases. Despite its widespread usage, the BETP data set has not been evaluated at the national‐scale, especially in data‐sparse high latitude environments. This study provides an evaluation of the BETP product across all of Canada using 333 climate stations made available from the homogenized Environment Canada station network (HTcan). Comparison between co‐located monthly air temperature anomalies for the two data sets suggests small differences between the two products for mean surface (∼2 m) air temperature. However, the relatively minimal bias in mean temperature is a consequence of contrasting cold and warm biases in minimum and maximum air temperatures, respectively, that are larger but effectively even out when averaged together. The BETP product is shown to exhibit systematic underestimation of recent regional warming in northern Canada which when combined with an overestimation of warmth earlier in the record results in an observable reduction in warming rates for minimum and mean temperature anomalies since 1950. The temporal evolution and spatial pattern of the observed biases suggest that the BETP ‐automated adjustments to station data in northern Canada miss some inhomogeneities in the raw station data. These results highlight the need for enhanced data recovery and homogenization efforts in data‐sparse high latitude regions and emphasize the importance of national‐scale climate data sets for evaluating global gridded products. We also recommend caution when using the BETP minimum and maximum monthly air temperature products for long‐term trend analyses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".