Monthly High‐Resolution Historical Climate Data for North America Since 1901
Bibliographic record
Abstract
ABSTRACT Interpolated grids of historical climate variables are widely used in climate change impact and adaptation research. Here, we contribute monthly historical time series grids since 1901 for our data product ClimateNA , which integrates historical data and future projections to generate high‐resolution gridded data and point estimates for North America. The historical climate grids in this study are based on interpolations of monthly anomalies (change factors) with thin‐plate splines, but a novel aspect is that we rely on high‐quality 1961–1990 normal estimates from ClimateNA to serve as reference for the change factor calculations instead of the reference being derived from station data itself. This allowed us to utilise records from 66,282 climate stations for interpolations, regardless of their temporal coverage. Another aspect that deviates from standard practice is that we reduce overfitting by optimising thin‐plate splines at a 0.5° grid level instead of fitting weather station observations directly. The high‐resolution grids generated with this approach compared favourably with other time series products, such as Daymet and advanced multi‐source products, such as MSWEP, in statistical and mapped visual comparisons, and provide additional historical coverage since the beginning of the 20th century.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".