The power of hourly weather data: Observed air temperature climate trends for pragmatic decision-making
Bibliographic record
Abstract
Analysis of hourly air temperature data from recent decades reveals trends and the degree of variability in the length of time above and below key temperature thresholds associated with the freezing point, heat stress, and energy usage. We examine hourly weather station data obtained from NOAA’s Integrated Surface Database for 340 stations in the contiguous US and southern Canada from 1978 to 2023. For each station, we compute decadal trends in hours below the freezing point (0 °C, 32 °F), hours above the threshold for heat stress in animals and plants (30 °C, 86 °F), and energy usage in terms of heating and cooling degree hours (weighted deviations from 18 °C, 65 °F). Many locations in southern Canada and the north central and western US lack clear decadal trends in hours below 0 °C and have high variability in below freezing temperatures year to year. In contrast, most locations east of the Mississippi River and north of 37 °N have lost the equivalent of ∼1.5 to 2 weeks per year of temperatures below freezing compared to the early 1980s. The same northeast region shows mostly insignificant trends in hours above 30 °C. The largest gains in the number of hours above 30 °C are concentrated in the southwestern US and parts of Texas. For most locations in the northern portions of the US, the rate at which heating degree hours are lost outpaces the rate at which cooling degree hours are gained. Trends in threshold exceedance are more easily related to lived experiences than incremental changes to seasonal or annual averages. Our examination of hourly data complements assessments of historical temperature changes based on daily minimum, maximum, and average temperatures. Information on regional exceedance trends and their magnitudes can aid local climate adaption planning.
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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 | 0.028 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".