Climate coupling between temperature, humidity, precipitation, and cloud cover over the Canadian Prairies
Bibliographic record
Abstract
Abstract This analysis uses over 50 years of hourly observations of temperature, relative humidity, and opaque cloud cover and daily precipitation from 11 climate stations across the Canadian Prairies to analyze the monthly, seasonal, and long‐term climate coupling in the warm season. On climate time scales, temperature depends on cloud forcing, while relative humidity depends on precipitation. The monthly climate depends on both opaque cloud cover for the current month and precipitation for both the present and past 2 months in summer. Multiple linear regression shows that anomalies of opaque cloud and precipitation explain 60–80% of the variance in the diurnal temperature range, afternoon relative humidity, and lifting condensation level on monthly time scales. We analyze the internal coupling of diurnal climate observables as a further guide to evaluating models. We couple the statistics to simplified energy and water budgets for the Prairies in the growing season. The opaque cloud observations have been calibrated against the incoming shortwave and longwave fluxes. We estimate that the drydown of total water storage on the landscape damps 56% of precipitation anomalies for the growing season on large spatial scales, although this drydown increases evapotranspiration. This couples the climatological surface fluxes to four key observables: cloud forcing, precipitation, temperature, and humidity. We estimate a climatological evaporative fraction of 0.61 for the Prairies. The observational relationships of the coupled Prairie climate system across time scale will be useful for evaluating these coupled processes in models for weather and seasonal forecasting and climate simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".