Environmental Factors Influencing 20th Century Diurnal Temperature Range Variations
Bibliographic record
Abstract
This dissertation explores the spatial-temporal patterns of diurnal temperature range (DTR) and its component variables, maximum temperature (Tmax) and minimum temperature (Tmin).The influences on DTR, Tmin, and Tmax variability from several environmental variables are also examined.Gridded time-series data from the Climate Research Unit (CRU TS 2.1) are used along with gridded Palmer Drought Severity Index (PDSI) data and nine teleconnection indices.The study focuses on the continental United States and surrounding land areas in southern Canada and northern Mexico.Data were converted to annual and seasonal averages to reduce the impact of the annual temperature cycle.Rotated principal components analyses (RPCA) of DTR data produced five or six geographic regions of similar DTR variability for each season and annually that were used in subsequent analysis.Regionally-averaged time-series of the locally measured variables Tmin, Tmax, DTR, cloud cover, precipitation, vapor pressure, and PDSI were examined for patterns and compared across regions and seasons.Stepwise multiple linear regression (SMLR) analyses were conducted with Tmin, Tmax, and DTR as the dependent variables and the other local variables (cloud cover, precipitation, vapor pressure, and PDSI) and teleconnection indices as independent variables to determine how the dependent variables impact Tmin, Tmax, and DTR variability.iii Results indicate regions of similar DTR variability occur annually and in most seasons in the west coast, southwest, central, northeast, and southeast United States.Other regions did emerge between the Great Plains and Rocky Mountains and in eastern Canada, but they were not seasonally consistent.Tmin generally showed an upward trend while the Tmax trends were usually near zero or downward.The DTR, which is the difference between Tmax and Tmin, had downward trends in many regions.SMLR analyses reveal that vapor pressure and cloud cover are the most influential variables on Tmin, Tmax, and DTR.Vapor pressure increases Tmin and Tmax, but the effect on Tmin is greater, so it tends to reduce DTR.Cloud cover increases Tmin and decrease Tmax making its impact on DTR greater than vapor pressure.Precipitation and PDSI had moderate impacts on Tmin, Tmax, and DTR but were not as consistent or influential as cloud cover and vapor pressure.The teleconnection indices used in the analysis generally had little effect on Tmin, Tmax, or DTR explaining very little (if any) of their variance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".