Detrended fluctuation analysis of daily atmospheric surface temperature records in Atlantic Canada
Bibliographic record
Abstract
The presence and properties of long‐range correlations in temperature data reflect interactions among climate components; therefore, the quantitative characterization of scaling aspects of temperature patterns is important to climate research and can serve as an effective constituent of tests for climate models. The article presents the results of a study using multiscale characterization of daily atmospheric surface temperature patterns in Atlantic Canada. Important influences in this region are exerted from the west (the Pacific Ocean), the south (the Gulf of Mexico) and the north (the Arctic), while a significant easterly impact is due to the Atlantic Ocean; the corresponding processes involve a wide range of spatial and temporal scales. The objective of the present study of long‐term temperature recordings was to evaluate the scaling properties produced in this geographical context. The data consist of homogenized daily atmospheric temperature time series recorded in stations from Atlantic Canada over a time interval of more than 100 years. Detrended fluctuation analysis (DFA) was applied both to maximum and minimum temperature records. The atmospheric temperature pattern produced by the interplay of factors of different strengths and dominating various time‐space scales was found to be characterized by consistent scaling properties, expressed over time intervals ranging from months to decades. Higher values of DFA scaling exponents were obtained for minimum temperature compared to maximum temperature records. Site‐specific properties include stronger pattern persistence—higher DFA exponents—for oceanic than for coastal locations; persistence tends to decrease with increasing distance from the coast for distances up to 10 kilometres. Scaling exponents tend to increase with decreasing difference between average minimum and maximum temperature, which may be relevant for the assessment of future changes in pattern variability if climate change involves modified contrasts between minimum and maximum temperature values.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".