Bibliographic record
Abstract
The quality of historical climate data is a fundamental consideration in climate change research. This thesis considers the problem of microclimatic biases in observed surface-level air temperatures, and the effect of these biases on the homogeneity of historical temperature records. An extensive literature review describes causes of potential biases in air temperature records, and demonstrates that climate station siting biases, in the form of microclimatic change in the surrounding environment, are particularly difficult to detect by conventional homogeneity analysis techniques. Such biases are therefore potentially still present in many of the records used to monitor global climate change. The microclimatic processes responsible for thermal biases in temperature records are reviewed to demonstrate the relevant physical principles. A new technique to detect microclimatic inhomogeneities in temperature records is presented. The technique is based on the construction of time series of cooling ratios derived from nocturnal cooling at neighbouring climate stations calculated from daily maximum and minimum temperatures. The 'cooling ratio' is shown to be a particularly sensitive measure of the relative microclimatic differences between neighbouring climate stations, because larger-scale climatic influences common to both stations are removed by the use of a ratio and, because the ratio is invariant in the mean with weather variables such as wind and cloud. Discontinuities in the time series of cooling ratios indicate microclimatic change in one of the temperature records. Hurst rescaling (Hurst, 1951) is applied to several time series of cooling ratios, and is shown to be helpful in identifying discontinuities in the time series. The technique is tested on several Canadian historical temperature records, and proven to effectively identify subtle microclimatic changes such as minor station relocations, vegetation growth, and encroachment of buildings and parking lots. Results of this technique are compared to those from other homogeneity assessment techniques, and the cooling ratio approach is shown to be better suited to the detection of microclimatic biases. The technique is also shown to be useful in the assessment of the homogeneity of urban temperature records. Finally, since the research highlights the significance of having detailed station metadata, recommendations are made to improve the utility of such records, along with suggestions for future research.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".