Estimation of global extreme temperature trends by merging Annual Maxima and Peaks Over Threshold
Bibliographic record
Abstract
Annual Maxima (AM) and Peaks over Threshold (POT) are the two most common approaches to define extreme time series in hydroclimatic variables. Both methods present limitations. AM frequently fails to include significant extremes that occur during the same year. Conversely, POT may only include clustered values from a few years thus excluding many years from the analysis, especially when the threshold is set high. Additionally, a big challenge in POT is identifying the threshold which can markedly affect the results.Here, we merge notions from both AM and POT, preserving the strengths of each approach, to extract extreme temperature series and estimate the trends in their frequency and magnitude. We select the values larger than or equal to the minimum of the AM series as high temperatures (HT) (lower than or equal to the maximum of the Annual Minima as the low temperatures – LT). Thus, each year of the HT, LT series has at least one extreme value (H1, L1). We apply the method to 4797 quality-controlled raw station observations from a global dataset of maximum and minimum temperatures over 1970-2019 when warming accelerates. To examine changes in H1-L1 frequency and magnitude, we estimate the ratio of observed to expected H1 (L1) annual occurrences, and the difference between the observed and expected mean H1 (L1) annual temperature values, respectively. We estimate the regression slopes of these ratios at the station level, regionally in 2°×2° grids, and globally. We then compare these trends with the ones obtained from AM and POT series. The proposed method adapts the threshold for each sample, and finds a compromise among all tested methods, thus being a flexible approach that can be applied to any non-intermittent variable. AcknowledgmentThis research was supported by a GWF Ph.D. Excellence Scholarship from the Global Institute for Water Security (GIWS), University of Saskatchewan
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".