A Trial of Climate Classification Based on Dynamic Climatology Using Distribution of Frontal Zone in Mid- and High Latitudes
Bibliographic record
Abstract
Here, I create a dataset of fronts in mid- and high latitudes by applying an objective front detection method to the JRA-55 reanalysis and try climate classification based on dynamic climatology from temperate to polar regions. Additionally, I describe the interannual variations and long-term trends in the frontal zone. The unique feature of this study lies in the methods used for frontal data creation. This includes adding the geopotential height condition at 500-hPa to the conventional thermal-based objective method with equivalent potential temperature, including incorporating latitude-dependent parameters. The former increased the similarity between fronts created by the objective method and manually counted fronts on surface weather maps, while the latter enabled an examination of climate classification based on dynamic climatology by increasing the frontal frequency at high latitudes. The areas where climatic zones can be clearly defined are limited to the east of the great mountains in the mid-latitudes and the region where the Siberia-Canada Arctic frontal zone exists due to the obscuration or unclear seasonal movement of the frontal zones in other areas. The interannual variability in frontal zones is generally consistent with the characteristics of the regional climate variability associated with the El Niño Southern Oscillation, Pacific Decadal Oscillation, and Arctic Oscillation, as reported by previous studies. This study also reveals significant trends in some frontal zones since 1979, such as the northward shift in the eastern part of the North Pacific polar frontal zone during boreal autumn and winter and the decreasing frontal frequency on the northern coast of Norway in the European Arctic frontal zone from boreal winter to summer, including around the Beaufort Sea in the Siberia-Canada Arctic frontal zone in boreal summer.
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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 | 0.002 | 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 | 0.000 |
| 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 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".