Trends of Cyclone Characteristics in the Arctic and Their Patterns From Different Reanalysis Data
Bibliographic record
Abstract
Abstract Cyclones in the Arctic are detected and tracked in four different reanalysis data sets from 1981 to 2010. In great detail the spatial and seasonal patterns of changes are scrutinized with regards to their frequencies, depths, and sizes. We find common spatial patterns for their occurrences, with centers of main activity over the seas in winter, and more activity over land and over the North Pole in summer. The deep cyclones are more frequent in winter, and the number of weak cyclones peaks in summer. Overall, we find a good agreement of our tracking results across the different reanalyses. Regarding the frequency changes, we find strong decreases in the Barents Sea and along the Russian coast toward the North Pole and increases over most of the central Arctic Ocean and toward the Pacific in winter. Areas of increasing and decreasing frequencies are of similar size in winter. In summer there is a longish region of increase from the Laptev Sea toward Greenland, over the Canadian archipelago, and over some smaller regions west of Novaya Zemlya and over the Russia. The larger part of the Arctic experiences a frequency decrease. All the summer changes are found statistically unrelated to the winter patterns. In addition, the frequency changes are found unrelated to changes in cyclone depth and size. There is generally good agreement across the different reanalyses in the spatial patterns of the trend sign. However, the magnitudes of changes in a particular region may strongly differ across the data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".