Impacts of Climate Change on Arctic Winter Cyclones
Bibliographic record
Abstract
Abstract We simulated Arctic climate using a high‐resolution implementation of WRF driven by HadGEM‐ES2 climate model outputs, following IPCC5 warming scenarios RCP 2.6, RCP 4.5 and RCP 8.5. The downscaled results indicate that, by the end‐of‐century, there are no significant changes in the average minimum central pressures or total number of winter Arctic cyclones. However, there are significant changes in the spatial patterns. For example, the frequency and vorticity of cyclones tend to increase over the western Arctic. Due to the poleward movement of the polar frontal zone and the increased low‐level tropospheric baroclinicity, more cyclones are expected to form within the Arctic Basin and migrate into the western‐central Arctic. We expect about 39% more cyclone tracks forming and dying in the Arctic Basin under RCP8.5, compared to present climate. On the other hand, with the reduced baroclinicity in the entire troposphere, there is a reduced genesis, frequency, and vorticity of cyclones over the Atlantic Arctic. Moreover, the depth of cyclones shows a robust decrease over the Arctic Basin, suggesting weakened eddy kinetic energy. With increasing greenhouse gases, the changes in cyclone vorticities and their depths tend to be stronger. In addition, the high resolution Polar WRF results demonstrate that changes in cyclone track density and intensities consistently become more pronounced with increasing radiative forcing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".