Projections and Physical Drivers of Extreme Precipitation in Greenland & Baffin Bay
Bibliographic record
Abstract
Abstract Extreme precipitation events can have substantial impacts on communities and the climate system in the warming Arctic, but their changes are not well understood. In this study, the characteristics of extreme precipitation days in the eastern Canadian Arctic and Greenland are examined in historical (1980–1999) and future (2080–2099, SSP5‐8.5) variable‐resolution Community Earth System Model simulations. Comparisons between the simulations illustrate potential changes in the frequency and intensity of these events in the region. Extreme precipitation is expected to increase broadly across the region. The frequency of the most intense daily precipitation rates is projected to rise, particularly in the northernmost areas. The seasonality of extremes does not shift substantially in the future simulation with most areas receiving the highest accumulations and increases during the summer. However, southeastern Greenland is projected to see decreases in extreme precipitation. Algorithms detecting atmospheric rivers and cyclones are employed to assess how their changes may factor into changes in extreme precipitation. Cyclone frequency remains largely consistent with slight decreases near southeastern Greenland, which may explain decreased extreme precipitation seen in the region. Atmospheric rivers are projected to become more frequent across the domain, largely during the summer. Although the majority of the region's extreme precipitation is associated with cyclones, this suggests that atmospheric rivers become more important in a warming climate. These results provide insight into potential changes in impactful precipitation events in the region and how the processes driving them may change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".