The atmospheric role in the Arctic water cycle: A review on processes, past and future changes, and their impacts
Bibliographic record
Abstract
Abstract Atmospheric humidity, clouds, precipitation, and evapotranspiration are essential components of the Arctic climate system. During recent decades, specific humidity and precipitation have generally increased in the Arctic, but changes in evapotranspiration are poorly known. Trends in clouds vary depending on the region and season. Climate model experiments suggest that increases in precipitation are related to global warming. In turn, feedbacks associated with the increase in atmospheric moisture and decrease in sea ice and snow cover have contributed to the Arctic amplification of global warming. Climate models have captured the overall wetting trend but have limited success in reproducing regional details. For the rest of the 21st century, climate models project strong warming and increasing precipitation, but different models yield different results for changes in cloud cover. The model differences are largest in months of minimum sea ice cover. Evapotranspiration is projected to increase in winter but in summer to decrease over the oceans and increase over land. Increasing net precipitation increases river discharge to the Arctic Ocean. Over sea ice in summer, projected increase in rain and decrease in snowfall decrease the surface albedo and, hence, further amplify snow/ice surface melt. With reducing sea ice, wind forcing on the Arctic Ocean increases with impacts on ocean currents and freshwater transport out of the Arctic. Improvements in observations, process understanding, and modeling capabilities are needed to better quantify the atmospheric role in the Arctic water cycle and its changes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".