Future increase in Arctic moisture transport dominated by midlatitude CO2 forcing
Bibliographic record
Abstract
Northward transport of moisture is a key driver of Arctic warming and is projected to increase in the future. However, the importance of local versus remote forcing, and the mechanisms through which they impact the Arctic, are unclear. Here, we determine the causes of increased Arctic moisture transport using climate model simulations in which idealised CO2 forcing is prescribed in distinct latitudinal regions. The sum of the regional responses reproduce the response to global forcing and resembles the future change seen in state-of-the-art climate model projections. Midlatitude forcing dominates future changes in Arctic moisture transport, while tropical and polar forcing play a secondary role. Increases in zonal-mean Arctic moisture transport result mostly from changes in transient eddy transport and scale with increased water vapour. Similar increases are found in an analogous set of simulations without the presence of land and orography, suggesting climatological zonal asymmetries, such as stationary eddies and gradients of sea-surface temperature, do not set the first-order zonal-mean response. In contrast, zonal changes in Arctic moisture transport are dominated by changes in stationary eddy transport, where both increased water vapour and meridional wind changes are important. Thus, the future increase in Arctic moisture transport depends primarily on dynamic and thermodynamic processes forced in midlatitudes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".