Extreme Moisture Transport into the Arctic Linked to Rossby Wave Breaking
Bibliographic record
Abstract
The transport of moisture into the Arctic is tightly connected to midlatitude dynamics. We show that the bulk of the transient poleward moisture transport across 60°N is driven by extreme transport (fluxes greater than the 90th percentile) events. We demonstrate that these events are closely related to the two types of Rossby wave breaking (RWB)—anticyclonic wave breaking (AWB) and cyclonic wave breaking (CWB). Using a RWB tracking algorithm, we determine that RWB can account for approximately 68% of the extreme poleward moisture transport by transients across 60°N in winter and 56% in summer. Additional analysis suggests that the seasonality of such RWB-related moisture transport is determined approximately equally by (1) the magnitude of transport (which is largely a function of the background moisture gradient) and (2) the frequency of RWB occurrence. The seasonality of RWB occurrence is, in turn, tied to the seasonal variation of the latitude of the jet streams—AWB-related (CWB-related) transport occurs more frequently when the jet is shifted poleward (equatorward). The interannual variability of RWB-related transport across 60°N in winter is shown to be strongly influenced by climate variability captured by the El Nino/Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). In the positive (negative) phase of ENSO, AWB transports less (more) moisture through the Bering Strait and CWB transports more (less) through western Canada. In the positive (negative) phase of the NAO, AWB transports more (less) moisture through the Norwegian Sea and CWB transports less (more) along the west coast of Greenland. These results highlight that low-frequency climate variability outside of the polar regions can influence the Arctic water vapor by modulating extreme synoptic transport events.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".