Investigating tropical and midlatitude drivers of Arctic atmospheric energy transport
Bibliographic record
Abstract
Planetary waves with zonal wavenumbers k ≤ 3 dominate poleward atmospheric energy transport and its associated Arctic warming and moistening impacts in reanalysis data. Previous work suggests planetary waves generated by tropical warm pool Sea-Surface Temperatures (SSTs) and midlatitude synoptic waves (k ≥ 4) can drive Arctic energy transport. Here, we investigate tropical and midlatitude drivers of Arctic planetary wave transport using an idealised aquaplanet model. First, we show that the zonally-symmetric model qualitatively captures the main characteristics of observed planetary wave transport, as well as its impacts in the Arctic. Next, we show that an idealised tropical warm pool, driven by regional SST forcing, amplifies but is not the dominant source of Arctic planetary wave transport. Finally, lag-regressions using reanalysis and model data suggest midlatitude synoptic waves compensate rather than drive Arctic planetary wave transport. The results do not support the simple geometric effect of midlatitude synoptic waves aliasing onto Arctic planetary waves on a sphere, but rather point towards more complex scale interactions and local drivers of Arctic planetary wave transport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".