Understanding the drivers of near-surface winds in Adélie Land, East Antarctica
Bibliographic record
Abstract
Abstract. Near-surface winds play a crucial role in the climate of Antarctica, but accurately quantifying and understanding their drivers is complex. They result from the contribution of two distinct families of drivers: the large-scale pressure gradient and surface-induced pressure gradients known as katabatic and thermal wind. The extrapolation of vertical potential temperature above the boundary layer down to the surface enables us to separate and quantify the contribution of these different pressure gradients in the momentum budget equations. Using this method applied to outputs of the regional atmospheric model MAR at a 3-hourly resolution, we find that the seasonal and spatial variability in near-surface winds in Adélie Land is dominated by surface processes. On the other hand, high-frequency temporal variability (3-hourly) is mainly controlled by large-scale variability everywhere in Antarctica, except on the coast. In coastal regions, although the katabatic acceleration surpasses all other accelerations in magnitude, none of the katabatic or large-scale accelerations can be identified as the single primary driver of near-surface wind variability. The angle between the large-scale acceleration and the surface slope is a key factor in explaining strong wind speed events: the highest-wind-speed events happen when the katabatic and large-scale forcing are aligned, although each acceleration, when acting alone, can also cause strong wind speed. This study underlines the complexity of the drivers of Antarctic surface winds and the value of the momentum budget decomposition to identify drivers at different spatial and temporal scales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".