Forcing Mechanisms of Strong Surface Winds in a Dust Storm–Prone, High-Latitude Proglacial Valley
Bibliographic record
Abstract
Abstract Proglacial valleys are important historical and contemporary sources of mineral dust emissions. These dust sources are situated within a complex mountainous terrain, which induce a series of superimposed meteorological phenomena. This paper assesses the forcing mechanisms of high wind speed (HWS) events in the dust storm–prone A’ą¨y Chu (Slims River) valley in southwest Yukon, Canada (60.96°N, 138.60°W). The period of 7 July 2021–30 September 2022 (≈15 months) is examined using surface meteorological stations distributed within and near the valley. A hierarchical indexing approach is applied, supported by high-resolution Weather Research and Forecasting (WRF) simulations, to identify dominant meteorological forcing mechanisms of strong wind events. Half (50.0%) of HWS events in the valley occur under high above-valley wind speeds. Little evidence was found that off-glacier winds drive HWS, despite frequent attribution of these winds to local dust emissions in the literature. A summertime valley wind system is likely responsible for persistent nocturnal HWS near the valley delta (61.00°N, 138.52°W), contributing to the 28% of summer HWS under distinctly calm aloft conditions. Efforts to constrain HWS characteristics from ambient conditions were not successful, partly confounded by adjacent valley wind systems. Ascertaining the configuration of phenomena responsible for high surface winds in these locations is key to yielding the predictive capacity of dust emissions from these rapidly deglaciating landscapes. Significance Statement Mineral dust emissions from high-latitude (>50°N) regions are poorly represented in current modeling approaches. Many dust sources are situated in a mountainous terrain, which heavily influences local wind systems. We investigated the drivers of strong surface winds in the A’ą¨y Chu valley (Canada) with a combination of surface observations and meteorological modeling. The relative importance of different meteorological systems is identified, indicating key phenomena to resolve in future efforts to simulate mineral dust emissions from this region and quantify contributions to the global dust budget.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".