Global Warming, El Niño, and High-Impact Storms at Extreme Altitude: Historical Trends and Consequences for Mountaineers
Bibliographic record
Abstract
Abstract The twentieth century was bracketed by two high-profile events on Mount Everest: the 1924 Mallory and Irvine disappearance and the 1996 Into Thin Air storm. During both events, fatalities occurred high on the mountain during deteriorating weather conditions. Although there have been dramatic improvements in knowledge of the mountain and in the technology used on it, it is shown that an unappreciated change that has also occurred, as a result of warming in the region, is an increase in barometric pressure. A rare and unique set of meteorological data collected at various elevations on the mountain during the 1924 British Everest expedition as well as modern datasets are used to compare and contrast conditions during the two storms and the two climbing seasons. It is shown that both storms were associated with weather systems known locally as western disturbances that resulted in summit barometric pressure drops sufficient to have exacerbated altitude-induced hypoxia. It is further shown that the Mallory and Irvine attempt occurred later in the season than typically is the case now and that this was most likely the result of a concurrent El Niño event. Despite the trend of increasing barometric pressure, the pressure drop associated with storms in the region should remain a concern for those who venture to extreme altitudes. The authors therefore argue that success and failure on Everest and other Himalayan peaks requires knowledge of the variability and trends in both the weather and climate.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".