Winter snowpack loss increases warm-season compound hot-dry extremes
Bibliographic record
Abstract
Ongoing warming intensifies snowpack extremes, posing significant hydroclimatic risks to socio-ecological systems. However, the relation between snowpack extremes and subsequent compound hydroclimatic extremes remains unclear. Here, we investigated the impact of snowpack extremes on warm-season compound hydroclimatic extremes in the Northern Hemisphere using multisource datasets from 1980 to 2022. We found widespread increases in deficient, short, and deficient-short snowpack extremes, triggering more compound hot-dry extremes within a month after snowpack disappearance (mean coincidence rate over 0.6, p < 0.05). The impact of compound snowpack extremes exceeded that of individual snowpack extremes in both areas (over 10%) and coincidence rates (over 0.2). Meanwhile, increased intensity, rather than frequency, of snowpack extremes drove mainly the occurrence of compound hydroclimatic extremes. Furthermore, background climate factors, followed by vegetation, topography, and soil, affected relations between snowpack and compound hydroclimatic extremes. These findings will deepen our understanding of the emerging consecutive extremes and improve their predictability. The Northern Hemisphere experiences a significant correlation between snowpack extremes and compound hydroclimatic extremes, with increased snowpack extremes triggering more hot-dry extremes, influenced by background climate, vegetation, topography, and soil, according to results from multisource remote sensing data analysis from 1980 to 2022.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".