A spatio-temporal analysis of trends in Northern Hemisphere seasonal snow-cover, 1971-2017
Bibliographic record
Abstract
Seasonal snow-cover (SSC) substantially alters surface physical properties over the Northern Hemisphere (NH). It modulates processes within the energy and water cycles, thereby influencing climatology, hydrology, geomorphology and ecology. In spring and summer, snowmelt provides an essential resource for humankind. The identification, quantification and explanation of changing spatial and temporal distributions of SSC helps to predict future impacts on natural and human environments, and informs development of mitigation and adaptation strategies. Because SSC is spatially and temporally heterogeneous, meaningful estimation of trends in its distribution and duration is dependent on long records of remotely-sensed imagery. The Rutgers University Global Snow Laboratory and the United States’ National Oceanic and Atmospheric Administration provide the longest such archive (NOAA-Rutgers Snow Archive, NRSA), dating from 1966. However, several studies have raised questions about the credibility of the signs and magnitudes of trends derived from the NRSA, suggesting that they may be artifacts of technological improvements introduced in 1999. This dissertation improves the spatial resolution at which NH SSC extent and duration trends during the NRSA’s longest continuous section (since 1971) are reported, building on previous hemispheric and continental studies. It demonstrates that the magnitudes of area-related trends are sensitive to assumptions adopted when estimating SSC extent from the NRSA, and that these sensitivities vary spatially. The study assesses whether temporal trajectories of SSConset trends imply abrupt changes in 1999, particularly over more complex terrain, and finds no evidence of this. It also explores the broader climatological contexts of these trends, together with estimated departures from mean conditions. Evidence is presented at monthly intervals for causative chains linking advection of mid-tropospheric warming from lower to higher latitudes, consequent inception of climatologically novel airflows, and the incidence of significant SSConset trends of both signs. Earlier onset of snow-dominated conditions is found to be driven by augmented moisture advected from lower latitudes (in eastern Eurasia) or zonally from oceanic sources (in North America) over regional monthly mean 0°C isotherms. Delayed onset is associated with drier or warmer airflows. These findings support the interpretation that the NRSA-based trends are plausible within their spatial and temporal contexts.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".