Bibliographic record
Abstract
Snow cover is known to exert a strong influence on climate, but quantifying its impact is difficult. This study investigates the global impact of terrestrial snow cover through a pair of GCM simulations run with prognostic snow cover and with all snow cover on land eliminated (NOSNOWCOVER). In this experiment all snowfall over land was converted into its liquid–water equivalent upon reaching the surface. Compared with the control run, NOSNOWCOVER produces mean-annual surface air temperatures up to 5 K higher over northern North America and Eurasia and 8–10 K greater during winter. The globally averaged warming of 0.8 K is one-third as large as the model’s response to 2 × CO2 forcing. The pronounced surface heating propagates throughout the troposphere, causing changes in surface and upper-air circulation patterns. Despite the large atmospheric warming, the absence of an insulating snow pack causes soil temperatures in NOSNOWCOVER to fall throughout northern Asia and Canada, including extreme wintertime cooling of over 20 K in Siberia and a 70% increase in permafrost area. The absence of snow melt water also affects extratropical surface hydrology, causing significantly drier upper-layer soils and dramatic changes in the annual cycle of runoff. Removing snow cover also drastically affects extreme weather. Extreme cold-air outbreaks (CAOs)—defined relative to the control climatology—essentially disappear in NOSNOWCOVER. The loss of CAOs appears to stem from both the local effects of eliminating snow cover in mid-latitudes and a remote effect over source regions in the Arctic, where −40°C air masses are no longer able to form.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".