Analysis of snow in the 20th and 21st century Geophysical Fluid Dynamics Laboratory coupled climate model simulations
Bibliographic record
Abstract
We evaluate the representation of the 20th century Northern Hemisphere, North American, and Eurasian snow cover extent, frequency, and mass by the Geophysical Fluid Dynamics Laboratory coupled climate model, version 2 (CM2) and then explore the 21st century trends and changes in these quantities. The CM2 simulations of 20th century climate capture the seasonal cycle in Northern Hemisphere snow cover extent and produce a mean annual snow area of 25 × 106 km2 that equals the satellite‐based observations for the period 1973–2000. The simulated snow cover frequency and snow mass generally decline from north to south, but longitudinal gradients in these variables are also found. Snow mass over North America, especially during spring, is underestimated by CM2. Simulations of 21st century climate using three Intergovernmental Panel on Climate Change Special Report on Emission Scenarios reveal strong trends in Northern Hemisphere snow cover extent, frequency, and mass. These simulations suggest that the annual Northern Hemisphere mean snow cover extent (total snow mass) will decrease by 12 to 26% (20 to 40%) by 2100 from their 21st century mean values. Large declines in 21st century snow cover frequency (up to 50%) and snow mass (up to 100 kg m−2) arise during fall, winter, and spring over southern Canada and the northern United States, the Western Cordillera of North America, and western Eurasia compared to the 20th century CM2 simulations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".