Connectivity between Eurasian snow cover extent and Canadian snow water equivalent and river discharge
Bibliographic record
Abstract
We explore pan‐Arctic climate connectivity by examining historical time series of satellite‐based measurements of Eurasian snow cover extent and of observed Canadian snow water equivalent (SWE) and freshwater discharge, with a focus on the Churchill River Basin of Labrador and the Chesterfield Inlet Basin of Nunavut. Analysis of the data reveals statistically significant positive (negative) correlations between spring and summer Eurasian standardized snow cover extent anomalies and annual maximum monthly SWE as well as freshwater discharge in the Churchill River (Chesterfield Inlet) Basin the following year. A spatially coherent response to the forcing is observed since 19 rivers draining more than 0.6 × 10 6 km 2 of northern Quebec and Labrador and with a mean annual total discharge of 320 km 3 yr −1 show statistically significant positive correlations to the annual Eurasian standardized snow cover extent anomalies. The origin of this pan‐Arctic climate connectivity is related to the persistent nature of the Eurasian snow cover extent anomalies and the associated accumulated gains or deficits in the surface radiation and water budgets that impose a memory in the climate system. The Eurasian snow cover extent anomalies provide some degree of predictability (up to 1 year in advance) of the surface water budget in the Churchill River and Chesterfield Inlet Basins. They further suggest that a declining trend in Eurasian snow cover extent will yield decreasing (increasing) SWE and river discharge in the Churchill River (Chesterfield Inlet) Basin in the 21st century.
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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 | 0.001 | 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) | 0.001 | 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 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".