Attribution of the Observed Spring Snowpack Decline in British Columbia to Anthropogenic Climate Change
Bibliographic record
Abstract
Abstract A detection and attribution analysis on the multidecadal trend in snow water equivalent (SWE) has been conducted in four river basins located in British Columbia (BC). Monthly output from a suite of 10 general circulation models (GCMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used, including 40 climate simulations with anthropogenic and natural forcing combined (ALL), 40 simulations with natural forcing alone (NAT), and approximately 4200 yr of preindustrial control simulations (CTL). This output was downscaled to ° spatial resolution and daily temporal resolution to drive the Variable Infiltration Capacity hydrologic model (VIC). Observed (manual snow survey) and VIC-reconstructed SWE, which exhibit declines across BC, are projected onto the multimodel ensemble means of the VIC-simulated SWE based on the responses to different forcings using an optimal fingerprinting approach. Results of the detection and attribution analysis shows that these declines are attributable to the anthropogenic forcing, which is dominated by the effect of increases in greenhouse gas concentration, and that they are not caused by natural forcing due to volcanic activity and solar variability combined. Anthropogenic influence is detected in three of the four basins (Fraser, Columbia, and Campbell Rivers) based on the VIC-reconstructed SWE, and in all basins based on the manual snow survey records. The simulations underestimate the observed snowpack trends in the Columbia River basin, which has the highest mean elevation. Attribution is supported by the detection of human influence on the cold-season temperatures that drive the snowpack reductions. These results are robust to the use of different observed datasets and to the treatment of low-frequency variability effects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".