Uncertainty in Regional Climate Model Mean Runoff Projections under Climate Change: Case Study of Labrador's Churchill River Basin
Bibliographic record
Abstract
An ensemble of seven climate models from the North American Regional Climate Change Assessment Program (NARCCAP) was used to examine uncertainty in simulated runoff changes from a base period (1971–2000) to a future period (2041–2070) for the Churchill River basin, Labrador, Canada. Three approximations for mean annual runoff from each ensemble member were included in the analysis: (i) atmospheric moisture convergence, (ii) the balance between precipitation and evaporation, and (iii) instantaneous runoff output from respective land-surface schemes. Using data imputation (i.e., reconstruction) and variance decomposition it was found that choice of regional climate model (RCM) made the greatest contribution to uncertainty in the climate change signal, whereas the boundary forcing of a general circulation model (GCM) played a smaller, though non-negligible, role. It was also found that choice of runoff approximation made a substantial contribution to uncertainty, falling between the contribution from RCM and GCM choice. The NARCCAP output and imputed data were used to calculate mean and median annual changes and results were presented via probability distribution functions to facilitate decision making. Mean and median increases in runoff for the basin were found to be 11.2% and 8.9%, respectively.
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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 | 0.001 |
| 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".