Large lakes moderate climate-change effects in boreal North America
Bibliographic record
Abstract
Abstract The interior biomes of North America are generally projected to experience rapid climatic change, especially with respect to drought indicators like potential evapotranspiration (PET). Focusing on the lake-rich and topographically varied North American boreal biome, we compared gradient-based climate velocity metrics for PET derived from the statistically downscaled ClimateNA product with dynamically downscaled metrics based on the Canadian regional climate model (CRCM5), which includes a 1-D freshwater lake model. We also developed regression tree models to examine the effects of land cover, topography, and geography on these differences. Across a range of global climate models (GCM), we found consistent, large differences in PET velocity–over 100 km/yr in some areas– between CRCM5 and ClimateNA. Within the boreal biome, we found that differences in the spatial gradient of change (mm/km), and hence in the gradient velocity metric (temporal gradient/spatial gradient) of annual PET, were largely explained by the percentage of lake coverage, especially at a broad scale (110-km × 110-km). Elevation effects were not detected. We found that the simple gradient velocity metric was remarkably robust to differences among GCMs in future PET projections, due to the high relative importance of spatial variation in temperature (i.e., spatial gradient), which often exceeded the magnitude of projected future change (i.e., temporal gradient). An important implication of our analysis is that regions surrounding large lakes are likely to be somewhat buffered from the full effects of climate change, serving as potential refugia for boreal ecosystems at a broad spatial scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".