Large lakes may moderate projected climate‐change velocity in boreal North America
Bibliographic record
Abstract
This repository contains gridded raster datasets used to quantify climate velocity and its components for annual potential evapotranspiration (PET) across the North American boreal region. The data support analyses presented in the associated manuscript “Large lakes may moderate projected climate-change velocity in boreal North America”. Climate velocity was calculated using the gradient method, defined as the ratio of the temporal gradient (rate of change over time) to the spatial gradient (rate of change across space). The datasets enable comparison of climate velocity patterns across alternative climate data products, downscaling approaches, spatial resolutions, and global climate model (GCM) inputs, with particular emphasis on lake-mediated climate buffering. Included data For each combination of climate product, spatial resolution, and GCM, the repository includes GeoTIFF rasters for: Climate velocity (cv, km yr⁻¹) Spatial gradient of PET (sg, mm km⁻¹) Temporal gradient of PET (tg, mm yr⁻¹) Climate data products include: ClimateNA (station-interpolated baseline with statistical downscaling) ERA5 reanalysis (model-based baseline with statistical downscaling) CRCM5 regional climate model (dynamical downscaling) Spatial resolutions: 1 km 22 km Global climate models: CanESM2 MPI-ESM-LR CNRM-CM5 All rasters are spatially aligned and projected consistently within each resolution. Methods summary PET was calculated from monthly temperature and precipitation using Hogg’s (1997) modified Penman–Monteith formulation. Climate velocity and its components were calculated using the gradient-based method implemented in the VoCC R package, following Loarie et al. (2009) and Burrows et al. (2011). Spatial gradients were derived from baseline climatologies using a moving-window approach, and temporal gradients represent mean projected change between historical (1981–2010) and late-century (2071–2100) periods under the RCP 8.5 scenario. Intended use These data are intended for: Climate-change exposure and vulnerability assessments Evaluation of climate velocity metrics across alternative baseline representations and downscaling approaches Identification of areas with low climate velocity and potential climate-change refugia Comparative or methodological studies of climate velocity in lake-rich or data-sparse regions Users should note that velocity estimates are sensitive to baseline climate representation, spatial resolution, and downscaling method, as discussed in the associated publication. Citation Users are requested to cite both this dataset and the associated article when using these data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".