Dynamical Downscaling over the Great Lakes Basin of North America Using the WRF Regional Climate Model: The Impact of the Great Lakes System on Regional Greenhouse Warming
Bibliographic record
Abstract
The Weather Research and Forecasting model (WRF) is employed to dynamically downscale global warming projections produced using the Community Climate System Model (CCSM). The analyses are focused on the Great Lakes Basin of North America and the climate change projections extend from the instrumental period (1979–2001) to midcentury (2050–60) at a spatial resolution of 10 km. Because WRF does not currently include a sufficiently realistic lake component, simulations are performed using lake water temperature provided by D.V. Mironov’s freshwater lake model “FLake” forced by atmospheric fields from the global simulations. Results for the instrumental era are first compared with observations to evaluate the ability of the lake model to provide accurate lake water temperature and ice cover and to analyze the skill of the regional model. It is demonstrated that the regional model, with its finer resolution and more comprehensive physics, provides significantly improved results compared to those obtained from the global model. It much more accurately captures the details of the annual cycle and spatial pattern of precipitation. In particular, much more realistic lake-induced precipitation and snowfall patterns downwind of the lakes are predicted. The midcentury projection is analyzed to determine the impact of downscaling on regional climate changes. The emphasis in this final phase of the analysis is on the impact of climate change on winter snowfall in the lee of the lakes. It is found that future changes in lake surface temperature and ice cover under warmer conditions may locally increase snowfall as a result of increased evaporation and the enhanced lake effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".