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
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it