Great Lakes Basin Heat Waves: An Analysis of Their Increasing Probability of Occurrence Under Global Warming
Bibliographic record
Abstract
Extreme heat events in the Great Lakes Basin (GLB) region of eastern North America are expected to increase in concert with greenhouse gas (GHG) induced global warming. The extent of this regional increase is also influenced by the direct effects of the Great Lakes themselves. This paper describes results from an ensemble of dynamically downscaled global warming projection using the Weather Research and Forecast (WRF) regional climate model coupled to the Freshwater Lake (FLake) model over the Great Lakes region. In our downscaling pipeline, we explore two sets of WRF physics configurations, with the initial and boundary conditions provided by four different fully coupled Global Climate Models (GCMs). Three time periods are investigated, namely an instrumental period (1979–1989) that is employed for validation, and a mid-century (2050–2060) and an end-century (2085–2100) periods that are used to understand the future impacts of global warming. Results from the instrumental period are characterized by large variations in climate states between the ensemble members, which is attributed to differences in both GCM forcing and WRF physics configuration. Results for the future periods, however, are such that the regional model results have good agreement with GCM results insofar as the rise of average temperature with GHG is concerned. Analysis of extreme heat events suggests that the occurrence rate of such events increase steadily with rising temperature, and that the Great Lakes exert strong lake effect influence on extreme heat events in this region.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".