Large lakes in climate models: A Great Lakes case study on the usability of CMIP5
Bibliographic record
Abstract
Large lakes have an impact on regional weather. In addition, they can be both sensitive to and influence regional climate changes. In the climate models that are used to investigate future climate changes, lakes are greatly simplified and sometimes absent. At the regional scale, this can have strong implications for the quality of the model information about the future. Through our work with climate information users in the Laurentian Great Lakes region, we have found that basic credibility of the information requires the underlying climate models simulate lake-atmosphere-land interactions. We are not aware of efforts within the scientific community to make known how individual large lakes are represented in models and how those representations translate to the quality of the data for particular regions. We share our framework for identifying how the Laurentian Great Lakes are represented in the Coupled Model Intercomparison Project (CMIP) version 5 climate models. We found that most CMIP5 models do not simulate the Great Lakes in a way that captures their impact on the regional climate, which is a credibility issue for their projections. We provide a perspective on the usability of CMIP5 for practitioners in the Great Lakes region and offer recommendations for alternative options.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".