Past and Future Hydro-climatological Drivers of Summer Precipitation Variability in the Great Lakes Region in Coupled Model Intercomparison Project Phase 6 (CMIP6)
Bibliographic record
Abstract
The Great Lakes Regions are the largest surface freshwater system on earth containing 20% of the global freshwater reserves and supply drinking water to 10% of the United States and 30% of the Canadian population. Nearly 25% of Canadian and 7% of American agricultural production are in the watershed. Trends and variability in precipitation over the Great Lakes basin has critical global and local implications for water resources management, which become especially problematic under climate change. Understanding how Great Lakes hydrometeorology will respond to anthropogenic climate change is complicated by the climatological complexity of the region. The Great Lakes basin spans over 1200 kilometers (about 745.65 mi) and straddles four different climate regions in two countries: the Midwestern and Eastern climate regions of the United States, and the Northeastern Forest and Laurentian Great Lakes climate regions of Canada. The atmospheric dynamics of hydroclimatic circulation vary across the Great Lakes basin. In the western Great Lakes, precipitation variability is strongly leveraged by the ENSO oscillation, in the eastern Great Lakes, precipitation variability is additionally impacted by circulation anomalies associated with the North Atlantic Hadley circulation. These atmospheric features will likely be impacted differently by climate change. Projecting pan-Great Lakes hydro-climatological shifts in the twenty-first century will require a diagnostic assessment of 1) how numerical climate models parameterize relationships between local circulation patterns and precipitation variability and 2) how these relationships will likely change under global warming. Currently, there is very little agreement in regional 21st century precipitation predictions between CMIP6 ensemble models. In this study, we evaluate precipitation simulations from 12 different general circulation models participating in CMIP6 that are representative of the full range of variability in numeric representation of North American climate. We quantify each model’s accuracy in capturing historic seasonal wet and dry anomalies in the Great Lakes region. We then evaluate consistency between characteristic modes of anomalous hydroclimatic circulation in historical observations and model simulations. Based on the physical mechanism by which climate models simulate historical anomalous hydroclimatic circulation, and translate these circulation anomalies into precipitation anomalies, we identify a subset of 21st century climate model predictions which we believe to be most physically plausible. Implications for future hydroclimate are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".