Investigating Multidecadal Trends in Ice Cover and Subsurface Temperatures in the Laurentian Great Lakes Using a Coupled Hydrodynamic–Ice Model
Bibliographic record
Abstract
Abstract While changing lake surface conditions have received significant research scrutiny, changes in subsurface conditions, including stratification and heat content, remain largely unexplored. In this work, we highlight changes in thermal structure, stratification dynamics, and ice characteristics in the Laurentian Great Lakes (Lakes Superior, Huron, Michigan, Erie, and Ontario) as simulated between 1979 and 2021. Three-dimensional lake hydrodynamics and ice cover were modeled using the Finite Volume Community Ocean Model (FVCOM) coupled with the Los Alamos sea ice model (CICE). Analysis revealed significant increases in surface (0.4°–0.6°C decade −1 ) and subsurface (0.1°–0.4°C decade −1 ) temperatures as well as dramatic losses in ice cover (1%–8% decade −1 ) and ice volume (0–3 km 3 decade −1 ) over the last 40 years. Estimated surface heating rates were strongest during the summer and fall, while subsurface warming was most rapid during the nearly isothermal winter and spring. Intensified (decreased) summer (winter) stratification led to shifts in lake turnover dynamics, with delayed fall turnover dates (2–6 days decade −1 ) and earlier spring overturn dates (2–9 days decade −1 ). Modeled surface temperatures (LST), bottom temperatures (LBT), and annual averaged ice cover (AAIC) were used to estimate low-frequency climate signals, which were highly correlated with the Atlantic multidecadal oscillation. Warming trends fit to residual climate signals (LST: 0.1°C decade −1 ; LBT: 0.03°C decade −1 ; AAIC: −1% decade −1 ), calculated by removing low-frequency variability from the raw climate signal, were lower than those fit to associated low-frequency components, suggesting that recent climate change in the Great Lakes may be strongly influenced by natural multidecadal climate variability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".