Simulated heat storage in a perennially ice‐covered high Arctic lake: Sensitivity to climate change
Bibliographic record
Abstract
Perennially ice‐covered, meromictic lakes occur along the northern coast of Ellesmere Island in the Canadian high Arctic and have distinctive conductivity and temperature profiles. They are salinity stratified and have deep thermal maxima that persist throughout the year at temperatures up to 60°C above the winter minimum in the overlying atmosphere. Heat transfer in one of these lakes (Lake A, latitude 83.0°N, longitude 75.4°W) was simulated using a high spatial resolution model based on a one‐dimensional heat diffusion and radiative transfer equation, which was solved through numerical integration. Boundary conditions were forced using climate data from an automated weather station installed next to the lake. There was a good fit between simulated and observed water column temperatures, including the midwater temperature maximum of 8.5°C, after 63 years of heating (RMSE = 0.10°C). This suggests that Lake A became ice‐free in the 1940s, a known period of intense warming of the circumpolar Arctic. The model was sensitive to forcing by photosynthetically active radiation (PAR, 400–700 nm), in addition to optically related parameters such as surface reflectance, snow and ice cover, and the PAR diffuse attenuation coefficient. The unusual thermal structure is affected by stratified layers of pigmented microbial communities, which enhance the absorption of solar radiation. Simulation of ice‐free summers revealed that the lake's thermal profile would lose its characteristic shape over several decades and that ongoing climate change could reduce the thermal maximum from 8.5° to 4°C within 50 years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".