Northern Lake Impacts on Local Seasonal Climate
Bibliographic record
Abstract
Abstract It is well known that large lakes can perturb local weather and climate through mesoscale circulations, for example, lake effects on storms and lake breezes, and the impacts on fluxes of heat, moisture, and momentum. However, for both large and small lakes, the importance of atmosphere–lake interactions in northern Canada is largely unknown. Here, the Canadian Regional Climate Model (CRCM) is used to simulate seasonal time scales for the Mackenzie River basin and northwest region of Canada, coupled to simulations of Great Bear and Great Slave Lakes using the Princeton Ocean Model (POM) to examine the interactions between large northern lakes and the atmosphere. The authors consider the lake impacts on the local water and energy cycles and on regional seasonal climate. Verification of model results is achieved with atmospheric sounding and surface flux data collected during the Canadian Global Energy and Water Cycle Experiment (GEWEX) program. The coupled atmosphere–lake model is shown to be able to successfully simulate the variation of surface heat fluxes and surface water temperatures and to give a good representation of the vertical profiles of water temperatures, the warming and cooling processes, and the lake responses to the seasonal and interannual variation of surface heat fluxes. These northern lakes can significantly influence the local water and energy cycles.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".