Interactive lakes in the Canadian Regional Climate Model, version 5: the role of lakes in the regional climate of North America
Bibliographic record
Abstract
Two one-dimensional (1-D) column lake models have been coupled interactively with a developmental version of the Canadian Regional Climate Model. Multidecadal reanalyses-driven simulations with and without lakes revealed the systematic biases of the model and the impact of lakes on the simulated North American climate. The presence of lakes strongly influences the climate of the lake-rich region of the Canadian Shield. Due to their large thermal inertia, lakes act to dampen the diurnal and seasonal cycle of low-level air temperature. In late autumn and winter, ice-free lakes induce large sensible and latent heat fluxes, resulting in a strong enhancement of precipitation downstream of the Laurentian Great Lakes, which is referred to as the snow belt. The FLake (FL) and Hostetler (HL) lake models perform adequately for small subgrid-scale lakes and for large resolved lakes with shallow depth, located in temperate or warm climatic regions. Both lake models exhibit specific strengths and weaknesses. For example, HL simulates too rapid spring warming and too warm surface temperature, especially in large and deep lakes; FL tends to damp the diurnal cycle of surface temperature. An adaptation of 1-D lake models might be required for an adequate simulation of large and deep lakes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".