A One‐Dimensional Lake Model in ECCC's Land Surface Prediction System
Bibliographic record
Abstract
Abstract In most of Environment and Climate Change Canada's (ECCC) current operational systems, inland water physical processes are simulated using a simple water scheme. Water surface temperatures and ice cover fractions are updated daily using analyses. However, ECCC recognizes the need for interactive lakes in its weather and environmental prediction systems, such as those used to forecast surface conditions and floods. As a first step toward this goal, the current study evaluates the impact of the Canadian Small Lake Model (CSLM) in an offline context on surface water temperature, ice phenology and near‐surface atmospheric conditions. The use of CSLM increases lake surface temperatures and decreases its RMSE during ice‐free months, which has a direct impact on the 2‐m air temperature by reducing the cold bias observed in the simulation without CSLM, particularly over larger lakes. CSLM improves ice cover in subgrid lakes, while having a neutral impact on intermediate lakes. On large lakes, CSLM tends to degrade ice cover simulation in southernmost lakes, while improving ice cover in northernmost lakes. The increased lake ice cover in CSLM, particularly over subgrid lakes and in the northern latitudes, has a strong impact on humidity fluxes at the surface during wintertime with a near‐interruption of evapotranspiration over lakes. In summertime, increased water temperature with CSLM leads to a 38% increase in evapotranspiration. With these results, it is expected that the synergy of CSLM and lake‐related observations will improve the simulation and initialization of lake conditions in ECCC's systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".