The representation of large lakes in high-resolution regional climate models
Bibliographic record
Abstract
It has been found that large lakes, a common component of the North American (NA) landscape, can affect the water cycle and modulate temperatures in the surrounding regions. The purpose of this study is to evaluate different lake representations in regional climate model simulations. We use the Weather Research and Forecasting (WRF) model, a widely-used regional climate model, forced by the ERA5 reanalysis product. The study is performed for a 40 year historic period (1979-2019) at a resolution of 12 km. The lakes of concern include the Laurentian Great Lakes, which straddle the US-Canada border; the Great Slave and Great Bear Lakes of the Northwest Territories; and the Lakes Winnipeg and Winnipegosis. Alongside the default lake model, two new column lake models are employed: FLake, a more widely used model, and GL25, a recent, physics-based model. These models have been somewhat successful at alleviating inadequacies of the default model by introducing additional process representations, such as a more realistic surface albedo formulation and a better parameterization for vertical overturning. Additionally, we consider the effect of vertical eddy diffusivity and lake stratification on the model performance. While no column lake model is expected to perform perfectly, our goal here is to identify when (seasonally) and where (geographically) a model produces reliable results, including ice cover distribution and near-surface temperature. The effect of the lake representation on the surrounding regions (e.g., lake-effect precipitation) is also evaluated. We find that the two new lake models perform reasonably well, but there are significant differences in seasonal biases with GL25 performing better in summer and FLake performing better in winter; in fact, FLake reproduces the ice cover of the Great Lakes very well. Furthermore, the biases do not seem to be affected by surface wind induced circulation, and hence 3D modelling may not be a requirement for lake modelling. This study can help with the selection of lake models for regional climate modelling in the NA region and inform the interpretation of the predictions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".