The Role of Northern Lakes in a Regional Energy Balance
Bibliographic record
Abstract
Abstract There are many lakes of widely varying morphometry in northern latitudes. For this study region, in the central Mackenzie River valley of western Canada, lakes make up 37% of the landscape. The nonlake components of the landscape are divided into uplands (55%) and wetlands (8%). With such abundance, lakes are important features that can influence the regional climate. This paper examines the role of lakes in the regional surface energy and water balance and evaluates the links to the frequency–size distribution of lakes. The primary purpose is to examine how the surface energy balance may influence regional climate and weather. Lakes are characterized by both the magnitude and temporal behavior of their surface energy balances during the ice-free period. The impacts of combinations of various-size lakes and land–lake distributions on regional energy balances and evaporation cycles are presented. Net radiation is substantially greater over all water-dominated surfaces compared with uplands. The seasonal heat storage increases with lake size. Medium and large lakes are slow to warm in summer. Their large cumulative heat storage, near summer’s end, fuels large convective heat fluxes in fall and early winter. The evaporation season for upland, wetland, and small, medium, and large lakes lasts for 19, 21, 22, 24, and 30 weeks, respectively. The regional effects of combinations of surface types are derived. The region is initially treated as comprising uplands only. The influences of wetland, small, medium, and large lakes are added sequentially, to build up to the energy budget of the actual landscape. The addition of lakes increases the regional net radiation, the maximum regional subsurface heat storage, and evaporation substantially. Evaporation decreases slightly in the first half of the season but experiences a large enhancement in the second half. The sensible heat flux is reduced substantially in the first half of the season, but changes little in the second half. For energy budget modeling the representation of lake size is important. Net radiation is fairly independent of size. An equal area of medium and large lakes, compared with small lakes, yields substantially larger latent heat fluxes and lesser sensible heat fluxes. Lake size also creates large differences in regional flux magnitudes, especially in the spring and fall periods.
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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 | 0.001 |
| 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) | 0.002 | 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".