Analysis of summer heat budget of lakes under a changing climate across a geographic gradient
Bibliographic record
Abstract
Warming surface water temperature is the most direct consequence of climate change in lakes and therefore, predicting the heat exchange at the air-water interface is important to understand how atmos-pheric forcing will affect lake temperature and thermal structure. Here, we forced a one-dimensional hydrodynamic lake model with outputs from four different climate models under three future green-house gas emission scenarios from 1976 to 2099. To investigate the changes in summer (June to August or December to February in the northern or southern hemisphere, respectively) net surface heat flux and the individual flux components for 47 lakes with varying in size and geographic location were analysed. The results show that in the most extreme case (RCP 8.5) summer lake surface temperature is projected to increase by 4.72±0.70 °C by the end of the 21st century, due to increasing absorption of solar radiation (17.40±8.81 W m-2) and of long-wave radiation (33.01±5.44 W m-2). The increased lake surface tem-perature, also lead to higher heat losses to the atmosphere by outgoing long-wave radiation (27.54±4.07 W m-2) and by latent heat flux (25.10±7.37 W m-2), while a lower heat loss by sensible heat flux is projected (-3.20±1.94 W m-2). Altogether, the net heat balance and thus the accumulation of heat in the lakes over summer remains almost unchanged. However, a shift in the contributions of the individuals heat fluxes is projected, with the latent heat flux gaining relative importance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".