Sensitivity of the Thermal Structure and Circulation Patterns of a Simple Idealized Lake and Lake Erie to External Driving Forces
Bibliographic record
Abstract
Lake Erie has been a great source of economic growth and drinking water for both Canada and the United States. All lakes in temperate regions that stratify during summer are prone to hypoxia as they will experience some degree of dissolved oxygen (DO) depletion in the hypolimnion, however, Lake Erie has been very unlucky as it experiences almost all the classes of hypoxic conditions due to its thin hypolimnion. Lately, many researchers focus on Lake Erie to understand the reasons for the abnormally large harmful algal blooms in Lake Erie and its hypoxic and anoxic conditions which has been negatively affecting its aquatic ecosystems and services, water quality, etc., which in turn has impacted the economy. \n \nUnderstanding the lake's thermal structure and circulation patterns are crucial for precise assessment of the water quality, physics, and biochemical characteristics, and also the effects of climate change on the lake in order to make informed management decisions. In this thesis, the 3-D hydrodynamics MITgcm was used to model a simple Idealized Lake and Lake Erie to study the sensitivity of their thermal structures and circulation patterns to different external driving forces using the two common 2-band short wave parameterizations, Jerlov IA and III and a 3-band short wave radiation model to simulate the motion. The simple idealized lake was forced with South-North linearly varying winds, long wave and short wave radiation, relative humidity, and air temperature while Lake Erie was modeled on a 500 m horizontal grid and forced with the meteorological data obtained from the National Water Research Institute of Environment Canada and the National Data Buoy Center for year 2008. The model results from simulating the simple Idealized Lake (the modeled current in the upper layer) has a good agreement with the analytical results, this confirms the robustness of MITgcm model. Our work suggests that the 2-band model (Jerlov IA and III) produced less warm water in the shallow areas than the 3-band model especially during summer period where it (the 3-band model) overestimated the water temperatures, thus, we suggest that the 3-band model should only be employed when modeling deep lakes for accurate predictions of the thermal structure. We also found out that the overly warm water in lakes is due to solar radiation (short wave and long wave radiation) and not the air temperature and the inflow water temperature forcings e.g.~(1)the water in the Idealized Lake warms up quickly when we modeled with no shortwave radiation but with long wave and cools down faster when modeled with no long wave and no shortwave and (2) the effects of the changes in external forcings in some of our model have slight influence on the thermal structure at 20 m depth and no impact at 1 m and 10 m depths in the eastern basin and central basin (image not shown).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".