Lake ice and temperature trends for Ontario and Manitoba: 2001 to 2014
Bibliographic record
Abstract
Abstract Lakes are a prominent geographic feature in northern landscapes and play an important role in understanding regional climate systems. In order to better model changes within climate systems, it is important to study lake ice processes. Although the availability of records for lake ice through ground measurements has declined in recent years, the increased use of remote sensing provides an alternative to this. Using a preclassified snow and ice remote sensing product with a 500‐m resolution, based on images from the Moderate Resolution Imaging Spectroradiometer (MODIS/MOD10A1), and the use of measured and reanalysis temperature data, this study evaluated lake ice phenology dates in connection to recent trends in temperature and 0 °C isotherms within Ontario and Manitoba between 2001 and 2014. Temperature trends indicated both regional warming and cooling, with significant cooling observed in Southern Ontario ( p < .05) and significant warming in Southern Manitoba ( p < .1) during the fall. Spatial analysis of the trends in the lake ice data showed significant clustering of significant trends in ice on dates ( p < .01). When analysing the trends in ice phenology in connection to the trends in temperature, it was found that 70% of lakes experienced a change in the ice on date with the expected change in temperature and 85% of lakes for ice off date. When shifting ice on and ice off dates are investigated in relation to 0 °C isotherms, it was seen that 80% of ice on dates and 100% of ice off dates shifted in sync with the isotherm dates. This demonstrates that the ice phenology of lakes in Ontario and Manitoba, Canada, is responding to short‐term variability in temperature. The MODIS product could be used to investigate ice phenology on a large scale and contribute towards expanding existing records of ice phenology. Establishing long‐term ice records could be a valuable asset for other research ranging from water balance studies to the response of lake biota under changing climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".