Seasonality in the Surface Energy Balance of Tundra in the Lower Mackenzie River Basin
Bibliographic record
Abstract
This study details seasonal characteristics in the annual surface energy balance of upland and lowland tundra during the 1998–99 water year (Y2). It contrasts the results with the 1997–98 water year (Y1) and relates the findings to the climatic normals for the lower Mackenzie River basin region. Both years were much warmer than the long-term average, with Y1 being both warmer and wetter than Y2. Six seasons are defined as early winter, midwinter, late winter, spring, summer, and fall. The most rapid changes in the surface energy balance occur in spring, fall, and late winter. Of these, spring is the most dynamic, and there is distinct asymmetry between rates of change in spring and those in fall. Rates of change of potential insolation (extraterrestrial solar radiation) in late winter, spring, and fall are within 10% of one another, being highest in late winter and smallest in spring. Rates of change in air temperature and ground temperature are twice as large in spring as in fall and late winter, when they are about the same. Rates of change in components of the energy balance in spring are twice and 4 times as large as in fall and late winter, respectively. The timing of snowpack ripening and snowmelt is the major agent determining the magnitude of asymmetry between fall and spring. This timing is a result of interaction between the solar cycle, air temperature, and snowpack longevity. Based on evidence from this study, potential surface responses to a 1°C increase in air temperature are small to moderate in most seasons, but are large in spring when increases range from 7% to 10% of average surface energy fluxes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".