Aircraft observations of surface–atmosphere exchange during and after snow melt for different arctic environments: MAGS 1999
Bibliographic record
Abstract
Abstract The arctic environment, and in particular the Mackenzie Basin, displays a very dynamic interrelationship between the atmosphere and the surface for the different ecosystems represented. The Canadian Twin Otter research aircraft flew a total of 24 grid and long regional transects, over tundra, forest and delta ecosystems, during the period of snow melt (late May–early June) and early summer (early July) as part of the 1999 Mackenzie Area GEWEX (Global Energy and Water Cycle Experiment) Study (MAGS) field campaign. Observations over tundra showed a sharp rise in the sensible heat flux at the onset of melt, reaching a maximum at the end of the melting period similar to those observed in early summer. The latent heat flux showed a more gradual rise from snowmelt to early summer with a Bowen ratio (sensible heat/latent heat) of two during melt. The forested system demonstrated a similar gradual rise in the latent heat flux, whereas the sensible heat flux was already high with Bowen ratios reaching three at the start of the observation period in late May. The gradual rise in latent heat flux can be tied to gradual thawing of the root zone and the onset of photosynthesis activity. The relatively low solar elevation angle and earlier start of snow melt along the regional transect may account for the much larger sensible heat flux. An analysis of the turbulent coherent structures indicates that the spatial density of structures for both latent heat and sensible heat increases strongly with season, from snow melt into the early summer conditions. This has implications for sampling criteria and optimum flux averaging period. There are distinct differences in energy partitioning between the various arctic ecosystems. At the beginning of the observation period, almost all the net radiation over the delta and tundra regions is utilized in non‐turbulent form, whereas the forested areas use less than 50%. Model simulations of the ground heat flux showed observed diurnal imbalances and suggest that the magnitudes depend on the position of the permafrost table and may partially account for the large non‐turbulent energy. Preliminary results from the Canadian MC2 model using the MAGS aircraft data for diagnosis and validation have identified sensitive model components that may merit further investigation. The findings from this study will help to fill gaps in our knowledge about surface–atmosphere interactions in arctic environments, particularly during snow melt, and broadens our contemporary view of evapotranspiration dynamics of wet surfaces. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 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".