Quantifying snow controls on vegetation greenness
Bibliographic record
Abstract
Abstract Snow is a key driver for biotic processes in Arctic ecosystems. Yet, quantifying relationships between snow metrics and biological components is challenging due to lack of temporally and spatially distributed observations at ecologically relevant scales and resolutions. In this study, we quantified relationships between snow, air temperature, and vegetation greenness (using annual maximum normalized difference vegetation index [MaxNDVI] and its timing [MaxNDVI_DOY]) from ground‐based and remote‐sensing observations, in combination with physically based models, across a heterogeneous landscape in a high‐Arctic, northeast Greenland region. Across the 98‐km distance from the Greenland Ice Sheet (GrIS) to the coast, we quantified significant inland–coast gradients of air temperature, winter precipitation (using pre‐melt snow‐water‐equivalent [SWE]), and snowmelt timing (using snow‐free day of year [SnowFree_DOY]). Near the coast, the mean annual air temperature was 4.5°C lower, the mean SWE was 0.3 m greater, and the mean SnowFree_DOY was 37 d later, than near the GrIS. The regional continentality gradient was eight times stronger than the south‐to‐north air–temperature gradient along the Greenland east coast. Across this strong gradient, the mean vegetation greening‐up period (SnowFree_DOY‐MaxNDVI_DOY) varied spatially by 24–57 d. We quantified significant non‐linear relationships between the vegetation characteristics of MaxNDVI and MaxNDVI_DOY, and SWE, SnowFree_DOY, and growing degree‐days‐sums during greening‐up (Greening_GDD) across the 16‐yr study period (2000–2015). These demonstrated that the snow metrics, both SWE and SnowFree_DOY, were more important drivers of MaxNDVI and MaxNDVI_DOY than Greening_GDD within this seasonally snow‐covered region. The methodologies that provided temporally and spatially distributed snow, air temperature, and vegetation greenness data are applicable to any snow‐ and vegetation‐covered area on Earth.
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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 | 0.000 |
| 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".