Inter‐ and intra‐annual variability of vegetation in the northern hemisphere and its association with precursory meteorological factors
Bibliographic record
Abstract
Determination of phenological variation is one of the most critical challenges in dynamic vegetation modeling, given the lack of a strong theoretical framework. Previous studies generally focused on the timing of a phenological event (e.g., bud‐burst or onset of growing season) and its atmospheric prompts, but not on the interactive variations across phenological stages. This study, therefore, investigated the inter‐ and intra‐annual variability existing in all the phenological stages and the relations of the variability with four meteorological variables (surface temperature ( T s ), shortwave radiation ( SW ), vapor pressure deficit ( VPD ), and precipitation ( PRCP )) using a 25‐year (1982‐2006) dataset of leaf area index (LAI) from the Advanced Very High Resolution Radiometer (AVHRR). Our six study sites of each 4° × 4° grids (mixed forest in China, deciduous needle‐leaf forest in Siberia, evergreen needle‐leaf forest in western Canada, grass in Gobi, and crops in the Central United States and southeastern Europe) include various vegetation types, local climates, and land‐use types in the mid‐latitudes of the northern hemisphere. Empirical orthogonal function (EOF) analysis with detrended LAI anomalies identified the two leading EOF modes that account for the amplitude and phase of the monthly LAI variations. The inter‐annual correlation between the principle components (PCs) of the two modes and the meteorological variables for spring and summer showed that the amplitude and phase modes (AM and PM, respectively) were affected by different dominant meteorological factors. Over most of the study regions, AM was positively correlated with PRCP and negatively with T s , SW , and VPD , while PM was predominantly positively correlated with T s . The contrasting responses of the two EOF modes to T s reflect environmental limitations on plant growth such as early start of growth, but with a reduced value of maximum LAI in a year with a warm spring. In addition, insignificant correlations between AM and PRCP , as well as negative correlations between PM and PRCP , in the crop regions suggest that human interventions such as advanced irrigation systems also play a key role in vegetative activity.
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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) | 0.000 | 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".