Heterogeneous spring phenology shifts affected by climate: supportive evidence from two remotely sensed vegetation indices
Bibliographic record
Abstract
Abstract The Northern Hemisphere spring greenup (SG) has advanced between 0–12 days per decade since early 1980s as inferred from multiple satellite time series. The wide range of SG shifts is mainly due to the fact that these studies cover different periods and regions, and using different satellite records. Assessing the spatial heterogeneity of SG trends associated with different satellites is essential for robustly interpreting phenological dynamics and their responses to climate. We investigated the heterogeneity of the SG trends and their responses to climate variability with two satellite products (1) Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and (2) Advanced Very High Resolution Radiometer (AVHRR) over the period 2001–2013. Both MODIS and AVHRR agreed in showing the spatial distribution of mean SG, and SG advancement in northern Canada, the eastern United States, and Russia, and SG delay in western North America, parts of Baltic Europe, and East Asia. However, we identified contrasting MODIS and AVHRR SG trends in the northern high latitudes. Our analyses of correlations between SG and preseason climate drivers indicated that temperature dominated the interannual variability of SG. Preseason, the period preceding SG and highly correlated with the timing of SG has experienced much stronger warming than the spring season. MODIS and AVHRR indicated consistent temperature sensitivity of SG across biomes, even though the MODIS inferred SG is better correlated and more sensitive to temperature across biomes as compared to AVHRR. The sensitivities of SG to temperature across biomes is stable but with a slight increase over 2001–2013, in comparison with that over 1988–2000. The increased SG-temperature sensitivity is associated with increased precipitation during the spring season, which regulated the sensitivity of SG to spring temperature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".