Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017
Bibliographic record
Abstract
Abstract. Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05° latitude by 0.05° longitude at 8-day interval by revising a light use efficiency model (i.e. EC-LUE). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 84 towers from the FLUXNET2015 dataset, covering nine major ecosystem types of the globe, were used to calibrate and validate the model. The revised EC-LUE model could explain 83 % and 68 % of the spatial variations in the annual GPP at 42 calibration and 43 validation sites, respectively. In particular, the revised EC-LUE model could very well reproduce (~ 74 % sites R2 > 0.5; averaged R2 = 0.65) the interannual variations in GPP at 51 sites with observations greater than 5-years. At global scale, sensitivity analysis indicated that the long-term changes of environmental variables could be well reflected in the global GPP dataset. The CO2 fertilization effect on the global GPP (0.14 ± 0.001 Pg C yr−1) could be offset by the increased VPD (−0.16 ± 0.02 Pg C yr−1). The global GPP derived from different datasets exist substantial uncertainty in magnitude and interannual variations. The magnitude of global summed GPP simulated by the revised EC-LUE model was comparable to other global models. While the revised EC-LUE model has a unique superiority in simulating the interannual variations in GPP at both site level and global scales. The revised EC-LUE model provides a reliable long-term estimate of global GPP because of integrating the important environmental variables. The dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v1 (Zheng et al., 2019).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".