Bibliographic record
Abstract
Abstract. Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool to provide benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these models often estimate GPP and ET at coarse daily or monthly step, hindering analysis of ecosystems dynamics at the diurnal (hourly) scales, and prescribe some essential parameters (i.e., the Ball-Berry slope (m) and the maximum carboxylation rate at 25 °C (Vcmax25)) as constant, inducing uncertainties in the estimates of GPP and ET. In this study, we present hourly estimation of global GPP and ET datasets at a 0.25° resolution from 2001 to 2020 simulated with a widely used diagnostic TBM – Biosphere-atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance observations and machine learning approaches to derive and upscale the seasonally varied m and Vcmax25 for carbon and water fluxes. The estimated hourly GPP and ET are validated against flux observation, remote sensing, and machine learning-based estimates across multiple spatial and temporal scales. The correlation coefficients (R2) and slopes between hourly tower-measured and modeled fluxes are: R2 = 0.83, regression slope = 0.92 for GPP and, R2 = 0.72, regression slope = 1.04 for ET. At the global scale, we estimated a global mean GPP of 137.78 ± 3.22 Pg C yr-1 (mean ± 1 SD) with a positive trend of 0.53 Pg C yr-2 (p < 0.001), and ET of 89.03 ± 0.82 × 103 km3 yr-1 with a slight positive trend of 0.10 × 103 km3 yr-2 (p < 0.001) from 2001 to 2020. The spatial pattern of our estimates agrees well with other products, with R2 = 0.77–0.85 and R2 = 0.74–0.90 for GPP and ET, respectively. Overall, this new global hourly dataset serves as a `handshake’ among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global GPP and ET with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles. The hourly and accumulated daily GPP and ET estimates are available at https://doi.org/10.5281/zenodo.8240492 (Leng et al., 2023).
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.025 | 0.024 |
| Insufficient payload (model declined to judge) | 0.150 | 0.116 |
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".