Modeling leaf area index in North America using a process‐based terrestrial ecosystem model
Bibliographic record
Abstract
Abstract Leaf area index (LAI) is often used to quantify plant production and evapotranspiration with terrestrial ecosystem models (TEMs). This study evaluated theLAIsimulation in North America using a data assimilation technique and a process‐based TEM as well as in situ and satellite data. We first optimized the parameters related toLAIin theTEMusing a Markov Chain Monte Carlo method, and AmeriFlux site‐level and regionalLAIdata from advanced very high‐resolution radiometer. The parameterized model was then verified with the observed monthlyLAIof major ecosystem types at site level. SimulatedLAIwas compared well with the observed data at sites of Harvard Forest (R2 = 0.96), University of Michigan Biological Station (R2 = 0.87), Howland Forest (R2 = 0.96), Morgan Monroe State Forest (R2 = 0.85), Shidler Tallgrass Prairie (R2 = 0.82), and Donaldson (R2 = 0.75). The root‐mean‐square error (RMSE) between modeled and satellite‐based monthlyLAIin North America is 1.4 m2/m2for the period of 1985–2010. The simulated average monthlyLAIin recent three decades increased by (3 ± 0.5)% in the region, with 1.24, 1.46, and 2.21 m2/m2on average, in Alaska, Canada, and the conterminous United States, respectively, which is consistent with satellite data. The model performed well for wet tundra, boreal forest, temperate coniferous forests, temperate deciduous forests, grasslands, and xeric shrublands (RMSE < 1.5 m2/m2), but not for alpine tundra and xeric woodlands (RMSE > 1.5 m2/m2). Both the spring and fallLAIin the 2000s are higher than that in the 1980s in the region, suggesting that the leaf phenology has an earlier onset and later senescence in the 2000s. The averageLAIincreased in April and September by 0.03 and 0.24 m2/m2, respectively. This study provides a way to quantifyLAIwith ecosystem models, which will improve future carbon and water cycling studies.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".