Effects of seasonal and interannual climate variability on net ecosystem productivity of boreal deciduous and conifer forests
Bibliographic record
Abstract
The response of net ecosystem productivity (NEP) and evaporation in a boreal aspen (Populus tremuloides Michx.) forest and a black spruce (Picea mariana (Mill.) BSP) forest in Canada was compared using a newly developed realistic model of surface-atmosphere exchanges of carbon dioxide (CO 2 ), water vapor, and energy as well as eddy covariance flux measurements made over a 6-year period (1994-1999). The model was developed by incorporating a process-based two-leaf (sunlit and shaded) canopy conductance and photosynthesis submodel in the Canadian Land Surface Scheme (CLASS). A simple submodel of autotrophic and heterotrophic respiration was combined with the photosynthesis model to simulate NEP. The model performed well in simulating half-hourly, daily, and monthly mean CO 2 exchange and evaporation values in both deciduous and coniferous forests. Modeled and measured results showed a linear relationship between CO 2 uptake and evaporation, and for each kilogram of water transpired, approximately 3 g of carbon (C) were photosynthesized by both ecosystems. The model results confirmed that the aspen forest was a weak to moderate C sink with considerable interannual variability in C uptake. In the growing season, the C uptake capacity of the aspen forest was over twice that of the black spruce forest. Warm springs enhanced NEP in both forests; however, high mid-summer temperatures appear to have significantly reduced NEP at the black spruce forest as a result of increased respiration. The model suggests that the black spruce forest is a weak C sink in cool years and a weak C source in warm years. These results show that the C balance of these two forests is sensitive to seasonal and interannual climatic variability and stresses the importance of continuous long-term flux measurement to confirm modeling results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".