Modelling decadal trends and the impact of extreme events on carbon fluxes in a deciduous temperate forest using the QUINCY model
Bibliographic record
Abstract
Abstract. Changing climatic conditions pose a challenge to accurately estimate the carbon sequestration potential of terrestrial vegetation, which is often mediated by Nitrogen availability. The close coupling between the Nitrogen and Carbon cycles controls plant productivity and shapes the structure and functional dynamics of ecosystems. However, how carbon and nitrogen interactions affect both carbon fluxes and plant functional traits in dynamic ecotones, which are experiencing disturbance and species compositional shifts remains unclear. In this work, we use in-situ measurements of leaf chlorophyll content (ChlLeaf, years 2013–2016) and leaf area index (LAI, years 1998–2018) to parameterise the seasonal dynamics of the QUINCY ('QUantifying Interactions between terrestrial Nutrient CYcles and the climate system') terrestrial biosphere model (TBM) to simulate the carbon fluxes at the Borden Forest Research Station flux tower site, Ontario, Canada, over 22 years from 1996–2018. QUINCY was able to simulate leaf-level maximum carboxylation capacity (Vc(max),25), ChlLeaf and leaf nitrogen quite consistent with observations. The improved model captured observed daily gross primary production (GPP) well (r² = 0.80). Nevertheless, we found that although observed GPP increased significantly during the study period, and NEE shifted towards a stronger sink, these trends were not captured in the model. Instead, QUINCY showed a significant increasing trend for total ecosystem respiration (TER), that was not present in the observations. The severe drought in 2007 affected observed carbon fluxes strongly, lowering both GPP and TER also in the following year. QUINCY was able to capture some of the decrease in GPP and TER in 2007. However, the legacy effect of the drought in 2008 was not captured by the model. These results call for further work on representing legacy effects in TBMs, as these can have long-lasting impacts on ecosystem functioning.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".