Modeling the diurnal variability of respiratory fluxes in the Canadian Terrestrial Ecosystem Model (CTEM)
Bibliographic record
Abstract
Abstract The Canadian Terrestrial Ecosystem Model (CTEM) coupled to the Canadian Land Surface Scheme (CLASS) is a dynamic vegetation model that incorporates photosynthesis and respiration submodules among many other physiological processes of the terrestrial biosphere. While the photosynthesis and leaf respiration submodules of CTEM operate at a time step of 30 min, other respiratory fluxes are estimated at a daily time step using the daily‐averaged values of canopy and soil temperature, and soil moisture content. Here we modify CTEM to be able to simulate the diurnal variation of ecosystem respiratory fluxes caused by the diurnal variation in the driving climate data. Simulating respiration at a 30 min time step changed the equilibrium states of primary carbon pools and fluxes. This required changes to some of the model's parameters in order to realistically simulate the equilibrium values of carbon pools and fluxes. The resulting estimated daily and annual carbon fluxes from the modified and original CTEM are similar with small relative differences. The similar annual cycles of daily values of primary CO2 fluxes confirm that modeling the respiratory fluxes at a subdaily time step does not affect the annual cycles of these fluxes. We also demonstrate that the modified version of the model is able to simulate the diurnal cycle of net ecosystem exchange of CO2 fluxes that are broadly comparable to observation‐based estimates at two flux tower sites and exhibit realistic seasonal patterns. Limitations remain in the modeled diurnal patterns of primary land‐atmosphere CO2 fluxes which will form the basis of further model improvements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".