Testing and development of the Canadian land surface scheme (class) for forests, agricultural crops and bare soils
Bibliographic record
Abstract
CLASS (Canadian Land Surface Scheme) is the land surface model currently used in the Canadian general circulation model. It features a single vegetation layer, three soil layers (and a snow layer when necessary) and physically-based calculations of the energy and water exchange between the atmosphere and land surface. This research focused on the validation of CLASS and the improvement of relevant parameterizations. CLASS was tested using continuous half-hourly energy balance and soil water content (θ) data collected during much of 1994 and from spring 1996 to the end of 1998 from a boreal aspen forest and during short summer periods over past 20 years from six west coast Douglas-fir forests, two agricultural crops and two bare soils. Tests identified the following deficiencies in CLASS : (1) evaporation from the soil surface was significantly overestimated, (2) transpiration from the aspen forest was underestimated under conditions of high solar irradiance, (3) winter albedo was too high, and (4) surface runoff after snowmelt was excessive. Two semi-empirical soil evaporation relationships (the α and β methods) were compared with Philip's relationship using measurements of evaporation from a bare loam/silt-loam soil. The latter, although physically-based, performed poorly when used with a thick surface soil layer as in CLASS. The β method performed significantly better than the a method. Parameterizations of canopy conductance (g[sub c]) based on the Jarvis- Stewart (JS) (also used in CLASS), the Ball-Woodrow-Berry (BWB) and a modified form of the BWB parameterization (MBWB) were evaluated for the aspen forest and a Douglas-fir forest. A new JS parameterization gave the best estimates of g[sub c], while the MBWB parameterization performed better than the BWB parameterization. The new JS and MBWB parameterizations worked well for five Douglas-fir forests of similar age with different leaf area indices under conditions of high θ but worked poorly for conditions of low θ because the response of Douglas-fir g[sub c] to soil water stress differed considerably from site to site. Adjusting the winter albedo for the aspen forest from 0.5 to the more realistic value of 0.25 significantly improved the calculation of winter net radiation, predicted the occurrence of snowmelt only 5-10 days later than observations and significantly reduced the overestimation of surface runoff following snowmelt. The near-field effect on flux calculations was examined using two approaches: (1) the near-field resistance was places in series with the aerodynamic resistance in CLASS, and (2) the performance of a Lagrangian two-layer canopy model was compared with a Ktheory two-layer canopy model and a /f-theory single-layer canopy model. The first approach was tested using data from a Douglas-fir forest, the aspen forest and an agricultural crop. The second approach was tested using data from the aspen forest because it had a thick understory canopy. Results from both approaches confirmed that the difference between simulations from AT-theory and the Lagrangian evaporation models was small due to the strong control by stomatal conductance. Furthermore, the two-layer canopy models were inferior to the single-layer canopy model in the calculation of the sensible and latent heat fluxes above the forest.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".