Inclusion of a dry surface layer and modifications to the transpiration and canopy evaporation partitioning in the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC)
Bibliographic record
Abstract
Land surface/Earth System models depend upon accurate simulation of evapotranspiration (ET) to avoid excessive biases in simulated energy, water, and carbon cycles. The Canadian Land Surface Scheme including biogeochemical Cycles (CLASSIC), the land surface scheme of the Canadian Earth System Model (CanESM) shows reasonable ET fluxes globally, but CLASSIC’s partitioning into evaporation ( E ) and transpiration ( T ) can be improved. Specifically, CLASSIC exhibited a high soil evaporation (Es) bias in sparsely vegetated areas during wet periods, which can deplete soil water and decrease photosynthesis and T later in the year. A dry surface layer (DSL) parameterization was implemented to address biases in Es through an increased surface resistance to water vapour and heat fluxes. In arid/semi-arid regions, the DSL decreased Es, leading to improved seasonality of ET and increased gross primary productivity (GPP) due to an increase in soil moisture. The DSL simulations significantly (t-test, p<0.01) increased T/ET from 0.25 in baseline CLASSIC to 0.30 in the DSL simulations. T/ET was further increased to 0.41 (p<0.01), comparable to the CMIP5 model mean, by allowing T to occur from the dry canopy fraction while water evaporates from the wet fraction. This mainly affected densely vegetated areas, where T and ET increased significantly (p<0.01) and canopy E was reduced (p<0.01). In seasonally dry tropical forests, higher T and ET reduced GPP. Despite increases in arid/semi-arid regions, the reduced GPP in tropical forests resulted in ∼1.6% lower global GPP (p=0.018) than baseline CLASSIC. Including these modifications in CanESM might reduce biases in climate.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".