Characterizing the diurnal patterns of errors in the prediction of evapotranspiration by several land‐surface models: An NACP analysis
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Bibliographic record
Abstract
Abstract Land‐surface models use different formulations of stomatal conductance and plant hydraulics, and it is unclear which type of model best matches the observed surface‐atmosphere water flux. We use the North American Carbon Program data set of latent heat flux (LE) measurements from 25 sites and predictions from 9 models to evaluate models' ability to resolve subdaily dynamics of transpiration. Despite overall good forecast at the seasonal scale, the models have difficulty resolving the dynamics of intradaily hysteresis. The majority of models tend to underestimate LE in the prenoon hours and overestimate in the evening. We hypothesize that this is a result of unresolved afternoon stomatal closure due to hydrodynamic stresses. Although no model or stomata parameterization was consistently best or worst in terms of ability to predict LE, errors in model‐simulated LE were consistently largest and most variable when soil moisture was moderate and vapor pressure deficit was moderate to limiting. Nearly all models demonstrate a tendency to underestimate the degree of maximum hysteresis which, across all sites studied, is most pronounced during moisture‐limited conditions. These diurnal error patterns are consistent with models' diminished ability to accurately simulate the natural hysteresis of transpiration. We propose that the lack of representation of plant hydrodynamics is, in part, responsible for these error patterns.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it