Incorporation of a permafrost model into a large‐scale ecosystem model: Evaluation of temporal and spatial scaling issues in simulating soil thermal dynamics
Bibliographic record
Abstract
This study evaluated whether a model of permafrost dynamics with a 0.5‐day resolution internal time step that is driven by monthly climate inputs is adequate for representing the soil thermal dynamics in a large‐scale ecosystem model. An extant version of the Goodrich model was modified to develop a soil thermal model (STM) with the capability to operate with either 0.5‐hour or 0.5‐day internal time steps and to be driven with either daily or monthly input data. The choice of internal time step had little effect on the simulation of soil thermal dynamics of a black spruce site in Alaska. The use of monthly climate inputs to drive the model resulted in an error of less than 1°C in the upper organic soil layer and in an accurate simulation of seasonal active layer dynamics. Uncertainty analyses of the STM driven with monthly climate inputs identified that soil temperature estimates of the upper organic layer were most sensitive to variability in parameters that described snow thermal conductivity, moss thickness, and moss thermal conductivity. The STM was coupled to the Terrestrial Ecosystem Model (TEM), and the performance of the coupled model was verified for the simulation of soil temperatures in applications to a black spruce site in Canada and to white spruce, aspen, and tundra sites in Alaska. A 1°C error in the temperature of the upper organic soil layer had little influence on the carbon dynamics simulated for the black spruce site in Canada. Application of the model across the range of black spruce ecosystems in North America demonstrated that the STM‐TEM has the capability to operate over temporal and spatial domains that consider substantial variation in surface climate given that spatial variability in key structural characteristics and physical properties of the soil thermal regime are described.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".