Quantification and practical solution for bottom boundary effects on long-term permafrost models
Bibliographic record
Abstract
Permafrost models are commonly used to simulate future ground temperatures under the influence of climate change and/or proposed infrastructure. Most, if not all model input decisions are made based on limited subsurface knowledge. Shallow model domains offer more efficient run-times especially with gridded one-dimensional schemes as well as two-dimensional and three-dimensional simulations over longer time periods. Recent focus has been on the development of surface boundaries; however, less attention is given to the bottom boundary condition. In this paper, first we quantify the effect of model domain depth and bottom boundary condition type on long-term ground temperature evolution in transient model simulations. Model domains less than 100 m deep with the bottom boundary condition set to both geothermal gradient and a perfectly insulated base ( q = 0) show significant overwarming and overestimate thawing in cold permafrost. Guidance is provided to interrogate model results to avoid bottom boundary condition effects. For cases where further models are needed, a practical solution to this challenge is developed to obtain model depth-independent results. This solution allows for reduced computational requirements while maintaining consistent results for century-scale transient simulations considering climate change effects on thawing permafrost.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".