Site-Level Permafrost Simulation in Remote Areas Driven by Atmospheric Re-Analyses: A Case Study from the Northwest Territories
Bibliographic record
Abstract
Simulating permafrost is impeded by the lack of meteorological forcing datasets at most locations of interest. Here, we present the first application of GlobSim, a new Python toolkit that extracts site-level meteorological time series from global atmospheric re-analyses and makes them easily available for forcing permafrost simulations using land-surface models. The software downloads, interpolates, and heuristically scales data to point locations. In this example, we use the model GEOtop and four re-analyses datasets to simulate permafrost in five terrain types that characterize the variability of tundra environments in the Northwest Territories. We evaluated model results using two years of air- and ground-temperature observations from sites near Lac de Gras. The range of observed mean annual ground surface temperatures during the two years was 6.0°C. Results show that the reanalysis data can support permafrost simulations quite well. Compared to individual re-analyses, the ensemble mean improved results for all terrain types. Using the ensemble mean reduced the root-mean-square error by 0.07–1.78°C for mean annual air temperature, 0.10–0.34°C for daily ground surface temperature, 0.11–0.31°C for mean annual ground surface temperature, and 0.08–1.03°C for surface offset. These results demonstrate that reanalysis-driven ensemble simulation is a promising tool for simulating permafrost in remote locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".