Modelling the temporal dynamics of subarctic surface temperature inversions from atmospheric reanalysis for producing point-scale multi-decade meteorological time series in mountains
Bibliographic record
Abstract
The vertical profile of air temperatures in subarctic regions is difficult to quantify, especially in areas with mountainous terrain subject to strong and lasting inversion events. Relying on observational data is not possible in most places due to sparse weather stations. To address this gap, we use reanalysis data to produce a model of the inversion strength. This model uses a single downscaled atmospheric column from reanalysis data and is calibrated with five weather stations close to Dawson City, Yukon, situated at various elevations. It is shown to perform better than bare reanalysis products and its parameters take into account the observed long-term decrease in frequency, strength, and depth of inversions since 1948, departing from the pattern of elevation-dependent warming found in lower latitude mountain regions. Once calibrated, the model only relies on global reanalysis data and hence can be applied in the vicinity of the calibration site, even where no observational data are available. Producing reliable time series for air temperature in complex terrain where inversions are strong and frequent is essential in modelling permafrost and understanding its future evolution. This model uses ever-improving physically based data, making it future-proof and versatile in its regional applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".