Spatial Modelling of Monthly Climate Across Mountainous Terrain in Southern Yukon and Northern British Columbia
Bibliographic record
Abstract
Two measures of air temperature trends across southern Yukon and northern British Columbia were modelled based on measurements from 83 monitoring sites across seven areas, operating for up to 14 years. Both mean monthly air temperature (MMAT) and freezing and thawing degree days (FDD and TDD, respectively) were modelled across this area (59 °N to 64.5 °N) at elevations ranging from 330-1480 m asl. Lapse rates in this region show inversions in the winter months (November - March) varying in inversion strength and length in relation to degree of continentality. The spatial and elevation range of these sites allowed for regional lapse rate modelling at the monthly scale for MMAT and at the annual scale for FDD and TDD. Lapse rates below treeline were found to be correlated (p < 0.1) with degree of continentality in the colder months (November - April) and August. In these months, lapse rates were modelled using kriging trend surfaces. In months where degree of continentality was not found to have a significant impact on lapse rates (p > 0.1) (May - October, excluding August), an average lapse rate calculated from the seven study regions was used across the study region. A combination of lapse rate trend surfaces, elevation, and temperatures at sea level were used to model MMAT and F/TDD below treeline. A treeline trend surface was created using a 4th order polynomial, allowing for temperatures at treeline to be determined. MMAT and F/TDD above treeline were calculated using a constant lapse rate of -6 °C/km, elevation, and temperature at treeline. The above and below treeline models were combined to create continuous models of MMAT and F/TDD. Modelled MMAT showed a high degree of homogeneity across the study region in warmer months. Inversions in lapse rates are evident in the colder months, especially December through February, when colder temperatures are easily identified in valley bottoms, increasing to treeline, and decreasing above treeline. Modelled MMAT values were validated using 20 sites across the study region, using both Environment and Climate Change Canada and University of Ottawa sites. The RMSE between modelled and observed MMAT was highest in January (4.4 °C) and lowest in June (0.7 °C). Sites below treeline showed a stronger relationship between modelled and observed values than sites above treeline. Edge effects of the model were evident in the northeast of the study region as well as in the ice fields in the southwest along the Alaska border. The new MMAT maps can be used to help understand species range change, underlying permafrost conditions, and climate patterns over time. FDD values were found to be highly influenced by both degree of continentality as well as latitude, whereas TDD values were mainly dependent on elevation, with degree of continentality and latitude being lesser influences. FDD and TDD were validated using the same 20 sites across the study region, with FDD showing a larger RMSE (368 degree days) between modelled and observed values than TDD (150 degree days). TDD modelling performed better on average, with a lower average absolute difference (254 degree days) between modelled and observed values at the validation sites than FDD modelling (947 degree days). The models of FDD and TDD represent a component of temperature at top of permafrost (TTOP) modelling for future studies. Two mean annual air temperature (MAAT) maps were created, one calculated from the MMAT models, and the other from the F/TDD models. Most of the study region showed negative MAAT, mainly between -6 °C and 0 °C for both methods. The average MAAT calculated from FDD and TDD values was -2.4 ºC, whereas the average MAAT calculated from MMAT values was -2.8 ºC. Models of MAAT were found to be slightly warmer than in previous studies, potentially indicating warming temperature trends.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".