Spatial Modelling of Monthly Climate Across Mountainous Terrain in Southern Yukon and Northern British Columbia
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».