Circum-Arctic Glaciers, Past, Present, and Future: Current Trends in Mass Balance and Simulation of Mass Balance Sensitivity to Temperature and Precipitation Increase
Notice bibliographique
Résumé
The circum-Arctic is a major contributor to sea level rise. Between 1991 and 2010, 70 % of eustatic sea level rise was attributable to glacier mass loss, 62 % of which was from glaciers in the circum-Arctic (Alaska, Arctic Canada North, Iceland, Svalbard, Scandinavia, and the Russian Arctic). In addition, Arctic temperatures are expected to increase at 2.4 times the magnitude of projected global average warming over the next 100 years. An understanding of how circum-Arctic glaciers are responding to temperature increase, and how they will respond under future climate conditions, is crucial to helping island nations and low-lying coastal communities predict and mitigate the impacts of sea level rise. This thesis has two objectives. The first objective is to a) identify the most effective methodology to calculate regional mass balance trends in the circum-Arctic using spatially and temporally sparse datasets and b) use these data to determine past and present (1961-2016) circum-Arctic mass balance trends. To accomplish this, I explore spatially interpolated mass balance from prior studies and compare these results to specific mass balance calculated using only observational data. I then compare two different time periods from the specific mass balance dataset (1961-2016 and 2000-2016) to determine regional mass balance trends. I find that mass balance calculated through spatial interpolation and specific mass balance are statistically likely to derive from the same population in regions that contain observational mass balance data. However, qualitatively, the variability between the datasets appears to be different for regions in which ≥50 % of observational data are geodetic. In addition, the mean magnitude of mass loss appears different in glacier regions with only high-variability glaciological mass balance data. A comparison of 1961-2016 and 2000-2016 mean specific mass balance in each region determines that glacier mass balance in Arctic Canada North has decreased at the largest rate, followed by Alaska and Svalbard (-0.20, -0.14, and -0.12 m w.e. a-1, respectively). The second objective of this thesis is to: a) determine the circum-Arctic glacier mass balance sensitivity temperature and precipitation increase and then b) investigate the factors driving the sensitivity. To achieve this objective, I use a degree-day model (the Python Glacier Evolution Model, PyGEM) to simulate circum-Arctic mass balance sensitivity to 1-3 °C temperature and 4%°C-1precipitation increase between 2000 and 2100. The model simulations suggest that Iceland glaciers are the most sensitive to temperature and precipitation increase (-0.70 m w.e. a-1 °C -1) of all regions studied, and Arctic Canada North is the least sensitive (-0.39 m w.e. a-1 °C -1). These results suggest that the degree of continentality (how warm/wet a region is) and the proximity of accumulation season temperatures (the rain/snow threshold) is the primary driver of mass balance sensitivity; warm, wet, ‘maritime’ regions (Iceland, Scandinavia) are more sensitive to the same temperature increase than cold, dry, ‘continental’ regions (Arctic Canada North, the Russian Arctic). Secondary factors such as glacier size, altitude, slope, and surface albedo may also impact regional glacier mass balance sensitivity. Small glacier size, low glacier altitude, large surface albedo, and steep glacier slope may increase mass balance sensitivity, while large glacier size, high glacier altitude, small surface albedo, and slight glacier slope may decrease mass balance sensitivity. Overall, the results of this thesis provide incentive for future data collection in rapidly changing regions like Arctic Canada North, and provides a better understanding of how the circum-Arctic may change in response to future climate change.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».