Global permafrost simulation and prediction from 2010 to 2100 under different climate scenarios

This paper aims to simulate and predict global permafrost distribution, and analyse its change from 2010 to 2100 under different climate scenarios. Based on different factors (topography, land cover, climate and location) and global permafrost distribution status, logistic regression model (LRM) is...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Environmental modelling & software : with environment data news 2022-03, Vol.149, p.105307, Article 105307
Hauptverfasser: Zhao, Shangmin, Cheng, Weiming, Yuan, Yecheng, Fan, Zemeng, Zhang, Jin, Zhou, Chenghu
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper aims to simulate and predict global permafrost distribution, and analyse its change from 2010 to 2100 under different climate scenarios. Based on different factors (topography, land cover, climate and location) and global permafrost distribution status, logistic regression model (LRM) is chosen and constructed to simulate and predict the global permafrost distributions. Thus, the global permafrost distributions at T1 (2010–2040), T2 (2040–2070) and T3 (2070–2100) are predicted under different climate scenarios (RCP26, RCP45 and RCP85). From T1 to T3, the area of global permafrost has the largest degradation under RCP85 scenarios. From RCP26 to RCP85 at T3, the area of the degraded permafrost reached 0.671 × 108 km2. The degraded permafrost mainly distributes in east Asia, west Asia, north Europe and north America. The west Asia has the highest degrading distance, about 600 km under the situations of both RCP85 from T1 to T3 and from RCP26 to RCP85 at T3. •Global permafrost is simulated and predicted from 2010 to 2100.•Global permafrost degradation various largely under different climate scenarios.•West Asia has the highest degrading distance under RCP85 scenario.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2022.105307