Estimation of snow melting incluiding the effect of red snow blooming in arctic region using a land surface model

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K19955
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 63010:Environmental dynamic analysis-related
• Research Institution: Japan Aerospace EXploration Agency (2022); The University of Tokyo (2020-2021)
• Principal Investigator: Onuma Yukihiko 国立研究開発法人宇宙航空研究開発機構, 第一宇宙技術部門, 宇宙航空プロジェクト研究員 (30800833)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: 雪氷学 / 雪氷微生物 / 数値モデリング / 陸面モデル / 赤雪 / 氷河暗色化 / 数理生物学

Outline of Final Research Achievements

In this study, the results more than expected toward the following issues were obtained for three years.
- Development of a numerical model to predict red snow phenomena based on field observations and estimation of the contribution of red snow to snow melting using a land surface model.
One of the most notable achievements for the entire research period was to develop global snow algae model Bio-MATSIRO, which enable us to estimate the contribution of red snow phenomena to snow melting in alpines and glaciers worldwide. In the latter half of the research period, numerical models to reproduce growth of glacier algae and vertical dynamics of cryoconite holes were established. These models will be incorporated into Bio-MATSIRO to evaluate snow and ice albedo reduction caused by the microbial activities. These model results have been published as peer reviewed papers on international journals.

Free Research Field

雪氷生物学

Academic Significance and Societal Importance of the Research Achievements

本研究で開発したBio-MATSIROを用いることで、雪氷微生物活動によるアルベド低下効果（バイオアルベド効果）を計算し、近年の雪氷融解加速の微生物活動による寄与を全球規模で評価できるようになった。バイオアルベド効果はIPCC第6次評価報告書で初めて取り上げられた新しい現象で、その効果を明らかにすることは学術的意義がある。本研究成果は、今後さらに発展させることでIPCC次期評価報告書に記載される可能性があり、国際社会への貢献も期待できる。