Inter-annual Variability of Snowfall in the Lower Peninsula of Michigan

• Published in: Frontiers in Water
• Main Authors: Lei Meng, Bandhan Dutta Ayon, Nirjala Koirala, Kathleen M. Baker
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2021
• Subjects: snowfall; inter-annual variability; Lake Michigan; trend; maximum ice cover; ENSO; Environmental technology. Sanitary engineering; TD1-1066; North Atlantic; North Atlantic oscillation
• Online Access: https://doi.org/10.3389/frwa.2021.746354; https://doaj.org/article/173985e8c7c14debb8670d466ff9625c

Winter snowfall, particularly lake-contributed snowfall, has a significant impact on the society and environment in the Great Lakes regions including transportation, tourism, agriculture, and ecosystem. Understanding the inter-annual variability of snowfall will provide sound basis for local community safety management and reduce its environmental impacts on agriculture and ecosystems. This study attempts to understand the trend and inter-annual variability in snowfall in the Lower Peninsula of Michigan (LPM) using statistical analysis based on snowfall measurements from eight weather stations. Our study demonstrates that snowfall has significantly increased from 1932 to 2015. Correlation analysis suggests that regional average air temperatures have a strong negative relationship with snowfall in the LPM. On average, approximately 27% of inter-annual variability in snowfall can be explained by regional average air temperatures. ENSO events are also negatively related to snowfall in the LPM and can explain ~8% of inter-annual variability. The North Atlantic Oscillation (NAO) does not have strong influence on snowfall. Composite analysis demonstrates that on an annual basis, more snowfall occurs during the years with higher maximum ice cover (MIC) than during the years with lower MIC in Lake Michigan. Higher MIC is often associated with lower air temperatures which are negatively related to snowfall. This study could provide insight on future snow related climate model improvement and weather forecasting.