Assessing Extreme Snowfall Trends in the White Mountains of New Hampshire Using KMWN Data and CMIP6 Simulations

Extreme snowfall events have profound impacts on the White Mountains of New Hampshire, affecting not only the natural environment but also the surrounding communities, local businesses, and recreational activities that are vital to the region. Despite their significance, there is limited understanding of how these extreme events may change in a warming climate within this specific area. This study addresses this gap by analyzing extreme snowfall trends in the White Mountains, utilizing data from the Mount Washington Observatory's summit weather station, KMWN. In our analysis, we define extreme snowfall events as those within the top 90th percentile of snowfall amounts, applying 3 to 5 day rolling averages to capture meaningful trends while accounting for short-term variability. Additionally, we conduct an in-depth examination of KMWN's manual observations, focusing on the frequency and counts of +SN (Heavy Snow) events. A critical aspect of this study is the verification of extreme snowfall estimates derived from simulations using several observational and reanalysis products. By incorporating actual point station data from MWOBS, we can accurately assess how well these simulations capture the magnitude and spatial distribution of extreme snowfall in this high-elevation region. Preliminary comparisons indicate that the simulations are in reasonable agreement with observed snowfall patterns, enhancing confidence in their use for future projections. This work builds on broader efforts to understand extreme weather in a changing climate, as exemplified by studies using the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. However, by focusing specifically on the White Mountains and validating the simulations with high-quality observational data, this study provides critical insights into how these trends might impact the region's communities, businesses, and recreational activities. Understanding these local trends is essential for preparing for future challenges posed by extreme snowfall, including potential risks to infrastructure, public safety, and the economic vitality of the region. This study is an important step in translating broad climate projections into actionable insights for regional planning and mitigation strategies in the face of evolving extreme weather patterns.