In order to join virtual sessions, you must be registered and logged-in(Were you registered for the in-person meeting in Denver? if yes, just log in.) 
Note: All session times are in Mountain Daylight Time.

Modeling seasonal snowfall: an attempt to better predict seasonal snowfall totals in Central New York

Authors: Justin Joseph Hartnett*, SUNY Oneonta
Topics: Climatology and Meteorology
Keywords: Snow, snowstorms, lake-effect
Session Type: Paper
Presentation File: No File Uploaded

For some, snowfall in Upstate New York is a lifeblood. Snowstorms are an integral part of the social, economic, ecological, hydrological and climatological processes in this region. Located to the lee of the Great Lakes, central New York State falls within the Great Lakes region, yet is also influenced by coastal dynamics from the Atlantic. Its unique geography is mostly responsible for the anomalously high seasonal snowfall totals experienced here compared to other similar latitudes.

Predicting seasonal snowfall totals has yet to be perfected. Snowfall totals are a product of snowfall from multiple storm types. These storms are influenced by environmental conditions such as teleconnections, lake temperatures, and elevation, among other variables. This study tests the use of linear models to improve seasonal snowfall predictions. Predictions are estimated for each of the eleven snowstorm types to influence Central New York using the environmental conditions shown to influence such storms.

As the climate changes, seasonal snowfall totals will likely be altered. However, a changing climate has a more complicated relationship with snowfall than simply warming temperatures resulting in less snow. The potential trajectory of future snowfall varies according to the type of storm producing the snow. Since the early 20th century, snowfall has increased to the lee of the Great Lakes and remained relatively unchanged or decreased outside of the Great Lakes basin. These varying trends further complicate seasonal snowfall predictions, and their effects are examined in these models.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login