Speaker III: David Coe
Title: Investigating Climate Connections: Using Regional Daily Weather Types and AI to Understand Variability in Timing of the Seasons in the Northeast U.S.
Abstract: Weather Type (WT) analysis identifies a region’s characteristic weather patterns that are useful to investigate underlying trends and variability in regional weather. Characteristic Weather Types for the spring season, Mar – May, and fall season, Sep – Nov, in the Northeast U.S. are identified using k-means clustering of ERA5 500-hPa height, MSLP, and 850-hPa u and v winds. The resulting WTs are analyzed for their seasonal, monthly, and daily frequency, their seasonal evolution, and relation to extreme temperature and precipitation events. The WTs are also used in conjunction with deep learning techniques, such as Convolutional Neural Networks, to train an AI model to identify the observed daily WTs based on their 500 hPa height fields. Once trained, the AI model can classify new data as one of the observed WTs. Using this model, we match winter dates to the fall and spring WTs to better understand changes in seasonal transition to and from the cold season. For the fall season, a preliminary trend analysis indicates an increase in early season WTs later in the season and a decrease in late season WTs earlier in the season; that is, a shift toward a longer period of warm season patterns and a shorter, delayed period of cold season patterns. Using daily frequencies of the Early and Late Season WTs, the middle of Spring is identified and changes in its timing are identified through splitting the time series into two equal halves. The middle of the spring season is found to come 12-15 days earlier, significant at the 95% level, and occurs 2-4 days longer. Combined with more Late Season WTs occurring in April, this shows a shift toward warmer, more summer-like circulation patterns occurring earlier in the season. We intend to further investigate the changing length of the winter season by applying an AI model to classify winter season data as one of our observed clusters. This model will also be used to classify observed WTs in CMIP6 climate model data and statistical analysis performed to understand the models’ ability to capture daily weather patterns.