Predicting Climate Extremes with Synoptic Climatology in the Chesapeake Bay

Synoptic climatology describes the relationship between the climate system and certain surface conditions. Many studies in the field have focused on understanding changes to long-term and broad climatic processes. This project aims to provide results that will benefit near-shore communities by focusing on better understanding climate extremes. Correlations made between climate modes and climate extremes are observed to be stronger than relationships between climate modes and average weather patterns (St. Laurent et al., 20022). For that reason, we correlated the climate modes North Atlantic Oscillation and Pacific Decadal Oscillation to 16 climate extreme indices (Pirhalla et al. 2024). We collated 39 years of historical sea level pressure (SLP) to weather data being temperature and precipitation for the region. We utilized the principal component analysis (PCA) to reduce high dimensionality among the SLP. We implemented the self-organizing map (SOM) algorithm to create sets of grids representing spatially consistent and temporally distinct pressure patterns. We then visualized the probability of climate extreme occurrence during the SOM’s seasonal distribution using star plots. Select star plots were chosen for correlation due to their high probability and seasonal occurrence. Scatter plots with linear best fit lines and shaded relief for degree of confidence were generated to represent these correlations. The final plots provide us with estimations for the likelihood of extreme events occurring during the respective phase of climate mode.

Presentation Preference: Poster

Primary Presenter: Tyrell Cooper, The College of Wooster (ty.cooper4242@gmail.com)

Authors:

Tyrell Cooper, The College of Wooster  (ty.cooper4242@gmail.com)

Dong Liang, University of Maryland Center for Environmental Sciences  (dliang@umces.edu)

Victoria Coles, University of Maryland Center for Environmental Sciences  (vcoles@umces.edu)