PhD Seminar Series: Data Assimilation under Environmental Disturbance with Jacob Epstein and Projected Changes to Western U.S. Atmospheric Rivers

Abstract: Jacob: Mathematical models are commonly used to monitor changes in soil and plant carbon pools over time. To ensure that modeled quantities align with observed data, a technique called State Data Assimilation (SDA) can be employed, which updates model states to better match observations. However, during ecological disturbance events such as wildfires, floods, or pest outbreaks, observed carbon pools can change rapidly—causing traditional SDA methods to struggle. This talk will focus on how a modified version of SDA, which combines a discrete multinomial state-and-transition framework with conventional ensemble filtering approaches, can be used to assimilate data during periods of ecological disturbance. Vlad: In a warming climate, the characteristics of landfalling atmospheric rivers (ARs) over the West Coast of the United States are expected to change. Recent work using a variable-intensity AR-identification method (Hughes et al. 2022) showed that the end-of-21st-century changes in West Coast AR landfall frequency depended on their intensity: extreme ARs increased in both frequency and intensity, whereas moderate ARs decreased in frequency by as much as 10%. Until now, this methodology has been applied only to a small set of regional climate models, however, in this work, we apply this methodology to a large set of global climate models (GCMs). We investigate the shifts of AR frequency as a function of AR intensity in the present and future climate over the United States in a set of 40 of members from the CESM2 Large Ensemble Community Project (LENS2). We find that the changes to ARs in GCMs largely confirm the trends in ARs seen in Hughes et al. 2022 but note limitations to using GCMs to study ARs. The observed shifts in AR frequency have direct implications for Western U.S. precipitation, with increased extreme precipitation and decreased moderate precipitation. Bio: Jacob: Jacob Epstein is a 1st-year PhD student at Boston University's Faculty of Computing and Data Sciences. As an undergraduate, he worked on multiple projects applying machine learning techniques to data analysis problems in environmental science, and received a Bachelor of Science in Computer Science and Mathematics at the University of Massachusetts Amherst. His current research interests are in applications of statistical and learning-based methods to environmental science. Vlad: Vlad Munteanu is a first year PhD student in CDS advised by Prof. Elizabeth Barnes and is interested in applications of data science and machine learning to answer complex questions about climate dynamics and extremes. He has conducted research on the response of atmospheric rivers to warming in climate simulations of the U.S. West Coast at the NOAA Physical Sciences Laboratory. Prior to that, he studied the formation and organization of deep tropical convection at the University of Washington.