High-impact weather and climate events such as temperature extremes, heavy rainfall, and lightning are all associated with numerous impacts on society and the environment. Furthermore, anthropogenic climate change is broadly projected to alter the frequency and severity of some types of extremes. However, some uncertainty in projections at local to regional scales persists despite advancements in climate modeling capabilities. One particular challenge is making projections of rare, high-impact events that occur at local scales or are influenced by local topography, as most state-of-the-art climate models are limited in their ability to resolve such scales. However, many of these local scale extremes are driven, at least in part, by large scale atmospheric circulation patterns. Therefore, one approach towards addressing this uncertainty is to gain a better understanding of the large-scale meteorological patterns associated with local phenomena, assess the ability of climate models to realistically simulate them, and assess whether models project systematic changes in these key patterns under further global warming. This talk will provide an overview of approaches, including machine learning methods, being used in the Portland State Climate Science Lab to better understand and characterize the large-scale meteorological patterns associated with several types of extremes in observations and climate models.
Dr. Paul Loikith is an Assistant Professor in the Department of Geography at Portland State and director of the Portland State Climate Science Lab. Dr. Loikith has a B.S. in Meteorology from Rutgers University as well as an M.S. and PhD in Atmospheric Science from Rutgers. Prior to joining the PSU faculty in 2015, he was a Caltech postdoc working at the NASA Jet Propulsion Laboratory on regional climate model evaluation. Dr. Loikith's research interests span the weather and climate timescales with a particular focus on linking large-scale meteorological features to local scale high-impact phenomena as a way to better understand the processes behind these events as well as how they may change in the future due to ongoing climate change.
Computer Sciences | Geography | Oceanography and Atmospheric Sciences and Meteorology
Loikith, Paul, "Using Large-Scale Meteorological Patterns to Better Understand Local-Scale Weather and Climate Extremes" (2019). Systems Science Friday Noon Seminar Series. 5.