Environmental Science and Engineering Seminar
As the climate changes, it is imperative that we improve our understanding of regional temperature and precipitation variability. To do so, it is necessary to merge insights from observations, dynamical models, and statistical models. In this talk, I will take this combined approach to answer questions about whether temperature variability is increasing, the influence of internal variability on climate signals, and links between climatic boundary conditions and extreme weather events. In particular, I will first present a method to concisely quantify trends in non-normal distributions, and apply it to daily summer temperature across the Northern Hemisphere. The vast majority of the observed changes in temperature can be explained by a shift in distributions without changes in shape. Trends in the observations, however, are reflective of both anthropogenic forcing and internal variability. Initial condition ensembles from climate models have illuminated the large influence of internal variability on perceived climate signals, but they suffer from biases that can limit their use for regional climate studies. As a complementary tool, I will present the Observational Large Ensemble (Obs-LE), which is based on a statistical model fit to the observations. The OLENS allows for more realistic simulation of internal variability that I will discuss in the context of climate change and the El Nino-Southern Oscillation. Finally, I will explore the use of statistical models to predict high-impact summer heat waves based on climatic boundary conditions. Both the land and the sea surfaces can be used to provide skillful predictions of Eastern US heat waves up to seven weeks in advance.