This event will be held remotely over Zoom; please go here to attend!
Title: Size of atmospheric blocking: Scaling law, response to climate change and implications for extreme weather events
Abstract: Atmospheric blocking events, which are persistent, large-scale high-pressure systems in the extra-tropics, can cause weather extremes such as heat waves, cold spells, droughts, and flooding events. Understanding the response of blocking events to climate change, and in particular to arctic amplification, has been of great interest in recent years, although the focus has been mostly on changes in the frequency of blocking events (which remains inconclusive). Potential changes in the area (size) of blocking events, which can affect the spatio-temporal characteristics of the resulting extreme events, have not been studied before. Here, using two large-ensemble, fully-coupled GCM simulations and two blocking indices, first I show that the size of blocking events increases with climate change, particularly in summers of the northern hemisphere. Building a model hierarchy, I then use a two-layer quasi-geostrophic model and a dimensional analysis technique to derive a scaling law for the size of blocking events, which shows that area mostly scales with the width of the jet times the Kuo scale (i.e., the length of stationary Rossby waves). The scaling law is validated in a range of idealized dry GCM simulations. This scaling's predictions agree well with changes in blocking events' size under climate change in fully-coupled GCMs in winters but not in summers. Further work with an idealized moist GCM points to the role of moist processes as the source of this discrepancy. I will discuss the implications of these results for the size, intensity, and impact of future heat waves. I will also briefly mention a few other ongoing projects in our group focused on understanding changes in future hurricanes’ movement and damage, dynamics of annular modes, and using deep learning for analog forecasting of extreme weather events and for improving climate/weather models.