Justin Finkel, University of Chicago
Nonlinear atmospheric dynamics produce rare events that are hard to predict and attribute due to many interacting degrees of freedom. A sudden stratospheric warming (SSW) event is a model example. Approximately once every other year, the winter polar vortex in the boreal stratosphere rapidly breaks down, inducing a shift in midlatitude surface weather patterns persisting for up to two to three months. In principle, lengthy numerical simulations can be used to predict and understand these rare transitions – but often at a prohibitive cost. We describe an alternative approach which only requires relatively short-duration computer simulations of the system. The methodology is illustrated by applying it to a prototype model of an SSW event developed by Holton and Mass (1976) and driven with stochastic forcing. While highly idealized, the model captures the essential nonlinear dynamics of SSW events and exhibits the key forecasting challenge: the dramatic separation in timescales between the dynamics of a single event and the return time between successive events. We compute optimal forecasts of sudden warming events and quantify the limits of predictability. Statistical analysis relates these optimal forecasts to a small number of interpretable physical variables. Remarkably, we are able to estimate these quantities using a data set of simulations much shorter than the timescale of the warming event. This methodology is designed to take full advantage of the high-dimensional data from models and observations and can be employed to find detailed predictors of many complex rare events arising in climate dynamics.
Abstract Author(s): Justin Finkel, Robert J. Webber, Edwin P. Gerber, Dorian S. Abbot, Jonathan Weare