A Study of Springtime Subseasonal Predictability

Friday, 19 December 2014
Matthew Newman, University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States; NOAA/ESRL/PSD, Boulder, CO, United States
The predictability of weekly averaged atmospheric anomalies over North America, and their relationship to predictable anomalies in the Tropics and over southeast Asia, is investigated in a linear inverse model (LIM) derived from their observed simultaneous and time-lag correlation statistics for boreal spring. Forecast skill is found to be comparable to that of the current operational forecast models (CFS and GFS) run at the National Centers for Environmental Prediction (NCEP). The geographical and temporal variations of forecast skill are also similar in the two models. This makes the much simpler LIM an attractive tool for assessing and diagnosing atmospheric predictability at these forecast ranges.

The LIM assumes that the dynamics of weekly averages are linear, asymptotically stable, and stochastically forced. In a forecasting context, the predictable signal is associated with the deterministic linear dynamics, and the forecast error with the unpredictable stochastic noise. In a low-order linear model of a high-order chaotic system, this stochastic noise represents the effects of both chaotic nonlinear interactions and unresolved initial components on the evolution of the resolved components. Its statistics are assumed here to be state independent.

In this framework, the predictable variations of forecast skill from case to case are associated with predictable variations of signal rather than of noise. In the LIM, the predictable variations of signal are associated with variations of the initial state projection on the growing singular vectors of the LIM’s propagator. At times of strong projection on such structures, the signal-to-noise ratio is relatively high, and springtime climate anomalies are not only potentially but also actually more predictable than at other times.