A Study of Subseasonal Surface Temperature Predictability

The predictability of weekly averaged surface temperatures (T2m) over North America is investigated in a linear inverse model (LIM) derived from the observed simultaneous and time-lag correlation statistics of Northern Hemisphere geopotential height and T2m anomalies and tropical OLR anomalies. In all seasons, the model’s North American surface temperature forecast skill at week 3 (days 15-21) and week 4 (days 22-28) is comparable to, and often is higher on average than, skill of coupled GCM (CGCM) forecasts taken from the S2S and SubX databases. The geographical and temporal variations of forecast skill of geopotential height anomalies are also similar between the CGCMs and LIM. 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. However, both the predictable dynamics and statistics of the noise can be seasonally varying.

An average signal-to-noise ratio is estimated at each grid point in North America and is then used to estimate the potential predictability of weekly variations at the point. 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, which have relatively large amplitude in the Tropics. At times of strong projection on such structures, the signal-to-noise ratio is relatively high, and North American surface temperatures are not only potentially but also actually more predictable than at other times.