Initial-value predictability of prominent modes of North Pacific subsurface temperature in a CGCM

Information in both the mean and spread of predicted ensembles contribute to this predictability. Among the leading 15 EOFs, EOF1 is the least predictable mode in terms of the rate at which the corresponding principal component disperses in the ensemble experiments. However, it can produce enhanced predictability of the whole system by inducing EOF2, which is one of the two EOFs with the slowest dispersion rate.

The first two EOFs can also enhance the ensemble mean (or "signal") component of predictability of the entire system. For typical amplitude initial states, this component contributes to predictability for about 6 years. For initial states with unusually high amplitude projections onto these two EOFs, this contribution can last much longer.

The major findings from the three ensemble experiments are replicated and generalized when the initial condition predictability for each of many hundreds of different initial states is estimated. These estimates are derived from the behavior of a linear inverse model (LIM) that is based on the intrinsic variability present in the control run.