Abstract

Three 40-member ensemble experiments and a 700 year control run are used to study initial value predictability in the North Pacific in Community Climate System Model version 3 (CCSM3). Our focus is on the leading two empirical orthogonal functions (EOFs) of subsurface temperature variability, whi... Show moreThree 40-member ensemble experiments and a 700 year control run are used to study initial value predictability in the North Pacific in Community Climate System Model version 3 (CCSM3). Our focus is on the leading two empirical orthogonal functions (EOFs) of subsurface temperature variability, which together produce an eastward propagating mode. Predictability is measured by relative entropy, which compares both the mean and spread of predictions of ensembles to the model’s climatological distribution of states. Despite the fact that EOF1, which is structurally similar to the observational Pacific Decadal Oscillation (PDO), has pronounced spectral peaks on decadal time scales, its predictability is less than 6 years. Additional predictability resides in the tendency of EOF1 to evolve to EOF2, primarily through simple advective processes. The propagating mode represented by the combination of EOF1 and EOF2 is predictable for about a decade. 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. Show less