Nonseasonal variability of sea level pressure (SLP) and sea surface temperature (SST) in the mid-latitude North Pacific Ocean is examined. The objective is examination of the basic scales of the variability and determination of possible causal connections which might allow prediction of short-term climatic (time scales between a month and a year) variability. Using empirical orthogonal function descriptions of the spatial structure, it is found that SLP variability is concentrated in a few large-scale modes but has a nearly white frequency spectrum. SST variability is spatially complex (being spread over many spatial modes, some of which have small-scale changes) but is dominated by low-frequency changes. The use of linear statistical estimators to examine predictability is discussed and the importance of limiting the number of candidate data used in a correlation starch is underscored. Using linear statistical predictors, it is found that (A) SST anomalies can be predicted from SST observations ... Abstract Nonseasonal variability of sea level pressure (SLP) and sea surface temperature (SST) in the mid-latitude North Pacific Ocean is examined. The objective is examination of the basic scales of the variability and determination of possible causal connections which might allow prediction of short-term climatic (time scales between a month and a year) variability. Using empirical orthogonal function descriptions of the spatial structure, it is found that SLP variability is concentrated in a few large-scale modes but has a nearly white frequency spectrum. SST variability is spatially complex (being spread over many spatial modes, some of which have small-scale changes) but is dominated by low-frequency changes. The use of linear statistical estimators to examine predictability is discussed and the importance of limiting the number of candidate data used in a correlation starch is underscored. Using linear statistical predictors, it is found that (A) SST anomalies can be predicted from SST observations ...