Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO

Abstract

Abstract A linear inverse model (LIM) is used to predict Pacific (30°S–60°N) sea surface temperature anomalies (SSTAs), including the Pacific decadal oscillation (PDO). The LIM is derived from the observed simultaneous and lagged covariance statistics of 3-month running mean Pacific SSTA for the years 1951–2000. The model forecasts exhibit significant skill over much of the Pacific for two to three seasons in advance and up to a year in some locations, particulary for forecasts initialized in winter. The predicted and observed PDO are significantly correlated at leads of up to four seasons, for example, the correlation exceeds 0.6 for 12-month forecasts initialized in January–March (JFM). The LIM-based PDO forecasts are more skillful than persistence or a first-order autoregressive model, and have comparable skill to LIM forecasts of El Niño SSTAs. Filtering the data indicates that much of the PDO forecast skill is due to ENSO teleconnections and the global trend. Within LIM, SST anomalies can grow due to constructive interference of the empirically determined modes, even though the individual modes are damped over time. For the Pacific domain, the basinwide SST variance can grow for ∼14 months, consistent with the skill of the actual predictions. The optimal structure (OS), the initial SSTA pattern that LIM indicates should increase the most rapidly with time, is clearly relevant to the predictions, as the OS develops into a mature ENSO and PDO event 6–10 months later. The OS is also consistent with the seasonal footprinting mechanism (SFM) and the meridional mode (MM); the SFM and MM involve a set of atmosphere–ocean interactions that have been hypothesized to initiate ENSO events.