Dominant modes of ensemble mean signal and noise in seasonal forecasts of SST

In contrast to the temporal evolution of forecast ensemble mean (signal) and spread (noise) in an ensemble of seasonal forecasts, the spatial patterns of signal and noise components for sea surface temperature (SST) predictions have not been analyzed. In this work, we examine the leading patterns of signal and noise components of SST forecasts by the National Centers for Environmental Prediction Climate Forecast System version 2. It is noted that the leading empirical orthogonal function pattern of SST is similar between the signal and the noise with maximum loading in the central and eastern tropical Pacific associated with El Nino-Southern Oscillation (ENSO) variability. The similarity implies that while some members of the forecasts predict a stronger (weaker) ENSO than others, the dominant pattern of SST anomalies from all members still resembles the ENSO SST pattern. This reflects the notion that for each forecast ensemble member, the evolution of ENSO is governed by the similar air-sea coupled interactions, the strength of which, however, differs due to unpredictable noise. On the other hand, the leading mode of the signal and the noise are found temporally independent. Thus, it is concluded that although the largest variability in the signal and the noise is spatially collocated, their temporal evolution is independent.

Automated summary

Spatial patterns of signal and noise components for sea surface temperature (SST) predictions have not been analyzed in contrast to the temporal evolution of forecast ensemble mean (signal) and spread (noise) in an ensemble of seasonal forecasts. It is found that the leading empirical orthogonal function pattern of SST is similar between the signal and the noise with maximum loading in the central and eastern tropical Pacific associated with El Nino-Southern Oscillation (ENSO) variability. Although the largest variability in the signal and the noise is spatially collocated, their temporal evolution is independent.