An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts

The loss of autocorrelations of tropical sea surface temperatures (SST) during late spring, also called the spring predictability barrier (SPB), is a factor that strongly limits the predictability of El Nino Southern Oscillation (ENSO), and especially the statistical SST-based ENSO forecasts starting from the winter-spring season. Recent studies show that Pacific atmospheric circulation anomalies in winter-spring may have a long-term impact on the summer tropical climate via the SST footprint. Here, we infer an index based on sea level pressure (SLP) data from February to March in a single area surrounding Hawaii, and show that this area is the most informative part of the large SLP pattern initiating the SST footprinting mechanism. We then construct a statistically optimal linear model of the Nino 3.4 index taking this atmospheric index as a forcing. We find that this forcing efficiently lowers the SPB and provides significant improvements of interseasonal Nino 3.4 forecasts.

Automated summary

Recent studies suggest that Pacific atmospheric circulation anomalies in winter-spring may impact summer tropical climate via SST footprinting. Researchers have inferred an index based on sea level pressure data surrounding Hawaii and constructed a statistically optimal linear model of the Nino 3.4 index with this atmospheric index as a forcing, leading to significant improvements in interseasonal Nino 3.4 forecasts by efficiently lowering the spring predictability barrier.