What is it about?
Interseasonal forecasting of El Niño Southern Oscillation (ENSO) is in high demand due to the impacts of ENSO on regional climatic conditions around the world as well as the global climate. Improvements in the quality of climate data in recent decades have led to the active use of statistical ENSO models, which compete with physical models in predictive power. The main disadvantage of statistical forecasts is the pronounced seasonal growth of uncertainty when predicting the upcoming summer‐fall ENSO conditions from winter‐spring months; this phenomenon is called the spring predictability barrier (SPB). A number of recent works revealed that winter‐spring atmospheric anomalies can substantially impact the ENSO system through the SPB via a complex atmosphere‐ocean interaction mechanism. Here, we introduce a reliable ENSO predictor constructed from sea level pressure data relating to this mechanism and show that the predictor significantly improves the multimonth (up to one year) ENSO forecast by lowering the SPB in a statistical model of the key ENSO index.
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This page is a summary of: An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts, Geophysical Research Letters, March 2021, American Geophysical Union (AGU),
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