loading page

An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts
  • +1
  • Dmitry Mukhin,
  • Andrey Gavrilov,
  • Aleksei Seleznev,
  • Maria Buyanova
Dmitry Mukhin
Institute of Applied Physics of the Russian Academy of Sciences, Institute of Applied Physics of the Russian Academy of Sciences

Corresponding Author:mukhin@ipfran.ru

Author Profile
Andrey Gavrilov
Institute of Applied Physics of the Russian Academy of Sciences, Institute of Applied Physics of the Russian Academy of Sciences
Author Profile
Aleksei Seleznev
Institute of Applied Physics of the Russian Academy of Sciences, Institute of Applied Physics of the Russian Academy of Sciences
Author Profile
Maria Buyanova
Institute of Applied Physics, Institute of Applied Physics of the Russian Academy of Sciences
Author Profile

Abstract

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-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.
28 Mar 2021Published in Geophysical Research Letters volume 48 issue 6. 10.1029/2020GL091287