EGU23-8904
https://doi.org/10.5194/egusphere-egu23-8904
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Forecasting the El Niño type well before the spring predictability barrier

Josef Ludescher1, Armin Bunde2, and Hans Joachim Schellnhuber1
Josef Ludescher et al.
  • 1Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 2Institute for Theoretical Physics, Justus-Liebig-Universität Gießen, Gießen, Germany

The El Niño Southern Oscillation (ENSO) is the most important driver of interannual global climate variability and can trigger extreme weather events and disasters in various parts of the globe. Depending on the region of maximal warming, El Niño events can be partitioned into 2 types, Eastern Pacific (EP) and Central Pacific (CP) events. The type of an El Niño has a major influence on its impact and can even lead to either dry or wet conditions in the same areas on the globe. Here we show that the zonal difference ΔTWP-CP between the sea surface temperature anomalies (SSTA) in the equatorial western Pacific and central Pacific is predictive of the type of an upcoming El Niño. When at the end of a calendar year, ΔTWP-CP is positive, an El Niño event developing in the following year will probably be an EP event, otherwise a CP event. Between 1950 and present, the index correctly indicates the type of 18 out of 21 El Niño events (p = 9.1⋅10-4).
For early actionable forecasts, the index has to be combined with a forecast for the actual onset of an El Niño event. The previously introduced climate network-based forecasting approach provides such forecasts for the onset of El Niño events also by the end of the calendar year before onset. Thus a combined approach can provide reliable forecasts for both the onset and the type of an event: at a lead time of about one year, 2/3 of the EP El Niño forecasts and all CP El Niño forecasts in the regarded period are correct. The combined model has considerably more predictive power than the current operational type forecasts with a mean lead time of about 1 month and should allow early mitigation measures.

How to cite: Ludescher, J., Bunde, A., and Schellnhuber, H. J.: Forecasting the El Niño type well before the spring predictability barrier, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8904, https://doi.org/10.5194/egusphere-egu23-8904, 2023.