Towards Probabilistic Multivariate ENSO Monitoring. Issue 17 (5th September 2019) - Zhang, Tao; Hoell, Andrew; Perlwitz, Judith; Eischeid, Jon; Murray, Donald; Hoerling, Martin; Hamill, Thomas M.
Abstract: A probabilistic approach to describing El Niño–Southern Oscillation (ENSO), based on consideration of the signal‐to‐noise ratio for the coupled ocean‐atmosphere ENSO state, is presented. The ENSO signal is estimated using an ensemble of historical atmospheric model simulations forced by observed sea surface temperatures and sea ice during 1980–2016. The noise is estimated from departures of individual model realizations from their ensemble average when subjected to identical forcing. It is found that this atmospheric noise effect is substantial and yields considerable uncertainty in detecting the true coupled ENSO mode. This uncertainty exceeds analysis errors by an order of magnitude. Greater atmospheric noise is found to prevail during El Niño than La Niña, suggesting that the intensity of the monitored ENSO state during El Niño is prone to greater misattribution. Our results demonstrate that a deterministic state estimate of ENSO conditions may not be representative of the true real‐time coupled ENSO mode. Key Points: A probabilistic approach to monitoring the El Niño–Southern Oscillation is introduced The probabilistic approach is based on a multivariate index in a large ensemble of atmospheric model simulations Probabilistic monitoring allows an assessment of uncertainties in El Niño–Southern Oscillation estimates