A Review of Real-Time Markov Model ENSO Forecast in 1996-2015: Why did it Forecast a Strong El Nino since March 2015?

Abstract:

The Markov model for real time ENSO forecast at Climate Prediction Center of National Centers for Environmental Prediction (NCEP) is based on observed sea surface temperature, sea level from the NCEP ocean reanalysis, and pseudo wind stress from the Florida State University in 1980-1995. The Markov model is constructed in a reduced multivariate EOF (MEOF) space with 3 MEOFs. The cross-validated hindcast skill of NINO3.4 in 1980-1995 is competitive among dynamical and statistical models. The model was implemented into operation at CPC in early 2000s since it successfully forecasted the El Nino in winter 1997/98 starting from November 1996 initial conditions (I.C.). In this study, we assessed the real time forecast skill of ENSO by the Markov model in 1996-2015 and compared it with that of other operational forecast models. It is found that the Markov model has lower forecast skill of ENSO in the 2000s than that in the 1980s and 1990s, which is common among ENSO forecast models. The lower forecast skill of the Markov model in the 2000s can be attributed to weak precursor of positive heat content anomaly in the equatorial Pacific and a shorter lead time of the precursor relative to NINO3.4, both of which is related to the decadal change of ENSO. However, out of surprise, the Markov model successfully forecasted the El Nino in winter 2014/15 starting from February 2014 I.C.. In addition, the Markov model forecasted the continuation of the El Nino into the spring/summer/fall of 2015. Starting from March 2015 I.C., the Markov model forecasted a strong El Nino in winter 2015/16. This surprising long-lead forecast skill can be attributed to the positive second principal component (PC) of MEOF that leads NINO3.4 by 6-9 months, a precursor commonly seen in the 1980s and 1990s. This provided us confidence in the model forecast of a strong El Nino in winter 2015/16 that is highly consistent with the ensemble forecast of dynamical models.