Abstract
It has been previously proposed that two El Nino (EN) regimes, strong and moderate, exist but the historical observational record is too short to establish this conclusively. Here, 1200 years of simulations with the GFDL CM2.1 model allowed us to demonstrate their existence in this model and, by showing that the relevant dynamics are also evident in observations, we present a stronger case for their existence in nature. In CM2.1, the robust bimodal probability distribution of equatorial Pacific sea surface temperature (SST) indices during EN peaks provides evidence for the existence of the regimes, which is also supported by a cluster analysis of these same indices. The observations agree with this distribution, with the EN of 1982–1983 and 1997–1998 corresponding to the strong EN regime and all the other observed EN to the moderate regime. The temporal evolution of various indices during the observed strong EN agrees very well with the events in CM2.1, providing further validation of this model as a proxy for nature. The two regimes differ strongly in the magnitude of the eastern Pacific warming but not much in the central Pacific. Observations and model agree in the existence of a finite positive threshold in the SST anomaly above which the zonal wind response to warming is strongly enhanced. Such nonlinearity in the Bjerknes feedback, which increases the growth rate of EN events if they reach sufficiently large amplitude, is very likely the essential mechanism that gives rise to the existence of the two EN regimes. Oceanic nonlinear advection does not appear essential for the onset of strong EN. The threshold nonlinearity could make the EN regimes very sensitive to stochastic forcing. Observations and model agree that the westerly wind stress anomaly in the central equatorial Pacific in late boreal summer has a substantial role determining the EN regime in the following winter and it is suggested that a stochastic component at this time was key for the development of the strong EN towards the end of 1982.
Highlights
Recent studies indicate that two types of El Niño (EN) have occurred over the last five decades, one with sea surface temperature (SST) anomalies peaking in the eastern Pacific and the other peaking in the central equatorial Pacific (Larkin and Harrison 2005; Ashok et al 2007; Kug et al 2009)
Forced linear models fitted to observations are able to capture some aspects of the ENSO diversity (e.g. Newman et al 2011b), but linear dynamics alone can not generate the nonlinear relation between the first two dominant statistical modes of equatorial Pacific SST anomalies, which has been proposed to emerge from dual EN regimes, with the 1982–1983 and 1997–1998 events corresponding to different dynamics from the other EN (Takahashi et al 2011; Capotondi et al 2015)
A well-known nonlinear ENSO process is associated with the existence of a threshold for SST to exceed in order for deep convection to take place (Graham and Barnett 1987), which in the eastern Pacific could be associated with the reversal of the meridional SST gradient required to bring the ITCZ to the equator (Lengaigne and Vecchi 2009; Cai et al 2014)
Summary
Recent studies indicate that two types of El Niño (EN) have occurred over the last five decades, one with SST anomalies peaking in the eastern Pacific and the other peaking in the central equatorial Pacific (Larkin and Harrison 2005; Ashok et al 2007; Kug et al 2009). Newman et al 2011b), but linear dynamics alone can not generate the nonlinear relation between the first two dominant statistical modes of equatorial Pacific SST anomalies, which has been proposed to emerge from dual EN regimes, with the 1982–1983 and 1997–1998 events corresponding to different dynamics from the other EN (Takahashi et al 2011; Capotondi et al 2015). We expand on previous studies (Takahashi et al 2011; Dommenget et al 2012) by investigating the possible existence and the nonlinear processes responsible for the ENSO regimes associated to strong and moderate EN events, using available observations and long-term simulations with the GFDL CM2.1 model. We discuss some implications of the results (Sect. 4) and summarize the main conclusions (Sect. 5)
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