GFDL - Geophysical Fluid Dynamics Laboratory

Understanding ENSO Diversity

March 23rd, 2015

A. Capotondi, A. T. Wittenberg, M. Newman, E. Di Lorenzo, J.-Y. Yu, P. Braconnot, J. Cole, B. Dewitte, B. Giese, E. Guilyardi, F.-F. Jin, K. Karnauskas, B. Kirtman, T. Lee, N. Schneider, Y. Xue, and S.-W. Yeh. Bulletin of the American Meteorological Society, in press, 2015. DOI: 10.1175/BAMS-D-13-00117.1

Summary

The El Niño / Southern Oscillation (ENSO) is Earth’s strongest interannual climate fluctuation, impacting weather, ecosystems, and economies around the world. Understanding the range of ENSO variation could help lead to longer range predictions of El Niño and La Niña events. The authors review the current state of understanding of diversity among different El Niño / Southern Oscillation (ENSO) events, which differ from event to event in their amplitude, spatial pattern, temporal evolution, dynamical mechanisms, and impacts.

ENSO diversity is apparent in historical climate records, paleo proxy records, and model simulations. As yet there is no clear evidence for distinct “types” of ENSO events, but rather a continuum of variability with striking extremes. ENSO warm events (El Niños) are generally more diverse than cold events (La Niñas). Compared to weaker El Niños, stronger El Niños tend to exhibit their peak warm sea surface temperature (SST) anomalies farther east, with a relatively greater role in the ocean mixed layer heat budget for thermocline motions, as opposed to oceanic zonal advection and air-sea heat fluxes. El Niño events of all types are often preceded (and in part triggered) by westerly wind events in the western and central equatorial Pacific, which, in turn, are favored by relaxation of the equatorial zonal SST gradient, recharge of the equatorial ocean heat content, and equatorward propagation of off-equatorial disturbances via wind-evaporation-SST feedbacks.

Present-day prediction systems can skillfully distinguish the flavor of ENSO evolution up to six months in advance. On longer time scales, simulations suggest that multi-decadal prevalence of a given ENSO flavor can occur at random, even in the absence of external changes in radiative forcings. Anthropogenic forcings are expected to increase the intensity of ENSO rainfall anomalies in the central equatorial Pacific.

Future work will focus on understanding how ENSO diversity relates to decadal- and longer-term variations in the background climate state, and on clarifying the sources and limits of predictability for different ENSO events. Additional goals include improving the representation of ENSO diversity in climate models and in observational and paleo reconstructions, and improving operational forecasts of ENSO.

 Figure 1. (Left) Distribution of boreal winter (NDJ) SSTA extrema in the longitude-amplitude plane. Anomalies were obtained from the NOAA Extended Reconstructed SST data set (Smith et al. 2004) over the period 1900-2013, as departures from the 1945-2013 climatology. Each dot corresponds to the extreme positive or negative value over the NDJ of each year in the region 2°S-2°N, 110°E-90°W. Events prior to 1945 are colored in gray. Events after 1945 are considered EP (red dots) when the Niño3 index exceeds one standard deviation. CP events are identified using the leading principal component of the SSTA residual after removing the SSTA regression onto the Niño3 index. Blue dots in the left panel correspond to events for which the leading principal component (used as CP index) exceeds one standard deviation. The spatial patterns of SSTA for specific warm and cold events of either type are shown on the right panels, with a contour interval of 0.25°C. (From Capotondi et al, 2015.)
Figure 1. (Left) Distribution of boreal winter (NDJ) SSTA extrema in the longitude-amplitude plane. Anomalies were obtained from the NOAA Extended Reconstructed SST data set (Smith et al. 2004) over the period 1900-2013, as departures from the 1945-2013 climatology. Each dot corresponds to the extreme positive or negative value over the NDJ of each year in the region 2°S-2°N, 110°E-90°W. Events prior to 1945 are colored in gray. Events after 1945 are considered EP (red dots) when the Niño3 index exceeds one standard deviation. CP events are identified using the leading principal component of the SSTA residual after removing the SSTA regression onto the Niño3 index. Blue dots in the left panel correspond to events for which the leading principal component (used as CP index) exceeds one standard deviation. The spatial patterns of SSTA for specific warm and cold events of either type are shown on the right panels, with a contour interval of 0.25°C. (From Capotondi et al, 2015.)