To clarify, coordinate and synthesize research devoted to achieve a better understanding of ENSO diversity, including: surface and sub-surface characteristics, tropical- extratropical teleconnections, physical mechanisms, predictability, and relationship with climate change Scientific Objectives: Examine the range of ENSO “flavors” with focus upon longitudinal variations of warming, identify basic surface and subsurface characteristics that are robust among different datasets, assess the existence of possible, and distinct precursors to the different flavors, and create a framework that will allow the community to better understand how the interplay of different oceanic, atmospheric, and coupled processes drive different ENSO flavors and impact their predictability. Examine the performance of the CMIP5 archive in reproducing the best observational estimate of ENSO diversity, and assess its projected changes. During its first year, the WG is charged with establishing the ability of data sets to reveal a range of ENSO types, and the ability of models to simulate the types revealed. A community workshop is being planned for fall 2012 to review the  representation of ENSO diversity in observational data sets,  model analyses of ENSO diversity and basic characteristics of different ENSO types, and  extra-tropical influences on ENSO diversity, and remote impacts of different ENSO flavors ENSO Diversity Working Group Chairs: Antonietta Capotondi (U Colorado) and Ben Kirtman (U Miami)

Fig. 1. SST anomalies of El Niño events during 1970–2005 based on the ERSST V2 dataset. The anomalies are averaged from September to the following February. Shading indicates normalized anomalies; contour interval is 0.3 K. The El Niño events are classified into (left) “Warm Pool” El Niño, (middle) “Cold Tongue” El Niño, and (right) mixed El Niño. The green boxes indicate (left) Niño4, (middle) Niño3, and (right) Niño3.4 regions (From Kug et al J. Climate)

Fig. 2. Composites of “Cold Tongue” El Niños (top) and “Warm Pool” El Niños (bottom) for the NCAR-CCSM4 (left) and SODA ocean reanalysis (right). The CT (WP) El Niños are identified using winter (DJF) values of the Niño3 (Niño4) indices, with the criterion that the Niño3 (Niño4) index is larger than its standard deviation, and larger than Niño4 (Niño3). (From Capotondi, in preparation)

Fig. 2. The longitudinal position of the equatorial SST anomalies influences the atmospheric teleconnections and large-scale impacts. Here we see the El Niño seasonal average U.S. Temperature Anomaly Associations for Autumn (SON) and Winter (DJF). The left two columns are based on the “conventional” El Niño seasons; the right column is based on the “Dateline” El Niños. The right two columns are masked for 80% significance. In general, the statistically significant “Dateline” temperature anomalies are of the opposite sign or do not overlap the “Conventional” anomalies spatially (From Larkin and Harrison 2005, GRL).