We discovered that variations in the spatial pattern of El Niño’s warming (ENSO diversity) substantially alter the relationship between ENSO and western US precipitation. In addition, using a physically-based ENSO index that fully captures ENSO diversity – in a single metric and for any climate state – better explains western US precipitation variability compared to traditional indices based on sea-surface temperature anomalies (SSTAs).
Considering ENSO diversity from the physically-based, atmospheric perspective of the ENSO Longitude Index (ELI; Williams and Patricola 2018) provides a powerful capability for understanding the response of a variety of extreme events, including mid-latitude precipitation and tropical cyclones, to ENSO diversity in a changing climate. ELI addresses the challenges with SSTA-based metrics that each capture only a portion of the ENSO spectrum, by 1) eliminating the need to choose which SSTA-based metric to use to understand teleconnections to a particular region and 2) eliminating the uncertainty as to whether that SSTA-based metric will be informative in a changing climate.
Until recently, the El Niño – Southern Oscillation (ENSO) was considered a reliable source of winter precipitation predictability in the western US, with a historically strong link between extreme El Niño events and extremely wet seasons. However, the 2015-2016 El Niño challenged our understanding of the ENSO-precipitation relationship. California precipitation was near-average during the 2015-2016 El Niño, which was characterized by warm sea-surface temperature (SST) anomalies of similar magnitude compared to the extreme 1997-1998 and 1982-1983 El Niño events. We demonstrate that this precipitation response can be explained by El Niño’s spatial pattern, rather than internal atmospheric variability. In addition, observations and large-ensembles of regional and global climate model simulations indicate that extremes in seasonal and daily precipitation during strong El Niño events are better explained using the ENSO Longitude Index (ELI), which captures the diversity of ENSO’s spatial patterns in a single metric, compared to the traditional Niño3.4 index, which measures SST anomalies in a fixed region and therefore fails to capture ENSO diversity. The physically-based ELI better explains western US precipitation variability because it tracks the zonal shifts in tropical Pacific deep convection that drive teleconnections through the response in the extratropical wave-train, integrated vapor transport, and atmospheric rivers. This research provides evidence that ELI improves the value of ENSO as a predictor of California’s seasonal hydroclimate extremes compared to traditional ENSO indices, especially during strong El Niño events.