The Full Extent of El Niño's Precipitation Influence on the United States and the Americas: The Suboptimality of the Niño 3.4 SST Index

Abstract

Key features of El Niño's influence on North American winter precipitation are well‐known but we show that this influence has, hitherto, been suboptimally characterized in winter, and especially in fall and spring. The suboptimality has arisen from the historical over‐reliance on regressions on the Niño 3.4 SST index—a widely used marker of El Niño variability. We show that El Niño's full influence is obtained from assembling the regressions on the spatiotemporal modes constituting El Niño variability, rather than from regressions on an SST index keyed just to its mature phase (Niño 3.4 index). The notably different influence of central and eastern Pacific El Niños on Eastern Seaboard is documented. The full influence of El Niño on North American precipitation is shown to be substantially larger than previously recognized and pronounced not just in northern winter but even in fall and spring—enhancing prospects for year‐round seasonal prediction.

Quantifying and Understanding Forced Changes to Unforced Modes of Atmospheric Circulation Variability over the North Pacific in a Coupled Model Large Ensemble

“…Given the greater and more consistent correlations between TPac_TS_SEOF1 and the extratropical Z500 SEOFs (except the NPO) in the future (Fig. 4a), this effectively translates into a more consistent hydroclimate response, especially when quantifying ENSO with an appropriate metric (Williams and Patricola 2018, Patricola et al 2020, Nigam and Sengupta 2021. On the other hand, when controlling for variability originating in the tropics, only NPac_Z500_SEOF1 and NPac_Z500_SEOF3 show large and robust patterns of change (recall Figs.…”

Hidden Tropical Pacific Sea Surface Temperature States Reveal Global Predictability for Monthly Precipitation for Sub‐Season to Annual Scales

“…The El Niño Southern Oscillation (ENSO) is the strongest interannual signal in the climate system. It develops around the tropical Pacific Ocean and is the most important mode of global climate variability (McPhaden et al., 2006; Nigam & Sengupta, 2021). There is an extensive literature on the teleconnections of ENSO to regional precipitation including future predictability (Arcodia et al., 2020; Chapman et al., 2021; Dai & Wigley, 2000; Emerton et al., 2017; Henderson et al., 2020; Ropelewski & Halpert, 1987; Sun et al., 2015; Tseng et al., 2021; Van Oldenborgh & Burgers, 2005; Vicente‐Serrano et al., 2011; Yan et al., 2021; Yang et al., 2021; Zhang et al., 2016).…”

“…Spatial patterns of sea surface temperature (SST) are key to each ENSO event's evolution and impacts. The NINO1.2 and NINO3.4 regions have been used most often for correlative teleconnection analyses, and also for benchmarking the performance of physics‐based models in reproducing ENSO dynamics and the associated teleconnections (Lenssen et al., 2020; Nigam & Sengupta, 2021). Recent ENSO events have exhibited strong anomalies in SST regions in the Central Tropical Pacific that are different from the traditional ones.…”

“…The El Niño Southern Oscillation (ENSO) is the strongest interannual signal in the climate system. It develops around the tropical Pacific Ocean and is the most important mode of global climate variability (McPhaden et al, 2006;Nigam & Sengupta, 2021). There is an extensive literature on the teleconnections of ENSO to regional precipitation including future predictability (…”

A SST-constructed Ocean Heat Content index in crossing ENSO spring persistence barrier

“…Seleznev and Mukhin (2023) proposed a joint SST-OHC model using Bayesian optimization schemes and revealed a substantial reduction in the seasonal predictability barrier of ENSO and winter barrier for the OHC index. Nigam and Sengupta (2021) proposed an SST index based on regressions of four spatiotemporal modes that better capture ENSO variability and related hydroclimate impact (relative to Nino 3.4 index) at multiple seasonal leads. Planton et al (2018) proposed the western Pacific OHC as a better predictor of La Niña events.…”