Multiyear versus single‐year El Niño events: Contrasting their impacts on South American seasonal precipitation

Abstract

Differences in the seasonal distribution of precipitation over South America (SA) associated with single‐year (SY) and multiyear (MY) El Niño (EN) events were analysed based on reanalysis data for the 1901–2012 period. The results suggest that Pacific Sea Surface Temperature (SST) anomalies associated with MY EN events interact with the tropical Atlantic and Indian Oceans SST from austral fall to spring, after the event's first year, affecting SA precipitation distribution in subsequent seasons. Compared to SY EN events, which reproduce well the north–south dipolar precipitation anomaly pattern over SA, the MY EN events show differences in the intensity and positioning of precipitation anomalies. Precipitation decreases over northern SA during all seasons are observed for SY and MY EN events except for differences in magnitudes. Variations in the position and longitudinal extension of the downward motions of Walker circulation in response to these events explain these differences. Over southern and southeastern SA, differences in anomaly positioning are more evident. The positive precipitation anomalies over these regions in the austral spring and summer are weakened and southward shifted during the MY EN in relation to those during SY EN events. These variations are associated with the Rossby‐wave train pattern path that depends on the EN and season. Consequently, the associated local atmospheric circulation patterns also depend on the season. The intense (weak) South American low‐level jet (SALLJ) for the first year of MY EN contrasts with the weak or inexistence (intense) SALLJ for the SY EN during summer (springer). Complementary to previous studies, the results indicate that the differences in the intensity and duration of the SY and MY EN events contribute to changes in the EN‐related atmospheric teleconnection pattern that impacts SA precipitation. The results can be useful for climate prediction and monitoring purposes.