Spatiotemporal patterns of ENSO-precipitation relationships in the tropical Andes of southern Peru and Bolivia

Precipitation in the outer tropical Andes is highly seasonal, exhibits considerable interannual variability, and is vital for regulating freshwater availability, flooding, glacier mass balance, and droughts. The primary driver of interannual variability is El Nino Southern Oscillation (ENSO), with most investigations reporting that the El Nino (La Nina) results in negative (positive) precipitation anomalies across the region. Recent investigations, however, have identified substantial spatiotemporal differences in ENSO-precipitation relationships. Motivated by the dissimilarity of these findings, this study examines a carefully selected data set (>= 90% completeness) of ground-based precipitation observations from 75 high-elevation (>= 2,500 m above sea level) meteorological stations in the tropical Andes of southern Peru and Bolivia for the period 1972-2016. Distinct groups of stations and associated variability in precipitation characteristics (e.g., total seasonal precipitation, wet season onset, and wet season length) are identified. Using no spatial constraints, the K-Means algorithm optimally grouped stations into five easily identifiable groups. The groups farthest from the Amazon basin had significant negative (positive) precipitation anomalies (p < .05) during El Nino (La Nina), aligning with the traditional view of ENSO-precipitation relationships while groups closest to the Amazon had opposite relationships. Additionally, though studies have reported delays in the wet season, years characterized by El Nino had an earlier wet season onset in all five groups. These findings may aid in improving seasonal climate prediction and managing water resources, and could allow for improved interpretation of tropical Andean ice cores.

Automated summary

Precipitation in the outer tropical Andes is highly seasonal and influenced by ENSO, with significant spatiotemporal differences. Analysis of high-elevation meteorological station data revealed distinct precipitation variability characteristics, aiding in improved seasonal climate prediction and water resource management.