The effects of El Niño's two distinct flavors, East Pacific (EP) and Central Pacific (CP)/Modoki El Niño, on global climate variability have been studied intensively in recent years. Most of these studies have made use of linear multivariate statistics or composite analysis. Especially the former assumes the same type of linear statistical dependency to apply across different phases of the El Niño--Southern Oscillation (ENSO), which appears not necessarily a justified assumption. Here, we statistically evaluate the likelihood of co-occurrences between very high or very low seasonal precipitation sums over vast parts of the global land surface and the presence of the respective EP and CP types of both, El Niño and La Niña. By employing event coincidence analysis, we uncover differential imprints of both flavors on very low and very high seasonal precipitation patterns over distinct regions across the globe, which may severely affect, among others, agricultural and biomass production or public health. We particularly find that EP periods exhibit statistically significant event coincidence rates with hydrometeorological anomalies at larger spatial scales, whereas sparser patterns emerge along with CP periods. Our statistical analysis confirms previously reported interrelations for EP periods and uncovers additional distinct regional patterns of very high/low seasonal precipitation, such as increased rainfall over Central Asia alongside CP periods that have to our knowledge not been reported so far. Our results demonstrate that a thorough distinction of El Niño and La Niña into their two respective flavors could be crucial for understanding the emergence of strong regional hydrometeorological anomalies and anticipating their associated ecological and socioeconomic impacts.
Precursors, Pathways, and State-Dependent Reliability of Long-range ENSO Prediction in the CESM2 Seasonal-to-Multiyear Large Ensemble
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；NSF National Center for Atmospheric Research (NCAR)（美国国家科学基金会国家大气研究中心）；North Carolina State University（北卡罗来纳州立大学）；University of Hawai’i at Mānoa（夏威夷大学马诺阿分校）
Seasonal cycle of Precipitation over Major River Basins in South and Southeast Asia: A Review of the CMIP5 climate models data for present climate and future climate projections
The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
Technical University of Darmstadt（达姆施塔特工业大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of São Paulo（圣保罗大学）；Technical University of Munich（慕尼黑工业大学）；Warsaw University of Technology（华沙工业大学）；Duquesne University（杜肯大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Massachusetts Institute of Technology（麻省理工学院）
Multiscale interactions between monsoon intra-seasonal oscillations and low pressure systems that produce heavy rainfall events of different spatial extents
Graph Neural Networks for Improved El Niño Forecasting
Technical University of Darmstadt（达姆施塔特工业大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of São Paulo（圣保罗大学）；TU Munich（慕尼黑工业大学）；Warsaw University of Technology（华沙理工大学）；Duquesne University（杜肯大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Massachusetts Institute of Technology（麻省理工学院）
