El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño-Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.

[1] Linear trend analysis is commonly applied to quantify sea level change, often over short periods because of limited data availability. However, the linear trend computed over short periods is complicated by large-scale climate variability which can affect regional sea level on interannual to inter-decadal time scales. As a result, the meaning of a local linear sea level trend over the short altimeter era (since 1993; less than 20 years) is unclear, and it is not straightforward to distinguish the regional sea level changes associated with climate change from those associated with natural climate variability. In this study, we use continuous near-global altimeter measurements since 1993 to attempt to separate interannual and decadal sea level variability in the Pacific from the sea level trend. We conclude that the rapid rates of sea level rise in the western tropical Pacific found from a single variable linear regression analysis are partially due to basin-scale decadal climate variability. The negligible sea level rise, or even falling sea level, in the eastern tropical Pacific and US west coast is a result of the combination of decreasing of sea level associated with decadal climate variability and a positive sea level trend. The single variable linear regression analysis only accounts for slightly more than 20% of the observed variance, whereas a multiple variable linear regression including filtered indices of the El Nino-Southern Oscillation and the Pacific Decadal Oscillation accounts for almost 60% of the observed variance.

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