PSD researchers develop a new approach for appraising the state of El Niño-Southern Oscillation

Flooded road closure, Photo credit: USGS
Photo Credit: USGS

The El Niño/Southern Oscillation (ENSO) is a naturally occurring variation of sea surface temperatures and atmospheric circulation over the equatorial Pacific Ocean. Because ENSO varies slowly, it can cause somewhat predictable changes to global weather patterns over several seasons and affect the frequency of floods, droughts, and other natural disasters. Knowing the possible variety of atmospheric circulation anomalies that are linked to the ENSO sea surface temperature anomalies is helpful for assessing the potential for societally-critical weather disruptions.

In a study to be published in Geophysical Research Letters, CIRES and NOAA researchers in the Physical Sciences Division developed a probabilistic approach for appraising ENSO, which allows for a clearer indication of the odds that ENSO may be in an extreme state. They used an adaptation of the Multivariate ENSO Index (MEI) that estimates ENSO from both the ocean temperature anomaly and the resulting atmospheric circulation patterns. The researchers ran multiple model predictions, which were influenced by the analyzed sea surface temperature anomalies. This approach makes it possible to identify the varieties of uncertainty in the ENSO MEI itself.

Atmospheric noise can lead to uncertainty in the atmospheric circulation associated with the ENSO SST anomaly. An approach that assesses the likelihood of coupled ocean-atmosphere ENSO magnitude is helpful to properly describe ENSO conditions. Larger (smaller) noise is found to prevail during El Niño (La Niña) environments, indicating that a single estimate of the ENSO state during its warm phase are prone to greater error than during its cold phase. A probabilistic assessment leads to a more realistic measure of the relative strength of ENSO events, with 2015 the strongest, 1997 the second strongest, and 1982 the third strongest among strongest warm events based on MEI conditions.

Because ENSO is a primary driver of global weather and climate variability and can endure over several seasons, quantifying it informs a host of predictions and global sectors including water supply, food security, health, and public safety. An accurate and complete assessment of the monitored state of ENSO is of great practical and societal importance.

PSD authors of 'Towards Probabilistic Multivariate ENSO Monitoring' are: Tao Zhang, Andrew Hoell, Judith Perlwitz, Jon Eischeid, Donald Murray, Martin Hoerling, and Thomas M. Hamill