Multivariate ENSO Index

Multivariate ENSO Index
Name	Multivariate ENSO Index
Abbreviation	MEI
Area	Tropical Pacific
Introduced	1990
Creator	Billie L. Jones and Antonietta Navarra

Multivariate ENSO Index The Multivariate ENSO Index summarizes the state of the El Niño–Southern Oscillation using a set of climate variables to represent tropical Pacific variability. Developed to capture coupled ocean–atmosphere interactions, the index is widely used alongside other indicators in National Oceanic and Atmospheric Administration, National Aeronautics and Space Administration, World Meteorological Organization assessments and scientific literature. It informs studies in Paleoclimatology, Humanitarian assistance, Agricultural economics, and Disaster risk reduction.

Overview

The index was created to integrate diverse observations of the tropical Pacific physical system including sea surface temperatures, sea level pressure, surface winds, precipitation, and outgoing longwave radiation from platforms like Advanced Very High Resolution Radiometer, Tropical Rainfall Measuring Mission, and Argo floats. It is frequently cited in publications from American Geophysical Union, European Centre for Medium-Range Weather Forecasts, Scripps Institution of Oceanography, and University Corporation for Atmospheric Research. Operational climate centers such as Climate Prediction Center and regional offices in Met Office briefings often reference the index along with paleoreconstructions from National Snow and Ice Data Center and reconstructions tied to events like the 1997–1998 El Niño and 1982–1983 El Niño.

Calculation and Components

Calculation employs a multivariate empirical orthogonal function (EOF) analysis applied to standardized monthly fields of sea surface temperature, sea level pressure, zonal and meridional surface wind components, surface air temperature, and total cloudiness or outgoing longwave radiation. Key observational datasets include Reynolds SST, HadISST, ERA-Interim, and NCEP/NCAR Reanalysis. The EOF procedure isolates principal components that represent coupled variability; leading modes are interpreted in the context of coupled model experiments from Coupled Model Intercomparison Project phases and diagnostics developed by Intergovernmental Panel on Climate Change. Statistical preprocessing follows protocols similar to those used in Principal component analysis studies and signal detection methods applied in Climatology research groups at institutions like Columbia University and Massachusetts Institute of Technology.

Versions and Updates

Multiple versions have been published as observational archives and methodology refinements emerged; notable updates align with improvements in datasets such as HadISST1 updates, introduction of Argo profiling floats, and new reanalyses like ERA5. Prominent revisions have been discussed in journals from Journal of Climate and Geophysical Research Letters and incorporated in diagnostics by the International Research Institute for Climate and Society. Each version recalibrates EOFs and variable selection to maintain consistency with evolving observing systems and paleoclimate proxies used in comparisons with Paleoceanography records and coral-based reconstructions tied to El Niño episodes.

Interpretation and Climate Impacts

Positive index phases correspond to El Niño conditions and are often associated with anomalous warming of the eastern equatorial Pacific, altered Walker circulation, and teleconnections that affect regions such as Indonesia, Australia, Peru, California, and East Africa. Negative phases align with La Niña patterns with impacts documented during events like the 2010–2011 La Niña and consequences observed in Amazon rainforest moisture budgets and Indian subcontinent monsoon variability. Impacts are evaluated in studies by organizations such as Food and Agriculture Organization, World Bank, and regional climate services, with emphasis on sectors including Fisheries of Peru and Ecuador, hydropower in Brazil, and wildfire risk in California and Australia.

Comparison with Other ENSO Indices

The index is compared with univariate indices like the Niño 3.4 index, Oceanic Niño Index, Southern Oscillation Index, and satellite-based metrics derived from instruments on NOAA polar-orbiting satellites. Unlike single-variable indices, the multivariate approach captures coupled anomalies and often shows different phasing or amplitude relative to indices such as the Trans-Niño Index or basin-wide SST metrics used in Model Intercomparison Projects and Seasonal climate forecasting assessments by agencies like NOAA and Met Éireann.

Applications in Research and Operational Forecasting

Researchers use the index in attribution studies, seasonal forecasting, and assessment of extreme event risk in collaborations between institutions like Bureau of Meteorology (Australia), Chinese Academy of Sciences, and Centre National de Recherches Météorologiques. Operational centers embed the index in outlook products, ensemble calibration, and teleconnection-based advisories for sectors coordinated with agencies such as United Nations Office for the Coordination of Humanitarian Affairs and International Fund for Agricultural Development. It is also a diagnostic in coupled forecast systems run by European Centre for Medium-Range Weather Forecasts, NOAA Geophysical Fluid Dynamics Laboratory, and national meteorological services.

Limitations and Criticisms

Critiques focus on sensitivity to dataset selection, temporal homogenization, and EOF rotation choices; these methodological dependencies can yield differing magnitudes or timing compared with other ENSO metrics, affecting attribution and prediction skill quantification in literature from Nature Climate Change and Bulletin of the American Meteorological Society. The index’s reliance on modern observing systems complicates comparisons with paleoclimate proxies in studies by National Academy of Sciences panels and international research consortia. Ongoing work addresses robustness through intercomparison projects coordinated by World Climate Research Programme and methodological transparency advocated by editorial standards of Science and Proceedings of the National Academy of Sciences.

Category:Climate indices