Reanalytical datasets used to analyze heat content in the Equatorial Pacific are more differentiated after strong El Niño events – Is there an increase with that?
ATMOSPHERIC INSTITUTE OF PHYSICS, CHINA ACADEMY
Meteorologists regularly use ocean reanalysis data to study the evolution of El Niño-Southern Oscillation (ENSO) over time. These datasets are powerful tools and can provide a reliable picture of sea surface temperatures in the equatorial Pacific over the past 40 years. However, ocean-specific reanalysis datasets can reproduce evolution in a different way. Due to these disparate data, a group of ENSO experts recently published a study on Advances in Atmospheric Science aimed to better understand the differences in reanalysis related to oceanic heat content in the equatorial Pacific. This important parameter is often used to diagnose the status of the ENSO.
“Interestingly, our study found that such differences change over the life cycle of El Niño events.” Professor Wen Zhou, the corresponding author of the study from the City University of Hong Kong. Her team notes that the difference between data sets increases as El Niño grows toward its extremes.
Then, shortly after the peak positive ENSO phase, El Niño rapidly declined. When ENSO is disabled, the resulting dataset begins to converge closer to the acceptable state. However, this process takes longer than El Niño to neutralize. The dataset disparity decay is even slower after a strong El Niño compared to a weaker El Niño.
Although a typical El Niño event rapidly declines after its climax, its subsurface signal persists in the region. This leads to a slow signal intensity attenuation during the neutral phase of El Niño events. As a result, the decay rate of differences between ocean reanalyze datasets also slowed. Thus, the researchers determined that signal strength in the equatorial Pacific is strongly correlated with the variation across ocean reanalysis datasets.
The asymmetry in the growth rates and the decreasing rates of differences between the analyzed data sets resulted in lower data consistency during El Niño. This makes mechanical analysis of El Niño in its decaying phase inherently more difficult than in its evolution.
JOURNEYS
Advances in Atmospheric Science
DOI
ARTICLE PUBLICATION DATE
January 12, 2022
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