Ocean salinity data sharply improves El Niño prediction accuracy

Ocean salinity data sharply increased the accuracy of El Niño prediction. This is stated in a study by scientists from NASA, published on the website of the space agency.

El Nino is a climatic phenomenon that is characterized by a deviation of the surface temperature of the ocean in the equatorial part to a greater side from average values. This leads to climate change in various regions of the Earth, in particular, affects the amount of precipitation and weather.

In a new study, scientists used the GEAO-S2S-2 model, which is a seasonal system for predicting the state of the ocean and atmosphere, to model the three past El Niños that occurred in 2015, 2017, and 2019.

Scientists have compiled two models, one of which takes into account data on the salinity of the ocean surface, and the other does not. The study showed that adding data on the salinity of the ocean surface significantly increases the accuracy of prediction.

“In modern forecasting systems, satellite and ocean observations are optimally combined using models and data assimilation methods to help determine the state of the oceans. This study shows that adding satellite-derived salinity data from the oceans to a set of current observations helps improve El Niño’s seasonal forecasts”.

Eric Hackert, lead author of the study

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