When modeling the El Niño-Southern Oscillation (ENSO) ocean-climate cycle, adding satellite sea surface salinity — or saltiness — data significantly improves model accuracy, according to a new NASA study.
When modeling the El Niño-Southern Oscillation (ENSO) ocean-climate cycle, adding satellite sea surface salinity — or saltiness — data significantly improves model accuracy, according to a new NASA study.
ENSO is an irregular cycle of warm and cold climate events called El Niño and La Niña. In normal years, strong easterly trade winds blow from the Americas toward southeast Asia, but in an El Niño year, those winds are reduced and sometimes even reversed. Warm water that was “piled up” in the western Pacific flows back toward the Americas, changing atmospheric pressure and moisture to produce droughts in Asia and more frequent storms and floods in the Americas. The reverse pattern is called a La Niña, in which the ocean in the eastern Pacific is cooler than normal.
The team used NASA’s Global Modelling and Assimilation Office (GMAO) Sub-seasonal-To-Seasonal (S2S) coupled ocean/atmosphere forecasting system (GEOS-S2S-2) to model three past ENSO events: The strong 2015 El Niño, the 2017 La Niña and the weak 2018 El Niño.
Pulling from NASA’s Soil Moisture Active Passive (SMAP) mission, the past NASA-CONAE (Argentinian Space Agency) Aquarius mission and the European Space Agency’s Soil Moisture Ocean Salinity (SMOS) mission, they compared the forecast model’s accuracy for each of the three events with and without assimilating SSS data into the models’ initialization. In other words: One model run’s initial conditions included SSS data, and the other did not.
Read more at NASA/Goddard Space Flight Center
Photo Credit: Couleur via Pixabay
