Turns out that dissolved salt – a.k.a. salinity – is a "key ingredient" in improving El Niño forecasts.
When modeling the El Niño-Southern Oscillation (ENSO) ocean-climate cycle, adding salinity data from satellites significantly improves model accuracy.
Let's find out more...
Trade winds blow warm water westward and induce upwelling – upward movement of cool seawater – off Peru.
Along with salinity and temperature, NASA monitors chlorophyll at the ocean surface. These data show upwelling-induced phytoplankton blooms across much of the equator!
Under normal conditions, the depth where temperature changes rapidly (i.e., "thermocline") looks like this blue line:
Not only is the surface pool of water warm, it is relatively fresh (i.e., less salty), thanks to rain.
As El Niño conditions develop, warm water moves toward the central Pacific and trade winds shift eastward, causing downwelling.
Large-scale equatorial waves, known as "Kelvin waves," move warm water eastward (green line). In big El Niños, a nearly flat thermocline (blue line) results from warm water capping the entire equatorial Pacific.
In most El Niños – including the 2015 event – equatorial upwelling continued in the east while the central Pacific heated up.
Chlorophyll data from the 2015 El Niño shows that upwelling was restricted to the eastern tropical Pacific Ocean.
In this region, salinity of the upper ocean layers affects the movement of Kelvin waves along the equator...
Let's dive deeper into the relationships between sea surface salinity, sea surface temperature, Mixed Layer Depth, and Barrier Layer Thickness for the 2015 El Niño.
The following maps show differences between an experiment that assimilates salinity data from two NASA instruments, Aquarius and SMAP, minus an experiment that withholds sea surface salinity.
Sea Surface Salinity: Assimilation of satellite data results in particularly high positive differences in the western tropical Pacific near the international dateline (180°W).
Barrier Layer Thickness: Decreased between 170°E and 150°W near the equator, corresponding to weaker downwelling in this region.
How does assimilation of sea surface salinity from satellites improve forecasts? By adjusting the Kelvin waves that are integral to ENSO development! This can be seen in mathematical equations...
Kelvin wave amplitude is inversely proportional to Mixed Layer Depth...
How much does satellite-derived salinity improve ENSO forecasts? Compare forecasts of sea surface temperature anomaly – i.e., how much temperature deviates from normal conditions – for the 2015 El Niño event.
Black line shows the actual temperature observations. Green lines show forecasts based on four different scenarios, all of which severely overestimate the amplitude of the 2015 El Niño.
Adding salinity data from NASA satellites significantly improved forecasts! Assimilating salinity from these satellites damped the Kelvin waves, resulting in more realistic predictions.
Why is accurately predicting ENSO important? It can affect ecosystems, economies, human health, and wildfire risk. making ENSO forecasts vital for many people around the world.
For example, forecasts and observations gave a strong indication that there would be a big El Niño in 1997, which would lead to drought in northeast Brazil.
This allowed the government of Brazil to issue a statement to farmers, encouraging them to plant drought-resistant corn instead of high-yield varieties.
In this case, good ENSO forecasts along with government action may have saved many lives.
"Rather than having one season of confidence in your forecast, you have two seasons. If your growing season is six months down the line, a longer quality forecast gives you an improved understanding of whether you need to plant high-yield or drought-resistant varieties." – Eric Hackert, Lead Author
Hackert, E., Kovach, R.M., Molod, A., Vernieres, G., Borovikov, A., Marshak, J., and Chang, Y. (2020). Satellite Sea Surface Salinity Observations Impact on El Niño/Southern Oscillation Predictions: Case Studies From the NASA GEOS Seasonal Forecast System, J. Geophys. Res.-Oceans, 125(4), doi: 10.1029/2019JC015788.