Penn State University (PSU) and Woods Hole Oceanographic Institution researchers have found that using preseason sea surface salinity in predictive models for summertime rainfall over the Midwest can make predictions 92% more accurate than sea surface temperature-based predictions alone.
As large-scale atmospheric patterns drive water evaporation from the ocean, salt concentrations increase. Thus, water salinity works as a “reliable” indicator for the amount of moisture in the atmosphere and patterns in where it will rain out.
Seasonal weather in the Midwest is atmospherically connected to both Pacific and Atlantic Oceans; wind patterns deliver heat and moisture to the central region at different times in the year. Scientists have long relied on sea-surface temperatures (alone) to predict the behavior of wind patterns and weather thousands of kilometers away from the coasts. However, sea surface temperature is highly variable.
Salinity data offers more consistency in patterns during years with extreme precipitation explained lead author Laifang Li of PSU’s Department of Meteorology and Atmospheric Science as well as the Institute of Computational and Data Science.
Remote sensing and floating sensors provide frequently updated ocean salinity data, allowing these oceanographers to draw connections between salinity and weather patterns.
Midwest rainfall data from 1948 to 2019 and compared it to sea surface temperature and salinity records in different parts of the Pacific and Atlantic oceans across the same time period.
“The relationship between sea surface temperatures and tropical precipitation can be complicated, especially when we consider warming oceans,” said NOAA research meteorologist Nathaniel Johnson; he wasn’t involved in this study.
Access the published research letter in Geophysical Research Letters.