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Webinar: Ocean Surface Salinity Response to Atmospheric River Precipitation in the CA Current System

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Tuesday, 02 August 2022, 2:00

Tuesday, August 2, 2022. 2:00 PM. Webinar: Ocean Surface Salinity Response to Atmospheric River Precipitation in the CA Current System. Lauren Hoffman, UC San Diego. Sponsored by NOAA NOS. More information here. Register here.

Abstract: Atmospheric rivers (ARs) result in precipitation over land and ocean. Rainfall on the ocean can generate a buoyant layer of fresh water that impacts exchanges between the surface and the mixed layer. These fresh lenses are important for weather and climate because they may impact the ocean stratification at all timescales. Here we use in situ ocean data, co-located with AR events, and a one-dimensional model, to investigate the impact of AR precipitation on surface ocean salinity in the California Current System (CCS) on (i) seasonal and (ii) event time scales. We find that the CCS freshens through the winter due to AR events and years with higher AR activity are associated with a stronger freshening signal. On shorter time scales, model simulations show that in response to a rain event the vertical salinity gradient near the surface has a linear dependence on rain rate and an inverse dependence on wind speed. Higher wind speeds induce mixing and distribute freshwater inputs over the top 5-10m of the ocean, while lower wind speeds allow freshwater lenses to remain at the surface. The results demonstrate that local precipitation is important in setting the freshwater seasonal cycle of the CCS and that the formation of freshwater lenses should be considered in the CCS on weather event time scales.

Bio(s): Lauren Hoffman is a PhD candidate researching at Scripps Institution of Oceanography, University of California, San Diego. She uses her expertise in data analysis and modeling to study freshwater processes in the upper ocean pertaining to interactions between the atmosphere, ocean, and sea ice. In 2021 she was a recipient of UCSD's Annual Interdisciplinary Research Award for her work in using machine learning to predict and understand sea-ice motion in the Arctic. Lauren earned her BS in Mechanical Engineering from University of San Diego in 2016, and MS in Chemical Engineering from University of California, San Diego in 2018.

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