Type | : | |
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Title | : | Impact of Rain-Adjusted Satellite Sea Surface Salinity on ENSO Predictions From the GMAO S2S Forecast System |
Pub Year | : | 2025 |
Author(s) | : | Hackert, E., Akella, S., Ruiz-Xomchuk, V., Nakada, K., Jacob, M., Drushka, K., Ren, L., and Molod, A. |
Source | : | J. Geophys. Res. Oceans, 130(5), e2024JC021773, doi: 10.1029/2023JC020451. |
Pub Url | : | https://doi.org/10.1029/2024JC021773 |
Description | : | Past research has shown that combining satellite sea surface salinity (SSS) observations with ocean models (i.e., using assimilation) can improve the surface density structure leading to improved ability to forecast large-scale ocean waves that comprise El NiƱo/Southern Oscillation (ENSO), a feature that has global socioeconomic impacts. Satellite SSS is measured at ~1 cm into the ocean so these observations incorporate short-lived fresh lenses from rainfall that are less dense than surrounding salty ocean water. Since ocean models cannot resolve 1 cm in the vertical, and their first layer is typically several meters deep, the fresh bias of the satellite observations (with respect to the model) needs to be removed somehow. In this paper, a new application for correcting for the fresh bias is presented. A model that calculates the near-surface salinity gradient is applied prior to assimilation in one experiment, and the fresh bias is retained in the other. Removing the fresh bias acts to destabilize the water column, allowing increased mixing from the wind action above, a deeper thermocline, and more heat storage in the upper ocean. Resulting ENSO forecasts are warmer and closer to the observed forecast metrics when the satellite SSS data have the fresh bias removed. |
Category | : | highlight06, ocean |
Preprint? | : | No |
Date Added | : | 2025-05-09 |