Satellite Sea Surface Salinity Observations Impact on El Niño/Southern Oscillation Predictions: Case Studies From the NASA GEOS Seasonal Forecast System. Issue 4 (26th March 2020)

• Record Type: Journal Article
• Authors: Hackert, Eric; Kovach, Robin M.; Molod, A.; Vernieres, G.; Borovikov, A.; Marshak, J.; Chang, Y.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-26
• Subjects: satellite sea surface salinity -- data assimilation -- ENSO forecasts -- GEOS seasonal forecast system -- mixed layer depth -- Kelvin wave; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2019JC015788
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: El Niño/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would have great societal benefit. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near‐surface ocean salinity. Satellite sea surface salinity (SSS), combined with temperature, help to improve the estimates of ocean density changes and associated near‐surface mixing. For the first time, we assess the impact of satellite SSS observations for improving near‐surface dynamics within ocean reanalyses and how these initializations impact dynamical ENSO forecasts using NASA's coupled forecast system (GEOS‐S2S‐2). For all initialization experiments, all available sea level and in situ temperature and salinity observations are assimilated. Separate observing system experiments additionally assimilate Aquarius, SMAP, SMOS, and these data sets combined. We highlight the impact of satellite SSS on ocean reanalyses by comparing experiments with and without the application of SSS assimilation. Next, we compare case studies of coupled forecasts for the big 2015 El Niño, the 2017 La Niña, and the weak El Niño in 2018 that are initialized from GEOS‐S2S‐2 spring reanalyses that assimilate and withhold along‐track SSS. For each of these ENSO‐event case studies, assimilation of satellite SSS improves the forecast validation with respect to observed NINO3.4 anomalies (or at least reduces the forecast uncertainty). Satellite SSS assimilation improved characterization of the mixed layer depth leading to more accurate coupled air/sea interaction and better forecasts. These results further underline the value of satellite SSS assimilation into operational forecast systems. Plain Language Summary: Improving the prediction of El Niño/Southern Oscillation (ENSO) is important because of the global impacts of ENSO and the associated socioeconomic implications. Only recently has satellite sea surface salinity (SSS) become available for improving our characterization of the global hydrological cycle. SSS, combined with temperature, helps to improve the estimates of near‐surface density changes and associated ocean mixing. Here we show results of experiments designed to highlight the impact of SSS on ENSO forecasts. In the control experiment, we assimilate a comprehensive set of in situ oceanographic information and satellite altimetry, as typically done in operational ocean data assimilation, but exclude satellite SSS. In the second set of reanalyses, we add different satellite SSS products to our assimilation. Air/sea coupled model hindcasts are then initialized for various case studies including the big El Niño (2015), the moderate La Niña (2017), and a weak El Niño (2018). For each example, satellite SSS assimilation improves coupled forecasts by adjusting the large‐scale equatorial waves that are integral to ENSO development. For 2015, SSS damps these waves resulting in a more realistic ENSO prediction. In 2017 and 2018, SSS assimilation acts to change the sign of ENSO forecasts, again leading to more realistic ENSO forecasts. Key Points: Assimilation of satellite sea surface salinity (SSS) into ocean reanalyses improves near‐surface density structure and mixed layer depth More realistic MLD improves air/sea interaction and acts to modulate the oceanic Kelvin waves of ENSO leading to improved forecasts Case studies for the El Nino and La Nina initialized in spring showed that SSS assimilation led to more realistic ENSO forecasts