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Impact of sea surface salinity assimilation on coupled forecasts in the tropical Pacific

AuthorsHackert, Eric C.; Ballabrera-Poy, Joaquim ; Busalacchi, Antonio J.; Zhang, Rong-Hua; Murtugudde, Raghu G.
Issue Date14-May-2011
PublisherAmerican Geophysical Union
CitationJournal of Geophysical Research: Oceans 116(C5): C05009 (2011)
AbstractIn this paper, we assess the impact of sea surface salinity (SSS) observations on seasonal variability of tropical dynamics as well as on dynamical El Niño–Southern Oscillation (ENSO) forecasts using a hybrid coupled model (HCM). The HCM is composed of a primitive equation ocean model coupled with a singular value decomposition–based statistical atmospheric model. An Ensemble Reduced Order Kalman Filter (EROKF) is used to assimilate observations to constrain tropical Pacific dynamics and thermodynamics for initialization of the HCM. Rather than trying to produce the best possible operational forecasts, point-wise subsurface temperature (sTz) has been assimilated separately and together with gridded observed sea surface salinity (SSS) from optimal interpolation to more efficiently isolate the impact of SSS. Coupled experiments are then initiated from these EROKF initial conditions and run for 12 months for each month, 1993–2007. The results show that adding SSS to sTz assimilation improves coupled forecasts for 6–12 month lead times. The main benefit of SSS assimilation comes from improvement to the spring predictability barrier (SPB) period. SSS assimilation increases correlation for 6–12 month forecasts by 0.2–0.5 and reduces RMS error by 0.3°C–0.6°C for forecasts initiated between December and March, a period key to long-lead ENSO forecasts. The positive impact of SSS assimilation originates from warm pool and Southern Hemisphere salinity anomalies. Improvements are brought about by fresh anomalies at the equator which increases stability, reduces mixing, and shoals the thermocline which concentrates the wind impact of ENSO coupling. This effect is most pronounced in June–August, helping to explain the improvement in the SPB. In addition, we show that SSS impact on coupled forecasts is more pronounced for the period 1993–2001 than for the period 2002–2007 due to the improved inherent predictability associated with the strong 1997–1998 ENSO. Rather than being the final say for the issue of SSS assimilation, this study should be considered as a necessary first step. Future work is still required to assess issues such as SSS satellite data coverage and the complementary nature of satellite/in situ assimilation. However, these results foreshadow the important positive potential impact that gridded satellite SSS provided by missions such as SMOS and Aquarius/SAC-D will have on coupled model predictions.
Description18 pages, 12 figures.
Publisher version (URL)http://dx.doi.org/10.1029/2010JC006708
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