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David Halpern
Dimitris Menemenlis
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Xiaochun Wang


The impact of data assimilation on the transports of eastward-flowing Equatorial Undercurrent (EUC) and North Equatorial Countercurrent (NECC) in the Pacific Ocean from 145°E to 95°W during 2004–05 and 2009–11 was assessed. Two Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2), solutions were analyzed: one with data assimilation and one without. Assimilated data included satellite observations of sea surface temperature and ocean surface topography, in which the sampling patterns were approximately uniform over the 5 years, and in situ measurements of subsurface salinity and temperature profiles, in which the sampling patterns varied considerably in space and time throughout the 5 years. Velocity measurements were not assimilated. The impact of data assimilation was considered significant when the difference between the transports computed with and without data assimilation was greater than 5.5 × 106 m3 s−1 (or 5.5 Sv; 1 Sv ≡ 106 m3 s−1) for the EUC and greater than 5.0 Sv for the NECC. In addition, the difference of annual-mean transports computed from 3-day-averaged data was statistically significant at the 95% level. The impact of data assimilation ranged from no impact to very substantial impact when data assimilation increased the EUC transport and decreased the NECC transport. The study’s EUC results had some correspondence with other studies and no simple agreement or disagreement pattern emerged among all studies of the impact of data assimilation. No comparable study of the impact of data assimilation on the NECC has been made.

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James A. Cummings
Ole Martin Smedstad


The impact of the assimilation of ocean observations on reducing global Hybrid Coordinate Ocean Model (HYCOM) 48-h forecast errors is presented. The assessment uses an adjoint-based data impact procedure that characterizes the forecast impact of every observation assimilated, and it allows the observation impacts to be partitioned by data type, geographic region, and vertical level. The impact cost function is the difference between HYCOM 48- and 72-h forecast errors computed for temperature and salinity at all model levels and grid points. It is shown that routine assimilation of large numbers of observations consistently reduces global HYCOM 48-h forecast errors for both temperature and salinity. The largest error reduction is due to the assimilation of temperature and salinity profiles from the tropical fixed mooring arrays, followed by Argo, expendable bathythermograph (XBT), and animal sensor data. On a per-observation basis, the most important global observing system is Argo. The beneficial impact of assimilating Argo temperature and salinity profiles extends to all depths sampled, with salinity impacts maximum at the surface and temperature impacts showing a subsurface maximum in the 100–200-m-depth range. The reduced impact of near-surface Argo temperature profile levels is due to the vertical covariances in the assimilation that extend the influence of the large number of sea surface temperature (SST) observations to the base of the mixed layer. Application of the adjoint-based data impact system to identify a data quality problem in a geostationary satellite SST observing system is also provided.

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