Barrier Layer and Relevant Variability of the Salinity Field in the Equatorial Pacific Estimated in an Ocean Reanalysis Experiment

Abstract

This paper investigates the feasibility of an ocean data assimilation system to analyze the salinity variability associated with the barrier layer in the equatorial Pacific. In order to validate reproducibility of the temperature and salinity fields, we perform an assimilation run where some temperature and salinity observations by TRITON buoys and Argo floats are withheld. The assimilation run reproduces interannual variability of salinity in the equatorial Pacific exhibited in the data that are withheld. Statistics shows that salinity values and variations in the assimilation run are closer to the data than the climatology and in the model free run. We also confirm that zonal currents in the equatorial Pacific in the reanalysis, where all available temperature and salinity data are assimilated, are consistent with an observation-based mapping and the data of the Acoustic Doppler Current Profiler mounted on TAO buoys. Variability of the barrier layer and relevant salinity field in the reanalysis is consistent with former studies. A thick barrier layer area generally exists west of the equatorial salinity front and is displaced zonally with the migration of the front in the response to El Niño-Southern Oscillation, although the area moved to the east over the front in the 1997 El Niño. It is confirmed that the barrier layer thickness is closely correlated with the near-surface temperature in the equatorial Pacific.

Assessing the impact of observations on ocean forecasts and reanalyses: Part 1, Global studies

“…The systems used in this study include the 1/4°-resolution global ocean forecast systems developed by Mercator Océan (Lellouche et al 2013), the Met Office (Storkey et al 2010;Blockley et al 2013) and the Canadian consortium (Smith et al 2014), a 1/10°-resolution reanalysis system developed under Bluelink (Oke et al 2013a;Oke et al 2013b), a 1/4°-resolution model-independent analysis system developed at CLS (Guinehut et al 2012), two operational seasonal prediction systems, currently operated at JMA (Takaya et al 2010;Fujii et al 2012) and ECMWF (Balmaseda et al 2013). Several of these systems share a common source code for the model (i.e.…”

Evaluating the impacts of the tropical Pacific observing system on the ocean analysis fields in the global ocean data assimilation system for operational seasonal forecasts in JMA

“…COM-G (Usui et al 2006;Fujii et al 2012) (hereafter MOVE-G) is employed in this study. MOVE-G is adopted in the operation of the Japan Meteorological Agency (JMA) in order to provide ocean initial condition for the coupled atmosphere-ocean general circulation model in the seasonal forecast system (Takaya et al 2010).…”

“…Statistical parameters for MOVE, including the variances of background and observation errors and the EOF modes, are calculated from deviations of temperature and salinity (TS) profiles, in World Ocean Database 2001 (Conkright et al 2002) and the Global Temperature-Salinity Profile Program (GTSPP) database (Hamilton 1994) before 2006, from monthly climatology based on the World Ocean Atlas 2001 Antonov et al 2006). MOVE generates an analysis from observation data and a model-forecasted field slightly nudged to the monthly climatology (Fujii et al 2012). Analysis is reflected in the OGCM through incremental analysis updates (Bloom et al 1996) with 10-day assimilation cycles.…”

Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting

“…It is also considered to induce a temperature rise in the mixed layer because it prevents warm water at the surface from mixing with cold water in the thermocline. This tendency is confirmed in the equatorial Pacific (e.g., Ando & McPhaden, 1997;Fujii et al, 2012;Maes et al, 2006). Some studies (e.g., Maes & Belamari, 2011) have further used CGCMs to confirm the impact of the barrier layer at the onset of El Niños.…”

“…Here, we compare the temperature and salinity fields in three assimilation runs in order to demonstrate the difference between the first-and second-generation ocean data assimilation systems. One is the assimilation run named MOVE-G VAL in Fujii et al (2012). The atmospheric reanalysis dataset produced by the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP-R1; Kalnay et al, 1996) is employed as the external forcing in MOVE-G VAL.…”