Generalized Vertical Coordinates for Eddy-Resolving Global and Coastal Ocean Forecasts

Chassignet, Eric P.; Hurlburt, Harley E.; Smedstad, Ole Martin; Halliwell, George R.; Wallcraft, Alan J.; Metzger, E. Joseph; Blanton, Brian; Lozano, Carlos J.; Rao, Desiraju B.; Hogan, Patrick

doi:10.5670/oceanog.2006.95

Cited by 79 publications

(49 citation statements)

References 17 publications

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“…Most of the spatial and highfrequency temporal variability from the ocean surface is acquired by satellite measurements such as the sea surface height (SSH) and sea surface temperature (SST). However, these observations are available for only a few decades and they are insufficient to determine the sub-surface variability (Ezer and Mellor, 1994;Chassignet et al, 2006). Therefore, the implementation of the Argo network with more than 3300 profilers freely reporting temperature and salinity data down to 2000 m has transformed the in situ ocean observing system in the new millennium (Schiller and Brassington, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

et al. 2015

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Abstract. An ocean data assimilation system to assimilate Argo temperature (T ) and salinity (S) profiles into the HYbrid Coordinate Ocean Model (HYCOM) was constructed, implemented and evaluated for the first time in the Atlantic Ocean (78 • S to 50 • N and 98 • W to 20 • E). The system is based on the ensemble optimal interpolation (EnOI) algorithm proposed by Xie and Zhu (2010), especially made to deal with the hybrid nature of the HYCOM vertical coordinate system with multiple steps. The Argo T -S profiles were projected to the model vertical space to create pseudoobserved layer thicknesses ( p obs ), which correspond to the model target densities. The first step was to assimilate p obs considering the sub-state vector composed by the model layer thickness ( p) and the baroclinic velocity components. After that, T and S were assimilated separately. Finally, T was diagnosed below the mixed layer to preserve the density of the model isopycnal layers. Five experiments were performed from 1 January 2010 to 31 December 2012: a control run without assimilation, and four assimilation runs considering the different vertical localizations of T , S and p. The assimilation experiments were able to significantly improve the thermohaline structure produced by the control run. They reduced the root mean square deviation (RMSD) of T and S calculated with respect to Argo independent data in 34 and 44 %, respectively, in comparison to the control run. In some regions, such as the western North Atlantic, substantial corrections in the 20 • C isotherm depth and the upper ocean heat content towards climatological states were achieved.The runs with a vertical localization of p showed positive impacts in the correction of the thermohaline structure and reduced the RMSD of T (S) from 0.993 • C (0.149 psu) to 0.905 • C (0.138 psu) for the whole domain with respect to the other assimilation runs.

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Section: Introductionmentioning

confidence: 99%

“…The model was configured to use surface pressure as reference for potential density, aiming for improved representation of near-surface fields, at the cost of not representing accurately the Antarctic Bottom Water (Chassignet et al, 2003). The vertical domain was discretized in 21 vertical layers.…”

Section: Introductionmentioning

confidence: 99%

Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

et al. 2015

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“…It is a primitive equation model with advantages of isopycnal, terrain-following (s) and pressure (approximately z level) coordinates in optimally simulating coastal and open-ocean circulation features [Chassignet et al, 2006]. Only a brief description of the model is provided here (see Appendix A for some details).…”

Section: Ocean Modelmentioning

confidence: 99%

Which surface atmospheric variable drives the seasonal cycle of sea surface temperature over the global ocean?

et al. 2009

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[1] The impact of near-surface atmospheric variables used in driving the seasonal cycle of climatological mean sea surface temperature (SST) is quantified over the global ocean. The six atmospheric variables are air temperature, vapor mixing ratio, wind speed, precipitation, net shortwave radiation, and net longwave radiation, the first (last) three just above (at) the sea surface. Atmospherically forced ocean general circulation model (OGCM) simulations with no data assimilation are performed using monthly and annual means of those variables under the assumption that variations in climatological monthly SSTs are driven by atmospheric variables. SSTs resulting from these simulations are compared with those from a satellite-based field to determine the impact of each atmospheric variable. Large spatial variability is found in the order of impact (most to least) of six atmospheric variables. In general, the SST seasonal cycle is driven primarily by shortwave radiation at midlatitudes, but wind speed is the major controlling variable in the Indian Ocean. Precipitation has almost no significant influence on monthly SST. Overall, shortwave radiation is the most influential variable controlling the seasonal cycle of SST over 34.3% of the global ocean. Wind speed is the second most important variable (27.2%). In tropical regions and the Arabian Sea, sources other than the atmospheric thermal forcing are found to play a significant role in regulating the SST seasonal cycle.

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“…For the sea surface, satellites collect remotely sensed data of sea surface temperature (SST), sea surface height (SSH), and, recently, sea surface salinity (SSS), with high temporal and spatial resolution. Despite the high coverage of the remotely sensed data, it is restricted to the surface and is insufficient to characterize the subsurface thermodynamic variability (Chassignet et al 2006). In the subsurface, the Argo system plays an important role with over 3500 free-drifting profiling floats measuring temperature and salinity in the upper 2000 m providing many in situ data for assimilation systems and other purposes (Oke et al 2008;Xie and Zhu 2010).…”

Section: Introductionmentioning

confidence: 99%

Assimilation of sea-level anomalies and Argo data into HYCOM and its impact on the 24 hour forecasts in the western tropical and South Atlantic

Costa

Tanajura

2015

Journal of Operational Oceanography

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Argo and along-track sea-level anomaly (SLA) data from satellites were assimilated into the Hybrid Coordinate Ocean Model in the western tropical and South Atlantic Ocean. An optimal interpolation method was employed in the assimilation, and the Cooper and Haines scheme projected the altimetry information into the subsurface. The run with assimilation of SLA and Argo data reduced the root mean square deviation of the 24 h forecasts of SLA, sea surface temperature, and subsurface temperature and salinity by 21.4%, 11.5%, 28.1%, and 15.8%, respectively, with respect to the control run without assimilation. Important improvements were also observed in the circulation.

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Generalized Vertical Coordinates for Eddy-Resolving Global and Coastal Ocean Forecasts

Abstract: cal implementation of several physical processes (e.g., mixed-layer detrainment, convective adjustment, sea-ice modeling)

Cited by 79 publications

References 17 publications

Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

Argo data assimilation into HYCOM with an EnOI method in the Atlantic Ocean

Which surface atmospheric variable drives the seasonal cycle of sea surface temperature over the global ocean?

Assimilation of sea-level anomalies and Argo data into HYCOM and its impact on the 24 hour forecasts in the western tropical and South Atlantic

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