Surface salinity budget in oceanic simulation using data from TOGA COARE

Dourado, Marcelo; Caniaux, Guy

doi:10.1029/2001jc001013

Cited by 3 publications

(5 citation statements)

References 39 publications

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“…It is recognized, however, that the fine vertical salinity structure observed in the western Pacific (Lukas and Lindstrom 1991;Weller and Anderson 1996) is undoubtedly important for the evolution and variability of the mixed layer through, for example, the presence of salinity-stratified barrier layers (Vialard and Delecluse 1998). The spatial heterogeneity of precipitation and the role of advection in such processes (Lukas and Lindstrom 1991;Dourado and Caniaux 2003) mean that the present 1D vertical model is an inappropriate tool for the study of salinity in the mixed layer in the warm pool. The results of such an investigation would most likely impose greater requirements on the vertical resolution throughout the mixed layer, rather than just that of the uppermost layer as suggested by this study.…”

Section: Discussionmentioning

confidence: 95%

“…However, the IMET buoy has fewer salinity sensors than temperature sensors, making it difficult to quantify the effects of salinity on the evolution of the mixed layer. In addition, the spatial heterogeneity of the precipitation field combined with the fact that advection is a significant term of the warm pool salinity budget (Dourado and Caniaux 2003;Feng et al 2000) has meant that only the thermal evolution of the mixed layer has been addressed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Diurnal and Intraseasonal Variability of the Ocean Mixed Layer

et al. 2005

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The intraseasonal variability of SST associated with the passage of the Madden–Julian oscillation (MJO) is well documented; yet coupled model integrations generally underpredict the magnitude of this SST variability. Observations from the Improved Meteorological Instrument (IMET) mooring in the western Pacific during the intensive observing period (IOP) of the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) showed a large diurnal signal in SST that is modulated by the passage of the MJO. In this study, observations from the IOP of the TOGA COARE and a one-dimensional (1D) ocean mixed layer model incorporating the K-Profile Parameterization (KPP) vertical mixing scheme have been used to investigate the rectification of the intraseasonal variability of SST by the diurnal cycle and the implied impact of the absence of a representation of this process on the modeled intraseasonal variability in coupled GCMs. Analysis of the SST observations has shown that the increase of the daily mean SST by the diurnal cycle of SST accounts for about one-third of the magnitude of intraseasonal variability of SST associated with the Madden–Julian oscillation in the western Pacific warm pool. Experiments from the 1D model forced with fluxes at a range of temporal resolutions and with differing vertical resolution of the model have shown that to capture 90% of the diurnal variability of SST, and hence 95% of the intraseasonal variability of SST, requires a 3-h or better temporal resolution of the fluxes and a vertical grid with an upper-layer thickness of the order of 1 m. In addition to the impact of the representation of the diurnal cycle on the intraseasonal variability of SST, the strength of the mixing across the thermocline was found to be enhanced by the proper representation of the nighttime deep mixing in the ocean, implying a possible impact of the diurnal cycle onto the mean climate of the tropical ocean.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Modeling Diurnal and Intraseasonal Variability of the Ocean Mixed Layer

et al. 2005

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“…In a realistic mesoscale simulation of the north‐east Atlantic, Caniaux and Planton [] estimated horizontal diffusion of the order of 3% of the mixed layer heat budget. In a mesoscale simulation of the tropical Pacific during the TOGA/COARE experiment, Dourado and Caniaux [] found that horizontal diffusion represented a cooling less than 2 W m −2 or 2% of a heat storage term. Other heat balance studies, performed from data in the same basin and during the same experiment found this term negligible compared to the other mixed layer processes [ Cronin and McPhaden , ; Smyth et al ., ; Feng et al ., ].…”

Section: Data Used and Forcingsmentioning

confidence: 99%

An inverse method to derive surface fluxes from the closure of oceanic heat and water budgets: Application to the north‐western Mediterranean Sea

et al. 2017

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The large amount of data collected during DeWEX, MOOSE, and HyMeX campaigns in the north‐western Mediterranean in 2012–2013 allowed to implement an inverse method to solve the difficult problem of heat and water budget closure. The inverse method is based on the simulation of the observed heat and water budgets, strongly constrained by observations collected during the campaigns and on the deduction of adjusted surface fluxes. The inverse method uses a genetic algorithm that generates 50,000 simulations of a single‐column model and optimizes some adjustable coefficients introduced in the surface fluxes. Finally, the single‐column model forced by the adjusted fluxes during 1 year and over a test area of about 300 × 300 km2 simulates the daily mean satellite bulk SST with an accuracy/uncertainty of 0.011 ± 0.072°C, as well as daily mean SSS and residual buoyancy series deduced from wintertime analyses with an accuracy of 0.011 ± 0.008 and 0.03 ± 0.012 m2 s−2, respectively. The adjusted fluxes close the annual heat and rescaled water budgets by less than 5 W m−2. To our knowledge, this is the first time that such a flux data set is produced. It can thus be considered as a reference for the north‐western Mediterranean and be used for estimating other flux data sets, for forcing regional models and for process studies. Compared with the adjusted fluxes, some operational numerical weather prediction models (ARPEGE, NCEP, ERA‐INTERIM, ECMWF, and AROME), often used to force oceanic models, were evaluated: they are unable to retrieve the mean annual patterns and values.

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“…In the determination of the conservation of salt in the surface mixed layer, E and P are important parameters that are incorporated in the mathematical expressions of the salinity budget [ Delcroix and Henin , 1991; Foltz and McPhaden , 2008]. The magnitude of variability in E and P especially, and that of other salinity tendency terms such as mixing, vertical and horizontal diffusions, may, however, not lead to an analogous magnitude in variability of salt transport as the processes affecting the tendency terms vary in time and space [ Dourado and Caniaux , 2003].…”

Section: Introductionmentioning

confidence: 99%

“…Many such studies have relied heavily on numerical simulations, which sometimes suffer especially from the estimation of freshwater flux terms [ Murtugudde and Busalacchi , 1998]. This has been explained as arising from the fact that the spatial distribution of atmospheric input functions is non‐Gaussian [ Tomczak , 1995; Dourado and Caniaux , 2003]. Other challenges have been the poor representation of coastal currents and the dominance of Ekman drift over geostrophy in the western coast of India that leads to differences between observed and modeled SSS [ Prasad and Ikeda , 2002b].…”

Section: Introductionmentioning

confidence: 99%

Seasonal variability of salt transport during the Indian Ocean monsoons

2011

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The seasonal variability of salinity transport in the Indian Ocean is investigated using the high‐resolution global HYbrid Coordinate Ocean Model (HYCOM). Mechanisms and physical parameters that control the salinity budget are examined. Results show the influence of freshwater forcing and zonal advection as the dominant mechanisms of sea surface salinity (SSS) variability. Precipitation is highest in the eastern Bay of Bengal (BoB), where it shows seasonal variation, and in the south equatorial eastern Indian Ocean (EIO), where it was consistently high year‐round. These patterns result in significant seasonal variation in the SSS in the BoB and almost no variation in the EIO. Zonal SSS transport was higher than meridional SSS transport with the strongest seasonality observed along the Sri Lankan region. Results of depth‐integrated transport show northward salt transport in the bottom layers and a southward salt transport in the surface layers. The 4 year mean net flux of depth‐integrated salt transport was southward (−154.8 × 106 kg/s to −552.4 × 106 kg/s) at all latitudes except at 20°N, where it was northward (396 × 106 kg/s). Transport generally increases southward with the highest transports occurring in the south (10°S–35°S) and a maximum at 30°S. Analyses of meridional Ekman volume and salt transport show a predominantly southward transport, an indication of the strong influence of SW monsoonal winds. It is anticipated that this study will be useful in computing salt transport using satellite‐derived salinity data from the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA Aquarius salinity missions.

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Surface salinity budget in oceanic simulation using data from TOGA COARE

Cited by 3 publications

References 39 publications

Modeling Diurnal and Intraseasonal Variability of the Ocean Mixed Layer

Modeling Diurnal and Intraseasonal Variability of the Ocean Mixed Layer

An inverse method to derive surface fluxes from the closure of oceanic heat and water budgets: Application to the north‐western Mediterranean Sea

Seasonal variability of salt transport during the Indian Ocean monsoons

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