scholarly journals Joint assimilation of A quarius‐derived sea surface salinity and AVHRR‐derived sea surface temperature in an ocean general circulation model using SEEK filter: Implication for mixed layer depth and barrier layer thickness

2015 ◽  
Vol 120 (10) ◽  
pp. 6927-6942 ◽  
Author(s):  
Abhisek Chakraborty ◽  
Rashmi Sharma ◽  
Raj Kumar ◽  
Sujit Basu
Keyword(s):  
General Circulation ◽  
Mixed Layer Depth ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Layer Depth ◽  
Surface Salinity ◽  
Barrier Layer Thickness ◽  
Ocean General Circulation

scholarly journals A global eddying hindcast ocean simulation with OFES2

2020 ◽  
Vol 13 (7) ◽  
pp. 3319-3336 ◽  
Author(s):  
Hideharu Sasaki ◽  
Shinichiro Kida ◽  
Ryo Furue ◽  
Hidenori Aiki ◽  
Nobumasa Komori ◽  
...  
Keyword(s):  
General Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Tidal Mixing ◽  
Surface Salinity ◽  
Previous Version ◽  
Spatiotemporal Scales ◽  
The Kuroshio

Abstract. A quasi-global eddying ocean hindcast simulation using a new version of our model, called OFES2 (Ocean General Circulation Model for the Earth Simulator version 2), was conducted to overcome several issues with unrealistic properties in its previous version, OFES. This paper describes the model and the simulated oceanic fields in OFES2 compared with OFES and also observed data. OFES2 includes a sea-ice model and a tidal mixing scheme, is forced by a newly created surface atmospheric dataset called JRA55-do, and simulated the oceanic fields from 1958 to 2016. We found several improvements in OFES2 over OFES: smaller biases in the global sea surface temperature and sea surface salinity as well as the water mass properties in the Indonesian and Arabian seas. The time series of the Niño3.4 and Indian Ocean Dipole (IOD) indexes are somewhat better in OFES2 than in OFES. Unlike the previous version, OFES2 reproduces more realistic anomalously low sea surface temperatures during a positive IOD event. One possible cause of these improvements in El Niño and IOD events is the replacement of the atmospheric dataset. On the other hand, several issues remained unrealistic, such as the pathways of the Kuroshio and Gulf Stream and the unrealistic spreading of salty Mediterranean overflow. Given the worldwide use of the previous version and the improvements presented here, the output from OFES2 will be useful in studying various oceanic phenomena with broad spatiotemporal scales.

scholarly journals A global eddying hindcast ocean simulation with OFES2

2020 ◽  
Author(s):  
Hideharu Sasaki ◽  
Shinichiro Kida ◽  
Ryo Furue ◽  
Hidenori Aiki ◽  
Nobumasa Komori ◽  
...  
Keyword(s):  
General Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Tidal Mixing ◽  
Surface Salinity ◽  
Previous Version ◽  
Spatiotemporal Scales ◽  
The Kuroshio

Abstract. A quasi-global eddying ocean hindcast simulation using a new version of our model called OFES2 (Ocean General Circulation Model for the Earth Simulator version 2) was conducted to overcome several issues with unrealistic properties in its previous version OFES. This paper describes the model and the simulated oceanic fields in OFES2 compared with OFES and also observed data. A sea-ice model and a tidal mixing scheme were implemented in OFES2, which was forced by a newly created surface atmospheric dataset called JRA55-do and simulated the oceanic fields from 1958 to 2016. We found several improvements in OFES2 over OFES: smaller biases in the global sea surface temperature and sea surface salinity and the water properties in the Indonesian and Arabian Seas. The time series of the Niño3.4 and Indian Ocean Dipole (IOD) indexes are somewhat better in OFES2 than in OFES. Unlike the previous version, OFES2 reproduces more realistic anomalous low sea surface temperatures during a positive IOD event. One possible cause for these improvements in El Niño and IOD events is the replacement of the atmospheric dataset. On the other hand, several issues remained unrealistic, such as the pathways of the Kuroshio and Gulf Stream and the unrealistic spreading of salty Mediterranean overflow. Given the worldwide use of the previous version and the improvements presented here on it, the output from OFES2 will be useful in studying various oceanic phenomena with broad spatiotemporal scales.

scholarly journals Simulated Sea Surface Salinity Variability in the Tropical Indian Ocean

2010 ◽  
Vol 23 (24) ◽  
pp. 6542-6554 ◽  
Author(s):  
Rashmi Sharma ◽  
Neeraj Agarwal ◽  
Imran M. Momin ◽  
Sujit Basu ◽  
Vijay K. Agarwal
Keyword(s):  
Indian Ocean ◽  
Time Scales ◽  
General Circulation ◽  
Circulation Model ◽  
Equatorial Indian Ocean ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Variability

Abstract A long-period (15 yr) simulation of sea surface salinity (SSS) obtained from a hindcast run of an ocean general circulation model (OGCM) forced by the NCEP–NCAR daily reanalysis product is analyzed in the tropical Indian Ocean (TIO). The objective of the study is twofold: assess the capability of the model to provide realistic simulations of SSS and characterize the SSS variability in view of upcoming satellite salinity missions. Model fields are evaluated in terms of mean, standard deviation, and characteristic temporal scales of SSS variability. Results show that the standard deviations range from 0.2 to 1.5 psu, with larger values in regions with strong seasonal transitions of surface currents (south of India) and along the coast in the Bay of Bengal (strong Kelvin-wave-induced currents). Comparison of simulated SSS with collocated SSS measurements from the National Oceanographic Data Center and Argo floats resulted in a high correlation of 0.85 and a root-mean-square error (RMSE) of 0.4 psu. The correlations are quite high (>0.75) up to a depth of 300 m. Daily simulations of SSS compare well with a Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoy in the eastern equatorial Indian Ocean (1.5°S, 90°E) with an RMSE of 0.3 psu and a correlation better than 0.6. Model SSS compares well with observations at all time scales (intraseasonal, seasonal, and interannual). The decorrelation scales computed from model and buoy SSS suggest that the proposed 10-day sampling of future salinity sensors would be able to resolve much of the salinity variability at time scales longer than intraseasonal. This inference is significant in view of satellite salinity sensors, such as Soil Moisture and Ocean Salinity (SMOS) and Aquarius.

scholarly journals FORTE 2.0: a fast, parallel and flexible coupled climate model

2021 ◽  
Vol 14 (1) ◽  
pp. 275-293
Author(s):  
Adam T. Blaker ◽  
Manoj Joshi ◽  
Bablu Sinha ◽  
David P. Stevens ◽  
Robin S. Smith ◽  
...  
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Climate Model ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Ocean Model ◽  
Coupled Climate Model ◽  
Layer Depth ◽  
Ocean General Circulation ◽  
Modular Ocean Model

Abstract. FORTE 2.0 is an intermediate-resolution coupled atmosphere–ocean general circulation model (AOGCM) consisting of the Intermediate General Circulation Model 4 (IGCM4), a T42 spectral atmosphere with 35σ layers, coupled to Modular Ocean Model – Array (MOMA), a 2∘ × 2∘ ocean with 15 z-layer depth levels. Sea ice is represented by a simple flux barrier. Both the atmosphere and ocean components are coded in Fortran. It is capable of producing a stable climate for long integrations without the need for flux adjustments. One flexibility afforded by the IGCM4 atmosphere is the ability to configure the atmosphere with either 35σ layers (troposphere and stratosphere) or 20σ layers (troposphere only). This enables experimental designs for exploring the roles of the troposphere and stratosphere, and the faster integration of the 20σ layer configuration enables longer duration studies on modest hardware. A description of FORTE 2.0 is given, followed by the analysis of two 2000-year control integrations, one using the 35σ configuration of IGCM4 and one using the 20σ configuration.

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