Le Sommer, J., Chassignet, E. P., & Wallcraft, A. J. (2018). Ocean Circulation Modeling for Operational Oceanography: Current Status and Future Challenges. In and J. Verron J. Tintoré A. Pascual E. P. Chassignet (Ed.),
New Frontiers in Operational Oceanography (pp. 289–305). Tallahassee, FL: GODAE OceanView.
Abstract: This chapter focuses on ocean circulation models used in operational oceanography, physical oceanography and climate science. Ocean circulation models area particular branch of ocean numerical modeling that focuses on the representation of ocean physical properties over spatial scales ranging from the global scale to less than a kilometer and time scales ranging from hours to decades. As such, they are an essential build-ing block for operational oceanography systems and their design receives a lot of attention from operational and research centers.
Leadbetter, A. M., Shepherd, A., Arko, R., Chandler, C., Chen, Y., Dockery, N., et al. (2016). Experiences of a “semantics smackdown”.
Earth Sci Inform, 9(3), 355–363.
Li, H., Kanamitsu, M., & Hong, S. - Y. (2012). California reanalysis downscaling at 10 km using an ocean-atmosphere coupled regional model system.
J. Geophys. Res., 117(D12).
Li, H., Kanamitsu, M., Hong, S. - Y., Yoshimura, K., Cayan, D. R., & Misra, V. (2014). A high-resolution ocean-atmosphere coupled downscaling of the present climate over California.
Clim Dyn, 42(3-4), 701–714.
Lombardi, K. C. (2004).
Resolving the Diurnal and Synoptic Variance of Scatterometer Vector Wind Observations. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Scatterometer observations of vector winds are used to examine the amplitudes of synoptic and diurnal cycles. Scatterometers have the advantage of providing global coverage over water; however, irregular temporal sampling complicates the analyses. A least squares technique is used in determination of the amplitudes and phases of the diurnal and synoptic cycles on spatial scales of 5°, 15°, and 30°. In open ocean areas and regions with sufficient open water, the magnitudes of the diurnal and synoptic cycles are 1.0 ms-1 and 3.5ms-1, respectively. Diurnal amplitudes are highest in the polar regions and close to land surfaces due to sea breeze effects. The fraction of variance explained by the diurnal cycle is greatest near the equator. Synoptic amplitudes are consistently larger downwind of land from storm tracks and in the southern polar region as the time analyzed is during the southern winter season.
Luecke, C. A., Arbic, B. K., Bassette, S. L., Richman, J. G., Shriver, J. F., Alford, M. H., et al. (2017). The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations.
J. Geophys. Res. Oceans, 122(11), 9126–9143.
Luecke, C. A., Arbic, B. K., Bassette, S. L., Richman, J. G., Shriver, J. F., Alford, M. H., et al. (2017). The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations: GLOBAL LOW-FREQUENCY EDDY APE.
J. Geophys. Res. Oceans, 122(11), 9126–9143.
Abstract: Global maps of the mesoscale eddy available potential energy (EAPE) field at a depth of 500 m are created using potential density anomalies in a high‐resolution 1/12.5° global ocean model. Maps made from both a free‐running simulation and a data‐assimilative reanalysis of the HYbrid Coordinate Ocean Model (HYCOM) are compared with maps made by other researchers from density anomalies in Argo profiles. The HYCOM and Argo maps display similar features, especially in the dominance of western boundary currents. The reanalysis maps match the Argo maps more closely, demonstrating the added value of data assimilation. Global averages of the simulation, reanalysis, and Argo EAPE all agree to within about 10%. The model and Argo EAPE fields are compared to EAPE computed from temperature anomalies in a data set of “moored historical observations” (MHO) in conjunction with buoyancy frequencies computed from a global climatology. The MHO data set allows for an estimate of the EAPE in high‐frequency motions that is aliased into the Argo EAPE values. At MHO locations, 15–32% of the EAPE in the Argo estimates is due to aliased motions having periods of 10 days or less. Spatial averages of EAPE in HYCOM, Argo, and MHO data agree to within 50% at MHO locations, with both model estimates lying within error bars observations. Analysis of the EAPE field in an idealized model, in conjunction with published theory, suggests that much of the scatter seen in comparisons of different EAPE estimates is to be expected given the chaotic, unpredictable nature of mesoscale eddies.
Maksimova, E. V. (2018). A conceptual view on inertial internal waves in relation to the subinertial flow on the central west Florida shelf.
Sci Rep, 8(1), 15952.
Abstract: The study reported here focuses on inertial internal wave currents on the west Florida midshelf in 50 m depth. In situ observations showed that the seasonal shifts in stratification change both the frequency range of inertial internal waves and their modulation time scales. According to the analysis, the subinertial flow evolution time scales also undergo compatible seasonal variations, and the inertial internal wave currents appear to be temporally and spatially related to the subinertial flow. Specifically, the subinertial flow evolving on frontal-/quasi-geostrophic time scales appears to be accompanied by the near-inertial oscillations/inertia-gravity waves in corresponding small/finite Burger number regimes, respectively. The quasi-geostrophic subinertial currents on the west Florida shelf are probably associated with the synoptic wind-forced flow, whereas the frontal-geostrophic currents are related to the evolution of density fronts. Further details of this conceptual view should, however, be elucidated in the future.
Michael, J. - P., Misra, V., & Chassignet, E. P. (2013). The El Niño and Southern Oscillation in the historical centennial integrations of the new generation of climate models.
Reg Environ Change, 13(S1), 121–130.
Misra, V., Mishra, A., & Bhardwaj, A. (2018). Simulation of the Intraseasonal Variations of the Indian Summer Monsoon in a Regional Coupled Ocean-Atmosphere Model.
J. Climate, 31(8), 3167–3185.