|Home||<< 1 2 3 4 >>|
|Jeon, C. - H., Buijsman, M. C., Wallcraft, A. J., Shriver, J. F., Arbic, B. K., Richman, J. G., et al. Improving surface tidal accuracy through two-way nesting in a global ocean model. Ocean Modelling, .|
|Jia, Y., Calil, P. H. R., Chassignet, E. P., Metzger, E. J., Potemra, J. T., Richards, K. J., et al. (2011). Generation of mesoscale eddies in the lee of the Hawaiian Islands. J. Geophys. Res., 116(C11).|
|Kara, A. B., Hurlburt, H. E., Wallcraft, A. J., & Bourassa, M. A. (2005). Black Sea Mixed Layer Sensitivity to Various Wind and Thermal Forcing Products on Climatological Time Scales. J. Climate, 18(24), 5266–5293.|
|Kara, A. B., Metzger, E. J., Hurlburt, H. E., Wallcraft, A. J., & Chassignet, E. P. (2008). Multistatistics metric evaluation of ocean general circulation model sea surface temperature: Application to 0.08° Pacific Hybrid Coordinate Ocean Model simulations. J. Geophys. Res., 113(C12).|
|Kara, A. B., Wallcraft, A. J., Barron, C. N., Hurlburt, H. E., & Bourassa, M. A. (2008). Accuracy of 10 m winds from satellites and NWP products near land-sea boundaries. J. Geophys. Res., 113(C10).|
|Kara, A. B., Wallcraft, A. J., & Bourassa, M. A. (2008). Air-sea stability effects on the 10 m winds over the global ocean: Evaluations of air-sea flux algorithms. J. Geophys. Res., 113(C4).|
|Kara, A. B., Wallcraft, A. J., Martin, P. J., & Chassignet, E. P. (2008). Performance of mixed layer models in simulating SST in the equatorial Pacific Ocean. J. Geophys. Res., 113(C2).|
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.
Keywords: OCEAN MODELING; OCEAN CIRCULATION; PARAMETERIZATIONS
|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.
Keywords: eddy available potential energy; mesoscale eddies; mixing; model‐ data comparison; ocean energy reservoirs; Argo