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Xu, X., Chassignet, E. P., Price, J. F., Özgökmen, T. M., & Peters, H. (2007). A regional modeling study of the entraining Mediterranean outflow. J. Geophys. Res., 112(C12).
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Xu, X., Rhines, P. B., & Chassignet, E. P. (2018). On Mapping the Diapycnal Water Mass Transformation of the Upper North Atlantic Ocean. J. Phys. Oceanogr., 48(10), 2233–2258.
Abstract: Diapycnal water mass transformation is the essence behind the Atlantic meridional overturning circulation (AMOC) and the associated heat/freshwater transports. Existing studies have mostly focused on the transformation that is forced by surface buoyancy fluxes, and the role of interior mixing is much less known. This study maps the three-dimensional structure of the diapycnal transformation, both surface forced and mixing induced, using results of a high-resolution numerical model that have been shown to represent the large-scale structure of the AMOC and the North Atlantic subpolar/subtropical gyres well. The analyses show that 1) annual mean transformation takes place seamlessly from the subtropical to the subpolar North Atlantic following the surface buoyancy loss along the northward-flowing upper AMOC limb; 2) mixing, including wintertime convection and warm-season restratification by mesoscale eddies in the mixed layer and submixed layer diapycnal mixing, drives transformations of (i) Subtropical Mode Water in the southern part of the subtropical gyre and (ii) Labrador Sea Water in the Labrador Sea and on its southward path in the western Newfoundland Basin; and 3) patterns of diapycnal transformations toward lighter and denser water do not align zonally�the net three-dimensional transformation is significantly stronger than the zonally integrated, two-dimensional AMOC streamfunction (50% in the southern subtropical North Atlantic and 60% in the western subpolar North Atlantic).
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Xu, X., Schmitz Jr., W. J., Hurlburt, H. E., Hogan, P. J., & Chassignet, E. P. (2010). Transport of Nordic Seas overflow water into and within the Irminger Sea: An eddy-resolving simulation and observations. J. Geophys. Res., 115(C12).
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Yin, J., E.P. Chassignet, W.G. Large, N.J. Norton, A.J. Wallcraft, and S.G. Yeager. (2009). Salinity boundary conditions and the Atlantic meridional overturning circulation in depth and quasi-isopycnic coordinate global ocean models. Ocean Modelling, , submitted.
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Yin, J., Griffies, S. M., & Stouffer, R. J. (2010). Spatial Variability of Sea Level Rise in Twenty-First Century Projections. J. Climate, 23(17), 4585–4607.
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Yin, J., Schlesinger, M. E., & Stouffer, R. J. (2009). Model projections of rapid sea-level rise on the northeast coast of the United States. Nature Geosci, 2(4), 262–266.
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Yu, B., Seed, A., Pu, L., & Malone, T. (2019). Integration of weather radar data into a raster GIS framework for improved flood estimation. Atmos. Sci. Lett., 6(1).
Abstract: We present in this paper the interannual variability of seasonal temperature and rainfall in the Indian meteorological subdivisions (IMS) for boreal winter and summer seasons that take in to account the varying length of the seasons.Our study reveals that accounting for the variations in the length of the sea-sons produces stronger teleconnections between the seasonal anomalies of surface temperature and rainfall over India with corresponding sea surface temperature anomalies of the tropical Oceans (especially over the northern Indian and the equatorial Pacific Oceans) compared to the same teleconnections from fixed length seasons over the IMS. It should be noted that the IMS show significant spatial heterogeneity in these teleconnections
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Yu, L., & Jin, X. (2012). Buoy perspective of a high-resolution global ocean vector wind analysis constructed from passive radiometers and active scatterometers (1987-present). J. Geophys. Res., 117(C11).
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Yu, P. (2006). Development of New Techniques for Assimilating Satellite Altimetry Data into Ocean Models. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: State of the art fully three-dimensional ocean models are very computationally expensive and their adjoints are even more resource intensive. However, many features of interest are approximated by the first baroclinic mode over much of the ocean, especially in the lower and mid latitude regions. Based on this dynamical feature, a new type of data assimilation scheme to assimilate sea surface height (SSH) data, a reduced-space adjoint technique, is developed and implemented with a three-dimensional model using vertical normal mode decomposition. The technique is tested with the Navy Coastal Ocean Model (NCOM) configured to simulate the Gulf of Mexico. The assimilation procedure works by minimizing the cost function, which generalizes the misfit between the observations and their counterpart model variables. The “forward” model is integrated for the period during which the data are assimilated. Vertical normal mode decomposition retrieves the first baroclinic mode, and the data misfit between the model outputs and observations is calculated. Adjoint equations based on a one-active-layer reduced gravity model, which approximates the first baroclinic mode, are integrated backward in time to get the gradient of the cost function with respect to the control variables (velocity and SSH of the first baroclinic mode). The gradient is input to an optimization algorithm (the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used for the cases presented here) to determine the new first baroclinic mode velocity and SSH fields, which are used to update the forward model variables at the initial time. Two main issues in the area of ocean data assimilation are addressed: 1. How can information provided only at the sea surface be transferred dynamically into deep layers? 2. How can information provided only locally, in limited oceanic regions, be horizontally transferred to ocean areas far away from the data-dense regions, but dynamically connected to it? The first problem is solved by the use of vertical normal mode decomposition, through which the vertical dependence of model variables is obtained. Analyses show that the first baroclinic mode SSH represents the full SSH field very closely in the model test domain, with a correlation of 93% in one of the experiments. One common way to solve the second issue is to lengthen the assimilation window in order to allow the dynamic model to propagate information to the data-sparse regions. However, this dramatically increases the computational cost, since many oceanic features move very slowly. An alternative solution to this is developed using a mapping method based on complex empirical orthogonal functions (EOF), which utilizes data from a much longer period than the assimilation cycle and deals with the information in space and time simultaneously. This method is applied to map satellite altimeter data from the ground track observation locations and times onto a regular spatial and temporal grid. Three different experiments are designed for testing the assimilation technique: two experiments assimilate SSH data produced from a model run to evaluate the method, and in the last experiment the technique is applied to TOPEX/Poseidon and Jason-1 altimeter data. The assimilation procedure converges in all experiments and reduces the error in the model fields. Since the adjoint, or “backward”, model is two-dimensional, the method is much more computationally efficient than if it were to use a fully three-dimensional backward model.
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Yu, P., Morey, S. L., & O'Brien, J. J. (2006). Development of new techniques for assimilating satellite altimetry data into ocean models (J. Cote, Ed.). Research Activities in Atmospheric and Ocean Modeling, Report No. 36. Geneva, Switzerland: World Meteorological Organization.
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