Browsing by Author "Zhang, Weiwei"
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Item Numerical study and remote sensing of the convection, restratification and mesoscale processes in the Labrador Sea and their implications on the subpolar North Atlantic warming(University of Delaware, 2015) Zhang, WeiweiThis dissertation focuses on quantifying the physical processes in the Labrador Sea, such as wintertime deep convections, subsequent restratification, and the mesoscale eddy activities. The Labrador Sea is one of the areas where deep ocean is warming since 1998, so it has an implication on the recent global surface warming slow-down, which is also termed the global warming “hiatus”. The restratification after the 2008 deep convection is studied using regional ocean model. The modeled mixed layer depth during wintertime resembles the ARGO observed mixed layer very well, and the lateral heat flux during the subsequent restratification is in line with observations. The Irminger Rings (IRs) are reproduced with fresher caps, and they are identified and tracked automatically. The model underestimates both the number of IRs into the convection area and the heat they carry. The underestimation is most likely caused by the errors in the direction of the West Greenland currents (WGC) in the model, which causes more IRs propagating westward. Yet, the model still observed three eddies propagating into the convection area during restratification phase in 2008, and they contribute from 1 to 4% of the total heat regain during central Labrador Sea restratification. Then a merged along track altimeter dataset is used to study the variability of eddy kinetic energy (EKE) in the Labrador Sea from 1993 to 2012. The WGC EKE propagates far into the central Labrador Sea during occasional years, and its annual cycle magnitude declines. The decreasing annual cycle is related to the central basin convections, while the WGC EKE strengths is controlled by a low frequency variation of the wind stress curl, which is related to the subpolar gyre (SPG) circulation. Finally, the subpolar North Atlantic (SPNA) warming during the recent global warming slow-down is studied by separately investigating the subsurface warming of the eastern and western SPNA. Temperature decompositions indicate buoyancy domination in the west, and wind stress forcing domination in the east prior hiatus period, and the contrary during the hiatus period. Atlantic Multidecadal Oscillation only shows shallow influence in the west SPNA.Item Reconstruction of Three-Dimensional Temperature and Salinity Fields From Satellite Observations(Journal of Geophysical Research: Oceans, 2021-11-07) Meng, Lingsheng; Yan, Chi; Zhuang, Wei; Zhang, Weiwei; Yan, Xiao-HaiObservation of the ocean is crucial to the studies of ocean dynamics, climate change, and biogeochemical cycle. However, current oceanic observations are patently insufficient, because the in situ observations are of difficulty and high cost while the satellite remote-sensed measurements are mainly the sea surface data. To make up for the shortage of ocean interior data and make full use of the abundant satellite data, here we develop a data-driven deep learning model to estimate ocean subsurface and interior variables from satellite-observed sea surface data. Exclusively and simply using satellite data, three-dimensional ocean temperature and salinity fields are successfully reconstructed, which are at 26 level depths from 0 to 2,000 m. We further design a scheme to increase the horizontal resolution from 1° to 1/4°, which is higher than the Argo gridded data. Estimations from our model are accurate, reliable, and stable for a wide range of research areas and periods. Dynamic height fields that are derived from the estimated temperature and salinity, as well as the associated ocean geostrophic flows, are also calculated and analyzed, which indicates the potentials of our model for reconstructing the ocean circulation fields as well. This study enriches oceanic observations with respect to vertical dimension and horizontal resolution, which can largely make up for the paucity of the subsurface and deep ocean observation, both before and during Argo era. This work also provides some new foundations for and insights into geoscience and climate change fields. Plain Language Summary: Ocean data are important for ocean science and climate change studies; however, oceanic observations are difficult and costly and thus are still very sparse in space and time. Satellites have been providing plentiful oceanic observations, but these data are only at the sea surface. To fully utilize the copious satellite data and to make up for the shortage of ocean interior data. Here, we establish a deep learning model to connect the surface ocean with the subsurface and deep oceans, through which, subsurface and deep ocean temperature and salinity data are estimated from the surface data observed by satellites. We also design a new scheme to improve the horizontal resolution of the obtained data. The results show that our model successfully reconstructed the three-dimensional field data of temperature and salinity. Our model could facilitate ocean science studies by largely enriching the ocean data availability.