Sea level variability in East China Sea and its response to ENSO

Sea level variability in the East China Sea （ECS） was examined based primarily on the analysis of TOPEX/Poseidon altimetry data and tide gauge data as well as numerical simulation with the Princeton ocean model （POM）. It is concluded that the inter-annual sea level variation in the ECS is negatively correlated with the ENSO index, and that the impact is more apparent in the southern area than in the northern area. Both data analysis and numerical model results also show that the sea level was lower during the typical E1 Niflo period of 1997 to 1998. E1 Nifio also causes the decrease of the annual sea level variation range in the ECS. This phenomenon is especially evident in the southern ECS. The impacts of wind stress and ocean circulation on the sea level variation in the ECS are also discussed in this paper. It is found that the wind stress most strongly affecting the sea level was in the directions of 70° and 20° south of east,, respectively, over the northern and southern areas of the ECS. The northwest wind is particularly strong when E1 Nifio occurs, and sea water is transported southeastward, which lowers the sea level in the southern ECS. The sea level variation in the southern ECS is also significantly affected by the strengthening of the Kuroshio. During the strengthening period of the Kuroshio, the sea level in the ECS usually drops, while the sea level rises when the Kuroshio weakens.

Sea level change under IPCC-A2 scenario in Bohai, Yellow, and East China Seas

Because of the environmental and socioeconomic impacts of anthropogenic sea level rise （SLR）, it is very important to understand the processes leading to past and present SLRs towards more reliable future SLR projections. A regional ocean general circulation model （ROGCM）, with a grid refinement in the Bohai, Yellow, and East China Seas （BYECSs）, was set up to project SLR induced by the ocean dynamic change in the 21st century. The model does not consider the contributions from ice sheets and glacier melting. Data of all forcing terms required in the model came from the simulation of the Community Climate System Model version 3.0 （CCSM3） under the International Panel on Climate Change （IPCC）-A2 scenario. Simulation results show that at the end of the 21st century, the sea level in the BYECSs will rise about 0.12 to 0.20 m. The SLR in the BYECSs during the 21st century is mainly caused by the ocean mass redistribution due to the ocean dynamic change of the Pacific Ocean, which means that water in the Pacific Ocean tends to move to the continental shelves of the BYECSs, although the local steric sea level change is another factor.

Memristor-based vector neural network architecture

Vector neural network(VNN)is one of the most important methods to process interval data.However,the VNN,which contains a great number of multiply-accumulate(MAC)operations,often adopts pure numerical calculation method,and thus is difficult to be miniaturized for the embedded applications.In this paper,we propose a memristor based vector-type backpropagation(MVTBP)architecture which utilizes memristive arrays to accelerate the MAC operations of interval data.Owing to the unique brain-like synaptic characteristics of memristive devices,e.g.,small size,low power consumption,and high integration density,the proposed architecture can be implemented with low area and power consumption cost and easily applied to embedded systems.The simulation results indicate that the proposed architecture has better identification performance and noise tolerance.When the device precision is 6 bits and the error deviation level(EDL)is 20%,the proposed architecture can achieve an identification rate,which is about 92%higher than that for interval-value testing sample and 81%higher than that for scalar-value testing sample.