A weakly coupled data assimilation system was established for a coupled physical–biological model for the northeastern South China Sea（NSCS）. The physical model used was the Regional Ocean Modeling System; the biol...A weakly coupled data assimilation system was established for a coupled physical–biological model for the northeastern South China Sea（NSCS）. The physical model used was the Regional Ocean Modeling System; the biological component was a seven-compartment nitrogen–phytoplankton–zooplankton–detritus ecosystem model; and the data assimilation method was Ensemble Optical Interpolation. To test the performance of the weakly coupled data assimilation system, two numerical experiments（i.e. control and assimilation runs） based on a process-oriented idealized case were conducted, and climatological SST was assimilated in the assimilation run. Only physical variables were adjusted in the weakly coupled data assimilation. The results showed that both the assimilated SST and other unassimilated physical variables had reasonable process responses. Due to the warmer SST observation, the water temperature（salinity） in the assimilation run increased（decreased） in coastal upwelling regions. Both the alongshore and bottom cross-shore currents were reduced, jointly demonstrating the weakening of the upwelling system. Meanwhile, ecosystem variables were also affected to some extent by the SST assimilation through the coupled model. For example, larger phytoplankton（chlorophyll） productivity was found in the upwelling region within the shallow layer due to the warmer waters in the assimilation run. Hence, the application of this data assimilation system could reasonably modify both physical and biological variables for the NSCS by SST assimilation.展开更多
The effects of sea surface temperature（SST） data assimilation in two regional ocean modeling systems were examined for the Yellow Sea（YS）. The SST data from the Operational Sea Surface Temperature and Sea Ice Anal...The effects of sea surface temperature（SST） data assimilation in two regional ocean modeling systems were examined for the Yellow Sea（YS）. The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis（OSTIA） were assimilated. The National Marine Environmental Forecasting Center（NMEFC） modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University（KNU） modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error（RMSE） of the SST from 1.78°C（1.46°C） to 1.30°C（1.21°C） for the NMEFC（KNU） modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature（OISST） SST dataset. A comparison with the buoy SST data indicated a 41%（31%） decrease in the SST error for the NMEFC（KNU） modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.展开更多
文摘A weakly coupled data assimilation system was established for a coupled physical–biological model for the northeastern South China Sea（NSCS）. The physical model used was the Regional Ocean Modeling System; the biological component was a seven-compartment nitrogen–phytoplankton–zooplankton–detritus ecosystem model; and the data assimilation method was Ensemble Optical Interpolation. To test the performance of the weakly coupled data assimilation system, two numerical experiments（i.e. control and assimilation runs） based on a process-oriented idealized case were conducted, and climatological SST was assimilated in the assimilation run. Only physical variables were adjusted in the weakly coupled data assimilation. The results showed that both the assimilated SST and other unassimilated physical variables had reasonable process responses. Due to the warmer SST observation, the water temperature（salinity） in the assimilation run increased（decreased） in coastal upwelling regions. Both the alongshore and bottom cross-shore currents were reduced, jointly demonstrating the weakening of the upwelling system. Meanwhile, ecosystem variables were also affected to some extent by the SST assimilation through the coupled model. For example, larger phytoplankton（chlorophyll） productivity was found in the upwelling region within the shallow layer due to the warmer waters in the assimilation run. Hence, the application of this data assimilation system could reasonably modify both physical and biological variables for the NSCS by SST assimilation.
基金The National Key Research and Development Program of China under contract Nos 2016YFC1401800 and 2016YFC1401605the Cooperation on the Development of Basic Technologies for the Yellow Sea and East China Sea Operational Oceanographic System(YOOS)+5 种基金the project of Development of Korea Operational Oceanographic System(KOOS),Phase 2 funded by the Ministry of Oceans and Fisheriesthe National Natural Science Foundation of China under contract Nos 41076011,41206023 and 41222038the National Basic Research Program(973 Program)of China under contract No.2011CB403606the Public Science and Technology Research Funds Project of Ocean under contract No.201205018the Strategic Priority Research Program of the Chinese Academy of Sciences under contract No.XDA1102010403Producing map of ocean currents for the neighboring seas of Korea funded by the Ministry of Oceans and Fisheries under contract No.2033-307-210-13
文摘The effects of sea surface temperature（SST） data assimilation in two regional ocean modeling systems were examined for the Yellow Sea（YS）. The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis（OSTIA） were assimilated. The National Marine Environmental Forecasting Center（NMEFC） modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University（KNU） modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error（RMSE） of the SST from 1.78°C（1.46°C） to 1.30°C（1.21°C） for the NMEFC（KNU） modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature（OISST） SST dataset. A comparison with the buoy SST data indicated a 41%（31%） decrease in the SST error for the NMEFC（KNU） modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.
