With the motivation to improve the simulation of the East Asian summer monsoon(EASM) in coupled climate models, oceanic data assimilation(DA) was used in CAS-ESM-C(Chinese Academy of Sciences–Earth System Model–Clim...With the motivation to improve the simulation of the East Asian summer monsoon(EASM) in coupled climate models, oceanic data assimilation(DA) was used in CAS-ESM-C(Chinese Academy of Sciences–Earth System Model–Climate Component) in this study. Observed sea surface temperature was assimilated into CAS-ESM-C. The climatology and interannual variability of the EASM simulated in CAS-ESM-C with DA were compared with a traditional AMIP-type run.Results showed that the climatological spatial pattern and annual cycle of precipitation in the western North Paci?c, and the ENSO-related and EASM-related EASM circulation and precipitation, were largely improved. As shown in this study, air–sea coupling is important for EASM simulation. In addition, oceanic DA synchronizes the coupled model with the real world without breaking the air–sea coupling process. These two successful factors make the assimilation experiment a more reasonable experimental design than traditional AMIP-type simulations.展开更多
The first version of the Brazilian Oceano- graphic Modeling and Observation Network （REMO） ocean data assimilation system into the Hybrid Coordi- nate Ocean Model （HYCOM） （RODAS H） has recently been constructed ...The first version of the Brazilian Oceano- graphic Modeling and Observation Network （REMO） ocean data assimilation system into the Hybrid Coordi- nate Ocean Model （HYCOM） （RODAS H） has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation （EnOI） scheme and considers the high fre- quency variability of the model error co-variance matrix. The EnOl can assimilate sea surface temperature （SST）, satellite along-track and gridded sea level anomalies （SLA）, and vertical profiles of temperature （T） and salinity （S） from Argo. The first observing system experiment was carried out over the Atlantic Ocean （78°S-50°N, 100°W-20°E） with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integra- tions were performed, including the control run without assimilation. In the other four, different observations were assimilated： SST only （A SST）; Argo T-S profiles only （AArgo）; along-track SLA only （A_SLA）; and all data employed in the previous runs （A_All）. The A_SST, A_Argo, and A_SLA runs were very effective in improv- ing the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to ob- servations. Overall, the A_All run produced a good analy- sis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%.展开更多
Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data ass...Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly(SLA); another, to assimilate sea surface temperature(SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDA19030403]the National Natural Science Foundation of China [grant numbers 41606027 and 41706028]+1 种基金the National Key R&D Program of China [grant number2017YFA0604201]the China Postdoctoral Science Foundation [grant number 2015M571095]
文摘With the motivation to improve the simulation of the East Asian summer monsoon(EASM) in coupled climate models, oceanic data assimilation(DA) was used in CAS-ESM-C(Chinese Academy of Sciences–Earth System Model–Climate Component) in this study. Observed sea surface temperature was assimilated into CAS-ESM-C. The climatology and interannual variability of the EASM simulated in CAS-ESM-C with DA were compared with a traditional AMIP-type run.Results showed that the climatological spatial pattern and annual cycle of precipitation in the western North Paci?c, and the ENSO-related and EASM-related EASM circulation and precipitation, were largely improved. As shown in this study, air–sea coupling is important for EASM simulation. In addition, oceanic DA synchronizes the coupled model with the real world without breaking the air–sea coupling process. These two successful factors make the assimilation experiment a more reasonable experimental design than traditional AMIP-type simulations.
基金financially supported by the Brazilian State oil company Petróleo Brasileiro S. A. (Petrobras) and Agência Nacional de Petróleo (ANP), Gás Natural e Biocombustíveis, Brazil, via the Oceanographic Modeling and Observation Network (REMO)support of the Coordenao de Aperfeioamento de Pessoal de Nível Superior (CAPES), Ministry of Education of Brazil (Proc. BEX 3957/13-6)
文摘The first version of the Brazilian Oceano- graphic Modeling and Observation Network （REMO） ocean data assimilation system into the Hybrid Coordi- nate Ocean Model （HYCOM） （RODAS H） has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation （EnOI） scheme and considers the high fre- quency variability of the model error co-variance matrix. The EnOl can assimilate sea surface temperature （SST）, satellite along-track and gridded sea level anomalies （SLA）, and vertical profiles of temperature （T） and salinity （S） from Argo. The first observing system experiment was carried out over the Atlantic Ocean （78°S-50°N, 100°W-20°E） with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integra- tions were performed, including the control run without assimilation. In the other four, different observations were assimilated： SST only （A SST）; Argo T-S profiles only （AArgo）; along-track SLA only （A_SLA）; and all data employed in the previous runs （A_All）. The A_SST, A_Argo, and A_SLA runs were very effective in improv- ing the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to ob- servations. Overall, the A_All run produced a good analy- sis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%.
基金the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406404)the Public Science and Technology Research Funds Projects of Ocean (Grant No. 201505013)Scientific Research Foundation of the First Institute of Oceanography, State Oceanic Administration (Grant No. 2012G24)
文摘Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly(SLA); another, to assimilate sea surface temperature(SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.
