排序方式： 共有8条查询结果，搜索用时 15 毫秒
1
1.
利用2001—2020年福建沿海赤潮事件记录资料和自然灾害风险判定方法,根据赤潮成灾面积、持续时间、危害类型、渔业直接经济损失等指标综合计算赤潮灾害指数。基于自然断点法,对赤潮灾害指数进行Ⅰ级、Ⅱ级、Ⅲ级、Ⅳ级等4个灾害级别分等定级。系统分析了福建沿海赤潮生物种类、时空分布特征和演变规律。结果表明:(1)2001—2020年福建沿海赤潮以灾害程度较轻的Ⅰ级和Ⅱ级为主,灾害程度较重的Ⅲ级和Ⅳ级仅占总次数的8.9%,但其造成的渔业直接经济损失达总损失的95.0%。(2)赤潮暴发次数和面积总体呈现下降趋势,但Ⅲ级和Ⅳ级灾害频次呈现波动特征。(3)季节尺度上表现为单波峰特征,5—6月是赤潮灾害最为严重的时段,赤潮暴发次数、面积和持续时间占总体的比例分别为73.3%、84.6%和74.9%,Ⅲ级和Ⅳ级灾害占总次数的95.2%。(4)空间尺度上,福州、宁德、厦门沿海赤潮累计次数和规模较大,但Ⅲ级和Ⅳ级赤潮灾害主要分布在泉州以北的福建沿海,泉州以南的福建海域赤潮灾害级别整体较低。(5)2001—2020年福建沿海赤潮原因种逐渐增多,硅藻占比减小、甲藻占比升高,有毒赤潮主要出现在宁德、福州、泉州海域,以米氏凯伦藻(Karenia mikimotoi)引发居多。(6)硅藻赤潮主要暴发在峡湾和海湾海域,而甲藻赤潮在峡湾、海湾和开阔的近岸海域均易暴发,近年来甲藻赤潮暴发位置呈现由福建北部向南部沿海扩张的趋势。引起这些变化的原因据推测与全球气候变化及福建近岸海域环境的变化密切相关。 相似文献
2.
OSTIA数据在中国近海业务化环流模型中的同化应用 总被引：3，自引：0，他引：3
The prediction of sea surface temperature(SST) is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea(BYECS). One is based on a surface net heat flux correction, named as Qcorrection(QC), which nudges the flux correction to the model equation; the other is ensemble optimal interpolation(En OI), which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis(OSTIA) system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error(RMSE) of the four experiments is 1.74, 1.16, 1.30 and 0.91°C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively.Although both two methods are effective in assimilating the SST, the En OI shows more advantages than the QC,and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions. 相似文献
3.
4.
东中国海碳循环时空变化特征:三维物理-生物地球化学数值模型研究 总被引：1，自引：1，他引：0
In the east of China's seas, there is a wide range of the continental shelf. The nutrient cycle and the carbon cycle in the east of China's seas exhibit a strong variability on seasonal to decadal time scales. On the basis of a regional ocean modeling system(ROMS), a three dimensional physical-biogeochemical model including the carbon cycle with the resolution(1/12)°×(1/12)° is established to investigate the physical variations, ecosystem responses and carbon cycle consequences in the east of China's seas. The ROMS-Nutrient Phytoplankton Zooplankton Detritus(NPZD) model is driven by daily air-sea fluxes(wind stress, long wave radiation, short wave radiation, sensible heat and latent heat, freshwater fluxes) that derived from the National Centers for Environmental Prediction(NCEP) reanalysis2 from 1982 to 2005. The coupled model is capable of reproducing the observed seasonal variation characteristics over the same period in the East China Sea. The integrated air-sea CO_2 flux over the entire east of China's seas reveals a strong seasonal cycle, functioning as a source of CO_2 to the atmosphere from June to October, while serving as a sink of CO_2 to the atmosphere in the other months. The 24 a mean value of airsea CO_2 flux over the entire east of China's seas is about 1.06 mol/(m~2·a), which is equivalent to a regional total of3.22 Mt/a, indicating that in the east of China's seas there is a sink of CO_2 to the atmosphere. The partial pressure of carbon dioxide in sea water in the east of China's seas has an increasing rate of 1.15 μatm/a(1μtm/a=0.101 325Pa), but p H in sea water has an opposite tendency, which decreases with a rate of 0.001 3 a~(–1) from 1982 to 2005.Biological activity is a dominant factor that controls the pCO_2 air in the east of China's seas, and followed by a temperature. The inverse relationship between the interannual variability of air-sea CO_2 flux averaged from the domain area and Ni?o3 SST Index indicates that the carbon cycle in the east of China's seas has a high correlation with El Ni?o-Southern Oscillation(ENSO). 相似文献
5.
中国近海生态动力学模型参数敏感性研究 总被引：3，自引：2，他引：1
In order to develop a coupled basin scale model of ocean circulation and biogeochemical cycling,we present a biogeochemical model including 12 components to study the ecosystem in the China coastal seas(CCS).The formulation of phytoplankton mortality and zooplankton growth are modified according to biological characteristics of CCS.The four sensitivity biological parameters,zooplankton assimilation efficiency rate(ZooAE_N),zooplankton basal metabolism rate(ZooBM),maximum specific growth rate of zooplankton(μ_(20)) and maximum chlorophyll to carbon ratio(Chl2C_m) are obtained in sensitivity experiments for the phytoplankton,and experiments about the parameter μ_(20'),half-saturation for phytoplankton NO_3 uptake(K_(NO_3)) and remineralization rate of small detritusN(SDeRRN) are conducted.The results demonstrate that the biogeochemical model is quite sensitive to the zooplankton grazing parameter when it ranges from 0.1 to 1.2 d~(-1).The K_(NO_3) and SDeRRN also play an important role in determining the nitrogen cycle within certain ranges.The sensitive interval of KNO_3 is from 0.1 to 1.5(mmol/m~3)~(-1),and interval of SEdRRN is from 0.01 and 0.1 d~(-1).The observational data from September 1998 to July 2000 obtained at SEATS station are used to validate the performance of biological model after parameters optimization.The results show that the modified model has a good capacity to reveal the biological process features,and the sensitivity analysis can save computational resources greatly during the model simulation. 相似文献
6.
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. 相似文献
7.
本文在构建黄海浒苔漂移输运模型的基础上耦合了生长消亡过程的生态模块,利用CFSR(Climate Forecast System Reanalysis)再分析数据、国家海洋环境预报中心全球业务化海洋学预报系统(Chinese Global operational Oceanography Forecasting System,CGOFS)黄东海再分析数据和CFS(Climate Forecast System products)预报数据,结合国家卫星海洋应用中心黄海绿潮遥感资料,选取浒苔灾害在时空动态演变过程方面存在明显差异的2016年和2019年,开展了黄海浒苔漂移输运和生长消亡过程的数值模拟,进行敏感实验和年度预测检验。结果表明,该模型可以有效刻画2016年黄海浒苔发展趋势的显著特征,对浒苔的漂移路径、影响范围和相对生物量变化特征的数值模拟结果与监测实况较为吻合。在2019年的年度预测应用上,针对浒苔漂移输运路径的方向、影响海域的时间、生物量较往年的变化等方面,模拟效果也都比较理想,体现出该模型在实际业务化预报应用中的可靠性和有效性。 相似文献
8.
Marginal seas play important roles in regulating the global carbon budget, but there are great uncertainties in estimating carbon sources and sinks in the continental margins. A Pacific basin-wide physical-biogeochemical model is used to estimate primary productivity and air-sea CO_2 flux in the South China Sea(SCS), the East China Sea(ECS), and the Yellow Sea(YS). The model is forced with daily air-sea fluxes which are derived from the NCEP2 reanalysis from 1982 to 2005. During the period of time, the modeled monthly-mean air-sea CO_2 fluxes in these three marginal seas altered from an atmospheric carbon sink in winter to a source in summer. On annualmean basis, the SCS acts as a source of carbon to the atmosphere(16 Tg/a, calculated by carbon, released to the atmosphere), and the ECS and the YS are sinks for atmospheric carbon(–6.73 Tg/a and –5.23 Tg/a, respectively,absorbed by the ocean). The model results suggest that the sea surface temperature(SST) controls the spatial and temporal variations of the oceanic pCO_2 in the SCS and ECS, and biological removal of carbon plays a compensating role in modulating the variability of the oceanic pCO_2 and determining its strength in each sea,especially in the ECS and the SCS. However, the biological activity is the dominating factor for controlling the oceanic pCO_2 in the YS. The modeled depth-integrated primary production(IPP) over the euphotic zone shows seasonal variation features with annual-mean values of 293, 297, and 315 mg/(m~2·d) in the SCS, the ECS, and the YS, respectively. The model-integrated annual-mean new production(uptake of nitrate) values, as in carbon units, are 103, 109, and 139 mg/(m~2·d), which yield the f-ratios of 0.35, 0.37, and 0.45 for the SCS, the ECS, and the YS, respectively. Compared to the productivity in the ECS and the YS, the seasonal variation of biological productivity in the SCS is rather weak. The atmospheric pCO_2 increases from 1982 to 2005, which is consistent with the anthropogenic CO_2 input to the atmosphere. The oceanic pCO_2 increases in responses to the atmospheric pCO_2 that drives air-sea CO_2 flux in the model. The modeled increase rate of oceanic pCO_2 is0.91 μatm/a in the YS, 1.04 μatm/a in the ECS, and 1.66 μatm/a in the SCS, respectively. 相似文献
1