RANS是工程中常用的CFD数值模拟模型,文中基于该模型对SUBOFF裸艇体的水动力特性开展数值模拟研究.传统SST(shear stress transport model)湍流模型采用了线性涡黏假设,难以描述复杂流场的各向异性流动现象.另外,传统SST模型对分离点的...RANS是工程中常用的CFD数值模拟模型,文中基于该模型对SUBOFF裸艇体的水动力特性开展数值模拟研究.传统SST(shear stress transport model)湍流模型采用了线性涡黏假设,难以描述复杂流场的各向异性流动现象.另外,传统SST模型对分离点的预测还可能出现延迟,使阻力预测值偏小.针对传统SST湍流模型的缺陷,提出使用各向异性的ASST(anisotropic shear stress transport)湍流模型及其再附修正来研究SUBOFF裸艇体的数值模拟计算问题,并对SST、SST(Reattach)、ASST及ASST(Reattach)4种湍流模型进行了比较研究.结果表明,相较于传统SST模型,ASST模型在预测SUBOFF裸艇的阻力上具有更高精确度,再附修正可有效克服阻力预测值偏小的问题,ASST(Reattach)模型在4种湍流模型中阻力预报性能最优.另外,针对不同站位的轴向及径向平均速度分布特性问题,4种湍流模型均能够取得与模型试验一致的数值模拟结果.展开更多
Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the...Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the period of 1979-2022.The results show that the TPA-DM,the dominant pattern of interannual variability in the tropical Pacific and Atlantic regions,exhibits a significant negative correlation with NCSP.The positive phase of TPA-DM induces subsidence over the Maritime Continent through a zonal circulation pattern,which initiates a Pacific-Japan-like wave train along the East Asian coast.The circulation anomalies lead to moisture deficits and convergence subsidence over North China,leading to below-normal rainfall.Further analysis reveals that cooler SST in the Southern Tropical Atlantic facilitates the persistence of the TPA-DM by stimulating the anomalous Walker circulation associated with wind-evaporation-SST-convection feedback.展开更多
The dominant annual cycle of sea surface temperature(SST)in the tropical Pacific exhibits an antisymmetric mode,which explains 83.4%total variance,and serves as a background of El Niño-Southern Oscillation(ENSO)....The dominant annual cycle of sea surface temperature(SST)in the tropical Pacific exhibits an antisymmetric mode,which explains 83.4%total variance,and serves as a background of El Niño-Southern Oscillation(ENSO).However,there is no consensus yet on its anomalous impacts on the phase and amplitude of ENSO.Based on data during 1982-2022,results show that anomalies of the antisymmetric mode can affect the evolution of ENSO on the interannual scale via Bjerknes feedback,in which the positive(negative)phase of the antisymmetric mode can strengthen El Niño(La Niña)in boreal winter via an earlier(delayed)seasonal cycle transition and larger(smaller)annual mean.The magnitude of the SST anomalies in the equatorial eastern Pacific can reach more than±0.3◦C,regulated by the changes in the antisymmetric mode based on random sensitivity analysis.Results reveal the spatial pattern of the annual cycle associated with the seasonal phase-locking of ENSO evolution and provide new insight into the impact of the annual cycle of background SST on ENSO,which possibly carries important implications for forecasting ENSO.展开更多
尝试利用卫星遥感高分辨率海表温度资料GHRSST(Group for High Resolution Sea Surface Temperature)与海表温度(sea surface temperature,SST)数值预报产品之间的误差,建立一种南海SST模式预报订正方法。首先,利用南海的Argo浮标上层...尝试利用卫星遥感高分辨率海表温度资料GHRSST(Group for High Resolution Sea Surface Temperature)与海表温度(sea surface temperature,SST)数值预报产品之间的误差,建立一种南海SST模式预报订正方法。首先,利用南海的Argo浮标上层海温数据对GHRSST海温数据进行验证,结果表明两者之间均方根误差约为0.3℃,相关系数为0.98,GHRSST海温数据可用于南海业务化数值预报SST的订正。预报订正后的SST与Argo浮标海温数据相比,24h、48h和72h的均方根误差均由0.8℃左右下降到0.5℃以内。与GHRSST海温数据相比,南海北部海域(110°E—121°E,13°N—23°N)订正后的24h、48h和72h的SST预报空间误差均显著减小,在冷空气影响南海期间或中尺度涡存在的过程中,SST预报订正效果也较为显著。因此,该方法可考虑在南海业务化SST数值预报系统中应用。展开更多
A prior observational study indicated an asymmetric link between sea surface temperature(SST)in the Tasman Sea and ENSO during austral summer.Specifically,El Niño is associated with a dipolar SST anomaly pattern,...A prior observational study indicated an asymmetric link between sea surface temperature(SST)in the Tasman Sea and ENSO during austral summer.Specifically,El Niño is associated with a dipolar SST anomaly pattern,featuring warming in the northwest and cooling in the southeast,whereas La Niña corresponds to basin-scale warming.This study employs the experiments of coupled models from the sixth phase of the Coupled Model Intercomparison Project(CMIP6)to assess ENSO’s impact on Tasman Sea SST.While all 15 models capture the observed dipolar SST anomalies(SSTAs)in the Tasman Sea during El Niño years,only 7 models capture the basin-scale warmth in the Tasman Sea during La Niña years.Consequently,the models are bifurcated into two groups:group-one models yield one physically reasonable asymmetric connection as observed,including the asymmetry of oceanic heat transport,especially the Ekman meridional transport anomalies induced by zonal wind stress driven by the asymmetric atmospheric circulation over the Tasman Sea.However,due to abnormal responses to ENSO and systematic biases in model simulations,including jet and storm tracks,oceanic heat fluxes,ocean currents,and SST,the group-two models fail to reproduce the asymmetric connection between the Tasman Sea and ENSO.This study not only validates the observational asymmetric connection of SSTAs in the Tasman Sea with respect to the two opposite ENSO phases,but also provides evidence and clues to reduce the bias in group-two models.展开更多
Sea surface temperature(SST)is an important ocean variable affecting climate change.It plays an important role in the interactions between the ocean and the atmosphere,and it also has an effect on the transport of hea...Sea surface temperature(SST)is an important ocean variable affecting climate change.It plays an important role in the interactions between the ocean and the atmosphere,and it also has an effect on the transport of heat,freshwater,and carbon.Therefore,accurate SST prediction is necessary for understanding climate change and protecting ocean ecosystems.In this study,we proposed a hybrid model to predict SST in the tropical Pacific Ocean based on two single deep-learning models.Results indicate that the proposed hybrid model shows superior prediction accuracy at all lead times compared to the single model.Specifically,during El Niño periods,the root mean square error,mean absolute error,and Pearson correlation coefficient of the hybrid model forecasts were approximately 0.54℃,0.40℃,and 0.98,respectively,while during La Niña periods,these metrics were 0.55℃,0.39℃,and 0.98,respectively.Notably,the hybrid model was able to capture the spatial distribution of SSTs during the El Niño-Southern Oscillation(ENSO)events more accurately relative to a single model.Moreover,the prediction results of the hybrid model in different ocean regions exhibited lower prediction errors and higher correlations.The ablation experiments showed that sea surface wind(SSW)had different effects on SST at different times.By combining SST and SSW data,the model can make more-accurate predictions under different climatic conditions.The proposed hybrid model is able to predict SSTs quickly and accurately with better robustness during ENSO.展开更多
The inter-model difference in the tropical Pacific SST warming pattern is a big stumbling block for reliable projections of global climate change. Here by conducting an inter-model Empirical Orthogonal Function(EOF) a...The inter-model difference in the tropical Pacific SST warming pattern is a big stumbling block for reliable projections of global climate change. Here by conducting an inter-model Empirical Orthogonal Function(EOF) analysis as well as an ocean mixed-layer heat budget, we find that the first two modes of inter-model difference in the SST warming pattern projected by 30 CMIP6 models, explaining more than three-quarters of the total inter-model variance, are both tied to different cloud–radiation feedbacks. The EOF1 mode that captures the different magnitudes of El Ni?o-like warming as well as the largest inter-model variance in the far eastern equatorial Pacific, is likely driven by highly diverse cloud–radiation feedbacks in the east and, to a lesser extent, by differing changes in the oceanic vertical temperature gradient. The EOF2 mode that mainly represents the different magnitudes of SST warming in the western equatorial Pacific, is associated with differing levels of negative cloud–radiation feedback over the central equatorial Pacific through a dynamic air–sea coupled process involving both the Bjerknes feedback and the wind–evaporation–SST feedback.Considering in isolation the robust common model bias of a weak negative cloud–radiation feedback over the central equatorial Pacific, the projected SST warming in the western equatorial Pacific is likely to be smaller than the multi-model ensemble mean, thereby presenting a more weakeened zonal SST gradient than expected, implying the potential for more severe climate extremes under global warming.展开更多
基金jointly supported by the Second Tibetan Plateau Scientific Expedition and Research Program[grant number-ber 2019QZKK0103]the National Natural Science Foundation of China[grant number 42293294]the China Meteorological Admin-istration Climate Change Special Program[grant number QBZ202303]。
文摘Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the period of 1979-2022.The results show that the TPA-DM,the dominant pattern of interannual variability in the tropical Pacific and Atlantic regions,exhibits a significant negative correlation with NCSP.The positive phase of TPA-DM induces subsidence over the Maritime Continent through a zonal circulation pattern,which initiates a Pacific-Japan-like wave train along the East Asian coast.The circulation anomalies lead to moisture deficits and convergence subsidence over North China,leading to below-normal rainfall.Further analysis reveals that cooler SST in the Southern Tropical Atlantic facilitates the persistence of the TPA-DM by stimulating the anomalous Walker circulation associated with wind-evaporation-SST-convection feedback.
基金jointly supported by the National Natural Science Foundation of China [grant numbers U2242205 and 41830969]the S&T Development Fund of CAMS [grant number 2023KJ036]the Basic Scientific Research and Operation Foundation of CAMS [grant number 2023Z018]。
文摘The dominant annual cycle of sea surface temperature(SST)in the tropical Pacific exhibits an antisymmetric mode,which explains 83.4%total variance,and serves as a background of El Niño-Southern Oscillation(ENSO).However,there is no consensus yet on its anomalous impacts on the phase and amplitude of ENSO.Based on data during 1982-2022,results show that anomalies of the antisymmetric mode can affect the evolution of ENSO on the interannual scale via Bjerknes feedback,in which the positive(negative)phase of the antisymmetric mode can strengthen El Niño(La Niña)in boreal winter via an earlier(delayed)seasonal cycle transition and larger(smaller)annual mean.The magnitude of the SST anomalies in the equatorial eastern Pacific can reach more than±0.3◦C,regulated by the changes in the antisymmetric mode based on random sensitivity analysis.Results reveal the spatial pattern of the annual cycle associated with the seasonal phase-locking of ENSO evolution and provide new insight into the impact of the annual cycle of background SST on ENSO,which possibly carries important implications for forecasting ENSO.
文摘尝试利用卫星遥感高分辨率海表温度资料GHRSST(Group for High Resolution Sea Surface Temperature)与海表温度(sea surface temperature,SST)数值预报产品之间的误差,建立一种南海SST模式预报订正方法。首先,利用南海的Argo浮标上层海温数据对GHRSST海温数据进行验证,结果表明两者之间均方根误差约为0.3℃,相关系数为0.98,GHRSST海温数据可用于南海业务化数值预报SST的订正。预报订正后的SST与Argo浮标海温数据相比,24h、48h和72h的均方根误差均由0.8℃左右下降到0.5℃以内。与GHRSST海温数据相比,南海北部海域(110°E—121°E,13°N—23°N)订正后的24h、48h和72h的SST预报空间误差均显著减小,在冷空气影响南海期间或中尺度涡存在的过程中,SST预报订正效果也较为显著。因此,该方法可考虑在南海业务化SST数值预报系统中应用。
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF0805101)the National Natural Science Founda-tion of China(Grant Nos.42376250 and 42405068).
文摘A prior observational study indicated an asymmetric link between sea surface temperature(SST)in the Tasman Sea and ENSO during austral summer.Specifically,El Niño is associated with a dipolar SST anomaly pattern,featuring warming in the northwest and cooling in the southeast,whereas La Niña corresponds to basin-scale warming.This study employs the experiments of coupled models from the sixth phase of the Coupled Model Intercomparison Project(CMIP6)to assess ENSO’s impact on Tasman Sea SST.While all 15 models capture the observed dipolar SST anomalies(SSTAs)in the Tasman Sea during El Niño years,only 7 models capture the basin-scale warmth in the Tasman Sea during La Niña years.Consequently,the models are bifurcated into two groups:group-one models yield one physically reasonable asymmetric connection as observed,including the asymmetry of oceanic heat transport,especially the Ekman meridional transport anomalies induced by zonal wind stress driven by the asymmetric atmospheric circulation over the Tasman Sea.However,due to abnormal responses to ENSO and systematic biases in model simulations,including jet and storm tracks,oceanic heat fluxes,ocean currents,and SST,the group-two models fail to reproduce the asymmetric connection between the Tasman Sea and ENSO.This study not only validates the observational asymmetric connection of SSTAs in the Tasman Sea with respect to the two opposite ENSO phases,but also provides evidence and clues to reduce the bias in group-two models.
基金Supported by the National Natural Science Foundation of China(Nos.42476024,42176010)the National Key Research and Development Program of China(No.2022YFF0801400)。
文摘Sea surface temperature(SST)is an important ocean variable affecting climate change.It plays an important role in the interactions between the ocean and the atmosphere,and it also has an effect on the transport of heat,freshwater,and carbon.Therefore,accurate SST prediction is necessary for understanding climate change and protecting ocean ecosystems.In this study,we proposed a hybrid model to predict SST in the tropical Pacific Ocean based on two single deep-learning models.Results indicate that the proposed hybrid model shows superior prediction accuracy at all lead times compared to the single model.Specifically,during El Niño periods,the root mean square error,mean absolute error,and Pearson correlation coefficient of the hybrid model forecasts were approximately 0.54℃,0.40℃,and 0.98,respectively,while during La Niña periods,these metrics were 0.55℃,0.39℃,and 0.98,respectively.Notably,the hybrid model was able to capture the spatial distribution of SSTs during the El Niño-Southern Oscillation(ENSO)events more accurately relative to a single model.Moreover,the prediction results of the hybrid model in different ocean regions exhibited lower prediction errors and higher correlations.The ablation experiments showed that sea surface wind(SSW)had different effects on SST at different times.By combining SST and SSW data,the model can make more-accurate predictions under different climatic conditions.The proposed hybrid model is able to predict SSTs quickly and accurately with better robustness during ENSO.
基金supported by the National Natural Science Foundation of China (Grant Nos.42227901, 42476020)the Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources (Grant No.QNYC2001)+4 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (Project No.SL2023MS020)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No.311024001)supported by the Natural Environment Research Council grant NE/W005239/1supported by the open fund of the State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR (No.QNHX2328)supported by the National Natural Science Foundation of China (Grant No.42222502)。
文摘The inter-model difference in the tropical Pacific SST warming pattern is a big stumbling block for reliable projections of global climate change. Here by conducting an inter-model Empirical Orthogonal Function(EOF) analysis as well as an ocean mixed-layer heat budget, we find that the first two modes of inter-model difference in the SST warming pattern projected by 30 CMIP6 models, explaining more than three-quarters of the total inter-model variance, are both tied to different cloud–radiation feedbacks. The EOF1 mode that captures the different magnitudes of El Ni?o-like warming as well as the largest inter-model variance in the far eastern equatorial Pacific, is likely driven by highly diverse cloud–radiation feedbacks in the east and, to a lesser extent, by differing changes in the oceanic vertical temperature gradient. The EOF2 mode that mainly represents the different magnitudes of SST warming in the western equatorial Pacific, is associated with differing levels of negative cloud–radiation feedback over the central equatorial Pacific through a dynamic air–sea coupled process involving both the Bjerknes feedback and the wind–evaporation–SST feedback.Considering in isolation the robust common model bias of a weak negative cloud–radiation feedback over the central equatorial Pacific, the projected SST warming in the western equatorial Pacific is likely to be smaller than the multi-model ensemble mean, thereby presenting a more weakeened zonal SST gradient than expected, implying the potential for more severe climate extremes under global warming.
