The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpec...The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.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
We present the approaches to implementing the k-√k L turbulence model within the framework of the high-order discontinuous Galerkin(DG)method.We use the DG discretization to solve the full Reynolds-averaged Navier-St...We present the approaches to implementing the k-√k L turbulence model within the framework of the high-order discontinuous Galerkin(DG)method.We use the DG discretization to solve the full Reynolds-averaged Navier-Stokes equations.In order to enhance the robustness of approaches,some effective techniques are designed.The HWENO(Hermite weighted essentially non-oscillatory)limiting strategy is adopted for stabilizing the turbulence model variable k.Modifications have been made to the model equation itself by using the auxiliary variable that is always positive.The 2nd-order derivatives of velocities required in computing the von Karman length scale are evaluated in a way to maintain the compactness of DG methods.Numerical results demonstrate that the approaches have achieved the desirable accuracy for both steady and unsteady turbulent simulations.展开更多
The Zebiak–Cane(ZC) model, renowned as a coupled ocean-atmosphere model specifically designed to simulate and predict El Ni??o-Southern Oscillation(ENSO), is an indispensable tool for ENSO studies. However, the origi...The Zebiak–Cane(ZC) model, renowned as a coupled ocean-atmosphere model specifically designed to simulate and predict El Ni??o-Southern Oscillation(ENSO), is an indispensable tool for ENSO studies. However, the original ZC model exhibits certain biases in reproducing the ENSO–related sea surface temperature anomalies and heating anomalies, limiting its broader applicability. To improve the accuracy of ENSO simulation, we propose a modified ZC model based on Xie et al.(2015), named the MZC_XJH model, through refining the heating parameterization scheme. The performance in simulating the nonlinear SST–precipitation relationship in the MZC_XJH model is firstly elaborated. Then, we investigate the impacts of three key atmospheric parameters on ENSO simulation by conducting experiments with the MZC_XJH model. Through assessing the performance in simulating five fundamental ENSO metrics(amplitude, periodicity,seasonality, diversity, and skewness), we uncover that the sensitivities of simulated ENSO behaviors to different parameters are distinct. Moreover, we explain why a particular parameter greatly affects some simulated ENSO behaviors while others exert minor influence. We also reveal that the nonlinear effect due to the covariation of multi-parameters on ENSO simulation warrants careful consideration when tuning multi-parameters synchronously. Lastly, we present an updated version of the MZC_XJH model, in which some biases have been mitigated but some remain obvious. Although there are no universally optimal parameters that would ensure flawless performance in simulating every aspect of ENSO, this study provides a valuable reference for tuning atmospheric parameters in the MZC_XJH model, rendering the MZC_XJH model applicable to some research objectives.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.12372214 and U2341231)。
文摘The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.
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.92252201 and 11721202)the Fundamental Research Funds for the Central Universities.
文摘We present the approaches to implementing the k-√k L turbulence model within the framework of the high-order discontinuous Galerkin(DG)method.We use the DG discretization to solve the full Reynolds-averaged Navier-Stokes equations.In order to enhance the robustness of approaches,some effective techniques are designed.The HWENO(Hermite weighted essentially non-oscillatory)limiting strategy is adopted for stabilizing the turbulence model variable k.Modifications have been made to the model equation itself by using the auxiliary variable that is always positive.The 2nd-order derivatives of velocities required in computing the von Karman length scale are evaluated in a way to maintain the compactness of DG methods.Numerical results demonstrate that the approaches have achieved the desirable accuracy for both steady and unsteady turbulent simulations.
基金supported by the National Key Research and Development Program of China (Grant No.2022YFF0802004)the Excellent Youth Natural Science Foundation of Jiangsu Province (BK20230061)+1 种基金the Joint Open Project of KLME&CIC-FEMD (Grant No.KLME202501)Jiangsu Innovation Research Group (Grant No.JSSCTD 202346)。
文摘The Zebiak–Cane(ZC) model, renowned as a coupled ocean-atmosphere model specifically designed to simulate and predict El Ni??o-Southern Oscillation(ENSO), is an indispensable tool for ENSO studies. However, the original ZC model exhibits certain biases in reproducing the ENSO–related sea surface temperature anomalies and heating anomalies, limiting its broader applicability. To improve the accuracy of ENSO simulation, we propose a modified ZC model based on Xie et al.(2015), named the MZC_XJH model, through refining the heating parameterization scheme. The performance in simulating the nonlinear SST–precipitation relationship in the MZC_XJH model is firstly elaborated. Then, we investigate the impacts of three key atmospheric parameters on ENSO simulation by conducting experiments with the MZC_XJH model. Through assessing the performance in simulating five fundamental ENSO metrics(amplitude, periodicity,seasonality, diversity, and skewness), we uncover that the sensitivities of simulated ENSO behaviors to different parameters are distinct. Moreover, we explain why a particular parameter greatly affects some simulated ENSO behaviors while others exert minor influence. We also reveal that the nonlinear effect due to the covariation of multi-parameters on ENSO simulation warrants careful consideration when tuning multi-parameters synchronously. Lastly, we present an updated version of the MZC_XJH model, in which some biases have been mitigated but some remain obvious. Although there are no universally optimal parameters that would ensure flawless performance in simulating every aspect of ENSO, this study provides a valuable reference for tuning atmospheric parameters in the MZC_XJH model, rendering the MZC_XJH model applicable to some research objectives.
