The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Ex...The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.展开更多
The El Nifio-Southern Oscillation （ENSO） is an interannual phenomenon involved in the tropical Pacific Oceanatmosphere interactions. In this paper, an asymptotic method of solving the nonlinear equation for the ENSO...The El Nifio-Southern Oscillation （ENSO） is an interannual phenomenon involved in the tropical Pacific Oceanatmosphere interactions. In this paper, an asymptotic method of solving the nonlinear equation for the ENSO model is used. And based on a class of oscillator of ENSO model, the approximate solution of a corresponding problem is studied by employing the perturbation method. Firstly, an ENSO model of nonlinear time delay equation of equatorial Pacific is introduced, Secondly, by using the perturbed method, the zeroth and first order asymptotic perturbed solutions are constructed. Finally, from the comparison of the values for a figure, it is seen that the first asymptotic perturbed solution using the perturbation method has a good accuracy. And it is proved from the results that the perturbation method can be used as an analytic operation for the sea surface temperature anomaly in the equatorial Pacific of the atmosphere-ocean oscillation for the ENSO model.展开更多
The Jin-Neelin model for the El Nio–Southern Oscillation(ENSO for short) is considered for which the authors establish existence and uniqueness of global solutions in time over an unbounded channel domain. The resu...The Jin-Neelin model for the El Nio–Southern Oscillation(ENSO for short) is considered for which the authors establish existence and uniqueness of global solutions in time over an unbounded channel domain. The result is proved for initial data and forcing that are sufficiently small. The smallness conditions involve in particular key physical parameters of the model such as those that control the travel time of the equatorial waves and the strength of feedback due to vertical-shear currents and upwelling; central mechanisms in ENSO dynamics.From the mathematical view point, the system appears as the coupling of a linear shallow water system and a nonlinear heat equation. Because of the very different nature of the two components of the system, the authors find it convenient to prove the existence of solution by semi-discretization in time and utilization of a fractional step scheme. The main idea consists of handling the coupling between the oceanic and temperature components by dividing the time interval into small sub-intervals of length k and on each sub-interval to solve successively the oceanic component, using the temperature T calculated on the previous sub-interval, to then solve the sea-surface temperature(SST for short) equation on the current sub-interval. The passage to the limit as k tends to zero is ensured via a priori estimates derived under the aforementioned smallness conditions.展开更多
Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit signifi...Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.展开更多
Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many f...Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific.To address this three-dimensional(3D)limitation and represent ENSO-related ocean-atmosphere interactions more accurately,a novel this 3D multivariate prediction model was proposed based on a Transformer architecture,which incorporates a spatiotemporal self-attention mechanism.This model,named 3D-Geoformer,offers several advantages,enabling accurate ENSO predictions up to one and a half years in advance.Furthermore,an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature(SST)variability in the eastern equatorial Pacific.Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events,particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator.By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields,the 3D-Geoformer represents a significant advancement in ENSO prediction.This study provides details in the model formulation,analysis procedures,sensitivity experiments,and illustrative examples,offering practical guidance for the application of the model in ENSO research.展开更多
The Indonesian Throughflow(ITF)plays important roles in global ocean circulation and climate systems.Previous studies suggested the ITF interannual variability is driven by both the El Niño-Southern Oscillation(E...The Indonesian Throughflow(ITF)plays important roles in global ocean circulation and climate systems.Previous studies suggested the ITF interannual variability is driven by both the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole(IOD)events.The detailed processes of ENSO and/or IOD induced anomalies impacting on the ITF,however,are still not clear.In this study,this issue is investigated through causal relation,statistical,and dynamical analyses based on satellite observation.The results show that the driven mechanisms of ENSO on the ITF include two aspects.Firstly,the ENSO related wind field anomalies driven anomalous cyclonic ocean circulation in the western Pacific,and off equatorial upwelling Rossby waves propagating westward to arrive at the western boundary of the Pacific,both tend to induce negative sea surface height anomalies(SSHA)in the western Pacific,favoring ITF reduction since the develop of the El Niño through the following year.Secondly,the ENSO events modulate equatorial Indian Ocean zonal winds through Walker Circulation,which in turn trigger eastward propagating upwelling Kelvin waves and westward propagating downwelling Rossby waves.The Rossby waves are reflected into downwelling Kelvin waves,which then propagate eastward along the equator and the Sumatra-Java coast in the Indian Ocean.As a result,the wave dynamics tend to generate negative(positive)SSHA in the eastern Indian Ocean,and thus enhance(reduce)the ITF transport with time lag of 0-6 months(9-12 months),respectively.Under the IOD condition,the wave dynamics also tend to enhance the ITF in the positive IOD year,and reduce the ITF in the following year.展开更多
Owing to the complexity of droughts,detailed assessments of drought events have become a key issue in water resource management and planning.In this study,three-dimensional copula models at Standard Precipitation Evap...Owing to the complexity of droughts,detailed assessments of drought events have become a key issue in water resource management and planning.In this study,three-dimensional copula models at Standard Precipitation Evapotranspiration Index(SPEI)-1,SPEI-3,SPEI-6,and SPEI-12 were used to assess drought risks in the Haihe River Basin(HRB)of China from 1961–2020.Drought duration,severity,and peak,as indicated by SPEI,were extracted based on run theory and fitted with suitable marginal distributions.The difference between the joint return period(Tor)and the co-occurrence return period(Tand)could explain the intrinsic correlation between drought characteristics.The smaller the difference,the stronger the correlation.The results showed that droughts in the north-western region of the HRB were characterized by high peak,intense severity,and long duration.In contrast,the eastern region exhibited a higher frequency of drought occurrence.Furthermore,the decreasing trend in precipitation dominated droughts,and topography of the northwest region creates the features of low annual precipitation with more days of precipitation.The drought events in the HRB were influenced by the phase shift between El Niño and La Niña.There was a strong negative phase coupling between SPEI-12 and Niño3.4(R^(2)≥0.77).The transition from La Niña to El Niño was responsible for severe droughts in the HRB.The El Niño-Southern Oscillation could predict droughts with lag times of 0.15–4.35 mon in mountainous areas.展开更多
The ocean heat content variability in the South China Sea(SCS)plays a pivotal role in regional climate and extreme weather events,such as tropical cyclones.Using high-resolution ocean reanalysis data,we show that the ...The ocean heat content variability in the South China Sea(SCS)plays a pivotal role in regional climate and extreme weather events,such as tropical cyclones.Using high-resolution ocean reanalysis data,we show that the SCS exhibits a summer subsurface temperature dipole mode that controls the interannual variability of ocean heat content in the upper SCS.This dipole mode manifests as warm anomalies in the north and cold anomalies in the south during strong monsoon years,and a reversed pattern during weak monsoons years.The monsoon variability is linked to large-scale climate variability associated with El Niño-Southern Oscillation transitions.Heat budget analysis indicates that this dipole pattern is primarily driven by vertical heat transport linked to opposite wind stress curl anomalies in the northern and southern basin.Accompanying the vertical heat transports is a shallow meridional overturning circulation that redistributes heat between the northern and southern SCS.展开更多
Internal solitary waves(ISWs)have considerable energy to drive the mixing of water masses in the Sulu Sea.The propagation speed is one of the critical parameters in quantifying the energy budget of the ISWs.We collect...Internal solitary waves(ISWs)have considerable energy to drive the mixing of water masses in the Sulu Sea.The propagation speed is one of the critical parameters in quantifying the energy budget of the ISWs.We collected 1354 groups of ISWs’speeds from tandem satellite remote sensing images with temporal intervals shorter than 25 min and analyzed their spatial and multi-scale temporal variations in the Sulu Sea.We found that water depth plays an important role in modulating the spatial variation of wave speeds,which increase exponentially with water depth with a power of 0.26.Tidal currents,ocean stratification,background circulation,and climate affect the temporal variations of wave speeds from days to months or years.The fortnightly spring/neap tidal currents cause daily variations of wave speeds up to 40%by modulating the ISW amplitudes.In addition to the well-accepted results that monthly variations of wave speeds are related to density stratification,we found that enhanced stratification increases wave speeds,and the background circulation leads to a maximum decrease of 0.27 m/s in the linear counterparts of wave speed.Moreover,the averaged wave speed collected in October is lower than the corresponding linear one possibly due to some unknown dynamical processes or underestimation of background current.As for the interannual variations,we show that wave speed increases in La Niña years and decreases in El Niño years as a result of the climatic modulation on the depth of the maximum value of buoyancy frequency.展开更多
El Niño-Southern Oscillation(ENSO)affects the changes in ocean physical elements in Taiwan Strait(TWS)primarily by regulating the strength of the East Asian Winter Monsoon(EAWM)and the intrusion of the Kuroshio.A...El Niño-Southern Oscillation(ENSO)affects the changes in ocean physical elements in Taiwan Strait(TWS)primarily by regulating the strength of the East Asian Winter Monsoon(EAWM)and the intrusion of the Kuroshio.Additionally,the fluctuating impact between nutrient-poor seawater with high dissolved inorganic carbon(DIC)that infiltrates owing to the Kuroshio during El Niño phases and nutrient-rich seawater with low DIC from the South China Sea(SCS)carried by the EAWM during La Niña phases determines the nutrient content in TWS,thereby sculpting appropriate or unsuitable biochemical environment.In this study,based on high-resolution sea-surface partial pressure of carbon dioxide(pCO_(2))data,we investigate the relationship between pCO_(2)level of TWS and ENSO events in winter.The physical mechanisms affecting the anomalous distribution of pCO_(2)level during ENSO are also explored.Stepwise regression was employed to identify the optimal influencing factors for modeling pCO_(2).Results indicate a significant positive correlation between Niño3.4 index and pCO_(2),which is significantly influenced by factors such as sea-surface temperature(SST),chlorophyll-a(Chl a),and DIC.These are related to the anomalously strong Kuroshio intrusion and weaker EAWM during El Niño years.It brings a large amount of high SST water with low nutrient concentration and high DIC,which is detrimental to CO_(2)dissolution and phytoplankton growth over the TWS,leading to an increase in pCO_(2).Conversely,pCO_(2)level is significantly low under the influence of SCS seawater during La Niña years.Based on the characterization of the pCO_(2)level response to ENSO,the carbon balance at TWS can be explored.展开更多
Understanding the catch composition of multispecies fisheries is fundamental to effective spatial fishery management.In the Equatorial Western and Central Pacific Ocean(EWCPO),the main catches of the tuna purse-seine ...Understanding the catch composition of multispecies fisheries is fundamental to effective spatial fishery management.In the Equatorial Western and Central Pacific Ocean(EWCPO),the main catches of the tuna purse-seine fishery include skipjack tuna(Katsuwonus pelamis),yellowfin tuna(Thunnus albacares),and bigeye tuna(Thunnus obesus).Studying the spatiotemporal distribution of the catch composition in the context of climate change contributes to the sustainable development of this fishery.Our study analyzed purse seine fishery data and environmental data from 1997 to 2019,using a random forest model to explore the changing mechanisms of catch composition under different El Niño-Southern Oscillation(ENSO)episodes with catch mean trophic level(CMTL)as the response variable.Emerging hot spot analysis was used to identify significant spatiotemporal hot(cold)spot areas.The results revealed two hot spot areas,namely the western hotspot area(WHA)and the eastern hotspot area(EHA),and two cold spot areas,namely the northern cold spot area(NCA)and the southern cold spot area(SCA).EHA spans the entire central Pacific east of 170°E among different ENSO episodes,expanding and contracting in tandem with the 28℃isotherm.WHA is mainly influenced by surface organic matter and the Western Boundary Currents and remains among different ENSO episodes.NCA is formed by the westerly anomalies and positive wind stress curl anomalies and exists only under La Niña episodes.SCA persists within the unproductive South Equatorial Current(SEC)and remains stable among different ENSO episodes.Our study contributes to revealing the spatiotemporal dynamics in tuna catch composition and their relationships with environmental factors and interspecies competition,providing valuable insights for ecosystem-based dynamic fishery management.展开更多
In this study,we conducted an experiment to construct multi-model ensemble(MME)predictions for the El Niño-Southern Oscillation(ENSO)using a neural network,based on hindcast data released from five coupled oceana...In this study,we conducted an experiment to construct multi-model ensemble(MME)predictions for the El Niño-Southern Oscillation(ENSO)using a neural network,based on hindcast data released from five coupled oceanatmosphere models,which exhibit varying levels of complexity.This nonlinear approach demonstrated extraordinary superiority and effectiveness in constructing ENSO MME.Subsequently,we employed the leave-one-out crossvalidation and the moving base methods to further validate the robustness of the neural network model in the formulation of ENSO MME.In conclusion,the neural network algorithm outperforms the conventional approach of assigning a uniform weight to all models.This is evidenced by an enhancement in correlation coefficients and reduction in prediction errors,which have the potential to provide a more accurate ENSO forecast.展开更多
El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive an...El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance.Meanwhile,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO modeling.We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made recently.As ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and improvements.Now,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and applications.Here,model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models,pinpointing to the areas where they need to be further improved for ENSO studies.These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.展开更多
文摘The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 40676016 and 10471039)the State KeyProgram for Basic Research of China (Grant Nos 2003CB415101-03 and 2004CB418304)+2 种基金the Key Project of the Chinese Academy of Sciences (Grant No KZCX3-SW-221)in partly by E-Institutes of Shanghai Municipal Education Commission (Grant NoN.E03004)the Natural Science Foundation of Zhejiang Province,China (Grant No Y606268)
文摘The El Nifio-Southern Oscillation （ENSO） is an interannual phenomenon involved in the tropical Pacific Oceanatmosphere interactions. In this paper, an asymptotic method of solving the nonlinear equation for the ENSO model is used. And based on a class of oscillator of ENSO model, the approximate solution of a corresponding problem is studied by employing the perturbation method. Firstly, an ENSO model of nonlinear time delay equation of equatorial Pacific is introduced, Secondly, by using the perturbed method, the zeroth and first order asymptotic perturbed solutions are constructed. Finally, from the comparison of the values for a figure, it is seen that the first asymptotic perturbed solution using the perturbation method has a good accuracy. And it is proved from the results that the perturbation method can be used as an analytic operation for the sea surface temperature anomaly in the equatorial Pacific of the atmosphere-ocean oscillation for the ENSO model.
基金supported by the Office of Naval Research Multidisciplinary University Research Initiative(No.N00014-16-1-2073)the National Science Foundation(Nos.OCE-1658357,DMS-1616981,DMS-1206438,DMS-1510249)the Research Fund of Indiana University
文摘The Jin-Neelin model for the El Nio–Southern Oscillation(ENSO for short) is considered for which the authors establish existence and uniqueness of global solutions in time over an unbounded channel domain. The result is proved for initial data and forcing that are sufficiently small. The smallness conditions involve in particular key physical parameters of the model such as those that control the travel time of the equatorial waves and the strength of feedback due to vertical-shear currents and upwelling; central mechanisms in ENSO dynamics.From the mathematical view point, the system appears as the coupling of a linear shallow water system and a nonlinear heat equation. Because of the very different nature of the two components of the system, the authors find it convenient to prove the existence of solution by semi-discretization in time and utilization of a fractional step scheme. The main idea consists of handling the coupling between the oceanic and temperature components by dividing the time interval into small sub-intervals of length k and on each sub-interval to solve successively the oceanic component, using the temperature T calculated on the previous sub-interval, to then solve the sea-surface temperature(SST for short) equation on the current sub-interval. The passage to the limit as k tends to zero is ensured via a priori estimates derived under the aforementioned smallness conditions.
基金Supported by the Laoshan Laboratory(No.LSKJ 202202404)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42000000)+1 种基金the National Natural Science Foundation of China(NSFC)(No.42030410)the Startup Foundation for Introducing Talent of NUIST,and the Jiangsu Innovation Research Group(No.JSSCTD 202346)。
文摘Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.
基金Supported by the Laoshan Laboratory(No.LSKJ202202402)the National Natural Science Foundation of China(No.42030410)+2 种基金the Startup Foundation for Introducing Talent of Nanjing University of Information Science&Technology,and Jiangsu Innovation Research Group(No.JSSCTD 202346)supported by the China National Postdoctoral Program for Innovative Talents(No.BX20240169)the China Postdoctoral Science Foundation(No.2141062400101)。
文摘Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific.To address this three-dimensional(3D)limitation and represent ENSO-related ocean-atmosphere interactions more accurately,a novel this 3D multivariate prediction model was proposed based on a Transformer architecture,which incorporates a spatiotemporal self-attention mechanism.This model,named 3D-Geoformer,offers several advantages,enabling accurate ENSO predictions up to one and a half years in advance.Furthermore,an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature(SST)variability in the eastern equatorial Pacific.Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events,particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator.By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields,the 3D-Geoformer represents a significant advancement in ENSO prediction.This study provides details in the model formulation,analysis procedures,sensitivity experiments,and illustrative examples,offering practical guidance for the application of the model in ENSO research.
基金The Fund of Laoshan Laboratory under contract No.LSKJ202202700the Basic Scientific Fund for National Public Research Institutes of China under contract No.2024Q02+1 种基金the National Natural Science Foundation of China under contract Nos 42076023 and 42430402the Global Change and Air-Sea InteractionⅡProject under contract No.GASI-01-ATP-STwin.
文摘The Indonesian Throughflow(ITF)plays important roles in global ocean circulation and climate systems.Previous studies suggested the ITF interannual variability is driven by both the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole(IOD)events.The detailed processes of ENSO and/or IOD induced anomalies impacting on the ITF,however,are still not clear.In this study,this issue is investigated through causal relation,statistical,and dynamical analyses based on satellite observation.The results show that the driven mechanisms of ENSO on the ITF include two aspects.Firstly,the ENSO related wind field anomalies driven anomalous cyclonic ocean circulation in the western Pacific,and off equatorial upwelling Rossby waves propagating westward to arrive at the western boundary of the Pacific,both tend to induce negative sea surface height anomalies(SSHA)in the western Pacific,favoring ITF reduction since the develop of the El Niño through the following year.Secondly,the ENSO events modulate equatorial Indian Ocean zonal winds through Walker Circulation,which in turn trigger eastward propagating upwelling Kelvin waves and westward propagating downwelling Rossby waves.The Rossby waves are reflected into downwelling Kelvin waves,which then propagate eastward along the equator and the Sumatra-Java coast in the Indian Ocean.As a result,the wave dynamics tend to generate negative(positive)SSHA in the eastern Indian Ocean,and thus enhance(reduce)the ITF transport with time lag of 0-6 months(9-12 months),respectively.Under the IOD condition,the wave dynamics also tend to enhance the ITF in the positive IOD year,and reduce the ITF in the following year.
基金Under the auspices of the Shandong Provincial Natural Science Foundation(No.ZR2024ME171,ZR2024QD207)the National Natural Science Foundation of China(No.41471160,42377077)。
文摘Owing to the complexity of droughts,detailed assessments of drought events have become a key issue in water resource management and planning.In this study,three-dimensional copula models at Standard Precipitation Evapotranspiration Index(SPEI)-1,SPEI-3,SPEI-6,and SPEI-12 were used to assess drought risks in the Haihe River Basin(HRB)of China from 1961–2020.Drought duration,severity,and peak,as indicated by SPEI,were extracted based on run theory and fitted with suitable marginal distributions.The difference between the joint return period(Tor)and the co-occurrence return period(Tand)could explain the intrinsic correlation between drought characteristics.The smaller the difference,the stronger the correlation.The results showed that droughts in the north-western region of the HRB were characterized by high peak,intense severity,and long duration.In contrast,the eastern region exhibited a higher frequency of drought occurrence.Furthermore,the decreasing trend in precipitation dominated droughts,and topography of the northwest region creates the features of low annual precipitation with more days of precipitation.The drought events in the HRB were influenced by the phase shift between El Niño and La Niña.There was a strong negative phase coupling between SPEI-12 and Niño3.4(R^(2)≥0.77).The transition from La Niña to El Niño was responsible for severe droughts in the HRB.The El Niño-Southern Oscillation could predict droughts with lag times of 0.15–4.35 mon in mountainous areas.
基金The National Key R&D Program of under contract No.2024YFF0506603.
文摘The ocean heat content variability in the South China Sea(SCS)plays a pivotal role in regional climate and extreme weather events,such as tropical cyclones.Using high-resolution ocean reanalysis data,we show that the SCS exhibits a summer subsurface temperature dipole mode that controls the interannual variability of ocean heat content in the upper SCS.This dipole mode manifests as warm anomalies in the north and cold anomalies in the south during strong monsoon years,and a reversed pattern during weak monsoons years.The monsoon variability is linked to large-scale climate variability associated with El Niño-Southern Oscillation transitions.Heat budget analysis indicates that this dipole pattern is primarily driven by vertical heat transport linked to opposite wind stress curl anomalies in the northern and southern basin.Accompanying the vertical heat transports is a shallow meridional overturning circulation that redistributes heat between the northern and southern SCS.
基金Supported by the National Natural Science Foundation of China(Nos.U23A2032,42006193)supported by the Hainan Provincial Excellent Talent Team Project(Space Observation of Deep-sea)。
文摘Internal solitary waves(ISWs)have considerable energy to drive the mixing of water masses in the Sulu Sea.The propagation speed is one of the critical parameters in quantifying the energy budget of the ISWs.We collected 1354 groups of ISWs’speeds from tandem satellite remote sensing images with temporal intervals shorter than 25 min and analyzed their spatial and multi-scale temporal variations in the Sulu Sea.We found that water depth plays an important role in modulating the spatial variation of wave speeds,which increase exponentially with water depth with a power of 0.26.Tidal currents,ocean stratification,background circulation,and climate affect the temporal variations of wave speeds from days to months or years.The fortnightly spring/neap tidal currents cause daily variations of wave speeds up to 40%by modulating the ISW amplitudes.In addition to the well-accepted results that monthly variations of wave speeds are related to density stratification,we found that enhanced stratification increases wave speeds,and the background circulation leads to a maximum decrease of 0.27 m/s in the linear counterparts of wave speed.Moreover,the averaged wave speed collected in October is lower than the corresponding linear one possibly due to some unknown dynamical processes or underestimation of background current.As for the interannual variations,we show that wave speed increases in La Niña years and decreases in El Niño years as a result of the climatic modulation on the depth of the maximum value of buoyancy frequency.
基金The Key R&D Project of Zhejiang Province under contract No.2023C03120the General Scientific Research Project of Zhejiang Province under contract No.Y202353957the National Natural Science Foundation of China under contract No.42106017.
文摘El Niño-Southern Oscillation(ENSO)affects the changes in ocean physical elements in Taiwan Strait(TWS)primarily by regulating the strength of the East Asian Winter Monsoon(EAWM)and the intrusion of the Kuroshio.Additionally,the fluctuating impact between nutrient-poor seawater with high dissolved inorganic carbon(DIC)that infiltrates owing to the Kuroshio during El Niño phases and nutrient-rich seawater with low DIC from the South China Sea(SCS)carried by the EAWM during La Niña phases determines the nutrient content in TWS,thereby sculpting appropriate or unsuitable biochemical environment.In this study,based on high-resolution sea-surface partial pressure of carbon dioxide(pCO_(2))data,we investigate the relationship between pCO_(2)level of TWS and ENSO events in winter.The physical mechanisms affecting the anomalous distribution of pCO_(2)level during ENSO are also explored.Stepwise regression was employed to identify the optimal influencing factors for modeling pCO_(2).Results indicate a significant positive correlation between Niño3.4 index and pCO_(2),which is significantly influenced by factors such as sea-surface temperature(SST),chlorophyll-a(Chl a),and DIC.These are related to the anomalously strong Kuroshio intrusion and weaker EAWM during El Niño years.It brings a large amount of high SST water with low nutrient concentration and high DIC,which is detrimental to CO_(2)dissolution and phytoplankton growth over the TWS,leading to an increase in pCO_(2).Conversely,pCO_(2)level is significantly low under the influence of SCS seawater during La Niña years.Based on the characterization of the pCO_(2)level response to ENSO,the carbon balance at TWS can be explored.
基金The National Key Research and Development Program of China under contract No.2023YFD2401303.
文摘Understanding the catch composition of multispecies fisheries is fundamental to effective spatial fishery management.In the Equatorial Western and Central Pacific Ocean(EWCPO),the main catches of the tuna purse-seine fishery include skipjack tuna(Katsuwonus pelamis),yellowfin tuna(Thunnus albacares),and bigeye tuna(Thunnus obesus).Studying the spatiotemporal distribution of the catch composition in the context of climate change contributes to the sustainable development of this fishery.Our study analyzed purse seine fishery data and environmental data from 1997 to 2019,using a random forest model to explore the changing mechanisms of catch composition under different El Niño-Southern Oscillation(ENSO)episodes with catch mean trophic level(CMTL)as the response variable.Emerging hot spot analysis was used to identify significant spatiotemporal hot(cold)spot areas.The results revealed two hot spot areas,namely the western hotspot area(WHA)and the eastern hotspot area(EHA),and two cold spot areas,namely the northern cold spot area(NCA)and the southern cold spot area(SCA).EHA spans the entire central Pacific east of 170°E among different ENSO episodes,expanding and contracting in tandem with the 28℃isotherm.WHA is mainly influenced by surface organic matter and the Western Boundary Currents and remains among different ENSO episodes.NCA is formed by the westerly anomalies and positive wind stress curl anomalies and exists only under La Niña episodes.SCA persists within the unproductive South Equatorial Current(SEC)and remains stable among different ENSO episodes.Our study contributes to revealing the spatiotemporal dynamics in tuna catch composition and their relationships with environmental factors and interspecies competition,providing valuable insights for ecosystem-based dynamic fishery management.
基金The fund from Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.SML2021SP310the National Natural Science Foundation of China under contract Nos 42227901 and 42475061the Key R&D Program of Zhejiang Province under contract No.2024C03257.
文摘In this study,we conducted an experiment to construct multi-model ensemble(MME)predictions for the El Niño-Southern Oscillation(ENSO)using a neural network,based on hindcast data released from five coupled oceanatmosphere models,which exhibit varying levels of complexity.This nonlinear approach demonstrated extraordinary superiority and effectiveness in constructing ENSO MME.Subsequently,we employed the leave-one-out crossvalidation and the moving base methods to further validate the robustness of the neural network model in the formulation of ENSO MME.In conclusion,the neural network algorithm outperforms the conventional approach of assigning a uniform weight to all models.This is evidenced by an enhancement in correlation coefficients and reduction in prediction errors,which have the potential to provide a more accurate ENSO forecast.
基金the National Key Research and Development Program of China (Nos.2017YFC1404102,2017YFC1404100)the Strategic Priority Research Program of Chinese Academy of Sciences (Nos.XDB 40000000,XDB 42000000)+4 种基金the National Natural Science Foundation of China (Nos.41690122(41690120),41705082,41421005)the Shandong Taishan Scholarship,the China Postdoctoral Science Foundation (Nos.2018M640659,2019M662453)YU Yongqiang is jointly supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Nos.XDA 19060102.XDB 42000000)REN Hong-Li is jointly supported by the China National Science Foundation (No.41975094)the China National Key Research and Development Program on Monitoring,Early Warning and Prevention of Major Natural Disaster (No.2018YFC1506004)
文摘El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance.Meanwhile,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO modeling.We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made recently.As ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and improvements.Now,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and applications.Here,model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models,pinpointing to the areas where they need to be further improved for ENSO studies.These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.
