A class of nonlinear coupled system for EI Nino-Southern Oscillation （ENSO） model is considered. Using the asymptotic theory and method of variational iteration, the asymptotic expansion of the solution for ENSO mod...A class of nonlinear coupled system for EI Nino-Southern Oscillation （ENSO） model is considered. Using the asymptotic theory and method of variational iteration, the asymptotic expansion of the solution for ENSO models is obtained.展开更多
A class of coupled system for the E1 Nifio-Southern Oscillation （ENSO） mechanism is studied. Using the method of variational iteration for perturbation theory, the asymptotic expansions of the solution for ENSO mode...A class of coupled system for the E1 Nifio-Southern Oscillation （ENSO） mechanism is studied. Using the method of variational iteration for perturbation theory, the asymptotic expansions of the solution for ENSO model are obtained and the asymptotic behaviour of solution for corresponding problem is considered.展开更多
By training a convolutional neural network(CNN) model, we successfully recognize different phases of the El Nino-Southern oscillation. Our model achieves high recognition performance,with accuracy rates of 89.4% for t...By training a convolutional neural network(CNN) model, we successfully recognize different phases of the El Nino-Southern oscillation. Our model achieves high recognition performance,with accuracy rates of 89.4% for the training dataset and 86.4% for the validation dataset.Through statistical analysis of the weight parameter distribution and activation output in the CNN, we find that most of the convolution kernels and hidden layer neurons remain inactive,while only two convolution kernels and two hidden layer neurons play active roles. By examining the weight parameters of connections between the active convolution kernels and the active hidden neurons, we can automatically differentiate various types of El Nino and La Nina,thereby identifying the specific functions of each part of the CNN. We anticipate that this progress will be helpful for future studies on both climate prediction and a deeper understanding of artificial neural networks.展开更多
基于ERA5的逐小时100m风场数据,利用时间序列K-means聚类方法,将中国沿海冬季风能年际变化划分为四个区域,分别为北中国海(NorthChina Sea,NCS)、东海(East China Sea,ECS)、南海北部(Northern South China Sea,NSCS)及南海南部(Souther...基于ERA5的逐小时100m风场数据,利用时间序列K-means聚类方法,将中国沿海冬季风能年际变化划分为四个区域,分别为北中国海(NorthChina Sea,NCS)、东海(East China Sea,ECS)、南海北部(Northern South China Sea,NSCS)及南海南部(SouthernSouthChinaSea,SSCS)。四个区域风能的年际变化受不同气候模态的影响,其中NCS风能的年际变化与北极涛动(ArcticOscillation,AO)有关;ECS风能的年际变化与中部型ENSO及西伯利亚高压有关;SSCS和NSCS的年际变化则和东部型ENSO及大陆高压的南北位置存在联系。鉴于影响各区域风能年际变化的气候模态具有较高的可预测性,进一步评估了多个气候模式对中国沿海风能年际变化的预测技巧。结果表明,气候模式对南中国海的风能年际变化预测技巧更高,这与气候模式对ENSO的高预测技巧有关。气候模式对北方海域风能年际变化的预测技巧较差,这和气候模式不能较好地预测AO和西伯利亚高压有关。展开更多
In this paper,we proposes and analyzes the mixed 4th-order Runge-Kutta scheme of conditional nonlinear perturbation(CNOP)approach for the EI Ni˜no-Southern Oscillation(ENSO)model.This method consists of solving the EN...In this paper,we proposes and analyzes the mixed 4th-order Runge-Kutta scheme of conditional nonlinear perturbation(CNOP)approach for the EI Ni˜no-Southern Oscillation(ENSO)model.This method consists of solving the ENSO model by using a mixed 4th-order Runge-Kutta method.Convergence,the local and global truncation error of this mixed 4th-order Runge-Kutta method are proved.Furthermore,optimal control problem is developed and the gradient of the cost function is determined.展开更多
利用河南省19个气象站点的逐日气温数据,并辅以海表温度距平指数(Sea Surface Temperature Anomaly,SSTA)和南方涛动指数,运用多项式拟合、相关性分析等方法,分析了1970—2019年河南省气温变化特征及其与厄尔尼诺-南方涛动(ENSO)的关系...利用河南省19个气象站点的逐日气温数据,并辅以海表温度距平指数(Sea Surface Temperature Anomaly,SSTA)和南方涛动指数,运用多项式拟合、相关性分析等方法,分析了1970—2019年河南省气温变化特征及其与厄尔尼诺-南方涛动(ENSO)的关系。结果表明:(1)1970—2019年河南省年、季节气温均呈明显的波动上升趋势,其中年均温以0.24℃/10a的速率递增,且春季气温增温速率最大,冬季气温增温速率最小。(2)过去50年,河南省的气温变化与ENSO事件的强度存在一定的相关关系,20世纪90年代以来,随着厄尔尼诺(El Nino)事件的增多和强度的加大,对应的河南省气温也显著增加。(3)在ENSO事件发生年份,河南省气温变化与SSTA值呈现比较明显的相关关系,且存在一定的滞后性。因此,河南省在强ENSO事件发生的当年或次年易发生极端灾害事件,需要提高警惕,加强防范。展开更多
黑潮是北太平洋副热带环流系统的一支重要的西边界流。前人对不同流段黑潮的季节和年际变化进行了诸多研究,然而基于不同数据所得结论仍存在差异,尤其是不同模式计算所得流量差别很大,而且以往研究往往着眼于某一流段,对不同流段黑潮变...黑潮是北太平洋副热带环流系统的一支重要的西边界流。前人对不同流段黑潮的季节和年际变化进行了诸多研究,然而基于不同数据所得结论仍存在差异,尤其是不同模式计算所得流量差别很大,而且以往研究往往着眼于某一流段,对不同流段黑潮变化之间的异同及其原因涉及较少。本文基于卫星高度计数据,评估了OFES(Ocean generalcir culation model For the Earth Simulator)和HYCOM(Hybrid Coordinate Ocean Model)两个模式对吕宋岛和台湾岛以东黑潮季节与年际变化的模拟能力,进而对两个海域黑潮变化的异同及其物理机制进行了分析。结果表明:HYCOM模式对黑潮季节变化的模拟较好,而OFES模式对黑潮年际变化的模拟较好。吕宋岛以东黑潮和台湾岛以东黑潮在季节与年际尺度上的变化规律均不相同,且受不同动力过程控制。吕宋岛以东黑潮呈现冬春季强而秋季弱的变化规律,主要受北赤道流分叉南北移动的影响;而台湾岛以东黑潮呈现夏季强冬季弱的变化特点,主要受该海区反气旋涡与气旋涡相对数目的季节变化影响。在年际尺度上,吕宋岛以东黑潮与北赤道流分叉及风应力旋度呈负相关,当风应力旋度超前于流量4个月时相关系数达到了-0.56;而台湾岛以东黑潮的流量变化则受制于副热带逆流区涡动能的变化,且滞后于涡动能9个月时达到最大正相关,相关系数为0.44。本研究对于深入理解不同流段黑潮的多尺度变异规律及其对邻近海区环流与气候的影响具有重要意义,同时对于黑潮研究的数值模式选取具有重要参考价值。展开更多
基金the National Natural Science Foundation of China (40676016)the National Key Project for Basics Research (2003CB415101-03+1 种基金 2004CB418304)the Key Project of the Chinese Academy of Sciences (KZCX3-SW-221)
文摘A class of nonlinear coupled system for EI Nino-Southern Oscillation （ENSO） model is considered. Using the asymptotic theory and method of variational iteration, the asymptotic expansion of the solution for ENSO models is obtained.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 90111011 and 10471039）, the National Key Basic Research Special Foundation of China （Grant Nos 2003CB415101-03 and 2004CB418304）, the Key Basic Research Foundation of the Chinese Academy of Sciences （Grant No KZCX3-SW-221） and in part by E-Institutes of Shanghai Municipal Education Commission （Grant No N.E03004）.
文摘A class of coupled system for the E1 Nifio-Southern Oscillation （ENSO） mechanism is studied. Using the method of variational iteration for perturbation theory, the asymptotic expansions of the solution for ENSO model are obtained and the asymptotic behaviour of solution for corresponding problem is considered.
基金supported by the National Natural Science Foundation of China (Grant No. 12135003)。
文摘By training a convolutional neural network(CNN) model, we successfully recognize different phases of the El Nino-Southern oscillation. Our model achieves high recognition performance,with accuracy rates of 89.4% for the training dataset and 86.4% for the validation dataset.Through statistical analysis of the weight parameter distribution and activation output in the CNN, we find that most of the convolution kernels and hidden layer neurons remain inactive,while only two convolution kernels and two hidden layer neurons play active roles. By examining the weight parameters of connections between the active convolution kernels and the active hidden neurons, we can automatically differentiate various types of El Nino and La Nina,thereby identifying the specific functions of each part of the CNN. We anticipate that this progress will be helpful for future studies on both climate prediction and a deeper understanding of artificial neural networks.
文摘基于ERA5的逐小时100m风场数据,利用时间序列K-means聚类方法,将中国沿海冬季风能年际变化划分为四个区域,分别为北中国海(NorthChina Sea,NCS)、东海(East China Sea,ECS)、南海北部(Northern South China Sea,NSCS)及南海南部(SouthernSouthChinaSea,SSCS)。四个区域风能的年际变化受不同气候模态的影响,其中NCS风能的年际变化与北极涛动(ArcticOscillation,AO)有关;ECS风能的年际变化与中部型ENSO及西伯利亚高压有关;SSCS和NSCS的年际变化则和东部型ENSO及大陆高压的南北位置存在联系。鉴于影响各区域风能年际变化的气候模态具有较高的可预测性,进一步评估了多个气候模式对中国沿海风能年际变化的预测技巧。结果表明,气候模式对南中国海的风能年际变化预测技巧更高,这与气候模式对ENSO的高预测技巧有关。气候模式对北方海域风能年际变化的预测技巧较差,这和气候模式不能较好地预测AO和西伯利亚高压有关。
基金supported in part by NSF of China(No.11371031),Technology Infrastructure Work(No.2014FY210100)Baoji Science and Technology Plan Projects(No.14SFGG-2-7),and the Key Project of Baoji University of Arts and Sciences(No.ZK15033).
文摘In this paper,we proposes and analyzes the mixed 4th-order Runge-Kutta scheme of conditional nonlinear perturbation(CNOP)approach for the EI Ni˜no-Southern Oscillation(ENSO)model.This method consists of solving the ENSO model by using a mixed 4th-order Runge-Kutta method.Convergence,the local and global truncation error of this mixed 4th-order Runge-Kutta method are proved.Furthermore,optimal control problem is developed and the gradient of the cost function is determined.
文摘利用河南省19个气象站点的逐日气温数据,并辅以海表温度距平指数(Sea Surface Temperature Anomaly,SSTA)和南方涛动指数,运用多项式拟合、相关性分析等方法,分析了1970—2019年河南省气温变化特征及其与厄尔尼诺-南方涛动(ENSO)的关系。结果表明:(1)1970—2019年河南省年、季节气温均呈明显的波动上升趋势,其中年均温以0.24℃/10a的速率递增,且春季气温增温速率最大,冬季气温增温速率最小。(2)过去50年,河南省的气温变化与ENSO事件的强度存在一定的相关关系,20世纪90年代以来,随着厄尔尼诺(El Nino)事件的增多和强度的加大,对应的河南省气温也显著增加。(3)在ENSO事件发生年份,河南省气温变化与SSTA值呈现比较明显的相关关系,且存在一定的滞后性。因此,河南省在强ENSO事件发生的当年或次年易发生极端灾害事件,需要提高警惕,加强防范。
文摘黑潮是北太平洋副热带环流系统的一支重要的西边界流。前人对不同流段黑潮的季节和年际变化进行了诸多研究,然而基于不同数据所得结论仍存在差异,尤其是不同模式计算所得流量差别很大,而且以往研究往往着眼于某一流段,对不同流段黑潮变化之间的异同及其原因涉及较少。本文基于卫星高度计数据,评估了OFES(Ocean generalcir culation model For the Earth Simulator)和HYCOM(Hybrid Coordinate Ocean Model)两个模式对吕宋岛和台湾岛以东黑潮季节与年际变化的模拟能力,进而对两个海域黑潮变化的异同及其物理机制进行了分析。结果表明:HYCOM模式对黑潮季节变化的模拟较好,而OFES模式对黑潮年际变化的模拟较好。吕宋岛以东黑潮和台湾岛以东黑潮在季节与年际尺度上的变化规律均不相同,且受不同动力过程控制。吕宋岛以东黑潮呈现冬春季强而秋季弱的变化规律,主要受北赤道流分叉南北移动的影响;而台湾岛以东黑潮呈现夏季强冬季弱的变化特点,主要受该海区反气旋涡与气旋涡相对数目的季节变化影响。在年际尺度上,吕宋岛以东黑潮与北赤道流分叉及风应力旋度呈负相关,当风应力旋度超前于流量4个月时相关系数达到了-0.56;而台湾岛以东黑潮的流量变化则受制于副热带逆流区涡动能的变化,且滞后于涡动能9个月时达到最大正相关,相关系数为0.44。本研究对于深入理解不同流段黑潮的多尺度变异规律及其对邻近海区环流与气候的影响具有重要意义,同时对于黑潮研究的数值模式选取具有重要参考价值。
