El Niño-Southern Oscillation (ENSO) Variations and Climate Changes Worldwide

This investigation aims to study the El-Niño-Southern Oscillation (ENSO) events in these three phases: El Niño, La Niña, and neutral. Warm and cold events relate to the Spring/Summer seasons. This paper will search for connections between the ENSO events and climate anomalies worldwide. There is some speculation that those events would be necessary for the climate anomalies observed worldwide. After analyzing the data from the reports to the ENSO, it shows almost periodicity from 1950-2023. We emphasized the occurrence of El Niño two years, when it was most prominent, and the climate anomalies (following NOAA maps), 2015 and 2023. The results indicated that the observed climate anomalies couldn’t be linked to the abnormal events observed. The worldwide temperatures in those years enhanced mostly in 2023. It shows an abnormal behavior compared with all the years scrutinized and analyzed since the records began. Therefore, there must be unknown factors beyond ENSO that rule the worldwide temperatures and the climate anomalies observed.

CMIP 6 models simulation of the connection between North/South Pacific Meridional Mode and ENSO

The subtropical North and South Pacific Meridional Modes(NPMM and SPMM)are well known precursors of El Niño-Southern Oscillation(ENSO).However,relationship between them is not constant.In the early 1980,the relationship experienced an interdecadal transition.Changes in this connection can be attributed mainly to the phase change of the Pacific decadal oscillation(PDO).During the positive phase of PDO,a shallower thermocline in the central Pacific is responsible for the stronger trade wind charging(TWC)mechanism,which leads to a stronger equatorial subsurface temperature evolution.This dynamic process strengthens the connection between NPMM and ENSO.Associated with the negative phase of PDO,a shallower thermocline over southeastern Pacific allows an enhanced wind-evaporation-SST(WES)feedback,strengthening the connection between SPMM and ENSO.Using 35 Coupled Model Intercomparison Project Phase 6(CMIP6)models,we examined the NPMM/SPMM performance and its connection with ENSO in the historical runs.The great majority of CMIP6 models can reproduce the pattern of NPMM and SPMM well,but they reveal discrepant ENSO and NPMM/SPMM relationship.The intermodal uncertainty for the connection of NPMM-ENSO is due to different TWC mechanism.A stronger TWC mechanism will enhance NPMM forcing.For SPMM,few models can simulate a good relationship with ENSO.The intermodel spread in the relationship of SPMM and ENSO owing to SST bias in the southeastern Pacific,as WES feedback is stronger when the southeastern Pacific is warmer.

Impacts of El Nino-Southern Oscillation events on China＇s rice production

This paper aims to demonstrate the relationships between ENSO and rice production of Jiangxi province in order to identify the reason that ENSO might have little effect on Chinese rice production. Using a data set with measures of Jiangxi＇s climate and rice production, we find the reason that during 1985 and 2004 ENSO＇s well correlated with rainfall did not promote Chinese rice production. First, the largest effects of ENSO mostly occur in the months when there is no rice in the field. Second, there is almost no temperature effect. Finally, the monthly distribution of rainfall is almost the same in ENSO and neutral years because the largest effects are during months when there is the least rain. In addition, due to the high irrigation share and reliable and effective irrigation facilities of cultivated land, China＇s rice production is less climate-sensitive.

从能量学角度理解气候背景场对ENSO热带和热带外遥相关的影响

厄尔尼诺—南方涛动(El Ni?o-Southern Oscillation,简称ENSO)通过遥相关过程影响全球天气气候。在热带地区,ENSO能通过影响热带对流层温度导致遥远海盆降水和海表温度异常;在热带外,ENSO能通过激发准定常罗斯贝波动造成北美、亚洲等地区气候异常。气候背景场对ENSO热带和热带外遥相关有重要影响。一方面,气候背景大气环流场可以通过正压和斜压能量转换影响ENSO遥相关波列的位置和强度。另一方面,热带气候背景海温和对流场会通过影响湿静力能分布影响ENSO热带遥相关过程。这些研究表明分析能量过程有助于理解气候背景场影响ENSO遥相关的机理。本文回顾了近几十年来国内外关于气候背景场对ENSO热带与热带外遥相关影响的能量分析研究进展,在此基础上,回顾了全球变暖背景下ENSO遥相关的可能变化,并提出了一些未来该领域内需要进一步研究的科学问题。

ENSO事件下赤道中东太平洋海温场非对称性特征

利用1950—2020年冬季HadISST逐月海面温度(sea surface temperature, SST)资料、SODAv2.2.4逐月SST和三维海洋流速同化资料以及NCEP/NCAR 2 m高度上的逐月气温(surface air temperature, SAT)资料,使用非对称合成差分析方法、海洋混合层热量收支诊断方法等,探究El Ni1o事件和La Ni1a事件下造成赤道东太平洋(E区:110°W~80°W,10°S~10°N)、赤道中太平洋(C区:160°E~170°W,10°S~10°N)SST异常场显著不同非对称性特征的可能海洋动力过程,分析ENSO事件非对称强迫下2 m高度上SAT异常场的非对称空间响应。结果表明:E区El Ni1o事件的强度显著强于La Ni1a事件,C区则相反。非线性动力学加热作用对E区和C区El Ni1o年和La Ni1a年SST异常场的非对称分量都起到了正反馈作用,是造成这两个区域SST异常场产生正、负非对称分量的主导动力因子。埃克曼输送作用不利于E区SST异常场正非对称分量的形成,但有利于C区SST异常场负非对称分量的形成。平均流、纬向平流和温跃层的非对称正反馈作用阻碍了C区SST异常场负非对称分量的形成。2 m高度上SAT异常场的非对称分布与SST异常场的非对称分布较为一致,但SAT异常场正、负非对称分量的显著范围明显减小,部分区域的非对称结果不显著。

THE EXCITING MECHANISM OF TROPICAL INTRASEASONAL OSCILLATION TO EL NINO EVENT

The data analyses indicated that the occurrence of D Nino event is closely related to intraseasonal oscillation (ISO) in the tropical atmosphere : The intraseasonal oscillation is very strong in tile tropics (particularly over the equatorial western Pacific) prior to the occurrence of El Nino; But the ISO is evidently reduced and the quasistationary system is enhanced after the outbreak of El Nino. A simple air-sea coupled model study shows that the periodical self-excited oscillation can be produced in the air-sea-coupled system, but the pattern is different from the observed ENSO mode. When there is external (atmospheric) forcing with interannual time scale, a coupled mode, which looks like the ENSO mode, will be excited in the air-sea system. Synthesizing the results in data analyses and the theoretical investigation. the mechanism of ISO in the tropical atmosphere exciting the EI Nino event can be suggested : The interannual anomalies (variations) of the tropical ISO play an important role in the exciting EI Nino event through the air-sea interaction.

用于厄尔尼诺-南方涛动(ENSO)研究的海气耦合模式综述:中间型和混合型模式

厄尔尼诺-南方涛动(El Niño-Southern Oscillation,ENSO)是地球气候系统中最强的年际变率信号,起源于热带太平洋海气相互作用过程,并对全球的天气和气候等产生显著的影响。过去几十年来,广泛、深入而细致的海气相互作用研究致力于发展和改进海气耦合模式以用于ENSO模拟和预测,各种类型的海气耦合模式应运而生。经过半个多世纪的努力,ENSO数值模式及其应用已经取得了巨大进展,包括已发展了一些高度理想化的概念(concept)模型来解释ENSO准周期性循环(包括正负反馈机制等);同时也已发展了几类复杂程度不同的海气耦合模式并用于对ENSO的真实模拟和实时预测等研究,尤其是已能提前6个月或更长时间对ENSO事件的发生和发展等进行有效的实时预测。其中最为复杂的模式是基于原始方程组的大气环流模式(Atmospheric General Circulation Models,AGCMs)与海洋环流模式(Oceanic General Circulation Models,OGCMs)等所组成的环流型耦合模式(Coupled General Circulation Models,CGCMs),这类模式变量取为完全变量的形式(如总的海表温度场,其可以分解为气候态部分和年际异常部分),还考虑了尽可能详尽的物理过程及其参数化方案。中间型耦合模式(Intermediate Coupled Models,ICMs)是一类介于高度理想化概念模型与复杂的环流型耦合模式之间的简化模式,其对应的控制方程组采用距平形式,直接取大气和海洋年际异常场作为预报变量(如海表温度年际异常),而相应的气候平均态部分则由对应的观测资料来给定;大气与海洋模式间的耦合采用异常耦合(anomaly coupling)。混合型耦合模式(Hybrid Coupled Models,HCMs)是另一类简化的海气耦合模式,其中海洋或大气模式有一个分量模式采用了简化的距平类模式(类似于ICMs),而另一个分量模式则采用环流型模式(General Circulation Models,GCMs);如可采用统计的大气风应力年际异常模式与OGCM间的耦合而构建一种HCMOGCM,也可采用简化的海洋距平类模式(如ICM中的海洋分量模式)与AGCM间的耦合而构建另一种HCMAGCM。历史上,ICMs、HCMs和CGCMs等这几类耦合模式都在ENSO理论体系的发展、数值模拟和实时预测等方面都起到了重要作用。本文主要回顾作者与合作者所研发的ICMs和HCMs的构建、特点和应用例子等。

北赤道逆流影响的热带西太平洋秋冬转换期海表叶绿素浓度与ENSO循环

在厄尔尼诺-南方涛动(El Nino-Southern Oscillation, ENSO)的发生、发展中,北赤道逆流也有明显的年际变化,但北赤道逆流的变化如何影响海洋水文生态条件的变化过程尚不清楚。本文以位于热带西太平洋的北赤道逆流影响区为研究海区,分析了2006-2022年ENSO循环期间研究区不同阶段秋冬转换期水文生态气候条件的变化特征。结果表明,北赤道逆流源区及其路径上存在海表叶绿素浓度高值条带,这是由北赤道逆流从其源区携带的和新几内亚海岸潜流携带的营养物质共同影响下形成的,棉兰老冷穹上升流也会对其营养盐供给产生较大影响。厄尔尼诺事件发生时,热带西太平洋西风事件增多,北赤道逆流增强,大量表层水东移,研究区海平面降低,深水向浅层补给增加,深层冷水上抬,共同增强的北赤道逆流、新几内亚海岸流、新几内亚海岸潜流和棉兰老冷穹上升流将更多的养分从水平和垂直层面输送到海面,导致海面叶绿素浓度和原位有机碳总量显著增加。拉尼娜事件发生时,北赤道逆流影响区生态水文气候条件变化与厄尔尼诺事件发生时的变化特征几乎相反,但其变化程度弱于厄尔尼诺时期,共同减弱的北赤道逆流、新几内亚海岸潜流和棉兰老冷穹上升流使从水平和垂直层面输送到海面的养分显著减少,导致海面叶绿素浓度和原位有机碳总量显著降低。本文提出了北赤道逆流影响区水文生态气候条件演化对厄尔尼诺事件和拉尼娜事件的响应机制模型,这有利于进一步分析ENSO循环在局地生态效应和水文气候演化中的作用,对认识全球气候变化对物质循环的影响具有重要意义。

Seasonal evolution of the effects of the El Nino–Southern Oscillation on lower stratospheric water vapor:Delayed effects in late winter and early spring

Water vapor in the stratosphere makes a significant contribution to global climate change by altering the radiative energy budget of the Earth’s climate system.Although many previous studies have shown that the El Ni?o–Southern Oscillation(ENSO)has significant effects on the water vapor content of the stratosphere in terms of the annual or seasonal mean,a comprehensive analysis of the seasonal evolution of these effects is still required.Using reanalysis data and satellite observations,we carried out a composite analysis of the seasonal evolution of stratospheric water vapor during El Ni?o/La Ni?a peaks in winter and decays in spring.The ENSO has a distinct hysteresis effect on water vapor in the tropical lower stratosphere.The El Ni?o/La Ni?a events moisten/dry out the tropical lower stratosphere in both winter and spring,whereas this wetting/dehydration effect is more significant in spring.This pattern is due to a warmer temperature in the upper troposphere and lower stratosphere during the El Ni?o spring phase,which causes more water vapor to enter the stratosphere,and vice versa for La Ni?a.This delayed warming/cooling in the lower stratosphere during the El Ni?o/La Ni?a decay in spring leads to the seasonal evolution of ENSO effects on water vapor in the lower stratosphere.

基于集成GCN-Transformer网络的ENSO预测模型

厄尔尼诺-南方涛动(El Niño-Southern Oscillation, ENSO)是热带太平洋海表面温度发生异常的现象,会导致冰雹、洪水、台风等极端天气的出现,因此对ENSO的准确预测意义重大。本文设计了集成GCN-Transformer(GCNTR)模型,首先运用Transformer网络的全局特征聚焦能力对数据特征进行编码,然后结合GCN提取图数据特征的能力,最后引入特征融合门控机制将经过编码的特征和GCN提取的特征进行融合,实现ENSO的精确预测。结果表明,GCNTR模型实现了对ENSO提前20个月的预测,比ENSOTR多了3个月,比Transformer多了5个月,并且模型绝大部分的预测精度优于其他模型。与现有的方法相比,GCNTR模型能够实现对ENSO更好的预测。

The homotopic method of travelling wave solution for El Nio tropic sea-air coupled oscillator

The EI Nimo and Southern Oscillation （ENSO） is an interannual phenomenon involved in the tropical Pacific sea-air interactions. In this paper, an asymptotic method of solving nonlinear equations for the ENSO model is proposed. And based on a class of oscillator of the ENSO model and by employing the method of homotopic mapping, the approximate solution of equations for the corresponding ENSO model is studied. It is proved from the results that homotopic method can be used for analysing the sea surface temperature anomaly in the equatorial Pacific of the sea-air oscillator for the ENSO model.

The IOD-ENSO precursory teleconnection over the tropical Indo-Pacific Ocean:dynamics and long-term trends under global warming

The dynamics of the teleconnection between the Indian Ocean Dipole(IOD) in the tropical Indian Ocean and El Ni?o-Southern Oscillation(ENSO) in the tropical Pacific Ocean at the time lag of one year are investigated using lag correlations between the oceanic anomalies in the southeastern tropical Indian Ocean in fall and those in the tropical Indo-Pacific Ocean in the following winter-fall seasons in the observations and in high-resolution global ocean model simulations. The lag correlations suggest that the IOD-forced interannual transport anomalies of the Indonesian Throughflow generate thermocline anomalies in the western equatorial Pacific Ocean, which propagate to the east to induce ocean-atmosphere coupled evolution leading to ENSO. In comparison, lag correlations between the surface zonal wind anomalies over the western equatorial Pacific in fall and the Indo-Pacific oceanic anomalies at time lags longer than a season are all insignificant, suggesting the short memory of the atmospheric bridge. A linear continuously stratified model is used to investigate the dynamics of the oceanic connection between the tropical Indian and Pacific Oceans. The experiments suggest that interannual equatorial Kelvin waves from the Indian Ocean propagate into the equatorial Pacific Ocean through the Makassar Strait and the eastern Indonesian seas with a penetration rate of about 10%–15% depending on the baroclinic modes. The IOD-ENSO teleconnection is found to get stronger in the past century or so. Diagnoses of the CMIP5 model simulations suggest that the increased teleconnection is associated with decreased Indonesian Throughflow transports in the recent century, which is found sensitive to the global warming forcing.The dynamics of the teleconnection between the Indian Ocean Dipole(IOD)in the tropical Indian Ocean and El Ni?o-Southern Oscillation(ENSO)in the tropical Pacific Ocean at the time lag of one year are investigated using lag correlations between the oceanic anomalies in the southeastern tropical Indian Ocean in fall and those in the tropical Indo-Pacific Ocean in the following winter-fall seasons in the observations and in high-resolution global ocean model simulations.The lag correlations suggest that the IOD-forced interannual transport anomalies of the Indonesian Throughflow generate thermocline anomalies in the western equatorial Pacific Ocean,which propagate to the east to induce ocean-atmosphere coupled evolution leading to ENSO.In comparison,lag correlations between the surface zonal wind anomalies over the western equatorial Pacific in fall and the Indo-Pacific oceanic anomalies at time lags longer than a season are all insignificant,suggesting the short memory of the atmospheric bridge.A linear continuously stratified model is used to investigate the dynamics of the oceanic connection between the tropical Indian and Pacific Oceans.The experiments suggest that interannual equatorial Kelvin waves from the Indian Ocean propagate into the equatorial Pacific Ocean through the Makassar Strait and the eastern Indonesian seas with a penetration rate of about 10%–15%depending on the baroclinic modes.The IOD-ENSO teleconnection is found to get stronger in the past century or so.Diagnoses of the CMIP5 model simulations suggest that the increased teleconnection is associated with decreased Indonesian Throughflow transports in the recent century,which is found sensitive to the global warming forcing.

Asymptotic solution for the El Nio time delay sea-air oscillator model

A sea-air oscillator model is studied using the time delay theory. The aim is to find an asymptotic solving method for the El Nino-southern oscillation （ENSO） model. Employing the perturbed method, an asymptotic solution of the corresponding problem is obtained. Thus we can obtain the prognoses of the sea surface temperature （SST） anomaly and the related physical quantities.

A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

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.

Interannual wave climate variability in the Taiwan Strait and its relationship to ENSO events

This study investigated the interannual wave climate variability in the Taiwan Strait(TS) and its relationship to the El Ni?o-Southern Oscillation(ENSO) phenomenon using a high-resolution numerical wave model. The results showed the interannual variability of significant wave height(SWH) in the TS, which exhibits significant spatial and seasonal variations, is typically weaker than the seasonal variability. The standard deviation of the interannual SWH anomaly(SWHA) showed similar spatial variations in the TS throughout the year, being largest in the middle of the strait and decreasing shoreward, except in summer, when there was no local maximum in the middle of the TS. Further analyses proved the interannual wave climate variability in the TS is controlled predominantly by tropical cyclone activities in summer and by the northeasterly monsoon winds in winter. Furthermore, the interannual SWHA in the TS was found correlated highly negatively with the ENSO phenomenon. This relationship mainly derives from that during the northeasterly monsoon seasons. During the northeasterly monsoon seasons in El Ni?o(La Ni?a) years, the negative(positive) SWHA in the TS derives from weakened(strengthened) northeasterly monsoon winds induced by a lower-tropospheric anomalous anticyclone(cyclone) over the western Pacific Ocean and the South China Sea. During the southwesterly monsoon season in El Ni?o(La Ni?a) years, however, the SWH in the TS tends to increase(decrease) anomalously because of intensified(weakened) TC activities over the western North Pacific Ocean and adjacent seas.

On the stability of an ENSO model

The system of equation discussed in this paper is a model that describes the phenomena of El Nino and Southern Oscillation （ENSO）. The stability of this model is studied with the method of stratification theory. A necessary and sufficient condition to justify whether an initial （boundary） problem is well posed or not is also obtained.

Seasonal Prediction Skill and Biases in GloSea5 Relating to the East Asia Winter Monsoon

The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.

Intraseasonal variability of the equatorial Pacific Ocean and its relationship with ENSO based on Self-Organizing Maps analysis

We investigated the intraseasonal variability of equatorial Pacific subsurface temperature and its relationship with El Nino-Southern Oscillation(ENSO) using Self-Organizing Maps(SOM) analysis.Variation in intraseasonal subsurface temperature is mainly found along the thermocline.The SOM patterns concentrate in basin-wide seesaw or sandwich structures along an east-west axis.Both the seesaw and sandwich SOM patterns oscillate with periods of 55 to 90 days,with the sequence of them showing features of equatorial intraseasonal Kelvin wave,and have marked interannual variations in their occurrence frequencies.Further examination shows that the interannual variability of the SOM patterns is closely related to ENSO;and maxima in composite interannual variability of the SOM patterns are located in the central Pacific during CP El Nino and in the eastern Pacific during EP El Nino.The se results imply that some of the ENSO forcing is manife sted through changes in the occurrence frequency of intraseasonal patterns,in which the change of the intraseasonal Kelvin wave plays an important role.

1970—2019年河南省气温变化特征及其对ENSO事件的响应

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。

Unpacking the black box of deep learning for identifying El Nino-Southern oscillation

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.