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.

Salinity effect-induced ENSO amplitude modulation in association with the interdecadal Pacific Oscillation

A 110-year ensemble simulation of an ocean general circulation model(OGCM)was analyzed to identify the modulation of salinity interdecadal variability on El Niño-Southern Oscillation(ENSO)amplitude in the tropical Pacific during 1901-2010.The simulating results show that sea surface salinity(SSS)variation in the region exhibits notable and coherent interdecadal variability signal,which is closely associated with the Interdecadal Pacific Oscillation(IPO).As salinity increases or reduces,the SSS modulations on ENSO amplitude during its warm/cold events vary asymmetrically with positive/negative IPO phases.Physically,salinity interdecadal variability can enhance or reduce ENSO-related conditions in upper-ocean stratification,contributing noticeably to ENSO variability.Salinity anomalies associated with the mixed layer depth and barrier layer thickness can modulate ENSO amplitude during positive and negative IPO phases,resulting in the asymmetry of sea surface temperature(SST)anomaly in the tropical Pacific.During positive IPO phases,SSS interdecadal variability contributes positively to El Niño amplitude but negatively to La Niña amplitude by enhancing or reducing SSS interannual variability,and vice versa during negative IPO phases.Quantitatively,the results indicate that the modulation of the ENSO amplitude by the SSS interdecadal variability is 15%-28%during negative IPO phases and 30%-20%during positive IPO phases,respectively.Evidently,the SSS interdecadal variability associated with IPO and its modulation on ENSO amplitude in the tropical Pacific are among factors essentially contributing ENSO diversity.

Estimating the Yield Loss of Winter Wheat from Drought in the United States Southern Plains Region as Influenced by El Niño-Southern Oscillation (ENSO)

Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projected to experience a drier climate in the future. Since the interannual variability in climate in this region is linked to an ocean-atmospheric phenomenon, called El Niño-Southern Oscillation (ENSO), droughts in this region may be associated with ENSO. Droughts that occur during the critical growth phases of wheat can be extremely costly. However, the losses due to an impending drought can be minimized through mitigation measures if it is predicted in advance. Predicting the yield loss from an imminent drought is crucial for stakeholders. One of the reliable ways for such prediction is using a plant physiology-based agricultural drought index, such as Agricultural Reference Index for Drought (ARID). This study developed ENSO phase-specific, ARID-based models for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to drought. The reasonable values of the drought sensitivity coefficients of the yield model for each ENSO phase (El Niño, La Niña, or Neutral) indicated that the yield models reflected reasonably well the phenomena of water stress decreasing the winter wheat yields in this region during different ENSO phases. The values of various goodness-of-fit measures used, including the Nash-Sutcliffe Index (0.54 to 0.67), the Willmott Index (0.82 to 0.89), and the percentage error (20 to 26), indicated that the yield models performed fairly well at predicting the ENSO phase-specific loss of wheat yields from drought. This yield model may be useful for predicting yield loss from drought and scheduling irrigation allocation based on the phenological phase-specific sensitivity to drought as impacted by ENSO.

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.

Wavelet Multiview-Based Hybrid Deep Learning Model for Forecasting El Niño-Southern Oscillation Cycles

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.

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 Frequency Asymmetry and the Pacific Decadal Oscillation in Observations and 19 CMIP5 Models

Using observational data and the pre-industrial simulations of 19 models from the Coupled Model Intercomparison Project Phase 5（CMIP5）, the El Ni o（EN） and La Ni a（LN） events in positive and negative Pacific Decadal Oscillation（PDO） phases are examined. In the observational data, with EN（LN） events the positive（negative） SST anomaly in the equatorial eastern Pacific is much stronger in positive（negative） PDO phases than in negative（positive） phases. Meanwhile,the models cannot reasonably reproduce this difference. Besides, the modulation of ENSO frequency asymmetry by the PDO is explored. Results show that, in the observational data, EN is 300% more（58% less） frequent than LN in positive（negative）PDO phases, which is significant at the 99% confidence level using the Monte Carlo test. Most of the CMIP5 models exhibit results that are consistent with the observational data.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

马来西亚东北部Setiu潟湖1970年以来沉积环境演化及其对ENSO的响应

马来半岛位于低纬热带地区,厄尔尼诺-南方涛动(El Nino-Southern Oscillation,ENSO)如何影响该区域的气候,尤其是降水仍然存在争议。本文以马来半岛东北部登嘉楼州Setiu潟湖钻孔NTT-3为研究对象,通过分析粒度、总有机碳及总氮含量、C/N比值和XRF岩心扫描等,探讨该钻孔的沉积环境变化及其对ENSO的响应。研究结果显示,钻孔记录中自1970年前后(84 cm处)出现两种不同的变化趋势。1970年之前钻孔下部沉积物粒度、有机和无机地球化学特征趋势波动明显,沉积速率较低。而1970年以来,潟湖沉积环境总体稳定,沉积物中的有机组分主要来自红树林,同时伴有河流输入的淡水浮游植物的贡献。频谱分析结果显示1970年以来钻孔上部存在明显的ENSO周期变化。强厄尔尼诺现象基本对应低Zr/Rb比值和低Zr/Ti比值,而强拉尼娜(La Nina)现象基本对应高Zr/Rb比值和高Zr/Ti比值。该结论不仅支持了现代观测对马来半岛东部沿海地区气候变化的认识,同时也在地质记录中发现了ENSO变化的直接证据,对全面认识和理解ENSO对亚洲气候变化的影响、区域陆海相互作用过程和环境响应等方面具有重要现实意义。

The El Niño-Southern Oscillation (ENSO) Effects on Cowpea and Winter Wheat Yields in the Semi-Arid Region of the Southern US

Information is limited on the effects of climate variability on cowpea (Vigna unguiculata L.) and winter wheat (Triticum aestivum L.) yields in the semiarid region of the southern US. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and weather data spanning 81 years, we assessed the impact of El Niño-Southern Oscillation (ENSO) on the grain yields of these crops in the Llano Estacado region of the southern US as affected by cowpea and wheat planting dates and N application rate. Simulated results showed that the El Niño phase of ENSO produced about 30% more yields of mono-cropped cowpea than those produced under the La Niña phase, especially with the cowpeas planted in July. The cowpea yields under El Niño were about 10% more than the 81-year average normal yield, whereas those under La Niña were about 20% less. At the N rates of 0, 50, and 100 kg·ha−1, regardless of wheat planting dates, the El Niño years produced, respectively, about 8%, 40%, and 60% higher wheat yields than those produced in the La Niña years, and about 5%, 20%, and 27% more than the 81-year average normal yield. In the La Niña years, the wheat yields at 0, 50, and 100 kg N ha−1 were, respectively, about 5%, 15%, and 20% less than the normal yield with similar N levels. The impact of ENSO on wheat yields under cowpea-wheat double-cropping systems was significant, especially for the wheat crops planted on October 15 (October 30) or later following the cowpea crops planted in June (July). At zero N, the mono-cropped wheat yields were not impacted by ENSO due to N limitation. However, the double-cropped wheat yields were impacted by ENSO even when no N fertilizer was applied due to high soil N status caused by N transfer from cowpea stover residues and roots. Results indicated that management strategies need to be attentive to ENSO forecasts and adjust potential planting dates and N application rates with the ENSO phase to avert risks of crop failure and economic loss.

A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses

El Niño-Southern Oscillation(ENSO)can be currently predicted reasonably well six months and longer,but large biases and uncertainties remain in its real-time prediction.Various approaches have been taken to improve understanding of ENSO processes,and different models for ENSO predictions have been developed,including linear statistical models based on principal oscillation pattern(POP)analyses,convolutional neural networks(CNNs),and so on.Here,we develop a novel hybrid model,named as POP-Net,by combining the POP analysis procedure with CNN-long short-term memory(LSTM)algorithm to predict the Niño-3.4 sea surface temperature(SST)index.ENSO predictions are compared with each other from the corresponding three models:POP model,CNN-LSTM model,and POP-Net,respectively.The POP-based pre-processing acts to enhance ENSO-related signals of interest while filtering unrelated noise.Consequently,an improved prediction is achieved in the POP-Net relative to others.The POP-Net shows a high-correlation skill for 17-month lead time prediction(correlation coefficients exceeding 0.5)during the 1994-2020 validation period.The POP-Net also alleviates the spring predictability barrier(SPB).It is concluded that value-added artificial neural networks for improved ENSO predictions are possible by including the process-oriented analyses to enhance signal representations.

The Natural Oscillation of Two Types of ENSO Events Based on Analyses of CMIP5 Model Control Runs

The eastern-and central-Pacific El Ni（n）o-Southem Oscillation （EP-and CP-ENSO） have been found to be dominant in the tropical Pacific Ocean,and are characterized by interannual and decadal oscillation,respectively.In the present study,we defined the EP-and CP-ENSO modes by singular value decomposition （SVD） between SST and sea level pressure （SLP） anomalous fields.We evaluated the natural features of these two types of ENSO modes as simulated by the pre-industrial control runs of 20 models involved in phase five of the Coupled Model Intercomparison Project （CMIP5）.The results suggested that all the models show good skill in simulating the SST and SLP anomaly dipolar structures for the EP-ENSO mode,but only 12 exhibit good performance in simulating the tripolar CP-ENSO modes.Wavelet analysis suggested that the ensemble principal components in these 12 models exhibit an interannual and multi-decadal oscillation related to the EP-and CP-ENSO,respectively.Since there are no changes in external forcing in the pre-industrial control runs,such a result implies that the decadal oscillation of CP-ENSO is possibly a result of natural climate variability rather than external forcing.

Underestimated relationship between westerly wind bursts and ENSO in CMIP6 models

观测表明,热带太平洋的西风爆发(WWBs)在厄尔尼诺南方涛动(ENSO)的发生,发展和多样性中起着关键作用.因此,在耦合模式中真实地再现WWBs对于改进ENSO的模拟和预测有重要意义.在本研究中,作者发现CMIP6的耦合模式的集合平均能很好地再现了热带太平洋WWB发生频率的纬向分布及其年际变动.然而,大多数CMIP6模型极大低估了WWB和ENSO的线性关系.这可能是因为大多数CMIP6模式里海气耦合强度低于观测:海气耦合强度与WWB-ENSO关系的模式间相关系数高达0.91.

The periodic oscillations in the ENSO recharge–discharge oscillator model

A class of recharge–discharge oscillator model for the El Ni？o/Southern Oscillation （ENSO） is considered. A stable limit cycle is obtained by transforming the ENSO model into the van der Pol-Duffing equation. We proved that there exists periodic oscillations in the ENSO recharge–discharge oscillator model.

模式垂直分辨率对模拟ENSO遥相关的影响

模式分辨率对气候模式的模拟效果具有重要影响。然而,当前模式开发对于垂直分辨率的重视不够。以ENSO(厄尔尼诺-南方涛动)遥相关为例,利用CESM(Community Earth System Model)模式,探究不同模式垂直分辨率设置下模式模拟的ENSO对平流层、对流层影响的差异,评估模式垂直分辨率在气候模拟中的重要性。结果表明,提高垂直分辨率可以显著改进模式对ENSO遥相关的模拟能力。以ECMWF(European Centre for Medium-Range Weather Forecasts)第五代再分析数据集(ERA5)为参照,ENSO对纬向平均温度的影响在北半球中高纬地区冬季呈现出“负正负”的三极子模态。CESM默认的垂直分辨率设置(L66)不能模拟出这一模态,而提高模式垂直分辨率(L103)后则可以较好地模拟出这个模态。对于水平分布而言,L66模拟的ENSO在对流层的信号与再分析资料相比明显偏强,L103则可以显著改善。同时,L103对ENSO影响平流层的模拟效果也比L66有所改善。进一步分析发现,L103模拟的行星波从对流层向平流层的传播更强,更接近再分析资料。提高垂直分辨率可以改善模式对大气波活动以及平流层-对流层动力耦合的模拟,重视模式的研发。

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。

A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions

2020–22年间热带太平洋经历了持续性多年的拉尼娜事件,多数耦合模式都难以准确预测其演变过程,这为厄尔尼诺-南方涛动(ENSO)的实时预测带来了很大的挑战.同时,目前学术界对此次持续性双拉尼娜事件的发展仍缺乏合理的物理解释,其所涉及的物理过程和机制有待于进一步分析.本研究利用再分析数据产品分析了热带东南太平洋东南风异常及其引起的次表层海温异常在此次热带太平洋海表温度(SST)异常演变中的作用,并构建了一个时空分离(Time-Space)的三维(3D)卷积神经网络模型(TS-3DCNN)对此次双拉尼娜事件进行实时预测和过程分析.通过将TS-3DCNN与中国科学院海洋研究所(IOCAS)中等复杂程度海气耦合模式(IOCAS ICM)的预测结果对比,表明TS-3DCNN模型对2020–22年双重拉尼娜现象的预测能力与IOCAS ICM相当,二者均能够从2021年初的初始场开始较好地预测2021年末El Niño3.4区SST的演变.此外,基于TS-3DCNN和IOCAS ICM的敏感性试验也验证了赤道外风场异常和次表层海温异常在2021年末赤道中东太平洋海表二次变冷过程中的关键作用.未来将神经网络与动力模式模式间的有效结合,进一步发展神经网络与物理过程相结合的混合建模是进一步提高ENSO事件预测能力的有效途径.

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。

Implementation of a particle-in-cell method for the energy solver in 3D spherical geodynamic modeling

The thermal evolution of the Earth’s interior and its dynamic effects are the focus of Earth sciences.However,the commonly adopted grid-based temperature solver is usually prone to numerical oscillations,especially in the presence of sharp thermal gradients,such as when modeling subducting slabs and rising plumes.This phenomenon prohibits the correct representation of thermal evolution and may cause incorrect implications of geodynamic processes.After examining several approaches for removing these numerical oscillations,we show that the Lagrangian method provides an ideal way to solve this problem.In this study,we propose a particle-in-cell method as a strategy for improving the solution to the energy equation and demonstrate its effectiveness in both one-dimensional and three-dimensional thermal problems,as well as in a global spherical simulation with data assimilation.We have implemented this method in the open-source finite-element code CitcomS,which features a spherical coordinate system,distributed memory parallel computing,and data assimilation algorithms.

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。