A CLASS OF HOMOTOPIC SOLVING METHOD FOR ENSO MODEL

The El Nio/La Nia and the Southern Oscillation(ENSO)is an interan- nual phenomenon involved in the tropical Pacific ocean-atmosphere interactions.In this article,the aim is to create an asymptotic solving method of nonlinear equation for the ENSO models.And on the basis of a class of oscillator of ENSO models,using the method of homotopic mapping,the approximation of solution of corresponding problem is stud- ied.It is proved from the results that homotopic method can be used for analyzing the SST anomaly...

A CLASS OF HOMOTOPIC SOLVING METHOD FOR ENSO MODEL

The El Nio/La Nia and the Southern Oscillation(ENSO)is an interan- nual phenomenon involved in the tropical Pacific ocean-atmosphere interactions.In this article,the aim is to create an asymptotic solving method of nonlinear equation for the ENSO models.And on the basis of a class of oscillator of ENSO models,using the method of homotopic mapping,the approximation of solution of corresponding problem is stud- ied.It is proved from the results that homotopic method can be used for analyzing the SST anomaly...

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.

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。

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.

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。

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.

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.

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.

Improved ENSO Forecasts by Assimilating Sea Surface Temperature Observations into an Intermediate Coupled Model

A simple method for initializing intermediate coupled models （ICMs） using only sea surface temperature （SST） anomaly data is comprehensively tested in two sets of hindcasts with a new ICM. In the initialization scheme, both the magnitude of the nudging parameter and the duration of the assimilation are considered, and initial conditions for both atmosphere and ocean are generated by running the coupled model with SST anomalies nudged to the observations. A comparison with the observations indicates that the scheme can generate realistic thermal fields and surface dynamic fields in the equatorial Pacific through hindcast experiments. An ideal experiment is performed to get the optimal nudging parameters which include the nudging intensity and nudging time length. Twelve-month-long hindcast experiments are performed with the model over the period 1984-2003 and the period 1997-2003. Compared with the original prediction results, the model prediction skills are significantly improved by the nudging method especially beyond a 6-month lead time during the two different periods. Potential problems and further improvements are discussed regarding the new coupled assimilation system.

Western Pacific Warm Pool and ENSO Asymmetry in CMIP3 Models

Theoretical and empirical studies have suggested that an underestimate of the ENSO asymmetry may be accompanied by a climatologically smaller and warmer western Pacific warm pool. In light of this suggestion, simulations of the tropical Pacific climate by 19 Coupled Model Intercomparison Project Phase 3 （CMIP3） climate models that do not use flux adjustment were evaluated. Our evaluation revealed systematic biases in both the mean state and ENSO statistics. The mean state in most of the models had a smaller and warmer warm pool. This common bias in the mean state was accompanied by a common bias in the simulated ENSO statistics： a significantly weak asymmetry between the two phases of ENSO. Moreover, despite the generally weak ENSO asymmetry simulated by all models, a positive correlation between the magnitude of the bias in the simulated warm-pool size and the magnitude of the bias in the simulated ENSO asymmetry was found. These findings support the suggested link between ENSO asymmetry and the tropical mean state--the climatological size and temperature of the warm pool in particular. Together with previous studies, these findings light up a path to improve the simulation of the tropical Pacific mean state by climate models： enhancing the asymmetry of ENSO in the climate models.

A Hybrid Coupled Ocean–Atmosphere Model and Its Simulation of ENSO and Atmospheric Responses

A new hybrid coupled model(HCM) is presented in this study, which consists of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model. The ocean component is the intermediate ocean model(IOM)of the intermediate coupled model(ICM) used at the Institute of Oceanology, Chinese Academy of Sciences(IOCAS). The atmospheric component is ECHAM5, the fifth version of the Max Planck Institute for Meteorology atmospheric general circulation model. The HCM integrates its atmospheric and oceanic components by using an anomaly coupling strategy. A100-year simulation has been made with the HCM and its simulation skills are evaluated, including the interannual variability of SST over the tropical Pacific and the ENSO-related responses of the global atmosphere. The model shows irregular occurrence of ENSO events with a spectral range between two and five years. The amplitude and lifetime of ENSO events and the annual phase-locking of SST anomalies are also reproduced realistically. Despite the slightly stronger variance of SST anomalies over the central Pacific than observed in the HCM, the patterns of atmospheric anomalies related to ENSO,such as sea level pressure, temperature and precipitation, are in broad agreement with observations. Therefore, this model can not only simulate the ENSO variability, but also reproduce the global atmospheric variability associated with ENSO, thereby providing a useful modeling tool for ENSO studies. Further model applications of ENSO modulations by ocean–atmosphere processes, and of ENSO-related climate prediction, are also discussed.

ENSO Phase-Locking in an Ocean-Atmosphere Coupled Model FGCM-1.0

The mean climatology and the basic characteristics of the ENSO cycle simulated by a coupled model FGCM-1.0 are investigated in this study. Although with some common model biases as in other directly coupled models, FGCM-1.0 is capable of producing the interannual variability of the tropical Pacific, such as the ENSO phenomenon. The mechanism of the ENSO events in the coupled model can be explained by “delayed oscillator” and “recharge-discharge” hypotheses. Compared to the observations, the simulated ENSO events show larger amplitude with two distinctive types of phase-locking： one with its peak phase-locked to boreal winter and the other to boreal summer. These two types of events have a similar frequency of occurrence, but since the second type of event is seldom observed, it may be related to the biases of the coupled model. Analysis show that the heat content anomalies originate from the central south Pacific in the type of events peaking in boreal summer, which can be attributed to a different background climatology from the normal events. The mechanisms of their evolutions are also discussed.

Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction

A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The ＂observation＂ of the SST anomaly, which is sampled from a ＂truth＂ model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.

Sensitivity of Nonlinearity on the ENSO Cycle in a Simple Air-Sea Coupled Model

In this paper,the influence of the El NioSouthern Oscillation (ENSO) cycle on the sensitivity of nonlinear factors in the numerical simulation is investigated by conducting numerical experiments in a simple air-sea coupled model for ENSO prediction.Two sets of experiments are conducted in which zonal nonlinear factors,meridional nonlinear factors,or both are incorporated into the governing equations for the atmosphere or ocean.The results suggest that the ENSO cycle is very sensitive to the nonlinear factor of the governing equation for the atmosphere or ocean.Thus,incorporating nonlinearity into air-sea coupled models is of exclusive importance for improving ENSO simulation.

WIND STRESS ANOMALY MODEL ON ENSO TIME SCALES IN COMPLEX MODELS

The purpose of this paper is to evaluate the tropical Pacific wind stress anomalies produccd on monthly to interannual time scales by the complex general circulation model (GCM) of the center for Ocean Land Atmosphere Interactions (C.O.L.A.) at low (R15) resolutions. The model is integraed using observed sea surface temperature (SST) for ten years 1979 through 1988. The model simulates generally realistic wind stress anomaly (WSA). The model-generated data set of WSA was used to force the Zebiax Cane ocean model (ZCOM) for ten years. The modeled (SST) anomalies were compared to the observed SST anomalies. The ZCOM simulation shows realistic 1982/83 and 1986/87 warm episodes along the equator, but could produce less realistic 1984/85and 1988/89 cold episodes along the equator due to lack of wind stress forcing in the mean model. Time series of the NINO3 index (measuring the SST anomaly in the mid-eastern Pacific) is realistic for the ZCOM simulation.

A Hybrid Coupled Ocean-Atmosphere Model and ENSO Prediction Study

A hybrid coupled ocean-atmosphere model is designed, which consists of a global AGCM and a simple anomaly ocean model in the tropical Pacific. Retroactive experimental predictions initiated in each season from 1979 to 1994 are performed. Analyses indicate that: (1) The overall predictive capability of this model for SSTA over the central-eastern tropical Pacific can reach one year, and the error is not larger than 0.8 degrees C. (2) The prediction skill depends greatly on the season when forecasts start. However, the phenomenon of SPB (spring prediction barrier) is not found in the model. (3) The ensemble forecast method can effectively improve prediction results. A new initialization scheme is discussed.

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.

The Optimal Precursors for ENSO Events Depicted Using the Gradientdefinition-based Method in an Intermediate Coupled Model

The predictability of El Ni-o-Southern Oscillation (ENSO) has been an important area of study for years. Searching for the optimal precursor (OPR) of ENSO occurrence is an effective way to understand its predictability. The CNOP (conditional nonlinear optimal perturbation), one of the most effective ways to depict the predictability of ENSO, is adopted to study the optimal sea surface temperature (SST) precursors (SST-OPRs) of ENSO in the IOCAS ICM (intermediate coupled model developed at the Institute of Oceanology, Chinese Academy of Sciences). To seek the SST-OPRs of ENSO in the ICM, non-ENSO events simulated by the ICM are chosen as the basic state. Then, the gradient-definition-based method (GD method) is employed to solve the CNOP for different initial months of the basic years to obtain the SSTOPRs. The experimental results show that the obtained SST-OPRs present a positive anomaly signal in the western-central equatorial Pacific, and obvious differences exist in the patterns between the different seasonal SST-OPRs along the equatorial western-central Pacific, showing seasonal dependence to some extent. Furthermore, the non-El Ni-o events can eventually evolve into El Ni-o events when the SST-OPRs are superimposed on the corresponding seasons;the peaks of the Ni-o3.4 index occur at the ends of the years, which is consistent with the evolution of the real El Ni-o. These results show that the GD method is an effective way to obtain SST-OPRs for ENSO events in the ICM. Moreover, the OPRs for ENSO depicted using the GD method provide useful information for finding the early signal of ENSO in the ICM.

一个可描写ENSO循环基本特点的简单热带海气耦合模式

在所构造的海气耦合模式中，采用Ｚｅｂｉａｋ海洋模式框架，并重建了一个海洋数值模式，大气模式采用了Ｇｉｌｌ模式。在大气模式中保留了时间发展项，潜热加热采用了Ｋｌｅｅｍａｎ方案。对模式的积分结果表明，海温异常具有３—７年的准周期振荡。在模式Ｅｌ Ｎｉｏ事件的初始阶段，西风异常，海温正距平（ＳＳＴＡ）首先在赤道西太平洋发生，然后向东传播、加强。在模式ＥＮＳＯ循环的位相转换过程中，ＳＳＴＡ的空间分布共有４种不同类型。模式模拟的Ｅｌ Ｎｉｏ。事件的初始阶段有两种发展类型，它们是：在初始阶段中西太平洋和东太平洋沿岸各有海表温度的正异常发生。在以后的发展中，一种情况是这两块正 ＳＳＴＡ都发展连成一片，形成Ｅｌ Ｎｉｏ事件；另一种情况是中西太平洋地区的正 ＳＳＴＡ衰减，中东太平洋地区的正 ＳＳＴＡ加强，向西传播，形成 Ｅｌ Ｎｉｏ事件。模式模拟的 Ｌａ Ｎｉａ事件的初始阶段也有两种发展类型，它们的发展过程和Ｅｌ Ｎｉｏ事件初始阶段的发展过程相似。