ENSO in a hybrid coupled model. Part I: sensitivity to physical parametrizations

 A hybrid coupled model (HCM) for the tropical Pacific ocean-atmosphere system is used to test the effects of physical parametrizations on ENSO simulation. The HCM consists of the Geophysical Fluid Dynamics Laboratory ocean general circulation model coupled to an empirical atmospheric model based on the covariance matrix of observed SST and wind stress anomaly fields. In this two-part work, part I describes the effects of ocean vertical mixing schemes and atmospheric spin-up time on ENSO period. Part II addresses ENSO prediction using the HCM and examines the impact of initialization schemes. The standard version of the HCM exhibits spatial and temporal evolution that compare well to observations, with irregular cycles that tend to exhibit 3- and 4-year frequency-locking behavior. Effects in the vertical mixing parametrization that produce stronger mixing in the surface layer give a longer inherent ENSO period, suggesting model treatment of vertical mixing is crucial to the ENSO problem. Although the atmospheric spin-up time scale is short compared to ENSO time scales, it also has a significant effect in lengthening the ENSO period. This suggests that atmospheric time scales may not be truly negligible in quantitative ENSO theory. Overall, the form and evolution mechanism of the ENSO cycle is robust, even though the period is affected by these physical parametrizations. Received: 17 April 1998 / Accepted: 22 July 1999     

A Hybrid Coupled Model for the Pacific Ocean–Atmosphere System.Part I: Description and Basic Performance

A hybrid coupled model(HCM) is constructed for El Nino–Southern Oscillation(ENSO)-related modeling studies over almost the entire Pacific basin. An ocean general circulation model is coupled to a statistical atmospheric model for interannual wind stress anomalies to represent their dominant coupling with sea surface temperatures. In addition, various relevant forcing and feedback processes exist in the region and can affect ENSO in a significant way; their effects are simply represented using historical data and are incorporated into the HCM, including stochastic forcing of atmospheric winds, and feedbacks associated with freshwater flux, ocean biology-induced heating(OBH), and tropical instability waves(TIWs). In addition to its computational efficiency, the advantages of making use of such an HCM enable these related forcing and feedback processes to be represented individually or collectively, allowing their modulating effects on ENSO to be examined in a clean and clear way. In this paper, examples are given to illustrate the ability of the HCM to depict the mean ocean state, the circulation pathways connecting the subtropics and tropics in the western Pacific, and interannual variability associated with ENSO. As satellite data are taken to parameterize processes that are not explicitly represented in the HCM, this work also demonstrates an innovative method of using remotely sensed data for climate modeling. Further model applications related with ENSO modulations by extratropical influences and by various forcings and feedbacks will be presented in Part II of this study.     

模式气候吸引子信息约束下的动力协调初始化方法及其在ENSO预测中的应用

为了改善模式初始场质量,减少初值与模式不协调对ENSO预测的影响,针对国家气候中心NCCo海-气耦合模式原初始化方案动力小协调的问题,从利用模式长期耦合模拟资料中的模式气候吸引子信息的角度出发,发展了一种获取观测资料中与模式相协调分量的信息重构方法,提出了一种模式气候吸引子信息约束下的动力协调初始化方案.对该方案回报检验的结果表明:通过反演NCCo海-气耦合模式模拟资料中的模式气候吸引子信息,有助于获取观测资料中与模式相协调的信息分量特征,实现了初始化过程中动力模式与所同化观测资料间的协调.这种基于信息重构方法的动力协调初始化方案,既可以延续原初始化方案利用观测信息较多的优势,又克服了原方案中观测资料和动力模式不协调的缺陷.这种新的初始化方案,消除了观测资料和模式不协调在初始场中产生的小尺度高频噪声,突出了与NCCo模式动力特征相适应的ENSO尺度信息.进而抑制了初始场中高频噪声所引起的快变预报误差的增长,提高了模式的预测技巧.     

IAP-DecPreS年代际气候预测系统及其预报技巧

针对未来1~10 a气候状态的近期气候预测(年代际预测)是当前国际气候领域的研究热点。本文综述了中国科学院大气物理研究所发展的基于耦合气候系统模式的年代际气候预测系统IAP-DecPreS相关的研究进展。IAP-DecPreS系统的核心部分是耦合模式海洋分量初始化方案,“集合最优插值-分析增量更新”(EnOI-IAU)方案,该方案将集合最优插值(EnOI)和增量分析更新(IAU)结合起来,能够同化原始的海洋次表层温度廓线观测资料,对耦合模式进行初始化。系统的年代际回报试验表明,IAP-DecPreS对太平洋年代际振荡和大西洋多年代际变率的预测技巧与耦合模式比较计划第五阶段(CMIP5)技巧较高的模式相当。IAP-DecPreS系统被广泛应用于气候预测相关研究,包括火山气溶胶对年代际预测技巧的影响,全场同化和异常场同化两种不同的初始化方法对ENSO、印度洋偶极子模态和印度洋洋盆模态等的预测技巧的影响。最后,结合国际发展态势,对未来IAP-DecPreS的发展进行了讨论。     

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.     

Impact of in-consistency between the climate model and its initial conditions on climate prediction

We investigated the influence of dynamical in-consistency of initial conditions on the predictive skill of decadal climate predictions. The investigation builds on the fully coupled global model “Coupled GCM for Earth Simulator” (CFES). In two separate experiments, the ocean component of the coupled model is full-field initialized with two different initial fields from either the same coupled model CFES or the GECCO2 Ocean Synthesis while the atmosphere is initialized from CFES in both cases. Differences between both experiments show that higher SST forecast skill is obtained when initializing with coupled data assimilation initial conditions (CIH) instead of those from GECCO2 (GIH), with the most significant difference in skill obtained over the tropical Pacific at lead year one. High predictive skill of SST over the tropical Pacific seen in CIH reflects the good reproduction of El Niño events at lead year one. In contrast, GIH produces additional erroneous El Niño events. The tropical Pacific skill differences between both runs can be rationalized in terms of the zonal momentum balance between the wind stress and pressure gradient force, which characterizes the upper equatorial Pacific. In GIH, the differences between the oceanic and atmospheric state at initial time leads to imbalance between the zonal wind stress and pressure gradient force over the equatorial Pacific, which leads to the additional pseudo El Niño events and explains reduced predictive skill. The balance can be reestablished if anomaly initialization strategy is applied with GECCO2 initial conditions and improved predictive skill in the tropical Pacific is observed at lead year one. However, initializing the coupled model with self-consistent initial conditions leads to the highest skill of climate prediction in the tropical Pacific by preserving the momentum balance between zonal wind stress and pressure gradient force along the equatorial Pacific.

     

ENSO机理及其预测研究

资料分析研究表明ENSO(El Ni?o和La Ni?a)实际上是热带太平洋次表层海温距平的循环,而次表层海温距平的循环是赤道西太平洋异常纬向风所驱动的,赤道西太平洋的异常纬向风又主要由异常东亚冬季风所激发。因此可以将ENSO的机理视为主要是由东亚季风异常造成的赤道西太平洋异常纬向风所驱动的热带太平洋次表层海温距平的循环。同时分析还表明,热带西太平洋大气季节内振荡(ISO)的明显年际变化,作为一种外部强迫,对ENSO循环起着十分重要的作用;El Ni?o的发生同大气ISO的明显系统性东传有关。资料分析也表明,El Ni?o持续时间的长短与大气环流异常有密切关系。 用非线性最优化方法研究El Ni?o－南方涛动(ENSO)事件的可预报性问题,揭示了最容易发展成ENSO事件的初始距平模态,即条件非线性最优扰动(CNOP)型初始距平;找出能够导致显著春季可预报性障碍(SPB),且对ENSO预报结果有最大影响的一类初始误差——CNOP型初始误差,进而探讨耦合过程的非线性在SPB研究中的重要作用,提出了关于ENSO事件发生SPB的一种可能机制;用CNOP方法揭示了ENSO强度的不对称现象,探讨ENSO不对称性的年代际变化问题,提出ENSO不对称性年代际变化的一种机制;建立了关于ENSO可预报性的最大可预报时间下界、最大预报误差上界和最大允许初始误差下界的三类可预报性问题,分别从三个方面揭示ENSO事件的春季可预报性障碍现象,比较有效地量化了模式ENSO事件的可预报性。 利用中国科学院大气物理研究所地球流体力学数值模拟国家重点实验室的ENSO预测系统,研究了海洋资料同化在ENSO预测中的应用,该系统可以同时对温、盐剖面资料和卫星高度计资料进行同化。并且在模式中采用次表层上卷海温的非局地参数化方法,可有效地改进ENSO模拟水平。采用集合卡曼滤波(Ensemble Kalman Filter,EnKF)同化方法以及在集合资料同化中“平衡的”多变量模式误差扰动方法为集合预报提供更加精确和协调的初始场,ENSO预报技巧得到提高。     

近期气候预测系统IAP-DecPreS对印度洋偶极子的回报技巧:全场同化和异常场同化的比较

印度洋偶极子（IOD）是热带印度洋年际变率主导模态之一,对于区域乃至全球气候有重要影响。准确预报IOD对于短期气候预测具有重要意义。中国科学院大气物理研究所最近建立了近期气候预测系统IAP-DecPreS,其初始化方案采用“集合最优插值—分析增量更新”（EnOI-IAU）方案,能够同化观测的海洋次表层温度廓线资料。本文分析了IAP-DecPreS季节回报试验对IOD的回报技巧,重点比较了全场同化和异常场同化两种初始化策略下预测系统对IOD的回报技巧。分析表明,8月起报秋季IOD,无论从确定性预报还是概率性预报的角度,基于全场同化的回报试验技巧均高于异常场同化的回报试验。对于5月起报的秋季IOD,基于两种初始化策略的回报试验技巧相当。研究发现,全场同化策略相对于异常场的优势主要源于它提高了对伴随ENSO发生的IOD的预报技巧。ENSO遥强迫触发的热带东印度洋“风—蒸发—SST”正反馈过程是IOD发展和维持的关键。采用全场同化策略的回报结果能够更好地模拟出IOD发展过程中ENSO遥强迫产生的异常降水场和异常风场的空间分布特征;而采用异常场同化策略,模拟的异常降水场和风场偏差较大。导致两种初始化策略预测结果技巧差异的主要原因是,全场同化能够减小模式对热带印度洋气候平均态降水固有的模拟偏差,从而提升了热带印度洋对ENSO遥强迫响应的模拟能力。而异常场同化由于在同化过程中保持了模式固有的气候平均态,因此模拟的热带印度洋对ENSO遥强迫的响应存在与模式自由积分类似的模拟偏差。     

The impact of atmospheric nonlinearities on the fastest growth of ENSO prediction error

In this paper we explore the impact of atmospheric nonlinearities on the optimal growth of initial condition error of El Niño and the Southern Oscillation (ENSO) prediction using singular vector (SV) analysis. This is performed by comparing and analyzing SVs of two hybrid coupled models (HCMs), one composed of an intermediate complexity dynamical ocean model coupled with a linear statistical atmospheric model, and the other one with the same ocean model coupled with a nonlinear statistical atmosphere. Tangent linear and adjoint models for both HCMs are developed. SVs are computed under the initial conditions of seasonal background and actual ENSO cycle simulated by the ocean model forced with the real wind data of 1980–1999. The optimization periods of 3, 6 and 9 months are individually considered. The results show that the first SVs in both HCMs are very similar to each other, characterized by a central east-west dipole pattern spanning over the entire tropical Pacific. The spatial patterns of the leading SV in both HCMs are not sensitive to optimization periods and initial time. However, the first singular value, indicating the optimal growth rate of prediction error, displays considerable differences between the two HCMs, indicating a significant impact of atmospheric nonlinearities on the optimal growth of ENSO prediction error. These differences are greater with increasing optimization time, suggesting that the impact of atmospheric nonlinearities on the optimal growth of prediction error becomes larger for a longer period of prediction.     

Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses

We investigate the effects of realistic oceanic initial conditions on a set of decadal climate predictions performed with a state-of-the-art coupled ocean-atmosphere general circulation model. The decadal predictions are performed in both retrospective (hindcast) and forecast modes. Specifically, the full set of prediction experiments consists of 3-member ensembles of 30-year simulations, starting at 5-year intervals from 1960 to 2005, using historical radiative forcing conditions for the 1960–2005 period, followed by RCP4.5 scenario settings for the 2006–2035 period. The ocean initial states are provided by ocean reanalyses differing by assimilation methods and assimilated data, but obtained with the same ocean model. The use of alternative ocean reanalyses yields the required perturbation of the full three-dimensional ocean state aimed at generating the ensemble members spread. A full-value initialization technique is adopted. The predictive skill of the system appears to be driven to large extent by trends in the radiative forcing. However, after detrending, a residual skill over specific regions of the ocean emerges in the near-term. Specifically, natural fluctuations in the North Atlantic sea-surface temperature (SST) associated with large-scale multi-decadal variability modes are predictable in the 2–5 year range. This is consistent with significant predictive skill found in the Atlantic meridional overturning circulation over a similar timescale. The dependency of forecast skill on ocean initialization is analysed, revealing a strong impact of details of ocean data assimilation products on the system predictive skill. This points to the need of reducing the large uncertainties that currently affect global ocean reanalyses, in the perspective of providing reliable near-term climate predictions.     

Uncertainty in the ocean–atmosphere feedbacks associated with ENSO in the reanalysis products

The evolution of El Ni?o-Southern Oscillation (ENSO) variability can be characterized by various ocean–atmosphere feedbacks, for example, the influence of ENSO related sea surface temperature (SST) variability on the low-level wind and surface heat fluxes in the equatorial tropical Pacific, which in turn affects the evolution of the SST. An analysis of these feedbacks requires physically consistent observational data sets. Availability of various reanalysis data sets produced during the last 15?years provides such an opportunity. A consolidated estimate of ocean surface fluxes based on multiple reanalyses also helps understand biases in ENSO predictions and simulations from climate models. In this paper, the intensity and the spatial structure of ocean–atmosphere feedback terms (precipitation, surface wind stress, and ocean surface heat flux) associated with ENSO are evaluated for six different reanalysis products. The analysis provides an estimate for the feedback terms that could be used for model validation studies. The analysis includes the robustness of the estimate across different reanalyses. Results show that one of the “coupled” reanalysis among the six investigated is closer to the ensemble mean of the results, suggesting that the coupled data assimilation may have the potential to better capture the overall atmosphere–ocean feedback processes associated with ENSO than the uncoupled ones.     

The retrospective prediction of ENSO from 1881 to 2000 by a hybrid coupled model: (II) Interdecadal and decadal variations in predictability

In this study, the retrospective predictions of ENSO (El Niño and Southern Oscillation) were performed for the period from 1881 to 2000 using a hybrid coupled model, which is an ocean general circulation model coupled to a linear statistical atmospheric model, and using a newly developed initialization scheme of SST assimilation by Ensemble Kalman Filter. With the retrospective predictions of the past 120 years, some important issues of ENSO predictability (measured by correlation and RMSE skills of NINO3 sea surface temperature anomaly index) were studied including decadal/interdecadal variations in ENSO predictability and the mechanisms responsible for these variations. Emphasis was placed on investigating the relationship between ENSO predictability and various characteristics of ENSO system such as the signal strength, the irregularity of periodicity, the noise and the nonlinearity. It is found that there are significant decadal/interdecadal variations in the prediction skills of ENSO during the past 120 years. The ENSO events were more predictable during the late nineteenth and the late twentieth centuries. The decadal/interdecadal variations of prediction skills are strongly related to the strength of sea-surface temperature anomaly (SSTA) signals, especially to the strength of SSTA signals at the frequencies of 2–4 year periods. The SSTA persistence, dominated by SSTA signals at frequencies over 4-year periods, also has a positive relationship to prediction skills. The high-frequency noise, on the other hand, has a strong inverse relationship to prediction skills, suggesting that it also probably plays an important role in ENSO predictability.     

Incorporating a semi-stochastic model of ocean-modulated westerly wind bursts into an ENSO prediction model

Prediction models of the El Niño-Southern Oscillation (ENSO) phenomenon often represent westerly wind bursts (WWBs), a significant player in ENSO dynamics, as stochastic forcing. A recent paper developed an observationally motivated semi-stochastic statistical model that quantifies the dependence of WWBs on large-scale sea-surface temperature. This WWB model is added here to a hybrid coupled model, thus activating a two-way SST-WWB feedback. The WWB model represents both the deterministic and stochastic elements of WWBs and thus is especially appropriate for ensemble ENSO prediction experiments. An ensemble of retrospective forecasts is performed for the years 1979–2002. Overall statistical measures of predictability are neither degraded nor improved relative to the hybrid, coupled general circulation model, perhaps because of the limitations of the hybrid coupled model and the initialization procedure used. While the present work is meant as a proof-of-concept, it is found that the addition of the WWB model does improve the prediction of the onset and the development of the large 1997 warm event, pointing to the potential for ENSO prediction skill improvement using this approach.     

Atmospheric properties of ENSO: models versus observations

Two important atmospheric features affecting El Niño-Southern Oscillation (ENSO) are atmospheric noise and a nonlinear atmospheric response to SST. In this article, we investigate the roles of these atmospheric features in ENSO in observations and coupled Global Climate Models (GCMs). We first quantify the most important linear couplings between the ocean and atmosphere. We then characterize atmospheric noise by its patterns of standard deviation and skewness and by spatial and temporal correlations. GCMs tend to simulate lower noise amplitudes than observations. Additionally, we investigate the strength of a nonlinear response of wind stress to SST. Some GCMs are able to simulate a nonlinear response of wind stress to SST, although weaker than in observations. These models simulate the most realistic SST skewness. The influence of the couplings and noise terms on ENSO are studied with an Intermediate Climate Model (ICM). With couplings and noise terms fitted to either observations or GCM output, the simulated climates of the ICM versions show differences in ENSO characteristics similar to differences in ENSO characteristics in the original data. In these model versions the skewness of noise is of minor influence on ENSO than the standard deviation of noise. Both the nonlinear response of wind stress to SST anomalies and the relation of noise to the background SST contribute to SST skewness. The ICM is not yet fully evolved, the results rather show that this is a promising route. Overall, atmospheric noise with realistic standard deviation pattern and spatial correlations seems to be important for simulating an irregular ENSO. Both a nonlinear atmospheric response to SST and the dependence of noise on the background SST influence the El Niño/La Niña asymmetry.     

The influence of climate regime shift on ENSO

Observations indicated that for the El Niño/Southern Oscillation (ENSO) there have been eastward displacements of the zonal wind stress (WS) anomalies and surface heat flux (short wave heat flux and latent heat flux) anomalies during El Niño episodes in the 1981–1995 regime relative to the earlier regime of 1961–1975 (without corresponding displacements during La Niña episodes). Our numerical experiments with the Zebiak–Cane coupled model generally reproduced such displacements when the model climatological fields were replaced by the observed climatologies [of sea surface temperature (SST), surface WS and surface wind atmospheric divergence] and simulated climatologies (of oceanic surface layer currents and associated upwelling) for the 1981–1995 regime. Sensitivity tests indicated that the background atmospheric state played a much more important role than the background ocean state in producing the displacements, which enhanced the asymmetry between El Niño and La Niña in the later regime. The later regime climatology state resulted in the eastward shifts in the ENSO system (WS and SST) only during El Niño, through the eastward shift of the atmosphere convergence heating rate in the coupled model. The ENSO period and ENSO predictability were also enhanced in the coupled model under the later regime climatology. That the change in the mean state of the tropical Pacific atmosphere and ocean after the mid 1970s could have produced the observed changes in ENSO properties is consistent with our findings.     

Argo大洋观测资料的同化及其在短期气候预测和海洋分析中的应用

国际Argo(Array for Real-time Geostrophic Oceanography)计划的实施,提供了前所未有的全球深海大洋0～2000 m水深范围内的海水温度和盐度观测资料,在大气和海洋科研业务中应用这一全新的资料,是深入认识大气和海洋变异、提高我国气候预测、海洋监测分析和预报能力的一个关键所在.通过开发非线性温—盐协调同化方案和利用同化高度计资料来调整模式的温度和盐度场,建立了可同化包括Argo等多种海洋观测资料的全球海洋资料变分同化系统,提高了对全球海洋的监测分析能力.实现了海洋资料同化系统与全球海气耦合模式的耦合,显著提高了短期气候预测水平.利用Argo资料改进了海洋动力模式中的物理过程参数化方案,有效提高了海洋模式对真实大洋的模拟能力和对厄尔尼诺/拉尼娜的预测能力.开发了利用Argo浮标漂流轨迹推算全球海洋表层和中层流的方法,提高了推算的全球表层流、中层流资料质量,有效弥补了洋流观测的匮乏.     

Coupled atmosphere–ocean data assimilation experiments with a low-order climate model

A simple idealized atmosphere–ocean climate model and an ensemble Kalman filter are used to explore different coupled ensemble data assimilation strategies. The model is a low-dimensional analogue of the North Atlantic climate system, involving interactions between large-scale atmospheric circulation and ocean states driven by the variability of the Atlantic meridional overturning circulation (MOC). Initialization of the MOC is assessed in a range of experiments, from the simplest configuration consisting of forcing the ocean with a known atmosphere to performing fully coupled ensemble data assimilation. “Daily” assimilation (that is, at the temporal frequency of the atmospheric observations) is contrasted with less frequent assimilation of time-averaged observations. Performance is also evaluated under scenarios in which ocean observations are limited to the upper ocean or are non-existent. Results show that forcing the idealized ocean model with atmospheric analyses is inefficient at recovering the slowly evolving MOC. On the other hand, daily assimilation rapidly leads to accurate MOC analyses, provided a comprehensive set of oceanic observations is available for assimilation. In the absence of sufficient observations in the ocean, the assimilation of time-averaged atmospheric observations proves to be more effective for MOC initialization, including the case where only atmospheric observations are available.     

Initialization and data assimilation experiments with a primitive equation model

The process of combining models of the ocean circulation with large data sets is known in meteorology as model initialization and data assimilation. This process is new to oceanographers, who only now are on the verge of having available world-wide synoptic maps of dynamic variables. In this paper we carry out a series of idealized initialization/assimilation experiments with a primitive equation (PE) model, which constitute a first step in developing a realistic process model and data assimilation techniques for the Gulf Stream system. The PE model is used in a spin-down mode and initialized with an analytic jet profile with geostrophically balanced fields.Two major questions are addressed in the present study. The first concerns the initialization process of a PE model during which internal/inertial gravity wave noise is produced. We ask: are the initialization shocks equally crucial for ocean models as they have been for their atmospheric counterparts? The results of an extensive series of balanced versus unbalanced initializations indicate that, for a PE model with a rigid lid, a brutally unbalanced initialization is required to produce strong internal gravity wave shocks. A geostrophically balanced initialization is sufficient to ensure smooth jet evolutions, with no apparent gravity waves, over long time durations in the spin-down mode. No sophisticated initialization procedures seem, therefore, to be required.The second question addressed is: which component of the flow is the most important in data assimilation to drive the model response towards a baseline reference ocean? We specifically compare the knowledge of the depth-integrated flow only, corresponding to measurements of the total transport, with the knowledge of the density field only, or equivalently the velocity shear. The knowledge of the interior density field is much more effective in decreasing the root-mean-square (r.m.s.) errors relative to the reference ocean. If the baroclinic structure is known, coarse horizontal resolutions of data insertion can be reached before significantly worsening the model estimates. If only the depth-averaged flow is known, a decrease in the horizontal resolution of data assimilation has an immediate effect: the r.m.s. errors sharply increase and the assimilation run diverges from the reference ocean. In the assimilation of the barotropic flow alone, even with dense resolution, the errors in the deep layers always show an increasing trend. The relative effectiveness of baroclinic versus barotropic data insertion can be rationalized in the context of geostrophic adjustment theory.     

A neural network atmospheric model for hybrid coupled modelling

 The possibility of using a nonlinear empirical atmospheric model for hybrid coupled atmosphere-ocean modelling has been examined by using a neural network (NN) model for predicting the contemporaneous wind stress field from the upper ocean state. Upper ocean heat content (HC) from a 6-layer ocean model was a better predictor of the wind stress than the (observed or modelled) sea surface temperature (SST). Our results showed that the NN model generally had slightly better skills in predicting the contemporaneous wind stress than the linear regression (LR) model in the off-equatorial tropical Pacific and in the eastern equatorial Pacific. When the wind stresses from the NN and LR models were used to drive the ocean model, slightly better SST skills were found in the off-equatorial tropical Pacific and in the eastern equatorial Pacific when the NN winds were used instead of the LR winds. Better skills for the model HC were found in the western and central equatorial Pacific when the NN winds were used instead of the LR winds. Why NN failed to show more significant improvement over LR in the equatorial Pacific for the wind stress and SST is probably because the relationship between the surface ocean and the atmosphere in the equatorial Pacific over the seasonal time scale is almost linear. Received: 2 March 1999 / Accepted: 13 July 2000     

An ENSO stability analysis. Part I: results from a hybrid coupled model

In this study, we use the Bjerknes stability (BJ) index as a tool to investigate overall El Niño-Southern Oscillation (ENSO) stability in a hybrid-coupled model (HCM) with various atmosphere and ocean background states. This HCM shows that ENSO growth rates as measured by the BJ index and linear growth rates estimated directly with a time series of the Niño 3.4 indices from the coupled model simulations exhibit similar dependence on background states, coupling strength, and thermodynamic damping intensity. That is, the BJ index and linear growth rates increase with a decrease in the intensity of the background wind, an increase in coupling strength, or a decrease in the intensity of thermodynamic damping, although the BJ index tends to overestimate the growth rate. A detailed analysis of the components of the BJ index formula suggests the importance of model climatological background states and oceanic dynamic parameters in determining ENSO stability. We conclude that the BJ index may serve as a useful tool for qualitatively evaluating the overall ENSO stability in coupled models or in observations without a detailed eigen-analysis that is difficult to perform in models more complex than relatively simple models.