What Kind of Initial Errors Cause the Severest Prediction Uncertainty of EI Nino in Zebiak-Cane Model

With the Zebiak-Cane (ZC) model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation (CNOP). The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOP- type errors, we find that for the normal states and the relatively weak EI Nino events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong EI Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of EI Nino in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most difficult. A linear singular vector (LSV) approach is also used to estimate the error growth of ENSO, but it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.     

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model

Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation （CNOP）-type initial errors and nonlinear forcing singular vector （NFSV）- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error （NFSV-type tendency error） represents the initial errors （model errors） that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model （perfect initial conditions） scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events.     

Time-Dependent Nonlinear Forcing Singular Vector-Type Tendency Error of the Zebiak-Cane Model

Based on the Zebiak-Cane model, the timedependent nonlinear forcing singular vector （NFSV）-type tendency errors with components of 4 and 12 （denoted by NFSV-4 and NFSV-12） are calculated for predetermined El Nifio events and compared with the constant NFSV （denoted by NFSV-1） from their patterns and resultant prediction errors. Specifically, NFSV-1 has a zonal dipolar sea surface temperature anomaly （SSTA） pattern with negative anomalies in the equatorial eastern Pacific and positive anomalies in the equatorial central-western Pa- cific. Although the first few components in NFSV-4 and NFSV-12 present patterns similar to NFSV-1, they tend to extend their dipoles farther westward; meanwhile, the positive anomalies gradually cover much smaller regions with the lag times. In addition, the authors calculate the predic- tion errors caused by the three kinds of NFSVs, and the results indicate that the prediction error induced by NFSV-12 is the largest, followed by the NFSV-4. However, when compared with the prediction errors caused by random tendency errors, the NFSVs generate significantly larger prediction errors. It is therefore shown that the spatial structure of tendency errors is important for producing large prediction errors. Furthermore, in exploring the tendency errors that cause the largest prediction error for E1 Nifio events, the timedependent NFSV should be evaluated.     

What Kind of Initial Errors Cause the Severest Prediction Uncertainty of El Nino in Zebiak-Cane Model

With the Zebiak-Cane(ZC)model,the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation(CNOP).The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model.By analyzing the behavior of CNOP- type errors,we find that for the normal states and the relatively weak El Nino events in the ZC model,the predictions tend to yield false alarms due to the uncertainties caused by CNOP.For the relatively strong El Nino events,the ZC model largely underestimates their intensities.Also,our results suggest that the error growth of El Nino in the ZC model depends on the phases of both the annual cycle and ENSO.The condition during northern spring and summer is most favorable for the error growth.The ENSO prediction bestriding these two seasons may be the most diffcult.A linear singular vector(LSV)approach is also used to estimate the error growth of ENSO,but it underestimates the prediction uncertainties of ENSO in the ZC model.This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes.CNOP yields the severest prediction uncertainty.That is to say,the prediction skill of ENSO is closely related to the types of initial error.This finding illustrates a theoretical basis of data assimilation.It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.     

Optimal Precursor Perturbations of El Nino in the Zebiak-Cane Model for Different Cost Functions

Optimal precursor perturbations of El Nino in the Zebiak-Cane model were explored for three different cost functions. For the different characteristics of the eastern-Pacific （EP） El Nino and the central-Pacific （CP） El Nino, three cost functions were defined as the sea surface temperature anomaly （SSTA） evolutions at prediction time in the whole tropical Pacific, the Nino3 area, and the Nino4 area. For all three cost functions, there were two optimal precursors that developed into El Nino events, called Precursor Ⅰ and Precursor Ⅱ. For Precursor Ⅰ, the SSTA component consisted of an east-west （positive-negative） dipole spanning the entire tropical Pacific basin and the thermocline depth anomaly pattern exhibited a tendency of deepening for the whole of the equatorial Pacific. Precursor Ⅰ can develop into an EP-El Nino event, with the warmest SSTA occurring in the eastern tropical Pacific or into a mixed El Nino event that has features between EP-El Nino and CP-El Nino events. For Precursor Ⅱ, the thermocline deepened anomalously in the eastern equatorial Pacific and the amplitude of deepening was obviously larger than that of shoaling in the central and western equatorial Pacific. Precursor Ⅱ developed into a mixed El Nino event. Both the thermocline depth and wind anomaly played important roles in the development of Precursor Ⅰ and Precursor Ⅱ.     

Application of the Analogue-Based Correction of Errors Method in ENSO Prediction

In this study, a method of analogue-based correction of errors（ACE） was introduced to improve El Ni？o-Southern Oscillation（ENSO） prediction produced by climate models. The ACE method is based on the hypothesis that the flow-dependent model prediction errors are to some degree similar under analogous historical climate states, and so the historical errors can be used to effectively reduce such flow-dependent errors. With this method, the unknown errors in current ENSO predictions can be empirically estimated by using the known prediction errors which are diagnosed by the same model based on historical analogue states. The authors first propose the basic idea for applying the ACE method to ENSO prediction and then establish an analogue-dynamical ENSO prediction system based on an operational climate prediction model. The authors present some experimental results which clearly show the possibility of correcting the flow-dependent errors in ENSO prediction, and thus the potential of applying the ACE method to operational ENSO prediction based on climate models.     

Role of Parameter Errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model

ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model.     

初始扰动对一次华南暴雨预报的影响的研究

本文选取了2006年华南前汛期的一次暴雨过程, 采用AREMv2.3中尺度数值模式进行数值模拟, 分别在模式初始场的物理量场 (温度场、 风场、 湿度场) 上加扰动, 分析不同物理量场上的扰动对降水预报的影响, 以及物理量预报误差和扰动能量的增长情况。同时, 通过本个例讨论误差增长与湿对流的关系, 扰动振幅对误差增长的影响和华南区域的中尺度降水的可预报性问题。数值试验结果表明: 初始时刻不同物理量场加实际振幅的正态分布的随机扰动时, 对降水的影响是不同的。对于24小时降水预报, 温度场对降水的影响最大。误差的增长与湿对流不稳定有着密切的关系。小尺度小振幅误差增长很快, 而且是非线性增长。这意味着短期的较小尺度降水的可预报性很小。与大振幅扰动相比, 小振幅扰动造成的误差较小。但是小振幅扰动的迅速发展, 很快就会对降水预报造成较大的影响。因此, 只能有限地提高预报质量, 而且由于扰动非线性增长很快, 在预报时间的提前上, 不会有太大的改善。     

Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nio events？ Results from a Theoretical Model

Within a theoretical ENSO model, the authors investigated whether or not theerrors superimposed on model parameters could cause a significant ``springpredictability barrier' (SPB) for El Nino events. First, sensitivityexperiments were respectively performed to the air--sea coupling parameter,α and the thermocline effect coefficient μ. The results showed that theuncertainties superimposed on each of the two parameters did not exhibit anobvious season-dependent evolution; furthermore, the uncertainties caused avery small prediction error and consequently failed to yield a significantSPB. Subsequently, the conditional nonlinear optimal perturbation (CNOP)approach was used to study the effect of the optimal mode (CNOP-P) of theuncertainties of the two parameters on the SPB and to demonstrate that theCNOP-P errors neither presented a unified season-dependent evolution fordifferent El Nino events nor caused a large prediction error, andtherefore did not cause a significant SPB. The parameter errors played onlya trivial role in yielding a significant SPB. To further validate thisconclusion, the authors investigated the effect of the optimal combined mode(i.e. CNOP error) of initial and model errors on SPB. The resultsillustrated that the CNOP errors tended to have a significantseason-dependent evolution, with the largest error growth rate in thespring, and yielded a large prediction error, inducing a significant SPB.The inference, therefore, is that initial errors, rather than modelparameter errors, may be the dominant source of uncertainties that cause asignificant SPB for El Nino events. These results indicate that theability to forecast ENSO could be greatly increased by improving theinitialization of the forecast model.     

Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model

Based on the high-resolution Regional Ocean Modeling System(ROMS) and the conditional nonlinear optimal perturbation(CNOP) method, this study explored the effects of optimal initial errors on the prediction of the Kuroshio large meander(LM) path, and the growth mechanism of optimal initial errors was revealed. For each LM event, two types of initial error(denoted as CNOP1 and CNOP2) were obtained. Their large amplitudes were found located mainly in the upper 2500 m in the upstream region of the LM, i.e., southeast of Kyushu. Furthermore, we analyzed the patterns and nonlinear evolution of the two types of CNOP. We found CNOP1 tends to strengthen the LM path through southwestward extension. Conversely,CNOP2 has almost the opposite pattern to CNOP1, and it tends to weaken the LM path through northeastward contraction.The growth mechanism of optimal initial errors was clarified through eddy-energetics analysis. The results indicated that energy from the background field is transferred to the error field because of barotropic and baroclinic instabilities. Thus, it is inferred that both barotropic and baroclinic processes play important roles in the growth of CNOP-type optimal initial errors.     

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。     

Impact of Subsurface Entrainment on ENSO Prediction: 1997-98 El Nino

Twenty-one-year hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific were performed to validate the influence of ocean subsurface entrainment on SST prediction.A new hybrid coupled model was used that considered the entrainment of subsurface temperature anomalies into the sea surface.The results showed that predictions were improved significantly in the new coupled model.The predictive correlation skill increased by about 0.2 at a lead time of 9 months,and the root-mean-square (RMS) errors were decreased by nearly 0.2°C in general.A detailed analysis of the 1997-98 El Nio hindcast showed that the new model was able to predict the onset,peak (both time and amplitude),and decay of the 1997-98 strong El Nio event up to a lead time of one year,factors that are not represented well by many other forecast systems.This implies,in terms of prediction,that subsurface anomalies and their impact on the SST are one of the controlling factors in ENSO cycles.Improving the presentation of such effects in models would increase the forecast skill.     

一个ENSO动力-相似误差订正模式及其后报初检验

为有效利用历史资料中的相似信息,减小模式误差对ENSO这类跨季节-年际尺度预测问题的影响提高动力模式的预测水平.作者利用一种基于统计相似的模式误差订正方法,以国家气候中心简化海气耦合模式为平台建立了相应的动力-相似误差订正(DAEC)模式,并着重探讨了系统相似程度(全相似或部分相似)、误差重估周期以及相似样本个数等因素对预报效果的影响.结果表明,利用该方法可以有效地改善原有模式的预报性能,其中 "全相似" 比 "部分相似" 更能反映海气耦合系统的相似程度,从而对模式误差做出更为准确的估计,使预报误差明显减小.海洋和大气的误差重估周期对结果也有较大影响,在不同相似程度下分别存在着某种最优配置使得预报效果达到最佳.另外,在对相似样本存在状况及影响的研究中则发现在当前资料长度内整体上只存在着有限个相似样本,在此范围内随着样本取样数目的增加DAEC模式的预报性能逐渐提高.     

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part Ⅱ: Model Uncertainty

This paper investigates the possible sources of errors associated with tropical cyclone (TC) tracks forecasted using the Global/Regional Assimilation and Prediction System (GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II. Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60°S and 60°N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.     

对流尺度集合预报与模式不确定性研究进展

本文回顾了国内外近10年来对流尺度集合预报系统以及有关模式不确定性研究的成果。对流尺度集合预报在提高局地强天气预报预警能力方面,因其可以提供丰富的概率预报信息而具有显著优势,相关研究和应用受到国内外学者和数值预报业务机构的重视。相对于全球集合预报,对流尺度集合预报中有关模式不确定性的研究缺乏系统性和理论基础,成为目前研究的热点和难点。目前常用的模式扰动方法有多模式、多物理过程、多物理参数、随机物理等。这些方法在强对流事件、热带气旋强度路径等预报中得到了广泛应用,但在提高对流尺度集合离散度方面作用仍有限,主要原因在于其并没有针对性描述影响对流系统发生发展的关键物理过程的不确定性,仍然属于全球集合预报中天气尺度范畴。在回顾相关研究的同时,也提出了值得探索和研究的方向。     

Investigating the Initial Errors that Cause Predictability Barriers for Indian Ocean Dipole Events Using CMIP5 Model Outputs

By analyzing the outputs of the pre-industrial control runs of four models within phase 5 of the Coupled Model Intercomparison Project, the effects of initial sea temperature errors on the predictability of Indian Ocean Dipole events were identified. The initial errors cause a significant winter predictability barrier(WPB) or summer predictability barrier(SPB).The WPB is closely related with the initial errors in the tropical Indian Ocean, where two types of WPB-related initial errors display opposite patterns and a west–east dipole. In contrast, the occurrence of the SPB is mainly caused by initial errors in the tropical Pacific Ocean, where two types of SPB-related initial errors exhibit opposite patterns, with one pole in the subsurface western Pacific Ocean and the other in the upper eastern Pacific Ocean. Both of the WPB-related initial errors grow the fastest in winter, because the coupled system is at its weakest, and finally cause a significant WPB. The SPB-related initial errors develop into a La Ni ?na–like mode in the Pacific Ocean. The negative SST errors in the Pacific Ocean induce westerly wind anomalies in the Indian Ocean by modulating the Walker circulation in the tropical oceans. The westerly wind anomalies first cool the sea surface water in the eastern Indian Ocean. When the climatological wind direction reverses in summer, the wind anomalies in turn warm the sea surface water, finally causing a significant SPB. Therefore, in addition to the spatial patterns of the initial errors, the climatological conditions also play an important role in causing a significant predictability barrier.     

动力季节预测中预报误差与物理因子的关系

为了在动力季节预测中更好地运用统计经验来改进预报, 从研究气候系统物理因子影响模式预报误差的角度入手, 探讨了与气候模式有关的统计经验获取问题, 并利用国家气候中心海-气耦合模式的历史回报数据, 考察了动力季节预测中夏季环流和降水的预报误差与主要物理因子, 包括Niño3区海温指数、太平洋年代际振荡指数、南北半球环状模指数以及北大西洋涛动指数相关关系。分析结果显示:上述物理因子与模式预报的夏季环流和降水误差之间存在前期或同期的某种显著相关关系, 并且显著关系分布随因子的不同而表现出明显不同的区域性特征, 这为发展一种基于预报因子的误差订正新方法提供了新思路。     

Revisiting Asymmetry for the Decaying Phases of El Nino and La Nina

This study investigated the relationship be- tween the asymmetry in the duration of El Nifio and La Nina and the length of their decaying phases. The results suggested that the duration asymmetry comes from the long decaying ENSO cases rather than the short decaying ones. The evolutions of short decaying El Nino and La Nina are approximately a mirror image with a rapid decline in the following summer for the warm and cold events. However, a robust asymmetry was found in long decaying cases, with a prolonged and re-intensified La Nina in the following winter. The asymmetry for long decaying cases starts from the westward extension of the zonal wind anomalies in a mature winter, and is further contributed to by the air-sea interaction over the tropical Pacific in the following seasons.     

厄尔尼诺事件对黑龙江省低温洪涝灾害的影响及其预报

在研究厄尔尼诺（拉尼娜）事件对黑龙江省低温洪涝灾害影响的基础上，提出一种表征厄尔尼诺的指数（ENI）。根据ENI谱分析结果，提出了一种厄尔尼诺事件和拉尼娜事件的统计预测方法。经1997年对强厄尔尼诺和1998年对拉尼娜事件预报检验证明，该方法可行，可在区域和省级短期气候预测中应用。