The Role of Nonlinear Forcing Singular Vector Tendency Error in Causing the “Spring Predictability Barrier” for ENSO     

《Journal of Meteorological Research》2016,(6)

With the Zebiak–Cane model, the present study investigates the role of model errors represented by the nonlinear forcing singular vector(NFSV) in the "spring predictability barrier"(SPB) phenomenon in ENSO prediction. The NFSV-related model errors are found to have the largest negative effect on the uncertainties of El Nio prediction and they can be classified into two types: the first is featured with a zonal dipolar pattern of SST anomalies(SSTA), with the western poles centered in the equatorial central–western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite to the first type. The first type of error tends to have the worst effects on El Nin?o growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSVrelated errors exhibits prominent seasonality, with the fastest error growth in spring and/or summer; hence,these errors result in a significant SPB related to El Nin?o events. The linear counterpart of NFSVs, the(linear) forcing singular vector(FSV), induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate an SPB for El Nio events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Nio events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central–western Pacific, which likely represent those areas sensitive to El Nio predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.  相似文献   

Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nio events？ Results from a Theoretical Model   总被引：3，自引：1，他引：2  

DUAN Wansuo  ZHANG Rui 《大气科学进展》2010,27(5):1003-1013

Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ``spring predictability barrier' (SPB) for El Nino events. First, sensitivity experiments were respectively performed to the air--sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation (CNOP) approach was used to study the effect of the optimal mode (CNOP-P) of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nino events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode (i.e. CNOP error) of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nino events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.  相似文献   

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

YU Liang  MU Mu  Yanshan  YU 《大气科学进展》2014,31(3):647-656

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.  相似文献   

Can the Uncertainties of Madden–Jullian Oscillation Cause a Significant “Spring Predictability Barrier” for ENSO Events?     

PENG Yuehu  DUAN Wansuo  XIANG Jie 《Acta Meteorologica Sinica》2012,26(5):566-578

With the Zebiak-Cane model and a parameterized stochastic representation of intraseasonal forcing, the impact of the uncertainties of Madden–Jullian Oscillation (MJO) on the “Spring Predictability Barrier (SPB)” for El Ni?no–Southern Oscillation (ENSO) prediction is studied. The parameterized form of MJO forcing is added physically to the Zebiak-Cane model to obtain the so-called Zebiak-Cane-MJO model and then the effects of initial error, stochastic model error, and their joint error mode on the SPB associated with El Ni?no prediction are estimated. The results show that the model errors caused by stochastic MJO forcing could hardly lead to a significant SPB while initial errors can do; furthermore, the joint error mode of initial error and model error associated with the stochastic MJO forcing can also lead to a significant SPB. These demonstrate that the initial error is probably the main error source of the SPB, which may provide a theoretical foundation of data assimilation for ENSO forecasts.  相似文献   

Initial Error-induced Optimal Perturbations in ENSO Predictions,as Derived from an Intermediate Coupled Model     

Ling-Jiang TAO  Rong-Hua ZHANG  Chuan GAO 《大气科学进展》2017,34(6):791-803

The initial errors constitute one of the main limiting factors in the ability to predict the El Nio–Southern Oscillation(ENSO) in ocean–atmosphere coupled models. The conditional nonlinear optimal perturbation(CNOP) approach was employed to study the largest initial error growth in the El Nio predictions of an intermediate coupled model(ICM). The optimal initial errors(as represented by CNOPs) in sea surface temperature anomalies(SSTAs) and sea level anomalies(SLAs) were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of El Nio, the El Nio event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier(SPB) phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly,weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.  相似文献   

Time-Dependent Nonlinear Forcing Singular Vector-Type Tendency Error of the Zebiak-Cane Model          下载免费PDF全文

ZHAO Peng  DUAN Wan-Suo 《大气和海洋科学快报》2014,(5):395-399

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.  相似文献   

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

XU Huiand DUAN Wansuo State Laboratory of Numerical Modeling for Atmospheric Sciences  Geophysical Fluid Dynamics 《大气科学进展》2008,(4)

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.  相似文献   

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

XU Hui  DUAN Wansuo 《大气科学进展》2008,25(4):577-584

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 CNOPtype errors, we find that for the normal states and the relatively weak E1 Nifio events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong E1 Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of E1 Nifio 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.  相似文献   

Insights into Convective-scale Predictability in East China: Error Growth Dynamics and Associated Impact on Precipitation of Warm-Season Convective Events     

Xiaoran ZHUANG  Jinzhong MIN  Liu ZHANG  Shizhang WANG  Naigeng WU  Haonan ZHU 《大气科学进展》2020,37(8):893-911

This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB) of East China. The scale-dependent error growth(ensemble variability) and associated impact on precipitation forecasts(precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale,while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing. Meanwhile, larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale. Consequently, these results can be used to explain underdispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.  相似文献   

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model          下载免费PDF全文

ZHAO Peng  DUAN Wan-Suo 《大气和海洋科学快报》2014,(5):447-452

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.  相似文献   

Revealing the most disturbing tendency error of Zebiak–Cane model associated with El Niño predictions by nonlinear forcing singular vector approach     

Wansuo?DuanEmail author  Peng?Zhao 《Climate Dynamics》2015,45(9-10):2351-2366

The nonlinear forcing singular vector (NFSV) approach is used to identify the most disturbing tendency error of the Zebiak–Cane model associated with El Niño predictions, which is most potential for yielding aggressively large prediction errors of El Niño events. The results show that only one NFSV exists for each of the predictions for the predetermined model El Niño events. These NFSVs cause the largest prediction error for the corresponding El Niño event in perfect initial condition scenario. It is found that the NFSVs often present large-scale zonal dipolar structures and are insensitive to the intensities of El Niño events, but are dependent on the prediction periods. In particular, the NFSVs associated with the predictions crossing through the growth phase of El Niño tend to exhibit a zonal dipolar pattern with positive anomalies in the equatorial central-western Pacific and negative anomalies in the equatorial eastern Pacific (denoted as “NFSV1”). Meanwhile, those associated with the predictions through the decaying phase of El Niño are inclined to present another zonal dipolar pattern (denoted as “NFSV2”), which is almost opposite to the NFSV1. Similarly, the linear forcing singular vectors (FSVs), which are computed based on the tangent linear model, can also be classified into two types “FSV1” and “FSV2”. We find that both FSV1 and NFSV1 often cause negative prediction errors for Niño-3 SSTA of the El Niño events, while the FSV2 and NFSV2 usually yield positive prediction errors. However, due to the effect of nonlinearities, the NFSVs usually have the western pole of the zonal dipolar pattern much farther west, and covering much broader region. The nonlinearities have a suppression effect on the growth of the prediction errors caused by the FSVs and the particular structure of the NFSVs tends to reduce such suppression effect of nonlinearities, finally making the NFSV-type tendency error yield much large prediction error for Niño-3 SSTA of El Niño events. The NFSVs, compared to the FSVs, are more applicable in describing the most disturbing tendency error of the Zebiak–Cane model since they consider the effect of nonlinearities. The NFSV-type tendency errors may provide information concerning the sensitive areas where the model errors are much more likely to yield large prediction errors for El Niño events. If the simulation skills of the states in the sensitive areas can be improved, the ENSO forecast skill may in turn be greatly increased.  相似文献   

The Initial Errors in the Tropical Indian Ocean that Can Induce a Significant “Spring Predictability Barrier” for La Ni?a Events and Their Implication for Targeted Observations     

Qian ZHOU  Wansuo DUAN  Xu WANG  Xiang LI  Ziqing ZU 《大气科学进展》2021,38(9):1566-1579

Initial errors in the tropical Indian Ocean (IO-related initial errors) that are most likely to yield the Spring Prediction Barrier (SPB) for La Ni?a forecasts are explored by using the CESM model. These initial errors can be classified into two types. Type-1 initial error consists of positive sea temperature errors in the western Indian Ocean and negative sea temperature errors in the eastern Indian Ocean, while the spatial structure of Type-2 initial error is nearly opposite. Both kinds of IO-related initial errors induce positive prediction errors of sea temperature in the Pacific Ocean, leading to under-prediction of La Ni?a events. Type-1 initial error in the tropical Indian Ocean mainly influences the SSTA in the tropical Pacific Ocean via atmospheric bridge, leading to the development of localized sea temperature errors in the eastern Pacific Ocean. However, for Type-2 initial error, its positive sea temperature errors in the eastern Indian Ocean can induce downwelling error and influence La Ni?a predictions through an oceanic channel called Indonesian Throughflow. Based on the location of largest SPB-related initial errors, the sensitive area in the tropical Indian Ocean for La Ni?a predictions is identified. Furthermore, sensitivity experiments show that applying targeted observations in this sensitive area is very useful in decreasing prediction errors of La Ni?a. Therefore, adopting a targeted observation strategy in the tropical Indian Ocean is a promising approach toward increasing ENSO prediction skill.  相似文献   

FGOALS-g模式关于ENSO事件的春季预报障碍现象          下载免费PDF全文

WEI Chao  DUAN Wan-Suo 《大气和海洋科学快报》2010,3(2):87-92

Using the sea surface temperature (SST) predicted for the equatorial Pacific Ocean by the Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g), an analysis of the prediction errors was performed for the seasonally dependent predictability of SST anomalies both for neutral years and for the growth/decay phase of El Niño/La Niña events. The study results indicated that for the SST predictions relating to the growth phase and the decay phase of El Niño events, the prediction errors have a seasonally dependent evolution. The largest increase in errors occurred in the spring season, which indicates that a prominent spring predictability barrier (SPB) occurs during an El Niño-Southern Oscillation (ENSO) warming episode. Furthermore, the SPB associated with the growth-phase prediction is higher than that associated with the decay-phase prediction. However, for the neutral years and for the growth and decay phases of La Niña events, the SPB phenomenon was less prominent. These results indicate that the SPB phenomenon depends extensively on the ENSO events themselves. In particular, the SPB depends on the phases of the ENSO events. These results may provide useful knowledge for improving ENSO forecasting.  相似文献   

四个耦合模式ENSO后报试验的“春季预报障碍”   总被引：2，自引：0，他引：2  

张雅乐  俞永强  段晚锁 《气象学报》2012,70(3):506-519

用CliPAS计划中3个气候模式和中国科学院大气物理研究所耦合模式FGOALS-g短期气候异常回报试验结果,将动力和统计方法相结合,考察了1982—2003年厄尔尼诺/拉尼娜事件发展期和衰减期海表温度春季可预报性障碍现象。结果表明,所考察的耦合模式对ENSO事件预报的误差发展存在明显的季节依赖性,最大误差增长通常发生在春季,发生显著的可预报性障碍现象。进一步分析发现厄尔尼诺事件和拉尼娜事件在发展期的季节预报障碍现象比衰减期明显,以厄尔尼诺事件发展期春季可预报性障碍现象最为显著,拉尼娜事件衰减期季节预报障碍现象不显著。研究还发现,预报误差的增长在ENSO事件冷暖位相具有显著的非对称性,发展期暖位相预报误差强于冷位相,而衰减期冷位相的预报误差比暖位相大。通过回归分析,诊断了海-气相互作用的强度,发现耦合系统在春季最不稳定,使预报误差最易在春季发展,从而导致可预报性障碍。  相似文献   

ENSO机理及其预测研究   总被引：19，自引：0，他引：19  

李崇银  穆穆  周广庆 《大气科学》2008,32(4):761-781

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。  相似文献   

AIR-SEA COUPLING FEATURES FROM THE WARM PHASE TO THE COLD PHASE OF ENSO CYCLES IN THE SEVENTIES TO THE EIGHTIES^*          下载免费PDF全文

Zhang Fengqi  He dinhai  Zhang Aihu  Cheng Yanjie 《Acta Meteorologica Sinica》1999,13(2):164-174

Using the sea surface temperature and wind anomalies(SSTA and SSWA for short) of the tropical Pacific from January 1970 to December 1989,main spatial patterns of tropical Pacific SSTA and SSWA coupling features in the transform course from the warm phase to the cold phase of El Nino-southern Oscillation(ENSO) cycles are discussed.The main conclusions are as follows:(1)air-sea coupling patterns at the mature stage of El Nino(La Nina) are main spatial ones of tropical Pacific SSWA and SSTA coupling:(2)at the mature stage of El Nino,the interaction of the anticyclonic anomaly wind,generated by the forcing of distinct meridional SSTA gradient in the Northern Hemisphere tropical central Pacific.with the California cold current and SSTA is mainly responsible for weakening of El Nino;(3)the second sea temperature increase along the South American coast in the decaying course of El Nino results from the eastward movement of the weakened positive SSTA in the tropical central-eastern Pacific forced by anomalous west wind stress:(4)La Nina results from the joint effect of Walker circulation,Ekman drift and negative SSTA in the tropical central-eastern Pacific.  相似文献   

    

Wu Aiming  Ni Yunqi 《大气科学进展》1999,16(3):405-418

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‡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. Supported by the National Natural Science Foundation of China under the project No. 49475256.  相似文献