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1.
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
2.
The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model 
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下载免费PDF全文 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
3.
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 Nino events in the ZC model,the predictions tend to yield false alarms due to the uncertainties caused by CNOP.For the relatively strong El Nino events,the ZC model largely underestimates their intensities.Also,our results suggest that the error growth of El 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 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
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 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
5.
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 Nio 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 Nio 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 Nio 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 Nio 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
6.
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
7.
Effect of Stochastic MJO Forcing on ENSO Predictability 总被引：2，自引：0，他引：2
Within the frame of the Zebiak-Cane model,the impact of the uncertainties of the Madden-Julian Oscillation(MJO) on ENSO predictability was studied using a parameterized stochastic representation of intraseasonal forcing.The results show that the uncertainties of MJO have little effect on the maximum prediction error for ENSO events caused by conditional nonlinear optimal perturbation(CNOP);compared to CNOP-type initial error,the model error caused by the uncertainties of MJO led to a smaller prediction uncertainty of ENSO,and its influence over the ENSO predictability was not significant.This result suggests that the initial error might be the main error source that produces uncertainty in ENSO prediction,which could provide a theoretical foundation for the data assimilation of the ENSO forecast. 相似文献
8.
The initial errors constitute one of the main limiting factors in the ability to predict the El Nio–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 Nio 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 Nia 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 Nio, the El Nio 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
9.
Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander 总被引：2，自引：0，他引：2
A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations.The results show that the model was able to capture the essential features of these path variations.We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method.Because of their relatively large uncertainties,three model parameters were considered:the interfacial friction coefficient,the wind-stress amplitude,and the lateral friction coefficient.We determined the CNOP-Ps optimized for each of these three parameters independently,and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm.Similarly,the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method.Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days.But the prediction error caused by CNOP-I is greater than that caused by CNOP-P.The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored.Hence,to enhance the forecast skill of the KLM in this model,the initial conditions should first be improved,the model parameters should use the best possible estimates. 相似文献
10.
The "summer prediction barrier"(SPB) of SST anomalies(SSTA) over the Kuroshio–Oyashio Extension(KOE) refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase,which is usually during the August–September–October(ASO) season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transition rates(Category-1) favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates(Category-2). The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling(downwelling) in the ASO season also causes SSTA cooling(warming), accelerating the transition rates of warm(cold) KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB. 相似文献
11.
Time-Dependent Nonlinear Forcing Singular Vector-Type Tendency Error of the Zebiak-Cane Model 下载免费PDF全文
下载免费PDF全文 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
12.
Optimal Parameter and Uncertainty Estimation of a Land Surface Model： Sensitivity to Parameter Ranges and Model Complexities 总被引：2，自引：0，他引：2
Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI) to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations. 相似文献
13.
Model errors offset by constant and time-variant optimal forcing vector approaches(termed COF and OFV, respectively)are analyzed within the framework of El Nio simulations. Applying the COF and OFV approaches to the well-known Zebiak–Cane model, we re-simulate the 1997 and 2004 El Nio events, both of which were poorly degraded by a certain amount of model error when the initial anomalies were generated by coupling the observed wind forcing to an ocean component. It is found that the Zebiak–Cane model with the COF approach roughly reproduced the 1997 El Nio, but the 2004 El Nio simulated by this approach defied an ENSO classification, i.e., it was hardly distinguishable as CP-El Nio or EP-El Nio. In both El Nio simulations, substituting the COF with the OFV improved the fit between the simulations and observations because the OFV better manages the time-variant errors in the model. Furthermore, the OFV approach effectively corrected the modeled El Nio events even when the observational data(and hence the computational time) were reduced.Such a cost-effective offset of model errors suggests a role for the OFV approach in complicated CGCMs. 相似文献
14.
Due to uncertainties in initial conditions and parameters,the stability and uncertainty of grassland ecosystem simulations using ecosystem models are issues of concern.Our objective is to determine the types and patterns of initial and parameter perturbations that yield the greatest instability and uncertainty in simulated grassland ecosystems using theoretical models.We used a nonlinear optimization approach,i.e.,a conditional nonlinear optimal perturbation related to initial and parameter perturbations (CNOP) approach,in our work.Numerical results indicated that the CNOP showed a special and nonlinear optimal pattern when the initial state variables and multiple parameters were considered simultaneously.A visibly different complex optimal pattern characterizing the CNOPs was obtained by choosing different combinations of initial state variables and multiple parameters in different physical processes.We propose that the grassland modeled ecosystem caused by the CNOP-type perturbation is unstable and exhibits two aspects:abrupt change and the time needed for the abrupt change from a grassland equilibrium state to a desert equilibrium state when the initial state variables and multiple parameters are considered simultaneously.We compared these findings with results affected by the CNOPs obtained by considering only uncertainties in initial state variables and in a single parameter.The numerical results imply that the nonlinear optimal pattern of initial perturbations and parameter perturbations,especially for more parameters or when special parameters are involved,plays a key role in determining stabilities and uncertainties associated with a simulated or predicted grassland ecosystem. 相似文献
15.
In this study, the predictability of the El Nino-South Oscillation(ENSO) in an operational prediction model from the perspective of initial errors is diagnosed using the seasonal hindcasts of the Beijing Climate Center System Model,BCC;SM1.1(m). Forecast skills during the different ENSO phases are analyzed and it is shown that the ENSO forecasts appear to be more challenging during the developing phase, compared to the decay phase. During ENSO development, the SST prediction errors are significantly negative and cover a large area in the central and eastern tropical Pacific, thus limiting the model skill in predicting the intensity of El Nino. The large-scale SST errors, at their early stage, are generated gradually in terms of negative anomalies in the subsurface ocean temperature over the central-western equatorial Pacific,featuring an error evolutionary process similar to that of El Nino decay and the transition to the La Nina growth phase.Meanwhile, for short lead-time ENSO predictions, the initial wind errors begin to play an increasing role, particularly in linking with the subsurface heat content errors in the central-western Pacific. By comparing the multiple samples of initial fields in the model, it is clearly found that poor SST predictions of the Nino-3.4 region are largely due to contributions of the initial errors in certain specific locations in the tropical Pacific. This demonstrates that those sensitive areas for initial fields in ENSO prediction are fairly consistent in both previous ideal experiments and our operational predictions,indicating the need for targeted observations to further improve operational forecasts of ENSO. 相似文献
16.
Optimal Precursor Perturbations of El Nino in the Zebiak-Cane Model for Different Cost Functions 下载免费PDF全文
下载免费PDF全文 XU Hui 《大气和海洋科学快报》2014,(4):297-303
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. 相似文献
17.
A possible mechanism is put forward in this paper for El Nino events from the viewpoint of plate tec-tonics and oceanic geology.A number of the data are cited to illustrate the views that sea-bottom volcanic ac-tivities and hot springs may cause El Nino events. 相似文献
18.
The numerical simulations,hindcasts and verifications of the tropical Pacific sea surfacetemperature anomaly(SSTA)have been conducted by using a dynamical tropical Pacific ocean-atmosphere coupled model named NCCo.The results showed that the model had performedreasonable simulations of the major El Nino episodes in the history,and the model forecast skill in1990s had been significantly improved.NCCo model has been used to predict the tropical PacificSSTA since January 1997.The comparisons between predictions and observations indicated thatthe occurrence,evolution and ending of the 1997/1998 El Nino episode have been predicted fairlywell by using this model.Also,the La Nina episode that began in the autumn of 1998 and thedeveloping tendency of the tropical Pacific SSTA during the year 1999 have been predictedsuccessfully.The forecast skills of NCCo model during the 1997-1999 El Nino and La Ninaevents are above 0.5 at 0—14 lead months. 相似文献
19.
Simulations and predictions using numerical models show considerable uncertainties, and parameter uncertainty is one of the most important sources. It is impractical to improve the simulation and prediction abilities by reducing the uncertainties of all parameters. Therefore, identifying the sensitive parameters or parameter combinations is crucial. This study proposes a novel approach: conditional nonlinear optimal perturbations sensitivity analysis(CNOPSA) method. The CNOPSA method fully consi... 相似文献
20.
Methods and approaches are discussed that identify and filter off affecting factors (noise) above
primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in
equatorial eastern and middle/western Pacific on the SSTA in the equatorial region and their contribution to the
latter are diagnosed and verified with observations of a number of significant El Nino and La Nina episodes. New
viewpoints are propsed. The methods of wavelet decomposition and reconstruction are used to build a predictive
model based on independent domains of frequency,which shows some advantages in composite prediction and
prediction validity.The methods presented above are of non-linearity, error-allowing and auto-adaptive/learning,
in addition to rapid and easy access,illustrative and quantitative presentation,and analyzed results that agree
generally with facts. They are useful in diagnosing and predicting the El Nino and La Nina problems that are just
roughly described in dynamics. 相似文献