Initial Error Growth Research Articles

The Potential Role of Seasonal Surface Heating on the Chaotic Origins of the El Niño/Southern Oscillation Spring Predictability Barrier

AbstractThe Spring Predictability Barrier (SPB) phenomenon is characterized by the reduced accuracy of El Niño/Southern Oscillation (ENSO) forecasts during the spring, which substantially limits our ability to predict ENSO events. By investigating the nonlinear dynamic characteristics of ENSO systems simulated by a box model, we found that the strong surface heating process in spring may contribute to the SPB by regulating the different coupling processes between the ocean and atmosphere. Specifically, the intensified springtime surface heating increases the Sea Surface Temperature (SST), further amplifying the thermal damping effect of SST anomalies and reducing the dynamic connection between zonal SST gradient and upwelling process, and finally increasing the chaotic degree of ENSO systems simulated by the box model. The enhanced chaotic degree of ENSO systems leads to a more rapid growth of initial errors in the forecast model in spring, potentially leading to the SPB phenomenon.

Predictability and forecast skill of solar irradiance over the contiguous United States

Current solar forecast verification processes place much attention on performance comparison of a group of competing methods. However, forecast verification ought to further answer how the best method within the group performs relative to the best-possible performance which one can attain under that forecasting situation, which makes the quantification of predictability and forecast skill immediately relevant. Unfortunately, the literature on the quantification of relative performance of solar irradiance has hitherto been lacking, and very few studies have focused on the spatial distributions of predictability and forecast skill of solar irradiance. The predictability and forecast skill of an atmospheric process depend on two concepts: (1) the growth of initial error in unresolved scale of motion, and (2) the forecast performance of the standard of reference. Based upon this formalism, predictability and forecast skill of solar irradiance in the United States are quantified and mapped. Through this study, a couple of common misconceptions in regard to irradiance predictability are refuted, and the original formulation of skill score revived.

Impact Analysis of Variable Resolution of MPAS on Intrinsic Predictability Using Bred Vectors

Variable resolution configuration is a defining feature of the NCAR MPAS (Model for Prediction Across Scales) model, which allows us to smoothly vary the horizontal resolution for taking a closer look at an area of interest. In this study, we aimed to analyze the impact of variable resolution on intrinsic predictability using bred vectors. Thus, the breeding cycles of the MPAS model with and without variable resolution configuration were implemented and tested with two different rescaling intervals of 6 h and 1 day. Rescaling within our breeding cycles were centered by the nature run, thus we could deal with the intrinsic predictability limited only by the initial error growth. We confirmed reasonable estimates of fast-growing errors by bred vectors at two different scales of convective and synoptic systems. We then found that the variable resolution configuration gave consistent improvement of intrinsic predictability not only over the high-resolution area but also outside. A quantitative analysis showed that an improvement with the variable resolution could be found in general for most vertical levels for both rescaling interval experiments. Additionally, we present the computational cost and experience of performing the variable resolution model which would help users in their decisions on this setting.

The Transition from Practical to Intrinsic Predictability of Midlatitude Weather

Abstract This study investigates the transition from current practical predictability of midlatitude weather to its intrinsic limit. For this purpose, estimates of the current initial condition uncertainty of 12 real cases are reduced in several steps from 100% to 0.1% and propagated in time with a global numerical weather prediction model (ICON at 40 km resolution) that is extended by a stochastic convection scheme to better represent error growth from unresolved motions. With the provision that the perfect model assumption is sufficiently valid, it is found that the potential forecast improvement that could be obtained by perfecting the initial conditions is 4–5 days. This improvement is essentially achieved with an initial condition uncertainty reduction by 90% relative to current conditions, at which point the dominant error growth mechanism changes: With respect to physical processes, a transition occurs from rotationally driven initial error growth to error growth dominated by latent heat release in convection and due to the divergent component of the flow. With respect to spatial scales, a transition from large-scale up-amplitude error growth to a very rapid initial error growth on small scales is found. Reference experiments with a deterministic convection scheme show a 5%–10% longer predictability, but only if the initial condition uncertainty is small. These results confirm that planetary-scale predictability is intrinsically limited by rapid error growth due to latent heat release in clouds through an upscale-interaction process, while this interaction process is unimportant on average for current levels of initial condition uncertainty. Significance Statement Weather predictions provide high socioeconomic value and have been greatly improved over the last decades. However, it is widely believed that there is an intrinsic limit to how far into the future the weather can be predicted. Using numerical simulations with an innovative representation of convection, we are able to confirm the existence of this limit and to demonstrate which physical processes are responsible. We further provide quantitative estimates for the limit and the remaining improvement potential. These results make clear that our current weather prediction capabilities are not yet maxed out and could still be significantly improved with advancements in atmospheric observation and simulation technology in the upcoming decades.

Prediction error growth in a more realistic atmospheric toy model with three spatiotemporal scales

Abstract. This article studies the growth of the prediction error over lead time in a schematic model of atmospheric transport. Inspired by the Lorenz (2005) system, we mimic an atmospheric variable in one dimension, which can be decomposed into three spatiotemporal scales. We identify parameter values that provide spatiotemporal scaling and chaotic behavior. Instead of exponential growth of the forecast error over time, we observe a more complex behavior. We test a power law and the quadratic hypothesis for the scale-dependent error growth. The power law is valid for the first days of the growth, and with an included saturation effect, we extend its validity to the entire period of growth. The theory explaining the parameters of the power law is confirmed. Although the quadratic hypothesis cannot be completely rejected and could serve as a first guess, the hypothesis's parameters are not theoretically justifiable in the model. In addition, we study the initial error growth for the ECMWF forecast system (500 hPa geopotential height) over the 1986 to 2011 period. For these data, it is impossible to assess which of the error growth descriptions is more appropriate, but the extended power law, which is theoretically substantiated and valid for the Lorenz system, provides an excellent fit to the average initial error growth of the ECMWF forecast system. Fitting the parameters, we conclude that there is an intrinsic limit of predictability after 22 d.

Role of initial error growth in the extended range prediction skill of Madden-Julian Oscillation (MJO)

Role of initial error growth in the extended range prediction skill of Madden-Julian Oscillation (MJO)

On the sensitive areas for targeted observations in ENSO forecasting

Using the outputs from CMCC-CM in CMIP5 experiments, the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth (IEG) method and the particle filter (PF) method. Results showed that the PF targets areas over the central-eastern equatorial Pacific, while the sensitive areas determined by the IEG method are slightly to the east of the former. Although a small part of the areas targeted by the IEG method also lie in the southeast equatorial Pacific, this does not affect the large-scale overlapping of the sensitive areas determined by these two methods in the eastern equatorial Pacific. Therefore, sensitive areas determined by the two methods are mutually supportive. When considering the uncertainty of methods for determining sensitive areas in realistic targeted observation, it is more reasonable to choose the above overlapping areas as sensitive areas for ENSO forecasting. This result provides scientific guidance for how to better determine sensitive areas for ENSO forecasting.摘要本研究分别从初始误差增长和粒子滤波同化的角度识别了ENSO预测的目标观测敏感区.结果表明:与粒子滤波同化方法确定的敏感区相比, 基于初始误差增长确定的敏感区位置稍微偏东, 且在东南太平洋也有分布, 但整体而言, 这两种方法确定的敏感区存在大范围重合区域, 是互为印证的.在实际目标观测中, 如果考虑使用确定敏感区方法的不确定性, 那么选择上述两种方法确定的敏感区的重合区域作为ENSO预测的目标观测敏感区将更为合理.

The Impact of Topography on the Initial Error Growth Associated with Moist Convection

The Impact of Topography on the Initial Error Growth Associated with Moist Convection

Quadratic Optimization of Initial State for Dynamic Controller in Linear Systems

This study presents an optimization-based controller state initialization method for improving the initial transient response. Uncertainties in the uncontrolled periods around transition events or a large change in the reference may result in unexpected growth of initial tracking error. The proposed method mainly aims to regulate out the initial error at the instance of feedback control initiation with a small amount of control effort and a short settling time. The proposed method minimizes a cost function given by a weighted sum of infinite-horizon square integrals, provided that the other controller parameters are fixed a priori to yield closed-loop stability. The performance-oriented initial controller state optimization is desirable for efficient and rapid initial error convergence in systems such as missiles where agility is of major concern. This study suggests performance-related optimization as an alternative for controller state setting besides zero filling or solving for bump-less transfer in switching systems. The concept and the effectiveness of the proposed method is demonstrated by numerical simulation considering the three-loop missile acceleration autopilot as an example application.

High-dimensional generalizations of the Lorenz system and implications for predictability

A set of (3N)- and (3N + 2)-dimensional ordinary differential equation systems for any positive integer N are newly derived as high-dimensional extensions of the three-dimensional Lorenz system, and their numerical solutions are analyzed using periodicity diagrams, bifurcation diagrams, solution trajectories, and initial condition experiments. Higher-dimensional Lorenz systems extended in this manner can be considered to be closer to the original governing equations describing Rayleigh-Bénard convection in the sense that they incorporate smaller-scale motions. This study focuses on how the solution characteristics react to incremental changes in the dimension of the Lorenz system. By plotting periodicity diagrams in dimension-parameter spaces, it is shown that the parameter ranges in which the systems have chaotic solutions tend to diminish with increasing dimensions. On the other hand, for particular parameter choices that result in chaotic solutions across many dimensions, having a higher dimension does not always result in a faster or slower initial error growth. Possible implications of these results are discussed in the context of predictability.

Predictability in process-based ensemble forecast of influenza.

Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks.

Season-dependent predictability and error growth dynamics for La Ni\xf1a predictions

The “spring predictability barrier” (SPB) is a well-known characteristic of ENSO prediction, which has been widely studied for El Niño events. However, due to the nonlinearity of the coupled ocean–atmosphere system and the asymmetries between El Niño and La Niña, it is worthy to investigate the SPB for La Niña events and reveal their differences with El Niño. This study investigates the season-dependent predictability of sea surface temperature (SST) for La Niña events by exploring initial error growth in a perfect model scenario within the Community Earth System Model. The results show that for the prediction through the spring season, the prediction errors caused by initial errors have a season-dependent evolution and induce an SPB for La Niña events. Two types of initial errors that often yield the SPB phenomenon are identified: the first are type-1 initial errors showing positive SST errors in the central-eastern equatorial Pacific accompanied by a large positive error in the upper layers of the eastern equatorial Pacific. The second are type-2 errors presenting an SST pattern with positive errors in the southeastern equatorial Pacific and a west–east dipole pattern in the subsurface ocean. The type-1 errors exhibit an evolving mode similar to the growth phase of an El Niño-like event, while the type-2 initially experience a La Niña-like decay and then a transition to the growth phase of an El Niño-like event. Both types of initial errors cause positive prediction errors for Niño3 SST and under-predict the corresponding La Niña events. The resultant prediction errors of type-1 errors are owing to the growth of the initial errors in the upper layers of the eastern equatorial Pacific. For the type-2 errors, the prediction errors originate from the initial errors in the subsurface layers of the western equatorial Pacific. These two regions may represent the sensitive areas of targeted observation for La Niña prediction. In addition, the type-2 errors in the equatorial regions are enlarged by the recharge process from 10°N in the central Pacific during the eastward propagation. Therefore, the off-equatorial regions around 10°N in the central Pacific may represent another sensitive area of La Niña prediction. Additional observations may be prioritized in these identified sensitive areas to better predict La Niña events.

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.

An ensemble forecast method combining CNOPs with BVs

Numerical forecasting can estimate the future state of the atmosphere or oceans. Because the atmosphere and oceans are complex and nonlinear systems, very small errors in the initial field can be amplified nonlinearly during integration of the numerical model, which may ultimately result in large errors. The true initial field cannot be obtained, and it is not possible to construct a perfect model that simulates the development of the atmospheric or oceanic state. Therefore, initial uncertainties and system instabilities result in large forecast errors. However, ensemble forecasting can be used to estimate the forecasting uncertainty. The ensemble initial perturbation has a crucial impact on the ensemble forecasting result. To obtain the initial perturbation that can capture initial field uncertainties and system instabilities, conditional nonlinear optimal perturbation (CNOP) is applied to generate initial perturbations for ensemble forecasting. CNOP is the initial perturbation that satisfies certain physical constraints and causes the largest forecast error at forecast time. More specifically, it is a generalization of the singular vector (SV) in nonlinear field. Using the Lorenz-96 model, we generate initial perturbations by combining CNOPs and breeding vectors (BVs), and then compare it with the traditional breeding growing modes (BGM) method. The spectral projected gradient 2 (SPG2) optimal algorithm is used to compute CNOPs based on basic states, and we sort the BVs according to the corresponding L 2 norm magnitude. We design two ensemble samples for the forecast experiments: sample 1 (S1) and sample 2 (S2). Ensemble initial perturbations all consist of BVs in S1. CNOPs and BVs are combined to form ensemble initial perturbations in S2; we use CNOPs to replace the several larger BVs in S1 according to the BVs’ L 2 norm magnitude order, and keep the remaining BVs unchanged. Both S1 and S2 are consist of 17 ensemble members, including one control forecast. Under the perfect model assumption, many ensemble forecasting numerical experiments are run on the Lorenz-96 model. The root mean square error (RMSE) and anomaly correlation coefficient (ACC) are used to evaluate the forecast results. We find that the forecast skill of S2 is higher than that of S1 in most experiments. This result suggests that initial perturbations generated using the new method better overcome the adverse impact from initial field uncertainty and system instability, and provide higher ensemble forecasting skill than the BGM method. When the initial analysis errors are fast-growing, ensemble forecasts generated by the new method can improve the control forecasts results. Furthermore, the rate of improvement increases with increasing forecast time, and is more obvious in the medium range (6–14 d). Ensemble forecasts generated using the new method can extend the effective forecast time about four days relative to the control forecasts based on the standard that the effective ACC forecast is not <0.6. Nevertheless, for slowly-growing initial analysis errors, ensemble forecasts generated using the new and BGM method may be worse than the control forecasts. The initial analysis errors that show much faster growth behavior are easily influenced by nonlinearity, and extreme events due to strong nonlinear instability may be more likely to cause fast growth of initial analysis errors. Therefore, ensemble forecasts generated by combining CNOPs and BVs will have higher forecasting skill than the BGM method, and can improve the forecast effect of control forecast.

Initial error-induced optimal perturbations in ENSO predictions, as derived from an intermediate coupled model

The initial errors constitute one of the main limiting factors in the ability to predict the El Nino–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 Nino 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 Nina 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˜no, the El Nino 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.

Seasonal predictability of sea surface temperature anomalies over the Kuroshio‐Oyashio Extension: Low in summer and high in winter

AbstractThe seasonal predictability of sea surface temperature anomalies (SSTA) in the Kuroshio‐Oyashio Extension (KOE) is explored by performing perfect model predictability experiments from the viewpoint of initial error growth in a global coupled model. It is found that prediction errors of KOE‐SSTA always increase in the boreal summer and decrease in the boreal winter. This leads to smaller (larger) prediction errors and higher (lower) prediction skills in boreal winter (summer). This seasonal characteristic of the KOE‐SSTA error growth implies a season‐dependent predictability that is lower in summer and higher in winter. The mechanism responsible for error growth associated with seasonal predictability is also explored. The error increase in summer and error decrease in winter in the KOE‐SSTA are both largely attributed to the seasonal evolution of latent heat flux error and mean temperature advection by vertical current error in the KOE region, both of which are forced by the prediction error of 1 month leading zonal wind stress per unit mass for the mixed layer over the KOE region. The shallowest (deepest) mixed layer in summer (winter) amplifies (reduces) the forcing of zonal wind stress errors on the error growth of KOE‐SSTA, thereby causing the seasonal evolution of prediction errors of KOE‐SSTA and ultimately resulting in the season‐dependent predictability of the KOE‐SSTA, i.e., low in summer and high in winter.

Intrinsic versus Practical Limits of Atmospheric Predictability and the Significance of the Butterfly Effect

Abstract Limits of intrinsic versus practical predictability are studied through examining multiscale error growth dynamics in idealized baroclinic waves with varying degrees of convective instabilities. In the dry experiment free of moist convection, error growth is controlled primarily by baroclinic instability under which forecast accuracy is inversely proportional to the amplitude of the baroclinically unstable initial-condition error (thus the prediction can be continuously improved without limit through reducing the initial error). Under the moist environment with strong convective instability, rapid upscale growth from moist convection leads to the forecast error being increasingly less sensitive to the scale and amplitude of the initial perturbations when the initial-error amplitude is getting smaller; these diminishing returns may ultimately impose a finite-time barrier to the forecast accuracy (limit of intrinsic predictability and the so-called “butterfly effect”). However, if the initial perturbation is sufficiently large in scale and amplitude (as for most current-day operational models), the baroclinic growth of large-scale finite-amplitude initial error will control the forecast accuracy for both dry and moist baroclinic waves; forecast accuracy can be improved (thus the limit of practical predictability can be extended) through the reduction of initial-condition errors, especially those at larger scales. Regardless of the initial-perturbation scales and amplitude, the error spectrum will adjust toward the slope of the background flow. Inclusion of strong moist convection changes the mesoscale kinetic energy spectrum slope from −3 to ~−5/3. This change further highlights the importance of convection and the relevance of the butterfly effect to both the intrinsic and practical limits of atmospheric predictability, especially at meso- and convective scales.

An Approach to Generating Mutually Independent Initial Perturbations for Ensemble Forecasts: Orthogonal Conditional Nonlinear Optimal Perturbations

Abstract Conditional nonlinear optimal perturbation (CNOP) is the initial perturbation that satisfies a certain physical constraint and causes the largest nonlinear evolution at prediction time. To yield mutually independent initial perturbations in ensemble forecasts, orthogonal CNOPs are developed. Orthogonal CNOPs are then applied to a Lorenz-96 model to generate initial perturbations for ensemble forecasting, as compared with orthogonal singular vectors (SVs). When the initial analysis errors are fast growing, the ensemble forecasts generated by orthogonal CNOPs of the control forecasts perform much more skillfully. Nevertheless, for slow-growing initial analysis errors, the ensemble forecasts generated by orthogonal SVs achieve higher skill when the ensemble initial perturbations are large, whereas the ensemble forecasts generated by orthogonal CNOPs achieve almost the same forecast skill as those generated by orthogonal SVs when the ensemble initial perturbations are sufficiently small. The initial analysis errors that possess much faster growth behavior are easily influenced by nonlinearity, and extreme events (extreme here refers to strong), because of strong nonlinear instability, may be much more likely to cause fast growth of initial analysis errors. Therefore, the ensemble forecasts generated by orthogonal CNOPs may have higher skill than those generated by orthogonal SVs for extreme events; in particular, the ensemble forecasts generated by orthogonal CNOPs, compared with those generated by orthogonal SVs, require a much smaller number of ensemble members to achieve high skill. Therefore, orthogonal CNOPs may provide another useful technique to generate initial perturbations for ensemble forecasting.

The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: results from an earth system model

The National Center for Atmospheric Research Community Earth System Model is used to study the “spring predictability barrier” (SPB) problem for El Nino events from the perspective of initial error growth. By conducting perfect model predictability experiments, we obtain two types of initial sea temperature errors, which often exhibit obvious season-dependent evolution and cause a significant SPB when predicting the onset of El Nino events bestriding spring. One type of initial errors possesses a sea surface temperature anomaly (SSTA) pattern with negative anomalies in the central–eastern equatorial Pacific, plus a basin-wide dipolar subsurface temperature anomaly pattern with negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific. The other type consists of an SSTA component with positive anomalies over the southeastern equatorial Pacific, plus a large-scale zonal dipole pattern of the subsurface temperature anomaly with positive anomalies in the upper layers of the eastern equatorial Pacific and negative anomalies in the lower layers of the central–western equatorial Pacific. Both exhibit a La Nina-like evolving mode and cause an under-prediction for Nino-3 SSTA of El Nino events. For the former initial error type, the resultant prediction errors grow in a manner similar to the behavior of the growth phase of La Nina; while for the latter initial error type, they experience a process that is similar to El Nino decay and transition to a La Nina growth phase. Both two types of initial errors cause negative prediction errors of Nino-3 SSTA for El Nino events. The prediction errors for Nino-3 SSTA are mainly due to the contribution of initial sea temperature errors in the large-error-related regions in the upper layers of the eastern tropical Pacific and/or in the lower layers of the western tropical Pacific. These regions may represent ‘‘sensitive areas’’ for El Nino–Southern Oscillation (ENSO) predictions, thereby providing information for target observations to improve the forecasting skill of ENSO.

Revisiting ENSO Coupled Instability Theory and SST Error Growth in a Fully Coupled Model

Abstract A coupled model framework is presented to isolate coupled instability induced SST error growth in the ENSO region. The modeling framework using CCSM4 allows for seasonal ensembles of initialized simulations that are utilized to quantify the spatial and temporal behavior of coupled instabilities and the associated implications for ENSO predictability. The experimental design allows for unstable growth of initial perturbations that are not prescribed, and several cases exhibit sufficiently rapid growth to produce ENSO events that do not require a previous ENSO event, large-scale wind trigger, or subsurface heat content precursor. Without these precursors, however, ENSO amplitude is reduced. The initial error growth exhibits strong seasonality with fastest growth during spring and summer and also dependence on the initialization month with the fastest growth occurring in the July ensemble. Peak growth precedes the peak error, and evidence suggests that the final state error may be sensitive to a slight temperature bias in the initialized SST. The error growth displays a well-defined seasonal limit, with ensembles initialized prior to fall exhibiting a clear seasonal halt in error growth around September, consistent with increased background stability typical during fall. Overall, coupled instability error growth in CCSM4 is deemed best characterized by strong seasonality, dependence on the initialization month, and nonlinearity. The results pose real implications for predictability because the final error structure is ENSO-like and occurs without a subsurface precursor, which studies have shown to be essential to ENSO predictability. Despite the large error growth induced by coupled instabilities, analysis reveals that ENSO predictability is retained for most seasonal ensembles.