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Richard Kleeman

Abstract

Three different mechanisms for the generation of ENSO SST anomalies within a simplified tropical Pacific Ocean model are examined: thermocline depth changes, Ekman-induced upwelling anomalies, and zonal advection changes. The effect of varying the relative influence of these terms on the realism of tropical Pacific coupled models is analyzed. The principal tool used to assess such realism is hindcast skill, with forced ocean and oscillatory behavior also being examined. Of the mechanisms considered, thermocline perturbations are shown to be crucially important for high coupled-model hindcast skill. Furthermore, it is concluded that the realism of the model (as measured by hindcast skill) deteriorates markedly when the influence on SST of Ekman upwelling becomes greater than a small fraction of the thermocline influence. This provides strong evidence for the hypothesis that Ekman upwelling anomalies (which are essentially a local response to wind stress anomalies) have only a small influence on the creation of real world SST anomalies. The implications of this latter point for coupled models involving ocean general circulation models is briefly discussed. It is also demonstrated that western boundary reflections provide a vital role by means of a negative feedback in ensuring realistic performance. The hindcast skill (as measured by NINO3 anomaly correlation) demonstrated by a model involving only the thermocline mechanism can be tuned to exceed that of the benchmark Cane and Zebiak model for hindcast lags up to 7 months (from 7 to 12 months the model skills are roughly equal).

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Andrew M. Moore
and
Richard Kleeman

Abstract

The idea that intraseasonal variability in the tropical west Pacific can act as an effective means of stochastically forcing ENSO episodes is explored. Using the ideas of generalized linear stability theory as they apply to nonnormal dynamical systems, the physical attributes of the coupled ocean–atmosphere system in the Tropics that allow perturbations with structures that are dissimilar to ENSO to act as precursors for ENSO episodes are examined. Using a coupled ocean–atmosphere model, two particularly important factors are identified that contribute to the nonnormality of the coupled system: nonsolar atmospheric heating directly related to SST changes, and the dissimilarity between the equatorial ocean wave reflection process at eastern and western boundaries. The latter is intrinsic to the dynamics of the ocean, while the former is related to the presence of the west Pacific warm pool and its relationship with the Walker circulation.

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Andrew M. Moore
and
Richard Kleeman

Abstract

The optimal perturbations (singular vectors) of a dynamical coupled model, a hybrid coupled model, and a linear inverse model of ENSO are compared. The hybrid coupled model consists of a dynamical ocean model and a statistical atmospheric model. The dynamical ocean model is identical to that used in the dynamical coupled model, and the atmospheric model is a statistical model derived from long time series of the dynamical coupled model. The linear inverse model was also derived from long time series from the dynamical coupled model. Thus all three coupled models are very closely related and all produce similar ENSO oscillations. The dynamical model and hybrid model also possess similar levels of hindcast skill. However, the optimal perturbations of the tangent linear versions of each model are not the same. The hybrid and linear inverse models are unable to recover the SST structure of the optimal perturbations of the dynamical model. The SST structure of the dynamical coupled model is a result of nonnormality introduced by latent heating of the atmosphere by deep convection over the west Pacific warm pool. It is demonstrated that standard statistical techniques remove the effects of the latent heating on the nonnormality of the hybrid and linear inverse models essentially rendering them more normal than their dynamical model counterpart. When the statistical components of the hybrid coupled model and the linear inverse models were recomputed using SST anomalies that are appropriately scaled by the standard deviation of SST variability, nonnormality was reintroduced into these models and they recovered the optimal perturbation structure of the dynamical model. Even though the hybrid and linear inverse model with scaled SSTs can recover the large-scale features of the correct optimal structure, state space truncation means that the dynamics of the resulting optimal perturbations is not the same as that governing optimal perturbation growth in the dynamical model. The consequences of these results for observed estimates of optimal perturbations for ENSO are discussed.

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William S. Kessler
and
Richard Kleeman

Abstract

An ocean general circulation model, forced with idealized, purely oscillating wind stresses over the western equatorial Pacific similar to those observed during the Madden–Julian oscillation (MJO), developed rectified low-frequency anomalies in SST and zonal currents, compared to a run in which the forcing was climatological. The rectification in SST resulted from increased evaporation under stronger than normal winds of either sign, from correlated intraseasonal oscillations in both vertical temperature gradient and upwelling speed forced by the winds, and from zonal advection due to nonlinearly generated equatorial currents. The net rectified signature produced by the MJO-like wind stresses was SST cooling (about 0.4°C) in the west Pacific, and warming (about 0.1°C) in the central Pacific, tending to flatten the background zonal SST gradient. It is hypothesized that, in a coupled system, such a pattern of SST anomalies would spawn additional westerly wind anomalies as a result of SST-induced changes in the low-level zonal pressure gradient. This was tested in an intermediate coupled model initialized to 1 January 1997, preceding the 1997–98 El Niño. On its own, the model hindcast a relatively weak warm event, but when the effect of the rectified SST pattern was imposed, a coupled response produced the hypothesized additional westerlies and the hindcast El Niño became about 50% stronger (measured by east Pacific SST anomalies), suggesting that the MJO can interact constructively with the ENSO cycle. This implies that developing the capacity to predict, if not individual MJO events, then the conditions that affect their amplitude, may enhance predictability of the strength of oncoming El Niños.

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Andrew M. Moore
and
Richard Kleeman

Abstract

Using the ideas of generalized linear stability theory, the authors examine the potential role that tropical variability on synoptic–intraseasonal timescales can play in controlling variability on seasonal–interannual timescales. These ideas are investigated using an intermediate coupled ocean–atmosphere model of the El Niño–Southern Oscillation (ENSO). The variability on synoptic–intraseasonal timescales is treated as stochastic noise that acts as a forcing function for variability at ENSO timescales. The spatial structure is computed that the stochastic noise forcing must have in order to enhance the variability of the system on seasonal–interannual timescales. These structures are the so-called stochastic optimals of the coupled system, and they bear a good resemblence to variability that is observed in the real atmosphere on synoptic and intraseasonal timescales. When the coupled model is subjected to a stochastic noise forcing composed of the stochastic optimals, variability on seasonal–interannual timescales develops that has spectral characteristics qualitatively similar to those seen in nature. The stochastic noise forcing produces perturbations in the system that can grow rapidly. The response of the system to the stochastic optimals is to induce perturbations that bear a strong resemblence to westerly and easterly wind bursts frequently observed in the western tropical Pacific. In the model, these “wind bursts” can act as efficient precursors for ENSO episodes if conditions are favorable. The response of the system to noise-induced perturbations depends on a number of factors that include 1) the phase of the seasonal cycle, 2) the presence of nonlinearities in the system, 3) the past history of the stochastic noise forcing and its integrated effect, and 4) the stability of the coupled ocean–atmosphere system. Based on their findings, they concur with the view adopted by other investigators that ENSO may be explained, at least partially, as a stochastically forced phenomena, the source of the noise in the Tropics being synoptic–intraseasonal variability, which includes the Madden–Julian oscillation, and westerly/easterly wind bursts. These ideas fit well with the observed onset and development of various ENSO episodes, including the 1997–98 El Niño event.

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Youmin Tang
,
Richard Kleeman
, and
Andrew M. Moore

Abstract

In this study, ensemble predictions of the El Niño–Southern Oscillation (ENSO) were conducted for the period 1981–98 using two hybrid coupled models. Several recently proposed information-based measures of predictability, including relative entropy (R), predictive information (PI), predictive power (PP), and mutual information (MI), were explored in terms of their ability of estimating a priori the predictive skill of the ENSO ensemble predictions. The emphasis was put on examining the relationship between the measures of predictability that do not use observations, and the model prediction skills of correlation and root-mean-square error (RMSE) that make use of observations. The relationship identified here offers a practical means of estimating the potential predictability and the confidence level of an individual prediction.

It was found that the MI is a good indicator of overall skill. When it is large, the prediction system has high prediction skill, whereas small MI often corresponds to a low prediction skill. This suggests that MI is a good indicator of the actual skill of the models. The R and PI have a nearly identical average (over all predictions) as should be the case in theory.

Comparing the different information-based measures reveals that R is a better predictor of prediction skill than PI and PP, especially when correlation-based metrics are used to evaluate model skill. A “triangular relationship” emerges between R and the model skill, namely, that when R is large, the prediction is likely to be reliable, whereas when R is small the prediction skill is quite variable. A small R is often accompanied by relatively weak ENSO variability. The possible reasons why R is superior to PI and PP as a measure of ENSO predictability will also be discussed.

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Youmin Tang
,
Richard Kleeman
, and
Sonya Miller

Abstract

Using a recently developed method of computing climatically relevant singular vectors (SVs), the error growth properties of ENSO in a fully coupled global climate model are investigated. In particular, the authors examine in detail how singular vectors are influenced by the phase of ENSO cycle—the physical variable under consideration as well as the error norm deployed. Previous work using SVs for studying ENSO predictability has been limited to intermediate or hybrid coupled models.

The results show that the singular vectors share many of the properties already seen in simpler models. Thus, for example, the singular vector spectrum is dominated by one fastest growing member, regardless of the phase of ENSO cycle and the variable of perturbation or the error norm; in addition the growth rates of the singular vectors are very sensitive to the phase of the ENSO cycle, the variable of perturbation, and the error norm. This particular CGCM also displays some differences from simpler models; thus subsurface temperature optimal patterns are strongly sensitive to the phase of ENSO cycle, and at times an east–west dipole in the eastern tropical Pacific basin is seen. This optimal pattern also appears for SST when the error norm is defined using Niño-4. Simpler models consistently display a single-sign equatorial signature in the subsurface corresponding perhaps to the Wyrtki buildup of heat content before a warm event. Some deficiencies in the CGCM and their possible influences on SV growth are also discussed.

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Rong-Hua Zhang
,
Richard Kleeman
,
Stephen E. Zebiak
,
Noel Keenlyside
, and
Stephane Raynaud

Abstract

An empirical model for the temperature of subsurface water entrained into the ocean mixed layer (Te ) is presented and evaluated to improve sea surface temperature anomaly (SSTA) simulations in an intermediate ocean model (IOM) of the tropical Pacific. An inverse modeling approach is adopted to estimate Te from an SSTA equation using observed SST and simulated upper-ocean currents. A relationship between Te and sea surface height (SSH) anomalies is then obtained by utilizing a singular value decomposition (SVD) of their covariance. This empirical scheme is able to better parameterize Te anomalies than other local schemes and quite realistically depicts interannual variability of Te , including a nonlocal phase lag relation of Te variations relative to SSH anomalies over the central equatorial Pacific. An improved Te parameterization naturally leads to better depiction of the subsurface effect on SST variability by the mean upwelling of subsurface temperature anomalies. As a result, SSTA simulations are significantly improved in the equatorial Pacific; a comparison with other schemes indicates that systematic errors of the simulated SSTAs are significantly small—apparently due to the optimized empirical Te parameterization. Cross validation and comparisons with other model simulations are made to illustrate the robustness and effectiveness of the scheme. In particular it is demonstrated that the empirical Te model constructed from one historical period can be successfully used to improve SSTA simulations in another.

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Amy Solomon
,
Julian P. McCreary Jr.
,
Richard Kleeman
, and
Barry A. Klinger

Abstract

Processes that cause decadal variability in an intermediate coupled ocean–atmosphere model of the Pacific basin, both at northern midlatitudes and in the Tropics, are studied. The model's ocean component is a variable-temperature 3½-layer system. Its atmospheric component consists of two basic parts: an empirical model, constructed from patterns obtained by the singular value decomposition (SVD) statistical technique that determines wind stress anomalies from model sea surface temperature (SST), and a simple representation of the planetary boundary layer to calculate the surface heat flux anomalies. A third part specifies stochastic wind stress forcing from observed variability. In addition, the model is specifically designed to separate tropical and extratropical interactions, such that the Tropics can force the extratropics through the atmosphere but the extratropics can only feed back to the Tropics through the ocean.

Solutions develop two types of oscillations: an ENSO-like interannual mode and a decadal mode. As in many models of ENSO, the interannual mode is driven by positive, ocean–atmosphere feedbacks near the equator, and time-delayed negative feedback is provided by off-equatorial Rossby waves. For parameter choices that amplify midlatitude coupling by 30% (ϕ o = 1.3), a self-sustained decadal oscillation develops in the North Pacific without any tropical interactions. Diagnostic analyses show that it is maintained by ocean-to-atmosphere feedbacks in the northwest and subtropical northeast Pacific, and by atmospheric teleconnections from those regions to the northeast ocean. For weaker coupling (ϕ o = 1.2), the decadal mode is damped. In this case, the mode can be sustained by atmospheric teleconnections from the Tropics associated with the interannual mode, but not by extratropical stochastic forcing. Although including stochastic forcing does generate variability at decadal timescales, a distinct decadal spectral peak only exists when the decadal mode is active.

Decadal variability is carried to the equator by variations in the transport, rather than temperature, of the North Pacific subtropical cell. These variations modulate near-equatorial SST by altering the amount of cool, thermocline water that upwells in the eastern equatorial Pacific, which in turn feeds back to the interannual mode.

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Cristina L. Perez
,
Andrew M. Moore
,
Javier Zavala-Garay
, and
Richard Kleeman

Abstract

A currently popular idea is that El Niño–Southern Oscillation (ENSO) can be viewed as a linear deterministic system forced by noise representing processes with periods shorter than ENSO. Also, there is observational evidence to suggest that the Madden–Julian oscillation (MJO) acts to trigger and/or amplify the warm phase of ENSO in this way. The feedback of the slower process, ENSO, to higher-frequency atmospheric phenomena, of which a large part of the variability in the intraseasonal band is due to the MJO, has received little attention. This paper considers the hypothesis that the probability of an El Niño event is modified by high MJO activity and that, in turn, the MJO is regulated by ENSO activity. If this is indeed the case, then viewing ENSO as a low-frequency oscillation forced by additive stochastic noise would not present a complete picture.

This paper tests the above hypothesis using a stochastically forced intermediate coupled model by allowing ENSO to directly influence the stochastic forcing. The model response to a variety of stochastic forcing types is found to be sensitive to the type of forcing applied. When the model is operated beyond its intrinsic Hopf bifurcation, its probability distribution function (PDF) is fundamentally altered when the stochastic forcing is changed from additive to multiplicative. The model integration period also influences the shape of the PDF, which is also compared to the PDF derived from observations. It is found that multiplicative stochastic forcing reproduces some measures of the observations better than the additive stochastic forcing.

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