Prediction of Niño 3 Sea Surface Temperatures Using Linear Inverse Modeling

Penland, Cécile; Magorian, Theresa

doi:10.1175/1520-0442(1993)006<1067:ponsst>2.0.co;2

Cited by 273 publications

(199 citation statements)

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“…If the system is a damped oscillator, then the expectation is that the predictability of the system is limited by the timing, intensity, and spatial structure of the atmospheric noise that imposes itself on the system. ENSO predictability under this paradigm has been addressed in a number of studies (e.g., Penland and Sardeshmukh 1995;Kleeman and Moore 1999;Thompson and Battisti 2000;Flugel et al 2004). The paradigm of a stochastically forced ENSO system was introduced in the literature a decade ago (e.g., Penland and Magorian 1993;Penland and Sardeshmukh 1995) and has persisted as an explanation for the initiation of ENSO events.…”

Section: Introductionmentioning

confidence: 99%

“…ENSO predictability under this paradigm has been addressed in a number of studies (e.g., Penland and Sardeshmukh 1995;Kleeman and Moore 1999;Thompson and Battisti 2000;Flugel et al 2004). The paradigm of a stochastically forced ENSO system was introduced in the literature a decade ago (e.g., Penland and Magorian 1993;Penland and Sardeshmukh 1995) and has persisted as an explanation for the initiation of ENSO events. In particular, this view has gained support since the 1997/98 El Niño event, which most models failed to forecast prior to the preceding summertime (Fedorov et al 2003).…”

Section: Introductionmentioning

confidence: 99%

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Predictability Loss in an Intermediate ENSO Model due to Initial Error and Atmospheric Noise*

Karspeck

Kaplan

2006

Journal of Climate

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The seasonal and interannual predictability of ENSO variability in a version of the Zebiak-Cane coupled model is examined in a perturbation experiment. Instead of assuming that the model is "perfect," it is assumed that a set of optimal initial conditions exists for the model. These states, obtained through a nonlinear minimization of the misfit between model trajectories and the observations, initiate model forecasts that correlate well with the observations. Realistic estimates of the observational error magnitudes and covariance structures of sea surface temperatures, zonal wind stress, and thermocline depth are used to generate ensembles of perturbations around these optimal initial states, and the error growth is examined. The error growth in response to subseasonal stochastic wind forcing is presented for comparison.In general, from 1975 to 2002, the large-scale uncertainty in initial conditions leads to larger error growth than continuous stochastic forcing of the zonal wind stress fields. Forecast ensemble spread is shown to depend most on the calendar month at the end of the forecast rather than the initialization month, with the seasons of greatest spread corresponding to the seasons of greatest anomaly variance. It is also demonstrated that during years with negative (and rapidly decaying) Niño-3 SST anomalies (such as the time period following an El Niño event), there is a suppression of error growth. In years with large warm ENSO events, the ensemble spread is no larger than in moderately warm years. As a result, periods with high ENSO variance have greater potential prediction utility.In the realistic range of observational error, the ensemble spread has more sensitivity to the initial error in the thermocline depth than to the sea surface temperature or wind stress errors. The thermocline depth uncertainty is the principal reason why initial condition uncertainties are more important than wind noise for ensemble spread.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictability Loss in an Intermediate ENSO Model due to Initial Error and Atmospheric Noise*

Karspeck

Kaplan

2006

Journal of Climate

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“…Introduction. Estimation of stochastic models from timeseries is an important tool in scientific disciplines ranging from econometrics [23,2,4] to chemistry [17,25,41,9] and atmosphere-ocean science [35,7,40]. A widely used class of such models are diffusion processes, described by stochastic differential equations (SDEs):…”

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confidence: 99%

Diffusion Estimation from Multiscale Data by Operator Eigenpairs

Crommelin¹,

Vanden‐Eijnden²

2011

Multiscale Model. Simul.

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Abstract. In this paper we present a new procedure for the estimation of diffusion processes from discretely sampled data. It is based on the close relation between eigenpairs of the diffusion operator L and those of the conditional expectation operator Pt, a relation stemming from the semigroup structure Pt = exp(tL ) for t ≥ 0. It allows for estimation without making time discretization errors, an aspect that is particularly advantageous in case of data with low sampling frequency. After estimating eigenpairs of L via eigenpairs of Pt, we infer the drift and diffusion functions that determine L by fitting L to the estimated eigenpairs using a convex optimization procedure. We present numerical examples where we apply the procedure to one-and two-dimensional diffusions, reversible as well as nonreversible.In the second part of the paper we consider estimation of coarse-grained (homogenized) diffusion processes from multiscale data. We show that eigenpairs of the homogenized diffusion operator are asymptotically close to eigenpairs of the underlying multiscale diffusion operator. This implies that we can infer the correct homogenized process from data of the multiscale process, using the estimation procedure discussed in the first part of the paper. This is illustrated with numerical examples.

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“…In this context, modern statistical techniques (e.g. Barnston and Ropelewski, 1992;Penland and Magorian, 1993;Xue et al, 1994;van den Dool and Barnston, 1995;Tangang et al, 1997) as well as climate models of different complexity (e.g. Cane et al, 1986;Zebiak and Cane, 1987;Blumenthal, 1991;Goswami and Shukla, 1991a, b;Balmaseda et al, 1994;Oberhuber et al, 1998;Stockdale et al, 1998;Ji et al, 1998;Mason et al, 1999;Grötzner et al, 1998) are being used in order to predict climate fluctuations sufficiently in advance for societies to take precautionary measures.…”

Section: Introductionmentioning

confidence: 99%

Cyclic Markov chains with an application to an intermediate ENSO model

Pasmanter

Timmermann²

2003

Nonlin. Processes Geophys.

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Abstract.We develop the theory of cyclic Markov chains and apply it to the El Niño-Southern Oscillation (ENSO) predictability problem. At the core of Markov chain modelling is a partition of the state space such that the transition rates between different state space cells can be computed and used most efficiently. We apply a partition technique, which divides the state space into multidimensional cells containing an equal number of data points. This partition leads to mathematical properties of the transition matrices which can be exploited further such as to establish connections with the dynamical theory of unstable periodic orbits. We introduce the concept of most and least predictable states. The data basis of our analysis consists of a multicentury-long data set obtained from an intermediate coupled atmosphere-ocean model of the tropical Pacific. This cyclostationary Markov chain approach captures the spring barrier in ENSO predictability and gives insight also into the dependence of ENSO predictability on the climatic state.

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Prediction of Niño 3 Sea Surface Temperatures Using Linear Inverse Modeling

Cited by 273 publications

References 14 publications

Predictability Loss in an Intermediate ENSO Model due to Initial Error and Atmospheric Noise*

Predictability Loss in an Intermediate ENSO Model due to Initial Error and Atmospheric Noise*

Diffusion Estimation from Multiscale Data by Operator Eigenpairs

Cyclic Markov chains with an application to an intermediate ENSO model

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