Ocean Data Assimilation, Initialization, and Predictions of ENSO with a Coupled GCM

Schneider, Edwin K.; Huang, Bohua; Zhu, Zhenfeng; DeWitt, David G.; Kinter, James L.; Kirtman, Ben P.; Shukla, J.

doi:10.1175/1520-0493(1999)127<1187:odaiap>2.0.co;2

Cited by 61 publications

(33 citation statements)

References 51 publications

(58 reference statements)

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“…This strategy is potentially less applicable for decadal prediction, because the smaller magnitude of the predictable signal is more likely to be masked by inaccuracies in the bias correction computed from the comparatively short period, and because nonlinearities will inevitably grow with the length of the experiments. An alternative approach, known as "anomaly initialization" (Schneider et al 1999), has therefore been tried (Barnett et al 2004;Pierce et al 2004;Smith et al 2007;Keenlyside et al 2008;Pohlmann et al 2009). In this approach, models are initialized with observed anomalies added to the model climate, and the mean model climate state is subtracted to obtain forecast anomalies.…”

Section: Science and Data Issues Initializationmentioning

confidence: 99%

Decadal Prediction

Meehl¹,

Goddard²,

Murphy³

et al. 2009

Bull. Amer. Meteor. Soc.

670

251

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Section: Science and Data Issues Initializationmentioning

confidence: 99%

Decadal Prediction

Meehl¹,

Goddard²,

Murphy³

et al. 2009

Bull. Amer. Meteor. Soc.

670

251

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“…The method has previously been used in several studies e.g. Schneider et al (1999), Pierce et al (2004) and Smith et al (2007). The rationale is to avoid an initialisation shock due to an initial state being far from the model attractor.…”

Section: Anomaly Initialisationmentioning

confidence: 99%

Evaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors

et al. 2012

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This study discusses and compares three different strategies used to deal with model error in seasonal and decadal forecasts. The strategies discussed are the socalled full initialisation, anomaly initialisation and flux correction. In the full initialisation the coupled model is initialised to a state close to the real-world attractor and after initialisation the model drifts towards its own attractor, giving rise to model bias. The anomaly initialisation aims to initialise the model close to its own attractor, by initialising only the anomalies. The flux correction strategy aims to keep the model trajectory close to the real-world attractor by adding empirical corrections. These three strategies have been implemented in the ECMWF coupled model, and are evaluated at seasonal and decadal timescales. The practical implications of the different strategies are also discussed. Results show that full initialisation results in a clear model drift towards a colder climate. The anomaly initialisation is able to reduce the drift, by initialising around the model mean state. However, the erroneous model mean state results in degraded seasonal forecast skill. The best results on the seasonal time-scale are obtained using momentum-flux correction, mainly because it avoids the positive feedback responsible for a strong cold bias in the tropical Pacific. It is likely that these results are model dependent: the coupled model used here shows a strong cold bias in the Central Pacific, resulting from a positive coupled feedback between winds and SST.At decadal time-scales it is difficult to determine whether any of the strategies is superior to the others.

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“…This technique is referred to as anomaly initialisation and is used in several studies e.g. Schneider et al (1999), Pierce et al (2004) and Smith et al (2007). In this study we use anomaly initialisation for the reference decadal integrations, so the model is initialised around its own climate.…”

Section: Flux Correctionmentioning

confidence: 99%

On the dependence of ENSO simulation on the coupled model mean state

2012

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Systematic model error remains a difficult problem for seasonal forecasting and climate predictions. An error in the mean state could affect the variability of the system. In this paper, we investigate the impact of the mean state on the properties of ENSO. A set of coupled decadal integrations have been conducted, where the mean state and its seasonal cycle have been modified by applying flux correction to the momentum-flux and a combination of heat and momentum fluxes. It is shown that correcting the mean state and the seasonal cycle improves the amplitude of SST inter-annual variability and also the penetration of the ENSO signal into the troposphere and the spatial distribution of the ENSO teleconnections are improved. An analysis of a multivariate PDF of ENSO shows clearly that the flux correction affects the mean, variance, skewness and tails of the distribution. The changes in the tails of the distribution are particularly noticeable in the case of precipitation, showing that without the flux correction the model is unable to reproduce the frequency of large events. For the interannual variability the momentum-flux correction alone has a large impact, while the additional heat-flux correction is important for the teleconnections. These results suggest that the current forecasts practices of removing the forecast bias a-posteriori or anomaly initialisation are by no means optimal, since they can not deal with the strong nonlinear interactions. A consequence of the results presented here is that the predictability on annual time-ranges could be higher than currently achieved. Whether or not the correction of the model mean state by some sort of flux correction leads to better forecasts needs to be addressed. In any case, flux correction may be a powerful tool for diagnosing coupled model errors and predictability studies.

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Ocean Data Assimilation, Initialization, and Predictions of ENSO with a Coupled GCM

Cited by 61 publications

References 51 publications

Decadal Prediction

Decadal Prediction

Evaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors

On the dependence of ENSO simulation on the coupled model mean state

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