ENSO and ENSO-related Predictability. Part I: Prediction of Equatorial Pacific Sea Surface Temperature with a Hybrid Coupled Ocean–Atmosphere Model

Barnett, T. P.; Graham, Nicholas E.; Pazan, Stephen E.; White, Warren B.; Latif, Mojib; Flügel, Moritz

doi:10.1175/1520-0442(1993)006<1545:eaerpp>2.0.co;2

Cited by 227 publications

(201 citation statements)

References 38 publications

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“…In recent decades, great advancements have been made in understanding ENSO and developing models for its real-time prediction (e.g., Bjerknes, 1969;Wyrtki, 1975;McCreary, 1983;Cane et al, 1986;Zhang et al, 2013). At present, various types of airsea coupled models have been developed, including intermediate coupled models (ICMs; e.g., Zebiak and Cane, 1987;Balmaseda et al, 1994;Zhang et al, 2003), hybrid coupled models (e.g., Neelin, 1990;Barnett et al, 1993;, and fully coupled general circulation models (e.g., Philander et al, 1992;Rosati et al, 1997). Currently, these coupled models enable us to make six-month to one-year real-time ENSO predictions in advance with reasonable success.…”

Section: Introductionmentioning

confidence: 99%

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

Gao

2016

Adv. Atmos. Sci.

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A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The "observation" of the SST anomaly, which is sampled from a "truth" model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.

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Section: Introductionmentioning

confidence: 99%

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

Gao

2016

Adv. Atmos. Sci.

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“…Indeed, since the 1980s, when the impact of the major 1982-83 El Niño event on climate was recognized, climate models, both empirical and dynamical, have become important tools for predicting the global climate (e.g. Zebiak and Cane, 1987;Barnett et al, 1993;Latif et al, 1993;Barnston et al, 1999;Goddard et al, 2001). …”

Section: Introductionmentioning

confidence: 99%

Variations of the surface temperature in Hong Kong during the last century

Ding

Zheng

Yang

2002

Intl Journal of Climatology

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A statistical analysis has been applied to obtain a better understanding of the variations of the surface climate in Southeast Asia. In particular, we have depicted the detailed features of the changes in the surface air temperature of Hong Kong (HK) during the past 115 years. Analysis of the time-frequency spectra of the wavelet transform indicates that although seasonal variations account for most of the temperature variations, strong signals also exist on subseasonal, interannual, and interdecadal time scales. Though the strong seasonal cycle is marked by a minimum temperature in February (and then in January) and a maximum temperature in July (and then in August), strong variations on the subseasonal and interannual time scales occur mostly in February and then in March. It is also found that a rising tendency exists in the long-recorded temperature data, with a rate of 0.09-0.15°C per decade.Temperature variations in HK are strongly related to changes in the regional and remote atmospheric circulation on various time scales. The East Asian monsoon circulation is the main factor controlling the seasonal cycle and the subseasonal and interannual variations of the HK temperature during winter. The subseasonal and seasonal variations of the temperature are also associated with changes in the atmospheric circulation over the North Pacific, which is closely linked to the East Asian jet stream. Strong signals are also found in both this mid-latitude circulation and the El Niño-southern oscillation phenomenon when the interannual variability of the HK temperature is apparent.

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“…Although this is only a short data set, predictions of a large suite of models are available and thus, it is convenient to determine the comparative skill of the IndOzy model. The physical models are: the Lamont model (Zebiak and Cane, 1987), the SIO model (Barnett et al, 1993), the European Centre for Mediumrange Weather Forecast model (Molteni et al, 1996), the NCEP coupled model (Ji et al, 1996), and the AGCM of NISPP/NASA (Bacmeister and Suarez, 2002) The statistical model includes the canonical correlation analysis (Barnston and Ropelewski, 1992) and the Markov (Xue et al, 2000) models of NCEP, the linear-inverse model of NOAA-CDC (Penland and Magorian, 1993), the constructed-analog models (van den Dool, 1994), climatology and persistence (CLIPER) model (Knaff and Landsea, 1997), and the UBC nonlinear canonical correlation analysis (Hsieh, 2001).…”

Section: Datamentioning

confidence: 99%

“…In this case, a wind stress is produced from sea surface temperature anomaly (SSTA) data using various statistical techniques ranging from CCA/linear regression and EOF analysis to neural network methods. The resulting wind stress is then used to drive the ocean model (e.g, Barnett et al, 1993;Balmaseda et al, 1994;Syu et al, 1995;Tang and Hsieh, 2002).…”

Section: Introductionmentioning

confidence: 99%

Complicated ENSO models do not significantly outperform very simple ENSO models

Halide

Ridd

2007

Intl Journal of Climatology

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An extremely simple univariate statistical model called 'IndOzy' was developed to predict El Niño-Southern Oscillation (ENSO) events. The model uses five delayed-time inputs of the Niño 3.4 sea surface temperature anomaly (SSTA) index to predict up to 12 months in advance. The prediction skill of the model was assessed using both short-and long-term indices and compared with other operational dynamical and statistical models. Using ENSO-CLIPER(climatology and persistence) as benchmark, only a few statistical models including IndOzy are considered skillful for short-range prediction. All models, however, do not differ significantly from the benchmark model at seasonal Lead-3-6. None of the models show any skill, even against a no-skill random forecast, for seasonal Lead-7. When using the Niño 3.4 SSTA index from 1856 to 2005, the ultra simple IndOzy shows a useful prediction up to 4 months lead, and is slightly less skillful than the best dynamical model LDEO5. That such a simple model such as IndOzy, which can be run in a few seconds on a standard office computer, can perform comparably with respect to the far more complicated models raises some philosophical questions about modelling extremely complicated systems such as ENSO. It seems evident that much of the complexity of many models does little to improve the accuracy of prediction. If larger and more complex models do not perform significantly better than an almost trivially simple model, then perhaps future models that use even larger data sets, and much greater computer power may not lead to significant improvements in both dynamical and statistical models. Investigating why simple models perform so well may help to point the way to improved models. For example, analysing dynamical models by successively stripping away their complexity can focus in on the most important parameters for a good prediction.

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ENSO and ENSO-related Predictability. Part I: Prediction of Equatorial Pacific Sea Surface Temperature with a Hybrid Coupled Ocean–Atmosphere Model

Cited by 227 publications

References 38 publications

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

Variations of the surface temperature in Hong Kong during the last century

Complicated ENSO models do not significantly outperform very simple ENSO models

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