Ensemble Construction and Verification of the Probabilistic ENSO Prediction in the LDEO5 Model

Yanjie Cheng Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, Canada

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Youmin Tang Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, Canada

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Peter Jackson Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, Canada

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Dake Chen Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York, and State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, China

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Ziwang Deng Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, Canada

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Abstract

El Niño–Southern Oscillation (ENSO) retrospective ensemble-based probabilistic predictions were performed for the period of 1856–2003 using the Lamont-Doherty Earth Observatory, version 5 (LDEO5), model. To obtain more reliable and skillful ENSO probabilistic predictions, first, four ensemble construction strategies were investigated: (i) the optimal initial perturbation with singular vector of sea surface temperature anomaly (SSTA), (ii) the realistic high-frequency anomalous winds, (iii) the stochastic optimal pattern of anomalous winds, and (iv) a combination of the first and the third strategy. Second, verifications were conducted to examine the reliability and resolution of the probabilistic forecasts provided by the four methods. Results suggest that reliability of ENSO probabilistic forecast is more sensitive to the choice of ensemble construction strategy than the resolution, and a reliable and skillful ENSO probabilistic prediction system may not necessarily have the best deterministic prediction skills. Among these ensemble construction methods, the fourth strategy produces the most reliable and skillful ENSO probabilistic prediction, benefiting from the joint contributions of the stochastic optimal winds and the singular vector of SSTA. In particular, the stochastic optimal winds play an important role in improving the ENSO probabilistic predictability for the LDEO5 model.

Corresponding author address: Dr. Youmin Tang, Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, Prince George, BC V2N 4Z9, Canada. Email: ytang@unbc.ca

Abstract

El Niño–Southern Oscillation (ENSO) retrospective ensemble-based probabilistic predictions were performed for the period of 1856–2003 using the Lamont-Doherty Earth Observatory, version 5 (LDEO5), model. To obtain more reliable and skillful ENSO probabilistic predictions, first, four ensemble construction strategies were investigated: (i) the optimal initial perturbation with singular vector of sea surface temperature anomaly (SSTA), (ii) the realistic high-frequency anomalous winds, (iii) the stochastic optimal pattern of anomalous winds, and (iv) a combination of the first and the third strategy. Second, verifications were conducted to examine the reliability and resolution of the probabilistic forecasts provided by the four methods. Results suggest that reliability of ENSO probabilistic forecast is more sensitive to the choice of ensemble construction strategy than the resolution, and a reliable and skillful ENSO probabilistic prediction system may not necessarily have the best deterministic prediction skills. Among these ensemble construction methods, the fourth strategy produces the most reliable and skillful ENSO probabilistic prediction, benefiting from the joint contributions of the stochastic optimal winds and the singular vector of SSTA. In particular, the stochastic optimal winds play an important role in improving the ENSO probabilistic predictability for the LDEO5 model.

Corresponding author address: Dr. Youmin Tang, Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, Prince George, BC V2N 4Z9, Canada. Email: ytang@unbc.ca

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