An extension of LDEO5 model for ENSO ensemble predictions
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An extension of LDEO5 model for ENSO ensemble predictions Yanqiu Gao1,3 · Ting Liu1,3 · Xunshu Song1,3 · Zheqi Shen1,3 · Youmin Tang2,3 · Dake Chen1,3 Received: 4 February 2020 / Accepted: 18 August 2020 © The Author(s) 2020
Abstract This study provided an extension to the latest version of Lamont-Doherty Earth Observation (LDEO5) prediction system. First, an ensemble coupled data assimilation (CDA) system, based on the Ensemble Kalman Filter, was established. Both the Kaplan sea surface temperature (SST) data from January 1856 to December 2018 and the ECMWF twentieth century reanalysis (ERA-20C) wind data from January 1900 to February 2010 were assimilated for prediction initialization. Second, an ensemble prediction (EP) system was established using stochastic optimal perturbation that represented the uncertainty in the physical process. The assimilation experiments showed that assimilating multi-source data yielded better results than assimilating single-source data. The analyses of Niño3.4 SST anomalies and zonal wind stress (ZWS) anomalies were in good agreement with the observed counterparts, respectively. The root mean square errors of both Niño3.4 SST anomalies and ZWS anomalies were found to be significantly reduced, compared to the values obtained before assimilation. The modeled upper layer depth anomalies along the equator, and subsurface temperature anomalies in the Niño3.4 region were also found to be similar to the observed counterparts. A long-term ensemble hindcast was conducted using the EP system for the past 163 years, from January 1856 to December 2018. Results showed that the predictions initialized by assimilating multisource data yielded best deterministic skill, reaching the international advanced level. A comparative analysis revealed that the EP system predicted the warm events well, followed by cold and neutral events. Keywords ENSO · Ensemble kalman filter · Weakly coupled data assimilation · Assimilating multi-source data · Ensemble probabilistic prediction
1 Introduction El Niño-Southern Oscillation (ENSO) is the most prominent short-term climate oscillation on Earth, which significantly influences the climate and weather anomalies in most regions globally. Therefore, it is significantly important to study and predict ENSO, which has been the focus of atmospheric and marine sciences since the 1980s (Cane et al. 1986; Zebiak and Cane 1987; Ji et al. 1996; Latif et al. 1998; Behringer et al. 1998; Kang and Kug 2000; Barnston et al. * Youmin Tang [email protected] 1
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
2
Environmental Science and Engineering, University of Northern British Columbia, Prince George, Canada
3
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China
2012; Ham et al. 2012; Chen et al. 2004, 2015; Tang et al. 2018; Peng et al. 2018; Ham et al. 2019). Although scientists have made significant progress in theorizing ENSO as well as in observi
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