Submitted as: model description paper 04 Jan 2022

Submitted as: model description paper | 04 Jan 2022

Review status: this preprint is currently under review for the journal GMD.

Simulation, Precursor Analysis and Targeted Observation Sensitive Area Identification for Two Types of ENSO using ENSO-MC v1.0

Bin Mu1,, Yuehan Cui1,, Shijin Yuan1, and Bo Qin1 Bin Mu et al.
  • 1School of Software Engineering, Tongji University, Shanghai, China
  • These authors contributed equally to this work.

Abstract. The global impact of an El Niño-Southern Oscillation (ENSO) event can differ greatly depending on whether it is an Eastern-Pacific-type (EP-type) event or a Central-Pacific-type (CP-type) event. Reliable predictions of the two types of ENSO are therefore of critical importance. Here we construct a deep neural network with multichannel structure for ENSO (named ENSO-MC) to simulate the spatial evolution of sea surface temperature (SST) anomalies for the two types of events. We select SST, heat content, and wind stress (i.e., three key ingredients of Bjerknes feedback) to represent coupled ocean-atmosphere dynamics that underpins ENSO, achieving skillful forecasts for the spatial patterns of SST anomalies out to one year ahead. Furthermore, it is of great significance to analyze the precursors of EP-type or CP-type events and identify targeted observation sensitive area for the understanding and prediction of ENSO. Precursors analysis is to determine what type of initial perturbations will develop into EP-type or CP-type events. Sensitive area identification is to determine the regions where initial states tend to have greatest impacts on evolution of ENSO. We use saliency map method to investigate the subsurface precursors and identify the sensitive areas of ENSO. The results show that there are pronounced signals in the equatorial subsurface before EP events, while the precursory signals of CP events are located in the North Pacific. It indicates that the subtropical precursors seem to favor the generation of the CP-type El Niño and the EP-type El Niño is more related to the tropical thermocline dynamics. And the saliency maps show that the sensitive areas of the surface and the subsurface are located in the equatorial central Pacific and the equatorial western Pacific, respectively. The sensitivity experiments imply that additional observations in the identified sensitive areas can improve forecasting skills. Our results of precursors and sensitive areas are consistent with the previous theories of ENSO, demonstrating the potential usage and advantages of the ENSO-MC model in improving the simulation, understanding and observations of two ENSO types.

Bin Mu et al.

Status: open (until 01 Mar 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-396', Anonymous Referee #1, 24 Jan 2022 reply

Bin Mu et al.

Bin Mu et al.


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Short summary
An ENSO deep learning forecast model (ENSO-MC) is built up to simulate the spatial evolution of sea surface temperature, analyze the precursor and identify the sensitive area. The results reveal the pronounced subsurface features before different types of events and indicate oceanic thermal anomaly in the central and western Pacific provides a key long-term memory for predictions, demonstrating the potential usage of the ENSO-MC model in simulation, understanding and observations of ENSO.