Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 10
Articles | Volume 15, issue 10
https://doi.org/10.5194/gmd-15-4105-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4105-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 10
Model description paper
 | 
25 May 2022
Model description paper |  | 25 May 2022

Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0

Bin Mu, Yuehan Cui, Shijin Yuan, and Bo Qin

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Short summary
An ENSO deep learning forecast model (ENSO-MC) is built to simulate the spatial evolution of sea surface temperature, analyse the precursor and identify the sensitive area. The results reveal the pronounced subsurface features before different types of events and indicate that 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.
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