Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 11
Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-6977-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6977-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 11
Model description paper
 | 
17 Nov 2021
Model description paper |  | 17 Nov 2021

ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler

Bin Mu, Bo Qin, and Shijin Yuan

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Cited articles

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Barnston, A. G., Tippett, M. K., L'Heureux, M. L., Li, S., and DeWitt, D. G.: Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing?, B. Am. Meteorol. Soc., 93, 631–651, 2012. 
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Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air–sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
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