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

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

doi:https://doi.org/10.5194/gmd-15-4105-2022

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

Data sets

Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century (https://rda.ucar.edu/) N. A. Rayner, D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan https://doi.org/10.1029/2002JD002670

El Niño variability in simple ocean data assimilation (SODA) (https://iridl.ldeo.columbia.edu/) Benjamin S. Giese and Sulagna Ray https://doi.org/10.1029/2010JC006695

Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean (https://psl.noaa.gov/data/gridded/) David Behringer and Yan Xue https://origin.cpc.ncep.noaa.gov/products/people/yxue/pub/13.pdf

An improved in situ and satellite SST analysis for climate (https://psl.noaa.gov/data/gridded/) Richard W. Reynolds, Nick A. Rayner, Thomas M. Smith, Diane C. Stokes, and Wanqiu Wang https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2

The ERA-Interim reanalysis: Configuration and performance of the data assimilation system (https://apps.ecmwf.int/datasets/) D. P. Dee, S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J.-N. Thépaut, and F. Vitart https://doi.org/10.1002/qj.828

Model code and software

ENSO-MC Model Skye Cui https://doi.org/10.5281/zenodo.5725987

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.