Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4599-2023
https://doi.org/10.5194/gmd-16-4599-2023
Model evaluation paper
 | 
11 Aug 2023
Model evaluation paper |  | 11 Aug 2023

ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1

Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang

Data sets

CMIP6.CMIP.AS-RCEC.TaiESM1.historical Wei-Liang Lee and Hsin-Chien Liang https://doi.org/10.22033/ESGF/CMIP6.9755

ERA5 monthly averaged data on single levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.f17050d7

ERA5 monthly averaged data on pressure levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.6860a573

rceclccr/TaiESM v1.0.0 (v1.0.0) rceclccr https://doi.org/10.5281/zenodo.3626654

Data Description CREATE-IP Project https://esgf-node.llnl.gov/projects/create-ip/data_description

SODA3.3.2 download page Simple Ocean Data Assimilation (SODA) project https://www2.atmos.umd.edu/~ocean/index_files/soda3.3.2_mn_download_b.htm

Model code and software

post-processing code for "ENSO statistics, teleconnections, and atmosphere-ocean coupling in the Taiwan Earth System Model version 1"" Yu-Luen Chen https://doi.org/10.5281/zenodo.7740033

Taiwan Earth System Model v1.0.0 rceclccr https://doi.org/10.5281/zenodo.3626654

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Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.