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

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Cited articles

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003. 
Bayr, T., Latif, M., Dommenget, D., Wengel, C., Harlaß, J., and Park, W.: Mean-state dependence of ENSO atmospheric feedbacks in climate models, Clim. Dynam., 50, 3171–3194, https://doi.org/10.1007/s00382-017-3799-2, 2018. 
Bayr, T., Wengel, C., Latif, M., Dommenget, D., Lübbecke, J., and Park, W.: Error compensation of ENSO atmospheric feedbacks in climate models and its influence on simulated ENSO dynamics, Clim. Dynam., 53, 155–172, https://doi.org/10.1007/s00382-018-4575-7, 2019. 
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: from CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018, https://doi.org/10.1007/s00382-013-1783-z, 2014. 
Beobide-Arsuaga, G., Bayr, T., Reintges, A., and Latif, M.: Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models, Clim. Dynam., 56, 3875–3888, https://doi.org/10.1007/s00382-021-05673-4, 2021. 
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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.