Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4599-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4599-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
11 Aug 2023
Model evaluation paper |
| 11 Aug 2023
| 11 Aug 2023
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Yi-Chi Wang
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
International Degree Program in Climate Change and Sustainable Development, National Taiwan University, Taipei, Taiwan
Yu-Luen Chen
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Shih-Yu Lee
CORRESPONDING AUTHOR
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Huang-Hsiung Hsu
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Hsin-Chien Liang
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
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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.
Bjerknes, J.: Atmospheric Teleconnections from the Equatorial Pacific, Mon.
Weather Rev., 97, 163–172,
https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2, 1969.
Cane, M. A.: The evolution of El Niño, past and future, Earth Planet. Sc.
Lett., 230, 227–240, https://doi.org/10.1016/j.epsl.2004.12.003, 2005.
Capotondi, A., Deser, C., Phillips, A. S., Okumura, Y., and Larson, S. M.:
ENSO and Pacific Decadal Variability in the Community Earth System Model
Version 2, J. Adv. Model. Earth Sy., 12, e2019MS002022, https://doi.org/10.1029/2019MS002022,
2020a.
Capotondi, A., Wittenberg, A. T., Kug, J., Takahashi, K., and McPhaden, M.
J.: ENSO Diversity, in: El Niño Southern Oscillation in a Changing
Climate, 65–86, https://doi.org/10.1002/9781119548164.ch4, 2020b.
Carton, J. A., Chepurin, G. A., and Chen, L.: SODA3: A New Ocean Climate
Reanalysis, J. Climate, 31, 6967–6983,
https://doi.org/10.1175/JCLI-D-18-0149.1, 2018.
Chen, C., Cane, M. A., Wittenberg, A. T., and Chen, D.: ENSO in the CMIP5
Simulations: Life Cycles, Diversity, and Responses to Climate Change, J. Climate, 30, 775–801, https://doi.org/10.1175/JCLI-D-15-0901.1, 2017.
Chen, C.-A., Hsu, H.-H., and Liang, H.-C.: Evaluation and comparison of
CMIP6 and CMIP5 model performance in simulating the seasonal extreme
precipitation in the Western North Pacific and East Asia, Weather Clim.
Extrem., 31, 100303, https://doi.org/10.1016/j.wace.2021.100303, 2021.
Chen, H.-C. and Jin, F.-F.: Simulations of ENSO Phase-locking in CMIP5 and
CMIP6, J. Climate, 34, 5135–5149, https://doi.org/10.1175/JCLI-D-20-0874.1, 2021.
Chen, J.-P., Tsai, I.-C., and Lin, Y.-C.: A statistical–numerical aerosol parameterization scheme, Atmos. Chem. Phys., 13, 10483–10504, https://doi.org/10.5194/acp-13-10483-2013, 2013.
Chen, Y.-L.: ENSO statistics, teleconnections, and atmosphere-ocean coupling in the Taiwan Earth System Model version 1 (Version 1), Zenodo [code], https://doi.org/10.5281/zenodo.7740033, 2023.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman,
P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri,
M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L.,
Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis
Project, Q. J. Roy. Meteor. Soc., 137, 1–28,
https://doi.org/10.1002/qj.776, 2011.
CREATE-IP Project: Data Description, ESGF [data set], https://esgf-node.llnl.gov/projects/create-ip/data_description (last access: December 2019), 2018.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
Diaz, H. F., Hoerling, M. P., and Eischeid, J. K.: ENSO variability, teleconnections and climate change, Int. J. Climatol., 21, 1845–1862, https://doi.org/10.1002/joc.631, 2001.
Fedorov, A. V., Hu, S., Wittenberg, A. T., Levine, A. F. Z., and Deser, C.:
ENSO Low-Frequency Modulation and Mean State Interactions, in: El Niño Southern Oscillation in a Changing Climate, 173–198,
https://doi.org/10.1002/9781119548164.ch8, 2020.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Glantz, M. H.: Currents of change: impacts of El Niño and La Niña on
climate and society, Cambridge University Press, ISBN 9780521786720, 2001.
Global Precipitation Climatology Project (GPCP): Global Precipitation Climatology Project (GPCP) Monthly Analysis Product, NOAA [data set], https://psl.noaa.gov/data/gridded/data.gpcp.html (last access: June 2020), 2009.
Guilyardi, E., Wittenberg, A., Fedorov, A., Collins, M., Wang, C.,
Capotondi, A., van Oldenborgh, G. J., and Stockdale, T.: Understanding El
Niño in Ocean–Atmosphere General Circulation Models: Progress and
Challenges, B. Am. Meteorol. Soc., 90, 325–340,
https://doi.org/10.1175/2008BAMS2387.1, 2009.
Guilyardi, E., Capotondi, A., Lengaigne, M., Thual, S., and Wittenberg, A.
T.: ENSO Modeling: history, progress, and challenges, in: El Niño
Southern Oscillation in a Changing Climate, edited by: McPhaden, M. J.,
Santoso, A., and Cai, W., American Geophsical Union, 199–226,
https://doi.org/10.1002/9781119548164.ch9, 2020.
Ham, Y.-G. and Kug, J.-S.: How well do current climate models simulate two
types of El Nino?, Clim. Dynam., 39, 383–398,
https://doi.org/10.1007/s00382-011-1157-3, 2012.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2023a.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.6860a573, 2023b.
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore,
J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: Extended
Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades,
Validations, and Intercomparisons, J. Climate, 30, 8179–8205,
https://doi.org/10.1175/JCLI-D-16-0836.1, 2017.
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5, NOAA [data set], https://doi.org/10.7289/V5T72FNM, 2018.
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Gu, G.: Improving the
global precipitation record: GPCP Version 2.1, Geophys. Res. Lett., 36, L17808,
https://doi.org/10.1029/2009GL040000, 2009.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner,
P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb,
W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P.,
Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl,
J., and Marshall, S.: The Community Earth System Model: A Framework for
Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360,
https://doi.org/10.1175/BAMS-D-12-00121.1, 2013.
Jha, B., Hu, Z.-Z., and Kumar, A.: SST and ENSO variability and change
simulated in historical experiments of CMIP5 models, Clim. Dynam., 42,
2113–2124, https://doi.org/10.1007/s00382-013-1803-z, 2014.
Jin, F.-F.: An Equatorial Ocean Recharge Paradigm for ENSO. Part I:
Conceptual Model, J. Atmos. Sci., 54, 811–829,
https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2, 1997.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic Characteristics,
J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015.
Kug, J.-S., Jin, F.-F., and An, S.-I.: Two Types of El Niño Events: Cold
Tongue El Niño and Warm Pool El Niño, J. Climate, 22, 1499–1515,
https://doi.org/10.1175/2008JCLI2624.1, 2009.
Kumar, A. and Hu, Z.-Z.: Uncertainty in the ocean–atmosphere feedbacks
associated with ENSO in the reanalysis products, Clim. Dynam., 39, 575–588,
https://doi.org/10.1007/s00382-011-1104-3, 2012.
Latif, M., Anderson, D., Barnett, T., Cane, M., Kleeman, R., Leetmaa, A.,
O'Brien, J., Rosati, A., and Schneider, E.: A review of the predictability
and prediction of ENSO, J. Geophys. Res.-Oceans, 103, 14375–14393,
https://doi.org/10.1029/97JC03413, 1998.
Lee, W.-L. and Liang, H.-C.: AS-RCEC TaiESM1.0 model output prepared for CMIP6 CMIP historical, Version 20200623[1], Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.9755, 2020.
Lee, W.-L., Liou, K. N., and Wang, C.: Impact of 3-D topography on surface
radiation budget over the Tibetan Plateau, Theor. Appl. Climatol., 113,
95–103, https://doi.org/10.1007/s00704-012-0767-y, 2013.
Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., Chen, J.-P., Pan, H.-L., and Hsu, H.-H.: Taiwan Earth System Model Version 1: description and evaluation of mean state, Geosci. Model Dev., 13, 3887–3904, https://doi.org/10.5194/gmd-13-3887-2020, 2020.
Lloyd, J., Guilyardi, E., Weller, H., and Slingo, J.: The role of atmosphere
feedbacks during ENSO in the CMIP3 models, Atmos. Sci. Lett., 10,
170–176, https://doi.org/10.1002/asl.227, 2009.
Lloyd, J., Guilyardi, E., and Weller, H.: The role of atmosphere feedbacks
during ENSO in the CMIP3 models. Part II: using AMIP runs to understand the
heat flux feedback mechanisms, Clim. Dynam., 37, 1271–1292,
https://doi.org/10.1007/s00382-010-0895-y, 2011.
McPhaden, M. J., Busalacchi, A. J., Cheney, R., Donguy, J.-R., Gage, K. S.,
Halpern, D., Ji, M., Julian, P., Meyers, G., Mitchum, G. T., Niiler, P. P.,
Picaut, J., Reynolds, R. W., Smith, N., and Takeuchi, K.: The Tropical
Ocean-Global Atmosphere observing system: A decade of progress, J. Geophys. Res.-Oceans, 103, 14169–14240, https://doi.org/10.1029/97JC02906, 1998.
McPhaden, M. J., Zebiak, S. E., and Glantz, M. H.: ENSO as an Integrating
Concept in Earth Science, Science, 314, 1740–1745,
https://doi.org/10.1126/science.1132588, 2006.
McPhaden, M. J., Lee, T., and McClurg, D.: El Niño and its relationship
to changing background conditions in the tropical Pacific Ocean, Geophys. Res. Lett., 38, L15709, https://doi.org/10.1029/2011GL048275, 2011.
Neelin, J. D., Battisti, D. S., Hirst, A. C., Jin, F.-F., Wakata, Y.,
Yamagata, T., and Zebiak, S. E.: ENSO theory, J. Geophys. Res.-Oceans, 103,
14261–14290, https://doi.org/10.1029/97JC03424, 1998.
NOAA Climate Prediction Center: Historical El Nino/La Nina episodes (1950–present), NOAA [data set], https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears_1971-2000_climo.shtml (last access: December 2020), 2000.
Onogi, K., Tsutsui, J., Koide, H., Sakamoto, M., Kobayashi, S., Hatsushika,
H., Matsumoto, T., Yamazaki, N., Kamahori, H., Takahashi, K., Kadokura, S.,
Wada, K., Kato, K., Oyama, R., Ose, T., Mannoji, N., and Taira, R.: The
JRA-25 Reanalysis, J. Meteorol. Soc. Jpn. Ser. II,
85, 369–432, https://doi.org/10.2151/jmsj.85.369, 2007.
Park, J., Kim, H., Simon Wang, S.-Y., Jeong, J.-H., Lim, K.-S., LaPlante,
M., and Yoon, J.-H.: Intensification of the East Asian summer monsoon
lifecycle based on observation and CMIP6, Environ. Res. Lett.,
15, 0940b9, https://doi.org/10.1088/1748-9326/ab9b3f, 2020.
Philander, S.: El Nino, La Nina, and the Southern Oscillation, edited by:
Philander, S. G., Academic Press, 1–293, ISBN 9780080570983, 1989.
Planton, Y. Y., Guilyardi, E., Wittenberg, A. T., Lee, J., Gleckler, P. J.,
Bayr, T., McGregor, S., McPhaden, M. J., Power, S., Roehrig, R., Vialard,
J., and Voldoire, A.: Evaluating Climate Models with the CLIVAR 2020 ENSO
Metrics Package, B. Am. Meteorol. Soc., 102, E193–E217,
https://doi.org/10.1175/BAMS-D-19-0337.1, 2021.
Potter, G. L., Carriere, L., Hertz, J., Bosilovich, M., Duffy, D., Lee, T.,
and Williams, D. N.: Enabling Reanalysis Research Using the Collaborative
Reanalysis Technical Environment (CREATE), B. Am. Meteorol. Soc., 99,
677–687, https://doi.org/10.1175/BAMS-D-17-0174.1, 2018.
rceclccr: rceclccr/TaiESM v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.3626654, 2020.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom,
S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J.,
Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P.,
Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz,
M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for
Research and Applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J.,
Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou,
C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge,
G., and Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010.
Shiu, C.-J., Wang, Y.-C., Hsu, H.-H., Chen, W.-T., Pan, H.-L., Sun, R., Chen, Y.-H., and Chen, C.-A.: GTS v1.0: a macrophysics scheme for climate models based on a probability density function, Geosci. Model Dev., 14, 177–204, https://doi.org/10.5194/gmd-14-177-2021, 2021.
Simple Ocean Data Assimilation (SODA) project: SODA3.3.2 download page, SODA [data set], https://www2.atmos.umd.edu/~ocean/index_files/soda3.3.2_mn_download_b.htm (last access: March 2023), 2018.
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J., Dukowicz, J., Fox-Kemper, B., Eden, C., Gent, P., Hecht,
M., Jayne, S., Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, N., Peacock, S., Vertenstein, M., and Yeager, S.: The Parallel Ocean Program (POP) Reference Manual: : Ocean component of the community climate system model, Los Alamos National Laboratory Tech. Rep. LAUR-10-01853, 2010.
Wang, C.: A review of ENSO theories, Natl. Sci. Rev., 5, 813–825,
https://doi.org/10.1093/nsr/nwy104, 2018.
Wang, C. and Picaut, J.: Understanding Enso Physics-A Review, in: Earth's
Climate: The Ocean-Atmosphere Interaction, edited by: Wang, C., Xie, S.-P.,
and Carton, J. A., American Geophysical Union, 21–48,
https://doi.org/10.1029/147GM02, 2013.
Wang, C.-C., Lee, W.-L., Chen, Y.-L., and Hsu, H.-H.: Processes Leading to
Double Intertropical Convergence Zone Bias in CESM1/CAM5, J. Climate, 28,
2900–2915, https://doi.org/10.1175/JCLI-D-14-00622.1, 2015.
Wang, Y., Hsu, H., Chen, C., Tseng, W., Hsu, P., Lin, C., Chen, Y., Jiang,
L., Lee, Y., Liang, H., Chang, W., Lee, W., and Shiu, C.: Performance of the
Taiwan Earth System Model in Simulating Climate Variability Compared With
Observations and CMIP6 Model Simulations, J. Adv. Model. Earth Sy., 13, e2020MS002353,
https://doi.org/10.1029/2020MS002353, 2021.
Wang, Y.-C., Pan, H. L., and Hsu, H. H.: Impacts of the triggering function
of cumulus parameterization on warm-season diurnal rainfall cycles at the
Atmospheric Radiation Measurement Southern Great Plains Site, J. Geophys. Res.,
120, 10681–10702, https://doi.org/10.1002/2015JD023337, 2015.
Wei, H., Subramanian, A. C., Karnauskas, K. B., DeMott, C. A., Mazloff, M.
R., and Balmaseda, M. A.: Tropical Pacific Air-Sea Interaction Processes and
Biases in CESM2 and Their Relation to El Niño Development, J. Geophys. Res.-Oceans, 126, 185–206, https://doi.org/10.1029/2020JC016967, 2021.
Yeh, S.-W., Cai, W., Min, S.-K., McPhaden, M. J., Dommenget, D., Dewitte, B., Collins, M., Ashok, K., An, S.-I., Yim, B.-Y., and Kug, J.-S.: ENSO Atmospheric Teleconnections and Their Response to Greenhouse Gas Forcing, Rev. Geophys., 56, 185–206, https://doi.org/10.1002/2017RG000568, 2018.
Zhao, B. and Fedorov, A.: The Effects of Background Zonal and Meridional
Winds on ENSO in a Coupled GCM, J. Climate, 33, 2075–2091,
https://doi.org/10.1175/JCLI-D-18-0822.1, 2020.
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.
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1...
