Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-3887-2020
© Author(s) 2020. 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-13-3887-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Taiwan Earth System Model Version 1: description and evaluation of mean state
Wei-Liang Lee
CORRESPONDING AUTHOR
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Yi-Chi Wang
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Chein-Jung Shiu
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
I-chun Tsai
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Chia-Ying Tu
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Yung-Yao Lan
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Jen-Ping Chen
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
Hua-Lu Pan
National Center for Environmental Protection, Camp Springs, Maryland, USA
Huang-Hsiung Hsu
Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
Related authors
Yung-Yao Lan, Huang-Hsiung Hsu, Wei-Liang Lee, and Simon Chou
Geosci. Model Dev., 19, 7479–7502, https://doi.org/10.5194/gmd-19-7479-2026, https://doi.org/10.5194/gmd-19-7479-2026, 2026
Short summary
Short summary
This study links wave and ocean models in real time to better simulate how our oceans breathe. Unlike older models that treat waves and oceans separately, this new approach captures their dynamic interactions. It reveals that wave-generated bubbles drive up to 40% of the gas exchange, boosting the estimated global ocean carbon sink by 1.8%. This helps scientists better predict climate change and understand the ocean's natural capacity to buffer global warming.
Wan-Ling Tseng, Huang-Hsiung Hsu, Yung-Yao Lan, Wei-Liang Lee, Chia-Ying Tu, Pei-Hsuan Kuo, Ben-Jei Tsuang, and Hsin-Chien Liang
Geosci. Model Dev., 15, 5529–5546, https://doi.org/10.5194/gmd-15-5529-2022, https://doi.org/10.5194/gmd-15-5529-2022, 2022
Short summary
Short summary
We show that coupling a high-resolution one-column ocean model to three atmospheric general circulation models dramatically improves Madden–Julian oscillation (MJO) simulations. It suggests two major improvements to the coupling process in the preconditioning phase and strongest convection phase over the Maritime Continent. Our results demonstrate a simple but effective way to significantly improve MJO simulations and potentially seasonal to subseasonal prediction.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
Short summary
Short summary
Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Yung-Yao Lan, Huang-Hsiung Hsu, Wei-Liang Lee, and Simon Chou
Geosci. Model Dev., 19, 7479–7502, https://doi.org/10.5194/gmd-19-7479-2026, https://doi.org/10.5194/gmd-19-7479-2026, 2026
Short summary
Short summary
This study links wave and ocean models in real time to better simulate how our oceans breathe. Unlike older models that treat waves and oceans separately, this new approach captures their dynamic interactions. It reveals that wave-generated bubbles drive up to 40% of the gas exchange, boosting the estimated global ocean carbon sink by 1.8%. This helps scientists better predict climate change and understand the ocean's natural capacity to buffer global warming.
Wen-Chien Lee, Ming-Hao Huang, Wei-Chieh Huang, Jen-Ping Chen, Yen-Jen Lai, Haojia Ren, and Hui-Ming Hung
Atmos. Chem. Phys., 26, 10947–10963, https://doi.org/10.5194/acp-26-10947-2026, https://doi.org/10.5194/acp-26-10947-2026, 2026
Short summary
Short summary
We studied nitrogen pollution in Taiwan's mountain forests to track how urban emissions reach and transform in remote areas. Isotope analysis and statistical modeling revealed that combustion sources contributed 50–83 % of ammonia, while nitrate forms continuously from urban to rural sampling sites. The findings show that persistent urban pollution strongly impacts mountain ecosystems, offering key insights for air quality management.
Tzu-Chin Tsai, Jen-Ping Chen, Zhiquan Liu, Siou-Ying Jiang, Rong Kong, Ying-Jhang Wu, Junmei Ban, Ling-Feng Hsiao, Yu-Shuang Tang, Pao-Liang Chang, and Jing-Shan Hong
Geosci. Model Dev., 19, 6231–6257, https://doi.org/10.5194/gmd-19-6231-2026, https://doi.org/10.5194/gmd-19-6231-2026, 2026
Short summary
Short summary
We developed a new cloud microphysics scheme that links simulated cloud and precipitation particles with radar signals. Idealized squall line and a real thunderstorm case in Taiwan show that the scheme produces physically meaningful storm structures and encouraging radar comparisons, while further refinement is still needed for ice particles, hydrometeor orientation, and radar-viewing assumptions.
Jen-Ping Chen, I-Chun Tsai, Li-Wei Kuo, and Gong-Do Hwang
Atmos. Chem. Phys., 26, 6909–6927, https://doi.org/10.5194/acp-26-6909-2026, https://doi.org/10.5194/acp-26-6909-2026, 2026
Short summary
Short summary
This study identifies a previously unrepresented process, Recondensation-Induced Nucleation (RIN), linking combustion microphysics to ambient ultrafine particle formation. Laboratory engine experiments, parcel modeling, and Community Multiscale Air Quality (CMAQ) simulations show that including RIN can significantly reduce number concentration underestimation from 75 % to 22 % while maintaining PM2.5 accuracy, providing a mechanistic basis for improving urban aerosol modeling.
Chao-Tzuen Cheng, Chihchung Chou, Ping-Yi Lin, Huang-Hsiung Hsu, and Yung-Ming Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-242, https://doi.org/10.5194/essd-2026-242, 2026
Preprint under review for ESSD
Short summary
Short summary
The TReAD dataset, developed by the TCCIP project, provides a high-resolution (2 km) 40-year climate reconstruction (1980–2019) for Taiwan’s complex terrain. By downscaling ERA5 data using the WRF model, we minimized physical biases to create a continuous historical record. Validated against ground observations, TReAD excels in capturing temperature, pressure, and extreme events like Typhoon Morakot. It is a vital tool for hydroclimatic and ecological research where station data is too sparse.
Katherine Shu-Min Li, Nadun Sinhabahu, Ben-Jei Tsuang, Fang-Chi Wu, Wan-Ling Tseng, Pei-Hsuan Kuo, Sying-Jyan Wang, Pang-Yen Liu, Jen-Her Chen, Bin-Ming Wang, Yung-Yao Lan, and Sun-Yuan Kung
EGUsphere, https://doi.org/10.5194/egusphere-2025-142, https://doi.org/10.5194/egusphere-2025-142, 2025
Short summary
Short summary
This study underscores the transformative potential of machine learning algorithms in environmental forecasting. The superior performance of Bi-LSTM in reducing SST bias, coupled with its broader applicability in time-series analysis, makes it a valuable tool for improving the accuracy and reliability of numerical weather prediction models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
Short summary
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.
Yung-Yao Lan, Huang-Hsiung Hsu, Wan-Ling Tseng, and Li-Chiang Jiang
Geosci. Model Dev., 15, 5689–5712, https://doi.org/10.5194/gmd-15-5689-2022, https://doi.org/10.5194/gmd-15-5689-2022, 2022
Short summary
Short summary
This study has shown that coupling a high-resolution 1-D ocean model (SIT 1.06) with the Community Atmosphere Model 5.3 (CAM5.3) significantly improves the simulation of the Madden–Julian Oscillation (MJO) over the standalone CAM5.3. Systematic sensitivity experiments resulted in more realistic simulations of the tropical MJO because they had better upper-ocean resolution, adequate upper-ocean thickness, coupling regions including the eastern Pacific and southern tropics, and a diurnal cycle.
Wan-Ling Tseng, Huang-Hsiung Hsu, Yung-Yao Lan, Wei-Liang Lee, Chia-Ying Tu, Pei-Hsuan Kuo, Ben-Jei Tsuang, and Hsin-Chien Liang
Geosci. Model Dev., 15, 5529–5546, https://doi.org/10.5194/gmd-15-5529-2022, https://doi.org/10.5194/gmd-15-5529-2022, 2022
Short summary
Short summary
We show that coupling a high-resolution one-column ocean model to three atmospheric general circulation models dramatically improves Madden–Julian oscillation (MJO) simulations. It suggests two major improvements to the coupling process in the preconditioning phase and strongest convection phase over the Maritime Continent. Our results demonstrate a simple but effective way to significantly improve MJO simulations and potentially seasonal to subseasonal prediction.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
Short summary
Short summary
Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Cited articles
Artale, V., Iudicone, D., Santoleri, R., Rupolo, V., Marullo, S., and
D'Ortenzio, F.: Role of surface fluxes in ocean general circulation models
using satellite sea surface temperature: Validation of and sensitivity to
the forcing frequency of the Mediterranean thermohaline circulation, J.
Geophys. Res.-Oceans, 107, 29-1–29-24, 2002.
Bretherton, C. S. and Park, S.: A new moist turbulence parameterization in
the Community Atmosphere Model, J. Climate, 22, 3422–2448, https://doi.org/10.1175/2008JCLI2556.1, 2009.
Byun, D. and Schere, K. L.: Review of the Governing Equations,
Computational Algorithms, and Other Components of the Models-3 Community
Multiscale Air Quality (CMAQ) Modeling System, Appl. Mech. Rev., 59,
51–77, 2006.
Carbone, R. E. and J. D. Tuttle: Rainfall occurrence in the U.S. warm
season: The diurnal cycle, J. Climate, 21, 4132–4146, https://doi.org/10.1175/2008JCLI2275.1, 2008.
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., Hall, A., and Liou, K. N.: Application of 3D solar radiative
transfer to mountains, J. Geophys. Res., 111, D21111,
https://doi.org/10.1029/2006JD007163, 2006.
Cook, K. H., Meel, G. A., and Arblaster, J. M.: Monsoon regimes and
processes in CCSM4, part II: African and American monsoon systems, J.
Climate, 25, 2609–2621, https://doi.org/10.1175/JCLI-D-11-00185.1, 2012.
Dirmeyer, P. A., Cash, B. A., Kinter, J. L., Jung, T., Marx, L., Satoh, M.,
Stan, C., Tomita, H., Towers, P., Wedi, N., Achuthavarier, D., Adams, J. M.,
Altshuler, E. L., Huang, B., Jin, E. K., and Manganello, J.: Simulating the
diurnal cycle of rainfall in global climate models: resolution versus
parameterization, Clim. Dynam., 39, 399–418, https://doi.org/10.1007/s00382-011-1127-9, 2011.
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo
evolution, J. Geophys. Res., 111, D12208, https://doi.org/10.1029/2005JD006834, 2006.
Gaspar, P., Gregoris, Y., and Lefevre, J.-M.: A simple eddy kinetic energy
model for simulations of the oceanic vertical mixing: Tests at station Papa
and long-term upper ocean study site, J. Geophys. Res.-Oceans, 95,
16179–16193, 1990.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C.,
Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M.,
Worley, P. H., Yang, Z.-L., and Zhang, M.: The Community Climate System
Model, version 4, J. Climate, 24, 4973–4991, 2011.
Gu, Y., Liou, K. N., Lee, W.-L., and Leung, L. R.: Simulating 3-D radiative transfer effects over the Sierra Nevada Mountains using WRF, Atmos. Chem. Phys., 12, 9965–9976, https://doi.org/10.5194/acp-12-9965-2012, 2012.
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for
climate models, J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007JD008972, 2008.
Griffies, S. M., Winton, M., Donner, L. J., horowitz, L. W., Downes, S. M.,
Farneti, R., Gnanadesikan, A., Hurlin, W. J., Lee, H. C., Liang, Z., Palter,
J. B., Samuels, B. L., Wittenberg, A. T., Wyman, B. L., Yin J., and Zadeh,
N.: The GFDL CM3 coupled climate model: characteristics of the ocean and sea
ice simulations, J. Climate, 24, 3520–3544, https://doi.org/10.1175/2011JCLI3964.1, 2009.
Han, J. and Pan, H.-L.: Revision of convection and vertical diffusion
schemes in the NCEP global forecast system, Weather Forecast., 26,
520–533, https://doi.org/10.1175/WAF-D-10-05038.1, 2011.
Hirota, N. and Takayabu, Y. N.: Reproducibility of precipitation
distribution over the tropical oceans in CMIP5 multi-climate models compared
to CMIP3, Climate Dyn., 41, 2909–2920, https://doi.org/10.1007/s00382-013-1839-0, 2013.
Hsu, H.-H., Chou, C., Wu, Y.-C., Lu, M.-M., Chen, C.-T., and Chen, Y.-M.:
Climate change in Taiwan: Scientific Report 2011 (Summary), National Science
Council, Taipei, Taiwan, 67 pp., 2011.
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.
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.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F.,
Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM multisatellite
precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/JHM560.1, 2007.
Hunke, E. C. and Lipscomb, W. H.: CICE: The Los Alamos sea ice model.
Documentation and Software User's Manual, Version 4.0, T-3 Fluid Dynamics
Group, Los Alamos National Laboratory, Tech. Rep. LA-CC-06-012, 76 pp.,
2008.
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, 1319–1360,
https://doi.org/10.1175/BAMS-D-12-00121, 2013.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113,
D13103, https://doi.org/10.1029/2008JD009944, 2008.
IPCC: Climate Change 2013: The Physical Science Basis,
Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia,
Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK,
New York, NY, USA, 2013.
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X., Smith, W. L., Su, Wenying, and Ham, S.-H.: Surface irradiances of Edition 4.0 Clouds and the Earth's Radiant
Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product, J.
Climate, 31, 4501–4527, https://doi.org/10.1175/JCLI-D-17-0523.1, 2018.
Kay, J. E. and Gettelman, A.: Cloud influence on and response to seasonal
Arctic sea ice loss, J. Geophys. Res., 114, D18204, https://doi.org/10.1029/2009JD011773, 2009.
Kay, J. E., Bourdages, L., Miller, N. B., Morrison, A., Yettella, V.,
Chepfer, H., and Eaton, B.: Evaluating and improving cloud phase in the
Community Atmosphere Model version 5 using spaceborne lidar observations, J.
Geophys. Res.-Atmos., 121, 416-4176, https://doi.org/10.1002/2015JD024699, 2016.
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S.
C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K., Bonan,
G. B., and Slater, A. G.: Parameterization improvements and functional and
structural advances in version 4 of the Community Land Model, J. Adv. Model.
Earth Syst., 3, M03001, https://doi.org/10.1029/2011MS000045,
2011.
Lee, M.-I., Schubert, S. D., Suarez, M. J., Schemm, J.-K. E., Pan, H.-L., Han, J., and Yoo, S. H.: Role of convection triggers in the simulation of the diurnal cycle of precipitation over the United States Great Plains in a general circulation model, J. Geophys. Res., 113, D02111, https://doi.org/10.1029/2007JD008984, 2008.
Lee, W.-L., Liou, K. N., and Hall, A.: Parameterization of solar fluxes
over mountain surfaces for application to climate models, J.
Geophys. Res., 116, D01101, https://doi.org/10.1029/2010JD014722, 2011.
Lee, W.-L., Liou, K. N., and Wang, C.-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., Gu, Y., Liou, K. N., Leung, L. R., and Hsu, H.-H.: A global model simulation for 3-D radiative transfer impact on surface hydrology over the Sierra Nevada and Rocky Mountains, Atmos. Chem. Phys., 15, 5405–5413, https://doi.org/10.5194/acp-15-5405-2015, 2015.
Lee, W.-L., Liou, K.-N., Wang, C.-c, Gu, Y., Hsu, H.-H., and Li, J.-L. F.:
Impact of 3-D radiation-topography interactions on surface temperature and
energy budget over the Tibetan Plateau in winter, J. Geophys. Res.-Atmos.,
124, 1537–1549, https://doi.org/10.1029/2018JD029592, 2019.
Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I.-C., Tu, C.-Y., Lan, Y.-Y., and Hwang, G.-D.: Taiwan Earth System Model v1.0.0, Zenodo, https://doi.org/10.5281/zenodo.3626654, 2020.
Lenssen, N., Schmidt, G., Hansen, J., Menne, M., Persin, A., Ruedy, R., and
Zyss, D.: Improvements in the GISTEMP uncertainty model, J. Geophys. Res.-Atmos., 124, 6307–6326, https://doi.org/10.1029/2018JD029522,
2019.
Lin, J.-L.: The double-ITCZ problem in IPCC AR4 coupled GCMs:
ocean-atmosphere feedback analysis, J. Climate, 20, 4497–4525, https://doi.org/10.1175/JCLI4272.1, 2007.
Lin, S. J.: A “vertically Lagrangian” finite-volume dynamical core for
global models, Mon. Weather Rev., 132, 2293–2307, 2004.
Liou, K. N., Lee, W.-L., and Hall, A.: Radiative transfer in mountains:
Application to the Tibetan Plateau, Geophys. Res. Lett., 34,
L23809, https://doi.org/10.1029/2007GL031762, 2007.
Liou, K. N., Gu, Y., Leung, L. R., Lee, W. L., and Fovell, R. G.: A WRF simulation of the impact of 3-D radiative transfer on surface hydrology over the Rocky Mountains and Sierra Nevada, Atmos. Chem. Phys., 13, 11709–11721, https://doi.org/10.5194/acp-13-11709-2013, 2013.
Morrison, H. and Gettelman, A.: A new two-moment bulk stratiform cloud
microphysics scheme in the Community Atmosphere Model, version 3 (CAM3) –
Part I: Description and numerical tests, J. Climate, 21, 3642–3659,
https://doi.org/10.1175/2008JCLI2105.1, 2008.
Neale, R. B., Richter, J. H., and Jochum, M.: The impact of convection on
ENSO: from a delayed oscillator to a series of events, J. Climate, 21,
5904–5924, https://doi.org/10.1175/2008JCLI2244.1, 2008.
Neale, R. B., Gettelman, A., Park, S., Chen, C.-C., Lauritzen, P. H.,
Williamson, D. L., Conley, A., J., Kinnison, D., Marsh, D., Smith, A. K.,
Lamarque, J.-F., Tilmes, S., Morrison, H., Cameron-Smith, P., Collins, W.
D., Iacono, M. J., Liu, X., Rasch, P. J., and Taylor, M. A.: Description of
the NCAR Community Atmosphere Model (CAM 5.0), NCAR Tech. Note, TN-486, National Center for Atmospheric Research, Boulder, CO, USA, 274 pp., 2010.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E.,
Lawrence, P. J., Levis, S., Swenson, S., C., Thornton, P. E., Dai, A.,
Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque,
J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J, Running, S.,
Sakaguchi, K, and Slater, A.: Technical description of version 4.0 of the
Community Land Model (CLM), Tech. Rep. NCAR/TN-478+STR, National
Center for Atmospheric Research, Boulder, CO, USA, 257 pp., 2010.
Pan, H. and Wu, W.: Implementing a mass flux convection parameterization
package for the NMC medium-range forecast model, Tech. Rep., NMC Office
Note, No. 409, Washington DC, 1995.
Park, S. and Bretherton, C. S.: The University of Washington shallow
convection and moist turbulence schemes and their impact on climate
simulations with the Community Atmosphere Model, J. Climate, 22,
3449–3469, https://doi.org/10.1175/2008JCLI2557.1, 2009.
Park, S., Bretherton, C. S., and Rasch, P. J.: Integrating cloud processes
in the community atmosphere model, version 5, J. Climate, 27,
6821–6856, 2014.
Peng, G., Meier, W. N., Scott, D. J., and Savoie, M. H.: A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring, Earth Syst. Sci. Data, 5, 311–318, https://doi.org/10.5194/essd-5-311-2013, 2013.
Pincus, R., Barker, H. W., and Morcrette, J.-J.: A fast, flexible,
approximation technique for computing radiative transfer in inhomogeneous
cloud fields, J. Geophys. Res., 108, 4376, https://doi.org/10.1029/2002JD003322, 2003.
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.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L.
V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface
temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
Rohde, R., Muller, R. A., Jacobsen, R., Muller, E., Perlmutter, S.,
Rosenfeld, A., Wurtele, J., Groom, D., and Wickham, C.: A new estimate of
the average Earth surface land temperature spanning 1753 to 2011, Geoinfor.
Geostat.: An Overview, 1, 1, https://doi.org/10.4172/2327-4581.1000101, 2013
Seigneur, C., Hudischewskyj, A. B., Seinfeld, J. H., Whitby, K. T., and
Whitby, E. R.: Simulation of Aerosol Dynamics: A Comparative Review of
Mathematical Models, Systems Applications, Inc., San Rafael, CA, USA, 1986.
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. Discuss., https://doi.org/10.5194/gmd-2020-144, in review, 2020.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, Duda, M.
G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the advanced
research WRF version 3, NCAR Tech. Note 475+STR, National Center
for Atmospheric Research, Boulder, CO, USA, 125 pp., 2005.
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J.,
Dukowicz, J., Eden, C., Fox-Kemper, B., 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, Los Alamos National Laboratory Tech. Rep. LAUR-10-01853, 140 pp.,
2010.
Sundqvist, H., Berge, E., and Kristjansson, J. E.: Condensation and cloud
parameterization studies with a mesoscale numerical weather prediction
model, Mon. Weather Rev., 117, 1641–1657, 1989.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design. B. Am. Meteorol. Soc., 93, 485–498,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Tompkins, A. M.: The parameterization of cloud cover, ECMWF Technical
Memorandum: Moist Processes Lecture Note Series, available at: https://www.ecmwf.int/sites/default/files/elibrary/2005/16958-parametrization-cloud-cover.pdf (last access: 15 August 2020),
2005.
Tsai, I.-C., Chen, J.-P., Lin, Y.-C., Chou, C. C.-K., and Chen, W.-N.:
Numerical investigation of the coagulation mixing between dust and
hygroscopic aerosol particles and its impacts, J. Geophys. Res.-Atmos., 120, 4213–4233,
https://doi.org/10.1002/2014JD022899, 2015.
Tsuang, B.-J., Tu, C.-Y., Tsai, J.-L., Dracup, J. A., Arpe, K., and Meyers,
T.: A more accurate scheme for calculating Earths skin temperature, Clim.
Dynam., 32, 251–272, 2009.
Tu, C.-Y. and Tsuang, B.-J.: Cool-skin simulation by a one-column ocean
model, Geophys. Res. Lett., 32, L22602, https://doi.org/10.1029/2005GL024252, 2005.
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.-C. and Hsu, H.-H.: Improving diurnal rainfall phase over the
Southern Great Plains in warm seasons by using a convective triggering
design, Int. J. Climatol., 39, 5181–5190, https://doi.org/10.1002/joc.6117, 2019.
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.-Atmos., 120, 10681–10702, https://doi.org/10.1002/2015JD023337, 2015.
Whitby, E. R. and McMurry, P. H.: Model aerosol dynamics modeling, Aerosol
Sci. Technol., 27, 673–688, https://doi.org/10.1080/02786829708965504, 1997.
Xie, S., Wang, Y.-C., Lin, W., Ma, H.-Y., Tang, Q., Tang, S., Zheng, X.,
Golaz, J.-C., Zhang, G. J., and Zhang, M.: Improved diurnal cycle of
precipitation in E3SM with a revised convective triggering function, J. Adv. Model. Earth Syst., 11, 2290–2310, https://doi.org/10.1029/2019MS001702, 2019.
Zhang, G. J. and McFarlane, N. A.: Sensitivity of climate simulations to
the parameterization of cumulus convection in the Canadian Climate Centre
general circulation model, Atmos.-Ocean, 33, 407–446, https://doi.org/10.1080/07055900.1995.9649539, 1995.
Short summary
The Taiwan Earth System Model (TaiESM) is a new climate model developed in Taiwan. It includes several new features, and therefore it can better simulate the occurrence of convective rainfall, solar energy received by mountainous surfaces, and more detail chemical processes in aerosols. TaiESM can capture the trend of global warming after 1950 well, and its overall performance in most meteorological quantities is better than the average of global models used in IPCC AR5.
The Taiwan Earth System Model (TaiESM) is a new climate model developed in Taiwan. It includes...