Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5689-2022
© Author(s) 2022. 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-15-5689-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Jul 2022
Development and technical paper |
| 22 Jul 2022
| 22 Jul 2022
Embedding a one-column ocean model in the Community Atmosphere Model 5.3 to improve Madden–Julian Oscillation simulation in boreal winter
Yung-Yao Lan
Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
Huang-Hsiung Hsu
CORRESPONDING AUTHOR
Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
Wan-Ling Tseng
Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
International Degree Program in Climate Change and Sustainable Development, National Taiwan University, Taipei 10617, Taiwan
Li-Chiang Jiang
Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
Related authors
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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.
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.
Wei-Liang Lee, Yi-Chi Wang, Chein-Jung Shiu, I-chun Tsai, Chia-Ying Tu, Yung-Yao Lan, Jen-Ping Chen, Hua-Lu Pan, and Huang-Hsiung Hsu
Geosci. Model Dev., 13, 3887–3904, https://doi.org/10.5194/gmd-13-3887-2020, https://doi.org/10.5194/gmd-13-3887-2020, 2020
Short summary
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.
Alex C. Ruane, Charlotte L. Pascoe, Claas Teichmann, David J. Brayshaw, Carlo Buontempo, Ibrahima Diouf, Jesus Fernandez, Paula L. M. Gonzalez, Birgit Hassler, Vanessa Hernaman, Ulas Im, Doroteaciro Iovino, Martin Juckes, Iréne L. Lake, Timothy Lam, Xiaomao Lin, Jiafu Mao, Negin Nazarian, Sylvie Parey, Indrani Roy, Wan-Ling Tseng, Briony Turner, Andrew Wiebe, Lei Zhao, and Damaris Zurell
EGUsphere, https://doi.org/10.5194/egusphere-2025-3408, https://doi.org/10.5194/egusphere-2025-3408, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This paper describes how the Coupled Model Intercomparison Project organized its 7th phase (CMIP7) to encourage the production of Earth system model outputs relevant for impacts and adaptation. Community engagement identified 13 opportunities for application across human and natural systems, 60 variable groups and 539 unique variables. We also show how simulations can more efficiently meet applications needs by targeting appropriate resolution, time slices, experiments and variable groups.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O’Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, Vanessa Hernaman, Céline Heuzé, Doroteaciro Iovino, Gaurav Madan, André L. Marquez, François Massonnet, Jenny Mecking, Dhrubajyoti Samanta, Patrick C. Taylor, Wan-Ling Tseng, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-3083, https://doi.org/10.5194/egusphere-2025-3083, 2025
Short summary
Short summary
The earth system model variables needed for studies of the ocean and sea ice are prioritized and requested.
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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.
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, Chia-Hao Chiang, Chiung-Jui Su, Ko-Chih Wang, Wan-Ling Tseng, Cheng-Ta Chen, and Hsin-Chien Liang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1022, https://doi.org/10.5194/egusphere-2024-1022, 2024
Preprint archived
Short summary
Short summary
Our study introduces a deep learning model, the EDA, to refine rainfall data in Taiwan. This model significantly improves bias correction by integrating surface wind and topography data, crucial in areas like Taiwan where traditional methods fall short. The EDA excels in adjusting low-intensity and misplaced rainfall, enhancing water management, agriculture, and disaster prevention. This work showcases deep learning's potential to improve climate downscaling in complex terrains.
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.
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.
Chein-Jung Shiu, Yi-Chi Wang, Huang-Hsiung Hsu, Wei-Ting Chen, Hua-Lu Pan, Ruiyu Sun, Yi-Hsuan Chen, and Cheng-An Chen
Geosci. Model Dev., 14, 177–204, https://doi.org/10.5194/gmd-14-177-2021, https://doi.org/10.5194/gmd-14-177-2021, 2021
Short summary
Short summary
A cloud macrophysics scheme utilizing grid-mean hydrometeor information is developed and evaluated for climate models. The GFS–TaiESM–Sundqvist (GTS) scheme can simulate variations of cloud fraction associated with relative humidity (RH) in a more consistent way than the default scheme of CAM5.3. Through better cloud–RH distributions, the GTS scheme helps to better represent cloud fraction, cloud radiative forcing, and thermodynamic-related climatic fields in climate simulations.
Wei-Liang Lee, Yi-Chi Wang, Chein-Jung Shiu, I-chun Tsai, Chia-Ying Tu, Yung-Yao Lan, Jen-Ping Chen, Hua-Lu Pan, and Huang-Hsiung Hsu
Geosci. Model Dev., 13, 3887–3904, https://doi.org/10.5194/gmd-13-3887-2020, https://doi.org/10.5194/gmd-13-3887-2020, 2020
Short summary
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.
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 Nelkini, E.: The Version 2.1 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.
Ahn, M.-S., Kim, D., Sperber, K. R., Kang, I.-S., Maloney, E., Waliser, D., and Hendon, H.: MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis, Clim. Dynam., 49, 4023–4045, https://doi.org/10.1007/s00382-017-3558-4, 2017.
Ahn, M.-S., Kim, D., Kang, D., Lee, J., Sperber, K. R., Glecker, P. J., Jiang, X., Ham, Y.-G., and Kim, H.: MJO propagation across the Maritime Continent: Are CMIP6 models better than CMIP5 models?, Geophys. Res. Lett., 47, e2020GL087250, https://doi.org/10.1029/2020GL087250, 2020.
Alappattu, D. P., Wang, Q., Kalogiros, J., Guy, N., and Jorgensen, D. P.: Variability of upper ocean thermohaline structure during a MJO event from DYNAMO aircraft observations, J. Geophys. Res.-Oceans, 122, 1122–1140, https://doi.org/10.1002/2016JC012137, 2017.
Amante, C. and Eakins, B. W.: ETOPO1 1 arc-minute globe relief model: Procedures, data sources and analysis, NOAA Tech. Memo. NESDIS NGDC-24, NOAA, Silver Spring, MD, 19 pp., https://doi.org/10.7289/V5C8276M, 2009.
Behringer, D. W. and Xue, Y.: Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean, Proc. Eighth Symp. on integrated observing and assimilation systems for atmosphere, oceans, and land surface, AMS 84th annual meeting in Washington State Convention and Trade Center, Seattle, Washington, 11–15 January 2004, Am. Meteorol. Soc., 2.3., http://origin.cpc.ncep.noaa.gov/products/people/yxue/pub/13.pdf (last access: 11 August 2020), 2004.
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013.
Bernie, D., Guilyardi, E., Madec, G., Slingo, J., Woolnough, S., and Cole, J.: Impact of resolving the diurnal cycle in an ocean–atmosphere GCM. Part 2: a diurnally coupled CGCM, Clim. Dynam., 31, 909–925, https://doi.org/10.1007/s00382-008-0429-z, 2008.
Boyle, J. S., Klein, S. A., Lucas, D. D., Ma, H.-Y., Tannahill, J., and Xie, S.: The parametric sensitivity of CAM5's MJO, J. Geophys. Res.-Atmos., 120, 1424–1444, https://doi.org/10.1002/2014JD022507, 2015.
Bui, H. X. and Maloney, E. D.: Changes in Madden-Julian Oscillation precipitation and wind variance under global warming, Geophys. Res. Lett., 45, 7148–7155, https://doi.org/10.1029/2018GL078504, 2018.
Chang, M.-Y., Li, T., Lin, P.-L., and Chang, T.-H.: Forecasts of MJO Events during DYNAMO with a Coupled Atmosphere-Ocean Model: Sensitivity to Cumulus Parameterization Scheme, J. Meteorol. Res.-PRC, 33, 1016–1030, https://doi.org/10.1007/s13351-019-9062-5, 2019.
Clivar Madden–Julian Oscillation Working Group: MJO simulation diagnostics, J. Climate, 22, 3006–3030, https://doi.org/10.1175/2008JCLI2731.1, 2009.
Crueger, T., Stevens, B., and Brokopf, R.: The Madden–Julian Oscillation in ECHAM6 and the introduction of an objective MJO metric, J. Climate, 26, 3241–3257, https://doi.org/10.1175/JCLI-D-12-00413.1, 2013.
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., Kampenhout, L. V., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
de Szoeke, S. P., Skyllingstad, E. D., Zuidema, P., and Chandra, A. S.: Cold pools and their influence on the tropical marine boundary layer, J. Atmos. Sci., 74, 1149–1168, https://doi.org/10.1175/JAS-D-16-0264.1, 2017.
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.
Delworth, T. L., Broccoli, A. J., Rosati, A., Stouffer, R. J., Balaji, V., Beesley, J. A., Cooke, W. F., Dixon, K. W., Dunne, J., Dunne, K. A., Durachta, J. W., Findell, K. L., Ginoux, P., Gnanadesikan, A., Gordon, C. T., Griggies, S. M., Gudgil, R., Harrison, M. J., Held, I. M., Hemler, R. S., Horowitz, L. W., Klein, S. A., Knutson, T. R., Kushner, P. J., Langenhorst, A. R., Lee, H. C., Lin, S. J., Lu, J., Malyshev, S. L., Milly, P. C. D., Ramaswamy, V., Russell, J., Schwarzkopf, M. D., Shevliakova, E., Sirutis, J. J., Spelman, M. J., Stern, W. F., Winton, M., Wittenberg, A. T., Wyman, B., Zeng, F., and Zhang, R.: GFDL's CM2 global coupled climate models. Part 1: Formulation and simulation characteristics, J. Climate, 19, 643–674, https://doi.org/10.1175/JCLI3629.1, 2006.
DeMott, C. A., Klingaman, N. P., and Woolnough, S. J.: Atmosphere-ocean coupled processes in the Madden-Julian oscillation, Rev. Geophys., 53, 1099–1154, https://doi.org/10.1002/2014RG000478, 2015.
DeMott, C. A., Klingaman, N. P., Tseng, W.-L., Burt, M. A., Gao, Y., and Randall, D. A.: The convection connection: How ocean feedbacks affect tropical mean moisture and MJO propagation, J. Geophys. Res.-Atmos., 124, 11910–11931, https://doi.org/10.1029/2019JD031015, 2019.
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, https://doi.org/10.1029/JC095iC09p16179, 1990.
Grassl, H.: The dependence of the measured cool skin of the ocean on wind stress and total heat flux, Bound.-Lay. Meteorol., 10, 465–474, https://doi.org/10.1007/BF00225865, 1976.
Haertel, P.: Prospects for Erratic and Intensifying Madden-Julian Oscillations, Climate, 8, 24, https://doi.org/10.3390/cli8020024, 2020.
Hannah, W. M. and Maloney, E. D.: The moist static energy budget in NCAR CAM5 hindcasts during DYNAMO, J. Adv. Model. Earth Sy., 6, 420–440, https://doi.org/10.1002/2013MS000272, 2014.
Hagos, S. M., Zhang, C., Feng, Z., Burleyson, C. D., De Mott, C., Kerns, B., Benedict, J. J., and Martini, M. N.: The impact of the diurnal cycle on the propagation of Madden-Julian Oscillation convection across the Maritime Continent, J. Adv. Model. Earth Sy., 8, 1552–1564, https://doi.org/10.1002/2016MS000725, 2016.
Hasse, L.: The sea surface temperature deviation and the heat flow at the sea–air interface, Bound.-Lay. Meteorol., 1, 368–379, https://doi.org/10.1007/BF02186037, 1971.
He, S., Yang, S., and Li, Z.: Influence of Latent Heating over the Asian and Western Pacific Monsoon Region on Sahel Summer Rainfall, Sci. Rep. 7, 7680, https://doi.org/10.1038/s41598-017-07971-6, 2017.
Heath, A., Gonzalez, A. O., Gehne, M., and Jaramillo, A.: Interactions of large-scale dynamics and Madden-Julian Oscillation propagation in multi-model simulations, J. Geophys. Res.-Atmos., 126, e2020JD033988, https://doi.org/10.1029/2020JD033988, 2021.
Hong, X., Reynolds, C. A., Doyle, J. D., May, P., and O'Neill, L.: Assessment of upper-ocean variability and the Madden-Julian Oscillation in extended-range air–ocean coupled mesoscale simulations, Dynam. Atmos. Oceans, 78, 89–105, https://doi.org/10.1016/j.dynatmoce.2017.03.002, 2017.
Hung, M.-P., Lin, J.-L., Wang, W., Kim, D., Shinoda, T., and Weaver, S. J.: MJO and convectively coupled equatorial waves simulated by CMIP5 climate models, J. Climate, 26, 6185–6214, https://doi.org/10.1175/JCLI-D-12-00541.1, 2013.
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-12-00121.1, 2013.
Jiang, X., Waliser, D. E., Xavier, P. K., Petch, J., Klingaman, N. P., Woolnough, S. J., Guan, B., Bellon, G., Crueger, T., DeMott, C., Hannay, C., Lin, H., Hu, W., Kim, D., Lappen, C.-L., Lu, M.-M., Ma, H.-Y., Miyakawa, T., Ridout, J. A., Schubert, S. D., Scinocca, J., Seo, K.-H., Shindo, E., Song, X., Stan, C., Tseng, W.-L., Wang, W., Wu, T., Wu, X., Wyser, K., Zhang, G. J., and Zhu, H.
Jiang, X., Waliser, D. E., Xavier, P. K., Petch, J., Klingaman, N. P., Woolnough, S. J., Guan, B., Bellon, G., Crueger, T., DeMott, C., Hannay, C., Lin, H., Hu, W., Kim, D., Lappen, C.-L., Lu, M.-M., Ma, H.-Y., Miyakawa, T., Ridout, J. A., Schubert, S. D., Scinocca, J., Seo, K.-H., Shindo, E., Song, X., Stan, C., Tseng, W.-L., Wang, W., Wu, T., Wu, X., Wyser, K., Zhang, G. J., and Zhu, H.: Vertical structure and physical processes of the MaddenJulian oscillation: Exploring key model physics in climate simulations, J. Geophys. Res.-Atmos., 120, 4718–4748, https://doi.org/10.1002/2014JD022375, 2015.
Jiang, X., Adames, Á. F., Kim, D., Maloney, E. D., Lin, H., Kim, H., Zhang, C., DeMott, C. A., and Klingaman, N. P.: Fifty years of research on the Madden-Julian Oscillation: Recent progress, challenges, and perspectives, J. Geophys. Res.-Atmos., 125, e2019JD030911, https://doi.org/10.1029/2019JD030911, 2020.
Johnson, R. H. and Ciesielski, P. E.: Multiscale variability of the atmospheric boundary layer during DYNAMO, J. Atmos. Sci., 74, 4003–4021, https://doi.org/10.1175/JAS-D-17-0182.1, 2017.
Kim, D., Sobel, A. H., Maloney, E. D., Frierson, D. M., and Kang, I.-S.: A systematic relationship between intraseasonal variability and mean state bias in AGCM simulations, J. Climate, 24, 5506–5520, https://doi.org/10.1175/2011JCLI4177.1, 2011.
Kim, D., Kim H., and Lee, M.-I.: Why does the MJO detour the Maritime Continent during austral summer?, Geophys. Res. Lett., 44, 2579–2587, https://doi.org/10.1002/2017GL072643, 2017.
Kim, H.-M., Hoyos, C. D., Webster, P. J., and Kang, I.-S.: Ocean–atmosphere coupling and the boreal winter MJO, Clim. Dynam., 35, 771–784, https://doi.org/10.1007/s00382-009-0612-x, 2010.
Kiranmayi, L. and Maloney, E. D.: Intraseasonal moist static energy budget in reanalysis data, J. Geophys. Res., 116, D21117, https://doi.org/10.1029/2011JD016031, 2011.
Lan, Y.-Y., Tsuang, B.-J., Tu, C.-Y., Wu, T.-Y., Chen, Y.-L., and Hsieh, C.-I.: Observation and Simulation of Meteorology and Surface Energy Components over the South China Sea in Summers of 2004 and 2006, Terr. Atmos. Ocean. Sci., 21, 325–342, https://doi.org/10.3319/TAO.2009.04.07.01(A), 2010.
Lan, Y.-Y., Hsu, H.-H., Tsuang, B.-J., and Tu, C.-Y.: rceclccr/CAM5_SIT_v1.0 (v1.0.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.5510795, 2021.
Lee, H.-T. and NOAA CDR Program: NOAA Climate Data Record (CDR) of Daily Outgoing Longwave Radiation (OLR), Version 1.2, NOAA National Climatic Data Center, https://doi.org/10.7289/V5SJ1HH2, 2011.
Li, T., Tam, F., Fu, X., Zhou, T., and Zhu, W.: Causes of the intraseasonal SST variability in the tropical Indian Ocean, Atmos. Ocean. Sci. Lett., 1, 18–23, https://doi.org/10.1080/16742834.2008.11446758, 2008.
Li, T., Ling, J., and Hsu, P.-C.: Madden–Julian Oscillation: Its discovery, dynamics, and impact on East Asia, J. Meteorol. Res.-PRC, 34, 20–42, https://doi.org/10.1007/s13351-020-9153-3, 2020.
Li, X., Tang, Y., Zhou, L., Chen, D., and Yao, Z.: Assessment of Madden–Julian oscillation simulations with various configurations of CESM, Clim. Dynam., 47, 2667–2690, https://doi.org/10.1007/s00382-016-2991-0, 2016.
Ling, J., Zhao, Y., and Chen, G.: Barrier effect on MJO propagation by the Maritime Continent in the MJO Task Force/GEWEX atmospheric system study models, J. Climate, 32, 5529–5547, https://doi.org/10.1175/JCLI-D-18-0870.1, 2019.
Madden, R. A. and Julian, P. R.: Description of global-scale circulation cells in the tropics with a 40–50 d period, J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2, 1972.
Madden, R. A. and Julian, P. R.: Observations of the 40–50 d tropical oscillation – A review, Mon. Weather Rev., 122, 814–837, https://doi.org/10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2, 1994.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys., 20, 851–875, https://doi.org/10.1029/RG020i004p00851, 1982.
Neale, R. B., Chen, C.-C., Gettelman, A., Lauritzen, P. H., Park, S., Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J.-F., Marsh, D., Mills, M., Smith, A. K., Tilmes, S., Vitt, F., Cameron-Smith, P., Collins, W. D., Iacono, M. J., Easter, R. C., Ghan, S. J., Liu, X., Rasch, P. J., and Taylor, M. A.: Description of the NCAR Community Atmosphere Model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+STR, Natl. Cent. for Atmos. Res., Boulder, CO, 289 pp., https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf (last access: 8 October 2022), 2012.
Newman, M., Sardeshmukh, P. D., and Penland, C.: How important is air–sea coupling in ENSO and MJO evolution?, J. Climate, 22, 2958–2977, https://doi.org/10.1175/2008JCLI2659.1, 2009.
Oh, J.-H, Kim, B.-M., Kim, K.-Y., Song, H.-J., and Lim, G.-H.: The impact of the diurnal cycle on the MJO over the Maritime Continent: a modeling study assimilating TRMM rain rate into global analysis, Clim. Dynam., 40, 893–911, https://doi.org/10.1007/s00382-012-1419-8, 2013.
Paulson, C. A. and Simpson, J. J.: The temperature difference across the cool skin of the ocean, J. Geophys. Res., 86, 11044–11054, https://doi.org/10.1029/JC086iC11p11044, 1981.
Pei, S., Shinoda, T., Soloviev, A., and Lien, R.-C.: Upper ocean response to the atmospheric cold pools associated with the Madden-Julian Oscillation, Geophys. Res. Lett., 45, 5020–5029, https://doi.org/10.1029/2018GL077825, 2018.
Pujiana, K., Moum, J. N., and Smyth, W. D.: The role of subsurface turbulence in redistributing upper-ocean heat, freshwater, and momentum in response to the MJO in the equatorial Indian Ocean, J. Phys. Oceanogr., 48, 197–220, https://doi.org/10.1175/JPO-D-17-0146.1, 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.
Ren, P. F., Gao, L., Ren, H.-L., Rong, X., and Li, J.: Representation of the Madden–Julian Oscillation in CAMSCSM, J. Meteorol. Res.-PRC, 33, 627–650, https://doi.org/10.1007/s13351-019-8118-x, 2019.
Reynolds, R. W. and Smith, T. M.: A high-resolution global sea surface temperature climatology, J. Climate, 8, 1571–1583, https://doi.org/10.1175/1520-0442(1995)008<1571:AHRGSS>2.0.CO;2, 1995.
Schreck, C. J., Lee, H.-T., and Knapp, K. R.: HIRS outgoing longwave radiation – Daily climate data record: Application toward identifying tropical subseasonal variability, Remote Sens.-Basel, 10, 1325, https://doi.org/10.3390/rs10091325, 2018.
Sobel, A. H., Maloney, E. D., Bellon, G., and Dargan, M. F.: The role of surface heat fluxes in tropical intraseasonal oscillations, Nat. Geosci., 1, 653–657, https://doi.org/10.1038/ngeo312, 2008.
Subramanian, A. C., Jochum, M., Miller, A. J., Murtugudde, R., Neale, R. B., and Waliser, D. E.: The Madden–Julian oscillation in CCSM4, J. Climate, 24, 6261–6282, https://doi.org/10.1175/JCLI-D-11-00031.1, 2011.
Tseng, W.-L., Tsuang, B.-J., Keenlyside, N. S., Hsu, H.-H., and Tu, C.-Y.: Resolving the upper-ocean warm layer improves the simulation of the Madden-Julian oscillation, Clim. Dynam., 44, 1487–1503, https://doi.org/10.1007/s00382-014-2315-1, 2014.
Tseng, W.-L., Hsu, H.-H., Keenlyside, N., Chang, C.-W. J., Tsuang, B.-J., Tu, C.-Y., and Jiang, L.-C.: Effects of Orography and Land–Sea Contrast on the Madden–Julian Oscillation in the Maritime Continent: A Numerical Study Using ECHAM-SIT, J. Climate, 30, 9725–9741, https://doi.org/10.1175/JCLI-D-17-0051.1, 2017.
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, W., Saha, S., Pan, H.-L., Nadiga, S., and White, G.: Simulation of ENSO in the new NCEP Coupled Forecast System Model (CFS03), Mon. Weather Rev., 133, 1574–1593, https://doi.org/10.1175/MWR2936.1, 2005.
Wang, W., Hung, M.-P., Weaver, S. J., Kumar, A., and Fu, X.: MJO prediction in the NCEP Climate Forecast System version 2, Clim. Dynam., 42, 2509–2520, https://doi.org/10.1007/s00382-013-1806-9, 2014.
Wheeler, M. and Kiladis, G. N.: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber-frequency domain, J. Atmos. Sci., 56, 374– 399, https://doi.org/10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2, 1999.
Wheeler, M. C. and Hendon, H. H.: An all-season real-time multivariate MJO index: development of an index for monitoring and prediction, Mon. Weather Rev., 132, 1917–1932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2, 2004.
Woolnough, S. J., Vitard, F., and Balmaseda, M. A.: The role of the ocean in the Madden–Julian oscillation: Implications for MJO prediction, Q. J. Roy. Meteor. Soc., 133, 117–128, https://doi.org/10.1002/qj.4, 2007.
Wu, C.-H. and Hsu, H.-H.: Potential Influence of Topography on the MJO in the Maritime Continent, J. Climate, 22, 5433–5448, https://doi.org/10.1175/2009JCLI2825.1, 2009.
Wu, J.: On the cool skin of the ocean, Bound.-Lay. Meteorol., 31, 203–207, https://doi.org/10.1007/BF00121179, 1985.
Yoneyama, K., Zhang, C., and Long, C.: Tracking pulses of the Madden–Julian oscillation, B. Am. Meteorol. Soc., 94, 1871–1891, https://doi.org/10.1175/BAMS-D-12-00157.1, 2013.
Zhang, C.: Madden-Julian oscillation, Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158, 2005.
Zhang, C. and Yoneyama, K.: CINDY/DYNAMO field campaign: Advancing our understanding of MJO initiation, in: World Scientific Series on Asia-Pacific Weather and Climate, Vol. 9, World Scientific Publishing Co. Pte Ltd., 339–348, https://doi.org/10.1142/9789813200913_0027, 2017.
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.
This study has shown that coupling a high-resolution 1-D ocean model (SIT 1.06) with the...
