Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3897-2024
© Author(s) 2024. 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-17-3897-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
15 May 2024
Model experiment description paper |
| 15 May 2024
| 15 May 2024
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
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
Ocean Center, National Taiwan University, Taipei 10617, 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 Nelkini, E.: The Version 2.1 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeor., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
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.
Banzon, V. F., Reynolds, R. W., Stokes, D., and Xue, Y.: A 1/4-spatial-resolution daily sea surface temperature climatology based on a blended satellite and in situ analysis, J. Climate, 27, 8221–8228, https://doi.org/10.1175/JCLI-D-14-00293.1, 2014.
Behringer, D. W. and Xue, Y.: Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean, Eighth Symposium on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, AMS 84th Annual Meeting, Washington State Convention and Trade Center, Seattle, Washington, 11–15 January 2004, https://ams.confex.com/ams/pdfpapers/70720.pdf (last access: 8 May 2024), 2004.
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., 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.
DeMott, C. A., Stan, C., Randall, D. A., and Branson, M. D.: Intraseasonal variability in coupled GCMs: The roles of ocean feedbacks and model physics, J. Climate, 27, 4970–4995, https://doi.org/10.1175/JCLI-D-13-00760.1, 2014.
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.
de Szoeke, S. P. and Maloney, E.: Atmospheric mixed layer convergence from observed MJO sea surface temperature anomalies, J. Climate, 33, 547–558, https://doi.org/10.1175/JCLI-D-19-0351.1, 2020.
de Szoeke, S. P., Edson, J. B., Marion, J. R., Fairall, C. W., and Bariteau, L.: The MJO and air–sea interaction in TOGA COARE and DYNAMO, J. Climate, 28, 597–622, https://doi.org/10.1175/JCLI-D-14-00477.1, 2014.
de Szoeke, S. P., Edson, J. B., Marion, J. R., Fairall, C. W., and Bariteau, L.: The MJO and air–sea interaction in TOGA COARE and DYNAMO, J. Climate, 28, 597–622, https://doi.org/10.1175/JCLI-D-14-00477.1, 2015.
Fu, J. X., Wang, W., Shinoda, T., Ren, H. L., and Jia, X.: Toward understanding the diverse impacts of air–sea interactions on MJO simulations, J. Geophys. Res.-Oceans, 122, 8855–8875, https://doi.org/10.1002/2017JC013187, 2017.
Gao, Y., Hsu, P.-C., Chen, L., Wang, L., and Li, T.: Effects of high-frequency surface wind on the intraseasonal SST associated with the Madden-Julian oscillation, Clim. Dynam., 54, 4485–4498, https://doi.org/10.1007/s00382-020-05239-w, 2020a.
Gao, Y., Klingaman, N. P., DeMott, C. A., and Hsu, P.-C.: Boreal summer intraseasonal oscillation in a superparameterized general circulation model: effects of air–sea coupling and ocean mean state, Geosci. Model Dev., 13, 5191–5209, https://doi.org/10.5194/gmd-13-5191-2020, 2020b.
Ge, X., Wang, W., Kumar, A., and Zhang, Y.: Importance of the vertical resolution in simulating SST diurnal and intraseasonal variability in an oceanic general circulation model, J. Climate, 30, 3963–3978, https://doi.org/10.1175/JCLI-D-16-0689.1, 2017.
Gonzalez, A. O. and Jiang, X.: Winter mean lower tropospheric moisture over the maritime continent as a climate model diagnostic metric for the propagation of the Madden-Julian Oscillation, Geophys. Res. Lett., 44, 2588–2596, https://doi.org/10.1002/2016GL072430, 2017.
Hagos, S. M., Zhang, C., Feng, Z., Burleyson, C. D., Mott, C. De, 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 Syst., 8, 1552–1564, https://doi.org/10.1002/2016MS000725, 2016.
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., De 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., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
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, Dyn. Atmos. Oceans, 78, 89–105, https://doi.org/10.1016/j.dynatmoce.2017.03.002, 2017.
Hsu, H.-H. and Lee, M.-Y.: Topographic effects on the eastward propagation and initiation of the Madden-Julian Oscillation, J. Climate, 18, 795–809, https://doi.org/10.1175/JCLI-3292.1, 2005.
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.
Jiang, X.: Key processes for the eastward propagation of the Madden-Julian Oscillation based on multimodel simulations, J. Geophys. Res.-Atmos., 122, 755–770, https://doi.org/10.1002/2016JD025955, 2017.
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., Schu-bert, 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., Zhao, M., Waliser, D., and Maloney, E.: A unified moisture mode framework for seasonality of the Madden–Julian oscillation, J. Climate, 31, 4215–4224, https://doi.org/10.1175/JCLI-D-17-0671.1, 2018.
Jiang, X., Adames, Á. F., Kim, D., Maloney, E. D., Lin, H., and 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.
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., Vitart, F., and Waliser, D. E.: Prediction of the Madden–Julian oscillation: A review, J. Climate, 31, 9425–9443, https://doi.org/10.1175/JCLI-D-18-0210.1, 2018.
Klingaman, N. P. and Demott, C. A.: Mean state biases and interannual variability affect perceived sensitivities of the Madden-Julian oscillation to air–sea coupling, J. Adv. Model. Earth Syst., 12, 1–22, https://doi.org/10.1029/2019MS001799, 2020.
Krishnamurti, T. N., Oosterhof, D. K., and Mehta, A. V.: Air–sea interaction on the time scale of 30 to 50 days, J. Atmos. Sci., 45, 1304–1322, https://doi.org/10.1175/1520-0469(1988)045<1304:AIOTTS>2.0.CO;2, 1988.
Lambaerts, J., Lapeyre, G., Plougonven, R., and Klein, P.: Atmospheric response to sea surface temperature mesoscale structures, J. Geophys. Res.-Atmos., 118, 9611–9621, https://doi.org/10.1002/jgrd.50769, 2020.
Lan, Y.-Y., Hsu, H.-H., Tsuang, B.-J., and Tu, C.-Y.: rceclccr/CAM5_SIT_v1.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.5510795, 2021.
Lan, Y.-Y., Hsu, H.-H., Tseng, W.-L., and Jiang, L.-C.: Embedding a one-column ocean model in the Community Atmosphere Model 5.3 to improve Madden–Julian Oscillation simulation in boreal winter, Geosci. Model Dev., 15, 5689–5712, https://doi.org/10.5194/gmd-15-5689-2022, 2022.
Li, K., Yu, W., Yang, Y., Feng, L., Liu, S., and Li, L.: Spring barrier to the MJO eastward propagation, Geophys. Res. Lett., 47, e2020GL087788, https://doi.org/10.1029/2020GL087788, 2020.
Li, T., Ling, J., and Hsu, P.-C.: Madden–Julian Oscillation: Its discovery, dynamics, and impact on East Asia, J. Meteor. Res., 34, 20–42, https://doi.org/10.1007/s13351-020-9153-3, 2020.
Li, Y., Han, W., Shinoda, T., Wang, C., Ravichandran, M., and Wang, J.-W.: Revisiting the wintertime intraseasonal SST variability in the tropical south Indian Ocean: Impact of the ocean interannual variation, J. Phys. Oceanogr., 44, 1886–1907, https://doi.org/10.1175/JPO-D-13-0238.1, 2014.
Liang, Y. and Du, Y.: Oceanic impacts on 50–80-day intraseasonal oscillation in the eastern tropical Indian Ocean, Clim. Dynam., 59, 1283–1296, https://doi.org/10.1007/s00382-021-06041-y, 2022.
Liang, Y., Du, Y., Zhang, L., Zheng, X., and Qiu, S.: The 30–50-Day Intraseasonal Oscillation of SST and Precipitation in the South Tropical Indian Ocean, Atmosphere, 9, 69, https://doi.org/10.3390/atmos9020069, 2018.
Liebmann, B.: Description of a complete (interpolated) outgoing longwave radiation dataset, B. Am. Meteorol. Soc., 77, 1275–1277, 1996.
Madden, R. A. and Julian, P. R.: Description of global-scale circulation cells in the tropics with a 40-50 day period, J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2, 1972.
Maloney, E. and Sobel, A. H.: Surface fluxes and ocean coupling in the tropical intraseasonal oscillation, J. Climate, 17, 4368–4386, https://doi.org/10.1175/JCLI-3212.1, 2004.
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.
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.
Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Bur- rows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron- Smith, P., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E., Bacmeister, J., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M., Leung, L. R., Zhang, Y., Brent, L., Branstet- ter, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D., Flan- ner, M., Foucar, J. G., Jacob, R., Keen, N., Klein, S. A., Liu, X., Salinger, A. G., Shrivastava, M., and Yang, Y.: An overview of the atmospheric component of the Energy Exascale Earth System Model, J. Adv. Model Earth Sy., 11, 2377–2411, https://doi.org/10.1029/2019ms001629, 2019.
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. Meteor. Res., 33, 627–650, https://doi.org/10.1007/s13351-019-8118-x, 2019.
Savarin, A. and Chen, S. S.: Pathways to better prediction of the MJO: 2. Impacts of atmosphere-ocean coupling on the upper ocean and MJO propagation, J. Adv. Model. Earth Sy., 14, e2021MS002929, https://doi.org/10.1029/2021MS002929, 2022.
Shinoda, T., Pei, S., Wang, W., Fu, J. X., Lien, R.-C., Seo, H., and Soloviev, A.: Climate process team: Improvement of ocean component of NOAA climate forecast system relevant to Madden-Julian Oscillation simulations, J. Adv. Model. Earth Sy., 13, e2021MS002658, https://doi.org/10.1029/2021MS002658, 2021.
Seo, H., Subramanian, A. C., Miller, A. J., and Cavanaugh, N. R.: Coupled impacts of the diurnal cycle of sea surface temperature on the Madden–Julian oscillation, J. Climate, 27, 8422–8443, https://doi.org/10.1175/JCLI-D-14-00141.1, 2014.
Sobel, A. H. and Gildor, H.: A simple time-dependent model of SST hot spots, J. Climate, 16, 3978–3992, https://doi.org/10.1175/1520-0442(2003)016<3978:ASTMOS>2.0.CO;2, 2003.
Sobel, A. H., Maloney, E. D., Bellon, G., and Frierson, D. M.: Surface Fluxes and Tropical Intraseasonal Variability: a Reassessment, J. Adv. Model. Earth Sy., 2, 2, https://doi.org/10.3894/JAMES.2010.2.2, 2010.
Sobel, A., Wang, S., and Kim, D.: Moist static energy budget of the MJO during DYNAMO, J. Atmos. Sci., 71, 4276–4291, https://doi.org/10.1175/JAS-D-14-0052.1, 2014.
Stan, C.: The role of SST variability in the simulation of the MJO, Clim. Dynam., 51, 2943–2964, https://doi.org/10.1007/s00382-017-4058-2, 2018.
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, 2015.
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.
Tseng, W.-L., Hsu, H.-H., Lan, Y.-Y., Lee, W.-L., Tu, C.-Y., Kuo, P.-H., Tsuang, B.-J., and Liang, H.-C.: Improving Madden–Julian oscillation simulation in atmospheric general circulation models by coupling with a one-dimensional snow–ice–thermocline ocean model, Geosci. Model Dev., 15, 5529–5546, https://doi.org/10.5194/gmd-15-5529-2022, 2022.
Tulich, S. N. and Kiladis, G. N.: On the Regionality of moist kelvin waves and the MJO: The critical role of the background zonal flow, J. Adv. Model. Earth Sy., 13, e2021MS002528. https://doi.org/10.1029/2021MS002528, 2021.
Voldoire, A., Roehrig, R., Giordani, H., Waldman, R., Zhang, Y., Xie, S., and Bouin, M.-N.: Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration, Geosci. Model Dev., 15, 3347–3370, https://doi.org/10.5194/gmd-15-3347-2022, 2022.
Wang, L. and Li, T.: Effect of vertical moist static energy advection on MJO eastward propagation: Sensitivity to analysis domain, Clim. Dynam., 54, 2029–2039, https://doi.org/10.1007/s00382-019-05101-8, 2020.
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.
Watterson, I. G.: The sensitivity of subannual and intraseasonal tropical variability to model ocean mixed layer depth, J. Geophys. Res., 107, 4020, https://doi.org/10.1029/2001JD000671, 2002.
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.
Wu, C.-H. and Hsu, H.-H.: Topographic Influence on the MJO in the Maritime Continent, J. Climate, 22, 5433–5448, https://doi.org/10.1175/2009JCLI2825.1, 2009.
Wu, J., Li, Y., Luo, J.-J., and Jiang, X.: Assessing the role of air–sea coupling in predicting Madden–Julian oscillation with an Atmosphere–Ocean coupled model, J. Climate, 34 9647–9663, https://doi.org/10.1175/JCLI-D-20-0989.1, 2021.
Zhang, C.: Madden-Julian oscillation, Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158, 2005.
Zhang, L. and Han, W.: Barrier for the eastward propagation of Madden–Julian Oscillation over the maritime continent: A possible new mechanism, Geophys. Res. Lett., 47, e2020GL090211, https://doi.org/10.1029/2020gl090211, 2020.
Zhao, N. and Nasuno, T.: How Does the Air–Sea Coupling Frequency Affect Convection During the MJO Passage?, J. Adv. Model. Earth Sy., 12, e2020MS002058, https://doi.org/10.1029/2020MS002058, 2020.
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.
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency...
