Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6249-2024
© 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-17-6249-2024
© 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
| 
23 Aug 2024
Model experiment description paper |
| 23 Aug 2024
| 23 Aug 2024
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Yangke Liu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Qing Bao
CORRESPONDING AUTHOR
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Bian He
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Xiaofei Wu
School of Atmospheric Sciences/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China
Jing Yang
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yimin Liu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Guoxiong Wu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Siyuan Zhou
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yao Tang
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Ankang Qu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Yalan Fan
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Anling Liu
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Dandan Chen
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Zhaoming Luo
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Xing Hu
National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
Tongwen Wu
Center for Earth System Modeling and Prediction, China Meteorological Administration, Beijing 100081, China
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Cited articles
Adames, Á. F. and Kim, D.: The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations, J. Atmos. Sci., 73, 913–941, https://doi.org/10.1175/JAS-D-15-0170.1, 2016.
Adames, Á. F. and Wallace, J. M.: Three-Dimensional Structure and Evolution of the MJO and Its Relation to the Mean Flow, J. Atmos. Sci., 71, 2007–2026, https://doi.org/10.1175/JAS-D-13-0254.1, 2014.
Adames, Á. F. and Wallace, J. M.: Three-Dimensional Structure and Evolution of the Moisture Field in the MJO, J. Atmos. Sci., 72, 3733–3754, https://doi.org/10.1175/JAS-D-15-0003.1, 2015.
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.
Ahn, M., Kim, D., Kang, D., Lee, J., Sperber, K. R., Gleckler, P. J., Jiang, X., Ham, Y., 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.
Bao, Q.: Outlook for El Niño and the Indian Ocean Dipole in autumn-winter 2018–2019, Chinese Sci. Bull., 64, 73–78, https://doi.org/10.1360/N972018-00913, 2019.
Bao, Q. and Li, J.: Progress in climate modeling of precipitation over the Tibetan Plateau, Natl. Sci. Rev., 7, 486–487, https://doi.org/10.1093/nsr/nwaa006, 2020.
Bao, Q., Liu, Y., Wu, G., He, B., Li, J., Wang, L., Wu, X., Chen, K., Wang, X., Yang, J., and Zhang, X.: CAS FGOALS-f3-H and CAS FGOALS-f3-L outputs for the high-resolution model intercomparison project simulation of CMIP6, Atmospheric and Oceanic Science Letters, 13, 576–581, https://doi.org/10.1080/16742834.2020.1814675, 2020.
Bao, Q., He, B., Wu, X., Yang, J., Liu, Y., Wu, G., Zhu, T., Zhou, S., Liu, Y., Tang, Y., Qu, A., Fan, Y., Liu, A., Chen, D., Luo, Z., Hu, X., and Wu, T.: An ensemble subseasonal-to-seasonal forecast system of IAP-CAS model (1.3), Zenodo [code], https://doi.org/10.5281/zenodo.10791355, 2024a.
Bao, Q., He, B., and Wu, X.: Input data of the IAP-CAS S2S system, Zenodo [data set], https://doi.org/10.5281/zenodo.10820243, 2024b.
Benedict, J. J. and Randall, D. A.: Observed Characteristics of the MJO Relative to Maximum Rainfall, J. Atmos. Sci., 64, 2332–2354, https://doi.org/10.1175/JAS3968.1, 2007.
Bessafi, M. and Wheeler, M. C.: Modulation of south Indian ocean tropical cyclones by the Madden-Julian oscillation and convectively coupled equatorial waves, Mon. Weather Rev., 134, 638–656, https://doi.org/10.1175/MWR3087.1, 2006.
Cassou, C.: Intraseasonal interaction between the Madden-Julian Oscillation and the North Atlantic Oscillation, Nature, 455, 523–527, https://doi.org/10.1038/nature07286, 2008.
Chen, G., Ling, J., Zhang, R., Xiao, Z., and Li, C.: The MJO From CMIP5 to CMIP6: Perspectives From Tracking MJO Precipitation, Geophys. Res. Lett., 49, e2021GL095241, https://doi.org/10.1029/2021GL095241, 2022.
Craig, A. P., Vertenstein, M., and Jacob, R.: A new flexible coupler for earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform. C., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2012.
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.
DeMott, C. A., Wolding, B. O., Maloney, E. D., and Randall, D. A.: Atmospheric Mechanisms for MJO Decay Over the Maritime Continent, J. Geophys. Res.-Atmos., 123, 5188–5204, https://doi.org/10.1029/2017JD026979, 2018.
Ferreira, R. N., Schubert, W. H., and Hack, J. J.: Dynamical aspects of twin tropical cyclones associated with the Madden-Julian oscillation, J. Atmos. Sci., 53, 929–945, https://doi.org/10.1175/1520-0469(1996)053<0929:DAOTTC>2.0.CO;2, 1996.
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.
Goswami, B. N.: South asian monsoon, Springer, 2012.
Gottschalck, J., Wheeler, M., Weickmann, K., Vitart, F., Savage, N., Lin, H., Hendon, H., Waliser, D., Sperber, K., Nakagawa, M., Prestrelo, C., Flatau, M., and Higgins, W.: A Framework for Assessing Operational Madden-Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project, B. Am. Meteorol. Soc., 91, 1247–1258, https://doi.org/10.1175/2010BAMS2816.1, 2010.
Hall, J. D., Matthews, A. J., and Karoly, D. J.: The modulation of tropical cyclone activity in the Australian region by the Madden-Julian oscillation, Mon. Weather Rev., 129, 2970–2982, https://doi.org/10.1175/1520-0493(2001)129<2970:TMOTCA>2.0.CO;2, 2001.
Hannah, W. M., Maloney, E. D., and Pritchard, M. S.: Consequences of systematic model drift in DYNAMO MJO hindcasts with SP-CAM and CAM5, J. Adv. Model. Earth Sy., 7, 1051–1074, https://doi.org/10.1002/2014MS000423, 2015.
Harris, L., Zhou, L., Lin, S.-J., Chen, J.-H., Chen, X., Gao, K., Morin, M., Rees, S., Sun, Y., Tong, M., Xiang, B., Bender, M., Benson, R., Cheng, K.-Y., Clark, S., Elbert, O. D., Hazelton, A., Huff, J. J., Kaltenbaugh, A., Liang, Z., Marchok, T., Shin, H. H., and Stern, W.: GFDL SHiELD: A Unified System for Weather-to-Seasonal Prediction, J. Adv. Model. Earth Sy., 12, e2020MS002223, https://doi.org/10.1029/2020MS002223, 2020.
Hendon, H. H. and Salby, M. L.: The Life Cycle of the Madden-Julian Oscillation, J. Atmos. Sci., 51, 2225–2237, https://doi.org/10.1175/1520-0469(1994)051<2225:TLCOTM>2.0.CO;2, 1994.
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 hourly 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.bd0915c6, 2023.
Hoffman, R. N. and Kalnay, E.: Lagged average forecasting, an alternative to Monte Carlo forecasting, Tellus A, 35, 100–118, https://doi.org/10.3402/tellusa.v35i2.11425, 1983.
Hou, D., Kalnay, E., and Droegemeier, K. K.: Objective Verification of the SAMEX ’98 Ensemble Forecasts, Mon. Weather Rev., 129, 73–91, https://doi.org/10.1175/1520-0493(2001)129<0073:OVOTSE>2.0.CO;2, 2001.
Hsu, H.-H.: Intraseasonal variability of the atmosphere–ocean–climate system: East Asian monsoon, in: Intraseasonal Variability in the Atmosphere-Ocean Climate System, edited by: Lau, W. K.-M. and Waliser, D. E., Springer Berlin Heidelberg, Berlin, Heidelberg, 73–110, https://doi.org/10.1007/978-3-642-13914-7_3, 2012.
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.
Hsu, P. and Li, T.: Role of the Boundary Layer Moisture Asymmetry in Causing the Eastward Propagation of the Madden-Julian Oscillation, J. Climate, 25, 4914–4931, https://doi.org/10.1175/JCLI-D-11-00310.1, 2012.
Huang, B., Liu, C., Banzon, V. F., Freeman, E., Graham, G., Hankins, W., Smith, T. M., and Zhang, H.-M.: NOAA 0.25-degree Daily Optimum Interpolation Sea Surface Temperature (OISST), Version 2.1, NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/RE9P-PT57, 2020.
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.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.: Cice: the los alamos sea ice model documentation and software user's manual version 4.1 la-cc-06-012, T-3 Fluid Dynamics Group, Los Alamos National Laboratory, 675, 500, 2010.
Inness, P. M. and Slingo, J. M.: The interaction of the Madden-Julian Oscillation with the Maritime Continent in a GCM, Q. J. Roy. Meteor. Soc., 132, 1645–1667, https://doi.org/10.1256/qj.05.102, 2006.
Jeuken, A. B. M., Siegmund, P. C., Heijboer, L. C., Feichter, J., and Bengtsson, L.: On the potential of assimilating meteorological analyses in a global climate model for the purpose of model validation, J. Geophys. Res.-Atmos., 101, 16939–16950, https://doi.org/10.1029/96JD01218, 1996.
Jiang, X.: Key processes for the eastward propagation of the Madden-Julian Oscillation based on multimodel simulations: Key Model Processes for MJO Propagation, J. Geophys. Res.-Atmos., 122, 755–770, https://doi.org/10.1002/2016JD025955, 2017.
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.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J., Fiorino, M., and Potter, G. L.: NCEP–DOE AMIP-II Reanalysis (R-2), Bulletin of the American Meteorological Society, 83, 1631–1644, https://doi.org/10.1175/BAMS-83-11-1631, 2002.
Kemball-Cook, S. R. and Weare, B. C.: The Onset of Convection in the Madden-Julian Oscillation, J. Climate, 14, 780–793, https://doi.org/10.1175/1520-0442(2001)014<0780:TOOCIT>2.0.CO;2, 2001.
Kerbyson, D. J. and Jones, P. W.: A Performance Model of the Parallel Ocean Program, Int. J. High Perform. C., 19, 261–276, https://doi.org/10.1177/1094342005056114, 2005.
Kim, D., Kug, J.-S., and Sobel, A. H.: Propagating versus Nonpropagating Madden-Julian Oscillation Events, J. Climate, 27, 111–125, https://doi.org/10.1175/JCLI-D-13-00084.1, 2014.
Kim, H.: MJO Propagation Processes and Mean Biases in the SubX and S2S Reforecasts, J. Geophys. Res.-Atmos., 124, 9314–9331, https://doi.org/10.1029/2019JD031139, 2019.
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.
Kim, H.-M.: The impact of the mean moisture bias on the key physics of MJO propagation in the ECMWF reforecast, J. Geophys. Res.-Atmos., 122, 7772–7784, https://doi.org/10.1002/2017JD027005, 2017.
Kim, H.-M., Webster, P. J., Toma, V. E., and Kim, D.: Predictability and Prediction Skill of the MJO in Two Operational Forecasting Systems, J. Climate, 27, 5364–5378, https://doi.org/10.1175/JCLI-D-13-00480.1, 2014.
Lau, K.-M. and Chan, P. H.: Aspects of the 40–50 Day Oscillation during the Northern Summer as Inferred from Outgoing Longwave Radiation, Mon. Weather Rev., 114, 1354–1367, https://doi.org/10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2, 1986.
Lau, W. K. M. and Waliser, D. E.: El Niño Southern Oscillation connection, in: Intraseasonal Variability in the Atmosphere-Ocean Climate System, Springer, Berlin, Heidelberg, 297–334, https://doi.org/10.1007/978-3-642-13914-7_9, 2012.
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 Sy., 3, M03001, https://doi.org/10.1029/2011MS00045, 2011.
Leutbecher, M. and Palmer, T. N.: Ensemble forecasting, J. Comput. Phys., 227, 3515–3539, https://doi.org/10.1016/j.jcp.2007.02.014, 2008.
Li, J., Bao, Q., Liu, Y., Wu, G., Wang, L., He, B., Wang, X., and Li, J.: Evaluation of FAMIL2 in Simulating the Climatology and Seasonal-to-Interannual Variability of Tropical Cyclone Characteristics, J. Adv. Model. Earth Sy., 11, 1117–1136, https://doi.org/10.1029/2018MS001506, 2019.
Liebmann, B. and Smith, C. A.: Description of a Complete (Interpolated) Outgoing Longwave Radiation Dataset, B. Am. Meteorol. Soc., 77, 1275–1277, 1996.
Lim, Y., Son, S.-W., and Kim, D.: MJO Prediction Skill of the Subseasonal-to-Seasonal Prediction Models, J. Climate, 31, 4075–4094, https://doi.org/10.1175/JCLI-D-17-0545.1, 2018.
Lin, H., Brunet, G., and Derome, J.: Forecast Skill of the Madden-Julian Oscillation in Two Canadian Atmospheric Models, Mon. Weather Rev., 136, 4130–4149, https://doi.org/10.1175/2008MWR2459.1, 2008.
Lin, S.-J.: A “Vertically Lagrangian” Finite-Volume Dynamical Core for Global Models, Mon. Weather Rev., 132, 2293–2307, https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2, 2004.
Liu, F., Wang, B., Ouyang, Y., Wang, H., Qiao, S., Chen, G., and Dong, W.: Intraseasonal variability of global land monsoon precipitation and its recent trend, npj Clim. Atmos. Sci., 5, 30, https://doi.org/10.1038/s41612-022-00253-7, 2022.
Liu, Y.: Dynamic MJO forecasts using an ensemble subseasonal-to-seasonal forecast system of IAP-CAS model, Zenodo [code], https://doi.org/10.5281/zenodo.10817813, 2024.
Liu, Y. Q.: Prediction of monthly-seasonal precipitation using coupled SVD patterns between soil moisture and subsequent precipitation, Geophys. Res. Lett., 30, 1827, https://doi.org/10.1029/2003GL017709, 2003.
Madden, R. A. and Julian, P. R.: Detection of a 40–50 Day Oscillation in the Zonal Wind in the Tropical Pacific, J. Atmos. Sci., 28, 702–708, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2, 1971.
Maloney, E. D.: An Intraseasonal Oscillation Composite Life Cycle in the NCAR CCM3.6 with Modified Convection, J. Climate, 15, 964–982, https://doi.org/10.1175/1520-0442(2002)015<0964:AIOCLC>2.0.CO;2, 2002.
Mu, M., Duan, W. S., and Wang, B.: Conditional nonlinear optimal perturbation and its applications, Nonlin. Processes Geophys., 10, 493–501, https://doi.org/10.5194/npg-10-493-2003, 2003.
Mureau, R., Molteni, F., and Palmer, T. N.: Ensemble prediction using dynamically conditioned perturbations, Q. J. Roy. Meteor. Soc., 119, 299–323, https://doi.org/10.1002/qj.49711951005, 1993.
Nasuno, T., Li, T., and Kikuchi, K.: Moistening Processes before the Convective Initiation of Madden-Julian Oscillation Events during the CINDY2011/DYNAMO Period, Mon. Weather Rev., 143, 622–643, https://doi.org/10.1175/MWR-D-14-00132.1, 2015.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6M043C6, 2000, updated daily.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D65D8PWK, 2015.
Neena, J. M., Lee, J. Y., Waliser, D., Wang, B., and Jiang, X.: Predictability of the Madden-Julian Oscillation in the Intraseasonal Variability Hindcast Experiment (ISVHE), J. Climate, 27, 4531–4543, https://doi.org/10.1175/JCLI-D-13-00624.1, 2014.
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0), Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, 2020.
Oleson, W., Lawrence, M., Bonan, B., Flanner, G., Kluzek, E., Lawrence, J., Levis, S., Swenson, C., Thornton, E., Dai, A., Decker, M., Dickinson, R., Feddema, J., Heald, L., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L., Zeng, X., and Zeng, X.: Technical Description of version 4.0 of the Community Land Model (CLM), University Corporation for Atmospheric Research, https://doi.org/10.5065/D6FB50WZ, 2010.
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.
Pham, D. T.: Stochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems, Mon. Weather Rev., 129, 1194–1207, https://doi.org/10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2, 2001.
Putman, W. M. and Lin, S.-J.: Finite-volume transport on various cubed-sphere grids, J. Comput. Phys., 227, 55–78, https://doi.org/10.1016/j.jcp.2007.07.022, 2007.
Rashid, H. A., Hendon, H. H., Wheeler, M. C., and Alves, O.: Prediction of the Madden-Julian oscillation with the POAMA dynamical prediction system, Clim. Dynam., 36, 649–661, https://doi.org/10.1007/s00382-010-0754-x, 2011.
Raymond, D. J. and Fuchs, Ž.: Moisture Modes and the Madden-Julian Oscillation, J. Climate, 22, 3031–3046, https://doi.org/10.1175/2008JCLI2739.1, 2009.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface Temperature, J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1, 2007.
Rousseeuw, P.: Silhouettes – a Graphical Aid to the Interpretation and Validation of Cluster-Analysis, J. Comput. Appl. Math., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987.
Rui, H. and Wang, B.: Development Characteristics and Dynamic Structure of Tropical Intraseasonal Convection Anomalies, J. Atmos. Sci., 47, 357–379, https://doi.org/10.1175/1520-0469(1990)047<0357:DCADSO>2.0.CO;2, 1990.
Vitart, F.: Madden—Julian Oscillation prediction and teleconnections in the S2S database, Q. J. Roy. Meteor. Soc, 143, 2210–2220, https://doi.org/10.1002/qj.3079, 2017.
Vitart, F. and Molteni, F.: Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India, Mon. Weather Rev., 137, 1480–1492, https://doi.org/10.1175/2008MWR2761.1, 2009.
Vitart, F. and Molteni, F.: Simulation of the Madden-Julian Oscillation and its teleconnections in the ECMWF forecast system, Q. J. Roy. Meteor. Soc., 136, 842–855, https://doi.org/10.1002/qj.623, 2010.
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C., Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D., Woolnough, S., Wu, T., Won, D.-J., Xiao, H., Zaripov, R., and Zhang, L.: The Subseasonal to Seasonal (S2S) Prediction Project Database, B. Am. Meteorol. Soc., 98, 163–173, https://doi.org/10.1175/BAMS-D-16-0017.1, 2017 (data available at: https://apps.ecmwf.int/datasets, last access: 1 July 2023).
Waliser, D. E., Lau, K. M., Stern, W., and Jones, C.: Potential Predictability of the Madden-Julian Oscillation, B. Am. Meteorol. Soc., 84, 33–50, https://doi.org/10.1175/BAMS-84-1-33, 2003.
Wang, B.: Dynamics of Tropical Low-Frequency Waves: An Analysis of the Moist Kelvin Wave, J. Atmos. Sci., 45, 2051–2065, https://doi.org/10.1175/1520-0469(1988)045<2051:DOTLFW>2.0.CO;2, 1988.
Wang, B. and Lee, S.-S.: MJO Propagation Shaped by Zonal Asymmetric Structures: Results from 24 GCM Simulations, J. Climate, 30, 7933–7952, https://doi.org/10.1175/JCLI-D-16-0873.1, 2017.
Wang, B., Chen, G., and Liu, F.: Diversity of the Madden-Julian Oscillation, Sci. Adv., 5, eaax0220, https://doi.org/10.1126/sciadv.aax0220, 2019.
Wang, W., Hung, M.-P., Weaver, S. J., Kumar, A., and Fu, X.: MJO prediction in the NCEP Climate Forecast System version 2, Clim. Dyn., 42, 2509–2520, https://doi.org/10.1007/s00382-013-1806-9, 2014.
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.
Wheeler, M. C., Hendon, H. H., Cleland, S., Meinke, H., and Donald, A.: Impacts of the Madden-Julian Oscillation on Australian Rainfall and Circulation, J. Climate, 22, 1482–1498, https://doi.org/10.1175/2008JCLI2595.1, 2009.
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, X., Deng, L., Song, X., Vettoretti, G., Peltier, W. R., and Zhang, G. J.: Impact of a modified convective scheme on the Madden-Julian Oscillation and El Niño-Southern Oscillation in a coupled climate model: MJO AND ENSO SIMULATED BY A COUPLED GCM, Geophys. Res. Lett., 34, L16823, https://doi.org/10.1029/2007GL030637, 2007.
Xiang, B., Zhao, M., Jiang, X., Lin, S.-J., Li, T., Fu, X., and Vecchi, G.: The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model, J. Climate, 28, 5351–5364, https://doi.org/10.1175/JCLI-D-15-0102.1, 2015.
Xiang, B., Harris, L., Delworth, T. L., Wang, B., Chen, G., Chen, J.-H., Clark, S. K., Cooke, W. F., Gao, K., Huff, J. J., Jia, L., Johnson, N. C., Kapnick, S. B., Lu, F., McHugh, C., Sun, Y., Tong, M., Yang, X., Zeng, F., Zhao, M., Zhou, L., and Zhou, X.: S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections, B. Am. Meteorol. Soc., 103, E463–E484, https://doi.org/10.1175/BAMS-D-21-0124.1, 2022.
Yang, C., Liu, J., and Xu, S.: Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations, J. Adv. Model. Earth Sy., 12, e2019MS001938, https://doi.org/10.1029/2019MS001938, 2020.
Zeng, L., Bao, Q., Wu, X., He, B., Yang, J., Wang, T., Liu, Y., Wu, G., and Liu, Y.: Impacts of humidity initialization on MJO prediction: A study in an operational sub-seasonal to seasonal system, Atmos. Res., 294, 106946, https://doi.org/10.1016/j.atmosres.2023.106946, 2023.
Zhang, C.: Madden-Julian Oscillation, Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158, 2005.
Zhou, L. and Harris, L.: Integrated Dynamics-Physics Coupling for Weather to Climate Models: GFDL SHiELD With In-Line Microphysics, Geophys. Res. Lett., 49, e2022GL100519, https://doi.org/10.1029/2022GL100519, 2022.
Zhou, L., Lin, S.-J., Chen, J.-H., Harris, L. M., Chen, X., and Rees, S. L.: Toward Convective-Scale Prediction within the Next Generation Global Prediction System, B. Am. Meteorol. Soc., 100, 1225–1243, https://doi.org/10.1175/BAMS-D-17-0246.1, 2019.
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences...
