Abstract
As one of the participants in the Subseasonal to Seasonal (S2S) Prediction Project, the China Meteorological Administration (CMA) has adopted several model versions to participate in the S2S Project. This study evaluates the models’ capability to simulate and predict the Madden-Julian Oscillation (MJO). Three versions of the Beijing Climate Center Climate System Model (BCC-CSM) are used to conduct historical simulations and re-forecast experiments (referred to as EXP1, EXP1-M, and EXP2, respectively). In simulating MJO characteristics, the newly-developed high-resolution BCC-CSM outperforms its predecessors. In terms of MJO prediction, the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M, and further to 24 days in EXP2. Within the first forecast week, the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill. However, during forecast weeks 2–3, EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3. Particularly at initial phases 2–3, EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection, leading to the highest prediction skill of the MJO. Our results reveal that, during the participation of the CMA models in the S2S Project, both the improved model initialization and updated model physics played positive roles in improving MJO prediction. Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.
摘 要
中国气象局(CMA)在参与国际次季节—季节(S2S)预测计划的第一到第二阶段,对S2S尺度重要现象—马登-朱利安振荡 (MJO)的预测能力逐步提升。本文利用中国气象局参加S2S计划的三个模式版本分别开展了历史模拟和回报试验(EXP1、EXP1-M和EXP2),进而探讨了模式对MJO预测的改进程度及可能原因。结果表明,在MJO 气候态模拟方面,新开发的高分辨率气候系统模式优于之前的中等分辨率模式。在MJO预测方面,由于初始条件的改进和模式的逐步更新,MJO的可用预测技巧从EXP1的15天提高到EXP1-M的22天,进一步在EXP2提高到24天。EXP2的MJO整体预测技巧最高,但其相比EXP1-M在预测第 2-3 周的改进并不明显。这主要是因为,针对第2-3初始位相的MJO预报,EXP1-M能够合理预测第2-3周的海洋大陆区域MJO对流异常和风场的响应关系,但EXP2对这种关系的预测能力快速下降。研究也同时指出,未来应侧重于进一步改进模式物理过程,尤其是海洋大陆区域MJO对流过程,从而提高MJO的预测技巧。
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References
Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19, 643–674, https://doi.org/10.1175/JCLI3629.1.
Fu, X., B. Wang, J.-Y. Lee, W. Q. Wang, and L. Gao, 2011: Sensitivity of dynamical intraseasonal prediction skills to different initial conditions. Mon. Wea. Rev., 139, 2572–2592, https://doi.org/10.1175/2011MWR3584.1.
Fu, X., J.-Y. Lee, P.-C. Hsu, H. Taniguchi, B. Wang, W. Q. Wang, and S. Weaver, 2013: Multi-model M/O forecasting during DYNAMO/CINDY period. Climate Dyn., 41, 1067–1081, https://doi.org/10.1007/s00382-013-1859-9.
Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447–462, https://doi.org/10.1002/qj.49710644905.
Griffies, S. M., 2012: Elements of the modular ocean model (MOM). GFDL Ocean Group Tech. Rep No.7.
Griffies, S. M., and Coauthors, 2005: Formulation of an ocean model for global climate simulations. Ocean Science, 1, 45–79, https://doi.org/10.5194/os-1-45-2005.
Hannah, W. M., and E. D. Maloney, 2014: The moist static energy budget in NCAR CAM5 hindcasts during DYNAMO. Journal of Advances in Modeling Earth Systems, 6, 420–440, https://doi.org/10.1002/2013MS000272.
Hannah, W. M., E. D. Maloney, and M. S. Pritchard, 2015: Consequences of systematic model drift in DYNAMO M/O hind-casts with SP-CAM and CAM5. Journal of Advances in Modeling Earth Systems, 7, 1051–1074, https://doi.org/10.1002/2014MS000423.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Jiang, X., and Coauthors, 2015: Vertical structure and physical processes of the Madden-Julian oscillation: Exploring key model physics in climate simulations. J. Geophys. Res., 120, 4718–4748, https://doi.org/10.1002/2014JD022375.
Kiladis, G. N., K. H. Straub, and P. T. Haertel, 2005: Zonal and vertical structure of the madden–julian oscillation. J. Atmos. Sci., 62, 2790–2809, https://doi.org/10.1175/JAS3520.1.
Kiladis, G. N., and Coauthors, 2014: A comparison of OLR and circulation-based indices for tracking the MJO. Mon. Wea. Rev., 142, 1697–1715, https://doi.org/10.1175/MWR-D-13-00301.1.
Kim, H.-M., P. J. Webster, V. E. Toma, and D. Kim, 2014: 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.
Kim, H., F. Vitart, and D. E. Waliser, 2018: Prediction of the Mad-den-Julian oscillation: A review. J. Climate, 31, 9425–9443, https://doi.org/10.1175/JCLI-D-18-0210.1.
Kim, H., M. A. Janiga, and K. Pegion, 2019: MJO propagation processes and mean biases in the SubX and S2S reforecasts. J. Geophys. Res., 124, 9314–9331, https://doi.org/10.1029/2019JD031139.
Klingaman, N. P., and S. J. Woolnough, 2014: Using a case-study approach to improve the Madden-Julian oscillation in the hadley centre model. Quart. J. Roy. Meteor. Soc., 140, 2491–2505, https://doi.org/10.1002/qj.2314.
Klingaman, N. P., X. N. Jiang, P. K. Xavier, J. Petch, D. Waliser, and S. J. Woolnough, 2015a: Vertical structure and physical processes of the Madden-Julian oscillation: Synthesis and summary. J. Geophys. Res., 120, 4671–4689, https://doi.org/10.1002/2015JD023196.
Klingaman, N. P., and Coauthors, 2015b: Vertical structure and physical processes of the Madden-Julian oscillation: Linking hindcast fidelity to simulated diabatic heating and moistening. J. Geophys. Res., 120, 4690–4717, https://doi.org/10.1002/2014JD022374.
Lee, J.-Y., X. Fu, and B. Wang, 2017: Predictability and prediction of the Madden-Julian oscillation: A review on progress and current status. The Global Monsoon System: Research and Forecast, 3rd ed, C.-P. Chang et al., Eds., WSPC, 147–159, https://doi.org/10.1142/9789813200913_0012.
Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 1275–1277.
Lin, H., G. Brunet, and J. Derome, 2008: Forecast skill of the Mad-den-Julian oscillation in two Canadian atmospheric models. Mon. Wea. Rev., 136, 4130–4149, https://doi.org/10.1175/2008MWR2459.1.
Ling, J., P. Bauer, P. Bechtold, A. Beljaars, R. Forbes, F. Vitart, M. Ulate, and C. D. Zhang, 2014: Global versus local MJO forecast skill of the ECMWF model during DYNAMO. Mon. Wea. Rev., 142, 2228–2247, https://doi.org/10.1175/MWR-D-13-00292.1.
Liu, X., and Coauthors, 2021: Development of coupled data assimilation with the BCC climate system model: Highlighting the role of sea-ice assimilation for global analysis. Journal of Advances in Modeling Earth Systems, 13, e2020MS002368, https://doi.org/10.1029/2020MS002368.
Liu, X. W., and Coauthors, 2014: Relationships between interannual and intraseasonal variations of the Asian-western Pacific summer monsoon hindcasted by BCC_CSM1.1(m). Adv. Atmos. Sci., 31, 1051–1064, https://doi.org/10.1007/s00376-014-3192-6.
Liu, X. W., T. W. Wu, S. Yang, W. H. Jie, S. P. Nie, Q. P. Li, Y. J. Cheng, and X. Y. Liang, 2015: Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m). Adv. Atmos. Sci., 32, 1156–1172, https://doi.org/10.1007/s00376-015-4194-8.
Liu, X. W., and Coauthors, 2017: MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Climate Dyn., 48, 3283–3307, https://doi.org/10.1007/s00382-016-3264-7.
Liu, X. W., and Coauthors, 2019: Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Climate Dyn., 52, 3823–3843, https://doi.org/10.1007/s00382-018-4369-y.
Madden, R. A., and P. R. Julian, 1971: 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.
Miyakawa, T., and Coauthors, 2014: Madden–Julian oscillation prediction skill of a new-generation global model demonstrated using a supercomputer. Nature Communications, 5, 3769, https://doi.org/10.1038/ncomms4769.
Neena, J. M., J. Y. Lee, D. Waliser, B. Wang, and X. N. Jiang, 2014: 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-33-00624.1.
Pegion, K., and Coauthors, 2019: The Subseasonal Experiment (SubX): A multimodel subseasonal prediction experiment. Bull. Amer. Meteor. Soc., 100, 2043–2060, https://doi.org/10.1175/BAMS-D-18-0270.1.
Rashid, H. A., H. H. Hendon, M. C. Wheeler, and O. Alves, 2011: Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system. Climate Dyn., 36, 649–661, https://doi.org/10.1007/s00382-010-0754-x.
Ren, H.-L., and Coauthors, 2017: Prediction of primary climate variability modes at the Beijing Climate Center. J. Meteor. Res., 31, 204–223, https://doi.org/10.1007/s13351-017-6097-3.
Reynolds, R. W., T. M. Smith, C. Y. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1.
Seo, K.-H., and W. Q. Wang, 2010: The Madden–Julian oscillation simulated in the NCEP climate forecast system model: The importance of stratiform heating. J. Climate, 23, 4770–4793, https://doi.org/10.1175/2010JCLI2983.1.
Shelly, A., P. Xavier, D. Copsey, T. Johns, J. M. Rodríguez, S. Milton, and N. Klingaman, 2014: Coupled versus uncoupled hind-cast simulations of the Madden-Julian oscillation in the year of tropical convection. Geophys. Res. Lett., 41, 5670–5677, https://doi.org/10.1002/2013GL059062.
Vitart, F., 2014: Evolution of ECMWF sub-seasonal forecast skill scores. Quart. J. Roy. Meteor. Soc., 140, 1889–1899, https://doi.org/10.1002/qj.2256.
Vitart, F., 2017: Madden–Julian oscillation prediction and teleconnections in the S2S database. Quart. J. Roy. Meteor. Soc., 143, 2210–2220, https://doi.org/10.1002/qj.3079.
Vitart, F., S. Woolnough, M. A. Balmaseda, and A. M. Tompkins, 2007: Monthly forecast of the Madden–Julian oscillation using a coupled GCM. Mon. Wea. Rev., 135, 2700–2715, https://doi.org/10.1175/MWR3415.1.
Vitart, F., and Coauthors, 2017: The subseasonal to seasonal (S2S) prediction project database. Bull. Amer. Meteor. Soc., 98, 163–173, https://doi.org/10.1175/BAMS-D-16-0017.1.
Waliser, D., and Coauthors, 2009: MJO simulation diagnostics. J. Climate, 22, 3006–3030, https://doi.org/10.1175/2008JCLI2731.1.
Wang, L., T. Li, E. Maloney, and B. Wang, 2017: Fundamental causes of propagating and nonpropagating MJOs in MJOTF/ GASS models. J. Climate, 30, 3743–3769, https://doi.org/10.1175/JCLI-D-16-0765.1.
Wang, W. Q., M.-P. Hung, S. J. Weaver, A. Kumar, and X. Fu, 2014: MJO prediction in the NCEP climate forecast system version 2. Climate Dyn., 42, 2509–2520, https://doi.org/10.1007/s00382-013-1806-9.
Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 1917–1932, https://doi.org/10.1175/1520-0493(2004)132<1917:AAR-MMI>2.0.CO;2.
Winton, M., 2000: A reformulated three-layer sea ice model. J. Atmos. Oceanic Technol., 17, 525–531, https://doi.org/10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2.
Wu, T. W., and Coauthors, 2013: Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century. J. Geophys. Res., 118, 4326–4347, https://doi.org/10.1002/jgrd.50320.
Wu, T. W., and Coauthors, 2014: An overview of BCC climate system model development and application for climate change studies. J. Meteor. Res., 28, 34–56, https://doi.org/10.1007/s13351-014-3041-7.
Wu, T. W., and Coauthors, 2019: The Beijing Climate Center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019.
Wu, T. W., and Coauthors, 2021: BCC-CSM2-HR: A high-resolution version of the Beijing Climate Center Climate System Model. Geoscientific Model Development, 14, 2977–3006, https://doi.org/10.5194/gmd-14-2977-2021.
Xavier, P. K., and Coauthors, 2015: Vertical structure and physical processes of the Madden-Julian Oscillation: Biases and uncertainties at short range. J. Geophys. Res., 120, 4749–4763, https://doi.org/10.1002/2014JD022718.
Xiang, B. Q., M. Zhao, X. N. Jiang, S.-J. Lin, T. Li, X. Fu, and G. Vecchi, 2015: 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.
Yang, B., Y. C. Zhang, Y. Qian, T. W. Wu, A. N. Huang, and Y. J. Fang, 2015: Parametric sensitivity analysis for the Asian summer monsoon precipitation simulation in the Beijing Climate Center AGCM, version 2.1. J. Climate, 28, 5622–5644, https://doi.org/10.1175/JCLI-D-14-00655.1.
Yao, J. C., F. Vitart, M. A. Balmaseda, T. W. Wu, and X. W. Liu, 2021: The impact of coupled data assimilation on Madden-Julian oscillation predictability initialized from coupled satellite-era reanalysis. Mon. Wea. Rev, 149, 2897–2912, https://doi.org/10.1175/MWR-D-20-0360.1.
Zhou, Y. H., S. G. Wang, J. Fang, and D. Yang, 2022: The maritime continent barrier effect on the MJO teleconnections during the boreal winter seasons in the Northern Hemisphere. J. Climate, 36, 171–192, https://doi.org/10.1175/JCLI-D-21-0492.1.
Zhu, J. S., W. Q. Wang, and A. Kumar, 2017: Simulations of MJO propagation across the maritime continent: Impacts of SST feedback. J. Climate, 30, 1689–1704, https://doi.org/10.1175/JCLI-D-16-0367.1.
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This study is supported by the National Natural Science Foundation of China (Grant No. 42075161).
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Article Highlights
• BCC-CSM2-HR has the highest skill in simulating MJO characteristics compared with the previous two model versions.
• S2S re-forecasts, referred to as EXP1, EXP1-M, and EXP2, have the useful MJO prediction skill of 15, 22, and 24 forecast days, respectively.
• EXP2 significantly improves the MJO prediction skill within the first forecast week due mainly to its better initial condition.
• EXP1-M better predicts the wind field and Kelvin-wave response beyond 20 days at MJO phase 2–3, leading to its higher prediction skill.
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Yao, J., Liu, X., Wu, T. et al. Progress of MJO Prediction at CMA from Phase I to Phase II of the Sub-Seasonal to Seasonal Prediction Project. Adv. Atmos. Sci. 40, 1799–1815 (2023). https://doi.org/10.1007/s00376-023-2351-z
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DOI: https://doi.org/10.1007/s00376-023-2351-z
Key words
- Madden-Julian Oscillation (MJO)
- Subseasonal to Seasonal (S2S)
- prediction skill
- improvement
- initial phase