Prediction of the Madden-Julian Oscillation: A Review

被引:137
|
作者
Kim, Hyemi [1 ]
Vitart, Frederic [2 ]
Waliser, Duane E. [3 ]
机构
[1] SUNY Stony Brook, Sch Marine & Atmospher Sci, Stony Brook, NY 11794 USA
[2] European Ctr Medium Range Weather Forecasts, Reading, Berks, England
[3] CALTECH, Jet Prop Lab, Pasadena, CA USA
关键词
Climate prediction; Climate models; Intraseasonal variability; SEA-SURFACE TEMPERATURE; MJO FORECAST SKILL; INTRASEASONAL VARIABILITY; ENSEMBLE PREDICTION; MARITIME CONTINENT; ECMWF MODEL; UNDERSTANDING ADVANCES; EASTWARD PROPAGATION; MOISTURE MODES; PART II;
D O I
10.1175/JCLI-D-18-0210.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
There has been an accelerating interest in forecasting the weather and climate within the subseasonal time range. The Madden-Julian oscillation (MJO), an organized envelope of tropical convection, is recognized as one of the leading sources of subseasonal predictability. This review synthesizes the latest progress regarding the MJO predictability and prediction. During the past decade, the MJO prediction skill in dynamical prediction systems has exceeded the skill of empirical predictions. Such improvement has been mainly attributed to more observations and computer resources, advances in theoretical understanding, and improved numerical models aided in part by multinational efforts through field campaigns and multimodel experiments. The state-of-the-art dynamical forecasts have shown MJO prediction skill up to 5 weeks. Prediction skill can be extended by improving the ensemble generation approach tailored for MJO prediction and by averaging multiensembles or multimodels. MJO prediction skill can be influenced by the tropical mean state and low-frequency climate mode variations, as well as by the extratropical circulation. MJO prediction skill is proven to be sensitive to model physics, ocean-atmosphere coupling, and quality of initial conditions, while the impact of the model resolution seems to be marginal. Remaining challenges and recommendations on new research avenues to fully realize the predictability of the MJO are discussed.
引用
收藏
页码:9425 / 9443
页数:19
相关论文
共 50 条
  • [1] The Madden-Julian Oscillation
    Lin, Hai
    [J]. ATMOSPHERE-OCEAN, 2022, 60 (3-4) : 338 - 359
  • [2] Madden-Julian oscillation
    Zhang, CD
    [J]. REVIEWS OF GEOPHYSICS, 2005, 43 (02) : 1 - 36
  • [3] Madden-Julian Oscillation
    Jones, Charles
    [J]. ATMOSPHERE, 2018, 9 (03):
  • [4] Machine learning prediction of the Madden-Julian oscillation
    Silini, Riccardo
    Barreiro, Marcelo
    Masoller, Cristina
    [J]. NPJ CLIMATE AND ATMOSPHERIC SCIENCE, 2021, 4 (01)
  • [5] Machine learning prediction of the Madden-Julian oscillation
    Riccardo Silini
    Marcelo Barreiro
    Cristina Masoller
    [J]. npj Climate and Atmospheric Science, 4
  • [6] The dynamics of the Madden-Julian Oscillation
    Zehnder, JA
    Reeder, MJ
    [J]. 24TH CONFERENCE ON HURRICANES AND TROPICAL METEOROLOGY/10TH CONFERENCE ON INTERACTION OF THE SEA AND ATMOSPHERE, 2000, : 96 - 97
  • [7] Incorrect computation of Madden-Julian oscillation prediction skill
    Suematsu, Tamaki
    Martin, Zane K.
    Barnes, Elizabeth A.
    Demott, Charlotte A.
    Hagos, Samson
    Ham, Yoo-Geun
    Kim, Daehyun
    Kim, Hyemi
    Koh, Tieh-Yong
    Maloney, Eric D.
    [J]. NPJ CLIMATE AND ATMOSPHERIC SCIENCE, 2024, 7 (01):
  • [8] Seasonality in the Madden-Julian oscillation
    Zhang, CD
    Dong, M
    [J]. JOURNAL OF CLIMATE, 2004, 17 (16) : 3169 - 3180
  • [9] Diversity of the Madden-Julian Oscillation
    Wang, Bin
    Chen, Guosen
    Liu, Fei
    [J]. SCIENCE ADVANCES, 2019, 5 (07):
  • [10] Prediction of the Madden-Julian oscillation with the POAMA dynamical prediction system
    Rashid, Harun A.
    Hendon, Harry H.
    Wheeler, Matthew C.
    Alves, Oscar
    [J]. CLIMATE DYNAMICS, 2011, 36 (3-4) : 649 - 661