4.6 Article

Progress of MJO Prediction at CMA from Phase I to Phase II of the Sub-Seasonal to Seasonal Prediction Project

Journal

ADVANCES IN ATMOSPHERIC SCIENCES
Volume 40, Issue 10, Pages 1799-1815

Publisher

SCIENCE PRESS
DOI: 10.1007/s00376-023-2351-z

Keywords

Madden-Julian Oscillation (MJO); Subseasonal to Seasonal (S2S); prediction skill; improvement; initial phase

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As part of the S2S Prediction Project, the China Meteorological Administration has tested different versions of the BCC-CSM model to predict the Madden-Julian Oscillation (MJO). The study found that the newer high-resolution BCC-CSM model outperforms its predecessors in simulating MJO characteristics. However, there was no significant improvement in MJO prediction skill between the different model versions. Overall, the study emphasizes the need for continued improvement in model physics to better predict the MJO.
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.

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