Abstract
By conducting several sets of hindcast experiments using the Beijing Climate Center Climate System Model, which participates in the Sub-seasonal to Seasonal (S2S) Prediction Project, we systematically evaluate the model’s capability in forecasting MJO and its main deficiencies. In the original S2S hindcast set, MJO forecast skill is about 16 days. Such a skill shows significant seasonal-to-interannual variations. It is found that the model-dependent MJO forecast skill is more correlated with the Indian Ocean Dipole (IOD) than with the El Niño–Southern Oscillation. The highest skill is achieved in autumn when the IOD attains its maturity. Extended skill is found when the IOD is in its positive phase. MJO forecast skill’s close association with the IOD is partially due to the quickly strengthening relationship between MJO amplitude and IOD intensity as lead time increases to about 15 days, beyond which a rapid weakening of the relationship is shown. This relationship transition may cause the forecast skill to decrease quickly with lead time, and is related to the unrealistic amplitude and phase evolutions of predicted MJO over or near the equatorial Indian Ocean during anomalous IOD phases, suggesting a possible influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21–22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases to 27 days in S2S_IEXP2. Nevertheless, even with improved initialization, it is still difficult for the model to predict MJO propagation across the western hemisphere–western Indian Ocean area and across the eastern Indian Ocean–Maritime Continent area. Especially, MJO prediction is apparently limited by various interrelated deficiencies (e.g., overestimated IOD, shorter-than-observed MJO life cycle, Maritime Continent prediction barrier), due possibly to the model bias in the background moisture field over the eastern Indian Ocean and Maritime Continent. Thus, more efforts are needed to correct the deficiency in model physics in this region, in order to overcome the well-known Maritime Continent predictability barrier.
Highlights
Short-term climate prediction is extremely important for both research and operation because of its vital close relationship with national economy and people’s livelihood
This study explores Madden Julian Oscillation (MJO) forecast skill and deficiency based on comprehensive hindcasts by the BCC Climate System Model (BCC_CSM) that participates in the S2S Prediction Project
A distinctive seasonal dependence of skill, featured by an about-19-day maximum in the boreal autumn rather than winter, appears in the predictions. It indicates that the seasonal-to-interannual variation of MJO forecast skill is more significantly correlated with Indian Ocean Dipole (IOD) index than with Niño 3.4 index, implying different degrees of impacts from the IOD and El Niño–Southern Oscillation (ENSO)
Summary
Short-term climate prediction is extremely important for both research and operation because of its vital close relationship with national economy and people’s livelihood. The low-order stochastic model with “past-noise forecasting” method (Kondrashov et al 2013) and the spatial–temporal projection statistical model (Hsu et al 2015; Zhu et al 2015) can provide useful skill of 25–30 days Dynamical model is another popular tool for MJO forecast, given that MJO’s main characteristics such as intensity, structure, spectrum, and propagation have been reasonably captured by state-of-the-art climate models (e.g., Kim et al 2009; Hung et al 2013; Jiang et al 2015). A Geophysical Fluid Dynamics Laboratory (GFDL) coupled model presented a 27-day skill for the MJO in the boreal winter (Xiang et al 2015) In this context, assessing the capability of climate models in forecasting MJO and further searching for improved methods are always necessary and meaningful, because the MJO is considered as a major signal source.
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