Madden-Julian Oscillation (MJO) is a quasiperiodic oscillation of the near-equatorial troposphere, particularly over the Indian Ocean and the western equatorial Pacific. The period of the oscillation varies between about 30 and 50 days, and appears to represent an eastward propagating disturbance with a phase speed of only about 8 m s-1. MJO has a great deal of impact on weather systems around the globe. Among others, the MJO may influence the variability of rainfall in the west coast of North America, affect the Indian monsoon development, and modulate the frequency and intensity of hurricanes in the Pacific Ocean and Caribbean Sea. For these reasons, the MJO is regarded as a forecast tool with great potential. The possible effect of the topography and land–sea contrast on the propagation nature of the MJO is an important but yet not well understood subject. In this study, we focus on the orographic effects on the propagation of the MJO 2007-08 when it passed over the New Guinea Mountains. This event is simulated by a numerical weather prediction model, the Advanced Research Weather Research and Forecasting (ARW-WRF) model. The model has a single domain of 5 km grid resolution. The simulated results are then analyzed and it was similar to observed results. The results of the simulations demonstrate that the MJO goes through three stages when it passes over the New Guinea Mountains, which are blocking, splitting and merging stages. In the blocking stage, the convection associated with the MJO is weakened by the orographic effects of the New Guinea Mountains until the eastward propagation is not continuous and there is a decline in rainfall amount. During the splitting stage, the MJO is divided into two parts while it moves southeastward across the island. In the merging stage, we see the split MJO come together and move eastward after moving away from the New Guinea Mountains. We also show that the higher resolution data from our mesoscale simulation was better at capturing the MJO than the lower resolution reanalysis data that was used in some previous studies.
Author: William Agyakwah
Co-authors: Justin Riley
Dr. Yuh-Lang Lin
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