Moistening Processes for Madden-Julian Oscillations During DYNAMO

Friday, 19 December 2014
Chung Hsiung Sui and Kai-Chih Tseng, National Taiwan University, Taipei, Taiwan
The multi-scale nature of TIV and possible dynamics are investigated through an analysis of the Low-tropospheric moistening processes of the two Madden-Julian Oscillations (MJOs) over the Indian Ocean during Dynamics of the MJO (DYNAMO) is performed by using soundings, operational assimilation and satellite data. Through the life cycles, the moistening processes responsible for MJO evolution is investigated by projecting the scale-separated moisture budget terms onto intraseasonal moisture anomaly and its time tendency change and by checking their correlation over their life cycles using time-decomposed wind and moisture fields. Results indicate that broad-scale advection by low-frequency and MJO flow and moisture fields are dominant moisture sources, while residual of moisture budget (-Q2) as dominant sink contributing to tendency term (propagation) and intaseasonal moisture anomaly (growth and decay). Dividing their life cycles into four phases, the two MJOs exhibit different budget balances in pre-moistening stage from the suppressed phase to cloud developing phase when low-frequency vertical motion is downward in MJO1 but upward in MJO2. The corresponding drying and moistening are balanced by negative Q2 (re-evaporation in non-raining cloud) in MJO1 and positive Q2 in MJO2. The result implies that seasonal cycle and interannual oscillations can affect the initiation of MJOs. The pre-moistening in the low-troposphere by boundary-layer moisture convergence leading the deep convection is observed but only in the cloud developing to convective phase of MJOs. Nonlinear moisture advection by synoptic disturbances and by MJOs always acts as diffusive terms. They are dominant moisture sources (sinks) in the suppress phase of MJO1 (MJO2). The above analysis is being extended to successive and primary MJO events in boreal summer and winter seasons using 10 years of data.