Statistical analyses are conducted with the use of 33-year dataset during 1979 and 2011 to examine the relationship between the interannual variations of MJO activity and TCG in the IO. We specifically focus on the season of October, November, and December (OND) during when the convectively active phase of MJO frequently appears. This season is also characterized by the enhanced frequency of TCG over the IO both in the northern and southern hemisphere. Figure 1 shows the locations of TCG in each season from the best-track data during 1979 and 2011. It is clearly seen that TCG frequently occurs over the whole IO in the OND season.
The TC best-track dataset of the United States Navy’s Joint Typhoon Warning Center (JTWC) is used for identifying the locations of TCG over the IO during the period between 1979 and 2011. In order to examine the environmental conditions for TCG, we use the reanalysis dataset produced by Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry: the Japanese 25-year Reanalysis (JRA-25)/JMA Climate Data Assimilation System (JCDAS) dataset [12]. The JRA-25/JCDAS dataset has a horizontal resolution of 1.25 degree and 6-hour temporal interval starting from 1979. This dataset was widely used in assessing the environmental conditions for TCG over the Indian Ocean [13–15], over the western North Pacific [11, 16], and over the Atlantic [17]. The representation of TCG environments in different reanalysis datasets including the JRA-25/JCDAS data can be found in Menkes et al. [18]; it is not seen that there are no pronounced differences among the datasets.
We divide the 33 years into MJO active years and MJO non-active years and compare the characteristics between the two categories. The MJO index of Wheeler and Hendon [19], archived at Australian Government Bureau of Meteorology and commonly referred to in MJO-related studies, is used to define the active years and the non-active years of MJO. The MJO index of Wheeler and Hendon is represented by two variables: real-time multivariate MJO series 1 (RMM1) and series 2 (RMM2), and is demonstrated by the phase space of RMM1 and RMM2. In the phase space, the amplitude of MJO is determined by the distance from the phase-space center, i.e., the square root of the sum of RMM1 squared and RMM2 squared. Phases 1 to 8 mean the approximate locations of enhanced convective signal of MJO. For example, phases 2 and 3 correspond to the period when MJO is active over the IO, and phases 4 and 5 correspond to the Maritime Continent. Phases 2–5 are chosen to divide active and non-active year from the total analysis years.
The procedure to define the MJO active year and the non-active year is described here. Firstly, we count a number of days in which the amplitude of an MJO event is larger than 1.0 in the phase space and at the same time the MJO phase shows any phase among phases 2, 3, 4, and 5 during the season of OND in each year. Secondly, we calculate the mean and standard deviation of the number of days of active MJO from the 33-year time series. Finally, the active (non-active) year is defined as a year in which the number of the active days is larger (smaller) than a half of the standard deviation from the 33-year mean. From this procedure, 11 years, i.e., 1979, 1982, 1984, 1987, 1993, 1994, 1999, 2000, 2002, 2008, and 2011, out of the 33 years are determined as active years, and 11 years, i.e., 1980, 1981, 1985, 1988, 1991, 1992, 1995, 1998, 2004, 2005, and 2010, are determined as non-active years. We did not apply any detrending of the dataset from the consideration that any trends that might exist in the dataset would be minimized by compositing data in each year category. Note that both the active and the non-active years include El Niño and La Niña phases.
To assess environmental conditions favorable/unfavorable for TCG, we use a genesis potential index (GPI) of Murakami et al. [11] that is a modified version of GPI originally defined by Emanuel and Nolan [10]. The GPI of Murakami et al. is defined as follows:
(1)
where η is the absolute vorticity (s-1) at the 850-hPa level, RH is the relative humidity (%) at the 700-hPa level, V
pot
is the maximum potential intensity (MPI; m s-1) of Emanuel [20], V
s
is the magnitude of the vertical wind shear (m s-1) between the levels of 850 and 200 hPa, and ω is the vertical wind speed (Pa s-1) at the 500-hPa level. Murakami et al. suggested that the vertical velocity term, which was newly added to the original GPI of Emanuel and Nolan [10], enables better reproducibility of TC genesis over regions with strong ascending motion due to the Inter-Tropical Convergence Zone and other convective circulation. The computation of MPI was conducted with the use of the Fortran code obtained from the Emanul’s web site (ftp://texmex.mit.edu/pub/emanuel/TCMAX/pcmin_revised.f).
The JRA-25/JCDAS dataset is used to compute GPI. As shown in Figure 1 GPI seems to well represent the spatial distribution of observed TCG as the 33-year climatology. GPI was used in diagnosing environments for TCG under the MJO conditions [8, 14, 21], because it is considered that the index takes into account the effects of low-level vorticity, vertical shear, middle-level humidity, and static stability including sea surface temperature which are important ingredients for TCG [22]. Therefore we regard GPI as a tool to diagnose environmental conditions for TCG and the relationship between MJO activity and TCG. Note that it is an open question whether and how GPI or other empirical indices can explain the interannual variation of TCG [18], and it may be the case that appropriate indices differ depending on the oceanic basins.