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Global Association of the Madden Julian Oscillation with Monsoon Lows and Depressions
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4 by Patrick Haertel and William Boos
Yale University, New Hav en, Connecticut, United States
5 Submitted to the Journal of Climate on 17 March 2016
Corresponding author address: Dr. Patrick Haertel Geology and Geophysics, Yale University,
210 Whitney Ave, New Hav en, CT 06510, USA E-mail:patrick.haertel@yale.edu.

6 ABSTRACT
7 Previous research has revealed that Indian monsoon lows and depressions, which produce
8 a large fraction of continental monsoon rainfall, are strongly modulated by intraseasonal
9 variations of the monsoon. This study examines whether such modulation occurs on a global
10 scale and, in particular, how the Madden-Julian Oscillation (MJO) is associated with changes in
11 synoptic-scale vortices in all monsoon regions.
12 Globally integrated counts of monsoon disturbances (i.e. lows and depressions) are found
13 to be insensitive to MJO amplitude. However, genesis frequencies and track densities of
14 monsoon disturbances are found to vary substantially with MJO phase. Composites based on a
15 widely used MJO index that depends strongly on zonal wind reveal large regions of the Indian
16 and Pacific Oceans with track density perturbations of +/- 40-60 percent during active and
17 suppressed MJO phases respectively. The anomalous track density has the same zonally
18 elongated, poleward-propagating structure that characterizes intraseasonal variability during
19 boreal summer. A separate composite based on MJO-related precipitation shows that the
20 frequency of strong monsoon disturbance genesis varies by an order of magnitude between MJO
21 regions having enhanced and suppressed rainfall.
22 An existing statistical model that successfully explains the climatological monthly mean
23 distribution of monsoon disturbance genesis has limited skill in predicting MJO-related
24 variations in monsoon disturbance activity. Although this model correctly predicts the sign of
25 MJO-related changes in monsoon disturbance activity in some regions, it predicts variations
26 about 2-5 times smaller than are observed. This suggests that genesis frequencies have different
27 sensitivities to intraseasonal and seasonal variations in environmental parameters.
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28 1. Introduction
29 The Madden-Julian Oscillation (MJO) is a planetary-scale tropical weather disturbance
30 with a convective signal that propagates slowly eastward over the Indian and West Pacific Oceans
31 (Madden and Julian 1971; 1972; 1995; Zhang 2005). The MJO’s heaviest rainfall is
32 accompanied by a deep moisture anomaly, a transition to strong westerly surface winds, and off-
33 equatorial upper-tropospheric cyclonic and anticyclonic gyres leading and trailing the
34 precipitation maximum, respectively (Kiladis et al. 2005). While the MJO’s cloudiness signal
35 primarily propagates eastward in boreal winter, during boreal summer its coupled wind and
36 convective anomalies also move northward in the Indian and West Pacific oceans (Wang and Rui
37 1990).
38 The MJO has many important impacts on tropical, mid-, and high-latitude weather and
39 climate. First, it is the dominant mode of tropical intraseasonal variability, with a stronger
40 spectral signal in cloudiness and rainfall than other convectively-coupled equatorial wav es such
41 as Kelvin, Rossby, mixed-Rossby-gravity, and inertio-gravity wav es (Wheeler and Kiladis 1999).
42 Second, it modulates tropical cyclone activity in both the Atlantic and Pacific basins (e.g.,
43 Maloney and Hartmann 2000). Third, it is associated with modulations in the strength of the
44 Asian, Australian, and North American monsoons (Wu et al. 1999; Lorenz and Hartmann 2006).
45 Fourth, it is known to alter mid-latitude weather (Moon et al. 2011), and even some high latitude
46 temperature variations phase with the MJO (Vecchi and Bond 2003). Finally, westerly wind
47 bursts associated with the MJO can impact both the timing and diversity of El Nino events (e.g.,
48 McPhaden 2004, Federov et al. 2015).
49 Here we focus on an aspect of the MJO that has not been thoroughly explored: its
50 association with synoptic-scale precipitating vortices in monsoon regions. These vortices are
51 well-known in the Indian monsoon, where weaker vortices are called monsoon lows and stronger
52 vortices are called monsoon depressions. An average of about 14 lows and depressions form
53 each summer in the Indian monsoon, primarily over the Bay of Bengal but also over continental
54 India and the Arabian Sea (Sikka 1977, Sikka 2006, Cohen and Boos 2014). Collectively, these
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55 storms are associated with at least half the rain that falls over continental India during summer;
56 they also serve as precursors for more intense tropical cyclones when the monsoon wind shear is
57 sufficiently weak in early or late summer. Monsoon lows and depressions are also prevalent in
58 the Australian summer monsoon and have highly similar structures to Indian monsoon lows and
59 depressions (Davidson and Holland 1987, Hurley and Boos 2015). About 10-15 lows and
60 depressions are observed each month, on average, in the Australian summer monsoon (Berry et
61 al. 2012). Storms called monsoon depressions also form in the West Pacific; although these form
62 in an environment of much weaker vertical wind shear than is observed in South Asia and
63 Australia, they hav e qualitatively similar dynamical structures with a warm-over-cold core and a
64 column of cyclonic potential vorticity extending from the surface to the upper troposphere
65 (Hurley and Boos 2015).
66 The genesis of Australian and Indian monsoon lows and depressions is modulated on
67 intraseasonal time scales. More Australian monsoon lows and depressions are observed when
68 the convectively active phase of the MJO lies over Australia than when the MJO is in its locally
69 convectively suppressed phase (Berry et al. 2012). Similarly, more Indian monsoon lows and
70 depressions are observed when there are positive intraseasonal anomalies of rainfall over central
71 India and the Bay of Bengal (Yasunari 1981, Chen and Weng 1999, Goswami et al. 2003,
72 Krishnamurthy and Ajayamohan 2010). However, it is difficult to compare the strength of this
73 modulation of synoptic-scale vortices between the two regions because of the fundamentally
74 different metrics used in previous studies, all of which have been regionally focused. For
75 example, Berry et al. (2012) used reanalyzed potential vorticity on the 315 K isentropic surface
76 to count Australian monsoon lows and depressions, then associated these counts with an MJO
77 index. In contrast, most studies of Indian monsoon synoptic activity rely on a subjectively
78 analyzed compilation of lows and depressions (Mooley and Shukla 1987, Sikka 2006) and
79 associate these tracks with the dominant empirical mode of convective variability over South
80 Asia. The latter is often referred to as the boreal summer intraseasonal oscillation (BSISO); its
81 northeastward- propagating bands of convection are also seen in composites of standard MJO
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82 indices compiled for the boreal summer period (e.g. Wheeler and Hendon 2004), but it is
83 nevertheless difficult to compare results based on different measures of intraseasonal variability.
84 Here we examine how the global distribution of monsoon lows and depressions is
85 associated with the dominant mode of intraseasonal variability. We use tracks of monsoon lows
86 and depressions compiled using objective, automated algorithms applied to reanalysis data
87 (Hurley and Boos 2015), and examine the association of genesis frequency and track density
88 with the widely used real-time, multivariate MJO index (Wheeler and Hendon 2004). This
89 provides the first global view of the statistical association between intraseasonal variability and
90 synoptic scale monsoon vortices. Indeed, the spatial structure of intraseasonal variability of
91 monsoon lows and depressions has only been documented in the South Asian region (Chen and
92 Weng 1999, Krishnamurthy and Ajayamohan 2010); the association of Australian monsoon lows
93 and depressions with the MJO was examined only in spatial aggregate (Berry et al. 2012).
94 This global perspective may be particularly valuable because we lack understanding of the
95 fundamental dynamics of monsoon lows and depressions, as well as of the MJO; a unified view
96 of their behavior across multiple regions may provide more insight than isolated regional studies.
97 Baroclinic instability, modified by the diabatic effects of precipitating convection, was for years
98 commonly cited as the growth mechanism of Indian monsoon depressions (e.g. Shukla 1978,
99 Mak 1983, Moorthi and Arakawa 1985, Krishnakumar et al. 1992, Krishnamurti et al. 2013).
100 However, Cohen and Boos (2016) showed that the potential vorticity structures of Indian
101 monsoon depressions do not satisfy a necessary criterion for baroclinic instability or transient,
102 non-modal baroclinic growth. They found this to be true for dry or moist forms of baroclinic
103 growth, insofar as they are described by existing theories. Monsoon depressions seem to have
104 dynamical structures similar to those of weak tropical cyclones (i.e. tropical depressions; Cohen
105 and Boos 2016). The climatological mean distribution of monsoon depression genesis
106 frequencies also has statistical relationships with environmental parameters (such as wind shear,
107 moisture, and convective available potential energy) similar to those those of tropical cyclones
108 (Ditchek et al. 2016), lending further support to the idea that monsoon depressions may be
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109 tropical cyclones whose intensification is inhibited by the strong vertical wind shear of
110 monsoons. This view has been implicit in some studies of trends in monsoon depression counts
111 (e.g. Prajeesh et al. 2013), but has only more recently been supported by statistical and
112 dynamical studies.
113 The fundamental mechanisms of the MJO are also poorly understood. Many theories have
114 been proposed to explain the MJO’s amplification and eastward propagation, including moisture
115 convergence by atmospheric wav es (Lau and Peng 1987), wind induced variations in surface
116 evaporation (Emanuel 1987, Sobel and Maloney 2012), frictionally driven boundary layer flow
117 (Wang and Rui 1990), radiation (Raymond 2001), circumnavigating Kelvin wav es (Haertel et al.
118 2015), and mid-latitude/tropical interactions (Strauss and Lindzen 2000). Many of these theories
119 have known defects, and none is widely accepted as correct (see review by Wang 2005).
120 Furthermore, climate models are biased in their simulation of the MJO. The MJO was found to
121 be too weak and too stationary in all but 1 or 2 of the climate models used for the recent
122 assessment of the Intergovernmental Panel on Climate Change (IPCC; Hung et al. 2013), with
123 many models overestimating the period of the MJO and only incremental improvements over
124 models used for the previous IPCC assessment (Lin et al. 2006).
125 This paper is organized as follows. Section 2 discusses the data and methods used for this
126 study. Basic MJO structure is reviewed in Section 3. Section 4 discusses the relationship
127 between the MJO and monsoon low and depression activity. Section 5 explores to what extent a
128 monsoon disturbance genesis index (MDGI; Ditchek et al. 2016) explains MJO-induced changes
129 in monsoon low and depression activity. Section 6 interprets the results presented here in terms
130 of tropical dynamics, and Section 7 is a summary.
131 2. Data and Methods
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132 2.1 Monsoon disturbance dataset
133 We use the track dataset developed by Hurley and Boos (2015), which is the first global
134 dataset of monsoon low pressure systems and which is hereafter referred to as the Yale dataset.
135 Previous track compilations existed only for Australia (Berry et al. 2012) and South Asia
136 (Mooley and Shukla 1987, Chen and Weng 1999, Sikka 2006), and these used highly distinct
137 methodologies that rendered comparison difficult. The Yale dataset was created by using a
138 feature tracking algorithm (Hodges 1995) to track extrema of 850 hPa relative vorticity in the
139 latest atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts
140 (ERA-Interim; Dee et al. 2011) from 1979 to 2012. Hurley and Boos (2015) classified storms
141 using criteria consistent with traditional definitions of the India Meteorological Department
142 (Table 1). The peak surface wind speed and the minimum mean sea level pressure (MSLP)
143 anomaly, relative to a 21-day running mean that filtered intraseasonal variability, were sampled
144 every 6 hours from ERA-Interim within 500 km of the vortex center (which in turn was defined
145 as the 850 hPa vorticity maximum, or minimum in the southern hemisphere). Disturbances were
146 classified as "lows," "depressions," and "deep depressions and stronger storms" according to the
147 peak surface wind speed and minimum MSLP anomaly achieved concurrently along their track
148 (Table 1). Note that the "low" category requires only a local minimum of MSLP with magnitude
149 larger than 2 hPa along the track of the 850 hPa vorticity maximum, while concurrent surface
150 wind speed and surface pressure thresholds must be met for disturbances to be placed in one of
151 the stronger categories. The "stronger storms" category includes so-called "deep depressions,"
152 "cyclonic storms," and other disturbances that overlap in intensity with traditional tropical
153 cyclones. For further details and a validation of the Yale dataset, see Hurley and Boos (2015). In
154 some figures we label monsoon lows, depressions, and deep depressions and stronger storms as
155 "cat 1", "cat 2", and "cat 3", respectively.
156 The Yale dataset only contains disturbances that originate in a generously defined
157 monsoon domain: the region on the equatorial side of a line drawn 500 km poleward of the 850
158 hPa temperature maximum. This includes the precipitating monsoon regions of Africa, Asia,
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159 Australia, and the tropical Americas, together with the desert regions that lie poleward of these
160 moist monsoon zones. One complication is that non-precipitating cyclonic vortices trapped
161 within the lower troposphere frequently form over deserts and on the poleward edges of the
162 precipitating monsoon regions (Thorncroft and Hodges 2001, Berry et al. 2012, Hurley and Boos
163 2015). Here we limit our focus to the precipitating low pressure systems that have wind
164 anomalies extending through the full depth of the troposphere. Ditchek et al. (2016) modified
165 the original Yale dataset by eliminating vortices that do not achieve a precipitable water content
166 of at least 53 mm at the vortex center along their track. This threshold is roughly the value of
167 precipitable water at which daily-mean precipitation increases sharply in analyses of oceanic
168 rainfall (Bretherton et al. 2004). This modification successfully eliminates nearly all genesis
169 points in desert regions of northern Africa, the Middle East, southwestern Asia, and southwestern
170 Australia (see maps in Ditchek et al. 2016).
171 We use two kinds of figures to illustrate variations in monsoon disturbance activity: 1)
172 scatter plots that show genesis locations and that depict peak intensity differences among storms,
173 and 2) contour and/or line plots that show variations in genesis counts or track densities for
174 particular regions and times. To construct the latter, we divide the low latitudes (45 S - 45 N)
175 into bins that each span 30 degrees in longitude and 10 degrees in latitude, and we average
176 genesis counts and track densities for each bin. Large bins are necessary for determining
177 percentage changes in genesis frequencies and track densities for individual seasons and MJO
178 phases without excessive noise.
179 2.2 Reanalysis
180 ERA-Interim data for 1979-2012 are used to construct composite MJO structures and to
181 assess which atmospheric variables have the largest association with monsoon disturbance
182 activity. To isolate variations associated with the MJO, we calculate the difference between the
183 7-day and 70-day running means of each variable (which is a simple way of filtering for
184 variations in the 7-70 day frequency band). The data are then averaged over the
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185 longitude/latitude bins described above, which both filters variability on small spatial scales and
186 ensures that comparisons between reanalysis data and monsoon disturbance data are conducted
187 for the same regions.
188 2.3 Rainfall
189 To generate composite rainfall patterns for the MJO we use data from the Global
190 Precipitation Climatology Project (GPCP; Huffman et al. 2001) for 1 October 1996 - 31
191 December 2012, which we averaged into 5x5 degree bins. While these observations cover a
192 shorter time period than the reanalysis and the monsoon disturbance data, comparisons of
193 atmospheric moisture and rainfall perturbations for individual MJO phases (see Section 3) yield a
194 consistent picture of where atmospheric convection is enhanced. Moreover, we hav e a
195 preference for using rainfall measurements as opposed to other data indicative of high cloudiness
196 (e.g. outgoing longwav e radiation), and thus are willing to accept the more limited historical
197 record of the former.
198 2.4 MJO index and compositing method
199 To identify the amplitude and phase of the MJO, we use the realtime multivariate MJO
200 (RMM) index of Wheeler and Hendon (2004), which was downloaded from the Australian
201 Bureau of Meteorology and is currently available from the website www.bom.gov.au/climate/mjo
202 under the heading "RMM data". This index is available for the entire 34-year duration of the
203 Yale dataset. To create composite MJO perturbations, we compute average values of time-
204 filtered reanalysis and satellite data for each phase of the MJO for all times when MJO amplitude
205 is greater than 1, then overlay plots of these data with monsoon disturbance genesis counts and
206 track densities averaged over these same periods.
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207 2.5 Alternative MJO Composite
208 Although the RMM index is based on winds and outgoing longwav e radiation (OLR), it is
209 known to depend much more strongly on zonal winds than OLR (Straub 2013). This might lead
210 to an underestimate of convectively induced (as opposed to wind-induced) changes in monsoon
211 disturbance activity associated with the MJO. To test this idea we also use an alternative MJO
212 composite, constructed by Haertel et al. (2015), which is based on tracking MJO precipitation
213 anomalies. As is noted in Section 5, this alternate way of viewing the MJO suggests that the
214 actual relationship between the MJO and monsoon disturbances might be even stronger than is
215 indicated by our RMM-based composites.
216 3. MJO basic structure
217 Before we discuss how the MJO impacts monsoon disturbances, we review the basic
218 structure of the MJO to highlight seasonal and phase dependencies of rainfall, mid-level
219 moisture, and low-level winds.
220 3.1 Boreal Summer MJO
221 We consider an extended summer period, from May through September, and present
222 average rainfall, 600 hPa relative humidity, and 850 hPa wind perturbations for four phase
223 divisions of the MJO. During phases 1-2 of the boreal summer MJO, rainfall is enhanced over
224 the central Indian Ocean and suppressed northeast of this region from southeast Asia into the
225 western Pacific (Fig. 1a). Low-level wind perturbations flow from the region of suppressed
226 rainfall toward the region of enhanced rainfall. The mid troposphere is moist where it is raining,
227 with positive 600 hPa relative humidity anomalies of 3 percent or more in much of the region
228 where there is enhanced rainfall; dry anomalies of a similar magnitude are seen where rainfall is
229 suppressed.
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230 Over time the region of enhanced rainfall grows and spreads to the north and east, forming
231 the zonally elongated, tilted band of moist convection that dominates boreal summer
232 intraseasonal variability and that is strongly associated with active and break periods of the
233 Indian monsoon (Figs. 1b-c; Goswami 2005). By phases 5-6 all fields have the opposite patterns
234 to those in phases 1-2, and the tilted band of convection has moved northward and eastward to
235 stretch from the northern Bay of Bengal to the equatorial western Pacific (Fig. 1c). By phases
236 7-8, areas with rainfall perturbations greater than 1 mm/day are limited to the northwest Pacific
237 and extreme eastern Pacific (Fig. 1d). Note that for all panels in Fig. 1 there is a high correlation
238 between rainfall and mid-level moisture, with a rough correspondence between the +/- 3%
239 relative humidity contours at 600 hPa and the +/- 1 mm/day rainfall perturbations.
240 3.2 Boreal Winter MJO
241 From November through March, in phases 1-2 of the MJO there is again a region of
242 enhanced rainfall accompanied by mid-level moisture in the central Indian Ocean, but it is
243 centered farther south and west than in boreal summer with 1 mm/day rainfall and 3% relative
244 humidity perturbations extending into central Africa (Fig. 2a). A region of suppressed rainfall
245 lies directly east of the rainy area, with low-level wind perturbations again flowing from the dry
246 area to the rainy area (Fig. 2a). Over time the region with enhanced rainfall expands and shifts
247 eastward (Fig. 2b) before turning southeast over the South Pacific Convergence Zone (SPCZ;
248 Fig. 2c). Between phases 3-4 and phases 5-6 there is both northward and southward propagation
249 of the enhanced rainfall in the general vicinity of the maritime continent, as the rainfall region
250 forms a sideways "V" pattern (Fig 2c). By and large, however, the rainy area propagates
251 eastward with a convectively suppressed region forming over the eastern Indian Ocean and
252 maritime continent while rainfall is enhanced over the SPCZ (Fig. 2d). As in boreal summer,
253 there is a rough correspondence between +/- 1 mm/day rainfall contours and +/- 3 percent 600
254 hPa relative humidity contours for most MJO phases.
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255 3.3 Rainfall/Moisture Relation
256 We show below that mid-level moisture is highly correlated not only with rainfall, but also
257 with monsoon disturbance activity. Before presenting that result, we discuss a few
258 interpretations of the observed MJO rainfall/moisture relation, as they eventually will be helpful
259 for understanding the association of the MJO with monsoon disturbances. There are at least
260 three different ways to interpret the positive correlation of rainfall with low- and mid-
261 tropospheric moisture in the MJO: 1) area-averaged rain rates are higher because of more
262 abundant moisture; 2) evaporation of falling rain enhances mid-level moisture in regions of moist
263 convection, and 3) some external forcing simultaneously enhances rainfall and mid-level
264 moisture. In other words, moisture might influence rainfall, rainfall might influence moisture, or
265 the two might independently respond to some other forcing. It is also possible that rainfall and
266 moisture amplify in a positive feedback. We further discuss this issue in Section 6, but for now
267 encourage the reader to keep these differing interpretations in mind while digesting observations
268 of how the MJO is associated with monsoon disturbances.
269 4. Relationship between MJO and monsoon disturbance activity
270 We now examine how genesis counts and track densities of monsoon disturbances are
271 associated with the amplitude and phase of the MJO. We essentially address two key questions:
272 1) Are there more monsoon disturbances when the MJO is active? and 2) How do the locations
273 of monsoon disturbances change with the amplitude and phase of the MJO?
274 4.1 Association of MJO amplitude with global mean genesis and track density
275 Figure 3 compares a histogram of MJO amplitude (thick gray line in each panel) to
276 normalized global integrals of genesis counts and track node counts binned by MJO amplitude
277 (thin colored lines). The genesis and track node counts are presented for each intensity category
278 of monsoon disturbance, with a track node being a 6-hourly position along a track. The track
279 node count provides a measure of the number of storms that exist at a given time, and it can
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280 change in response to variations in genesis frequency and in average storm lifetime. If the global
281 frequency or mean lifetime of monsoon disturbances were to depend on MJO amplitude, it would
282 cause the normalized count curves to deviate in shape from the histogram of MJO amplitude.
283 Instead, the count curves for each category of monsoon disturbance have the same basic shape as
284 the MJO amplitude histogram for both genesis counts (Fig. 3a) and track node counts (Fig. 3b).
285 While there is some deviation for the most intense category of storms (the red line in Fig. 3a),
286 this is likely due to small sample size (the total count for each category is shown in the figure;
287 uncertainty bounds are not plotted for clarity). Count curves for the larger samples, i.e. for
288 monsoon lows and depressions, follow the MJO amplitude histogram especially closely (e.g.,
289 blue and green lines in Fig. 3b). We conclude that MJO amplitude has little if any effect on the
290 global counts of monsoon disturbances.
291 4.2 Association of MJO amplitude with spatial distribution of monsoon disturbances
292 Next we consider how MJO amplitude affects the spatial distribution of monsoon
293 disturbances. We first restrict our analysis to the longitudinal distribution, which allows for a
294 more quantitative assessment than analysis of the latitude-longitude distribution. Figure 4 shows
295 meridionally integrated genesis counts for each category of monsoon disturbance for winter and
296 summer seasons, stratified by longitude and MJO amplitude. We consider counts for days when
297 MJO amplitude is above (solid lines) and below (dashed lines) its seasonal median value, so that
298 the same number of days is used in each group and a difference in count equates to a genesis
299 frequency difference. Overall, the longitudinal distribution of genesis counts is remarkably
300 insensitive to MJO amplitude; for both winter and summer seasons and for most longitudes,
301 about the same number of monsoon disturbances of each category develop in each longitudinal
302 bin when the MJO amplitude is above and below its median value. One exception is for the
303 strongest disturbances in northern winter near the International Date Line (Fig. 4b, red lines), for
304 which roughly 20-50 percent more storms form when MJO amplitude is above its median value.
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305 Scatter plots of moist genesis locations stratified by MJO amplitude and season (Fig. 5)
306 reveal the same overall result: the spatial distribution of monsoon disturbance genesis has little
307 sensitivity to MJO amplitude, except for the strongest disturbances in northern winter in the
308 South Pacific near the Date Line (red dots in Figs. 5c-d), which form more frequently when MJO
309 amplitude is above its median value.
310 To be clear, this analysis does not address the distribution of convectively coupled vortices
311 in general, but only cyclonic vortices that pass through a generously defined monsoon domain
312 and that have a signature in the sea level pressure field. The monsoon domain defined in
313 construction of the Yale dataset extends to about 25-30 degrees of latitude over and near all
314 continents, but extends only about 10 degrees off the equator in the east-central Pacific, the
315 central Atlantic, and the southwestern Indian Ocean (see Fig. 1 of Hurley and Boos 2015). This
316 should be considered when interpreting the low genesis frequencies in the off-equatorial central
317 Pacific and central Atlantic in Fig. 5 -- those regions lie outside nearly all definitions of the
318 global monsoon domain.
319 4.3 MJO phase and monsoon disturbance genesis
320 In contrast to the nearly undetectable influence of MJO amplitude, MJO phase has a
321 stronger association with monsoon disturbance genesis. Figure 6 shows monsoon disturbance
322 genesis locations for boreal summer partitioned by MJO phase. In regions where the MJO
323 enhances rainfall (shaded light and dark gray areas in Fig. 6), monsoon disturbance genesis is
324 also enhanced, although there is also a component of the genesis distribution that does not
325 change with MJO phase. For example, just south of India, more disturbances form in phases 1-4
326 (Fig. 6a,b) when there is heavy rainfall there than in phases 5-8 (Fig. 6c,d) when there is a
327 negative rainfall perturbation. Similarly, in the West Pacific just north of the equator, more
328 disturbances form in phases 5-6 (Fig. 6c), coincident with the heaviest rainfall, than in the other
329 phases when rainfall is lighter in that region. Moreover, the MJO’s impact is not limited to the
330 Indian and West Pacific Oceans. Near Central America, more disturbances form in phases 1-2
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331 and phases 7-8 (Figs 6a,d) than in phases 3-6 (Figs. 6b-c). During all MJO phases, zonally
332 elongated bands of high genesis frequency persist near 10 N in the East Pacific and near 20 N
333 over South Asia and into the West Pacific.
334 During boreal winter the MJO has a qualitatively similar association with monsoon
335 disturbances, with genesis counts elevated when and where there are positive MJO rainfall
336 perturbations (Fig. 7). For example, genesis counts are higher in the eastern Indian Ocean in
337 phases 3-4 than phases 7-8. Similarly, northern Australia has higher genesis counts during
338 phases 3-6 than phases 1-2 (Figs. 7a-c). Finally, more monsoon disturbances form over the
339 SPCZ during phases 5-8 than in phases 1-4. We conclude that although MJO amplitude has little
340 impact on the global mean number of monsoon disturbances, MJO phase is strongly associated
341 with the timing and location of their development.
342 4.4 MJO phase and spatial changes in track density
343 While scatter plots of genesis locations (e.g., Figs. 5-7) reveal general variations in
344 patterns of monsoon disturbance development, they do not precisely quantify MJO-related
345 changes in genesis frequency. In this section we present quantitative changes in track density by
346 MJO phase. Using all track positions for a monsoon disturbance’s lifetime yields a dense data
347 set that permits calculation of local percentage changes by MJO phase; such a quantitative
348 assessment was not possible for genesis frequency due to insufficient sample size.
349 In boreal summer, there is a 20-70 percent increase in track density during MJO phases 1-2
350 in the region of enhanced rainfall (shaded dark gray) in the Indian Ocean (Fig. 8a). Just east of
351 this, where rainfall is suppressed (light gray shading), there are 20-80 percent fewer lows and
352 depressions than in the seasonal mean (Fig. 8a). By phases 3-4, there is a factor of 2 more
353 monsoon disturbances than in the seasonal mean in the western Indian Ocean. The region where
354 track density is most enhanced lies west of the heaviest rainfall, perhaps because monsoon
355 disturbances often propagate westward (e.g. Lau and Lau 1990, Boos et al. 2015), opposite to the
356 main direction of propagation of the MJO. By phases 5-6, a region of suppressed rainfall and
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357 reduced track density has developed in the central Indian Ocean (Fig. 8c). North and east of this,
358 rainfall and track density are enhanced in the northward-propagating branch of the boreal
359 summer MJO (Fig. 8c). In phases 7-8 the region of suppressed rainfall with fewer than normal
360 monsoon disturbances expands to cover most of the Indian Ocean (Fig. 8d).
361 The fact that the track density of monsoon disturbances takes on spatio-temporal structures
362 similar to those of the boreal summer MJO, such as the band that stretches from central India to
363 the equatorial West Pacific during phases 5-6, is a novel result. The relative strength of the
364 modulation in the West Pacific is even stronger than that over the Bay of Bengal and central
365 India, where the intraseasonal modulation of the genesis of Indian monsoon disturbances is well
366 known (e.g. Krishnamurthy and Ajayamohan 2010).
367 During boreal winter there is a similar relationship between MJO phase and monsoon
368 disturbance track density. When MJO convection develops in the western Indian Ocean in phases
369 1-2, track densities are 20-80 percent higher than normal in much of that region of enhanced
370 rainfall (Fig. 9a). As this region of rainfall expands (Fig. 9b), and later spreads eastward and
371 poleward (Fig. 9c), track densities are typically 20-60 percent higher than the seasonal mean
372 where rainfall perturbations exceed 1 mm day
−
1
(Figs. 9b-c). By phases 7-8, when enhanced
373 MJO rainfall has migrated to the SPCZ, positive contours of track density perturbations have
374 moved to this general area as well, while negative rainfall and track density perturbations lie over
375 the Indian Ocean (Fig. 9d).
376 4.5 Genesis in a Precipitation-Based MJO Composite
377 The analyses presented above used the RMM index of Wheeler and Hendon (2004) to
378 construct composite MJO fields. We selected this index because it is a relatively simple year-
379 round, global index that has become a standard in recent years. However, as noted by Straub
380 (2013), the RMM index is much more sensitive to zonal wind than to OLR, which might result in
381 an underestimate of the association between monsoon disturbance activity and the MJO’s
382 convective signal. So we also constructed MJO composites by tracking time-filtered rainfall
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383 perturbations using the method of Haertel et al. (2015). In Fig. 10 monsoon disturbance genesis
384 points are plotted in MJO-relative coordinates, with perturbations to low-level specific humidity
385 contoured. By construction, the MJO’s heaviest rainfall perturbation is centered on longitude
386 zero. This rainfall-based composite yields a stronger association between genesis locations and
387 the MJO than was obtained with the RMM index. For example, 24 times as many disturbances
388 in the strongest intensity category form in regions where the MJO causes a positive low-level
389 moisture perturbation (shaded areas in Fig. 10) than in regions with a negative low-level moisture
390 perturbation (unshaded areas in Fig. 10). The modulation of monsoon lows and depressions has
391 a similar order of magnitude. This sensitivity test suggests that our RMM-based analysis
392 produces a conservative estimate of how strongly the MJO patterns the genesis of monsoon
393 disturbances, and that alternate ways of defining the MJO can yield a more pronounced
394 association with genesis frequencies. Note that a similar composite in MJO-relative coordinates
395 would be difficult to produce using the RMM index because that index does not track MJO
396 features. The analysis presented in Fig. 10 thus provides a complementary view of the problem,
397 rather than one that can be precisely compared with the RMM-based results.
398 We conclude that MJO phase, and in particular the locations of MJO convective and
399 moisture anomalies, are key factors to consider when attempting to predict both genesis
400 frequency and track density of monsoon disturbances.
401 5. Using a Genesis Index to explain MJO-induced chang es to monsoon distur-
402 bances
403 Results presented above suggest that the development of monsoon disturbances is strongly
404 sensitive to the phase of the MJO. Now we explore one possible explanation for this result -- that
405 the MJO perturbs the environmental variables that determine the frequency of monsoon
406 disturbances.
17

407 5.1 The Monsoon Disturbance Genesis Index
408 Following Emanuel and Nolan (2004), Camargo et al. (2007) and Tippett et al. (2011),
409 who developed Genesis Potential Indices (GPIs) for tropical cyclones, Ditchek et al. (2016)
410 created a Monsoon Disturbance Genesis Index (MDGI) for moist vortices in the Yale dataset (the
411 same set of storms examined here). Their index is based on four variables: 850 hPa absolute
412 vorticity, 600 hPa relative humidity, an estimated convective available potential energy (ECAPE),
413 and total column water vapor. Their MDGI explains much of the spatial and temporal variance
414 in genesis counts of monsoon disturbances in a monthly climatology (e.g., see Figs. 5-7 from
415 Ditchek et al. 2016), and shows that genesis is enhanced in humid, convectively unstable
416 environments that are rich in low-level vorticity. The coefficients of the MDGI suggest that
417 monsoon disturbances and tropical cyclones have similar associations with environmental
418 variables, but monsoon disturbances are less inhibited by vertical wind shear. The MDGI is
419 novel in that it is defined over both land and ocean, in contrast to the indices (GPIs) used for
420 tropical cyclones.
421 5.2 MJO-related perturbations in MDGI and track density
422 Figure 11 compares changes in the MDGI (color contours) to changes in monsoon
423 disturbance track density (black contours and gray shading) for different phases of the boreal
424 summer MJO. For most of the region where the MJO is strongly coupled to convection (the
425 equatorial Indian and West Pacific Oceans), positive MDGI perturbations are colocated with
426 positive track density perturbations. For example, track density and the MDGI are both elevated
427 in the equatorial western Indian Ocean in phases 1-2, when MJO precipitation is high in that
428 same region (Fig. 11a, cf. Fig. 1a). At the same time, there are negative MDGI and track density
429 perturbations in the western Pacific. During phases 3-4, positive MDGI and track densities
430 spread eastward (Fig. 11b), then northward during phases 5-6, at which time there is a striking
431 overlap of postive MDGI and track density perturbations extending westward into Southeast
432 Asia, with negative MDGI and track density perturbations in the central Indian Ocean (Fig. 11c).
18

433 By phases 7-8 there is a large region with negative MDGI and track density perturbations
434 covering the Indian Ocean (Fig. 11d). The MDGI and track densities do, however, hav e opposite
435 signs in portions of the southern hemisphere in phases 5-6.
436 In boreal winter there is a similar relationship between MDGI and track density
437 perturbations, although the spatial correlation appears lower than in boreal summer. During
438 phases 1-2 there are maxima in the MDGI and track density where convection is active in the
439 equatorial Indian Ocean near 80 E, with negative MDGI and track density perturbations in the
440 western equatorial Pacific (Fig. 12a). During phases 3-6 the positive MDGI and track density
441 perturbations spread to the east, north, and south. During phases 7-8 the positive MDGI and
442 track density perturbations shift to the east over the SPCZ (Fig. 12d), together with the region of
443 enhanced rainfall (Fig. 2d). Again, there are regions in the subtropics where track density and
444 MDGI anomalies have opposite signs.
445 While the MDGI successfully explains qualitative variations in track densities in the
446 equatorial Indian and West Pacific oceans, there are important quantitative differences. In
447 general, the MDGI predicts frequency changes that are roughly a factor of 2-5 smaller than
448 observed changes in track density. For example, during MJO phases 1-2 in boreal summer, peak
449 changes in the MDGI in the Indian Ocean are between 15 and 20 percent (Fig. 11a), whereas
450 track density changes peak between 60 and 80 percent (Figs. 11a, 8a). Similarly, at this time in
451 the western Pacific MDGI perturbations reach a minumum near -20 percent (Fig. 11a) whereas
452 track density perturbations are about -80 percent (Figs. 11a, 8a). In boreal winter, MDGI and
453 track density amplitudes are a little closer at times, with a peak MDGI anomaly around 25
454 percent just north of Australia during phases 5-6, where track density perturbations range from
455 40-60 percent (Figs. 9c, 12c). One factor that likely contributes to the differences in amplitude
456 between MDGI-predicted variations and those observed in the MJO, is that the MDGI was
457 constructed using climatological monthly mean data, and not intended to be used for
458 intraseasonal time scales.
19

459 5.3 Decomposing MDGI perturbations
460 We now use the intraseasonal variations in the MDGI to obtain insight into the
461 mechanisms underlying the association between the MJO and the activity of monsoon
462 disturbances, with the caveat that the MDGI variations are weaker than those of track density.
463 We partition the MDGI perturbations into contributions from constituent variables; because the
464 logarithm of the MDGI (i.e. of the expected genesis frequency) is linearly related to the predictor
465 variables, the fractional change in the MDGI due to an environmental variable is simply the
466 product of the change in that variable and the regression coefficient for that variable (Tippett et
467 al. 2011),
469 468
δ
MDGI
MDGI
= b
T δ x
470 Here b is the vector of regression coefficents (i.e. four scalar coefficients plus a single
471 scalar offset), and x is the vector of environmental variables (i.e. a latitude-longitude map for
472 each of the four variables), with the coefficients given in Ditchek et al. (2016). As seen in the
473 decomposition for phases 5-6 of the boreal summer MJO, the contribution from 600 hPa relative
474 humidity perturbations is by far the greatest (Fig. 13a), providing most of the structure seen in
475 the total MDGI perturbation for this time (Fig. 11c). Vorticity perturbations contribute slightly to
476 the positive MDGI perturbation over central India (Fig. 13b), the contribution of total column
477 water vapor perturbations remains smaller than 5% throughout the region (Fig. 13c), and ECAPE
478 reduces MDGI over northern Austalia (Fig. 13d). Partitioning of MDGI for other phases and for
479 boreal winter (not shown) yields the same general result: MJO-induced MDGI perturbations are
480 dominated by mid-level moisture variations, with small contributions from the other three
481 variables.
482 The fact that amplitudes of changes predicted by the MDGI are much smaller than
483 observed MJO-induced track density changes suggests that there are dynamics not captured by
484 the MDGI that contribute to track density changes when the MJO is active. One possible
20

485 additional mechanism relates to how the MJO perturbs mid-level stratification. Raymond and
486 Sessions (2007) showed that a positive temperature perturbation in the upper troposphere
487 produces a convective mass flux that is bottom heavy and that converges low-level vorticity,
488 leading to cyclogenesis. Figure 14 shows that there is good agreement between regions with
489 high mid-level stability and high track density for the time of the partitioning shown in Fig. 13,
490 with an even better pattern match than with mid-level moisture (Fig. 13a) or with the MDGI (Fig.
491 11c). While it is beyond the scope of this paper to fully explore the mechanism by which the
492 MJO alters mid-level stratification and how this contributes to monsoon disturbance genesis, this
493 seems worthy of future research. Ditchek et al. (2016) did consider mid-level stability as a
494 candidate variable for the MDGI, but it did not robustly improve the fit of their Poisson
495 regression model for the climatological monthly mean genesis distribution.
496 6. Discussion
497 Our results leave little doubt that the frequencies of monsoon disturbances change during
498 different phases of the MJO over much of the tropics. However, questions remain about the
499 mechanism(s) of these variations. For example, do changes in monsoon disturbance frequencies
500 in different MJO phases go beyond those expected from changes in rainfall? Monsoon
501 disturbances are one of many forms of convective org anization that occur in the tropics, and one
502 might expect counts of each kind of convective system to scale with convective rainfall,
503 assuming that the total rain falling within each kind of system is fixed. Figures 8-9 reveal that, in
504 general, there are positive rainfall perturbations when and where there are higher frequencies of
505 monsoon disturbances. Thus, at least a portion of the MJO dynamics that enhance monsoon
506 disturbance counts may do so by enhancing convective rainfall and increasing the number of
507 convective systems of all types.
508 One candidate for causing such a change in general convective activity is large-scale
509 upward motion. Figures 1-2 and 8-10 show that regions with positive rainfall and monsoon
510 disturbance count perturbations are generally accompanied by low-level wind convergence,
21

511 which indicates large-scale ascent. These same regions are generally also moist (Figs. 1-2, 10,
512 13a), which could be a consequence of moistening from the upward motion and/or a signal of
513 moistening by the enhanced convection itself. In fact, it is quite possible that the success of mid-
514 level moisture as a predictor of varitions in rainfall (Figs. 1-2) and monsoon disturbance counts
515 (e.g., Fig. 13a) is a result of it being a good proxy for large scale vertical motion. However,
516 Ditchek et al. (2016) did consider 500 hPa vertical velocity as a candidate predictor for their
517 MDGI, but it was not found to robustly improve the fit of the underlying Poisson regression
518 model; in other words, they found that mid-level moisture was a better predictor of
519 climatological mean genesis frequency than mid-level vertical motion. One might think that
520 vorticity might play a role, and it surely does contribute to the climatological mean equatorial
521 minimum in monsoon disturbance genesis (e.g. Fig. 5). Yet the MJO-related variations in track
522 density often peak on the equator (e.g. Fig. 8a,c) and vorticity made little contribution to the
523 MJO-related variations in MDGI.
524 Finally, we note that our analysis and other studies of monsoon disturbance activity have
525 taken a vortex-centered view of the tropical atmosphere. This contrasts with wav e-centered
526 views, which have been captured by wav enumber-frequency analyses and which have shown that
527 the activity of convectively coupled equatorial wav es covaries with that of the MJO (e.g. Kiladis
528 et al. 2009). Straub and Kiladis (2003) showed via spectral analysis that the activity of mixed
529 Rossby-gravity wav es and tropical depressions is enhanced in the convectively active region of
530 the MJO during boreal summer. They furthermore suggested that the Rossby-gravity wav es and
531 the tropical depressions may produce a significant part of the boreal summer MJO signal. This
532 raises an important point that we have thus far not made explicit: there may be a two-way
533 coupling between the MJO and monsoon disturbances, with the latter supplying some of the
534 synoptic-scale convective heating that has been hypothesized to drive the MJO (e.g. Biello and
535 Majda 2007). Furthermore, vortices in the Yale dataset that we refer to as monsoon lows and
536 depressions likely have some overlap with mixed Rossby-gravity wav es and other modes in the
537 equatorial wave spectrum (e.g. Lau and Lau 1990). Modulation of monsoon vortices also likely
22

538 occurs in spectral bands other than those of the MJO, such as the quasi-biweekly mode of the
539 South Asian monsoon (e.g. Chen and Weng 1999).
540 7. Conclusions
541 The results presented here show that while MJO amplitude has little impact on global
542 counts of monsoon disturbances, MJO phase strongly patterns the timing and location of their
543 development. In general, frequencies of monsoon disturbances at a given location are highest in
544 MJO phases with the heaviest rainfall. Both rainfall and monsoon disturbance counts are highly
545 correlated with mid-level moisture. In analyses based on the RMM index, which depends
546 strongly on zonal wind, composite 600 hPa relative humidity anomalies of about 5 percent were
547 accompanied by track density anomalies of 20-70 percent of the seasonal mean. An MJO
548 composite based on precipitation suggests an even stronger modulation, with the frequency of
549 monsoon disturbance genesis enhanced by an order of magnitude in the humid, precipitating
550 regions of the MJO relative to the dry, non-precipitating regions (Fig. 10).
551 Some of the MJO-related variations in monsoon disturbance counts can be explained by
552 how the MJO perturbs a monsoon disturbance genesis index (MDGI) that is based on mid-level
553 moisture, low-level vorticity, total column water vapor, and an approximate CAPE. Of these
554 variables, mid-level moisture contributes the most to MDGI perturbations in different phases of
555 the MJO. However, quantitative changes in monsoon disturbance frequency are a factor of 2-5
556 greater than those predicted by the MDGI, perhaps because there are additional dynamics at play.
557 We suggest that changes in mid-tropospheric static stability may play a role, but there are
558 presumably numerous other possible mechanisms. The discrepancy between the amplitude of
559 MDGI variations and frequency changes in monsoon disturbances also suggests that genesis
560 frequencies have different sensitivities to environmental parameters on intraseasonal and
561 seasonal time scales.
562 Recent studies suggest that the MJO is becoming more frequent and intense as the oceans
563 warm (e.g. Slingo et al. 1999; Jones 2006; Arnold et al. 2015). If this is true, what implications
23

564 are there for monsoon disturbances? Our research suggests that significant changes in global
565 counts are unlikely. Howev er, if the MJO is indeed intensifying, it seems plausible that month-
566 to-month variability in the frequency of monsoon lows and depressions will increase, especially
567 in regions where the MJO strongly modulates rainfall.
568 Acknowledgements. This research was supported by ONR grant N00014-15-1-2531, by
569 NSF grants AGS-1116885 and AGS-125322, and by the Yale Center for Research Computing.
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30

Table 1. Monsoon Disturbance Categories (adapted from Hurley and Boos 2015).
surface pressure anomaly surface wind speed monsoon low monsoon depression deep depression and stronger
2 - 4 hPa 4 - 10 hPa >= 10 hPa
8.5 - 13.5 m s
−
1
>= 13.5 m s
−
1
31

Figure Captions
1. Rainfall (shaded), 600 hPa relative humidity (green contours), and 850 hPa winds for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the composite boreal summer MJO. Dark gray shading denotes regions with rainfall perturbations greater than 1 mm day
−
1
, and light gray marks regions with perturbations less -1 mm day
−
1
. The contour interval for relative humidity is 3%, and the zero contour omitted.
2. Rainfall (shaded), 600 hPa relative humidity (green contours), and 850 hPa winds for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the composite boreal winter MJO. Shading and contouring are as in Figure 1.
3. Histogram of MJO amplitude (gray line in each panel) compared with normalized (a) genesis count and (b) track node count for each MJO amplitude range and each intensity category of monsoon low and depression. Total counts for each data type are provided in the key to the right of each panel.
4. Relation of longitudinal distribution of monsoon low and depression genesis to MJO amplitude for (a) May through September and (b) November through February. In each panel solid lines are for times in which the MJO amplitude is greater than its seasonal median value, and dashed lines are for times in which the MJO amplitude is less than its seasonal median value. Color coding differentiates between different intensity categories of monsoon lows and depressions.
5. Spatial relation of moist monsoon low/depression genesis to MJO amplitude for (a-b) May through September and (c-d) November through Februrary. In panels (a,c) MJO amplitude is less than its seasonal median value, and in panels (b,d) MJO amplitude is greater than its seasonal median value.
6. Relation of moist monsoon low/depression genesis locations to the phase of the MJO for
May through September. Color dots denote genesis locations for (a) phases 1-2, (b) phases
3-4, (c) phases 5-6, and (d) phases 7-8. Rainfall perturbations are contoured with a 2
32

mm day
−
1 contour interval, with negative contours dashed, values greater than 1 mm day
−
1 shaded light gray, and values greater than 3 mm day
−
1 shaded dark gray.
7. Relation of moist monsoon low/depression genesis locations to the phase of the MJO for
November through March. Color dots denote genesis locations for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8. Rainfall perturbations are contoured as in
Figure 6.
8. Percentage change in monsoon low/depression track density compared to seasonal mean for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the MJO for May through September. Red contours indicate positive track density perturbations, blue contours mark negative perturbations, and the contour interval is 20 percent. Rainfall perturbations greater than 1 mm day
−
1 are shaded dark gray, pertrubations less than -1 mm day
−
1 are shaded light gray, and vectors indicate 850 hPa winds. Regions in which monsoon lows and depressions form on fewer than 1 percent of the days considered are whited out.
9. Percentage change in monsoon low/depression track density compared to seasonal mean for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the MJO for
November through March. Contoured and shaded as in Figure 8.
10. Monsoon low/depression genesis locations in MJO relative cooridinates. Average lowlevel (500-1000 hPa) moisture perturbations are contoured with a 0.1 g/kg contour interval, the zero contour is dotted, and negative contours are dashed. Values between 0.0 and 0.1
g/kg are shaded light gray, and values greater than 0.1 g/kg are shaded dark gray. The composite MJO moisture and wind perturbation fields were constructed by tracking MJO precipitation centers and analyzing atmospheric sounding data in MJO relative coordinates
(Haertel et al 2015).
11. Monsoon Disturbance Genesis Index (MDGI) for May through September for (a) phases
1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the MJO. Red contours denote positive MDGI perturbations, blue contours indicate negative perturbations, and the
33

contour interval is 0.05. Black contours and grey shading are of percentage changes in track density, which have a 20 percent contour interval.
12. MDGI for November through March for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and
(d) phases 7-8 of the MJO. Contoured as in Figure 11.
13. Partitioning of MDGI for May through September for MJO phases 5-6 (compare with Fig.
11c). Contributions from (a) 600 hPa relative humidity, (b) 850 hPa relative vorticity, (c) total column water vapor, and (d) Ecape. Red contours denote positive MDGI perturbations, blue contours mark negative pertubations, and the contour interval is 0.05.
14. Mid-level stability perturbations (red/blue contours) and flow (vectors) for MJO phases 5-6 for May through September. As in Figs. 11-13, percentage changes in track density are indicated with black contours and gray shading.
34

(a)
MJJAS MJO Phases 1-2
40 N
20 N
Eq
20 S
40 S
0 E/W
600 hPa RH:
3% contour int.
rainfall shaded:
+-1 mm/day
850 hPa flow:
1 m/s
0 E/W
(b)
90 E 180 E/W 90 W
MJJAS MJO Phases 3-4
40 N
20 N
Eq
20 S
40 S
0 E/W
600 hPa RH:
3% contour int.
rainfall shaded:
+-1 mm/day
850 hPa flow:
1 m/s
0 E/W
(c)
90 E 180 E/W 90 W
MJJAS MJO Phases 5-6
40 N
20 N
Eq
20 S
40 S
0 E/W
600 hPa RH:
3% contour int.
rainfall shaded:
+-1 mm/day
850 hPa flow:
1 m/s
0 E/W
(d)
90 E 180 E/W 90 W
MJJAS MJO Phases 7-8
40 N
20 N
Eq
20 S
40 S
0 E/W
600 hPa RH:
3% contour int.
rainfall shaded:
+-1 mm/day
850 hPa flow:
1 m/s
0 E/W 90 E 180 E/W 90 W
Figure 1. Rainfall (shaded), 600 hPa relative humidity (green contours), and 850 hPa winds for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the composite boreal summer MJO. Dark gray shading denotes regions with rainfall perturbations greater than
1 mm day
−
1
, and light gray marks regions with perturbations less -1 mm day
−
1
. The contour interval for relative humidity is 3%, and the zero contour omitted.
35

(a)
NDJFM MJO Phases 1-2
40 N
20 N
Eq
20 S
40 S
0 E/W
600 hPa RH:
3% contour int.
rainfall shaded:
+-1 mm/day
850 hPa flow:
1 m/s
0 E/W
(b)
90 E 180 E/W 90 W
NDJFM MJO Phases 3-4
40 N
20 N
Eq
20 S
40 S
0 E/W
600 hPa RH:
3% contour int.
rainfall shaded:
+-1 mm/day
850 hPa flow:
1 m/s
0 E/W
(c)
90 E 180 E/W 90 W
NDJFM MJO Phases 5-6
40 N
20 N
Eq
20 S
40 S
0 E/W
600 hPa RH:
3% contour int.
rainfall shaded:
+-1 mm/day
850 hPa flow:
1 m/s
0 E/W
(d)
90 E 180 E/W 90 W
NDJFM MJO Phases 7-8
40 N
20 N
Eq
20 S
40 S
0 E/W
600 hPa RH:
3% contour int.
rainfall shaded:
+-1 mm/day
850 hPa flow:
1 m/s
0 E/W 90 E 180 E/W 90 W
Figure 2. Rainfall (shaded), 600 hPa relative humidity (green contours), and 850 hPa winds for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the composite boreal winter MJO. Shading and contouring are as in Figure 1.
36

(a)
1 moist genesis: cat 1 (3481) cat 2 (1468) cat 3 (463)
0.5
MJO (12419)
0
0 1 2
3 4
(b)
1 track density: cat 1 (105392) cat 2 (64848) cat 3 (26076)
0.5
MJO (12419)
0
0 1 2
3 4
Figure 3. Histogram of MJO amplitude (gray line in each panel) compared with normalized (a) genesis count and (b) track node count for each MJO amplitude range and each intensity category of monsoon low and depression. Total counts for each data type are provided in the key to the right of each panel.
37

(a)
MJJAS
250
200
150
100 cat 1 cat 2 cat 3
50
(b)
0
0 E 180 E
NDJFM
360 E
140
120
100
80
60
40
20
0
0 E cat 1 cat 2 cat 3
180 E
360 E
Figure 4. Relation of longitudinal distribution of monsoon low and depression genesis to MJO amplitude for (a) May through September and (b) November through February. In each panel solid lines are for times in which the MJO amplitude is greater than its seasonal median value, and dashed lines are for times in which the MJO amplitude is less than its seasonal median value.
Color coding differentiates between different intensity categories of monsoon lows and depressions.
38

(a)
(b)
40 N
20 N
Eq
20 S
40 S
0 E/W
(c)
40 N
20 N
Eq
20 S
40 S
0 E/W
40 N
20 N
Eq
20 S
40 S
0 E/W
(d)
40 N
20 N
Eq
20 S
40 S
0 E/W
90 E 180 E/W 90 W
90 E
90 E
180 E/W
180 E/W
90 W
90 W
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
90 E 180 E/W 90 W
Figure 5. Spatial relation of moist monsoon low/depression genesis to MJO amplitude for
(a-b) May through September and (c-d) November through Februrary. In panels (a,c) MJO amplitude is less than its seasonal median value, and in panels (b,d) MJO amplitude is greater than its seasonal median value.
39

(a)
(b)
40 N
20 N
Eq
20 S
40 S
0 E/W
(c)
40 N
20 N
Eq
20 S
40 S
0 E/W
40 N
20 N
Eq
20 S
40 S
0 E/W
(d)
40 N
20 N
Eq
20 S
40 S
0 E/W
90 E
90 E
90 E
180 E/W
90 W
180 E/W
90 W
180 E/W
90 W
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
90 E 180 E/W 90 W
Figure 6. Relation of moist monsoon low/depression genesis locations to the phase of the
MJO for May through September. Color dots denote genesis locations for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8. Rainfall perturbations are contoured with a 2 mm day
−
1 contour interval, with negative contours dashed, values greater than 1 mm day shaded light gray, and values greater than 3 mm day
−
1 shaded dark gray.
−
1
40

(a)
(b)
40 N
20 N
Eq
20 S
40 S
0 E/W
(c)
40 N
20 N
Eq
20 S
40 S
0 E/W
40 N
20 N
Eq
20 S
40 S
0 E/W
(d)
40 N
20 N
Eq
20 S
40 S
0 E/W
90 E
90 E
90 E
180 E/W
90 W
180 E/W
90 W
180 E/W
90 W
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
0 E/W category
= 1
= 2
= 3
90 E 180 E/W 90 W
Figure 7. Relation of moist monsoon low/depression genesis locations to the phase of the
MJO for November through March. Color dots denote genesis locations for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8. Rainfall perturbations are contoured as in Figure
6.
41

(a)
(b)
40 N
20 N
Eq
20 S
40 S
0 E/W
(c)
40 N
20 N
Eq
20 S
40 S
0 E/W
40 N
20 N
Eq
20 S
40 S
0 E/W
(d)
40 N
20 N
Eq
20 S
40 S
0 E/W
90 E
90 E
90 E
180 E/W
180 E/W
180 E/W
90 W
90 W
90 W track den. prt.
20% cont. int.
rainfall shaded
+-1 mm/day
850 hPa wind
1 m/s
0 E/W track den. prt.
20% cont. int.
rainfall shaded
+-1 mm/day
850 hPa wind
1 m/s
0 E/W track den. prt.
20% cont. int.
rainfall shaded
+-1 mm/day
850 hPa wind
1 m/s
0 E/W track den. prt.
20% cont. int.
rainfall shaded
+-1 mm/day
850 hPa wind
1 m/s
0 E/W 90 E 180 E/W 90 W
Figure 8. Percentage change in monsoon low/depression track density compared to seasonal mean for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the MJO for May through September. Red contours indicate positive track density perturbations, blue contours mark negative perturbations, and the contour interval is 20 percent. Rainfall perturbations greater than 1 mm day
−
1 are shaded dark gray, pertrubations less than -1 mm day
−
1 are shaded light gray, and vectors indicate 850 hPa winds. Regions in which monsoon lows and depressions form on fewer than 1 percent of the days considered are whited out.
42

(a)
(b)
40 N
20 N
Eq
20 S
40 S
0 E/W
(c)
40 N
20 N
Eq
20 S
40 S
0 E/W
40 N
20 N
Eq
20 S
40 S
0 E/W
(d)
40 N
20 N
Eq
20 S
40 S
0 E/W
90 E
90 E
90 E
180 E/W
180 E/W
180 E/W
90 W
90 W
90 W track den. prt.
20% cont. int.
rainfall shaded
+-1 mm/day
850 hPa wind
1 m/s
0 E/W track den. prt.
20% cont. int.
rainfall shaded
+-1 mm/day
850 hPa wind
1 m/s
0 E/W track den. prt.
20% cont. int.
rainfall shaded
+-1 mm/day
850 hPa wind
1 m/s
0 E/W track den. prt.
20% cont. int.
rainfall shaded
+-1 mm/day
850 hPa wind
1 m/s
0 E/W 90 E 180 E/W 90 W
Figure 9. Percentage change in monsoon low/depression track density compared to seasonal mean for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the MJO for November through March. Contoured and shaded as in Figure 8.
43

30 N category
= 1 (330)
= 2 (208)
= 3 (72)
Eq
850 hPa flow
1 m/s
30 S
-60 0
60
Figure 10. Monsoon low/depression genesis locations in MJO relative cooridinates.
Av erage low-level (500-1000 hPa) moisture perturbations are contoured with a 0.1 g/kg contour interval, the zero contour is dotted, and negative contours are dashed. Values between 0.0 and
0.1 g/kg are shaded light gray, and values greater than 0.1 g/kg are shaded dark gray. The composite MJO moisture and wind perturbation fields were constructed by tracking MJO precipitation centers and analyzing atmospheric sounding data in MJO relative coordinates
(Haertel et al 2015).
44

(a)
(b)
40 N
20 N
Eq
20 S
40 S
0 E/W
(c)
40 N
20 N
Eq
20 S
40 S
0 E/W
40 N
20 N
Eq
20 S
40 S
0 E/W
(d)
40 N
20 N
Eq
20 S
40 S
0 E/W
90 E
90 E
90 E
180 E/W
90 W
180 E/W
90 W
180 E/W
90 W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W 90 E 180 E/W 90 W
Figure 11. Monsoon Disturbance Genesis Index (MDGI) for May through September for
(a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the MJO. Red contours denote positive MDGI perturbations, blue contours indicate negative perturbations, and the contour interval is 0.05. Black contours and grey shading are of percentage changes in track density, which have a 20 percent contour interval.
45

(a)
(b)
40 N
20 N
Eq
20 S
40 S
0 E/W
(c)
40 N
20 N
Eq
20 S
40 S
0 E/W
40 N
20 N
Eq
20 S
40 S
0 E/W
(d)
40 N
20 N
Eq
20 S
40 S
0 E/W
90 E
90 E
90 E
180 E/W
90 W
180 E/W
90 W
180 E/W
90 W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W 90 E 180 E/W 90 W
Figure 12. MDGI for November through March for (a) phases 1-2, (b) phases 3-4, (c) phases 5-6, and (d) phases 7-8 of the MJO. Contoured as in Figure 11.
46

(a)
40 N
20 N
Eq
20 S
40 S
0 E/W 90 E 180 E/W 90 W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
(b)
40 N
20 N
Eq
20 S
40 S
0 E/W 90 E 180 E/W 90 W
(c)
40 N
20 N
Eq
20 S
40 S
0 E/W 90 E 180 E/W 90 W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
(d) track den. prt.
20% cont. int.
0 E/W
MDGI prt.
0.05 cont. int.
40 N
20 N
Eq
20 S
40 S
0 E/W
90 E 180 E/W 90 W
MDGI prt.
0.05 cont. int.
track den. prt.
20% cont. int.
0 E/W
Figure 13. Partitioning of MDGI for May through September for MJO phases 5-6
(compare with Fig. 11c). Contributions from (a) 600 hPa relative humidity, (b) 850 hPa relative vorticity, (c) total column water vapor, and (d) Ecape. Red contours denote positive MDGI perturbations, blue contours mark negative pertubations, and the contour interval is 0.05.
47

40 N
20 N
Eq
20 S
40 S
0 E/W mid level stab.
0.2 K cont. int.
track den. gray:
20% cont. int.
0 E/W mid level flow
1 m/s
90 E 180 E/W 90 W
Figure 14. Mid-level stability perturbations (red/blue contours) and flow (vectors) for
MJO phases 5-6 for May through September. As in Figs. 11-13, percentage changes in track density are indicated with black contours and gray shading.
48