1 indiansummer monsoondepressionsdecreasedover cohenandboos: trendsinindianmonsoondepressioncounts...

GEOPHYSICAL RESEARCH LETTERS, VOL. ???, XXXX, DOI:10.1029/,

Supporting Information for “Has the number of1

Indian summer monsoon depressions decreased over2

the last thirty years?”3

Naftali Y. Cohen and William R. Boos

Department of Geology and Geophysics, Yale University, New Haven,

Connecticut

Corresponding author: Naftali Y. Cohen, Department of Geology and Geophysics, Yale Uni-

versity, P. O. Box 208109, New Haven, CT 06520-8109. E-mail: [email protected]

D R A F T October 26, 2014, 9:32pm D R A F T

X - 2 COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS

Introduction

This document presents supporting material to the main text.4

Trend estimates (Fig. 2) are obtained using nonlinear Poisson regression which is par-5

ticularly appropriate for count data with non-negative integers (Solow and Moore [2000];6

Wilks [2011]). In addition, the superiority of the nonlinear fit over the linear fit was ver-7

ified using the deviance measure. The deviance in nonlinear regression fits was found to8

be about three times smaller than in linear regression fits. The regression fits are shown9

in Supplemental Table 1. The standard errors of each fit’s coefficients are shown paren-10

thetically below the coefficients themselves (multiply by 1.96 to get the 95% confidence11

interval). For the time averaged synoptic kinetic energy (Fig. 4d) we used a simple linear12

regression [Wilks , 2011], as it is not count data.13

Supplemental Figs. 1-3 complement Fig. 3 of the main text by showing the details14

of five additional monsoon depressions that formed over the Indian domain during the15

summer monsoon seasons of 2002, 2010 and 2012. These tracks were obtained from the16

Yale dataset, which was based entirely on ERA-Interim data, but the NYUAD datasets17

(based on ERA-Interim and MERRA using a different tracking algorithm) as well as the18

satellite-derived estimates of surface wind speeds and precipitation support classification19

of these disturbances as monsoon depressions. For example, scatterometer surface wind20

speeds easily exceed 8.5 m s−1 near the 850 hPa vortex center, and the scatterometer21

surface wind vectors show cyclonic curvature around the storm center in most cases (al-22

though in some cases the proximity of the vortex centers to the coast makes it difficult to23

unambiguously identify circular flow from oceanic winds alone). In addition, satellite and24


COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS X - 3

gauge precipitation measurements show intense precipitation within the typical radius25

from the storm center.26

It is possible that monsoon depressions that are missing from the IMD dataset were27

classified by the IMD as lows. We have examined the IMD Monsoon reports for 201028

and 2012 (Tyagi et al. [2011]; Pai and Bhan [2013]; 2002 is not available from the IMD29

website) and found that all of the storms for those years that are shown in Fig. 3 and30

Supp. Figs. 1-3 are indeed listed by the IMD as “low pressure areas” or “well-marked31

low pressure areas”. Most are listed as having cyclonic circulation extending up to mid-32

tropospheric levels.33

Supplemental Fig. 4 complements Fig. 4b of the main text by showing the synoptic KE34

count over the main genesis region of the Bay of Bengal domain (15-25◦N and 80-100◦E)35

as a function of time for the summers of 2002 and 2012 using SSM/I scatterometer data.36

Extreme events, our satellite-derived proxy for monsoon depressions, are defined when37

the synoptic KE exceeds its 90th percentile (marked by the dashed line). Arrows in the38

figures correspond to lows and depressions that occurred roughly around the same time39

according to the Yale dataset.40

In the main text we presented a sensitivity test for the robustness of this synoptic41

KE threshold (see Fig. 4d). Supplemental Fig. 5 shows a second sensitivity test for42

the synoptic KE threshold. The solid black line in panel (a) shows how the summer43

mean synoptic KE storm count (using SSM/I scatterometer data for 1987-2012) varies44

as a function of the synoptic KE threshold. Clearly, as the threshold is raised fewer45

extreme events are identified. The dashed lines correspond to the summer mean monsoon46



depression counts in the Yale (blue) and IMD (red) datasets. Identifying the cross-cutting47

points with the solid black line allows us to tune the synoptic KE threshold so that the48

synoptic KE count will yield the same summer mean monsoon depression count, for 1987-49

2012, as in the Yale or IMD datasets. Panel (b) shows time series of summer synoptic KE50

extremes using these two thresholds. The key point here is that either threshold produces51

multiple extreme events during the summers of 2002, 2010 and 2012. In addition, neither52

threshold provides evidence for a statistically significant decreasing trend in the extreme53

KE activity in the summer over the Bay of Bengal since 1987.54

Lastly, Supplemental Fig. 6 shows the IMD summer monsoon depression count over the55

Bay of Bengal (5-27◦N, 70-100◦E) since 1891, together with an 11-year moving average56

of this quantity. The moving average remains at or above 5 storms per summer from57

the 1920s through the 1980s, then declines steeply over the next two decades. Definitive58

proof or disproof of the existence of this decline thus requires examination of storm counts59

before and in the very early part of the satellite era, before the availability of the satel-60

lite scatterometer record. This may be a difficult task, given that monsoon depressions61

have relatively weak surface pressure anomalies and relatively disorganized cloud patterns62

(e.g. compared to tropical cyclones).63

References

Pai, D., and S. Bhan (2013), Monsoon 2012–a report,Meteorological monographs (available64

from India Meteorological Department, New Delhi).65

Solow, A. R., and L. Moore (2000), Testing for a trend in a partially incomplete hurricane66

record., Journal of climate, 13 (20).67



Tyagi, A., A. Mazumdar, and D. Pai (2011), Monsoon 2010–a report, Meteorological68

monographs (available from India Meteorological Department, New Delhi).69

Wilks, D. (2011), Statistical Methods in the Atmospheric Sciences, Academic Press.70



longitude

latit

ude

a) June 21, 2002, 00 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

994

998

1002

1006

1010

longitude

latit

ude

b) June 21, 2002, 00 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

8.5

10.5

12.5

14.5

16.5

longitude

latit

ude

c) September 09, 2002, 12 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

994

998

1002

1006

1010

longitude

latit

ude

d) September 09, 2002, 12 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

8.5

10.5

12.5

14.5

16.5

Supplemental Fig. 1 – Two monsoon depressions observed in the Yale and NYUAD datasetsduring the summer of 2002. In all panels storm tracks (from the Yale dataset) are shown byblue lines with vortex center marked with a black star. Left column: ERA-Interim sea-levelpressure (in hPa) is shaded and black arrows show the ERA-Interim surface wind vector field.Right column: SSM/I surface wind speed is shaded, while arrows show surface wind vector fromQuikSCAT scatterometer. The thick magenta contour shows the 40 mm day−1 daily TRMMprecipitation.



longitude

latit

ude

a) July 24, 2010, 06 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

994

998

1002

1006

1010

longitude

latit

ude

b) July 24, 2010, 06 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

8.5

10.5

12.5

14.5

16.5

longitude

latit

ude

c) August 04, 2010, 12 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

994

998

1002

1006

1010

longitude

latit

ude

d) August 04, 2010, 12 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

8.5

10.5

12.5

14.5

16.5

Supplemental Fig. 2 – Two monsoon depressions observed in the Yale and NYUAD datasetsduring the summer of 2010. In all panels storm tracks (from the Yale dataset) are shown byblue lines with vortex center marked with a black star. Left column: ERA-Interim sea-levelpressure (in hPa) is shaded and black arrows show the ERA-Interim surface wind vector field.Right column: SSM/I surface wind speed is shaded, while arrows show surface wind vectorfrom WindSat scatterometer. The thick magenta contour shows the 40 mm day−1 daily TRMMprecipitation.



longitude

latit

ude

a) August 04, 2012, 12 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

994

998

1002

1006

1010

longitude

latit

ude

b) August 04, 2012, 12 UTC

8.5 m/s

50 60 70 80 90 1000

5

10

15

20

25

30

8.5

10.5

12.5

14.5

16.5

Supplemental Fig. 3 – A monsoon depression observed in the Yale and NYUAD datasets duringthe summer of 2012. In all panels storm tracks (from the Yale dataset) are shown by blue lineswith vortex center marked with a black star. Left column: ERA-Interim sea-level pressure (inhPa) is shaded and black arrows show the ERA-Interim surface wind vector field. Right column:SSM/I surface wind speed is shaded, while arrows show surface wind vector from WindSatscatterometer. The thick magenta contour shows the 40 mm day−1 daily TRMM precipitation.



Jun. Jul. Aug. Sep. Oct.0

2

4

6

8

10

12

mea

n K

E (

m2 s

−2 )

a) Daily mean KE, JJAS 2002

Jun. Jul. Aug. Sep. Oct.0

2

4

6

8

10

12

mea

n K

E (

m2 s

−2 )

b) Daily mean KE, JJAS 2012

Supplemental Fig. 4 – The synoptic KE, using SSM/I data, as a function of time for the summersof 2002 and 2012. The 90th percentile threshold for extreme events is marked by the dashed line.Arrows correspond to monsoon lows (dashed) and monsoon depressions (solid) that took placeroughly around the same time in the main genesis region of the Bay of Bengal domain (15-25◦Nand 80-100◦E).



4 5 6 7 8 9 101.5

2

2.5

3

3.5

4

4.5

5a) Mean summer storms vs. KE threshold

KE threshold (m2 s−2)

sum

mer

mea

n st

orm

s

KEYaleIMD

88 91 94 97 00 03 06 09 120

2

4

6

8

year

# of

sto

rms

b) KE count and Monsoon depression frequency (#/year)

✼ ❂ ❡①♣✦�✿✷✹✾✭✁✽✂✄✶✄✮☎ ✵✿✵✵✸✭✆✂✆✶✝✮ ★ ✞

✧

✼ ❂ ❡①♣✦✷✵✿✸✸✾✭✟✁✂✽✻✆✮☎ ✵✿✵✠✵✭✆✂✆✶✻✮ ★ ✞

✧

KE (tuned using the Yale MD mean)KE (tuned using the IMD MD mean)

Supplemental Fig. 5 – Sensitivity test for the synoptic KE count. Panel (a) shows the summermean synoptic KE storm count as a function the synoptic KE threshold. The dashed linescorrespond to the summer mean monsoon depression in the Yale (blue) and IMD (red) datasets.The cross-cutting points are used in panel (b) to tune the synoptic KE threshold to produce KEcount that will have the same summer mean as in the Yale and IMD datasets. In panel (b) thetuned KE counts are shown (solid lines). The nonlinear regression fits to the tuned KE countsare shown too (dashed lines).



1900 1920 1940 1960 1980 20000

2

4

6

8

10

12

year

# of

sto

rms

Monsoon depression frequency (#/year)

IMD smoothed IMD

Supplemental Fig. 6 – Monsoon depression frequency over Bay of Bengal (5-27◦N and 70-100◦E)using the IMD dataset (black) for 1891-2012, and its smoothed trend using 11-year centeredmoving average (red).



Supplemental Table 1 – Linear and nonlinear Poisson regression fits to the datasets presented inFigs. 1b, 1c, 4c and 4d. The standard errors of each fit’s coefficients are shown parentheticallybelow the coefficients themselves (multiply by 1.96 to get the 95% confidence interval).

Dataset Linear and Nonlinear Poisson regression fitIMD (Fig. 1b) lnµ = 65.342(19.092) − 0.032(0.010) × x

SIKKA (Fig. 1b) lnµ = 118.159(29.496) − 0.059(0.015) × x

ERA-Interim Yale (Fig. 1c) lnµ = −18.617(17.127) + 0.010(0.009) × x

MERAA NYUAD (Fig. 1c) lnµ = −16.012(12.824) + 0.009(0.006) × x

ERA-Interim NYUAD (Fig. 1c) lnµ = 19.632(14.303) − 0.009(0.007) × x

Averaged Reanalysis data (Fig. 1c) lnµ = −4.54(14.413) + 0.003(0.007) × x

KE (Figs. 4c and 4d) lnµ = 7.250(28.920) − 0.003(0.015) × x

ERA-Interim Yale (Fig. 4c) lnµ = −31.981(28.896) + 0.017(0.014) × x

IMD (Fig. 4c) lnµ = 22.330(32.614) − 0.010(0.016) × x

Summer average KE (Fig. 4d) µ = 44.860(26.737) − 0.021(0.013) × x


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