1 indiansummer monsoondepressionsdecreasedover cohenandboos: trendsinindianmonsoondepressioncounts...
TRANSCRIPT
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
D R A F T October 26, 2014, 9:32pm D R A F T
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
D R A F T October 26, 2014, 9:32pm D R A F T
X - 4 COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS
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
D R A F T October 26, 2014, 9:32pm D R A F T
COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS X - 5
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
D R A F T October 26, 2014, 9:32pm D R A F T
X - 6 COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS
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.
D R A F T October 26, 2014, 9:32pm D R A F T
COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS X - 7
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.
D R A F T October 26, 2014, 9:32pm D R A F T
X - 8 COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS
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.
D R A F T October 26, 2014, 9:32pm D R A F T
COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS X - 9
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).
D R A F T October 26, 2014, 9:32pm D R A F T
X - 10 COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS
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).
D R A F T October 26, 2014, 9:32pm D R A F T
COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS X - 11
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).
D R A F T October 26, 2014, 9:32pm D R A F T
X - 12 COHEN AND BOOS: TRENDS IN INDIAN MONSOON DEPRESSION COUNTS
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
D R A F T October 26, 2014, 9:32pm D R A F T