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V, P., S. Sukumaran, and R. Ajayamohan, 2015: On the relationship between
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Praveen et al. Monsoon synoptic activity
1
1
On the relationship between mean monsoon precipitation and low
2
pressure systems in climate model simulations
3
V. Praveen, S. Sandeep, and R. S. Ajayamohan*
4
Center for Prototype Climate Modeling, New York University Abu Dhabi, UAE
5
*Corresponding author address: [email protected]
Praveen et al. Monsoon synoptic activity
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6
ABSTRACT:
7
The north north-west propagating Low Pressure Systems (LPS) are an important component of
8
the Indian Summer Monsoon (ISM). The objective detection and tracking of LPS in reanalysis
9
products and climate model simulations are challenging due to the weak structure of the LPS
10
compared to tropical cyclones. Therefore the skill of reanalysis and climate models in simulating
11
the monsoon LPS is unknown. A robust method is presented here to objectively identify and
12
track LPS, which mimics the conventional identification and tracking algorithm based on
13
detecting closed isobars on surface pressure charts. The new LPS tracking technique allows a fair
14
comparison between the observed and simulated LPS. The analysis based on the new tracking
15
algorithm shows that the ERA-interim and MERRA reanalysis were able to reproduce the
16
observed climatology and interannual variability of the monsoon LPS with a fair degree of
17
accuracy. Further, the newly developed LPS detection and tracking algorithm is also applied to
18
the climate model simulations of the Coupled Model Inter-comparison Project phase five
19
(CMIP5). The CMIP5 models show considerable spread in terms of their skill in LPS simulation.
20
About 60% of the observed total summer monsoon precipitation over east-central India is found
21
to be associated with LPS activities, while that in model simulations this ratio varies between 5 –
22
60%. Those models which simulate synoptic activity realistically, are found have better skill in
23
simulating seasonal mean monsoon precipitation.
24
simulated synoptic activity is found to be linked to the inter-model spread in zonal wind shear
25
over Indian region, which is further linked to inadequate representation of Tropical Easterly Jet
26
in climate models. These findings elucidate the mechanisms behind the model simulation of ISM
27
precipitation, synoptic activity and their interdependence.
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KEYWORDS: Indian Monsoon; Low Pressure Systems; Climate Model; Tracking
The model-to-model variability in the
Praveen et al. Monsoon synoptic activity
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1.
Introduction
30
The tropical cyclones are high impact weather systems that form during summer season, which
31
often bring havoc to coastal regions. A distinct feature of the northern Indian Ocean, as
32
compared to the rest of the tropical oceans, is that strong vertical shear of the monsoon winds
33
prevents the tropical cyclone development during summer monsoon season. Nevertheless
34
relatively weak cyclonic storms form during Indian Summer Monsoon (ISM) season and they
35
play a crucial role in determining the amount and distribution of the summer rainfall over India,
36
as they penetrate deep inland. These synoptic scale systems of varying strength mainly form over
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the Bay of Bengal and generally follow a north north-westward track (Mooley 1973; Sikka 1977;
38
Krishnamurthy and Ajayamohan 2010). These cyclonic systems are collectively called Low
39
Pressure Systems (LPS; Mooley and Shukla 1989; Ajayamohan et al. 2010). The LPS have a life
40
cycle of 3 – 6 days and a horizontal dimension of about 1000 – 2000 km (Mooley 1973;
41
Krishnamurti et al. 1975), which bring copious rainfall to the central and north-west Indian
42
subcontinent during June – September (JJAS) season (Krishnamurthy and Ajayamohan 2010) .
43
Further, Krishnamurthy and Ajayamohan (2010) noted that the LPS tracks reach farther
44
northwest India during flood years as against drought years, during which they mainly confine to
45
central India. The increasing trend in extreme rainfall events during ISM season in the recent
46
decades are also found to be linked with LPS (Ajayamohan et al. 2010). Despite its important
47
role in the ISM, the dynamics and thermodynamics of LPS are not well studied as in the case of
48
tropical cyclones. Moreover, skill of the present day state-of-the-art climate models in simulating
49
LPS has not been reviewed. One hurdle for such studies to be carried out is the difficulty in the
50
detection and tracking of LPS in reanalysis and climate model simulations.
Praveen et al. Monsoon synoptic activity
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The objective detection and tracking of tropical cyclones are relatively easy, as their severe
52
strength and well-defined structure makes them distinguishable from the mean atmospheric flow
53
pattern. However, this is not the case with relatively weak cyclonic storms such as extra-tropical
54
cyclones (Neu et al. 2012). Tropical cyclones are also vividly clear in satellite pictures to the
55
extent that its dimensions can be measured in contrast to relatively weak monsoon low pressure
56
systems. The objective tracking of rather weak cyclonic systems that form over Indian region
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during summer monsoon season can be equally challenging as extra-tropical cyclones. The
58
presence of monsoon trough, which is a semi-permanent low pressure region over the Indian
59
landmass during summer, makes the detection of LPS complicated for the objective algorithms
60
that rely on minimum Sea Level Pressure (SLP) criteria. Various methods are developed for the
61
objective identification and tracking of rotating weather systems (e.g. Murray and Simmonds
62
(1991); Hodges (1994); Sinclair (1997); see Neu et al. (2012) for a detailed review). However,
63
the monsoon LPS are weaker than tropical cyclones and often mid-latitude storms. Hence the
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methods developed primarily to detect tropical cyclones or mid-latitude storms may not be
65
optimal for the identification of monsoon LPS both from reanalysis data products and climate
66
model simulations. Few previous studies (Sabre et al. 2000; Stowasser et al. 2009) used known
67
tropical cyclone tracking algorithms to detect LPS in Coupled General Circulation Models
68
(CGCM) and regional climate models. However, none of them validated their LPS tracking
69
technique by applying it to an observationally constrained reanalysis data and the cross
70
comparing it with actual LPS observations. Hence, the reliability of those techniques in detecting
71
monsoon LPS is unknown. A comparison of storm frequency (mainly depressions) in ERA-40
72
reanalysis with observations during ISM season shows that the reanalysis data over-estimates the
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storm events by about two storms in an year, although the linear trend (decrease of depressions
Praveen et al. Monsoon synoptic activity
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74
in the recent decades) is somewhat reasonably reproduced (Stowasser et al. 2009). The reason for
75
over-estimation of storm events in ERA-40 reanalysis data cannot be ascertained without a
76
proper validation of the technique used to track storms. If the reanalysis data are able to reliably
77
capture the observed LPS tracks and intensity, it would open up the possibility of exploring the
78
dynamics of LPS using three dimensional meteorological fields from the reanalysis.
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The impact of climate change on monsoon LPS are not identified especially in the future climate.
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The intensity of post-monsoon tropical cyclones over the Bay of Bengal is found to be increasing
81
in the recent decades, with no significant changes in the number of systems (Balaguru et al.
82
2014). In contrast,
83
(increasing) in the recent decades during boreal summer season (Rajeevan et al. 2000; Dash et al.
84
2004; Ajayamohan et al. 2010; Krishnamurti et al. 2013; Prajeesh et al. 2013). The exact reason
85
for this contrasting trend in monsoon LPS compared to tropical cyclones needs to be identified in
86
order to make a reliable projection of natural hazards over Indian region in a warming climate.
87
CGCMs are the main tool for making future projections of the global climate. Most of the state
88
of the art current generation climate models are not successful in simulating a reasonable mean
89
climate of the ISM (Sperber et al. 2013; Ramesh and Goswami 2014; Sandeep and Ajayamohan
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2014b, 2014a), which may be linked to the models’ inability to simulate monsoon internal
91
dynamics (Ajayamohan and Goswami 2007; Xavier et al. 2010). In a recent study, Sabin et al.
92
(2013) note that an improved LPS activity in a high resolution Atmospheric GCM (AGCM)
93
results in realistic mean ISM precipitation. This suggests that understanding the simulation of
94
monsoon synoptic activity by the climate models is imperative for improved mean monsoon
95
simulation and to make reliable projections of the future climate. However, the current
96
understanding of the climate models’ fidelity in simulating synoptic features of ISM is limited.
the frequency of the stronger (weaker) monsoon LPS is decreasing
Praveen et al. Monsoon synoptic activity
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97
Development of a robust algorithm to track LPS in climate model simulations can provide new
98
insights to the model simulation of monsoon.
99
The primary goal of this study is to assess the skill of climate models and reanalysis products in
100
simulating monsoon LPS that form over the Bay of Bengal. An objective technique based on
101
closed isobar identification is developed to identify and track LPS from climate model
102
simulations and reanalysis data. The relationship of mean monsoon rainfall and synoptic activity
103
in climate model simulations is examined. In addition, we are making the tracks of LPS dataset
104
in the ECMWF Interim Reanalysis (ERAI; Dee et al. (2011)) and Modern Era-Retrospective
105
Analysis for Research and Applications (MERRA; Rienecker et al. (2011)) data publicly
106
available in electronic form. This will help the monsoon research community to use an LPS data
107
set which is very well compared with the observed data. Note that an inventory of Indian
108
monsoon LPS data based on reanalysis data products like ERA/MERRA is non-existent today
109
due to different identification criteria employed for specific research purposes in various studies.
110
The next section of this paper describes the data and methodology used, LPS tracking algorithm
111
and diagnostics used for comparing simulated synoptic activity. Section 3 validates our results
112
with the observed LPS data set and compares with few CGCM outputs from the CMIP5 archive.
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The systematic biases seen in the CGCMs in simulating synoptic activity and the dynamics
114
associated with it is discussed in Section 4. A brief summary of results and concluding remarks
115
are presented in Section 5.
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Praveen et al. Monsoon synoptic activity
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2.
Data and Methods
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2.1 Data
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We used LPS data compiled by Mooley and Shukla (1987) and Sikka (2006) by a careful
120
examination of the India Meteorological Department (IMD) surface pressure charts (see
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Ajayamohan et al. (2010) for details). Since the observed LPS data are derived from surface
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pressure charts, we also use pressure data to retrieve LPS information from reanalysis and
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climate model simulations. For stronger systems such as tropical cyclones, vorticity field or
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precipitation observation from satellites may serve as better parameters for tracking the vortex
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trajectory. Weaker vortex combined with lack of eye-wall structure and organized precipitation
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band makes it difficult for tracking of LPS using precipitation/vorticity fields. We used six
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hourly SLP data from ERAI reanalysis and daily mean SLP from CMIP5 models. MERRA
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reanalysis provides high frequency data output and we used hourly SLP data from it. The ERAI
129
uses a four dimensional variational data assimilation system to ingest satellite and conventional
130
observations in its model at every 12 hour cycles (Dee et al. 2011). The MERRA assimilates a
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substantial amount of satellite observations in addition to conventional data sources using a three
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dimensional variational analysis at every 6 hour cycles (Rienecker et al. 2011). Thus ERAI and
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MERRA can be considered as good choices in studying synoptic scale systems such as LPS.
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Daily precipitation from IMD observations (Rajeevan et al. 2006) and CMIP5 simulations are
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also used. The length of data archive varies among different datasets. We choose data during
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1979 – 2003 for this study, as all products have data during this time span. Daily temperature and
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meridional winds from ERAI/MERRA reanalysis as well as historical All Forcing (AF)
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simulations of CMIP5 models during 1990 – 1999 are used to construct storm centered
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composites of LPS vertical structure. Monthly mean specific humidity and winds during 1979 –
Praveen et al. Monsoon synoptic activity
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2003 from ERAI reanalysis and AF simulations are used to calculate moisture convergence,
141
geostrophic vorticity, and zonal wind shear. CMIP5 coupled models used in this study are listed
142
in Table 3. Only the first ensemble (r1i1p1) of the CMIP5 experiments is used. All analyses are
143
done for June – September (JJAS) season.
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145
2.2 LPS tracking technique
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As explained earlier, the objective tracking of LPS is challenging. Nevertheless a rich set of data
147
spanning the whole 20th century has been constructed by the careful manual evaluation of India
148
Meteorological Department’s (IMD) daily surface pressure charts (Mooley and Shukla 1987;
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Sikka 2006), here after referred as Sikka archive in the rest of the paper. This observed LPS data
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can be used as a benchmark to evaluate LPS simulated by reanalysis. We aim to devise a
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technique that is as close as possible to the manual detection and tracking criteria used by Sikka
152
(2006). The manual tracking of LPS in Sikka archive is based on the identification of closed
153
contours in the interval of 2 hPa on daily surface pressure charts. Such closed-contour
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identification technique has been objectively applied in reanalysis data mainly to detect extra-
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tropical storms (Wernli and Schwierz 2006; Hanley and Caballero 2012). The advantages of
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Hanley and Caballero (2012) technique are that it can detect multi-center cyclones and it does
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not rely on ellipsoidal best fit to identify the closed contours as in the case of earlier algorithms
158
(Murray and Simmonds 1991). The proposed LPS tracking technique imbibe the basic principles
159
of contour detection from Hanley and Caballero (2012). However the unique regional features,
160
such as heat lows over the land and the presence of semi-permanent low-pressure area called
161
monsoon trough, can have annulling effects on automated algorithms that are successful in
Praveen et al. Monsoon synoptic activity
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tracking extra-tropical storms. This is evident from the spatial distribution of cyclone frequency
163
in Wernli and Schwierz (2006), where the pattern over south Asia resembles heat low over the
164
deserts. The monsoon trough in their study extends from heat low over northwest India to Indo-
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Gangetic plain aligned parallel to the Himalayas (see their figure 4c). In order to overcome these
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hurdles in objectively detecting monsoon LPS, a new detection and tracking technique is
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designed as explained in the following steps.
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169
2.2.1 Contour detection
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(1) At each grid point, search for the local minima from the surrounding 8 grid points.
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(2) In the above step, local minima that do not satisfy the criteria of a threshold pressure
172
gradient are considered as heat lows and removed. The heat lows are identified in
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following sub-steps.
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(a) Mean pressure gradient of central minima (∇SLP) with respect to surrounding 8 grid
175
points is calculated.
176
(b) If the numerical value of ∇SLP is less than 1/10th of grid resolution of the dataset (e.
177
g. 0.15 hPa degree-1 for ERAI which has a grid resolution of 1.5 degrees), then it is
178
considered as a heat low. A systematic sensitivity analysis is carried out by varying ∇SLP
179
as a function of data grid resolution to arrive at an optimal value. This procedure is
180
similar to that of Hanley and Caballero (2012), but with a modified threshold pressure
181
gradient to account for summer heat lows over India. The data is then re-gridded to a
Praveen et al. Monsoon synoptic activity
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0.5x0.5 degree resolution before proceeding to the next step, in order to get smoother
183
contours.
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(3) Identify closed contour around the local minimum with an increment of 1 hPa interval.
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(4) The pressure depth (∆SLP) is calculated as the pressure difference between the outermost
186
closed contour and the local minimum. If there are more than two local minima inside the
187
outermost closed contour, take the lowest among them (see Fig. 1).
188
For both ERAI and MERRA datasets, contours are identified for two sampling times that are
189
closer to the IMD sampling time (0230Z). The detection is performed for ERAI at 00Z, 06Z and
190
MERRA at 02Z, 03Z respectively. Since CMIP5 model SLPs are available as daily means, only
191
one time slice per day is used.
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2.2.2 Tracking
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(1) A first guess position of the track is taken as the first member in the first time slice (e.g.:
195
for MERRA 02Z data). If the search returns none, then the search is extended to the next
196
time slice (e.g.: for MERRA 03Z).
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(2) The second position in the track is determined by searching in a radius of 3 degrees from
198
first guess position after 24 hours. The search radius for the subsequent positions is taken
199
as the distance travelled by the system in the previous 24 hours (R). Since the systems
200
slow down over the land, the search radius is also reduced as 0.75*R. The land-sea
201
separation is identified using a 15m isobath extracted from the ETOPO2 dataset
202
(http://www.ngdc.noaa.gov/mgg/fliers/06mgg01.html). The local minima identified
Praveen et al. Monsoon synoptic activity
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203
outside the search radius are considered as independent systems and are tracked
204
simultaneously to account for multiple systems.
205
(3) If the nearest neighbor search for the next LPS position does not find an LPS in the
206
ensuing 24 hours, then those tracks are treated as terminated.
207
(4) The systems with a lifecycle less than 2 days are not considered.
208
(5) The algorithm runs for 122 days of monsoon season starting 1st June.
209
Since CMIP5 models have only daily means, step-1 is performed only for one time slice per day.
210
The above objective tracking algorithm is performed over the eastern half of the Indian
211
peninsula, spanning Bay of Bengal (70oE-100oE and 5oN-27oN). Note that systems forming over
212
Arabian Sea are not included in this study as our goal is to study the systems originating over the
213
Bay of Bengal and adjoining land regions.
214
The different categories of LPS based on the intensity of the storm are shown in Table 1. The
215
original classification used by Mooley and Shukla (1987) is used in the present work to avoid
216
ambiguity in determining the category of the LPS by the objective detection and tracking
217
algorithm.
218
219
2.3 Comparison of LPS tracks with observations
220
LPS have a considerable range of intensities and hence, for the comparison purpose, we derive
221
an aggregate Synoptic Activity Index (SAI) by summing the number of LPS days in each 3°x 3°
222
grid cell after weighting with LPS intensity, for each JJAS season (see Appendix of
Praveen et al. Monsoon synoptic activity
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223
Krishnamurthy and Ajayamohan, 2010 for details). LPS days are defined as days in the monsoon
224
season in which an LPS is present. If ‘N’ systems occur in the same day, then it will be counted
225
as ‘N’ LPS days. The spatial patterns of SAI computed from ERAI/MERRA reanalysis and
226
CMIP5 model simulations are quantitatively compared with those of Sikka archive by
227
calculating spatial correlation and Root Mean Square Error (RMSE). The cyclone track inter-
228
comparison procedure (Blender and Schubert 2000; Neu et al. 2012) is employed to compare
229
LPS tracks derived from ERAI and MERRA reanalysis with the observed LPS tracks (see
230
APPENDIX-1 for details).
231
232
2.4 Diagnostics of CGCM simulated synoptic activity
233
The diagnostics that involve spatial maps of SAI are done for a subset of five CMIP5 models, for
234
the sake of brevity. The subset of CMIP5 CGCMs and their stand-alone version (atmospheric)
235
are chosen in such a way that good, bad, and moderately performing ones are represented in an
236
unbiased manner. The model performance in simulating ISM is evaluated using a Taylor diagram
237
(see figure 13 of Sandeep and Ajayamohan (2014b)). The scatter plots and regression maps that
238
show the model spread in synoptic activity use all the available 17 models which provide daily
239
data for the variables considered for this study.
240
The JJAS mean wind shear is calculated as the difference between 200 and 850 hPa mean zonal
241
winds. In order to examine, to what extent the model to model spread in wind shear affects the
242
inter-model variance in synoptic activity, we regressed the area averaged SAI climatology of 17
243
models on their zonal wind shear climatology. This regression technique is identical to the one
244
used by Sandeep and Ajayamohan (2014a) which can be explained as follows.
Praveen et al. Monsoon synoptic activity
13
1
245
T  1
1

The regression coefficient,    U i U i   U i  S i   ;
n
 n

246
where U denotes JJAS climatological zonal wind shear, < S > the area averaged SAI climatology
247
over 77°E – 90°E and 15°N – 25°N, the subscript i stands for the index of CMIP5 models (span
248
from 1 to 17) and ‘n’ for total number of models used in the calculation (in this case n=17). For
249
easier understanding, one may imagine the regression of ‘model series’ of area averaged SAI
250
climatology S(model) regressed on the ‘model series’ of 2-dimensional climatological maps of
251
zonal wind shear U(model, lat, lon). The map of regression slopes is expected to reveal the
252
model to model covariance in SAI and zonal wind shear. In the case of regression of SAI on
253
vertical profiles of zonal winds, the ‘model series’ of SAI climatology S(model) is regressed on
254
model series of zonal wind profiles U(model, level). The statistical significance of the regression
255
slopes are estimated using a two tailed t-test.
256
257
3. Synoptic Variability in reanalysis and climate models
258
3.1 Inter-annual variability of observed and reconstructed LPS tracks
259
The success of LPS track retrieval from the reanalysis data depends on robustness of the tracking
260
algorithm. The modern reanalysis products are observationally constrained by the assimilation of
261
satellite and conventional meteorological observations using sophisticated data assimilation
262
systems (Dee et al. 2011; Rienecker et al. 2011). Therefore we may expect the current generation
263
reanalysis such as ERAI and MERRA to be closer to the actual observations. Nonetheless, the
264
application of reanalysis data in climate change studies should be done with caution due to the
Praveen et al. Monsoon synoptic activity
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spurious trends in some fields, mainly arising from the changes in observing systems (Bengtsson
266
et al. 2004; Robertson et al. 2011). It is therefore essential that the storm positions and intensity
267
derived from the reanalysis should be compared with the corresponding observations in terms of
268
mean climatology as well as year-to-year variability. If the inter-annual variability of the LPS
269
frequency and intensity are realistically reproduced by the reanalysis data, then our confidence in
270
using the tracking-algorithm for long term climate analysis will be enhanced.
271
A comparison of the LPS statistics generated by the proposed technique from ERAI/MERRA
272
data with the observed daily weather data (Sikka archive) would be helpful in a qualitative
273
understanding of the limitations of the technique/data before venturing in to further investigation.
274
Table.2 lists the LPS statistics generated by the new tracking technique as compared to the Sikka
275
archive. Qualitatively, both the reanalysis products perform reasonably well in reconstructing the
276
overall LPS numbers in a season with comparable standard deviations during the 1979-2003
277
period, with MERRA detecting about 1 storm more than the Sikka archive (Table. 2). The
278
tracking algorithm systematically underestimates (overestimates) the number of lows
279
(depressions & deep depressions) in a season over the Bay of Bengal. We note here that, a recent
280
study (Hurley and Boos 2014) using an entirely different tracking algorithm detects ~16 storms
281
(~13 in Sikka archive) in a season over North Indian Ocean (both Bay of Bengal & Arabian Sea).
282
This highlights the promise and pitfalls in tracking LPS using reanalysis data. Errors can
283
percolate from the reanalysis data due to its coarse resolution, data assimilation and several other
284
related issues. On the other hand, errors in the LPS observational data (Sikka archive) due to the
285
subjectivity involved in the manual detection technique cannot be overruled. The narrow division
286
between various LPS categories (Lows, Depressions, Deep Depressions & Cyclonic Storms) also
287
poses challenges to an automated tracking algorithm.
Praveen et al. Monsoon synoptic activity
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288
As indicated earlier, an accurate reproduction of interannual variability in the LPS activity by the
289
reanalysis products is important for their usefulness in the long term climate analysis. Thus the
290
next step is to evaluate the reconstructed LPS frequency and intensity against observations
291
during the period of combined availability of both the datasets. Monsoon LPS systems rarely
292
achieve intensity above that of category 2 cyclones in Saffir-Simpson scale (Ajayamohan et al.
293
2010). Therefore we split the number of LPS systems that form in each monsoon season during
294
1979 – 2003 into two intensity categories (Table. 1), viz., category 1 & 2 (lows and depressions)
295
and category >2 (deep depressions and storms). Both ERAI and MERRA moderately reproduce
296
the observed number of category 1 & 2 LPS during the analysis period (Fig. 2a). The number of
297
stronger LPS (> category 2) are better reproduced by MERRA than ERAI (Fig. 2b). During 1979
298
– 2003, 322 LPS systems formed over the Bay of Bengal, with a total of 1478 days of activity. In
299
this period, ERAI (MERRA) has 317 (351) systems totaling 1339 (1442) days of storm activity,
300
which indicates robustness of the reanalysis data as well as the tracking technique. Despite the
301
reliability of the reanalysis products in reconstructing the total number of systems and stormy
302
days, the category-wise reproduction seems to be sketchy. During 1999 – 2003 period, there are
303
no stronger LPS (> category 2) in the observations. However, ERAI/MERRA shows a moderate
304
number of stronger LPS during this period. This hints at the uncertainty in the category-wise
305
reproduction of LPS, especially category 3 and greater, by the reanalysis products. The total June
306
– September LPS (figure not shown) in ERAI (MERRA) has a better correlation of 0.45 (0.6)
307
compared to the category-wise comparison with Sikka archive during 1979 – 2003.
308
The month-wise cumulative days of LPS activity during 1979 – 2003 are shown as time series
309
(Fig. 3). Both ERAI and MERRA reasonably capture the interannual variability in monthly LPS
310
activity. The observed interannual variability in June, July, and August LPS days (days when
Praveen et al. Monsoon synoptic activity
16
311
LPS are present) are reproduced by both the reanalysis data products (Fig. 3a – c). Overall, both
312
reanalysis have moderately strong to high correlations with the observed interannual variability
313
in LPS days for the months of June, July, and August. It is noted that LPS days in the month of
314
July has a significant (p<0.05) positive trend in the observations as well as in ERAI/MERRA
315
reanalysis. The interannual variability in LPS days derived from reanalysis data are weakly
316
correlated with observations for the month of September, in contrast to the other three monsoon
317
months (June, July & August). The reason for this weak correlation in the month of September
318
between observations and reanalysis is unclear. A more detailed analysis of daily pressure charts
319
in the month of September is needed to uncover this ambiguity, which is beyond the scope of the
320
present study.
321
The cumulative seasonal (JJAS) LPS days are calculated for Sikka archive, ERAI/MERRA, and
322
five AGCM simulations of AMIP experiment carried out as part of CMIP5 exercise (Fig. 4a).
323
These AGCM simulations are forced with observed monthly SSTs and other anthropogenic as
324
well as natural forcing agents (Taylor et al. 2011). The total number of JJAS LPS days in
325
ERAI/MERRA has moderate correlation with observations (r=0.49/0.54). When the LPS days
326
during only June – August are considered, the reanalysis data have stronger correlations with the
327
observed LPS days, with r=0.65 (0.76) for ERAI (MERRA). These results indicate that the LPS
328
identified using ERAI/MERRA data are not artifacts of the reanalysis data. Moreover, it
329
highlights the skill of the tracking technique to capture the interannual variability of the observed
330
data. It is worth noting here that similar studies to track LPS data fail to capture interannual
331
variability of the observed synoptic activity (Hurley and Boos 2014). As the LPS information
332
from the reanalysis products extracted by the proposed technique is similar to Sikka archive, this
333
LPS tracking technique can be applied to CMIP5 models as a means of evaluating their
Praveen et al. Monsoon synoptic activity
17
334
performance in LPS simulation. It is interesting to note that two out of five models (CNRM-CM5
335
and CCSM4) analyzed here simulate the seasonal sum of LPS days closer to the observed range.
336
While MRI-CGCM3 and MIROC5 overestimate the number of LPS days, ACCESS1.3
337
underestimate it. Further, this indicates that many of the current generation climate models are
338
able to simulate monsoon LPS. However, a comprehensive analysis of the simulated spatial
339
structure and life cycle is required to assess the skill of climate models in successfully simulating
340
the observed characteristics of the monsoon LPS (see Section 3.3).
341
342
3.2. Track inter-comparison
343
Probably, the most difficult test for any cyclone (or LPS) tracking algorithm is to simulate the
344
correct trajectory of the system. The probability of coincidence (Pc) of tracks (Blender and
345
Schubert (2000); see Appendix) in observations and reanalysis is a robust measure of how close
346
is the LPS track reproduced by the reanalysis with the observed (Sikka archive) track. Pc of LPS
347
tracks from ERAI and MERRA with respect to observations are calculated for each monsoon
348
season during 1979 – 2003 (Fig. 5). A considerable range of coincidence probability (~ 7 – 53%)
349
is found for ERAI and MERRA tracks with observed tracks for individual seasons. 100%
350
probability means exact reproduction of all the observed tracks by the reanalysis product for one
351
particular monsoon season. The overall agreement of all storm tracks captured by ERAI
352
(MERRA) with respect to the observed tracks during the entire span of the analysis period is
353
found to be 25% (29%). This means that 25% (29%) of all LPS tracks in ERAI (MERRA) during
354
1979 – 2003 have an exact spatio-temporal match with the observed tracks. The horizontal
355
resolution of the reanalysis data, subjectivity in the observed tracks and the temporal frequency
Praveen et al. Monsoon synoptic activity
18
356
of the observations and reanalysis are all crucial in determining the probability of track
357
coincidence. Thus a low value of Pc does not mean that the reanalysis data is not useful, rather
358
majority of LPS tracks reproduced by the reanalysis do not show an exact match with the
359
observed tracks. In the case of mid-latitude storms, a value of Pc ≥ 70% is considered as a good
360
agreement between different tracking algorithms (Neu et al. 2012). It shall be noted that Neu et
361
al. (2012) did not compare reanalysis tracks with observed tracks as in the present study. The
362
value of Pc between ERAI and MERRA tracks is found to be 70%, indicating that the trajectories
363
of LPS in the two reanalysis products have reasonable agreement. Taken together the LPS
364
numbers, days, and probability of track coincidence, we can conclude that the reanalysis data are
365
reasonably successful in reproducing the number and lifecycle of the LPS systems, but with
366
major disagreements in the trajectories of individual storms compared to the observed trajectory.
367
368
3.3. Spatial pattern of LPS activity
369
The track disagreements between reanalysis and observations may not be crucial in simulating
370
the spatial pattern associated with LPS in the reanalysis. Both ERAI and MERRA reliably
371
reproduce the number and lifecycle of the systems. As the LPS involve a considerable range of
372
intensities, SAI which is an index weighted by the storm intensity (Section2.3, (Ajayamohan et
373
al. 2010) is used to construct the spatial density maps of the LPS activity (Fig. 6). The
374
climatological mean spatial pattern of the observed SAI shows the strongest LPS activity over
375
Indo-Gangetic plain region closer to the Bay of Bengal (Fig. 6a). This observed pattern in SAI is
376
consistent with the composite structure of all LPS trajectories (Ajayamohan et al. 2010). The
377
spatial pattern of SAI in ERAI (Fig. 6b) and MERRA (Fig. 6c) closely resemble that of
Praveen et al. Monsoon synoptic activity
19
378
observations, with the latter having maxima located slightly farther inland. The spatial patterns
379
of SAI in ERAI and MERRA have spatial correlations of 0.94 and 0.92, respectively with
380
observed pattern. ERAI has a lower Root Mean Square Error (RMSE, 22.4) compared to
381
MERRA (30). The spatial densities of SAI in CGCM simulations (Fig. 6d – h) are much weaker
382
compared to observations and reanalysis. Among the CGCMs analyzed here, MIROC5 performs
383
better than others in terms of spatial structure of SAI, with a spatial correlation value of 0.84
384
with observations. MIROC5 and CCSM4 also have relatively low values of RMSE (33.6 and
385
33.2 respectively), as compared to other models (~42 and 53). Although MIROC-ESM has a
386
high spatial correlation (r=0.8), its RMSE is also very high (53.4) due to weak amplitude of SAI.
387
It may be noted that MIROC5 is identified as one of the best among the CMIP5 coupled models
388
for the simulation of the mean monsoon precipitation (Wang et al. 2013). The present results
389
suggest that the improved simulation of mean monsoon precipitation by the climate models may
390
be linked to their performance in simulating LPS. The AGCM simulations are found to be better
391
in simulating the mean spatial pattern of SAI in few cases (Fig. 7, e.g. MIROC5 r=0.94), which
392
indicates that the errors in ocean-atmosphere coupling processes may affect the simulation of
393
LPS in CGCMs to a certain extent. However, the standalone version of MIROC5 grossly
394
overestimates the intensity of SAI (RMSE=98) as compared to its coupled version
395
(RMSE=33.6). All models except CCSM4 have overestimated the strength of SAI in AGCM
396
simulations. In summary, AGCM experiments do not show an overall improvement of LPS
397
simulation when compared with CGCM experiments. The time series of area averaged JJAS
398
mean SAI index confirms that ERAI/MERRA captures the observed interannual variability in
399
SAI (r=0.69/0.54), consistent with LPS days during the analysis period (Fig. 8). AGCM
400
simulations of MIROC5 and MRI-CGCM3 tend to overestimate the magnitude of SAI, the
Praveen et al. Monsoon synoptic activity
20
401
reason for which is found to be the simulation of more number of stronger LPS by these models
402
(figure not shown). When the analysis is restricted to JJA season, ERAI/MERRA yields better
403
correlation (r=0.7) with the observed interannual variability in SAI index, consistent with the
404
uncertainty found in the month of September (Fig.3).
405
The monsoon LPS are the main rain-bearing systems which brings copious rains over the central
406
Indian region (Sikka 2006; Krishnamurthy and Ajayamohan 2010). Hence, the rainfall associated
407
with LPS days with respect to the total rainfall in a season assumes significance. Fig. 9 shows the
408
relation between total JJAS seasonal precipitation (PT) and the LPS day precipitation (PL) over
409
the core monsoon region. It is to be noted that the observations are available only over the land
410
and hence oceanic grids are discarded in these calculations. Almost 60% of the observed total
411
precipitation during 1979 – 2003 is found to be contributed during the LPS days. The CGCMs
412
also show a strong dependence of PT to PL, with considerable spread in the simulation of ISM
413
precipitation. There seems to be a linear relationship between the skill of the CGCMs in
414
simulating mean monsoon and simulation of synoptic activity. The models that realistically
415
simulate monsoon synoptic activity also show skill in the simulation of ISM precipitation
416
(Fig.9). It is noted that MIROC-ESM and MIROC-ESM-CHEM simulates total seasonal
417
precipitation closer to the observed amount, with substantially less contribution from synoptic
418
activities, suggesting that a few CGCMs have different mechanisms for ISM precipitation
419
simulation. MIROC5 simulates excessive precipitation over the Gangetic plain, with most of it
420
coming from synoptic activities, consistent with the stronger values of SAI index in that model.
421
In general, CESM1-BGC, CCSM4, and GFDL-ESM2G models perform better among the
422
CGCMs analyzed here, in terms of the ISM precipitation simulation over the core monsoon
423
region. These models also simulate the precipitation contribution associated with LPS closer to
Praveen et al. Monsoon synoptic activity
21
424
the observations. It may be noted that the models (ACCESS1-3, CSIRO-Mk3-6-0, IPSL-CM5B-
425
LR, MRI-CGCM3, and MRI-ESM1) that have weaker contribution from LPS-related
426
precipitation are already found as having strong cold SST bias over the northern Arabian Sea and
427
a dry bias over Indian land region (Sandeep and Ajayamohan 2014a). The weaker monsoon
428
circulation in the models with strong cold SST bias may affect the LPS activity in those models.
429
430
4. Potential factors contributing to inter-model spread in LPS activity
431
It may be difficult to find one single factor that explains the deficiency in simulating synoptic
432
activity across all models. In other words the biases in LPS simulation may be due to different
433
reasons in different models. However, in order to improve the model performance in future, it is
434
important to have a deeper understanding of the probable factors that are contributing to the
435
biases in LPS simulation. Finer model resolution may be required to resolve the dynamical
436
features of the storms. At the same time parameterized physics, such as cumulus convection, also
437
plays an important role in representing the processes that are responsible for storm development.
438
In the present study, we do not investigate the effect of sub-grid scale processes on the LPS
439
simulation, as inferring the role of such processes from the various CMIP models is difficult.
440
Instead, the roles of large-scale dynamical features such as moisture convergence, geostrophic
441
vorticity and wind shear on the LPS simulation are examined, in addition to the effect of model
442
resolution. Further, the thermodynamical structure of the reanalyzed and simulated LPS is
443
analyzed.
444
Praveen et al. Monsoon synoptic activity
22
445
4a. Horizontal and vertical resolution of the models
446
The horizontal (latitude x longitude) grid spacing of the models considered for this study varies
447
from 1.125° x 1.125° to 2.8° x 2.8° and the number of vertical levels range between 18 and 80.
448
While the increased horizontal resolution is found to have a positive effect on the model
449
performance, the impact of increased vertical levels is not clear always (Roeckner et al. 2006).
450
A balanced choice of horizontal and vertical resolutions is needed for optimal model
451
performance (Roeckner et al. 2006). In general, the simulation of LPS gets better with decreased
452
grid spacing, as indicated by the correlation of -0.62 between SAI and model resolution (Table
453
3). However, it is difficult to attribute this correlation to horizontal resolution alone, as the model
454
physics also plays an important role in the LPS simulation. The scale interaction between
455
parameterized physics and model dynamics is hard to elucidate from the analysis of multi-model
456
CMIP5 experiments. Although the increased model resolution helps in reducing numerical
457
errors, a systematic analysis of model integrations at various resolutions is necessary to
458
understand the improvements in the performance of particular model (Boer et al. 1992). It may
459
be noted that Sabin et al. (2013) found an improved simulation of monsoon synoptic activity
460
when the horizontal resolution of an AGCM is increased.
461
A negative correlation (r = -0.42) is obtained between SAI and the number of vertical levels of
462
models, suggesting that CMIP5 models with higher number of vertical levels are poorer in
463
simulating LPS activity. The models that have more vertical levels are the ones with coarser
464
horizontal resolution. The poor performance of these models may be because of the coarser
465
horizontal resolution rather than the increased vertical levels. It is better to have a balance
466
between the horizontal and vertical resolutions for the optimal model performance.
Praveen et al. Monsoon synoptic activity
23
467
4b. Moisture convergence
468
The moisture convergence plays an important role in the development and maintenance of
469
synoptic scale weather systems such as LPS. The column integrated seasonal mean moisture
470
convergence of each of the 17 CMIP5 models is calculated as
471
where q is the specific humidity, and V the vector wind. Seasonal mean (JJAS) M and SAI are
472
found to be moderately correlated (r = 0.55), suggesting that the moisture convergence alone
473
may not determine the synoptic activity in the models. The models with weaker moisture
474
convergence, in general, tend to have poor synoptic activity. Some of the models have a net
475
divergence of the moisture during JJAS season, as indicated by the negative values of M (Table
476
3). The models that have a negative moisture convergence are also the ones already identified as
477
having strong cold SST bias over the Arabian Sea and weaker ISM circulation (Sandeep and
478
Ajayamohan 2014a).
479
4c. Geostrophic vorticity
480
The large-scale vorticity field at low levels (850 hPa) is a feature of ISM circulation. The low
481
level vorticity of the monsoon circulation also favors the development of LPS. The geostrophic
482
vorticity at 850 hPa is calculated as ζg = (1/f0)∇2Φ; where Φ is the geopotential height. SAI and
483
ζg are rather strongly correlated (r=0.65), indicating that the mean vorticity field at lower levels
484
of the model is important for the simulation of LPS activity. Stronger ζg can be considered as a
485
necessary but not a sufficient condition for the development of LPS.
486
487
∫
;
Praveen et al. Monsoon synoptic activity
24
488
4d. Spread in wind shear and synoptic systems across model simulations
489
It was suggested that the monsoon disturbances grow by drawing up on zonal kinetic energy
490
(Keshavamurty et al. 1978). The roles of barotropic, baroclinic and combined barotropic
491
baroclinic instability in developing LPS are examined in earlier modeling (Shukla 1978; Mishra
492
and Salvekar 1980; Krishnakumar et al. 1992) and observational studies (Sikka 1977; Sanders
493
1984). Shukla (1977) argued that the barotropic instability of the mean zonal winds at 150 hPa is
494
the primary mechanism that excites the largest unstable mode in the mean monsoon flow. The
495
strong westward zonal wind shear over the Indian region during summer monsoon season is vital
496
for the development of the LPS (Goswami et al. 1980).
497
Here, we examine the mechanisms responsible for the large inter-model variability seen in the
498
climate models in simulating the LPS activity and hence the seasonal mean monsoon. As
499
outlined above, one of the most prominent dynamical feature for the generation of LPS is the
500
easterly shear seen in zonal winds over the monsoon trough region. Here we do not attempt to
501
investigate the role of barotropic or baroclinic instability on the development of individual
502
storms; rather we explore how the mean state zonal winds and synoptic activity are related in
503
CMIP5 models. The ensemble mean zonal wind shear (Fig. 10a) is comparable with that from
504
ERAI. However, the models exhibit a considerable spread in the wind shear, with a standard
505
deviation of about 4 m s-1 over the monsoon trough region (Fig. 10b). This suggests that the
506
model to model variability in the synoptic activity and wind shear over the monsoon trough
507
region may be linked. The regression pattern of SAI on zonal wind shear shows statistically
508
significant (p<0.05) slopes over Indian land region that encompass monsoon trough (Fig. 10c).
509
The scatter plot between area averaged SAI and the area averaged zonal wind shear shows a
Praveen et al. Monsoon synoptic activity
25
510
moderate correlation of -0.62 (p<0.05), suggesting that the models with weak wind shear
511
simulate less synoptic activity (Fig. 10d).
512
To unravel the large inter-model variability seen in the easterly zonal wind shear, we further
513
analyzed the vertical structure of zonal winds. The spread in the area averaged vertical profiles of
514
seasonal mean zonal wind climatology reveals that the models have a large disagreement in the
515
zonal winds at 300 – 150 hPa levels (Fig. 11a). This indicates that the models have a substantial
516
spread in the simulation of the mean strength of Tropical Easterly Jet (TEJ). A comparison with
517
ERAI wind profile shows that almost all models analyzed here simulates weaker TEJ. Consistent
518
with previous analysis, the wind profile of MIROC5 is closer to ERAI, while that of IPSL-
519
CM5A-MR (an outlier) is away from ERAI. In order to examine the dependence of model to
520
model variance in LPS simulation on the vertical structure of zonal wind profile, a linear
521
regression analysis is performed by regressing area averaged SAI climatology of 17 models on
522
the wind profile climatology (Fig. 11b). This regression is also done in the same way as the
523
regression map in Fig.10c, except that the SAI is projected on vertical profiles of area averaged
524
zonal winds. The vertical structure of regression coefficient shows that the model to model
525
variance in SAI is deeply associated with the spread in the zonal wind profiles between 300 and
526
150 hPa. This further shows that the models that simulate weaker TEJ are also the ones with
527
weaker SAI.
528
529
4e. Thermodynamical biases
530
The three-dimensional dynamical and vertical structure of the simulated LPS can provide further
531
insights in to the model skill in simulating monsoon synoptic systems. The meridionally
Praveen et al. Monsoon synoptic activity
26
532
averaged (over 10 grids on both sides of storm center) longitude – height view of the storm-
533
centered composite of meridional wind and potential temperature anomalies is shown in Fig. 12.
534
Consistent with the earlier studies (Hurley and Boos 2014), both ERAI and MERRA show a
535
vertical structure with a cold core at lower levels and a warm core at the upper levels with a
536
south-westward tilt in the meridional winds (Figs.12a-b,13). Similar analysis on two CGCMS –
537
MIROC5 and MIROC-ESM – reveals the poor vertical structure in the latter (Fig. 12d), which
538
partly explains its failure in the simulation of LPS, consistent with the mean structure of zonal
539
wind profiles. The MIROC5 simulation shows a dynamical structure of the LPS that is closer to
540
ERAI, with a cold (warm) core in the lower (upper) levels. The development of a well-defined
541
tilted vertical structure of the LPS in MIROC-ESM seems to be curtailed by a weak zonal shear.
542
The warm-on-top-cold thermal structure of LPS is due to latent heating (evaporative cooling) at
543
the upper (lower) levels. The weaker thermodynamic structure in MIROC-ESM suggests that the
544
low level evaporative cooling and upper level latent heating are not well simulated in that model.
545
This indicates that the moist processes related to monsoon are not well represented in MIROC-
546
ESM.
547
In order to get a comprehensive view of the vertical structure of monsoon LPS, the storm
548
centered composite of potential temperature anomaly is shown as a three dimensional plot in Fig.
549
13. The warm-over-cold structure is very clear in the three dimensional view. When averaged
550
over a latitude domain (10 grids on both sides of the storm center), a cold core extending up to
551
700hpa beneath the warm core is visible (meridional pane, Fig. 13). The wind vectors shown in
552
two levels (surface and 200hPa) indicate the deep first baroclinic structure of the monsoon
553
depressions.
554
Praveen et al. Monsoon synoptic activity
27
555
5.
Summary and conclusions
556
The monsoon LPS in CGCMS/AGCM as well as in ERAI/MERRA reanalysis are detected and
557
tracked using a robust tracking algorithm. The reliability of the new tracking method is verified
558
by the fact that, it succeeded in extracting LPS information from two independent reanalysis data
559
sets, which agree fairly well with observations. The rigorous comparison of the LPS tracks from
560
the reanalysis data with observed LPS tracks, using trajectory inter-comparison protocol,
561
strengthens the confidence in the new tracking technique. Further, the algorithm presented here
562
mimics the manual method used to derive the observed LPS data from daily synoptic charts.
563
Thus the LPS data captured by the new technique can be fairly compared with the reanalysis
564
datasets. As the tracking results from the reanalysis products and Sikka archive are similar, the
565
proposed technique is capable of providing useful results when applied to CMIP5 model
566
simulations. The three dimensional composite of potential temperature anomaly derived from
567
ERAI/MERRA reanalysis reveals finer-scale vertical structure of the LPS which enhances the
568
confidence in using reanalysis data in further exploring the dynamics of monsoon LPS.
569
The skill of CMIP5 coupled models in simulating monsoon LPS is assessed with the help of
570
newly devised LPS tracking algorithm. Although the biases in the simulation of mean monsoon
571
precipitation is well known (Levine et al. 2013; Sperber et al. 2013; Sandeep and Ajayamohan
572
2014a), the role of model skill in LPS simulation in such biases has not been explored hitherto.
573
The present analysis reveals that the model skill in simulating mean monsoon precipitation is
574
closely linked to how well the models simulate the monsoon synoptic activity. The ratio of LPS-
575
day precipitation to total precipitation is found to be about 60% in reanalysis and few CGCMs
576
that are skillful in simulating LPS. The models with poor LPS simulation skills have
577
substantially smaller ratio of LPS-day precipitation to total precipitation, leading to poor
Praveen et al. Monsoon synoptic activity
28
578
simulation of the seasonal mean monsoon. The model-to-model variability in LPS simulation is
579
found to be related to a number of factors, such as model’s horizontal resolution, biases in
580
moisture convergence, geostrophic vorticity, and zonal wind shear. Increasing vertical levels at
581
the expense of horizontal resolution can be counter-productive in realistically simulating LPS.
582
The biases in moisture convergence may be linked to model biases in large-scale circulation
583
features, which is often linked to issues related to parameterized convection (Hwang and
584
Frierson 2013). The biases in zonal wind shear indicate problems related to the simulation
585
Tropical Easterly Jet (TEJ). This hinds at the importance of better representation of TEJ in
586
climate models to improve the simulation of mean monsoon precipitation over India. It may be
587
noted that the subtropical jets are also inadequately represented in climate models (Sandeep and
588
Ajayamohan 2014a).
589
The proposed tracking technique is also promising in the context of climate change impact on
590
monsoon LPS. The effects of climate change on monsoon LPS are unknown. With the help of
591
the new algorithm, we are analyzing LPS characteristics on future projections by the CMIP5
592
models, which will be reported elsewhere. Since the tracking algorithm is developed based on
593
surface pressure, there is an ambit for extending this analysis to the twentieth century reanalysis
594
data products (e.g. Compo et al. (2011) ) to analyze the trends and associated dynamics.
595
Acknowledgements
596
The Center for Prototype Climate Modeling is fully funded by the Government of Abu Dhabi
597
through New York University Abu Dhabi (NYUAD) Research Institute grant. The NYUAD
598
High Performance Computing resources are used for the computations. We thank Dr. William
Praveen et al. Monsoon synoptic activity
29
599
Boos, Dr Kevin Walsh and the two anonymous reviewers for their valuable comments on an
600
earlier version of the manuscript, which led to significant improvement of this paper.
Praveen et al. Monsoon synoptic activity
30
601
APPENDIX-1
602
Track Inter-comparison algorithm
603
We calculate The probability of coincidence (Pc) of tracks in the ERA/MERRA data using
604
(Blender and Schubert 2000) algorithm. Let the observed LPS track be represented as
605
{
} for latitude (Φ), longitude (λ), and time (k), with time-steps
606
. The track in the dataset to be compared (ERAI/MERRA) with the observations
607
may be represented as {
608
temporal distance between the two storm tracks can be calculated as
609
610
611
612
613
} for time steps
[
]; where
of ERAI/MERRA tracks;
∫
∫
] 〉 ; where
. The spatio-
is the variance of the observed LPS tracks and
that
is the variance of the combined tracks that is computed as
〈 {[
]
[
] }
is a spatial weighting and
. In our calculations U = 10 ms-1 and
[
temporal weighting which are related as
are used.
614
σ1 and σ2 are estimated in the same as σ12, except that identical paths are used. The probability of
615
coincidence of the tracks Pc is calculated as Pc = L/L1, with L1 being the number of LPS of the
616
dataset with fewer LPS. L is the sum of identical tracks.
617
618
Praveen et al. Monsoon synoptic activity
31
619
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Praveen Page 34
1
Figure Legends
2
Figure 1: Schematic diagram showing the identification of LPS center and calculation of
3
pressure gradient for (a) single center system and (b) multi-center system.
4
Figure 2: Comparison of observed and reconstructed annual number of Low Pressure Systems
5
formed over Indian monsoon region for (a) category 1 and 2 and (b) category >2 systems. The
6
correlations of the annual number of LPS computed from ERAI and MERRA datasets with the
7
Sikka archive are indicated for JJAS and JJA.
8
Figure 3: Frequency of LPS days for (a) June, (b) July, (c) August, and (d) September during
9
1979 – 2003 calculated from Sikka archive, ERAI, and MERRA reanalysis. Correlation of the
10
frequency of LPS days computed using ERAI and MERRA datasets with the Sikka archive are
11
also indicated.
12
Figure 4: Frequency of LPS days during 1979 – 2003 calculated from Sikka archive,
13
ERAI/MERRA reanalysis, and various AMIP model simulations from CMIP5 for (a) June –
14
September and (b) June – August Seasons. Correlation of the frequency of LPS days computed
15
using ERAI and MERRA datasets with the Sikka archive are also indicated.
16
Figure 5: Probability of track coincidence (%) calculated for LPS systems in ERAI and MERRA
17
with respect to the Sikka archive. Probability of track coincidence calculated for all LPS during
18
1979 – 2003 are indicated.
19
Figure 6: Spatial density maps of Synoptic Activity Index (SAI) from (a) Sikka archive, (b)
20
ERAI, (c) MERRA, (d) MIROC5, (e) CCSM4, (f) CNRM-CM5, (g) ACCESS1-3, and (h) MRIMonsoon synoptic activity
Praveen Page 35
1
CGCM3. (d) – (f) are from historical All Forcing experiments of CMIP5 simulations. The spatial
2
patterns are mean of 1979 – 2003 JJAS LPS days’ frequency. The spatial correlations and RMSE
3
of model simulated SAI index with Sikka archive are indicated. The black box in panel (a) shows
4
the region (77°E – 90°E and 15°N – 25°N) selected for calculating area averaged SAI in Fig.9.
5
Figure 7: Same as Fig. 6, except that (d) – (f) are computed using AGCM experiments (AMIP)
6
of CMIP5 simulations.
7
Figure 8: Inter-annual variability of synoptic activity from the Sikka archive, ERAI/MERRA
8
reanalysis and AGCM simulations for (a) JJAS and (b) JJA seasons. Synoptic activity is
9
represented by SAI index (see text for details) averaged over 77°E – 90°E and 15°N – 25°N (see
10
the box in Fig.6(a). Correlations of SAI calculated from ERAI and MERRA with Sikka archive
11
are indicated.
12
Figure 9: Total precipitation index against LPS day precipitation index. PL is the sum of
13
precipitation over all grid points in the box 77°E – 90°E and 15°N – 25°N for all LPS days
14
during 1979 - 2003. Similarly, PT is the sum of precipitation for all days during 1st June – 30th
15
September in the same box. Both PL and PT are normalized using observed total precipitation. As
16
observations are available only over land, the oceanic grids are masked for the calculations. The
17
models that have strong cold SST bias over the northern Arabian Sea (Sandeep and Ajayamohan
18
2014a) are indicated in blue color.
19
Figure 10: (a) Ensemble mean JJAS zonal wind shear (U200 – U850, ms-1). Contours show the
20
JJAS zonal wind shear from ERAI, (b) Standard deviation in the zonal wind shear among 17
21
CMIP5 models, (c) Linear regression map generated by regressing inter-model SAI on zonal
Monsoon synoptic activity
Praveen Page 36
1
wind shear; see section 2.4 for details of generating this regression map; the stippling shows
2
regression slopes significant at 95% level. (d) Scatter plot between area averaged SAI and zonal
3
wind shear. SAI is area averaged over 77°E – 90°E and 15°N – 25°N, while zonal wind shear is
4
area averaged over the box shown in panel (c) (65°E – 90°E and 17.5°N – 27.5°N).
5
Figure 11: (a) JJAS mean zonal wind profiles averaged over the monsoon trough (65°E – 90°E
6
and 17.5°N – 27.5°N). Black, red, magenta and green colors indicates ensemble mean, ERAI,
7
IPSL-CM5A-MR and MIROC5 respectively.
8
standard deviation of zonal wind profiles among 17 CGCMs, (b) linear regression of SAI index
9
from 17 models on JJAS mean zonal wind profiles (units are standardized); see section 2.4 for
The error bars (blue horizontal lines) show
10
details of regression technique.
11
Figure 12: Storm-centered composite vertical structure of anomalous potential temperature
12
(shading, K) and meridional winds (ms-1) calculated using (a) ERAI reanalysis, (b) MERRA, (c)
13
MIROC5, and (d) MIROC-ESM historical All Forcing experiments. Only 1990 – 1999 data were
14
used for these calculations. The day on which each LPS event achieved maximum intensity is
15
considered for constructing composites. Contours range between -1.5 and 1.5 with an interval of
16
0.3 m s-1. Positive (negative) contours show southerly (northerly) winds.
17
Figure 13: Three dimensional thermodynamical structure of monsoon LPS in ERAI reanalysis
18
as revealed by the storm centered composite of potential temperature anomalies (K). Blue and
19
black arrows represent wind vectors at 850 hPa, and 200 hPa respectively. Meridional mean of
20
the thermodynamical structure is shown in the background (same as in Fig. 12a).
21
22
Monsoon synoptic activity
Praveen Page 37
1
Table 1: Classification of monsoon Low Pressure Systems based on pressure depth adapted in
2
this study. This classification is similar to the one used in Sikka (2006) and Ajayamohan et al.
3
(2010).
∆SLP (hPa)
LPS category
Estimated Wind Speed (ms-1)
SAI Weighting
<=2
Low
<8.5
4.25
>2 and <=4
Depression
8.5-13.4
11
>4 and <=10
Deep
13.5-16.4
15
16.5-23.4
20
Depression
>10 and <=16
Cyclonic storm
>16
Severe cyclonic >=23.5
storm
4
5
6
7
8
9
10
11
12
Monsoon synoptic activity
27.5
Praveen Page 38
1
2
Table 2: Mean number (June – September) of category-wise storms formed over the Bay of
Bengal during 1979 – 2003.
3
Data Source/
Category
Low
Depression
Deep Depression
Total
(Std.Dev)
Sikka archive
9.2
2.5
1.2
12.9 (2.7)
ERA
6.1
4.5
2
12.6 (2.7)
MERRA
6.6
4.5
2.9
14 (2.8)
4
5
6
7
8
9
10
11
12
13
14
Monsoon synoptic activity
Praveen Page 39
1
Table 3: Various factors that contribute to the inter-model variance in LPS activity. The models
2
with strong cold SST bias over the northern Arabian Sea (Sandeep and Ajayamohan 2014a) are
3
indicated by an asterisk.
Model/Reanal Horizontal Number Vertica Total
ysis Product Resolution of Grid
l
number
lato x lono Points in Levels of Grid
Horizont
(B)
points
al
(x106)
(A)
Grid
Points
Ratio
(A)/(B)
Vertically
integrated
Moisture
convergence
x10-5 kgm-2s-1
SAI
Index
Wind Shear
(U200-U850, ms-1)
Geostrophic
Vorticity
x10-6 s-1
1) ACCESS1- 1.24 x 1.88
27840
38
1.058
732.63
-2.99
43.96
-15.38
2.78
8192
26
0.213
315.08
-1.13
26.87
-12.39
3.90
3*
2) Bcc-csm1-1 2.81 x 2.81
3) CanESM2
2.81 x 2.81
8192
35
0.287
234.06
-0.21
19.78
-8.53
2.09
4) CCSM4
0.94 x 1.25
55296
26
1.438
2126.77
2.44
64.76
-11.90
5.30
5) CESM1-
0.94 x 1.25
55296
26
1.438
2126.77
2.55
73.47
-11.77
5.67
1.41 x 1.41
32768
31
1.016
1057.03
-0.33
66.58
-13.62
5.63
1.88 x 1.88
18432
18
0.332
1024.00
-0.19
28.00
-13.41
5.61
2.00 x 2.50
12960
24
0.311
540.00
1.80
64.93
-12.71
6.82
2.00 x 2.50
12960
24
0.311
540.00
1.40
54.45
-10.31
5.25
1.26 x 2.50
20592
39
0.803
528.00
0.22
17.37
-4.66
5.49
BGC
6) CNRMCM5
7) CSIROMK3-6-0*
8) GFDLESM2G
9) GFDLESM2M
10) IPSLCM5A-MR
Monsoon synoptic activity
Praveen Page 40
11) IPSL-
1.88 x 3.75
9216
39
0.359
236.31
-2.75
10.77
-1.85
4.29
12) MIROC5 1.41 x 1.41
32768
40
1.311
819.20
3.33
70.45
-12.84
8.37
13) MIROC-
2.81 x 2.81
8192
80
0.655
102.40
1.07
21.30
-8.27
3.39
2.81 x 2.81
8192
80
0.655
102.40
1.11
25.24
-7.94
3.50
1.13 x 1.13
51200
48
2.458
1066.67
-2.01
43.50
-5.45
4.52
1.13 x 1.13
51200
48
2.458
1066.67
-1.92
45.44
-5.27
4.38
17) NorESM1- 1.88 x 2.50
13824
26
0.359
531.69
1.16
73.99
-13.01
5.76
CM5B-LR*
ESM-CHEM
14) MIROCESM
15) MRICGCM3*
16) MRIESM1*
M
ERAI
0.75 x 0.75
115680
37
4.280
3126.49
3.18
57.00
-13.70
5.90
Correlation
-0.62
0.44
-0.42
0.30
0.54
0.55
1.00
-0.62
0.65
with SAI
Index
1
2
Monsoon synoptic activity
Praveen Page 41
1
2
Figure 1: Schematic diagram showing the identification of LPS center and calculation of
3
pressure gradient for (a) single center system and (b) multi-center system.
4
5
6
7
8
9
10
11
Monsoon synoptic activity
Praveen Page 42
1
2
Figure 2: Comparison of observed and reconstructed annual number of Low Pressure Systems
3
formed over Indian monsoon region for (a) category 1 and 2 and (b) category >2 systems. The
4
correlations of the annual number of LPS computed from ERAI and MERRA datasets with the
5
Sikka archive are indicated for JJAS and JJA.
Monsoon synoptic activity
Praveen Page 43
1
2
3
Figure 3: Frequency of LPS days for (a) June, (b) July, (c) August, and (d) September during
4
1979 – 2003 calculated from Sikka archive, ERAI, and MERRA reanalysis. Correlation of the
5
frequency of LPS days computed using ERAI and MERRA datasets with the Sikka archive are
6
also indicated.
7
Monsoon synoptic activity
Praveen Page 44
1
2
Figure 4: Frequency of LPS days during 1979 – 2003 calculated from Sikka archive,
3
ERAI/MERRA reanalysis, and various AMIP model simulations from CMIP5 for (a) June –
4
September and (b) June – August Seasons. Correlation of the frequency of LPS days computed
5
using ERAI and MERRA datasets with the Sikka archive are also indicated.
Monsoon synoptic activity
Praveen Page 45
1
2
3
4
5
6
Figure 5: Probability of track coincidence (%) calculated for LPS systems in ERAI and MERRA
7
with respect to the Sikka archive. Probability of track coincidence calculated for all LPS during
8
1979 – 2003 are indicated.
9
10
11
12
Monsoon synoptic activity
Praveen Page 46
1
2
Figure 6: Spatial density maps of Synoptic Activity Index (SAI) from (a) Sikka archive, (b)
3
ERAI, (c) MERRA, (d) MIROC5, (e) CCSM4, (f) CNRM-CM5, (g) ACCESS1-3, and (h) MRI-
4
CGCM3. (d) – (f) are from historical All Forcing experiments of CMIP5 simulations. The spatial
5
patterns are mean of 1979 – 2003 JJAS LPS days’ frequency. The spatial correlations and RMSE
6
of model simulated SAI index with Sikka archive are indicated. The black box in panel (a) shows
7
the region (77°E – 90°E and 15°N – 25°N) selected for calculating area averaged SAI in Fig.9.
8
9
10
11
12
13
Monsoon synoptic activity
Praveen Page 47
1
2
Figure 7: Same as Fig. 6, except that (d) – (f) are computed using AGCM experiments (AMIP)
3
of CMIP5 simulations.
Monsoon synoptic activity
Praveen Page 48
1
2
Figure 8: Inter-annual variability of synoptic activity from the Sikka archive, ERAI/MERRA
3
reanalysis and AGCM simulations for (a) JJAS and (b) JJA seasons. Synoptic activity is
4
represented by SAI index (see text for details) averaged over 77°E – 90°E and 15°N – 25°N (see
5
the box in Fig.6(a). Correlations of SAI calculated from ERAI and MERRA with Sikka archive
6
are indicated.
Monsoon synoptic activity
Praveen Page 49
1
2
3
Figure 9: Total precipitation index against LPS day precipitation index. PL is the sum of
4
precipitation over all grid points in the box 77°E – 90°E and 15°N – 25°N for all LPS days
5
during 1979 - 2003. Similarly, PT is the sum of precipitation for all days during 1st June – 30th
6
September in the same box. Both PL and PT are normalized using observed total precipitation. As
7
observations are available only over land, the oceanic grids are masked for the calculations. The
8
models that have strong cold SST bias over the northern Arabian Sea (Sandeep and Ajayamohan
9
2014a) are indicated in blue color.
10
11
12
13
Monsoon synoptic activity
Praveen Page 50
1
2
Figure 10: (a) Ensemble mean JJAS zonal wind shear (U200 – U850, ms-1). Contours show the
3
JJAS zonal wind shear from ERAI, (b) Standard deviation in the zonal wind shear among 17
4
CMIP5 models, (c) Linear regression map generated by regressing inter-model SAI on zonal
5
wind shear; see section 2.4 for details of generating this regression map; the stippling shows
6
regression slopes significant at 95% level. (d) Scatter plot between area averaged SAI and zonal
7
wind shear. SAI is area averaged over 77°E – 90°E and 15°N – 25°N, while zonal wind shear is
8
area averaged over the box shown in panel (c) (65°E – 90°E and 17.5°N – 27.5°N).
9
Monsoon synoptic activity
Praveen Page 51
1
2
Figure 11: (a) JJAS mean zonal wind profiles averaged over the monsoon trough (65°E – 90°E
3
and 17.5°N – 27.5°N). Black, red, magenta and green colors indicates ensemble mean, ERAI,
4
IPSL-CM5A-MR and MIROC5 respectively.
5
standard deviation of zonal wind profiles among 17 CGCMs, (b) linear regression of SAI index
6
from 17 models on JJAS mean zonal wind profiles (units are standardized); see section 2.4 for
7
details of regression technique.
8
9
Monsoon synoptic activity
The error bars (blue horizontal lines) show
Praveen Page 52
1
2
Figure 12: Storm-centered composite vertical structure of anomalous potential temperature
3
(shading, K) and meridional winds (ms-1) calculated using (a) ERAI reanalysis, (b) MERRA, (c)
4
MIROC5, and (d) MIROC-ESM historical All Forcing experiments. Only 1990 – 1999 data were
5
used for these calculations. The day on which each LPS event achieved maximum intensity is
6
considered for constructing composites. Contours range between -1.5 and 1.5 with an interval of
7
0.3 m s-1. Positive (negative) contours show southerly (northerly) winds.
Monsoon synoptic activity
Praveen Page 53
1
2
Figure 13: Three dimensional thermodynamical structure of monsoon LPS in ERAI reanalysis
3
as revealed by the storm centered composite of potential temperature anomalies (K). Blue and
4
black arrows represent wind vectors at 850 hPa, and 200 hPa respectively. Meridional mean of
5
the thermodynamical structure is shown in the background (same as in Fig. 12a).
6
Monsoon synoptic activity