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Investigation of temperature changes over India in association with meteorological parameters in a warming climate

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International Journal of Climatology
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We have used 1 ∘ × 1 ∘ resolution maximum temperature (T MAX) data sets developed by India Meteorological Department (IMD) to examine the summer time warming over India during the period 2001–2014 in comparison with the period 1971–2000. The two study periods have been arrived at based on the drastic change of Moisture Index (I M) trends over India between the two epochs. The T MAX variations over India are discussed with the corresponding changes in Potential Evapotranspiration (PET) data of the Climate Research Unit (CRU) and Outgoing Longwave Radiation (OLR) data of NOAA ESRL. The study shows a considerable warming over northern parts of India compared to southern parts. Western Himalayas (WH) and Northwest (NW) regions experienced highest warming with 1.4 ∘ C and 0.8 ∘ C increases during Epoch 2 (2001–2014) as compared Epoch 1 (1971–2000) during the summer (March, April and May). Using MERRA Black Carbon Surface Mass Concentration (BCSMC) data, we have analysed the relation of increasing BCSMC with the T MAX over different homogeneous temperature regions of India and found that BCSMC has increased upto 1.6 times between the two epochs. Strong linear association is found between T MAX , PET and OLR evidenced by Coherence Wavelet Spectral analysis. It is also found that the highest warming occurred in the month of March and is 2.2 ∘ C in WH and 1.4 ∘ C in NW parts of India. We calculated mass stream function based on zonal mean meridional velocity for the two periods. In the recent periods we observed the weakening of polar cell and northward expansion of Hadley cell. These changes may be related to warming conditions of the atmosphere which may explain the intensification and northward expansion of the Ferrel cell with favourable conditions during the summer season.
Difference in T MAX on space scale over India for the periods 2001-2014 and 1971-2000 for (a) summer season (March, April and May average), (b) March, (c) April and (d) May. [Colour figure can be viewed at wileyonlinelibrary.com]. even a decrement of T MAX during the 2001-2014 epoch than in the corresponding 1971-2000 epoch. The region of Deccan Plateau has yielded least change in temperature of 0.5 ∘ C. It has been reported by Kothawale and Rupakumar (2005) that all India mean annual temperature increased by about 0.22 ∘ C per decade during 1971-2003. Also, they have reported that some of the regions of India have shown significant warming trend during 1971-2003. Pal and Al-Tabbaa (2010) in their study on long term changes of extreme temperatures over India, found that the maximum temperatures increased unevenly over India during the last century. They have used the monthly maximum and minimum temperature data of homogeneous temperature regions provided by Indian Institute of Tropical Meteorology, Pune, India for their analysis. They have concluded that there are large variations in temperature changes with highest values in WH region. The present investigation also portrays that there is a large difference of T MAX over WH region between the two epochs. The study of Ganguly and Iyer (2009) on long term variations of surface air temperatures during summer in India showed that the air temperatures increased over NW, NC and IP regions when compared with the EC, WC and WH regions of India. According to their study, the Aerosol Optical Depth (AOD) and BC optical depth
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INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. (2017)
Published online in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.5216
Investigation of temperature changes over India in association
with meteorological parameters in a warming climate
G. Purnadurga,aT.V. Lakshmi Kumar,a*K. Koteswara Rao,bM. Rajasekharc
and M.S. Narayanand
aDepartment of Physics, Atmospheric Science Research Laboratory, SRM University, Chennai, India
bSchool of Earth and Environmental sciences, Seoul National University, Seoul, South Korea
cSathish Dhawan Space Centre, ISRO, Srihari Kota, India
dDRVE, SRM University, Chennai, India
ABSTRACT: We have used 1×1resolution maximum temperature (TMAX) data sets developed by India Meteorological
Department (IMD) to examine the summer time warming over India during the period 2001– 2014 in comparison with
the period 1971–2000. The two study periods have been arrived at based on the drastic change of Moisture Index (IM)
trends over India between the two epochs. The TMAX variations over India are discussed with the corresponding changes
in Potential Evapotranspiration (PET) data of the Climate Research Unit (CRU) and Outgoing Longwave Radiation (OLR)
data of NOAA ESRL. The study shows a considerable warming over northern parts of India compared to southern parts.
Western Himalayas (WH) and Northwest (NW) regions experienced highest warming with 1.4 C and 0.8 C increases during
Epoch 2 (2001–2014) as compared Epoch 1 (1971 –2000) during the summer (March, April and May). Using MERRA Black
Carbon Surface Mass Concentration (BCSMC) data, we have analysed the relation of increasing BCSMC with the TMAX over
different homogeneous temperature regions of India and found that BCSMC has increased upto 1.6 times between the two
epochs. Strong linear association is found between TMAX, PET and OLR evidenced by Coherence Wavelet Spectral analysis.
It is also found that the highest warming occurred in the month of March and is 2.2 C in WH and 1.4 CinNWpartsof
India. We calculated mass stream function based on zonal mean meridional velocity for the two periods. In the recent periods
we observed the weakening of polar cell and northward expansion of Hadley cell. These changes may be related to warming
conditions of the atmosphere which may explain the intensication and northward expansion of the Ferrel cell with favourable
conditions during the summer season.
KEY WORDS maximum temperatures; pet; OLR; mass stream function; India
Received 5 November 2016; Revised 28 April 2017; Accepted 27 June 2017
1. Introduction
It is reported by IPCC (2013) that the change in global
mean surface temperatures will be around 0.3 Cto
0.7 C during 2016–2035 when compared to the period
1986–2005. Hegerl et al. (2007) reported that the global
warming from the beginning of industrial era is caused
by the anthropogenic greenhouse gas emissions. Studies
of Scott and Jones (2012) have shown, based on the
temperature records till 2010, that the global warming
is set to continue in 21st century. It is also reported that
annual surface temperatures over India have signicantly
increased during 1901–2013 (Annual Climate Summary,
2014). This increase in temperature over India is a result of
signicant occurrence of heat waves with higher frequency
and increasing duration (Rohini et al., 2016). The conse-
quent numbers of days with highest temperature, known
as heat waves, are strongly linked to ENSO (Ratnam et al.,
* Correspondence to: T.V. Lakshhmi Kumar, Department of Physics,
Atmospheric Science Research Laboratory, SRM University, Chennai
603203, India. E-mail: lkumarap@hotmail.com
2016). Many studies over India have found that there is an
increase in mean and maximum temperatures in space and
time scales (Arora et al., 2005; Srivastava et al., 2017).
These analyses have been carried out based on the station
data and also from data of homogeneous temperature
regions. The main reason for the increase of temperatures
are found to be global warming and global teleconnections
(Kothawale and Rupa Kumar, 2005; Jaswal et al., 2015).
Besides the above, the contribution of aerosols can also
be thought of. black carbon (BC) aerosols which mainly
originate from fossil fuel and incomplete combustion,
absorb the solar radiation and contribute to climate warm-
ing (Ban-Weiss et al., 2012). The atmospheric heating is
further increased by the anthropogenic emissions of BC.
Jacobson (2010) found that the increase in BC, particularly
biofuel soot gases result in cloud and atmospheric heating
due to their hygroscopic nature. Meehl et al. (2008) used
the coupled ocean-atmospheric climate model to study
the 21st century simulations and reported that the BC and
organic aerosols increase the lower tropospheric heating
over south Asian region. Hence, it is very important to see
the relation of BC and surface temperatures to understand
© 2017 Royal Meteorological Society
G. PURNADURGA et al.
the warming over a region. It is also well known that the
Hadley Cell is the principal controller of the precipitation
patterns in the tropical regions. There is an increasing
interest on the changes of width and strength of Hadley
Cell in the context of global warming. Kang and Lu (2012)
studied the expansion of Hadley Cell and reported that it
is extending polewards in all seasons. It is also reported
that the changes in meridional temperature gradient of the
troposphere suggest a total widening of 2in latitude
since 1979 (Johanson and Fu, 2009).
In the present study, we analyse the gridded surface
air temperature data over India to evaluate the variations
of TMAX for the two epochs 1971–2000 (Epoch 1) and
2001–2014 (Epoch 2). The main aim of this study is to
show that there is a considerable warming over India dur-
ing Epoch 2 compared to Epoch 1. The starting year of
the study period is chosen as 1971, mainly to see the
temperature rise after the rapid industrialization in India
(Kothawale et al., 2016). The two Epochs 1971– 2000 and
2001–2014 are arrived at, based on the break trend anal-
ysis of Moisture Index (IM) time series derived over India
using Thornthwaite and Mather (1955) revised water bal-
ance concept. The break trend analysis which is applied
in this study is useful to analyse the abrupt changes in IM
for the two study epochs. A drastic decline was observed
in IMover India during the summer months of March,
April and May around the year 2000 and hence the total
study period was divided into two epochs as 1971– 2000
and 2001–2014. The TMAX differences over these two
epochs are examined along with the corresponding varia-
tions of Potential Evapotranspiration and Outgoing Long-
wave Radiation (OLR) over India. The possible reasons for
the TMAX rise over different homogeneous regions along
with those over All India are discussed by associating them
with PET and OLR variations. The Black Carbon Sur-
face Mass Concentration (BCSMC) data from MERRA
has been used to examine the relation of BC with the
TMAX over India. Further, we also analysed the width of
the Hadley Cell for the above mentioned two epochs using
mass stream function (MSF).
2. Data and methodology
The main data sources for this study are: India Meteoro-
logical Department (IMD), Climate Research Unit (CRU)
and NOAA ESRL for data on daily TMAX, monthly PET
and interpolated monthly OLR, respectively. The daily
TMAX data have been averaged over a month and is used
as monthly mean TMAX for the present study. The grid
resolution of TMAX data is 1×1covering the Indian land
mass for the period 1951–2014 (Srivastava et al., 2009).
The PET data sets are available with the 0.5×0.5grid
resolution and OLR at 2.5×2.5resolution. All the three
data sets were averaged over all the grids falling over each
of the seven temperature homogeneous regions of Indian
land mass (Figure 1, IMD – www.tropmet.res.in). Thus,
average TMAX over the seven homogenous regions and
over whole of India were computed. Monthly rainfall and
69°E
WH- Western Himalaya, NW- North-west, NC- North central
NE - Northeast, WC- West coast, EC- East coast
IP- Interior peninsula
9°N
12°N
15°N
18°N
21°N
24°N
27°N
30°N
33°N
36°N
WH
NW
NC
WC
NE
IP
EC
72°E 75°E 78°E 81°E 84°E 87°E 90°E 93°E 96°E
Figure 1. Temperature homogeneous regions of India.
PET data have been used to run the Thoronthwaite and
Mather water balance model and Humidity and Aridity
indices are thus, derived. The difference of humidity
and aridity index is known as IMby which climate of
a region is categorized as perhumid (A) (IM>100%),
Humid (B4) (100<IM>80), Humid (B3) (80<IM>60),
Humid (B2) (60<IM>40), Humid (B1) (40<IM>20),
Moist subhumid (C2) (20<IM>0), Dry subhumid (C1)
(0 <IM>33.3), Semi arid, D (66.7 <IM>33.3)
and Arid, E (100 <IM>66.7) as per Thoronthwaite
Climate Classication (1948). The rainfall data for this
analysis have been taken from IMD with 1×1resolution
developed by Rajeevan et al. (2006) from the station data.
The seven temperature homogeneous regions of India are
(1) Western Himalayas (WH), (2) North West (NW), (3)
North Central (NC), (4) North East (NE), (5) West Coast
(WC), (6) East Coast (EC) and (7) Interior Peninsula (IP).
The difference of maximum temperatures for each of
the three summer months, March, April and May and for
average of all these three summer months, were obtained
to see the TMAX rise over India on spatial scale. Simi-
larly, the differences, as above, were obtained for PET
and OLR to understand their association with TMAX. Daily
temperatures have been calculated for different homoge-
neous regions and for All India been studied to quantify
the corresponding rise in TMAX rise over different homoge-
neous regions as well as for All India. Coherence Wavelet
Spectrum has been obtained (details are given in Section 3)
for TMAX and PET and also with OLR to examine the vari-
ation of PET and OLR with TMAX. Pearson correlation
technique is also used to understand the strength of the
relation among these variables.
The BCSMC data (in microgram per cubic meter) have
been obtained from Modern ERA Retrospective Analy-
sis for Research (MERRA) products which are developed
by Global Modelling and Assimilation Ofce Research
© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
TEMPERATURES OVER INDIA IN ASSOCIATION WITH METEOROLOGICAL PARAMETERS
Site – National Aeronautics and Space Administration
(GMAO NASA). The data are available on monthly scale
with 0.5×0.625grid resolution over the globe from the
year 1980 till to date. The authors extracted the same data
for the different homogeneous regions of India as well
for the whole of India from 1980–2014. This data for the
above period has been compared with the monthly TMAX
data and a correlation analysis was carried out for the
aforementioned period.
Mass stream function (Oort and Yienger, 1996), which
is the integral the northward mass ux above a particular
pressure level has been calculated using the formula
𝜓=2Πacos 𝜓
gvp
0
dp
where 𝜓is Mass Stream Function, 𝜈is the
zonally-averaged meridional velocity, ais the Earth
radius, gis the gravity, Ψis the latitude and pis the
pressure level.
The MSF has been calculated for the two epochs and
studied in the context of increasing temperatures over
India. The wind data for the MSF calculation is taken from
the NCEP-NCAR Reanalysis data sets with 2.5×2.5
resolution.
3. Results and discussion
Figures 2(a) –(d) depict the break trends for the time series
of IMover India derived from the Thoronthwaite and
Mather revised water balance model for the Epoch 1
(1971–2000) and Epoch 2 (2001 2014) for the summer
season and for the 3 months. Due to southwest monsoon
showers over India from June, we have not included June
for this analysis.
From visual inspection of the gures we infer that IMis
distinctly different in all the individual months and also for
the season. From Figure 2(a), it can be seen that IMvaried
from - 40 to 50 units during Epoch 1 which corresponds
to semi arid climatic nature. The period Epoch 2 has shown
the variation from - 60 to - 45 units, which also corresponds
to semi arid nature but the value of IM(60 units) is very
close to arid category. From Figures 2(a)– (d) it is found
that there is a drastic dip in IMvalues during 1998 to 2005
and the trend is different during the two epochs. The aver-
age value of IMfor the summer season (MAM average)
for Epoch 1 is - 44 units and is - 55 units for Epoch 2. The
difference of IMis more in the month of March and is of
about 15 units. The value of IMreects the climatology
of a region and further allows us to infer the changes in
climatic indices such as rainfall and temperature. Ravin-
dranath (2011) studied the Moisture Index derived from
the Thoronthwaite Climatic Approach over tropical region
from 1901 to 2006. It is found from their study that the
period from 1970 showed negative deviations of Moisture
Index which revealed the dry weather conditions over trop-
ics. The analysis of climatology over India carried out
by Raju et al. (2013) indicated the increased regions of
semi-arid, arid and dry sub humid in different parts of India
–40
–35
–50
–45
–60
–55
–30
–65
–40
–35
–50
–45
–60
–55
–30
–20
–50
–40
–70
1970 1975 1980 1985 1990
Year
May
April
March
MAM Average
(a)
(b)
(c)
(d)
Moisture index (IM)
1995 2000 2005 2010 2015
–60
–30
–60
–55
–50
–45
–40
–35
–30
Figure 2. Break trends of Moisture Index (IM) of All India for the periods
1971– 2000 and 2001 –2014 obtained for (a) summer season (March,
April and May average), (b) March, (c) April and (d) May months.
during 1971 to 2005. They also reported that the rise in
temperatures and no noticeable trend in rainfall will lead
to more arid nature over a region. In the present study, the
difference in IMbetween the two study periods has been
veried for the level of signicance using student t test
(two tailed). It is found that the difference in IMvalue dur-
ing summer season, March, April and May are at 0.01level
of signicance.
This prompted us to further examine the changes in
surface air temperature over India on spatial and tempo-
ral scale for the aforementioned periods. We also have
seen the changes in temperatures associated with poten-
tial evapotarnspiration (PET) and OLR over India. Since
the temperature impacts the estimation of evapotranspira-
tion and it is very important parameter for agriculture, we
examined the corresponding variations in them. In the sub-
sequent sections, the spatial as well as time scale changes
of TMAX, PET and OLR have been discussed.
Figures 3(a)– (d) depict the space scale variations of
TMAX difference over India for the two epochs for the aver-
age summer season and for, March, April and May months,
respectively. It is found from the gures that the tempera-
ture rise was highest in WH region, followed by NW parts
and NE parts of India. The same has been observed dur-
ing the months of March, April and May but the incre-
ment of TMAX is pronounced during March followed by
April and May. The gures also reveal that the southern
parts of India have undergone less warming effects than
the northern parts. Some parts of Karnataka have shown
© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
G. PURNADURGA et al.
MAM
9°N
12°N
15°N
18°N
21°N
24°N
27°N
30°N
33°N
36°N
(a)
69°E 72°E 75°E 78°E 81°E 84°E 87°E 90°E 93°E 96°E
March
9°N
12°N
15°N
18°N
21°N
1. 8
1. 6
1. 4
1. 2
1
0.8
0.6
0.4
0.2
0
24°N
27°N
30°N
33°N
36°N
(b)
69°E 72°E 75°E 78°E 81°E 84°E 87°E 90°E 93°E 96°E
April
9°N
12°N
15°N
18°N
21°N
24°N
27°N
30°N
33°N
36°N
(c)
69°E 72°E 75°E 78°E 81°E 84°E 87°E 90°E 93°E 96°E
May
9°N
12°N
15°N
18°N
21°N
24°N
27°N
30°N
33°N
36°N
(d)
69°E 72°E 75°E 78°E 81°E 84°E 87°E 90°E 93°E 96°E
Figure 3. Difference in TMAX on space scale over India for the periods 2001– 2014 and 1971–2000 for (a) summer season (March, April and May
average), (b) March, (c) April and (d) May. [Colour gure can be viewed at wileyonlinelibrary.com].
even a decrement of TMAX during the 2001– 2014 epoch
than in the corresponding 1971–2000 epoch. The region of
Deccan Plateau has yielded least change in temperature of
0.5 C. It has been reported by Kothawale and Rupakumar
(2005) that all India mean annual temperature increased
by about 0.22 C per decade during 1971 2003. Also,
they have reported that some of the regions of India have
shown signicant warming trend during 1971– 2003. Pal
and Al-Tabbaa (2010) in their study on long term changes
of extreme temperatures over India, found that the max-
imum temperatures increased unevenly over India during
the last century. They have used the monthly maximum and
minimum temperature data of homogeneous temperature
regions provided by Indian Institute of Tropical Meteorol-
ogy, Pune, India for their analysis. They have concluded
that there are large variations in temperature changes with
highest values in WH region. The present investigation
also portrays that there is a large difference of TMAX over
WH region between the two epochs.
The study of Ganguly and Iyer (2009) on long term
variations of surface air temperatures during summer in
India showed that the air temperatures increased over
NW, NC and IP regions when compared with the EC,
WC and WH regions of India. According to their study,
the Aerosol Optical Depth (AOD) and BC optical depth
showed increasing trend over NW India. Ramachandran
and Cherian (2008) studied the seasonal variations of
aerosol characteristics from 2001 to 2005 over India using
MODIS Terra data. It is found from their study that Srina-
gar, located in WH region, has shown increased tendency
of AOD from 2001 to 2005. Also, it is reported in their
study that the states of Jammu and Kashmir and parts
of WH region have low SO2uxes. It is also found by
Nair et al. (2013) that BC concentration attains maximum
values in premonsoon season over Western Himalayas.
Also, they have reported that the direct and surface albedo
radiative forcing leads to considerable warming over
Himalayan region during premonsoon season. A gradual
increase in absorbing aerosols due to biomass burning and
transport activities raises the possibility that they could
have contributed to warmer temperature scenario over WH
region. Similar analysis has been carried out in the present
study using the BCSMC data obtained from the Modern
ERA Retrospective Analysis for Research (MERRA)
products developed by GMAO NASA. Different data sets
from MERRA have been used to study the aerosol charac-
teristics over India. Gaurav et al. (2015) used wind elds
at different atmospheric levels obtained from MERRA
to understand the inuence of transport on aerosols
over Indian subcontinent. Mean modelled surface ozone
© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
TEMPERATURES OVER INDIA IN ASSOCIATION WITH METEOROLOGICAL PARAMETERS
(a)
(b)
Figure 4. The Seasonal variation of BCSMC (𝜇gm
3) of MERRA over (a) Dibrugarh (2008– 2014) and (b) Chennai (2011–2014). [Colour gure
can be viewed at wileyonlinelibrary.com].
concentration from MERRA has been used to understand
the air pollution impacts on Indian agriculture (Burney and
Ramanathan, 2014). By using the satellite aerosol mea-
surements and MERRA reanalysis data, Kim et al. (2016)
found that the absorbing aerosols can impact seasonal
and interannual variations in Indian summer monsoon
season. In the present study, the authors compared the
seasonal variations of BCSMC obtained from MERRA
and Black Carbon Mass Concentration from in-situ
measurements which are reported at Dibrugarh (27.3N,
94.6E) and Chennai (12.8N, 80.03E) located in NE
and EC regions, respectively. Figures 4(a) and (b) depict
the seasonal variations of BCSMC from the year 2008
to 2014 over Dibrugarh and 2011– 2014 over Chennai,
obtained from MERRA. It can be observed that over
Dibrugarh (Figure 4(a)), the BCSMC has shown higher
values during the winter season in general (January and
February), followed by Summer (March, April and May),
Northeast monsoon (October, November and December)
and Southwest monsoon season (June, July, August and
September), respectively. Studies of Pathak et al. (2010)
reported that the Black Carbon Mass Concentration
obtained from in-situ measurements have also shown
higher values during the winter season (16.3 μgm
3)
followed by northeast monsoon (10.9 μgm
3), premon-
soon (7.5 μgm
3) and monsoon (3.4 μgm
3) seasons,
respectively. Similarly, in-situ measurements of BC over
Chennai also displayed the broad maximum of BC during
NE and winter months when compared to other seasons
(Aruna et al., 2013). From these analyse, it can be inferred
that the BCSMC from MERRA is able to show the sea-
sonal variability as depicted by the in-situ measurements
over Dibrugarh and Chennai. However, the bias of BC
mass concentration of MERRA is still to be examined
with the in-situ measurements.
In the present study, the BC data over all the
homogeneous regions have shown increment during
the period 2001–2014 when compared to 1980 2000.
During 2001–2014, the BC in WH and NW parts have
increased 1.6 and 1.5 times, respectively compared to
that during the period 1980–2000. The BC values for
WH and NW regions for the periods 1980–2000 and
2001–2014 during summer season are 0.12, 0.18 and
0.77, 1.17 𝜇gm
3, respectively. It is also observed that
the other regions such as NC, NE, IP, WC, EC have
shown, respectively 1.6, 1.6, 1.3, 1.4 and 1.6 times of BC
during epoch 2 vis a vis during Epoch 1. To understand
the relation of BCSMC with TMAX, we have carried out
the correlation analysis for the months March, April and
May months of the study period. The Pearson correlation
co-efcient along with the level of signicance are given
in Table 1. Among all temperature homogeneous regions,
NE part showed the highest correlation of +0.69 with 0.01
level of signicance during the month of March compared
to all other months. The regions WH and NW parts showed
the correlations of 0.56 and 0.32 which are statistically
signicant. It is also observed that the correlation is high
in March between TMAX and BCSMC in all temperature
homogeneous regions than in other months. The correla-
tion is low in the regions of IP and EC. The average of
BCSMC for all temperature homogeneous regions con-
sidered as the All India average and the correlation in this
case is 0.52, 0.33 and 0.20 during March, April and May
month, respectively. For the whole summer season, the
correlation between BCSMC and TMAX are, respectively,
0.57, 0.28, 0.29, 0.66, and 0.33 for WH, NW, NC, NE and
WC regions with 0.01, 0.10, 0.10, 0.01 and 0.05 levels
of signicance. The correlations of EC and IP regions
are found statistically insignicant. The scatter plots
© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
G. PURNADURGA et al.
Table 1. Correlation co-efcient of TMAX with BCSMC (𝜇gm
3)
for all India and for all temperature homogeneous regions of
India* denotes 0.01 and ** denotes 0.05 levels of signicance.
Region/ March April May
Year 1980–2014 1980 2014 1980– 2014
All India 0.52* 0.33** 0.20
WH 0.56* 0.48* 0.30
NW 0.32** 0.18 0.03
NC 0.35** 0.27 0.06
NE 0.69* 0.51* 0.40**
WC 0.49* 0.36** 0.04
IP 0.21 0.06 0.08
EC 0.18 0.18 0.09
for the regions with signicant correlations are given in
Figures 5(a)–(e).
The spatial scale variations of TMAX of the present
study are compared with the PET and OLR variations for
the same period to understand the proportionate relation
among them. Figures 6 and 7 depict the spatial variations
of PET and OLR. The differences in these parameters
between the two epochs for the summer season over India
are shown here. Similar features of increments have also
been observed in PET and OLR. The increment/decrement
of PET/OLR were subjected to student t- test (two tailed)
to know the level of signicance. The difference in PET
over NW, NC, NE, WC and EC are found at 0.01, 0.20,
0.01, 0.20 and 0.05 levels of signicance. The difference
in OLR during the two study periods is found at 0.01,
0.05, 0.20, 0.01, 0.01 levels of signicance over WH, NW,
NC, NE, EC regions, respectively. WH and NW parts of
India experienced 0.1–0.3 mm increase of monthly mean
PET, which gradually decreased during months of April
and May. Studies of Madhu et al. (2014) revealed that the
PET estimated from Hargreeves method over India has
shown increasing trend from 1901 to 2007 during sum-
mer time and is more prominent in NW region of India
which is shown as due to increase of summer TMAX over
that region. The OLR maps also revealed the same fea-
tures as PET. NW parts of India have shown an increase
of OLR ranging from 5 w m2to 15 w m2. NE parts also
showed variation of about 5 w m2during the summer
months. The higher values of OLR over NW parts, due to
low cloud cover resulting in higher surface temperatures
(Chandrasekhara et al., 1993). It is also observed that the
southern parts of India witnessed decrease of OLR during
Epoch 2 (2001–2014) than during Epoch 1 (1971 2000).
The overall spatial analysis reveals that TMAX considerably
increased over NW and WH regions of India and is main-
tained in close association with PET and OLR.
4. Association of TMAX, PET and OLR over India
This section presents the correlation analysis of TMAX
with PET and OLR for different homogeneous tempera-
ture regions of India and also for India as a whole along
with the daily time series analysis of TMAX. Also, we
present in Figures 8(a) and (b) the coherence wavelet
spectrums (CWS) for TMAX with PET and OLR for the
period 1971–2014 for whole of India. The CWS is a very
useful technique to explore the common power among
the time series of two variables and the relative phase
in time-frequency domain (Grinsted et al., 2004). The
wavelet coherence reveals the local periodicities in a given
time series and the wavelet transform coherence can be
treated as the local correlation between two time series
in time-frequency zone. Grinsted et al. (2004) have devel-
oped this technique and applied to the time series of Arc-
tic Oscillation and the Ice extent of Baltic Sea to report
the relationship among them. Many researchers have used
this algorithm of CWS for understanding the response of
ENSO on interannual sea level variability in South China
(Rong et al., 2007), variations in drought and wet spells in
China (Su and Wang, 2007) and for studying precipitation
variability (Vargas et al., 2012).
In Figures 8(a) and (b), xaxis denotes the year and yaxis
denotes the period of the wave. The gures were drawn
with cone of inuence. The arrow marks pointing towards
right in the gure, indicate that the two parameters to be
in-phase and the arrows pointing left indicate them to be
in anti-phase. From Figures 8(a) and (b) are found that the
relation between TMAX and PET is stronger than the rela-
tion between TMAX and OLR. The common power is very
high among TMAX and PET than with OLR. The arrows
in Figure 8(a) point towards right which reveal that TMAX
and PET are in phase with each other whereas TMAX and
OLR maintained in phase when the common power is
high. When the low power happens to be between Max-
imum Temp and OLR, these parameters are in anti-phase.
It is known that increased radiative forcing increases the
surface heating and surface latent heating which leads to
increase in surface temperature and increase in evapora-
tion. This may be the reason for the strong relation of
TMAX with PET. Also, OLR is measured from the top
of the atmosphere, whereas PET is measured at surface.
This might be the reason for the anti-phase relation of
TMAX with OLR in some years. The correlation analy-
sis of TMAX with OLR and PET also revealed the strong
association among them (Table 2). The correlations are
very strong with 0.05–0.01 level of signicance over India
with values of 0.42 and 0.96 for OLR and PET, respec-
tively. Over homogeneous regions also strong correlations
of TMAX with PET and OLR are seen. Among all homoge-
neous regions, WC region has shown negative correlation
between TMAX and PET and the regions EC and IP have
shown very less and insignicant correlations with OLR.
The lowest correlation over WC region between TMAX
and PET might be due to topography and climatic con-
ditions. Figures 9(a)– (h) represent the mean daily TMAX
for the periods 1971–2000 and 2001 2014 during the
months of March, April and May for All India, WH, NW,
NC, NE, WC, IP and EC regions, respectively. It is clear
that the daily TMAX continuously increased from March to
May except for WC region whereas in this region, TMAX
decreased during the end of May. In general, it is also
observed that the daily TMAX for the have shown higher
© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
TEMPERATURES OVER INDIA IN ASSOCIATION WITH METEOROLOGICAL PARAMETERS
(a) (b) (c)
(d) (e)
Figure 5. Scatter plots of BCSMC (𝜇gm
3) and Maximum Temperature (C) for the period 1980– 2014 over (a) WH, (b) NW, (c) NC, (d) NE and
(e) WC regions of India. [Colour gure can be viewed at wileyonlinelibrary.com].
70°E
10°N
15°N
20°N
25°N
30°N
35°N
MAM
75°E 80°E 85°E
0 0.05 0.1 0.15 0.2 0.25 0.3
90°E 95°E 100°E
Figure 6. Difference in PET on space scale over India for the periods
2001– 2014 and 1971–2000 for summer season (March, April and May
average). [Colour gure can be viewed at wileyonlinelibrary.com].
values than during except for a few days in all the 3
months. The All India average TMAX during Epoch 2 is
34.7 C and it is 34.1 C during Epoch 1 (Table 3). Thus,
a warming of 0.6 C is witnessed over India between the
two epochs. In the case of homogeneous regions, WH
has shown highest warming trend of about 1.4 C during
70°E
10°N
15°N
20°N
25°N
30°N
35°N
MAM
75°E 80°E 85°E
0 5 10 15 20 25 30
90°E 95°E 100°E
Figure 7. Difference in OLR on space scale over India for the periods
2001– 2013 and 1975–2000 for summer season (March, April and May
average). [Colour gure can be viewed at wileyonlinelibrary.com].
Epoch 2 followed by NW which is of about 0.9 C. The
lowest warming was observed in IP region of about 0.2 C
with TMAX values of 38.9 C degree and 38.7 Cdegree
during Epoch 2 and Epoch 1, respectively. Remaining
regions such as NC, NE, WC and EC have shown warm-
ing of 0.4 C, 0.5 C, 0.4 C and 0.3 C, respectively. It also
seen that India as a whole experiences a warming of 0.8 C,
© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
G. PURNADURGA et al.
4
8
8
510
Period
15 20 25 30 35 40
51015
Year
1
0.9
0.8
0.7
0.6
0.8
1
0.4
0.2
0
0.6
0.5
0.4
0.3
0.2
0.1
0
20 25 30
4
(a)
(b)
Figure 8. Coherence Wavelet Spectrum for (a) mean annual TMAX
and PET during 1971– 2014 and (b) mean annual TMAX and OLR
during 1975– 2013 over India. [Colour gure can be viewed at
wileyonlinelibrary.com].
0.4 C and 0.4 C during months March, April and May,
respectively. WH part of India has shown highest incre-
ment of TMAX during March of about 2.2 C followed by
NW which is of about 1.4 C between the two epochs.
Overall, it is to be noted that the daily TMAX of March
in all homogeneous regions have shown higher warming
than in any other month of summer season. The analysis
on high temperature days in India by Jaswal et al. (2015)
has clearly shown that there is an increase of respec-
tively, 3% and 5% in the north and west regions during
1969–2013. They also have shown that contrasting fea-
tures of summer high temperature days during the periods
1991–2013 and 1969 1990. Also, it is reported that there
is a strong link between SST of eastern pacic region and
temperatures over India (Kothawale et al., 2010). We also
nd a considerable increase of 0.3 C in Nino 3 SST during
November, December than during other months between
the periods 2001–2014 and 1971 2000. Similar features
of increase have been observed in PET and OLR during
the months of March, April and May and for the total
season. The PET over India increased by 3% during the
Epoch 2 when compared to that during Epoch 1. OLR has
shown 11 w m2and 10 w m2increments over WH and
NW parts and a decrement of 4 w m2and 8 w m2over IP
and EC regions, respectively during the summer season.
5. Changes in meridional circulation during the two
epochs
The MSF of the mean meridional circulation is obtained
with pressure as vertical coordinate. Figures 10(a) and (b)
show the MSF for the two distinct periods 1971– 2000 and
Table 2. Correlation co-efcient of TMAX with OLR and PET
over India and other homogeneous regions (* and ** denotes 0.01
and 0.05 levels of signicance, respectively).
Region 1975–2014 1971 2014
OLR PET
All India 0.43* 0.96*
IP 0.12 0.92*
NC 0.72* 0.97*
NE 0.26* 0.83*
NW 0.83* 0.96*
WH 0.92* 0.94*
WC 0.94* 0.70*
EC 0.42** 0.92*
2001–2014. We can observe that the contour lines of zero
value are located around 35N and 60N, respectively in
the epochs, which are separating the three cells, Hadley,
Ferrel, and Polar cells with their centres at 20N, 50Nand
70N. Flow circulates around positive (negative) centres
in a clockwise (anti-clockwise) sense. Thus, in the sea-
sonal mean (March, April and May), air rises just north
of the equator and sinks around 35N. In the Epoch 2,
the descending branch of Hadley cell was strengthening
and it has some northward expansion over the subtropics.
There is a seasonal variation in the position of the cells.
The latitudinal positions where the values of MSF are
zero in the subtropical regions shift poleward in both the
epochs, resulting from the intensied MSF in the subtrop-
ics in the recent periods. Widening of Hadley cell also
can be conrmed from this intensication of Hadley cell.
A strong Hadley cell dominates with its rising branch in
the summer hemisphere in the both the epochs. Obser-
vational analysis have demonstrated that the Hadley cells
expanded poleward in recent decades (Hu and Fu, 2007),
and various modelling studies have investigated the Hadley
cell expansion under global warming (Lu et al., 2007).
A recent modelling study (Hu et al., 2013) suggests that
increasing greenhouse gases play an important role in
causing the observed poleward expansion of the Hadley
circulation. Te Ferrel cell has maximum negative values
over 47–55N and it shows a signal of enhancement
as well as pole ward expansion compared to the epoch
1971–2000. A weakening and poleward shrinking of the
polar cell is also observed in both the epochs. The anal-
ysis shows that the Ferrel cell strongly intensied and
that intensication stretched over the entire troposphere
at around 50N in the recent period. This is may be
due to anomalous warm advection from tropics to higher
latitudes. These changes may be signicantly related to
warming conditions of the atmosphere during summer
season. The strength and width of the Hadley Circulation
during the two epochs experienced substantial changes
associated primarily to greenhouse warming and contribut-
ing to temperatures rises in the recent periods. Climate
models predict a weakening of the atmospheric convective
overturning in response to surface warming driven by an
increase in greenhouse gases (Knutson and Manabe, 1995;
Tanaka et al., 2004; Held and Soden, 2006).
© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
TEMPERATURES OVER INDIA IN ASSOCIATION WITH METEOROLOGICAL PARAMETERS
(a)
(c)
(e)
(g)
(b)
(d)
(f)
(h)
Figure 9. Mean daily time series of TMAX for the months March, April and May over (a) All India, (b) WH, (c) NW, (d) NC, (e) NE, (f) WC, (g) IP
and (h) EC regions of India. [Colour gure can be viewed at wileyonlinelibrary.com].
Table 3. Mean TMAX in C over India and homogeneous regions for the periods 1971– 2000 and 2001–2014.
Region March April May MAM
1971–2000 2001 2014 1971– 2000 2001 2014 1971– 2000 2001 2014 1971– 2000 2001 2014
All India 31.232.034.935.336.336.734.134.7
WH 16.218.422.623.526.727.621.823.2
NW 29.931.336.737.540.240.535.636.4
NC 32.433.238.438.941.241.137.337.7
NE 28.129.029.329.430.431.029.329.8
WC 34.935.336.937.436.837.136.236.6
IP 36.436.639.339.440.440.738.738.9
EC 35.035.437.237.438.538.936.937.2
6. Conclusions
Many earlier studies have pointed out, the warming over
India is continuing and is evidenced by increase in TMAX.
The main aim of the present analysis is to quantify the
differential warming trend over India during the Epoch
2 (2001–2014) compared to Epoch 1 (1971 2000). The
difference of TMAX over India during these two epochs
were studied in the context of PET and OLR variations
and the study has been extended also to homogeneous
temperature regions of India. TMAX has been related to
BCSMC to understand the role of aerosols in warming over
India. The results of the study show that:
(i) Warming over India during the epoch 20012014 was
more in northern India than in southern India when
compared to the epoch of 1971–2014.
(ii) WH and NW regions are the warmest regions with
warming of 1.4 C and 0.8 C, respectively during the
summer season.
(iii) The correlations are signicant between BCSMC and
TMAX over the different temperature homogeneous
regions of India except for IP and EC regions. This
revealed the linear association of BCSMC with the
rise in maximum temperatures.
(iv) A strong association is found between TMAX and, PET
when compared to TMAX and OLR.
© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
G. PURNADURGA et al.
(a)
EQ
1000
900
800
700
600
500
400
300
200
100
10°N 20°N 30°N 40°N 50°N 60°N 70°N 80°N 90°N
(b)
EQ
1000
900
800
700
600
500
400
300
200
100
10°N 20°N 30°N 40°N 50°N 60°N 70°N 80°N 90°N
Figure 10. (a) The mass stream function of the mean meridional circulation in pressure coordinates, for March, April and May in the period
1971– 2000. (b) The mass stream function of the mean meridional circulation in pressure coordinates, for March, April and May in the period
2001– 2014. [Colour gure can be viewed at wileyonlinelibrary.com].
(v) Most of the warming have been observed during
March with highest values in WH region (2.2 C)
followed by NW region (1.4 C).
(vi) It is found that the intensication and northward
broadening of Hadley cell and northward expansion
of the Ferrel cell during the Epoch 2 is more compared
to the Epoch 1.
Acknowledgements
The authors are thankful to Department of Science and
Technology (DST) Scientic and Engineering Research
Board (SERB), Govt of India for sponsoring this work
under EMR scheme.
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© 2017 Royal Meteorological Society Int. J. Climatol. (2017)
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The study explores variability and dynamical characteristics of heatwaves during March–June for 1990–2020 over India. Normalised T max anomaly is used to identify different heatwave spells in vulnerable regions of North‐central India (NCI) and Southeast coast of India (SECI) using India Meteorological Department (IMD, 1° × 1° resolution) observations, Indian Monsoon Data Assimilation and Analysis (IMDAA, 0.12° × 0.12°), and ECMWF Reanalysis v5 (ERA5, 0.25° × 0.25°). Results highlight that IMDAA exhibited a total 202 days (181 days) heatwaves duration in NCI (SECI) regions while ERA5 exhibited a total 132 days (89 days), respectively, compared with those of IMD (195 and 163 days). The primary heatwave periods for NCI (10 April to 20 June) and SECI region (1 May to 10 June) are well captured by IMDAA, unlike ERA5. The average length of the heatwave is 7.8, 7.5, and 7.76 days (8.15, 7.72, and 6.1 days) over NCI (SECI) in IMD, IMDAA, and ERA5, respectively. The high heat stress is more frequent in SECI than in the NCI region and is common during May–June (May only), as seen in IMDAA (ERA5). The middle to upper‐level anticyclone over NCI is stronger than SECI during heatwaves. Heat advection with stronger 850‐hPa north‐westerlies (~10 ms⁻¹) abates sea breeze in the coastal region, aiding longer heatwaves in the SECI region. Ascending motion induced by surface heating is confined to the lower levels due to the subsidence by the upper‐level anomalous anticyclone, stagnating higher temperatures in the lower atmosphere, depicting a heat dome. The surface temperatures are slightly higher in NCI (31°C–39°C) than in SECI (30°C–37°C). However, the double moist heat dome in SECI has witnessed higher heat stress conditions than NCI. Higher relative humidity in the SECI region is contributed by maritime winds from the Bay of Bengal and Arabian Sea, soil moisture, and so forth. The study highlights the value of atmospheric moisture in differentiating the study regions for heat stress conditions.
... However, all of the earlier cited works lacked novel gridded temperature datasets due to a gridded data shortage for Bangladesh. In contrast, other countries like China (Fan et al. 2022), India (Purnadurga et al. 2018) and the Mediterranean and Sahara region (Babaousmail et al. 2022) are experiencing an analogous warming trend. Interestingly, between the near and far futures and under the SSP2-4.5 scenario, a discernible difference in cold days and cool nights (TX10p/ TN10p) was observed throughout the research region. ...
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... The results obtained from the Student's t-test indicate that there will be an abrupt increase in AMT during the post-2035 period. As the rate of PET is directly associated with temperature (Purnadurga et al. 2017), the hydrological losses over the study area are very likely to increase in the near-term future, which may lead to augment the agricultural water demand. A rise in PET due to warmer conditions can increase aridity in semi-arid regions (Ramarao et al. 2018). ...
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... Despite the complexity of the Indian monsoon system and the abundance of studies investigating its variability [37], an in-depth analysis of multifractal characteristics for temperature datasets of India has never been attempted by researchers. In addition to analyzing the characteristics of mean temperature (T mean ), it is important to examine the characteristics of maximum temperature (T max ), minimum temperature (T min ), and diurnal temperature range (DTR) or T DTR , to fully understand the dynamics of the Indian climatic system. ...
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... Based on the spatio-temporal distribution of surface air temperature, India is classified (https://www.tropmet.res.in/) into seven temperature homogeneous zones (THZ) (Dash and Mamgain 2011;Purnadurga et al. 2017;Kumar and Sarthi 2019;Madhu et al. 2015;Chakraborty et al. 2017). Zones are North West (NW), North Central (NC), West Coast (WC), East Coast (EC), Interior Peninsula (IP), Western Himalaya (WH) and North East (NE) (Fig. 1a). ...
Conference Paper
Heat waves are the extended periods of extremely high surface temperature. Numerous studies have consistently demonstrated that heat waves are becoming more intense, frequent, and longer- lasting both on a regional and global scale. Heat waves pose significant threats to human health, agriculture, ecosystem, economy etc. The impact is so severe in densely populated regions like India. It is imperative to develop effective mitigation strategies to reduce the heat stress exposure. For policymakers, having accurate future projections at the regional level is essential for climate risk management. The present study addresses projected heat stress over India over the seven temperature homogeneous zones (THZ) of India, viz. North West (NW), North Central (NC), West Coast (WC), East Coast (EC), Interior Peninsula (IP), Western Himalaya (WH), and North East (NE). Here we have used the historical (1951-2014) and projections (2015-2100) of the Coupled Model Intercomparison Project phase-6 (CMIP6) under multiple climate change scenarios based on Shared Socioeconomic Pathways (SSP) SSP126, SSP245, SSP370, and SSP585. Studies on the projections are mostly done by the multi-model mean even though wide dispersion exists between the climate models. Here we identified the suitable reliable model for each THZ. The reliability assessment shows the selected model composite showed modest skill than all model composite, in the multiple aspects of observed heat wave features over each zone. The heat stress is assessed by the metric EHF severity. The health hazard is projected to increase in all temperature homogeneous zones, where the number of days with moderate heat stress increases uniformly under all scenarios, while the days with severe heat stress will increase significantly during 2076-2100 (far future) and is maximum under SSP585. Beyond 2051, the moderate heat stress days are likely to increase about 20-30 days in most of the THZ. In the far future, severe heat stress days are projected to increase to 45 days in WC, EC, and IP under SSP585, while NE and NC is about 35 days and in NW and WH the increase is less than 20 days. The days with extreme heat stress are exacerbated in the southern parts of WC, EC, and IP. Currently, southern peninsular India is least impacted to heat waves, however it is likely to be more susceptible to heat exposure in future.
... Over India, rainfall variations owing to diverse geographical, topographical and climatological conditions directly aAect the economy and lives of billions of people because agriculture, the prime source of livelihood, is directly controlled by rainfall distribution (Purnadurga et al. 2018;Zende and Bhagawati 2021). Further, its variations in the form of prolonged dry/wet days and spells during the cropping season negatively aAect agricultural production. ...
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This study explores variations and trends in dry/wet days and spells over Banas River basin during 1961–2020. For this, daily rainfall data acquired from Water Resources Department of Rajasthan, India have been utilised. The variations have been identiBed in relation to number (dry/wet days and spells), mean and maximum length of dry/wet spells. For identifying trends, Mann–Kendall (MK), innovative trend analysis (ITA) and Sen’s slope estimator tests have been executed, whereas change points have been detected with the help of Pettit, Buishand range, standard normal homogeneity and Neumann ratio tests. In Banas River basin, dry days have been found much higher (330 days) as compared to wet (35 days), while the number of dry/wet spells has been found almost identical (18). A decreasing trend in dry days has been detected, whereas wet days have increased. In addition, change point detection tests have detected 1993 as the year of noteworthy change in dry/wet days and spells, whereas most of the stations witnessed such change in 1995 and 2009. These results will be valuable for water resource and risk reduction managers in managing the risks of drought and Cood over the Banas River basin. Keywords. Rainfall; dry spell; wet spell; trend; change point; Rajasthan.
... Based on the spatio-temporal distribution of surface air temperature, India is classified (https://www.tropmet.res.in/) into seven temperature homogeneous zones (THZ) (Dash and Mamgain 2011;Purnadurga et al. 2017;Kumar and Sarthi 2019;Madhu et al. 2015;Chakraborty et al. 2017). Zones are North West (NW), North Central (NC), West Coast (WC), East Coast (EC), Interior Peninsula (IP), Western Himalaya (WH) and North East (NE) (Fig. 1a). ...
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Heat waves (HW) are increasing in intensity, frequency, and duration. The projected changes in the characteristics of HW at the regional level are essential input to develop mitigation strategies to minimize social risks in densely populated regions. In this study, we examine the projected spatiotemporal changes in heat wave characteristics under different climate change scenarios using simulations of Coupled Model Intercomparison Project phase 6 (CMIP6) in seven temperature homogeneous zones of India, i.e., North West (NW), North Central (NC), West Coast (WC), East Coast (EC), Interior Peninsula (IP), Western Himalaya (WH) and North East (NE). The results show that the area of occurrence of a daily maximum temperature above 43 • C is projected to increase about 16-fold over WC, 10-fold over EC, and in other zones in the range of 1-3 fold. The warm days are projected to increase fivefold over WC and threefold over NW, EC, IP, and WH. In India, HW days are projected to increase by 7-8 days in the near future (2025-2050) and by 10-17 days in the far future (2076-2100), while under SSP585 over WH (24 days), NW (19 days), and other zones 12-15 days in the far future. EC and WC are plausible to be more vulnerable under SSP370 and SSP585, with an increase in HW intensity (>1.5 • C). The area of occurrence of long-lasting heat waves over WC is expected to have a drastic increase of more than 20-fold under all scenarios, while increasing 12-fold over IP and 8-fold over NC, EC, and WH under SSP585. The projected HW days will be more intense in the coastal zones and more frequent over WH and NW.
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This study analysed the spatio-temporal variability in the trend of 9 extreme rainfall indices (ERIs) and 4 extreme temperature indices (ETIs) over India, estimated from 1° × 1° resolution daily datasets of 1951–2015 period. The trend analysis showed that 20% of grids exhibited a significant increase in Simple Daily Intensity Index (SDII) series, while Consecutive Wet Day (CWD) index of 32% of the grids exhibited significant reduction. The CWD index showed significant decrease in all the rainfall homogeneous regions of India, while the indices of heavy rainfall (R95p and R99p) and SDII showed significant increase in North East and North West regions. All the ETIs of North West India showed significant rise while the warm and cold spell duration indices showed significant increase in the North Central region and Eastern Coast. The temporal variability of trend analysed about the global climate shift of 1977 revealed that tropical nights of 21% grids with significant reduction prior to the shift showed a significant rise after the shift. In general, a significant change in statistical characteristics in ETI series is noticed after the climatic shift deciphering that the non-stationarity of ETI is more apparent than that of ERI across the Indian mainland.
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Industrialization and urbanization are the most dominant causal factors for long-term changes in surface air temperatures. To examine this fact, the long term changes in the surface-air temperatures have been evaluated by the linear trend for the different periods, i.e. 1901-2013, 1901-1970 and recent period 1971-2013 as rapid industrialization was observed during the recent four decades. In the present study, seasonal and annual mean, maximum and minimum temperature data of 36 stations for the period 1901-2013 have been used. These stations are classified into 4 groups, namely major, medium, small cities and hill stations. During the period 1901-1970, less than 50% stations from each group showed a significant increasing trend in annual mean temperature, whereas in the recent period 1971-2013, more than 80% stations from all the groups except small city group showed a significant increasing trend. The minimum temperature increased faster than that of the maximum temperature over major and medium cities, while maximum temperature increased faster than the minimum temperature over the small cities and hill stations. The annual mean temperature of all the coastal stations showed a significant increasing trend and positive correlation with Precipitable Water Vapour (PWV). The effect of PWV is more pronounced on minimum temperature than that of the maximum.
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Over India, heat waves occur during the summer months of April to June. A gridded daily temperature data set for the period, 1961–2013 has been analyzed to examine the variability and trends in heat waves over India. For identifying heat waves, the Excess Heat Factor (EHF) and 90th percentile of maximum temperatures were used. Over central and northwestern parts of the country, frequency, total duration and maximum duration of heat waves are increasing. Anomalous persistent high with anti-cyclonic flow, supplemented with clear skies and depleted soil moisture are primarily responsible for the occurrence of heat waves over India. Variability of heat waves over India is influenced by both the tropical Indian Ocean and central Pacific SST anomalies. The warming of the tropical Indian Ocean and more frequent El Nino events in future may further lead to more frequent and longer lasting heat waves over India.
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India suffers from major heatwaves during March-June. The rising trend of number of intense heatwaves in recent decades has been vaguely attributed to global warming. Since the heat waves have a serious effect on human mortality, root causes of these heatwaves need to be clarified. Based on the observed patterns and statistical analyses of the maximum temperature variability, we identified two types of heatwaves. The first-type of heatwave over the north-central India is found to be associated with blocking over the North Atlantic. The blocking over North Atlantic results in a cyclonic anomaly west of North Africa at upper levels. The stretching of vorticity generates a Rossby wave source of anomalous Rossby waves near the entrance of the African Jet. The resulting quasi-stationary Rossby wave-train along the Jet has a positive phase over Indian subcontinent causing anomalous sinking motion and thereby heatwave conditions over India. On the other hand, the second-type of heatwave over the coastal eastern India is found to be due to the anomalous Matsuno-Gill response to the anomalous cooling in the Pacific. The Matsuno-Gill response is such that it generates northwesterly anomalies over the landmass reducing the land-sea breeze, resulting in heatwaves. http://www.nature.com/articles/srep24395
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In this study, we present observational evidence, based on satellite aerosol measurements and MERRA reanalysis data for the period 1979–2011, indicating that absorbing aerosols can have strong influence on seasonal-to-interannual variability of the Indian summer monsoon rainfall, including amplification of ENSO effects. We find a significant correlation between ENSO (El Nino Southern Oscillation) and aerosol loading in April–May, with La Nina (El Nino) conditions favoring increased (decreased) aerosol accumulation over northern India, with maximum aerosol optical depth over the Arabian Sea and Northwestern India, indicative of strong concentration of dust aerosols transported from West Asia and Middle East deserts. Composite analyses based on a normalized aerosol index (NAI) show that high concentration of aerosol over northern India in April–May is associated with increased moisture transport, enhanced dynamically induced warming of the upper troposphere over the Tibetan Plateau, and enhanced rainfall over northern India and the Himalayan foothills during May–June, followed by a subsequent suppressed monsoon rainfall over all India, consistent with the elevated heat pump (EHP) hypothesis (Lau et al. in Clim Dyn 26:855–864, 2006. doi:10. 1007/ s00382-006-0114-z). Further analyses from sub-sampling of ENSO years, with normal (<1-σ), and abnormal (>1-σ) NAI over northern India respectively show that the EHP may lead to an amplification of the Indian summer monsoon response to ENSO forcing, particularly with respect to the increased rainfall over the Himalayan foothills, and the warming of the upper troposphere over the Tibetan Plateau. Our results suggest that absorbing aerosol, particular desert dusts can strongly modulate ENSO influence, and possibly play important roles as a feedback agent in climate change in Asian monsoon regions.
Chapter
In this chapter, long-term trends in annual and seasonal surface air temperatures over India for the period 1901–2010 have been examined. During the period 1901–2010, annual mean, maximum, and minimum temperatures averaged over the country as a whole exhibited a significant increasing trend of 0.60, 1.0, and 0.18 °C per hundred years, respectively. Further, the rate of increase in the annual mean temperatures since 1908s is much higher, mainly due to sharp increase in minimum temperatures. For the period 1981–2010, the increase in mean, maximum, and minimum temperatures was almost 0.2 °C per decade. On the seasonal scale, the highest increasing trend in the mean temperatures was observed in the postmonsoon and winter seasons, during the period of 1901–2010 and in the recent 30 years (1981–2010). Maximum and minimum temperatures over India showed an accelerated warming during the recent 30-year period (1981–2010). Further, the rise of the maximum and minimum temperatures, in the recent 30 years, is mostly confined to the northern, central, and eastern/northeastern parts of the country. Peninsular India experienced the least warming during the recent 30-year period (1981–2010). The annual upper-air temperature series for the country as a whole for the period 1971–2007 also showed significant increasing trend at the lower tropospheric levels, viz. 850 and 700 hPa levels.