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RESEARCH ARTICLE
Evidence of asymmetric change in diurnal temperature
range in recent decades over different agro-climatic zones
of India
Rajesh Kumar Mall
1
| Manisha Chaturvedi
1
| Nidhi Singh
1
|
Rajeev Bhatla
2
| Ravi Shankar Singh
2
| Akhilesh Gupta
3
| Dev Niyogi
4,5
1
DST-Mahamana Centre of Excellence in
Climate Change Research, Institute of
Environment and Sustainable
Development, Banaras Hindu University,
Varanasi, 221005, India
2
Department of Geophysics, Banaras
Hindu University, Varanasi, 221005, India
3
Department of Science and Technology,
Ministry of Science and Technology, Govt.
of India, New Delhi, 110 016, India
4
Department of Geological Sciences,
Jackson School of Geosciences, The
University of Texas at Austin, Austin,
Texas
5
Department of Civil, Architectural, and
Environmental Engineering, Cockrell
School of Engineering, The University of
Texas at Austin, Austin, Texas
Correspondence
R. K. Mall, DST-Mahamana Centre of
Excellence in Climate Change Research,
Institute of Environment and Sustainable
Development, Banaras Hindu University,
Varanasi, India.
Email: rkmall@bhu.ac.in
Funding information
Climate Change Programme, Department
of Science and Technology, Grant/Award
Number: DST/CCP/CoE/80/2017(G)
Abstract
Diurnal temperature range (DTR) is an important indicator of climatic change
and a critical thermal metric to assess the impact on agriculture and human
health. This study investigates the seasonal, annual and decadal changes in
the spatio-temporal trend in DTR and air temperatures (maximum: T
max
and
minimum: T
min
) during 1951–2016 and solar radiation (Srad) during
1984–2016 over 14 different agro-climatic zones (ACZs) in India. The changes
in the DTR trend between two time periods:1951–2016 and 1991–2016 (recent
period) are also assessed. The results indicate an overall increasing trend in DTR
(0.038C/decade), T
max
(0.078C/decade, significant), T
min
(0.049C/decade) dur-
ing 1951–2016 and Srad (0.10 MJ/m
2
/day/decade) during 1984–2016. However,
a decreasing trend in DTR (−0.02C/decade) and a significant increasing trend
in T
min
(0.210C/decade) was noted during 1991–2016. The decadal changes
showed an evident decline in DTR during the recent period since 1991. The rela-
tive increase in T
min
(0.21C/decade, significant) compared to T
max
(0.18C/
decade) resulted in a decreasing DTR trend. This was evident across the 5 out of
the 14 agro-climatic zones for the 1991–2016 period. The seasonal analysis
showed a significant (95%) increasing trend in DTR during pre-monsoon and
monsoon (1951–2016), and a negative trend for the post-monsoon and monsoon
since 1991. There were also interesting spatial differences found with the ACZs
in the north-west, parts of Gangetic plain, north-east, and central India
exhibiting negative DTR trends. The effect of Srad is larger on T
max
than T
min
;
therefore, the decrease in Srad in parts of Gangetic plain likely contributed to a
smaller increase in T
max
relative to T
min
and led to a decreasing trend in DTR.
At the same time, the west coast, east coast, and southern region show positive
trends. The observational analysis finds a distinct increase in the T
min
and also
highlights the need for future assessments to continue investigate the causes of
these spatio-temporal changes found in this study.
KEYWORDS
agro-climatic zones, decadal changes, diurnal temperature range
Received: 5 May 2020 Revised: 23 November 2020 Accepted: 22 December 2020
DOI: 10.1002/joc.6978
Int J Climatol. 2021;1–14. wileyonlinelibrary.com/journal/joc © 2020 Royal Meteorological Society 1
1|INTRODUCTION
Across parts of the world, the minimum temperature
(T
min
) is increasing at a much faster rate than maximum
temperature (T
max
) and hence causing diurnal tempera-
ture range (DTR) to decrease (Karl et al., 1991, 1993; Bra-
ganza et al., 2004; He et al., 2015). Past studies related to
DTR changes reported a global decrease of −0.07Cper
decade during 1950–1980 (Vose et al., 2005). In another
study, a significant decreasing trend in global DTR
(−0.036Cdecade
−1
) was reported with a relatively smaller
increase in the T
max
as compared to the T
min
(1.1C
vs. 1.6C) between 1901 and 2014 (Sun et al., 2018).
Despite these global conclusions, the DTR trend is highly
heterogeneous with variable trends found for parts of
northern Eurasia, western North America, Australia, and
the Indian subcontinent (Kumar et al., 1994).
Although DTR has become an important factor for
climate change, few studies have discussed its spatio-
temporal changes and trends. As the changes in T
max
and
T
min
are not uniform, an asymmetry between T
max
and
T
min
can cause an increase/decrease in the DTR (Karl
et al., 1991, 1993; Dai et al., 1999; Zhou et al., 2008).
Importantly, any changes in DTR would lead to an
increase in the risks of drought and heat stress Braganza
et al. (2004) that in turn may add up to cause crop failure
(Mueller and Seneviratne, 2012; Bhatt et al., 2019),
increase morbidity in humans Hirschi et al. (2011) and
mortality rate (Yang et al., 2018). The DTR characteristics
strongly influence public health (He et al., 2015; Yang
et al., 2018; Singh et al., 2020a). Failure to adjust to the
DTR variation might cause increased blood pressure, heart
rate, and oxygen requirement (Lim et al., 2012). A study
conducted for 308 cities in 10 countries showed an
increase in attributable risk fraction to DTR increased
from 2.4% (2.1–2.7%) to 2.7% (2.4–2.9%) between 1972 and
2013 (Lee et al., 2017). A decreasing trend in DTR and a
negative association with mortality was evident in a study
from India (Singh et al., 2019). The health and agronomic
impacts are often regional, and additional studies have
been sought (Lee et al., 2017; Mall et al., 2017; Tyagi
et al., 2019; Singh et al., 2020b; Singh et al., 2021).
A change (lowering) in DTR (often due to high nighttime
temperatures) was found to cause an overall adverse effect
on vegetative growth in maize in the form of a decrease in
total sugars (linear) as well as non-reducing sugars, plant
height, total leaf area, and total biomass accumulation (Mall
et al., 2006; Sunoj et al., 2016). The negative response of high
day and night time temperature on physiological and bio-
chemical processes has been studied for many crops. Exam-
ples exist for wheat-Triticumaestivum (Prasad et al., 2011),
soybean Glycine max (Mall et al., 2006; Djanaguiraman
et al., 2013), sorghum, and rice Oryza sativa (Aggarwal and
Mall, 2002; Lobell and Ortiz-Monasterio, 2007). There are
mixed results about the possibility of future projections and
yields. Lobell et al. (2017) study considered DTR changes
from 11 models (2046–2065) and found an increase in DTR
in wheat-growing areas and a decrease in rice-growing areas.
ThenegativeeffectofDTRhasreducedtheU.S.maizeand
India rice yields. On the other hand, an increase/decrease in
DTR is known to have a beneficial impact on crops where
grain filling and development rates are more sensitive to T
min
than T
max
(Wilkens and Singh, 2001; Singh et al., 2016; Mall
et al., 2018; Sonkar et al., 2019) and where chilling tempera-
tures can cause crop injury or death (Lobell et al., 2006).
There are several additional mechanisms through which
DTR can influence crop development and yield, but the
current understanding is limited.
Recognizing the heterogeneity in the trends for DTR
around the globe, a better understanding is required to
study the impact of regional DTR on crop and human
health. The diurnal asymmetry of temperature over
India, with its active monsoon pattern, is quite different
from the other parts of the world. Studies have reported
an overall increase in DTR with a significant increase in
T
max
relative to the 1901–2003 period (Rai et al., 2012).
For the same period, Kothawale and Rupa (2005) also
reported an increase in annual Tmean (0.05C decade
−1
).
Kumar et al. (1994) studied the DTR changes from 1960
to 1987, while Rai et al. (2012) and Qu et al. (2014)
analysed the same from 1901 to 2003, and the studies
report an increase in DTR annually and seasonally. The
seasonal analysis of DTR showed the highest increase in
winter and lowest in the post-monsoon period.
In different agro-ecological zones that witness wide
diurnal temperature variation, a small relative change in
temperature can have a notable impact plausibly nega-
tive. A study by Vinnarasi et al. (2017) over different cli-
mate zones in India reported an overall 0.36 (C) increase
in mean DTR till 1980 and a decline further. They
reported a positive trend in the west coast and sub-
tropical forest in the north-east and a sound change in
DTR in winter and post-monsoon in the arid desert and
warm-temperate grasslands. Notably, a decrease in DTR
by up to 2C was observed, in places where the increase
in the rate of T
min
was higher than the T
max
was
observed. The changes in DTR were heterogeneous and
highly dependent on the local climatic zone.
We found there has been no study on the decadal
changes in DTR that could show a much clear picture of
the advent of recent warming. Also, it is important from the
context of regional agro-climatic adaptation approaches to
analyse the rate of increase in two different periods, one
that shows the background rate of change and the other
that shows warming in recent times. As discussed, the local
climate zone can change the rate of change through its
2MALL ET AL.
diverse characteristics. Accordingly, in this study, the
changes in DTR over different agro-climatic zones across
India are considered. Keeping the existing research gap and
need, the core of the study seeks to assess the annual sea-
sonal and decadal trends in air temperature (maximum and
minimum) and DTR over 14 different agro-climatic zones
in India for 66 years (1951–2016). The spatio-temporal
trends in DTR and air temperatures for two different
periods, that is, the entire period of 66 years (1951–2016)
and the recent period (1991–2016) are also undertaken.
2|MATERIAL AND METHODS
2.1 |Study area
India covers an area of about 3.28 million sq. km.
between the latitude of 840to 3760N and longitude of
6870to 97250E. India possesses great diversity over
landforms from deep valleys, extensive plains to high
mountains, plateau and coastal Ghats, islands and the
desert. Therefore, the analysis of DTR changes over
14 agro-climatic zones of India is undertaken. Figure 1
shows these agro-climatic zones that have been devel-
oped based on soil, climate and cropping patterns
(Alagh, 1990). Note that one additional zone lies out-
side the mainland and is not considered. The 14 agro-
climatic zones are referred to as Western Himalayan
(WH), Eastern Himalayan (EH), Lower Gangetic
Plain (LGP), Middle Gangetic Plain (MGP), Upper
Gangetic Plain (UGP), Trans Gangetic Plain (TGP),
Eastern Plateaus & Hills (EPH), Central Plateau Hills
Region (CPH), Western Plateau Hills (WPH), Southern
Plateau Hills Region (SPH), East Coast Plains (ECP),
West Coast Plains (WCP), Gujarat Plain Hills (GPH),
Western Dry (WD) regions.
2.2 |Data and methodology
The observed daily minimum and maximum air tempera-
ture data of the past 66 years (1951–2016) is acquired from
India Meteorological Department (IMD) for the study area
and analysed for 1,167 grid boxes at 0.5
0
×0.5
0
resolution.
Initially, the daily air temperature data for the period of
1951–1979 was available at 1x1
resolution, while the
FIGURE 1 Different agro-climatic zones of India [Colour figure can be viewed at wileyonlinelibrary.com]
MALL ET AL.3
data from 1980 to 2016 was available at 0.5 x0.5
.To
obtain a homogeneous, high-resolution temperature dataset
for the entire study period (1951–2016), the 1x1
resolu-
tion was re-gridded to 0.5using a bilinear interpolation
method. Additionally, to understand the possible reasons
for the changes in the temperature patterns, the daily sur-
face Srad data was obtained from the NASA POWER
(Prediction of Worldwide Energy Resources- power.larc.
nasa.gov). This data was at 1resolution for the period
1984–2016. The data has been screened, taking into account
theproblemofmissingdatabydiscardingtheyearswith
monthly records of less than 6 months or excluded grids
where data were missing substantially. Ultimately, data
from 1,099 grids were used in this analysis. The dataset was
divided into four seasons; winter (January–February), pre-
monsoon (March–May), monsoon (June–September) and
post-monsoon (October–December) according to IMD. The
DTR was calculated by subtracting the daily T
min
from the
T
max
at each grid box (DTR =T
max
-T
min
).
These daily DTR, T
max
,T
min
and Srad values were
then averaged on a seasonal and annual basis for further
analysis, including the annual and seasonal trends in the
DTR, T
max
,T
min
and Srad. The trend was also calculated
to assess the spatial variation among different agro-
climatic zones for the two study periods, that is,
1951–2016 (1984–2016 for Srad) and 1991–2016. The
trend was obtained by the ordinary linear least-square
method to calculate the linear trend between time
(1951–2016) and temperature (C) and Srad
(MJ m
−2
day
−1
). Moreover, the decadal analysis of DTR
seasonally and annually was also calculated to cover the
decadal variation in DTR. The trend for the recent past
(1991–2016) was then calculated to review the changes in
DTR. The 1990 threshold corresponds to when rapid
urbanization and economic liberalization saw regional
changes post-1990s in India. The modified Mann-Kendall
test (Hamed and Rao, 1998) using (Kendall's tau and
Sen's slope) were used to detect the change in trend at
95% confidence level (p<.05). The modified Mann Ken-
dall test takes into account the problem of autocorrela-
tion, and thus, the tau and slope value are free of
autocorrelation and data normalization. Pearson's corre-
lation coefficient (r) was calculated to estimate the
strength of the correlation between DTR and T
max
,T
min
,
and Srad. Values at p<.05 were considered significant.
3|RESULT AND DISCUSSION
3.1 |Correlation analysis
Figure S1a–c shows the relation between the annual DTR
with T
max
,T
min
, and Srad. DTR is positively correlated
with T
max
(r=.6, significant) and Srad (r=.2) and nega-
tively correlated with T
min
(r=−0.3, significant). Similar
relationships were found for the upper Second Songhua
River Basin (Wang et al., 2014), Northeast India
(Jhajharia and Singh, 2011), and in lower-elevation sites
in the Swiss Alps (Rebetez and Beniston, 1998). The
effect of Srad is thus more on T
max
than T
min
due to its
presence only during the day (Wang et al., 2014). Thus an
increase in Srad may cause an increase in T
max
and thus
a subsequent increase in DTR.
3.2 |Annual and seasonal trends in air
temperature, Srad, and DTR over India
Figure 2 shows the annual and seasonal variations for
DTR, T
max
, and T
min
over India covering the entire
period of 66 years (1951–2016), the recent period of
26 years (1991–2016) and Srad (1984–2016). The non-
parametric MK trend indicated an overall increase in
DTR by 0.25C for the 66 years (1951–2016), while the
recent 26 years (1991–2016) DTR showed a decrease by
−0.05C (Table 1). Also, the annual linear trend showed
that there is an increase in T
max
and T
min
for both periods
and Srad during 1984–2016. However, it also shows a
(significantly) larger rate of increase in T
min
during the
recent period compared to T
max
, which attributes to the
decline in DTR. Similarly, Vinnarasi et al. (2017) found
an increase in DTR (0.36C) over India during 1951–1980
and then a decline during 1981–2010, primarily due to an
increase in T
min
. Similar findings were noted in the study
by Jhajharia and Singh (2011) and Sun et al. (2018),
highlighting the robustness of the results.
The monsoon and pre-monsoon season show a signif-
icant increase of DTR by 0.07C/decade and 0.07C/
decade, respectively, and a declining trend in the winter
season during 1951–2016 Figure 1. While the recent
period of 1991–2016, DTR showed a small declining trend
during monsoon season (−0.01C/decade) and post-
monsoon season (−0.06C/decade) and a small increase
in winter and pre-monsoon season Figure 2. A relatively
high increase in T
min
and T
max
was noted with a faster
increase in T
min
during the recent period. The post-
monsoon season showed the highest increase in both T
max
and T
min
during 1951–2016, while during the recent period
(1991–2016), T
max
and T
min
showed more increase during
the pre-monsoon season Figure 2. The post-monsoon sea-
son witnessed the highest warming in 1951–2016, which is
different in the recent period where the pre-monsoon sea-
son showed the largest seasonal warming (from 1991
through 2016).
Srad, on the other hand, displayed a mixed effect with
a consistently increasing trend for the monsoon period
4MALL ET AL.
and a decreasing trend during winter for both the periods
(insignificant). However, as the Srad analysis is present
only since 1984, the variation in Srad would be much
more representative of the recent variation in DTR (and
not since 1951). The increasing trend in Srad during
monsoon and the decreasing trend in post-monsoon
could lead to a less negative trend in DTR in monsoon
and a more decreasing trend in DTR in Post monsoon
during 1991–2016. This conclusion is supported by
Jhajharia and Singh (2011) analysis over northeast India.
The decrease in DTR is supported by the increased load-
ing in atmospheric aerosols that reflect solar radiation
and modify cloud properties (e.g., Niyogi et al., 2007;
Roy, 2008; Stjern et al., 2020). The findings are consistent
with those reported by Sun et al. (2018), who reported a
1.5 times higher increase in T
min
than T
max
, which led to
a significant decrease in global DTR between 1901 and
2014 that increased in the first halves of the century and
declined later. A similar finding was reported in the work
of Rai et al. (2012) and Vinnarasi et al. (2017) over India
(though for a smaller and different period). The analysis
by Kothawale et al. (2010), Mondal et al. (2015), and
Jaswal et al. (2016) over India considering the seasonal
changes in DTR showed significant changes during pre-
monsoon, post-monsoon, and winter. Thus the analysis
in this study further confirms and extends the broader
conclusion emerging regarding the warming in the latter
half of the century.
TABLE 1 Annual and seasonal long-term temperature values
over India for the period of 1951–2016 and 1991–2016 (‘*’denote
trends at 95% significance level)
DTR
Maximum
temperature
Minimum
temperature
Annual 1951–16 0.25 0.52* 0.32
1991–16 −0.05 0.47 0.55*
Winter 1951–16 −0.14 0.17 0.33
1991–16 0.10 0.52 0.52
Pre-
monsoon
1951–16 0.44* 0.40* 0.18
1991–16 0.07 0.65 0.65
Monsoon 1951–16 0.48* 0.73* 0.17
1991–16 −0.03 0.16 0.36*
Post-
monsoon
1951–16 0.21 0.86* 0.61*
1991–16 −0.16 0.42* 0.60
Abbreviation: DTR, diurnal temperature range.
FIGURE 2 Annual and seasonal variation of spatially averaged DTR, T
max
,T
min
, and Srad over India with linear time trends. The
values within the graph show the yearly trend for two time periods of 1951–2016 and 1991–2016 [Colour figure can be viewed at
wileyonlinelibrary.com]
MALL ET AL.5
Apart from T
max
and T
min
, Srad and total cloud cover
(TCC) are considered as part of the DTR analysis in the
regional and global analysis (Makowski et al., 2008;
Stjern et al., 2020). The effect of Srad is positive on DTR
because of the apparent effect on daytime T
max
relative to
T
min
due (Wild et al., 2007; Makowski et al., 2008);
whereas, clouds can have a negative effect on DTR as
they reflect sunlight during the day but enhance down-
ward longwave radiation during the night thus causing a
decrease in T
max
but increase in T
min
(Dai et al., 1999;
Zhou et al., 2008; Wang et al., 2014). Other possible fac-
tors that influence DTR are surface soil moisture and pre-
cipitation (Dai et al., 1999), land surface temperature and
land-use changes (Kalnay and Cai, 2003), vegetation and
leaf area index (Collatz et al., 2000), and atmospheric
aerosols (Wang et al., 2014).
Decreasing DTR has several adverse consequences.
For example, a significant reduction in crop yield due to
decreased photosynthetic rate, antioxidant scavenging
capacity, photochemical efficiency, increased respiration,
and carbon loss leading to altered sugar metabolism and
lowered biomass accumulation was observed in winter
wheat and other crops under low DTR and high T
min
exposure (Peng et al., 2004; Lobell et al., 2006; Lobell and
Ortiz-Monasterio, 2007; Matsuda et al., 2014). Further,
the incremental risk in mortality concomitant with the
change in DTR was also reported in different multi-
country and multi-community studies (Carreras
et al., 2015; Lee et al., 2017; Yang et al., 2018; Singh
et al., 2019). In a study by Singh et al. (2019), over
Varanasi city, India, a decrease in DTR was noted to cor-
respond to an increased risk of mortality by 0.61% (95%
CI: 0.25%,1.01%). In the majority of mortality cases, the
leading cause of death is obstructive pulmonary diseases
(Song et al., 2008), coronary heart diseases (Cao et al.,
2009), or cerebrovascular diseases (Smolensky et al.,
2015). It has been suggested that failure to get heat relief,
particularly at night after sustained high day tempera-
ture, that is low DTR, increases the risk of heat-related
mortality (Kovats and Hajat, 2008).
3.3 |DTR trend over diverse agro-
climatic zones
To further understand the temporal evolution of different
temperature metrics and Srad of annual and seasonal
(explained in a subsequent section), the analysis was
repeated and analysed for the 14 different agro-climatic
zones of India Table S1.
3.3.1 |The annual variation
A consistent increasing trend (except for WH, MGP,
UGP, and TGP) was observed for T
max
and T
min
, which
results in variation in DTR trend from the increase of
0.4C per decade (ECP, WCP, and SPH) to decrease to
−0.2C per decade (TGP, UGP, and CPH) across the
zones (1951–2016; Figure 3).
FIGURE 3 Decadal trends of diurnal temperature range over the different agro-climatic zones of India during 1951–2016 and
1991–2016 [Colour figure can be viewed at wileyonlinelibrary.com]
6MALL ET AL.
During the later period of 1991–2016, a quantitative
increase in a negative trend of DTR in major parts of
northern and western India (0–0.6C/decade) was visible,
attributed mainly to the large increase in T
min
relative to
T
max
Figures 4 and 5. However, an increasing trend in
DTR was also noted in most parts of EH and parts of
south India. The significance of trends is assessed at a 5%
significance level, and the zones showing the significant
trends for all variables are shown in Figures S2 and S3
and Table S1. Analysis of T
max
reveals an increasing
trend over most parts of India in both periods. However,
a larger increase in trend was noted for the EH region
(0.4–0.8C/decade) and the peninsular (WPH and SPH)
and south-west coast region (0.2–0.4C/decade) for the
recent period (1991–2016; Figure 4). Unlike T
max
, a large
increase in the T
min
trend at a rate of 0–0.8C/decade
FIGURE 4 Decadal trends of T
max
over the different agro-climatic zones of India during 1951–2016 and 1991–2016 [Colour figure can
be viewed at wileyonlinelibrary.com]
FIGURE 5 Decadal trends of T
min
over the different agro-climatic zones of India during 1951–2016 and 1991–2016 [Colour figure can
be viewed at wileyonlinelibrary.com]
MALL ET AL.7
(a significant increase of 0.3C/decade into Gangetic
plain region) was observed for the recent decades
(1991–2016; Figure 5). A gradual increase in Srad (up to
+0.6 MJ m
−2
/decade from 1951 to 2016 to 1.0 MJ m
−2
/decade during 1991–2016) was also observed for most
parts of India except Himalayan and Gangetic plain
where a declining trend was observed (Figure 6). This
explains the decrease in DTR for the recent warming
period (1991–2016) in which a larger increase in T
min
over the Himalayan region and Gangetic plain was
observed. Our results are in line with the findings of
Sonkar et al. (2019), who reported an overall increasing
trend in T
max
(0.02–0.29C/decade highest over the
southern region) and T
min
(0.16–0.29C/decade) and Srad
(0.013–0.027 MJ m
−2
day
−1
/decade) with a notable
increase in T
min
over northern India. The persistent
warming over the southern and north-western region
likely coincides with the presence of anthropogenic
brown haze that usually absorbs the short-wave solar
radiation (Kulkarni et al., 2012; Ross et al., 2018).
The surface net radiation is also influenced by land
surface changes, which are rapidly underway across
India (Niyogi et al., 2018). The landuse/land cover change
(LULC) primarly modifies the surface albedo which in
turn alters the surface radiative properties and the sur-
face temperatures (Wen-Jian and Hai-Shan, 2013). An
overview of the pathways causing the DTR change due to
LULC is outlined in Pielke et al. (2011). The change in
land surface alters the surface energy balance, which in
turn modifies the daytime maximum temperature as well
as the nocturnal radiative cooling which can alter the
T
min
(Niyogi, 2019). LULC influences regional and spatial
differences in the trend of DTR as shown by Gallo et al.
(1996) for the U.S. Historical Climate Network data, and
by Mohan and Kandya (2015) for Indian airshed as an
example. LULC changes could lead to a decrease in DTR,
which is mainly caused by the reduction in daily maxi-
mum temperature (Wen-Jian and Hai-Shan, 2013). In
general, the LUCC significantly controls the DTR change
through the changes in land evaporation and vegetation
transpiration, which is altered as the land surface charac-
teristics change. In the context of Indian region,
agroclimatic-based LULC- DTR has also been noted for
few locales (e.g., Majumder et al., 2020), and a more com-
prehensive analysis is pending.
3.3.2 |The seasonal variation
In the seasonal analysis, the post-monsoon and winter
season showed a negative trend in DTR primarily over
the northern agro-climatic zones (EH, WD, TGP, UGP,
MGP, and TGP) with values typically varying from −0.1
to −0.4C per decade (Figure 3) and a positive trend in
other regions during 1951–2016. The spatial extent of the
negative trend increased during 1991–2016, and in fact, a
more robust trend was noted with a decrease of almost
1.6C(−0.6C /decade for winter) in UGP and 2C
(−0.83C per decade; post-monsoon) in WD region
(Figure 3). Moreover, the increasing trend in DTR
FIGURE 6 Decadal trends of Srad over the different agro-climatic zones of India during 1984–2016 and 1991–2016 [Colour figure can
be viewed at wileyonlinelibrary.com]
8MALL ET AL.
remained consistent or unchanged for other zones
namely: WH and EH, Peninsular India (WCP, ECP,
WPH, and SPH) during the pre-monsoon season, and
other parts across India have shown unanimous decrease
(Figure 3).
Putting the above results in a broader context, Waqas
and Athar (2019) reported a decrease in DTR over the
Hindukush Karakoram Himalaya region, and Roy and
Balling (2005) found no significant trend over different
regions of India but mostly declining (−0.30 to 0.14C;
1931–2002) trend for the winter season. The increasing
trend in DTR during the post-monsoon season was
found, as stated earlier, in the work of Jhajharia and
Singh (2011) over EH (1976–2000) and by Jamir
et al. (2016) over North-east (EH) and west coast
(1901–2010). The overall seasonal changes in the DTR
trend also follow the conclusions discussed in Kumar
et al. (1994) and Vinnarasi et al. (2017). Other seasons
like pre-monsoon and monsoon also showed a negative
DTR trend for both periods. A quantifiable increase in
negative trend was apparent for 1991–2016 for most of
the zones, including parts of North-west (WD and GPH),
Indo-Gangetic Plain (UGP and MGP), CPH and WPH
during pre-monsoon and monsoon season Figure 3.
The variation in seasonal DTR can be attributed
based on further understanding of the variation in T
max
,
T
min
, and Srad (Figures 4–6). The increasing trend in
T
max
was observed for all the seasons during both
1951–2016 (0.01–0.8C/decade in all seasons) and
1991–2016 (0.78C/decade in EH during winter). How-
ever, the increase in T
max
was weak or declining over
parts of Indo-Gangetic plain (TGP, UGP, and MGP), WD
and CPH, particularly in winter, monsoon and post-
monsoon season 1991–2016. Similarly, like T
max
, there
has been a significant increase in T
min
prominently for
the WD, over EPH during winter, UGP, CPH, GPH, and
WD region during pre-monsoon season and EH, GP, and
WD Region during monsoon and post-monsoon season
with an increase of up to 2C (Figure 5). As the warming
was intense and apparent for all seasons and that the rate
of increase in T
min
is substantially higher than T
max
, the
end outcome is a decrease in DTR. This decrease is more
in recent decades from 1991 to 2016 relative to the
66 years from 1951 to 2016. We found that the unani-
mous increase in both T
min
and T
max
in diverse agro-
climatic zones of India has vastly influenced the evolu-
tion in DTR, where local and regional factors can further
explain the variation at a finer level.
Surface radiation is affected by local variability in
cloudiness and aerosols, and large spatial heterogeneity
was observed for the trend in Srad Figure 6. A consistent
positive trend in all season was reported in different parts
of the western and peninsular region in both time
periods, but a declining trend was prominent in Himala-
yan (1MJm
−2
/decade in 1991–2016 during monsoon)
and Gangetic plain (0.87 MJ m
−2
/decade in monsoon in
Middle Gangetic Plain during 1991–2016). Solar
radiation-clouds-aerosols may impact DTR by altering
radiative flux, modifications in cloud microphysical prop-
erties, the thermal balance of lower atmosphere, and sur-
face insolation. There are reports of the persistence of
thick aerosol layer over the Indo-Gangetic Plain (IGP;
Kumar et al., 2018), that has been indirectly linked to
cloudiness and solar dimming over IGP and rise in T
min
(Padma Kumari and Goswami, 2010). The consistent
decrease in Srad due to a systematic rise in airborne par-
ticulate concentration was reported in several other stud-
ies (Hu et al., 2017). However, a focused topical
investigation is beyond the scope of this study.
3.3.3 |The decadal variation
The spatio-temporal decadal analysis of DTR shows an
increase in DTR from 1951–1960 till 1981–1990 and
decreased after that Figure 5. There is an increase of
about 2C from 1951–1960 to 2010–2016. The increase
was most notable in 1981–1990 and showed a decrease,
particularly in terms of spatial extent in later decades.
The increase has been consistent and followed the same
pattern across the seasons, and the increase was most vis-
ible in grids of northern, central, and western India Fig-
ure 7. The decrease in DTR in recent decades over a
larger part of India and more specific to the north-west
(WD and TGP) and CPH, is consistent with the broader
regions reported in Zhou et al. (2007, 2008) and appears
to be part of large-scale climatic changes. Chen and
Dirmeyer (2019) recently summarized that the climate
forcing from LULC exerts relatively strong impacts on
hot extremes and DTR compared with other anthropo-
genic forcings. A number of studies indicate that LULC
alter the energy and water cycles, thus contribute signifi-
cantly to the changes in the climate variables such as
maximum and minimum temperature, evapotraspiration
and hence the DTR (Kishtawal et al., 2010; Niyogi
et al., 2011; Mohan and Kandya, 2015; Shen et al., 2017).
Nayak and Mandal (2019a) studied and shows that even
though the LULC contributed towards overall cooling
during 1981–2006 over India, it contributed towards
warming during 1991–2006. In a study, Nayak and
Mandal (2012) highlighted that LULC over Western
India contributed to warming by 0.06C per decade
mainly due to the decrease of forests and increase of agri-
cultural lands. In another study, Nayak and Mandal
(2019b) find LULC over Eastern India contributed
towards the warming at a rate of 0.2C per decade due
MALL ET AL.9
to the conversions of shrubs/agricultural/fallow land into
bare land. Thus, there is a clear signal of LULC feedback
on the DTR changes, which remains to be systematically
extracted in the context of the agroclimatic zones across
and will be reported in a follow up study.
Considering the likely projected (2.6C) rise in
global surface temperature in the mid-century
(IPCC, 2013), and about 2.9C (under RCP 4.5, Rao
et al., 2016) for India by 2,100, there is a widespread chal-
lenge to precisely quantify the extent of adverse impact
due to change in DTR in several dimensions of
agriculture, water, and health (Krishnan et al., 2020). The
above findings indicate that the change in DTR is region-
ally heterogeneous and necessitates investigations of fac-
tors that influence DTR at the regional scale.
4|CONCLUSIONS
The study found an overall increasing trend of DTR dur-
ing 1951–2016, and a decreasing trend during the recent
period 1991–2016 across the different agro-climatic zones
FIGURE 7 Decadal spatial
variation for diurnal temperature range
during 1951–2016 [Colour figure can be
viewed at wileyonlinelibrary.com]
10 MALL ET AL.
in India. For recent decades, the decreasing DTR trend is
primarily because of the relatively faster increase in T
min
relative to the T
max
and the Srad.
The results also show distinct spatial and temporal
variations in the DTR trends. The monsoon and pre-
monsoon seasons show significantly increasing DTR
trend during 1951–2016 and decreasing trend during the
recent period 1991–2016, while winter season has a
decreasing DTR trend during 1951–2016 and increasing
trend during the recent period. The DTR over the SPH,
WCP, ECP, and EH region showed an increasing trend in
all seasons during both the period (1951–2016 and
1991–2016). A significant declining trend in DTR was
noted for parts of Gangetic Plain, WD, and CPH. The
DTR trends and rates over all other regions differ not just
in value but also for the seasonal variations.
The rise in nighttime temperature (T
min
) may affect
the plant growth and grain formation (Peraudeau
et al., 2015; Sonkar et al., 2019), and the decreasing trend
of DTR may cause an increase in mortality rate (Lee
et al., 2017; Singh et al., 2019). Though the effects from
DTR changes may not be evident in the near short term,
the higher intensity of change necessitates its consider-
ation in developing sustainable agro-climatic and biocli-
matic assessments to help the inhabitants to better adapt
to these changes (Mall et al., 2019). Although the analysis
of T
max
,T
min
, and Srad can help to understand DTR
trends, it would be interesting to investigate other factors
related to temporal and spatial changes.
Atmospheric aerosols may be causing a decrease in
DTR and modify cloud properties. Clouds reflect the
incoming solar radiation during day time and enhance the
downward longwave radiations towards earth at night,
thus decreasing T
max
in the day and increase T
min
at night
(Zhou et al., 2007). On the other hand, land surface change
also influences surface energy and hydrological balance
and, in turn, may cause a decline in DTR (Zhou
et al., 2007; Wang et al., 2014; Pielke et al., 2007, 2011;
Niyogi, 2019). Future studies need to address these interac-
tions and synthesize the spatiotemporal patterns noted in
the DTR trends across the Indian monsoon region.
ACKNOWLEDGEMENTS
Authors thankfully acknowledge India Meteorological
Department, New Delhi, for providing observed air tem-
perature data used in the study. Authors also thank the
Climate Change Programme, Department of Science and
Technology, New Delhi, for financial support (DST/CCP/
CoE/80/2017(G)).
CONFLICT OF INTEREST
Theauthorsdeclarethattheyhavenoconflictof
interest.
ORCID
Rajesh Kumar Mall https://orcid.org/0000-0002-3118-
096X
Rajeev Bhatla https://orcid.org/0000-0002-8031-8857
REFERENCES
Aggarwal, P.K. and Mall, R.K. (2002) Climate change and rice
yields in diverse agro-environments of India. II. Effect of uncer-
tainties in scenarios and crop models on impact assessment.
Climatic Change, 52(3), 331–343.
Alagh, Y.K. (1990) Agro-climatic planning and regional development.
Indian Journal of Agricultural Economics.,45(3),244–268.
Bhatt, D., Sonkar, G. and Mall, R.K. (2019) Impact of climate vari-
ability on the rice yield in Uttar Pradesh: an agro-climatic zone
based study. Environmental Processes, 6(1), 135–153. https://
doi.org/10.1007/s40710-019-00360-3.
Braganza, K., Karoly, D.J. and Arblaster, J. (2004) Diurnal tempera-
ture range as an index of global climate change during the
twentieth century. Geophysical Research Letters, 31, L13217.
https://doi.org/10.1029/2004GL019998.
Cao, J., Cheng, Y., Zhao, N., Song, W., Jiang, C., Chen, R. and
Kan, H. (2009) Diurnal temperature range is a risk factor for
coronary heart disease death. Journal of Epidemiology, 19,
328–332.
Carreras, H., Zanobetti, A. and Koutrakis, P. (2015) Effect of daily
temperature range on respiratory health in Argentina and its
modification by impaired socio-economic conditions and PM10
exposures. Environmental Pollution, 206, 175–182.
Chen, L. and Dirmeyer, P.A. (2019) The relative importance among
anthropogenic forcings of land use/land cover change in affect-
ing temperature extremes. Climate Dynamics, 52(3–4),
2269–2285. https://doi.org/10.1007/s00382-018-4250-z.
Collatz, G.J., Bounoua, L., Los, S.O., Randall, D.A., Fung, I.Y. and
Sellers, P.J. (2000) A mechanism for the influence of vegetation
on the response of the diurnal temperature range to changing
climate. Geophysical Research Letters, 27, 273381–273384.
https://doi.org/10.1029/1999GL010947.
Dai, A., Trenberth, K.E. and Karl, T.R. (1999) Effects of clouds,
soilmoisture, precipitation, and water vapor on diurnal temper-
ature range. Journal of Climate, 12, 122451–122473.
Djanaguiraman, M., Prasad, P.V.V. and Schapaugh, W.T. (2013)
High day or nighttime temperature alters leaf assimilation
reproductive success, and phosphatidic acid of pollen grain in
soybean [Glycine max (L.) Merr.]. Crop Science, 53, 1594–1604.
https://doi.org/10.2135/cropsci2012.07.0441.
Gallo, K.P., Easterling, D.R. and Peterson, T.C. (1996) The influence
of land use/land cover on climatological values of the diurnal
temperature range. Journal of climate, 9(11), 2941–2944.
Hamed, K.H. and Rao, A.R. (1998) A modified Mann–Kendall trend
test for autocorrelated data. Journal of Hydrology, 204, 182–196.
He, B., Huang, L. and Wang, Q. (2015) Precipitation deficits
increase high diurnal temperature range extremes. Scientific
Reports, 5, 12004. https://doi.org/10.1038/srep12004.
Hirschi, M., Seneviratne, S.I., Alexandrov, V., Boberg, F.,
Boroneant, C., Christensen, O.B., Formayer, H., Orlowsky, B.
and Stepanek, P. (2011) Observational evidence for soil-
moisture impact on hot extremes in southeastern Europe.
Nature Geoscience, 4, 417–421. https://doi.org/10.1038/
NGEO1032.
MALL ET AL.11
Hu, B., Zhao, X., Liu, H., Liu, Z., Song, T., Wang, Y., Xia, X.,
Tang, G., Ji, D., Wen, T., Wang, L. and Xin, J. (2017) Quantifi-
cation of the impact of aerosol on broadband solar radiation in
North China. Scientific Reports, 7, 44851. https://doi.org/10.
1038/srep44851.
IPCC. (2013) Climate change: the physical science basis. In:
Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.)
Contribution of Working Group I to the Fifth Assessment Report
of the Intergovernmental Panel on Climate Change. Cambridge
University Press; Cambridge.
Jamir, T., Gadgil, A., De, U. and Krishna Kumar, G. (2016) Temper-
ature patterns over northeast and west coast regions of India.
Bulletin of Geography Phys Geogr, 10, 1051–1063. https://doi.
org/10.1515/bgeo-2016-0004.
Jaswal, A.K., Kore, P.A. and Singh, V. (2016) Trends in diurnal tem-
perature range over India (1961-2010) and their relationship
with low cloud cover and rainy days. Journal of Climatic
Change, 2, 235–255. https://doi.org/10.3233/jcc-160016.
Jhajharia, D. and Singh, V.P. (2011) Trends in temperature, diurnal
temperature range and sunshine duration in Northeast India.
International Journal of Climatology, 31, 1353–1367.
Kalnay, E. and Cai, M. (2003) Erratum: corrigendum: impact of
urbanization and land-use change on climate. Nature, 425,
425102–425102. https://doi.org/10.1038/nature01952.
Karl, T.R., Jones, P.D., Knight, R.W., Kukla, G., Plummer, N.,
Razuvayev, V., Gallo, K.P., Lindseay, J., Charlson, R.J. and
Peterson, T.C. (1993) Asymmetric trends of daily maximum
and minimum temperature. Bulletin of the American Meteoro-
logical Society, 74, 1007–1023. https://doi.org/10.1175/1520-
0477(1993)074<1007:ANPORG>2.0.CO;2.
Karl, T.R., Kukla, G., Razuvayev, V.N., Changery, M.J., Quayle, R.
G., Heim, R.R., Easterling, D.R. and Fu, C.B. (1991) Global
warming: evidence for asymmetric diurnal temperature change.
Geophysical Research Letters, 18, 182253–182256. https://doi.
org/10.1029/91GL02900.
Kishtawal, C.M., Niyogi, D., Tewari, M., Pielke, R.A. and
Shepherd, J.M. (2010) Urbanization signature in the observed
heavy rainfall climatology over India. International Journal of
Climatology, 30, 1908–1916.
Kothawale, D.R., Munot, A.A. and Kumar, K.K. (2010) Surface
air temperature variability over India during 1901–2007, and
its association with ENSO. Climate Research, 42, 89–104.
Retrieved from https://www.researchgate.net/publication/
225357462.
Kothawale, D.R. and Rupa, K.K. (2005) On the recent changes in
surface temperature trends over India. Geophysical Research
Letters, 32, L18714. https://doi.org/10.1029/2005GL023528.
Kovats, R.S. and Hajat, S. (2008) Heat stress and public health: a
critical review. Annual Review of Public Health, 29, 41–55.
Krishnan, R., Sanjay, J., Gnanaseelan, C., Mujumdar, M.,
Kulkarni, A. and Chakraborty, S. (2020) Assessment of Climate
Change over the Indian Region: A Report of the Ministry of Earth
Sciences (MoES). New Delhi, India: Government of India, 1–
226. https://doi.org/10.1007/978-981-15-4327-2.
Kulkarni, J.R., Maheskumar, R.S., Morwal, S.B., Padma Kumari, B.,
Konwar, M., Deshpande, C.G., Joshi, R.R., Bhalwankar, R.V.,
Pandithurai, G., Safai, P.D. and Narkhedkar, S.G. (2012) The
cloud aerosol interactions and precipitation enhancement
experiment (CAIPEEX): overview and preliminary results. Cur-
rent Science, 102(3), 413–425.
Kumar,K.R.,Kumar,K.K.andPant,G.B.(1994)Diurnalasym-
metry of surface temperature trends over India. Geophysical
Research Letters, 21, 21677–21680. https://doi.org/10.1029/
94GL00007.
Kumar, M., Parmar, K.S., Kumar, D.B., Mhawish, A., Broday, D.M.,
Mall, R.K. and Banerjee, T. (2018) Long-term aerosol climatol-
ogy over Indo-Gangetic Plain: trend, prediction and potential
source fields. Atmospheric Environment, 180, 37–50.
Lee, W.H., Lim, Y.H., Dang, T.N., Seposo, X., Honda, Y., Guo, Y.L.
L., Jang, H.M. and Kim, H. (2017) An investigation on attri-
butes of ambient temperature and diurnal temperature range
on mortality in five East-Asian countries. Scientific Reports,7,
10207–10130. https://doi.org/10.1016/j.envint.2017.10.018.
Lim, Y.H., Hong, Y.C. and Kim, H. (2012) Effects of diurnal temper-
ature range on cardiovascular and respiratory hospital admis-
sions in Korea. Science of the Total Environment, 57, 597–603.
Lobell, D.B. and Asseng, S. (2017) Comparing estimates of climate
change impacts from process-based and statistical crop models.
Environmental Research Letters, 12(1), 015001.
Lobell, D.B., Field, C.B., Cahill, K.N. and Bonfils, C. (2006) Impacts
of future climate change on California perennial crop yields:
modelprojections with climate and crop uncertainties. Agricul-
tural and Forest Meteorology, 141, 208–218.
Lobell, D.B. and Ortiz-Monasterio, J.I. (2007) Impacts of day versus
night temperatures on spring wheat yields: a comparison of
empirical and ceres model predictions in three locations.
Agronomy Journal, 99, 469–477.
Majumder, A., Kingra, P.K., Setia, R., Singh, S.P. and Pateriya, B.
(2020) Influence of land use/land cover changes on surface
temperature and its effect on crop yield in different agro-
climatic regions of Indian Punjab. Geocarto International, 35(6),
663–686. https://doi.org/10.1080/10106049.2018.1520927.
Makowski, K., Wild, M. and Ohmura, A. (2008) Diurnal tempera-
ture range over Europe between 1950 and 2005. Atmospheric
Chemistry and Physics, 34, 86483–86498. https://doi.org/10.
1007/s13593-013-0179-0.
Mall, R.K., Singh, N., Prasad, R., Tompkins, A. and Gupta, A.
(2017) Impact of climate variability on human health: a pilot
study in tertiary care hospital of eastern Uttar Pradesh, India.
Mausam, 68(3), 429–438.
Mall, R.K., Singh, N., Singh, K.K., Sonkar, G. and Gupta, A. (2018)
Evaluating the performance of RegCM4. 0 climate model for
climate change impact assessment on wheat and rice crop in
diverse agro-climatic zones of Uttar Pradesh, India. Climatic
Change, 149(3–4), 503–515.
Mall, R.K., Singh, R., Gupta, A., Srinivasan, G. and Rathore, L.S.
(2006) Impact of climate change on Indian agriculture: a
review. Climatic Change, 78(2–4), 445–478.
Mall, R.K., Srivastava, R.K., Banerjee, T., Mishra, O.P., Bhatt, D.
and Sonkar, G. (2019) Disaster risk reduction including climate
change adaptation over South Asia: challenges and ways for-
ward. International Journal of Disaster Risk Science, 10(1),
14–27. https://doi.org/10.1007/s13753-018-0210-9.
Matsuda, R., Ozawa, N. and Fujiwara, K. (2014) Leaf photosynthe-
sis plant growth, and carbohydrate accumulation of tomato
under different photoperiods and diurnal temperature differ-
ences. Scientia Horticulturae, 170, 150–158.
12 MALL ET AL.
Mishra, D., Shekhar, S., Agrawal, L., Chakraborty, S. and
Chakraborty, N. (2016) Cultivar-specific high temperature
stress responses in bread wheat (Triticum aestivum L.) associ-
ated with physicochemical traits and defense pathways. Food
Chemistry, 221, 1077–1087. https://doi.org/10.1016/j.foodchem.
2016.11.053.
Mohan, M. and Kandya, A. (2015) Impact of urbanization and land-
use/land-cover change on diurnal temperature range: a case
study of tropical urban airshed of India using remote sensing
data. Science of the Total Environment, 506, 453–465.
Mondal, A., Khare, D. and Kundu, S. (2015) Spatial and temporal
analysis of rainfall and temperature trend of India. Theoretical
and Applied Climatology, 122, 122143–122158. https://doi.org/
10.1007/s00704-014-1283-z.
Mueller, B. and Seneviratne, S.I. (2012) Hot days induced by precip-
itation deficits at the global scale. Proceedings of the National
Academy of Sciences of the United States of America, 109,
12398–12403.
Nayak, S. and Mandal, M. (2012) Impact of land use and land cover
change on temperature trends over Western India. Current Sci-
ence, 102(8), 1166–1173.
Nayak, S. and Mandal, M. (2019a) Impact of land use and land
cover changes on temperature trends over India. Land Use Pol-
icy, 89, 104238. https://doi.org/10.1016/j.landusepol.2019.
104238.
Nayak, S. and Mandal, M. (2019b) Examining the impact of
regional land use and land cover changes on temperature: the
case of eastern India. Spatial Information Research,89
(104238), 1–11.
Niyogi, D. (2019). Land surface processes. In: Randall, D.,
Srinivasan, J., Nanjundiah, R., Mukhopadhyay, P. (Eds.) Cur-
rent Trends in the Representation of Physical Processes in
Weather and Climate Models. Springer Atmospheric Sciences.
Springer, Singapore
Niyogi, D., Chang, H.I., Chen, F., Gu, L., Kumar, A., Menon, S. and
Pielke, R.A. (2007) Potential impacts of aerosol–land–
atmosphere interactions on the Indian monsoonal rainfall char-
acteristics. Natural Hazards, 42(2), 345–359.
Niyogi, D., Pyle, P., Lei, M., Arya, S.P., Kishtawal, C.M.,
Shepherd, M., Chen, F. and Wolfe, B. (2011) Urban modifica-
tion of thunderstorms: an observational storm climatology and
model case study for the Indianapolis urban region. Journal of
Applied Meteorology and Climatology, 50, 1129–1144.
Niyogi, D., Subramanian, S., Mohanty, U.C., Kishtawal, C.M.,
Ghosh, S., Nair, U.S., EK, M. and Rajeevan, M. (2018) The
impact of land cover and land use change on the Indian mon-
soon region hydroclimate. In Land-atmospheric research appli-
cations in South and Southeast Asia. Cham: Springer, 553–575.
Padma Kumari, B. and Goswami, B.N. (2010) Seminal role of clouds
on solar dimming over the Indian monsoon region. Geophysical
Research Letters, 37, L06703. https://doi.org/10.1029/
2009GL042133.
Peng, S., Huang, J., Sheehy, J., Laza, R., Visperas, R., Zhong, X.,
Centeno, G., Khush, G. and Cassman, K. (2004) Rice yields
decline with higher night temperature from global warming.
Proceedings of the National Academy of Sciences of the United
States of America, 101, 9971–9975.
Peraudeau, S., Lafarge, T., Roques, S., Quinones, C.O., Clement-
Vidal, A., Ouwerkerk, P.B.F., Jeroen Van Rie, J., Fabre, D.,
Jagadish, S.V.K. and Dingkuhn, M. (2015) Effect of carbohy-
drates and night temperature on night respiration in rice. Jour-
nal of Experimental Botany, 66, 3931–3944.
Pielke Sr, R.A., Adegoke, J., BeltraáN-Przekurat, A., Hiemstra, C.
A., Lin, J., Nair, U.S. and Nobis, T.E. (2007) An overview of
regional land-use and land-cover impacts on rainfall. Tellus B:
Chemical and Physical Meteorology, 59(3), 587–601.
Pielke Sr, R.A., Pitman, A., Niyogi, D., Mahmood, R., McAlpine, C.,
Hossain, F. and Reichstein, M. (2011) Land use/land cover
changes and climate: modeling analysis and observational evi-
dence. Wiley Interdisciplinary Reviews: Climate Change, 2(6),
828–850.
Prasad, P.V.V., Pisipati, S.R., Momcilovic, I. and Ristic, Z. (2011)
Independent and combined effects of high temperature and
drought stress during grain filling on plant yield and chloro-
plast EF-Tu expression in spring wheat. Journal of Agronomy
and Crop Science, 197, 430–441.
Qu, M., Wan, J. and Hao, X. (2014) Analysis of diurnal air temperature
range change in the continental United States. Weather and Cli-
mate Extremes,4,86–95. https://doi.org/10.1016/j.wace.2014.05.002.
Rai, A., Joshi, M.K. and Pandey, A.C. (2012) Variations in diurnal
temperature range over India: under global warming scenario.
Journal of Geophysical Research: Atmospheres, 117, D02114.
https://doi.org/10.1029/2011JD016697.
Rao, C.S., Gopinath, K.A., Prasad, J.V.N.S. and Singh, A.K. (2016)
Climate resilient villages for sustainable food security in tropi-
cal India: concept, process, technologies, institutions, and
impacts. In: Sparks, Donald L., (Ed.), Advances in Agronomy,
Vol. 140. London: Academic Press, pp. 101–214.
Rebetez, M. and Beniston, M. (1998) Changes in sunshine duration
are correlated with changes in daily temperature range this
century: an analysis of Swiss climatological data. Geophysical
Research Letters, 25(19), 3611–3613.
Ross, R.S., Krishnamurti, T.N., Pattnaik, S. and Pai, D.S. (2018)
Decadal surface temperature trends in India based on a new
high-resolution data set. Scientific Reports, 8(1), 7452.
Roy, S. (2008) Impact of aerosol optical depth on seasonal tempera-
tures in India: a spatio-temporal analysis. International Journal
of Remote Sensing, 29(3), 727–740.
Roy, S. and Balling, R. (2005) Analysis of trends in maximum and
minimum temperature, diurnal temperature range, and cloud
cover over India. Geophysical Research Letters, 32, L12702.
https://doi.org/10.1029/2004GL022201.
Shen, X., Liu, B. and Lu, X. (2017) Effects of land use/land cover on
diurnal temperature range in the temperate grassland region of
China. Science of the Total Environment, 575, 1211–1218.
Singh, H., Singh, N. and Mall, R.K. (2020b) Japanese encephalitis
and associated environmental risk factors in eastern Uttar
Pradesh: a time series analysis from 2001 to 2016. Acta Tropica,
212, 105701. https://doi.org/10.1016/j.actatropica.2020.105701.
Singh, N., Mhawishb, A., Ghoshc, S., Banerjee, A. and Mall, R.K.
(2019) Attributing mortality from temperature extremes: a time
series analysis in Varanasi, India. Science of the Total Environ-
ment, 665, 665453–665464. https://doi.org/10.1016/j.scitotenv.
2019.02.074.
Singh, P.K., Singh, K.K., Rathore, L.S., Baxla, A.K., Bhan, S.C.,
Gupta, A. and Mall, R.K. (2016) Rice (Oryza sativa L.) yield gap
using the CERES-rice model of climate variability for different
agroclimatic zones of India. Current Science, 110(3), 405–413.
MALL ET AL.13
Singh, S., Mall, R.K., Dadich, J., Verma, S., Singh, J.V. and Gupta, A.
(2021) Evaluation of CORDEX- South Asia regional climate models
for heat wave simulations over India. Atmospheric Research, 248,
105228. https://doi.org/10.1016/j.atmosres.2020.105228.
Singh, S., Mall, R.K. and Singh, N. (2020a) Changing spatio-
temporal trends of heat wave and severe heat wave events over
India: an emerging health hazard. International Journal of Cli-
matology,1–15. https://doi.org/10.1002/joc.6814.
Smolensky, M.H., Portaluppi, F., Manfredini, R., Hermida, R.C.,
Tiseo, R., Sackett-Lundeen, L.L. and Haus, E.L. (2015) Diurnal
and twenty-four hour patterning of human diseases: cardiac,
vascular, and respiratory diseases, conditions, and syndromes.
Sleep Medicine Reviews, 21, 3–11.
Song, G., Chen, G., Jiang, L., Zhang, Y., Zhao, N., Chen, B. and
Kan, H. (2008) Diurnal temperature range as a novel risk factor
for COPD death. Respirology, 13, 1066–1069.
Sonkar, G., Mall, R.K., Banerjee, T., Singh, N., Kumar, T.L. and
Chand, R. (2019) Vulnerability of Indian wheat against rising
temperature and aerosols. Environmental Pollution, 254,
112946. https://doi.org/10.1016/j.envpol.2019.07.114.
Stjern, C.W., Samset, B., Boucher, O., Iversen, T., Lamarque, J.F.,
Myhre, G., Shindell, D. and Takemura, T. (2020) How aerosols
and greenhouse gases influence the diurnal temperature range.
Atmospheric Chemistry and Physics Discussions, 20, 13467–
13480.
Sun, X., Ren, G., You, Q., Ren, Y., Xu, W., Xue, X. and Zhang, P.
(2018) Global diurnal temperature range (DTR) changes since
1901. Climate Dynamics, 52, 1–14. https://doi.org/10.1007/
s00382-018-4329-6.
Sunoj, V.S.J., Shroyer, K.J., Jagadish, S.V.K. and Prasad, P.V.V.
(2016) Diurnal temperature amplitude alters physiological and
growth response of maize (Zea mays L.) during the vegetative
stage. Journal of Experimental Botany, 130, 113–121. https://
doi.org/10.1016/j.envexpbot.2016.04.007.
Tyagi, S., Singh, N., Sonkar, G. and Mall, R.K. (2019) Sensitivity of
evapotranspiration to climate change using DSSAT model in
sub humid climate region of eastern Uttar Pradesh. Modelling
Earth Systems and Environment,5,1–11. https://doi.org/10.
1007/s40808-018-0513-2.
Vinnarasi, R., Dhanya, C.T., Chakravorty, A. and Aghakouchak, A.
(2017) Unravelling diurnal asymmetry of surface temperature
in different climate zones. Scientific Reports,7,71–78. https://
doi.org/10.1038/s41598-017-07627-5.
Vose, R.S., Easterling, D.R. and Gleason, B. (2005) Maximum and
minimum temperature trends for the globe: an update through
2004. Geophysical Research Letters, 32, L23822. https://doi.org/
10.1175/JAMC-D-13-0248.1.
Wang, F., Zhang, C., Peng, Y. and Zhou, H. (2014) Diurnal temper-
ature range variation and its causes in a semiarid region from
1957 to 2006. International Journal of Climatology, 34, 343–354.
https://doi.org/10.1002/joc.3690.
Waqas, A. and Athar, H. (2019) Observed diurnal temperature
range variations and its association with observed cloud cover
in northern Pakistan. International Journal of Climatology, 38,
3323–3336. https://doi.org/10.1002/joc.5503.
Wen-Jian, H. and Hai-Shan, C. (2013) Impacts of regional-scale
land use/land cover change on diurnal temperature range.
Advances in Climate Change Research, 4(3), 166–172. https://
doi.org/10.3724/SP.J.1248.2013.166.
Wild, M., Ohmura, A. and Makowski, K. (2007) Impact of global
dimming and brightening on global warming. Geophysical
Research Letters, 34, L04702. https://doi.org/10.1029/
2006gl028031.
Wilkens, P. and Singh, U. (2001) A code-level analysis for tempera-
ture effects in the CERES models. In: White, J (Ed.), Modeling
Temperature Response in Wheat and Maize. CIMMYT, El Batan,
Mexico, pp. 34181–34198
Yang, J., Zhou, M., Li, M., Yin, P., Wang, B., Pilot, E., Liu, Y., Van
der Hoek, W., Van Asten, L., Krafft, T. and Liu, Q. (2018) Diur-
nal temperature range in relation to death from stroke in
China. Environmental Research, 164, 669–675.
Zhou, L., Dickinson, R., Dirmeyer, P., Chen, H., Dai, Y. and
Tian, Y. (2008) Asymmetric response of maximum and mini-
mum temperatures to soil emissivity change over the northern
African Sahel in a GCM. Geophysical Research Letters, 35, 1–6.
https://doi.org/10.1029/2007GL032953.
Zhou,L.,Dickinson,R.E.,Tian,Y.,Vose,R.S.andDai,Y.(2007)
Impact of vegetation removal and soil aridation on diurnal
temperature range in a semiarid region: application to the
Sahel. Proceedings of the National Academy of Sciences of the
United States of America, 32, 429–440. https://doi.org/10.
1073/pnas.0700290104.
SUPPORTING INFORMATION
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in the Supporting Information section at the end of this
article.
How to cite this article: Mall RK, Chaturvedi M,
Singh N, et al. Evidence of asymmetric change in
diurnal temperature range in recent decades over
different agro-climatic zones of India. Int
J Climatol. 2021;1–14. https://doi.org/10.1002/
joc.6978
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