ArticlePDF Available

Evidence of asymmetric change in Diurnal Temperature Range in recent decades over different Agro‐climatic Zones of India



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: Tmax and minimum: Tmin) during 1951‐2016 and solar radiation (Srad) during 1984‐2016 over fourteen 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 (0.038°C/decade), Tmax (0.078°C/decade, significant), Tmin (0.049°C/decade) during 1951‐2016 and Srad (0.10 MJ/m2/day/decade) during 1984‐2016 . However, a decreasing trend in DTR (‐0.02°C/decade) and a significant increasing trend in Tmin (0.210°C/decade) was noted during 1991‐2016. The decadal changes showed an evident decline in DTR during the recent period since 1991. The relative increase in Tmin (0.21°C /decade, significant) compared to Tmax (0.18°C /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 Tmax than Tmin; therefore, the decrease in Srad in parts of Gangetic plain likely contributed to a smaller increase in Tmax relative to Tmin 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 Tmin and also highlights the need for future assessments to continue investigate the causes of these spatio‐temporal changes found in this study.
Evidence of asymmetric change in diurnal temperature
range in recent decades over different agro-climatic zones
of India
Rajesh Kumar Mall
| Manisha Chaturvedi
| Nidhi Singh
Rajeev Bhatla
| Ravi Shankar Singh
| Akhilesh Gupta
| Dev Niyogi
DST-Mahamana Centre of Excellence in
Climate Change Research, Institute of
Environment and Sustainable
Development, Banaras Hindu University,
Varanasi, 221005, India
Department of Geophysics, Banaras
Hindu University, Varanasi, 221005, India
Department of Science and Technology,
Ministry of Science and Technology, Govt.
of India, New Delhi, 110 016, India
Department of Geological Sciences,
Jackson School of Geosciences, The
University of Texas at Austin, Austin,
Department of Civil, Architectural, and
Environmental Engineering, Cockrell
School of Engineering, The University of
Texas at Austin, Austin, Texas
R. K. Mall, DST-Mahamana Centre of
Excellence in Climate Change Research,
Institute of Environment and Sustainable
Development, Banaras Hindu University,
Varanasi, India.
Funding information
Climate Change Programme, Department
of Science and Technology, Grant/Award
Number: DST/CCP/CoE/80/2017(G)
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
minimum: T
) during 19512016 and solar radiation (Srad) during
19842016 over 14 different agro-climatic zones (ACZs) in India. The changes
in the DTR trend between two time periods:19512016 and 19912016 (recent
period) are also assessed. The results indicate an overall increasing trend in DTR
(0.038C/decade), T
(0.078C/decade, significant), T
(0.049C/decade) dur-
ing 19512016 and Srad (0.10 MJ/m
/day/decade) during 19842016. However,
a decreasing trend in DTR (0.02C/decade) and a significant increasing trend
in T
(0.210C/decade) was noted during 19912016. The decadal changes
showed an evident decline in DTR during the recent period since 1991. The rela-
tive increase in T
(0.21C/decade, significant) compared to T
decade) resulted in a decreasing DTR trend. This was evident across the 5 out of
the 14 agro-climatic zones for the 19912016 period. The seasonal analysis
showed a significant (95%) increasing trend in DTR during pre-monsoon and
monsoon (19512016), 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
than T
therefore, the decrease in Srad in parts of Gangetic plain likely contributed to a
smaller increase in T
relative to T
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
and also
highlights the need for future assessments to continue investigate the causes of
these spatio-temporal changes found in this study.
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;114. © 2020 Royal Meteorological Society 1
Across parts of the world, the minimum temperature
) is increasing at a much faster rate than maximum
temperature (T
) 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 19501980 (Vose et al., 2005). In another
study, a significant decreasing trend in global DTR
) was reported with a relatively smaller
increase in the T
as compared to the T
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
are not uniform, an asymmetry between T
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% (2.12.7%) to 2.7% (2.42.9%) 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 (20462065) and found an increase in DTR
in wheat-growing areas and a decrease in rice-growing areas.
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
than T
(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
relative to the 19012003 period (Rai et al., 2012).
For the same period, Kothawale and Rupa (2005) also
reported an increase in annual Tmean (0.05C decade
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
was higher than the T
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
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 (19512016). The spatio-temporal
trends in DTR and air temperatures for two different
periods, that is, the entire period of 66 years (19512016)
and the recent period (19912016) are also undertaken.
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 (19512016) is acquired from
India Meteorological Department (IMD) for the study area
and analysed for 1,167 grid boxes at 0.5
Initially, the daily air temperature data for the period of
19511979 was available at 1x1
resolution, while the
FIGURE 1 Different agro-climatic zones of India [Colour figure can be viewed at]
data from 1980 to 2016 was available at 0.5 x0.5
obtain a homogeneous, high-resolution temperature dataset
for the entire study period (19512016), the 1x1
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. This data was at 1resolution for the period
19842016. The data has been screened, taking into account
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 (JanuaryFebruary), pre-
monsoon (MarchMay), monsoon (JuneSeptember) and
post-monsoon (OctoberDecember) according to IMD. The
DTR was calculated by subtracting the daily T
from the
at each grid box (DTR =T
These daily DTR, T
and Srad values were
then averaged on a seasonal and annual basis for further
analysis, including the annual and seasonal trends in the
and Srad. The trend was also calculated
to assess the spatial variation among different agro-
climatic zones for the two study periods, that is,
19512016 (19842016 for Srad) and 19912016. The
trend was obtained by the ordinary linear least-square
method to calculate the linear trend between time
(19512016) and temperature (C) and Srad
(MJ m
). 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
(19912016) 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
and Srad. Values at p<.05 were considered significant.
3.1 |Correlation analysis
Figure S1ac shows the relation between the annual DTR
with T
, and Srad. DTR is positively correlated
with T
(r=.6, significant) and Srad (r=.2) and nega-
tively correlated with T
(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
than T
due to its
presence only during the day (Wang et al., 2014). Thus an
increase in Srad may cause an increase in T
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
, and T
over India covering the entire
period of 66 years (19512016), the recent period of
26 years (19912016) and Srad (19842016). The non-
parametric MK trend indicated an overall increase in
DTR by 0.25C for the 66 years (19512016), while the
recent 26 years (19912016) DTR showed a decrease by
0.05C (Table 1). Also, the annual linear trend showed
that there is an increase in T
and T
for both periods
and Srad during 19842016. However, it also shows a
(significantly) larger rate of increase in T
during the
recent period compared to T
, which attributes to the
decline in DTR. Similarly, Vinnarasi et al. (2017) found
an increase in DTR (0.36C) over India during 19511980
and then a decline during 19812010, primarily due to an
increase in T
. 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 19512016 Figure 1. While the recent
period of 19912016, 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
and T
was noted with a faster
increase in T
during the recent period. The post-
monsoon season showed the highest increase in both T
and T
during 19512016, while during the recent period
(19912016), T
and T
showed more increase during
the pre-monsoon season Figure 2. The post-monsoon sea-
son witnessed the highest warming in 19512016, 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
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 19912016. 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
than T
, 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 19512016 and 19912016 (*denote
trends at 95% significance level)
Annual 195116 0.25 0.52* 0.32
199116 0.05 0.47 0.55*
Winter 195116 0.14 0.17 0.33
199116 0.10 0.52 0.52
195116 0.44* 0.40* 0.18
199116 0.07 0.65 0.65
Monsoon 195116 0.48* 0.73* 0.17
199116 0.03 0.16 0.36*
195116 0.21 0.86* 0.61*
199116 0.16 0.42* 0.60
Abbreviation: DTR, diurnal temperature range.
FIGURE 2 Annual and seasonal variation of spatially averaged DTR, T
, and Srad over India with linear time trends. The
values within the graph show the yearly trend for two time periods of 19512016 and 19912016 [Colour figure can be viewed at]
Apart from T
and T
, 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
relative to
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
but increase in T
(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
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
and T
, 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 (19512016; Figure 3).
FIGURE 3 Decadal trends of diurnal temperature range over the different agro-climatic zones of India during 19512016 and
19912016 [Colour figure can be viewed at]
During the later period of 19912016, a quantitative
increase in a negative trend of DTR in major parts of
northern and western India (00.6C/decade) was visible,
attributed mainly to the large increase in T
relative to
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
reveals an increasing
trend over most parts of India in both periods. However,
a larger increase in trend was noted for the EH region
(0.40.8C/decade) and the peninsular (WPH and SPH)
and south-west coast region (0.20.4C/decade) for the
recent period (19912016; Figure 4). Unlike T
, a large
increase in the T
trend at a rate of 00.8C/decade
FIGURE 4 Decadal trends of T
over the different agro-climatic zones of India during 19512016 and 19912016 [Colour figure can
be viewed at]
FIGURE 5 Decadal trends of T
over the different agro-climatic zones of India during 19512016 and 19912016 [Colour figure can
be viewed at]
(a significant increase of 0.3C/decade into Gangetic
plain region) was observed for the recent decades
(19912016; Figure 5). A gradual increase in Srad (up to
+0.6 MJ m
/decade from 1951 to 2016 to 1.0 MJ m
/decade during 19912016) 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 (19912016) in which a larger increase in T
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
(0.020.29C/decade highest over the
southern region) and T
(0.160.29C/decade) and Srad
(0.0130.027 MJ m
/decade) with a notable
increase in T
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
(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 19512016. The spatial extent of the
negative trend increased during 19912016, 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 19842016 and 19912016 [Colour figure can
be viewed at]
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;
19312002) 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 (19762000) and by Jamir
et al. (2016) over North-east (EH) and west coast
(19012010). 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 19912016 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
, and Srad (Figures 46). The increasing trend in
was observed for all the seasons during both
19512016 (0.010.8C/decade in all seasons) and
19912016 (0.78C/decade in EH during winter). How-
ever, the increase in T
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 19912016. Similarly, like T
, there
has been a significant increase in T
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
is substantially higher than T
, 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
and T
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
/decade in 19912016 during monsoon)
and Gangetic plain (0.87 MJ m
/decade in monsoon in
Middle Gangetic Plain during 19912016). 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
(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 19511960 till 19811990 and
decreased after that Figure 5. There is an increase of
about 2C from 19511960 to 20102016. The increase
was most notable in 19811990 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 19812006 over India, it contributed towards
warming during 19912006. 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
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.
The study found an overall increasing trend of DTR dur-
ing 19512016, and a decreasing trend during the recent
period 19912016 across the different agro-climatic zones
FIGURE 7 Decadal spatial
variation for diurnal temperature range
during 19512016 [Colour figure can be
viewed at]
in India. For recent decades, the decreasing DTR trend is
primarily because of the relatively faster increase in T
relative to the T
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 19512016 and decreasing trend during the
recent period 19912016, while winter season has a
decreasing DTR trend during 19512016 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 (19512016 and
19912016). 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
) 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
, 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
in the day and increase T
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.
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/
Rajesh Kumar Mall
Rajeev Bhatla
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), 331343.
Alagh, Y.K. (1990) Agro-climatic planning and regional development.
Indian Journal of Agricultural Economics.,45(3),244268.
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), 135153. https://
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.
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,
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, 175182.
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(34),
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, 273381273384.
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, 122451122473.
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, 15941604.
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), 29412944.
Hamed, K.H. and Rao, A.R. (1998) A modified MannKendall trend
test for autocorrelated data. Journal of Hydrology, 204, 182196.
He, B., Huang, L. and Wang, Q. (2015) Precipitation deficits
increase high diurnal temperature range extremes. Scientific
Reports, 5, 12004.
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, 417421.
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.
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, 10511063. https://doi.
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, 235255.
Jhajharia, D. and Singh, V.P. (2011) Trends in temperature, diurnal
temperature range and sunshine duration in Northeast India.
International Journal of Climatology, 31, 13531367.
Kalnay, E. and Cai, M. (2003) Erratum: corrigendum: impact of
urbanization and land-use change on climate. Nature, 425,
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, 10071023.
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, 182253182256. https://doi.
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, 19081916.
Kothawale, D.R., Munot, A.A. and Kumar, K.K. (2010) Surface
air temperature variability over India during 19012007, and
its association with ENSO. Climate Research, 42, 89104.
Retrieved from
Kothawale, D.R. and Rupa, K.K. (2005) On the recent changes in
surface temperature trends over India. Geophysical Research
Letters, 32, L18714.
Kovats, R.S. and Hajat, S. (2008) Heat stress and public health: a
critical review. Annual Review of Public Health, 29, 4155.
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
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), 413425.
metry of surface temperature trends over India. Geophysical
Research Letters, 21, 2167721680.
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, 3750.
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,
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, 597603.
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, 208218.
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, 469477.
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),
Makowski, K., Wild, M. and Ohmura, A. (2008) Diurnal tempera-
ture range over Europe between 1950 and 2005. Atmospheric
Chemistry and Physics, 34, 8648386498.
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), 429438.
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(34), 503515.
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(24), 445478.
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),
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, 150158.
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, 10771087.
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, 453465.
Mondal, A., Khare, D. and Kundu, S. (2015) Spatial and temporal
analysis of rainfall and temperature trend of India. Theoretical
and Applied Climatology, 122, 122143122158.
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,
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), 11661173.
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.
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), 111.
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 aerosolland
atmosphere interactions on the Indian monsoonal rainfall char-
acteristics. Natural Hazards, 42(2), 345359.
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, 11291144.
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, 553575.
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.
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, 99719975.
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, 39313944.
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), 587601.
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),
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, 430441.
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,4,8695.
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.
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. 101214.
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), 36113613.
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), 727740.
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.
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, 12111218.
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.
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, 665453665464.
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), 405413.
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,
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-
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, 311.
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, 10661069.
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,
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
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, 114.
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, 113121. https://
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,5,111.
Vinnarasi, R., Dhanya, C.T., Chakravorty, A. and Aghakouchak, A.
(2017) Unravelling diurnal asymmetry of surface temperature
in different climate zones. Scientific Reports,7,7178. https://
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.
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, 343354.
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,
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), 166172. https://
Wild, M., Ohmura, A. and Makowski, K. (2007) Impact of global
dimming and brightening on global warming. Geophysical
Research Letters, 34, L04702.
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. 3418134198
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, 669675.
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, 16.
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, 429440.
Additional supporting information may be found online
in the Supporting Information section at the end of this
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;114.
... However, even these decreasing trends depend on the study period and time scale (annual/seasonal/monthly). The study of Makowski et al. [29] has revealed a reversal in the DTR trend from decreasing to increasing between the 1970s and the 1980s, depending on the region in Europe. Increasing trends in DTR have also been reported in several regions, like Bangladesh during the monsoon period [30], some regions in India [31,32], Spain [33] and the Baltic region depending on the season [23]. Employing simulated global data coupled with atmosphere-ocean general circulation models between 1900 and 2099, Zhou et al. [34] found a decrease in the global DTR as well as in the zonal DTR of lower latitudes and subpolar region by 0.3 • C, 0.16 • C and 0.61 • C in 2099, respectively. ...
... Asymmetrical changes in maximum and minimum air temperatures lead to a mosaic of either downward or upward trends in DTR depending on the region/site, suggesting topographical and geographical variabilities, although there are cities with no significant trend. Most previous studies have shown a decrease in global DTR over recent decades [19][20][21]59], while some other studies that focus on specific regions have reported increasing trends in DTR [23,[30][31][32][33]60]. In Spain, for example, the maximum air temperature has increased at a higher rate than the minimum air temperature since 1961 [33], and in the Baltic region, an increasing trend in DTR depending on the season has also been reported [23]. ...
Full-text available
An important indicator of climate change is the diurnal temperature range (DTR), defined as the difference between the daily maximum and daily minimum air temperature. This study aims to investigate the DTR distribution in European cities of different background climates in relation to the season of the year, climate class and latitude, as well as its response to exceptionally hot weather. The analysis is based on long-term observational records (1961–2019) coupled with Regional Climate Model (RCM) data in order to detect any projected DTR trends by the end of the 21st century under intermediate and high emission greenhouse gases (GHGs) scenarios. The analysis reveals marked variations in the magnitude of DTR values between the cities, on the one hand, and distinct patterns of the DTR distribution according to the climate class of each city, on the other. The results also indicate strong seasonal variability in most of the cities, except for the Mediterranean coastal ones. DTR is found to increase during hot days and heat wave (HW) days compared to summer normal days. High latitude cities experience higher increases (3.7 °C to 5.7 °C for hot days, 3.1 °C to 5.7 °C for HW days) compared to low latitude cities (1.3 °C to 3.6 °C for hot days, 0.5 °C to 3.4 °C for HW days). The DTR is projected to significantly decrease in northernmost cities (Helsinki, Stockholm, Oslo), while it is expected to significantly increase in Madrid by the end of the 21st century under both the intermediate- and high-emission scenarios, due to the asymmetric temperature change. The asymmetrical response of global warming is more pronounced under the high-emission scenario where more cities at higher latitudes (Warsaw, Berlin, Rotterdam) are added to those with a statistically significant decrease in DTR, while others (Bucharest, Nicosia, Zurich) are added to those with an increase in DTR.
... [10][11][12] The delay of monsoon, low evaporative cooling, and depleted soil moisture increase the sensible heat flux and exacerbate the prevailing heat wave conditions. 8,12,13 Heat waves have emerged as an immediate health hazard increasing morbidity and huge mortality episodes across the globe such as during European heat wave of 2010 (70,000 deaths), Russian heat wave (54,000 deaths), and Indian heat wave of 2015 (>2,500 deaths). 11,[14][15][16] India has reported a marked increase in heat wave intensity, frequency, and duration in the past half century. ...
Full-text available
Future changes in heat wave characteristics over India have been analyzed using Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) regional climate model simulations for mid-term (2041–2060) and long-term (2081–2099) future under the representative concentration pathway (RCP) 4.5 and RCP 8.5 emission scenarios, respectively. SMHI_CSIRO-MK3.6 was found to be the best model in simulating heat wave trend over India for historical period. Future projections show a four-to-seven-fold increase in heat wave frequency for mid-term and long-term future under RCP 4.5 scenario, and five-to-ten-fold increase under RCP 8.5 scenario with increase in frequency dominating intensity in both the scenarios. Northwestern, Central, and South-central India emerged as future heat wave hotspots with largest increase in the south-central region. This high-resolution regional future projection of heat wave occurrence will serve as a baseline for developing transformational heat-resilient policies and adaptation measures to reduce potential impact on human health, agriculture, and infrastructure.
... End of the 21st century, the world's temperature is projected to rise by 5.7°C, making the atmosphere more volatile and hotter (Wu et al., 2022). Many research studies showed that an increasing global mean temperature reveals an increasing trend in seasonal monsoonal rainfall over South Asia (Loo et al., 2015;Mall et al., 2021). Hydroclimatic Extremes comprise extreme precipitation, drought, and flash floods, which pose a grave threat to man-made ecosystems, agriculture, humans, and natural systems (Giorgi et al., 2018;Maurya et al., 2021;Sonkar et al., 2020). ...
Full-text available
The changing frequency of extreme rain events in the past few decades over the Indian river basins (IRBs) contributed to floods and drought and resulted in economic losses and gross domestic product. In this study, we evaluated the performance of 12 Global Circulation Models from the Coupled Model Intercomparison Project Phase 6‐ experiment with India Meteorological Department observed data sets to reproduce the extreme rainfall events as well as project the changes in frequency and intensity of the hydroclimate extremes in future. We found that under low emission scenarios (SSP1‐2.6), the frequency of extreme rainfall is going to increase over the western ghat and northeast IRBs, while an increase in heavy rainfall intensity (14.3%) noticed under SSP2‐4.5 in the upper Ganga and Indus basin. Also, approximately 4%–10% of the heavy rainfall is projected to increase over the western part of IRBs during the Near (2021–2040) and Mid (2041–2060) future. The study explored the new hotspot regions for future urban flooding due to increasing pattern of heavy rainfall in future. Moreover, the lower Ganga basin will experience agricultural drought in near future due to decreasing areal mean rainfall, which needs to be seen by policymakers for managing the excess (less) water. Also, India's northern, central, and western river basins may experience more extremes under high‐emission (SSP5‐8.5) scenarios that indicate challenges to mitigation. The findings of this study highlight the importance of developing long‐term adaptation and mitigation strategies aimed at reducing hydroclimate vulnerability. It emphasizes the need to implement measures that enhance resilience and minimize risks associated with hydroclimate extremes at the basin level.
... There are pieces of evidence of adaptation gaps i.e., the difference between actual adaptation and recommended adaptation (IPCC, 2022). Possible reasons are a misinterpretation of climate trends, and barriers like social, institutional, individual, technological, and economical at different levels (Mall et al., 2019;Singh, 2020;Mall et al., 2021). It may also be influenced by external factors that develop a biased belief (Myers et al., 2013). ...
Full-text available
Effective adaptation is crucial for building climate resilience in agriculture. This study attempted to understand the perception of farmers about changing climate and its impact on agriculture, its consistency with observed trends. It further assessed the major adaptation strategies opted-in by the farmers along with the identification of the motivation that led to opt-in or opt-out. Multi-stage sampling was used to collect responses from farmers (n = 300) of eastern Uttar Pradesh, India. The validity of responses was verified through secondary data analysis. The findings revealed that 82% farmers perceived rise in temperature, 85% believed that the rainfall has altered, and 95% believed that the intensity of rainfall has changed. More than 60% of the farmers agreed that alterations in temperature and precipitation reduce the production as well as the revenue. A large fraction of farmers opted-in strategies like shifting of sowing dates (87%), change of variety (86%), and increase in irrigation (83%). While, resource saving strategies like conservation agriculture, water harvesting, were not considered (<25%). Interestingly, the motivation behind opting-in was not the knowledge but the monetary benefit generated by doing so i.e., passive adaptation. Among the non-adopters, a large fraction opted-out because they believed that 'It is not needed'. Constructive policies need to prioritize generation of awareness and sensitization of farmers for active adaptation preferably through participatory approach.
... Moreover, the increasing DTR in sub-humid LTB and concurrent intensification of atmospheric temperature and humidity would adversely impact human health by upsurging cardiovascular and respiratory diseases (Cheng et al. 2014) and increase outdoor thermal stress and the overall energy consumption and power requirement for indoor cooling (Sherwood 2020). The warming tendency reported in the current study closely corroborates with past studies on TRB (Sharma et al. 2018;Chandole et al. 2019;Jibhakate et al. 2023a), Indian regions (Mall et al. 2021;Rehana et al. 2022), and regions across the globe (Donat et al. 2013;Alexander 2016). The studies by Sherwood (2020) Kumar et al. (2020Kumar et al. ( , 2021 on the Indian region affirm the similar remarkable rise in the temperature mean and extremes during the future periods. ...
Full-text available
This study explored co-occurring climate scale changes across the physioclimatically heterogeneous Tapi River basin (TRB) for baseline (1991–2020) and future periods (2021–2100). We used a novel multivariate framework comprising multi-model ensembles of bias-corrected rainfall and temperature from 5 global climate models (CMIP-5), 12 climate indices (6 for each variable), and principal component analysis (PCA). The univariate assessment showed statistically significant warming of 1.1–1.8 °C (1.5–4.0 °C) under RCP-4.5 (RCP-8.5) scenarios. The Middle Tapi basin showed a substantial shift towards a wetter climate regime in the future. The multivariate assessment of spatially varying climate indices resulted in four significant principal components (PCs). The relative evaluation of these PCs showed that nearly 41.6% (47.0%) of the TRB is vulnerable to the transition of the current climatic patterns to the dry-warm (wet-warm) regime under RCP-8.5 (RCP-4.5) in the near (distant) future. On the optimistic side, under RCP-4.5 and RCP-8.5, 53.0 and 69.8% of the TRB displayed signs of uniform temporal distribution with wet rainfall regimes and profound warming towards the end of the 21st century, respectively. The study outcomes would help to devise policies for regional sustainability and adopt mitigation measures to enhance resiliency in a changing climate.
... Decreasing minimum temperatures (or variability in minimum temperature) trends with respect to maximum temperature has also been recorded by various studies for the north Indian region (Das et al., 2007;Pal and Al-Tabbaa, 2010) as well as the Himalaya (Kumar et al., 1994;Sharma et al., 2000;Arora et al., 2005). The rise in mean temperature in recent decades might be due to decrease in DTR in India (Mall et al., 2021). It has also been observed that cloud cover plays a major role in DTR variability. ...
Full-text available
Long-term climate records which help decipher past climate variability and its impact are scarce in the tough terrain of the Himalayan region. Therefore, in order to fill the climate data gap and understand the glacier climate linkage, we developed a 231 year long (1785–2015 CE) March–June temperature record using ring-width chronology of Himalayan fir (Abies pindrow (Royle ex D.Don) Royle) for the Din Gad valley, Dokriani glacier region, Uttarkashi, Uttarakhand, in the Western Himalaya. The Din Gad, originating from the Dokriani glacier, is a meltwater river contributing to Bhagirathi catchment in the headwaters of the socio-economically vital Ganga River. The 21-year running mean of the temperature record showed 1978–1998 CE as the coldest period followed by 1925–1945 CE, and 1890–1910 CE as the warmest period followed by 1946–1966 CE over the entire time series. The reconstruction matches well with tree-ring based temperature records available from the Garhwal Himalaya. It also shows similarity to tree-ring based temperature reconstructions from the Western Himalaya, Nepal, Tibetan Plateau and Bhutan, thus displaying a regional scale climate signal. The low frequency fluctuation patterns of the March–June temperature also matches with Asia and Northern hemisphere temperature records. Reconstructed March–June temperature record showed a statistically negligible warming temperature trend during 1901–1989 CE in the 20th century. It, however, captured a warming spike from 1990s CE which continues rising into the 21st century, which is also evident in the Northern hemisphere temperature record. Moreover, temperature rise is not anomalous in the past 231 years and well within range of the rest of the series. The present temperature record exclusively from the glacier region revealed a strong linkage with the benchmark Dokriani glacier’s winter mass balance (November–April) revealing mass loss (gain) episodes occurred in warm (cool) phases. This first such record from the glacier valleys in Ganga headwaters would be of great value at providing insight into past climate variability and glacier behaviour with respect to climate change in long term perspective, and thus would provide valuable information for water resource management in light of climate change.
... The variability of daily temperature over the UTB is increased due to higher rate of increase in maximum temperature relative to that of minimum temperature, and the opposite nature is observed over LTB. The daily temperature variability over UTB increased by 0.39 °C, surpassing the mean rise of 0.25 °C over India during 1951-2016 reported by Mall et al. (2021). Such variability in daily temperature may likely to increase cardiovascular and respiratory diseases in humans in the basin (Cheng et al., 2014;Ding et al., 2016;Wang et al., 2021). ...
Full-text available
The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space–time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951–2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen’s slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45 days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.
... Meanwhile, over northeast India, Jhajharia and Singh (2011) found decreasing and increasing trends in DTR at some points corresponding to annual, seasonal, and monthly time scales. Some studies have found the contrary, e.g., Waqas (2018) in Pakistan and Niyogi and Singh (2021) in India. Scafetta (2021) observed a generally declining pattern of DTR over Africa. ...
The variability in the diurnal temperature range (DTR), an indicator of climate change, remains limited, especially over Africa, due to the scarcity of observed maximum and minimum temperature data. This work investigates the ability of the Coupled Model Intercomparison Project (CMIP6) to simulate DTR over Africa for the period 1980–2014. Datasets from the Climatic Research Unit (CRU TS4.05) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC) gridded temperature datasets are utilized as observed data. Similar to the high variability in topography and climate across the continent, the DTR exhibits high heterogeneity over Africa. The Sahara and its environs record the highest DTR, while Central Africa and coastal areas experience the least, given the thermal inertia of water bodies. CMIP6 models overestimate and underestimate DTR over different parts of the continent. Moreover, the multi-model ensemble mean of CMIP6 models shows significant decreasing trends both in seasonal and annual trends. Overall, five CMIP6 models such as EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, EC-Earth3-veg, and IPSL-CM6A-LR show robust skill scores (0.48–0.54). The findings form the basis for investigating the role of temperature extremes on DTR. Further, the variability in DTR across parts of the continent prompts the need for future assessments to investigate future changes in DTR.
... For example, past observational studies reported a global decrease in DTR by 0.07 °C/decade during 1950-1980(Vose et al. 2005 to 0.036 °C/decade between 1901 and 2014 with a relatively smaller increase in the Tmax (1.1 °C) as compared to the Tmin (1.6 °C) (Sun et al. 2019). However, there have been regional inconsistencies such as in the mid-latitudes and low-latitude regions like East Asia, the decrease in DTR is mainly attributed to a reduction in daily Tmax, whereas in India, the decline in DTR is mainly due to the increase in daily Tmin (Hua and Chen 2013;Vinnarasi et al. 2017;Waqas and Athar 2018;Mall et al. 2021;IPCC 2021). ...
Full-text available
Diurnal temperature range (DTR) which reflects the difference between the daily maximum (Tmax) and minimum temperature (Tmin) is an important indication of changing climate and a critical thermal metric to assess the impact on agriculture, biodiversity, water resources, and human health. The major aim of this study is to assess the probable future spatio-temporal changes in the Tmax, Tmin, and DTR and their long-term warming trend from 2006 to 2099 under two representative concentration pathways (hereafter RCP4.5 and RCP8.5) over diverse agroclimatic regions of India. The observed data from India Meteorological Department (IMD) was used to evaluate the performance of climate models (1970–2005). The result shows a very slight underestimation in DTR by models compared to the observed. In future projections, we found a reduction in DTR (0.001 to 0.020 °C/year) partly linked to the substantial increase in Tmin (0.020 to 0.071 °C/year) than Tmax (0.031 to 0.060 °C/year) that was stronger in far twenty-first-century future under RCP8.5. The decline in DTR was profound and consistent over northern India (up to 3 °C) surrounding the Indo-Gangetic Plain, western dry region, and part of central India with the highest decline observed in winter and pre-monsoon season. However, a decline in DTR was also anticipated over the plateau, coastal, and eastern Himalayas region. Change in land use land cover (LULC) also complimented the decline in DTR. The main findings of the study advocate implementation of a robust framework for climate change adaptation strategies to mitigate adverse consequences to the natural ecosystem and human health over specific regions arising due to declining DTR.
Full-text available
The diurnal temperature range (DTR) (or difference between the maximum and minimum temperature within a day) is one of many climate parameters that affects health, agriculture and society. Understanding how DTR evolves under global warming is therefore crucial. Physically different drivers of climate change, such as greenhouse gases and aerosols, have distinct influences on global and regional climate. Therefore, predicting the future evolution of DTR requires knowledge of the effects of individual climate forcers, as well as of the future emissions mix, in particular in high-emission regions. Using global climate model simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), we investigate how idealized changes in the atmospheric levels of a greenhouse gas (CO2) and aerosols (black carbon and sulfate) influence DTR (globally and in selected regions). We find broad geographical patterns of annual mean change that are similar between climate drivers, pointing to a generalized response to global warming which is not defined by the individual forcing agents. Seasonal and regional differences, however, are substantial, which highlights the potential importance of local background conditions and feedbacks. While differences in DTR responses among drivers are minor in Europe and North America, there are distinctly different DTR responses to aerosols and greenhouse gas perturbations over India and China, where present aerosol emissions are particularly high. BC induces substantial reductions in DTR, which we attribute to strong modeled BC-induced cloud responses in these regions.
Full-text available
This open access book discusses the impact of human-induced global climate change on the Indian subcontinent and regional monsoon, the adjoining Indian Ocean and the Himalayas. It also examines the regional climate change projections based on the climate models used by the IPCC Fifth Assessment Report (AR5) and national climate change modeling studies using the IITM Earth System Model (ESM) and CORDEX South Asia datasets. The IPCC assessment reports, published every 6–7 years, provide important reference material for major policy decisions on climate change, adaptation, and mitigation. While the IPCC assessment reports largely provide a global perspective on climate change, they offer limited information on the regional aspects of climate change. Regional climate change effects over the Indian subcontinent, especially relating to the Indian monsoon, are unique to the region, and in particular, the climate in this region is shaped by the Himalayas, Western Ghats, the Tibetan Plateau, the Indian Ocean, Arabian Sea, and Bay of Bengal. Climatic variations in this region are influenced by (a) regional-scale interactions between the atmosphere, ocean, land surface, cryosphere and biosphere on different time scales, (b) remote effects from natural phenomena such as the El Nino / Southern Oscillation, North Atlantic Oscillation, Indian Ocean Dipole, and Madden Julian Oscillation, and (c) human-induced climate change. This book presents policy-relevant information based on robust scientific analysis and assessments of the observed and projected future climate change over the Indian region.
Full-text available
This chapter discusses observed and projected changes in global climate to set the context, after which it discusses the scientific issues around the complexity of regional climate over the Indian subcontinent, with a focus on the Indian Monsoon. It introduces the Earth System Model from India (IITM-ESM), and synthesises the major findings of the report and the links between the regional and global climate.
Full-text available
In the backdrop of the established fact that the climate and agricultural produce foster a close-knit relation, the present study explores the impacts of climate variability on the rice yields across diverse agro-climatic zones of Uttar Pradesh, India. The time-series non-parametric Mann-Kendall trend test was applied to study long term (both annual and seasonal) weather and yield data sets. Minimum temperature, encompassing all the zones, was found to be increasing within the range of 0.06 to 0.44 °C per decade. The ‘kharif’ season maximum temperature trends were found increasing in most zones. In terms of annual and seasonal rainfall trends, the results were mostly non-significant, except for Bhabhar and Tarai Zone which had witnessed a very high decadal trend indicating towards the occurrences of intense rainfall events. North Eastern Plain Zone needs a special mention owing to its large number of extreme rainfall events in three categories (>50 to <100 mm/day; >100 to <150 mm/day; >150 mm/day). Considering the annual/seasonal temperature and rainfall variability in the region, the warming trend along with spatio-temporally uncertain rainfall is likely to inflict significant impact upon the rice crop. Consequently, there is a dire need to devise strategies capable of dealing with the impacts of the prevailing climate variability on rice yields in this state of India through development of suitable adaptation options for sustainable production. The continuous and rigorous studies into this field of agro-meteorology subjected to impact assessment call for international action plans that are designed in a frame of ‘bottom-up approach’ or a ‘local to regional to country level’ strategic implementation of adaptation options to sustain yields in the rice fields.
India and other Southeast Asian countries are severely affected by Japanese encephalitis (JE), one of the deadliest vector-borne disease threat to human health. Several epidemiological observations suggest climate variables play a role in providing a favorable environment for mosquito development and virus transmission. In this study, generalized additive models were used to determine the association of JE admissions and mortality with climate variables in Gorakhpur district, India, from 2001-2016. The model predicted that every 1 unit increase in mean (Tmean;°C), and minimum (Tmin;°C) temperature, rainfall (RF; mm) and relative humidity (RH; %) would on average increase the JE admissions by 22.23 %, 17.83 %, 0.66 %, and 5.22 % respectively and JE mortality by 13.27 %, 11.77 %, 0.94 %, and 3.27 % respectively Conversely, every unit decrease in solar radiation (Srad; MJ/m2/day) and wind speed (WS; Kmph) caused an increase in JE admission by 17% and 11.42% and in JE mortality by 9.37% and 4.88% respectively suggesting a protective effect at higher levels. The seasonal analysis shows that temperature was significantly associated with JE in pre-monsoon and post-monsoon while RF, RH, Srad, and WS are associated with the monsoon. Effect modification due to age and gender showed an equal risk for both genders and increased risk for adults above 15 years of age, however, males and age groups under 15 years outnumbered females and adults. Sensitivity analysis results to explore lag effects in climate variables showed that climate variables show the strongest association at lag 1 to 1.5 months with significant lag effect up tp lag 0-60 days. The exposure-response curve for climate variables showed a more or less linear relationship, with an increase in JE admissions and mortality after a certain threshold and decrease were reported at extreme levels of exposure. The study concludes that climate variables could influence the JE vector development and multiplication and parasite maturation and transmission in the Gorakhpur region whose indirect impact was noted for JE admission and mortality. In response to the changing climate, public health interventions, public awareness, and early warning systems would play an unprecedented role to compensate for future risk.
The episodes of heat wave events have strengthened in recent decades causing great concern for human health, agriculture and natural ecosystem. In the present study, Regional Climate Models (RCMs) namely, CCAM and RegCM, from Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) are evaluated for simulating heat waves (March–June) for a long-term period (1971 to 2005) over India in comparison with observations from India Meteorological Department (IMD). The statistical analysis (correlation, RMSE, MAE, ECDF) results reveal differences in RCMs in simulating spatial pattern and trends of maximum temperature before bias correction. Variance scaling bias correction is found to remove bias and improve model simulations in capturing temperature variability. An increase in correlation in daily observations from 0.24 to 0.70 and reduction in RMSE from 8.08 °C to 2.02 °C and MAE from 3.87 °C to 2.43 °C after bias correction is observed between model and observation. LMDZ4 and GFDL-ESM2M are found to perform best in simulating interannual variability of seasonal mean maximum temperature with an underestimation of −7.74% and − 15.41% which improved significantly to around −1.51% and − 0.78%, respectively after bias correction over India. LMDZ4 and GFDL-ESM2M (RegCM ensemble) are best-performing models (ensemble) in significantly reproducing the heat wave frequency and spatial variability in closer proximity with observations over India amongst all models after bias correction. Over NW and western regions, the LMDZ4 and GFDL-ESM2M ensemble models successfully capture the increasing trend of 0.2 events/year and 0.4 events/year accordance to IMD and IITM criteria, respectively. However, the ACCESS1.0, CNRM-CM5 and CCSM4 ensemble experiments overestimated heat waves by ±40 events in most sub-divisions in India. Over the central Indian regions, the ACCESS 1.0 and CNRM-CM5 model output show a negative trend of −0.2 events/year and large spatial variability possibly due to model associated uncertainties. Overall the results show an improvement in capturing maximum temperature and heat waves across the regions of Indian sub-continent in the bias-corrected downscaled CORDEX-SA ensemble RCMs than without bias-corrected output. The study suggests a way forward to assess RCMs performance and uncertainty in extreme weather analysis in future projections.
Heat wave (HW) and Severe Heat Wave (SHW) events are the manifestations of extreme temperature causing an array of impacts on health, ecosystem, and economy. Since the mid‐20th century, an increasing trend in the characteristics of heat waves has been observed over India causing an increased rate in human mortality. Our study aimed to analyze monthly, seasonal & decadal variations along with long‐term trends of HW and SHW events for pre‐monsoon (March‐May) and early summer monsoon (June‐July) season during 1951‐2016. HW and SHW events were identified using revised criteria given by India Meteorological Department (IMD) using daily gridded maximum temperature data at 0.5° x 0.5° resolution from IMD. The study found a Spatio‐temporal shift in the occurrence of HW events with a significantly increasing trend in three prominent heat wave prone regions i.e., Northwestern, Central and South‐Central India, highest being in West Madhya Pradesh (0.80 events/year), while a significantly decreasing trend was observed over eastern region i.e., Gangetic West Bengal (‐0.13events/year). SHW events showed a southward expansion and a spatial surge during the decades of 2001‐2010 and 2010‐2016. Tri‐decadal comparative assessment shows a decadal increase of around 12 HW and 5 SHW events post the 1980s. State‐wise Pearson's correlation between HW/SHW events and observed mortality reveals that the eastern coastal states i.e., Odisha and Andhra Pradesh show a significant positive correlation of 0.62 and 0.73 respectively. This significantly increasing trend in HW and SHW events may pose a grave risk to human health, predominantly on the vulnerable sections of the society. Heat waves need to be recognized as a potential health risk and demand further study, robust preparedness, and policy intervention. This article is protected by copyright. All rights reserved.
This study estimates the temperature trends over India and seeks to understand the contribution of land use and land cover (LULC) changes towards the change in the temperature trends (warming or cooling) during 1981–2006 by using ‘Observation minus Reanalysis’ (OMR) method. We find that the India got warmer by 0.1 °C per decade during 1981–2006 and the LULC changes contributed to cooling over India by 0.02 °C per decade during this period. The contribution of land use changes to the temperature trends depends on the type of LULC and their conversion from one type to another. With the exception of dense forest, all land cover conversions to agriculture lead to cooling whereas conversion from dense forest to agriculture results in warming. The contribution of LULC changes towards cooling over India during 1981–2006 is due to the reduction of area under shrubs/ small vegetation and subsequent increase of the area under agricultural/ fallow land. The analysis shows that even though the LULC changes contributed towards overall cooling during 1981–2006 over India, it contributed towards warming during 1991–2006. We find that the cooling caused by LULC changes during 1981–2006 is due to the cooling contributed during 1981–1990. Our overall results have implications for future land use change strategies that can be undertaken over India in order to avoid further worsening the Indian climate.
This study investigates the temperature trend (warming or cooling) over Eastern India during the period 1981–2006 and its response to the changes in land use and land cover (LULC). The ‘Observation minus Reanalysis’ (OMR) method is used to investigate the LULC impact on the temperatures over the region. We find that the Eastern India got warmer at a rate of 0.077 °C per decade during 1981–2006 and the changes in LULC contributed towards warming during 1991–2006 at a rate of ~ 0.2 °C per decade. We investigated the LULC changes during the period 1981–2006 over Eastern India by using satellite datasets for four different time periods viz. 1981, 1991, 2001, and 2006. Results indicate that shrubs/small vegetations, agricultural/fallow land and open forest are increased by 0.15%, 0.1% and 0.07% respectively over Eastern India during the period 1981–2006. On the other hand, bare land/snow cover and dense forest are decreased by 0.23% and 0.09% respectively over the region. Overall results indicate that the cooling is due to the conversion of open forest/shrubs/small vegetation into dense forest/agricultural/fallow land and the warming is due to the conversions of shrubs/agricultural/fallow land into bare land.
Potential impact of change in climate on Indian agriculture may be significantly adverse, if not disastrous. There are projections of potential loss in wheat yield due to the rise in daily minimum (Tmin) and maximum (Tmax) temperature, but only few researchers have considered the extent of such loss on a spatial scale. We therefore, systematically studied the effect of change in Tmax, Tmean (daily average temperature) and Tmin, solar radiation (Srad) and precipitation (RAIN) during wheat growing seasons (from 1986 to 2015) on crop yield over five wheat growing zones across India, taking into account the effect modification by aerosol loading (in terms of aerosol optical depth, 2001–2015). We note that for the entire India, 1 °C rise in Tmean resulted a 7% decrease in wheat yield which varied disproportionally across the crop growing zones by a range of −9% (peninsular zone, PZ) to 4% (northern hills zone, NHZ). The effect of Tmean on wheat yield was identical to the marginal effect of Tmax and Tmin, while 1% increase in Srad enhance wheat yield by 4% for all India with small geographical variations (2–5%), except for the northern hill region (−4%). Rise in 1 °C Tmean exclusively during grain filling duration was noted positive for all the wheat growing regions (0–2%) except over central plain zone (−3%). When estimates of weather variables on wheat yield was combined with the estimated impact of aerosols on weather, the most significant impact was noted over the NHZ (−23%), which otherwise varied from −7 to −4%. Overall, the study brings out the conclusive evidence of negative impact of rising temperature on wheat yield across India, which we found spatially inconsistent and highly uncertain when integrated with the compounding effect of aerosols.