Temperature variability over Dokriani glacier region, Western Himalaya, India

Abstract

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.

Full text

Quaternary International 664 (2023) 33–41
Available online 1 June 2023
1040-6182/© 2023 Elsevier Ltd and INQUA. All rights reserved.
Temperature variability over Dokriani glacier region, Western
Himalaya, India
Tanupriya Rastogi
a
, Jayendra Singh
a
,
*
, Nilendu Singh
a
, Pankaj Chauhan
a
, Ram R. Yadav
a
,
Bindhyachal Pandey
b
a
Wadia Institute of Himalayan Geology, Dehradun, 248001, India
b
Department of Geology, Banaras Hindu University, Varanasi, 221 005, India
ARTICLE INFO
Keywords:
Climate reconstruction
March–June temperature
Tree-ring
Dokriani glacier
Western Himalaya
India
ABSTRACT
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 ll 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 r (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 uctuation
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 con-
tinues 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 rst 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.
1. Introduction
Inter-hemispheric temperature records coherently reect an un-
precedented warm phase in the latter half of the 20th century relative to
the past millennium, testifying the signal of recent warming at a global
scale (Neukom et al., 2014). Global surface temperature rise of 1.09 ◦C
over 2011–2020 CE in comparison to 1850–1900 CE is unprecedented in
the past 2000 years (IPCC, 2021). Recent warming is linked with the rise
in extreme precipitation and heat events and is also projected to cause a
persistent rise in their magnitude and frequency overtime (Meehl and
Tebaldi, 2004; Fischer and Knutti, 2015; Horton et al., 2016). The
temperature rise is affecting the global climate (Dai, 2013; Donat et al.,
2016) and, consequently, the biosphere (Teskey et al., 2015; Worm and
Lotze, 2021), including the human systems (Levy and Patz, 2015;
Duchenne-Moutien and Neetoo, 2021).
High altitude areas of the world are particularly vulnerable to the
warming climate. The rate of temperature rise in these regions is more
acute with increasing altitude and shows much higher variability (Diaz
and Bradley, 1997; Ohmura, 2012; Pepin et al., 2015). This is apparent
most signicantly in the Himalayan region (Ohmura, 2012) where
* Corresponding author.
E-mail address: jayendrasingh@wihg.res.in (J. Singh).
Contents lists available at ScienceDirect
Quaternary International
journal homepage: www.elsevier.com/locate/quaint
https://doi.org/10.1016/j.quaint.2023.05.013
Received 20 September 2022; Received in revised form 16 May 2023; Accepted 22 May 2023

Quaternary International 664 (2023) 33–41
34
maximum warming has been observed at higher altitudes (Shrestha
et al., 1999). The temperature here has been warming at a rate three
times higher than global average (Xu et al., 2009). In the Western
Himalaya, the annual temperature has warmed by 1.6 ◦C in the 20th
century, however higher temperatures were observed in winters relative
to other seasons (Bhutiyani et al., 2007; Mondal et al., 2015; Sonali
et al., 2017). An abrupt rise in annual temperature trend has also been
noticed from 1990s. (Bhutiyani et al., 2007). With projected warming,
the High Mountains of Asia are estimated to lose 36 ±7% ice mass from
its glaciers by the end of the 21st century, even if we succeed in curbing
global temperature rise to 1.5 ◦C, as per the Paris Agreement of 2015
(Kraaijenbrink et al., 2017). The long-term net mass balance for glaciers
in the Indian Himalaya also reveal mass loss for the past four decades
with a few exceptions (Pratap et al., 2016). This region is home to large
river systems that emerge from these vast ice sanctuaries and supports
among the highest population densities on the planet, along with being
home to biodiversity hotspots. Given this scenario and the anticipated
socioeconomic and ecological impact of warming, it is essential to
investigate the long-term climate variability and its impact on glacier
behaviour in order to devise effective adaptation and mitigation stra-
tegies for the adversity caused by glacier decay. The paucity of long-term
instrumental climate data in Himalayan terrain has been addressed to a
certain extent by tree-ring based climate reconstructions from the
Eastern (Bhattacharyya and Chaudhary, 2003; Krusic et al., 2015),
Central (Sano et al., 2005; Thapa et al., 2015; Aryal et al., 2020) and
Western Himalaya (Singh and Yadav, 2014; Yadav et al., 1997, 1999),
along with the Tibetan plateau region (Zhu et al., 2011). But, tree ring
based temperature reconstructions from close vicinity of glaciers are few
and, particularly, from the Western Himalaya are limited, with only one
such temperature record (August–September) from the Uttarkashi dis-
trict of Uttarakhand (Chaudhary et al., 2013).
The Dokriani glacier, located in proximity of Bhagirathi river
catchment in Uttarkashi, is among the best studied glaciers in the Indian
Himalaya considering the multifarious studies on its mass balance
(Dobhal et al., 2008; Pratap et al., 2016; Azam and Srivastava, 2020),
recession (Dobhal et al., 2004; Dobhal and Mehta, 2010), hydrological
(Hasnain and Thayyen, 1999; Hasnain et al., 2001; Singh et al., 2004;
Thayyen et al., 2007; Thayyen and Gergan, 2010; Pratap et al., 2015)
and meteorological aspects (Thayyen et al., 2005; Kumar et al., 2014).
But in spite of these, no long-term climate record exists for this area
which could greatly expand the scope of studies on this glacier with
regard to its dynamics in the long-term perspective. This becomes
essential as the Dokriani glacier along with the Gangotri glacier forms
part of the system of glaciers that contribute to the Bhagirathi River
system that eventually merges with the Alaknanda River to form the
socioeconomically and culturally vital, river Ganga. Recognising the
importance of climate variability in this region and to address this gap,
we prepared a ring-width chronology of Abies pindrow (A. pindrow) from
the Din Gad valley with the aim to develop a temperature record for the
Dokriani glacier region and explore its potential in unveiling glacier
behavior in long-term perspective.
2. Regional setting
The study site, Tela, lies in the Din Gad valley in the Uttarkashi
district, Uttarakhand (Fig. 1), and is a part of the Bhagirathi catchment
that forms the headwaters of the river Ganga. Tela is situated on the left
bank of river Din Gad which is a proglacial meltwater stream originating
from the Dokriani glacier ~12 km away aerially. The Dokriani glacier
(30◦50′N and 78◦50′E) is a valley type glacier that is formed by the
merging of two cirques bound by the western slope of Jaonli peak (6632
m a.s.l.) and the northern slope of Draupadi ka Danda peak (5716 m a.s.
l.) (Garg et al., 2022). The Dokriani glacier is ~5 km in length and has an
area of 7.03 km
2
while the area of Dokriani glacier catchment is 15.71
km
2
(Azam and Srivastava, 2020). The glacier has a northwest orien-
tation and is bound by two large lateral moraines.
Geologically the area is bound by two major thrusts – the Munsiari
Thrust (MCT-1) in the south and the Trans Himadri Fault (THF) to the
north (Heim and Gansser, 1939; Valdiya, 1998). The region comprises of
metamorphic and granitic rocks. The climate is inuenced by both the
Indian Summer Monsoon (ISM) and the Westerlies. The ISM contributes
~71% of the annual precipitation during June to September whereas
remaining occurs in the rest of the months (Singh et al., 2019).
3. Material and methods
3.1. Tree-ring data and chronology development
Himalayan r (A. pindrow (Royle ex D.Don) Royle), a constituent of
Mid-montane needle-leaf evergreen forest formation type (Champion
Fig. 1. Map showing location of tree-ring study area, meteorological station used in the present study and tree-ring based temperature reconstructions (1-Singh and
Yadav, 2014 and 2-Yadav et al., 2004) used in comparison.
T. Rastogi et al.

Quaternary International 664 (2023) 33–41
35
and Seth, 1968), was selected for the present study. It is found in the
Western Himalaya in pure forest stands or as a part of Picea-Cedrus-Abies
pindrow-Pinus wallichiana forest assemblage at elevations of 2500–3000
m a.s.l. or higher (Champion and Seth, 1968). The cores were sampled at
Tela (2534 m a.s.l.), Uttarkashi, Uttarakhand, on the left bank of the Din
Gad River, which emerges from the snout of Dokriani glacier approxi-
mately ~12 km away aerially, and is a tributary to the Bhagirathi river
system (Fig. 1). A. pindrow trees were growing on an east facing slope in
a mixed deciduous evergreen conifer forest stand comprising Picea
smithiana (Wall.) Boiss and Aesculus indica (Wall. ex Camb.) (Singh et al.,
2019) along with Quercus, Carpinus, Pinus, Taxus, and Cedrus which
represent the forest assemblage at the altitude (Phadtare, 2000; Bhat-
tacharyya et al., 2011). Cores were retrieved from trees that were old
and undisturbed, and those with any visible marks of injury were
avoided. Tree cores were extracted using an increment borer at 1.37 m
above the ground and processed in the laboratory using standard
dendrochronological techniques (Stokes and Smiley, 1968; Fritts, 1976).
The annual growth rings of each core were measured using the LINTAB
measuring system that measures up to 0.001 mm precision (Rinntech,
Heidelberg, Germany) and each growth ring was then crossdated using
the TSAP program (Rinn, 2003). The quality of dating and measurement
was crosschecked in COFECHA software to lter out any dating and
measurement errors (Holmes, 1983). Finally, 34 cores (21 trees) were
used for further analysis. The individual ring-width series were stan-
dardized using the program ARSTAN to remove age-size related growth
trends and disturbances due to stand dynamics (Cook et al., 1990). Out
of total 34 cores, 26 cores were detrended with a smoothing spline of
2/3rd of the series length while 8 series were treated with a 50 year
cubic spline to account for growth dynamics different than the rest of the
series. Finally, a mean chronology was generated by applying biweight
robust mean.
3.2. Climate data
The tough terrain and high elevation of the Himalaya make it
logistically difcult to put in place and maintain a systematic, wide-
spread meteorological observation network. For the present study, to
understand ring-width chronology and climate relationship we used
climate data of nearby available homogenous datasets from climate
stations [Shimla (2202 m a.s.l.), Mussoorie (1988 m a.s.l.), Dehradun
(682 m a.s.l.) and Mukteshwar (2311 m a.s.l.)]. We also used regional
climate data series prepared using these four stations and gridded data
of the sampling location to test the relationship (Fig. 1). The regional
climate data series was prepared by converting individual station data of
the four stations (Shimla, Mussoorie, Dehradun and Mukteshwar) to
anomalies with respect to their mean and standard deviation over the
common period, and then these anomalised series were averaged. The
tree growth-climate analysis revealed weak relationship with tempera-
ture of Shimla, Dehradun and the regional climate series. Mukteshwar
and Mussoorie temperature data showed signicant correlation with the
chronology while CRU gridded data showed a slightly weaker correla-
tion. However, combining the Mussoorie and Mukteshwar climate data
could not enhance the signal and therefore, we used Mukteshwar data
available up to recent time (2311 m a.s.l.; 1897–2019 CE) in further
analysis (http://climexp.knmi.nl/). This station best represents our
study area in terms of its 1) climate regime and 2) ora at similar
elevation (Shah and Joshi, 1971; Negi, 2000). The dataset has been used
by multiple tree ring-based climate studies in the Western Himalaya
region for precipitation (Singh and Yadav, 2005; Singh et al., 2006;
Yadav and Park, 2000) and temperature reconstructions for Nepal (Sano
et al., 2005; Thapa et al., 2013, 2015) and Garhwal region (Chaudhary
et al., 2013). This further highlights the reliability and suitability of the
Mukteshwar station record for climate analysis in the Western Hima-
laya. Finally, for developing the reconstruction we adjusted the tem-
perature data of Mukteshwar station using the lapse rate estimated by
Kattel et al. (2013), in order to account for the elevation difference
between the study area and the climate station (Supplementary Fig. S1).
Climate data show that maximum temperature in this region is recorded
in June while the lowest is observed in January. The region receives
most of the precipitation in the monsoon months (June–September) with
highest rainfall in July and minimum in November (Supplementary
Fig. S1).
3.3. Tree growth-climate relationship
For analyzing the relationship of tree growth with climate, correla-
tion analyses was performed between standard ring-width chronology of
A. pindrow and Mukteshwar precipitation and temperature data over a
common period (1898–2015 CE). As cambial activity of A. pindrow starts
in April/May and ceases in month of October, climate data of previous
year October to October of the following year was taken in correlation
analyses using program DENDROCLIM2002 (Biondi and Waikul, 2004).
Although cambial activity stops in winter, the photosynthetic food re-
serves generated are utilized at the commencement of the ensuing year’s
growth (Malik et al., 2020). Signal identied in the climate analyses was
further used to develop climate record.
3.4. Temperature reconstruction
We used linear regression analyses to develop the reconstruction. In
order to validate the calibration models the temperature series was split
into two sub-periods (1898–1956 CE and 1957–2015 CE) and each of
these sub-periods were validated with their respective verication pe-
riods. Both the calibration models were rigorously veried using mul-
tiple statistical analyses [Reduction of error (RE), Pearson correlation
coefcient, sign test, coefcient of efciency (CE), Root mean square
error (RMSE) and t-test (Fritts, 1976; Cook et al., 1999)] in their
respective verication periods (Table 1). To capture low frequency
signals the entire instrumental temperature series (1898–2015 CE) was
used in developing the reconstruction.
To explore the potential association between the reconstructed
temperature record and glacier uctuations we used reconstructed mass
balance data (1979–2018 CE) developed by Azam and Srivastava
(2020). The mass balance time series was developed on the basis of a
Table 1
March–June temperature reconstruction calibration-verication statistics obtained in linear regression analysis.
Calibration Verication
Period ar
2
% Period r T value Sign test RE CE RMSE
1898–1956 42 1957–2015 0.655 (p <0.00001) 3.68 (p =0.0005) 43
+
/16
-
(p =0.0006) 0.416 0.415 0.725
1957–2015 42 1898–1956 0.655 (p <0.00001) 5.14 (p <0.00001) 43
+
/16
-
(p =0.0006) 0.420 0.419 0.794
1898–2015 42
Note: ar
2
is captured variance adjusted for degrees of freedom which explains the percent variation in the dependent variable that can be explained by variation in the
independent variable in a regression; r is Pearson correlation coefcient which gives strength of association between the independent and dependent variable; RE is the
reduction of error and CE is coefcient of efciency (Fritts, 1976). Both are used for verication purposes in reconstructions where positive values indicate the
climatological potential (Cook et al., 1999). RMSE is root mean square error explains standard deviation of unexplained variance. The two tailed p values are given in
brackets.
T. Rastogi et al.

Quaternary International 664 (2023) 33–41
36
glacier mass balance model that comprised an accumulation module and
temperature index ablation module made using the ERA5 daily tem-
perature and precipitation data (1979–2018 CE) (Azam and Srivastava,
2020).
We also performed spatial correlation analysis between our tem-
perature series and March–June CRU TS4.01 temperature data
(1901–2015 CE) using KNMI climate explorer (http://climexp.knmi.nl/)
to understand regional scale features in our data.
4. Results and discussion
4.1. Ring-width chronology
We developed a ring-width chronology spanning 360 years
(1656–2015 CE) which was truncated at 1785 CE as Expressed Popu-
lation Signal (EPS) (Wigley et al., 1984) was ≥0.85 (Supplementary
Fig. S2). Mean sensitivity which is a measure of year to year variability
in ring-width chronology was 0.161 and mean rbar, that is the mean
correlation among ring-widths of all the trees, was 0.427. The standard
deviation depicting the variability in the series was 0.171. High mean
sensitivity, rbar and standard deviation point towards the den-
droclimatic potential of the chronology.
4.2. Tree growth-climate response analysis
The present study registered a signicant inverse relationship be-
tween temperature and radial growth in A. pindrow extending from
previous years’ October to ensuing year June, and August, while pre-
cipitation had a signicant direct relationship with growth in January,
March and April (Supplementary Fig. S3). This signies that sufcient
moisture availability with low temperatures is ideal for growth of
A. pindrow in the Din Gad valley in these months. (Supplementary
Fig. S3). Whereas rise in temperature in premonsoon months (March-
April-May) combined with low precipitation causes moisture deciency
in soil. This causes plant stress and increases evapotranspiration
resulting in reduced cambial activity at high elevation regions under the
inuence of strong solar radiation (Borgaonkar et al., 1996, 1999). This
results in a negative relationship between tree growth and temperature
during March–May. However, slightly more than average precipitation
in these months causes replenishment of minimum moisture levels and
positively aids growth (Borgaonkar et al., 1996). A similar negative
response of A. pindrow to premonsoon temperature was also observed in
other studies from the Western (Borgaonkar et al., 1999; Ram, 2012)
and Central Himalayan region (Thapa et al., 2013). We also performed
correlation analysis of chronology with the Palmer Drought Severity
Index (PDSI) which revealed a poor relationship that was found un-
suitable for reconstruction. And nally, the signicant strong relation-
ship with March–June temperature was used in developing temperature
reconstruction for the region.
4.3. Temperature reconstruction
4.3.1. Analyses of March–June temperature reconstruction
The March–June temperature reconstruction resembles well with the
instrumental temperature record (r =0.65, p <0.00001, 1898–2015
CE) and captures 42% variance (Fig. 2) (Table 1). Linear trends of the
reconstruction and instrumental data further validated the resemblance
among both the datasets (Supplementary Fig. S4a). The March–June
temperature in both the datasets depicts an increasing trend which is
also mirrored by the CRU TS4.06 gridded temperature data (http
://climexp.knmi.nl/) for the study area. However, it is less in these
months in comparison to that observed in winter and at annual level
(Bhutiyani et al., 2007). Borgaonkar et al. (2011) also observed similar
variations in the month of March-April-May and
June-July-August-September. To understand the variability in
sub-periods, we calculated 5, 11, and 21-year running means over the
reconstructed series (Table 2). A 5-year running mean showed
1788–1792 CE as the coldest and 1890–1894 CE as the warmest period
in the entire record. An 11-year running mean depicted 1986–1996 CE
as the coldest and 1890–1900 CE as the warmest followed by 1946–1956
CE and 2000–2010 CE. The 21-year running mean showed 1978–1998
CE, 1925–1945 CE as cool and 1890–1910 CE, 1946–1966 CE as warm
periods (Table 2) (Fig. 3). The annual uctuations in the present
reconstruction showed high coherency with those recorded in Feb–June
mean temperature reconstruction from Tons region of Uttarakhand (r =
0.65, p <0.00001, 1785–2002 CE) (Singh and Yadav, 2014) (Fig. 4a and
b). This reconstruction captured cool periods during 1911–1940 CE and
warming in 1879–1908 CE and 1941–1970 CE along with the decline in
Fig. 2. Instrumental March–June temperature data (black line) plotted with
corresponding months’ reconstruction (red line) for the period
(1898–2015 CE).
Table 2
A 5-year, 11-year and 21-year running means of March–June temperature
reconstruction.
Period Cool (◦C) Period Warm (◦C)
5 Year
1788–1792 13.35 1890–1894 15.43
1989–1993 13.79 1802–1806 15.37
1851–1855 13.93 1946–1950 15.34
1934–1938 13.95 2009–2013 15.29
1835–1839 13.98 1846–1850 15.18
11 Year
1986–1996 13.99 1890–1900 15.22
1786–1796 14.14 1946–1956 14.98
1910–1920 14.18 2000–2010 14.93
1933–1943 14.21 1797–1807 14.92
1836–1846 14.25 1813–1823 14.80
21 Year
1978–1998 14.21 1890–1910 14.96
1925–1945 14.32 1946–1966 14.91
1826–1846 14.36 1802–1822 14.78
1866–1886 14.40 1995–2015 14.75
Fig. 3. March–June temperature reconstruction (1785–2015 CE) for the Dok-
riani glacier region. Red bold line represents the 20 year low pass lter and
black dotted line is the long-term mean of the reconstructed series. Shaded area
represents the upper and lower error limits.
T. Rastogi et al.

Quaternary International 664 (2023) 33–41
37
temperature in the last few decades of the 20th century (Fig. 4b) (Singh
and Yadav, 2014). Our reconstruction also resembles well (r =0.48, p <
0.00001, 1785–2000 CE) with spring (March-April-May) temperature
reconstruction showing similar cool and warm phases (Yadav et al.,
2004) (Fig. 4c), and with premonsoon (April–May) temperature record
from Garhwal Himalaya (Yadav et al., 1997). The temperature vari-
ability of the latter half of 20th century captured in these studies from
the Western Himalaya were in agreement with our observations of this
period with no abnormal warming observed in the reconstruction as well
as the instrumental record.
Our record also registers temperature signal at a regional scale. Cool
periods in the rst half of the 19th century are also observed in tem-
perature records from Karakoram (Esper et al., 2002), and Central Asia
(Briffa et al., 2001; Esper et al., 2002) (Table 2). The prominent late 18th
century cooling in our record has been captured in Tian Shan (Chen
et al., 2019), Tibetan plateau (Xu et al., 2019) and Bhutan (Krusic et al.,
2015). And the reconstructions from Nepal (Cook et al., 2003), Tibet and
Central Asia (Briffa et al., 2001) are also in agreement with cool phase
observed in the last few decades of the 20th century in our reconstruc-
tion. Cool phases recorded in 1826–1846 CE, 1910–1920 CE and
1978–1998 CE resembled with the recent temperature reconstruction
from southeast Tibet (Yang et al., 2010) (Table 2). Spatial correlation of
the March–June reconstructed temperature series with corresponding
months gridded data (1901–2015 CE) also endorsed signicant associ-
ation with Western Himalaya, Central Himalaya and the Tibetan
plateau, indicating regional scale signatures in our reconstruction
(Fig. 5). High spatial correlation (r ≥0.5) was noticed between latitudes
~25◦-35◦N and longitude ~73◦-85◦E (Fig. 5).
Low frequency signals captured in our March–June temperature re-
cord match with Asia 2k temperature (June-July-August) reconstruction
(Pages 2k Consortium, 2013) and northern hemisphere temperature
(May-June-July-August) reconstruction (Wilson et al., 2016) (Fig. 6).
Cooling in the late 18th century was comparable among the three re-
constructions. Low temperature phases during the rst half of the 19th
century during the Little Ice Age (LIA) in the Asia 2k and Northern
hemisphere record was observed as a stable to low phase in our record
with, however, a similar uctuation pattern. A high temperature phase
is seen during 1890–1900s CE in Asia 2k and present temperature re-
cord, but that is not well registered in the Northern hemisphere record.
High temperatures were also recorded in these three records during
1940–1960s CE. Overall, in the 20th century the uctuation pattern is
similar, but steady rise in temperature was observed in the northern
hemisphere from the 1910s. However, the Asia 2k record showed this to
a lesser degree, whereas March–June temperature showed no such trend
(Fig. 6). It is worth noting that these reconstructions correspond to
different seasonalities and inclusion/exclusion of months inuences the
magnitude and is ultimately responsible for variations in trend/-
uctuations among the three reconstructions. The Asia 2k and Northern
hemisphere record represent summer months’ temperature,
Fig. 4. Comparison of (a) March–June temperature reconstruction with (b)
Feb–June temperature reconstruction from Tons valley, Uttarakhand (Singh
and Yadav, 2014) and (c) March-April-May temperature reconstruction from
Uttarakhand (Yadav et al., 2004). Shaded blue areas represent common cool
periods. A 10 year low pass lter was applied to all temperature anomaly series
which were derived from mean and standard deviation of temperature data for
the common period (1785–2000 CE).
Fig. 5. Spatial correlation between reconstructed March–June temperature and corresponding months’ gridded CRU TS4.01 temperature data (1901–2015 CE).
Figure was generated using KNMI climate explorer (http://climexp.knmi.nl/).
T. Rastogi et al.

Quaternary International 664 (2023) 33–41
38
June-July-August and May-June-July-August temperature, respectively.
Our March–June temperature record has an overlap of only one month
(i.e. June) with Asia 2k and two months (i.e. May–June) with Northern
hemisphere temperature record and that could be major cause of
magnitude difference. Asia 2k record matches well with March–June
temperature reconstruction up to 1970 CE. Further, to understand the
recent scenario, we analyzed linear trends in sub-periods of recent part
(i.e. since 1901 CE up to available data of respective series) of Northern
Hemisphere, Asia 2k and the present study (March–June temperature;
instrumental and reconstruction), which revealed negligible increase
during 1901–1989 CE in March–June temperature record with respect
to other two records. However, trends from 1990 CE up to recent
available data revealed surge in rise and resembled well with Northern
hemisphere record (Supplementary Fig. S4).
To understand in depth the variability and its plausible causes during
recent part, we analyzed sub-periods of our March–June temperature
record, which showed no signicant temperature trend in the 20th
century (1901–2000 CE.). However, it captured a slight insignicant
increase, supporting the temperature rise, over 1901–2015 CE. This
implies that the warming in the 21st century, though not anomalous, has
contributed to the rising trend in the region. The March–June instru-
mental data also depicts an increasing but statistically insignicant
trend during entire available period (i.e. 1901-2019 CE). Recent part of
reconstruction and instrumental data showed surge in temperature from
1990s, which was also reported by Bhutiyani et al. (2007) in the Western
Himalayan region. Low temperature phase observed in our present re-
cord during 1980–2000 CE corresponds to decreasing mean temperature
because of divergent temperature trends of premonsoon maximum and
minimum temperatures, and increase in diurnal temperature range
(DTR), in the latter half of the 20th century in the Himalayan region
which was associated with large scale land degradation and deforesta-
tion (Yadav et al., 2004). Decreasing minimum temperatures (or vari-
ability 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. Low or no cloud cover allows
for more radiation to reach the earth’s surface causing the daytime
maximum temperature to increase, whereas high cloud cover has the
opposite effect, thus, decreasing daytime maximum temperature (Pyr-
gou et al., 2019). Hamal et al. (2021) have reported that high cloud
cover causes an increase in overall minimum temperature and decrease
in maximum temperature. Therefore, we postulate that recent mean
temperature surge could be a result of a rise in minimum temperatures,
due to increase in cloud cover associated DTR decrease. However, this
needs to be further investigated for better understanding.
To understand the recurrence behavior, we analyzed the spectral
characteristics of our March–June temperature reconstruction using the
multi-taper method (Mann and Lees, 1996) which revealed peaks at
2.08, 2.61–2.63, 2.67, 2.81–2.89, 3.54–3.55, 3.64–3.68 and 8.83–8.98
year cycles (signicant at 99% condence level) (Supplementary
Fig. S5). The shorter periodicities at 2.08, 2.61–2.63, 2.67, 2.81–2.89,
3.54–3.55 and 3.64–3.68 seem to reect the El Nino Southern Oscilla-
tion (ENSO) (Trenberth, 1976) signal of the Pacic Ocean while the
periodicities at 8.83–8.98 fall in the range of the Northern Atlantic
Oscillation (NAO). A similar ENSO signal was registered in temperature
reconstructions in various studies from Western Himalaya (Shah et al.,
2019; Singh and Yadav, 2014), Nepal (Thapa et al., 2015; Aryal et al.,
2020; Gaire et al., 2020) and Tibetan Plateau (Liang et al., 2008). Peaks
within the ENSO variability range were also observed in precipitation
records from the Western Himalaya (Singh et al., 2006, 2009; Yadav
et al., 2014). Similarly, NAO signal was registered in temperature (Khan
et al., 2021) and precipitation (Singh et al., 2009; Yadav, 2011) records
from Western Himalaya. The presence of periodicities in our recon-
struction within the range of ENSO and NAO cycles suggests that the
temperature of our study area is inuenced by global circulation
systems.
4.3.2. March–June temperature and Dokriani glacier mass balance linkage
To understand the relationship between glacier mass balance and
temperature, and explore its potential in understanding long-term
glacier variability, we correlated the March–June instrumental and
reconstructed temperature data with the modelled winter (Novem-
ber–April), summer (May–October) and annual mass balance (Azam and
Srivastava, 2020). The analysis revealed high correlation of winter mass
balance with instrumental (r = − 0.97, p =0.03, n =4) and
Fig. 6. Comparison of 20 year low pass ltered plots of a) March–June temperature reconstruction (black solid line) with b) Asia 2k June-July-August temperature
reconstruction (blue solid line) (Pages 2k Consortium, 2013) and c) Northern hemisphere temperature record (red solid line) (Wilson et al., 2016). Shaded yellow
areas represent common warm periods.
T. Rastogi et al.

Quaternary International 664 (2023) 33–41
39
reconstructed (r = − 0.98, p =0.02, n =4) temperature data when
average winter mass balance from each of the four periods (1980–1988
CE, 1989–1997 CE, 1998–2006 CE, 2007–2018 CE) used by Azam and
Srivastava (2020) to understand the decadal variability was correlated
with averaged temperature from the corresponding overlapping periods.
On the basis of this signicant linkage between winter mass balance and
reconstructed temperature we identied periods of mass gain and mass
loss, with cool and warm temperature periods registering mass accu-
mulation and ablation, respectively. Thus, cool periods during
1786–1796 CE, 1836–1846 CE, 1910–1920 CE, 1933–1943 CE, and
warm periods during, 1890–1900 CE and 1946–1956 CE in our recon-
struction could have witnessed mass accumulation and mass wasting
episodes, respectively (Fig. 7). Similarly, comparison with the modelled
dataset showed that the glacier has witnessed a period of mass gain
through the late 1980s CE and 1990s CE, and loss thereafter which
increased until 2015 CE (Fig. 7). This trend is mirrored by our temper-
ature reconstruction which depicts cooling during 1986–1996 CE and
subsequent temperature high in the 21st century (Azam and Srivastava,
2020). Studies from the Central Himalaya also reveal an increase in
temperature and drying in the recent decades (Cannon et al., 2015;
Norris et al., 2019). A recent composite annual glacier mass balance
reconstruction developed from four glaciers (including Dokriani glacier)
in Uttarakhand Himalaya also depicts drastic increase in mass loss in the
last 30 years, indicating that the glaciers are in a state of accelerated
degradation (Singh et al., 2021). Thus, the phases of mass loss and mass
gain identied through comparison between modelled winter mass
balance dataset and temperature record are in agreement with other
studies in this region, corroborating the strong linkage between tem-
perature and mass balance, and further, indicating its potential in
unfolding the Dokriani glacier winter dynamics in the long-term
perspective.
5. Conclusions
We developed a ring-width chronology extending back to 1656 CE
from Himalayan r (A. pindrow) which was further used to develop
March–June temperature reconstruction spanning 231 years
(1785–2015 CE) for the Dokriani glacier region, Uttarakhand, Western
Himalaya. In the present study, the 21-year running mean periods
1978–1998 CE, 1925–1945 CE, and 1890–1910 CE, 1946–1966 CE were
observed as cool and warm, respectively, over the span of the record.
The reconstruction captured strong coherence with tree-ring based
temperature reconstructions from the Garhwal Himalaya. There was
also a broad agreement with other tree-ring based reconstructions from
the Western Himalaya, Nepal Himalaya and the Tibetan plateau, indi-
cating the relevance of the reconstruction in understanding climate
variability at regional scale. Present record also showed association at
low frequency with global temperature record (Asia and Northern
Hemisphere). We observed a negligible rise in temperature during
1901–1989 CE with respect to Asia 2k and Northern hemisphere
records, however, a surge in rise was observed from 1990 CE that
continued in the 21st century, which is also evident in the Northern
hemisphere temperature record. We postulate that cloud cover is among
the factors governing recent temperature in the region.
March–June temperature reconstruction also showed strong associ-
ation with Dokriani glacier winter mass balance (November–April),
reecting cool (warm) periods could have witnessed mass accumulation
(mass wasting) episodes. Rapid mass loss of the Dokriani glacier since
1990s leading to a rise in mass wasting in the late 20th and early 21st
century is in good agreement with rising temperature and increased
drying in the Central Himalaya.
Such rst temperature record exclusively from the Dokriani region in
the upper reaches of the Ganga river, revealing a strong linkage with the
Dokriani glacier winter mass balance, provides a valuable dataset in
understanding climate variability and glacier response in the past with
respect to climate change. This is essential for planning mitigation and
resource management strategies in light of the recent and predicted
future warming.
Data availability
Data would be available on request.
Funding
This study was funded by the Wadia Institute of Himalayan Geology
(An Autonomous Institution of Department of Science & Technology,
Government of India), Dehradun, India.
CRediT authorship contribution statement
Tanupriya Rastogi: conceived and designed the study, developed
reconstruction, performed analyses, wrote the paper. Jayendra Singh:
conceived and designed the study, participated in sample collection,
generated data, developed reconstruction, performed analyses, wrote
the paper. Nilendu Singh: participated in sample collection. Pankaj
Chauhan: participated in sample collection. Ram R. Yadav: developed
reconstruction, performed analyses, wrote the paper. Bindhyachal
Pandey: wrote the paper.
Declaration of competing interest
The authors declare that they have no competing interests.
Acknowledgements
Authors (TR, JS, RRY) express their sincere thanks to Director, Wadia
Institute of Himalayan Geology, Dehradun for necessary facilities.
Generous help offered by the Forest ofcials of Uttarakhand in collection
of research materials is sincerely acknowledged. This work comprises
Fig. 7. A 10 year low pass ltered March–June temperature reconstruction (black solid line) plotted with modelled winter mass balance data (1980–2015 CE) (after
Azam and Srivastava, 2020). Temperature anomaly series was derived using mean and standard deviation of the 20th century (1901–2000 CE).
T. Rastogi et al.

Quaternary International 664 (2023) 33–41
40
Wadia Institute of Himalayan Geology contribution number WIHG/
0143. Authors express their sincere thanks to the anonymous reviewers
for valuable suggestions which substantially improved the manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.quaint.2023.05.013.
References
Arora, M., Goel, N.K., Singh, P., 2005. Evaluation of temperature trends over India.
Hydrol. Sci. J. 50, 81–93.
Aryal, S., Gaire, N.P., Pokhrel, N.R., Rana, P., Sharma, B., Kharal, D.K., Poudel, B.S.,
Dyola, N., Fan, Z.-X., Grießinger, J., Br¨
auning, A., 2020. Spring season in western
Nepal Himalaya is not yet warming: a 400-year temperature reconstruction based on
tree-ring widths of Himalayan Hemlock (Tsuga dumosa). Atmosphere 11, 132.
Azam, M.F., Srivastava, S., 2020. Mass balance and runoff modelling of partially debris-
covered Dokriani Glacier in monsoon-dominated Himalaya using ERA5 data since
1979. J. Hydrol. 590, 125432.
Bhattacharyya, A., Chaudhary, V., 2003. Late-summer temperature reconstruction of the
eastern Himalayan region based on tree-ring data of Abies densa. Arctic Antarct.
Alpine Res. 35, 196–202.
Bhattacharyya, A., Ranhotra, P.S., Gergan, J.T., 2011. Vegetation vis-a-vis climate and
glacier history during 12,400 to 5,400 yr BP from Dokriani valley, Garhwal
Himalaya, India. J. Geol. Soc. India 77 (5), 401–408.
Bhutiyani, M.R., Kale, V.S., Pawar, N.J., 2007. Long-term trends in maximum, minimum
and mean annual air temperatures across the Northwestern Himalaya during the
twentieth century. Clim. Change 85, 159–177.
Biondi, F., Waikul, K., 2004. DENDROCLIM2002: a C++ program for statistical
calibration of climate signals in tree-ring chronologies. Comput. Geosci. 30,
303–311.
Borgaonkar, H.P., Pant, G.B., Rupa Kumar, K., 1996. Ring-width variations in Cedrus
deodara and its climatic response over the western Himalaya. Int. J. Climatol. 16,
1409–1422.
Borgaonkar, H.P., Pant, G.B., Kumar, K.R., 1999. Tree-ring chronologies from western
Himalaya and their dendroclimatic potential. IAWA J. 20, 295–309.
Borgaonkar, H.P., Sikder, A.B., Ram, S., 2011. High altitude forest sensitivity to the
recent warming: a tree-ring analysis of conifers from Western Himalaya, India. Quat.
Int. 236 (1–2), 158–166.
Briffa, K.R., Osborn, T.J., Schweingruber, F.H., Harris, I.C., Jones, P.D., Shiyatov, S.G.,
Vaganov, E.A., 2001. Low-frequency temperature variations from a northern tree
ring density network. J. Geophys. Res. Atmos. 106, 2929–2941.
Cannon, F., Carvalho, L.M., Jones, C., Bookhagen, B., 2015. Multi-annual variations in
winter westerly disturbance activity affecting the Himalaya. Clim. Dynam. 44,
441–455.
Champion, H.G., Seth, S.K., 1968. A Revised Survey of the Forest Types of India. Manager
of Publication, Government of India, New Delhi.
Chaudhary, V., Bhattacharyya, A., Guiot, J., Shah, S.K., Srivastava, S.K., Edouard, J.L.,
Thomas, A., 2013. Reconstruction of August-September Temperature in North-
Western Himalaya since AD 1773, Based on Tree-Ring Data of Pinus Wallichiana and
Abies Pindrow. Holocene: Perspectives, Environmental Dynamics and Impact Events;
Nova Science Publishers, New York, NY, USA, pp. 145–156.
Chen, F., Yuan, Y., Yu, S., Chen, F., 2019. A 391-year summer temperature
reconstruction of the Tien Shan, reveals far-reaching summer temperature signals
over the midlatitude Eurasian continent. J. Geophys. Res. Atmos. 124 (22),
11850–11862.
Cook, E.R., Briffa, K.R., Shiyatov, S., Mazepa, V., 1990. Tree-ring standardization and
growth-trend estimation. In: Methods of Dendrochronology: Applications in the
Environmental Sciences, pp. 104–123.
Cook, E.R., Meko, D.M., Stahle, D.W., Cleaveland, M.K., 1999. Drought reconstructions
for the continental United States. J. Clim. 12, 1145–1162.
Cook, E.R., Krusic, P.J., Jones, P.D., 2003. Dendroclimatic signals in long tree-ring
chronologies from the Himalayas of Nepal. Int. J. Climatol. 23, 707–732.
Dai, A., 2013. Increasing drought under global warming in observations and models. Nat.
Clim. Change 3, 52–58. https://doi.org/10.1038/nclimate1633.
Das, S.K., Jenamani, R.K., Kalsi, S.R., Panda, S.K., 2007. Some evidence of climate
change in twentieth-century India. Clim. Change 85, 299–321. https://doi.org/
10.1007/s10584-007-9305-9.
Diaz, H.F., Bradley, R.S., 1997. Temperature variations during the last century at high
elevation sites. In: Climatic Change at High Elevation Sites. Springer, Dordrecht,
pp. 21–47.
Dobhal, D.P., Gergan, J.T., Thayyen, R.J., 2004. Recession and morphogeometrical
changes of Dokriani glacier (1962-1995) Garhwal Himalaya, India. Curr. Sci. 86,
692–696.
Dobhal, D.P., Gergan, J.T., Thayyen, R.J., 2008. Mass balance studies of the Dokriani
glacier from to, Garhwal Himalaya, India. Bull. Glaciol. Res. 25, 9–17.
Dobhal, D.P., Mehta, M., 2010. Surface morphology, elevation changes and terminus
retreat of Dokriani Glacier, Garhwal Himalaya: implication for climate change.
Himal. Geol. 31, 71–78.
Donat, M., Lowry, A., Alexander, L., et al., 2016. More extreme precipitation in the
world’s dry and wet regions. Nat. Clim. Change 6, 508–513. https://doi.org/
10.1038/nclimate2941.
Duchenne-Moutien, R.A., Neetoo, H., 2021. Climate change and emerging food safety
issues: a review. J. Food Protect. 84 (11), 1884–1897.
Esper, J., Schweingruber, F.H., Winiger, M., 2002. 1300 years of climatic history for
Western Central Asia inferred from tree-rings. Holocene 12, 267–277.
Fischer, E., Knutti, R., 2015. Anthropogenic contribution to global occurrence of heavy-
precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564.
https://doi.org/10.1038/nclimate2617.
Fritts, H.C., 1976. Tree-Rings and Climate. Academic Press, San Diego, California.
Gaire, N.P., Fan, Z.X., Br¨
auning, A., Panthi, S., Rana, P., Shrestha, A., Bhuju, D.R., 2020.
Abies spectabilis shows stable growth relations to temperature, but changing
response to moisture conditions along an elevation gradient in the central Himalaya.
Dendrochronologia 60, 125675.
Garg, P.K., Yadav, J.S., Rai, S.K., Shukla, A., 2022. Mass balance and morphological
evolution of the Dokriani Glacier, central Himalaya, India during 1999–2014.
Geosci. Front. Times 13 (1), 101290.
Hamal, K., Sharma, S., Talchabhadel, R., Ali, M., Dhital, Y.P., Xu, T., Dawadi, B., 2021.
Trends in the diurnal temperature range over the southern slope of central Himalaya:
retrospective and prospective evaluation. Atmosphere 12 (12), 1683.
Hasnain, S.I., Thayyen, R.J., 1999. Discharge and suspended-sediment concentration of
meltwaters, draining from the Dokriani glacier, Garhwal Himalaya, India. J. Hydrol.
218, 191–198.
Hasnain, S.I., Jose, P.G., Ahmad, S., Negi, D.C., 2001. Character of the subglacial
drainage system in the ablation area of Dokriani glacier, India, as revealed by dye-
tracer studies. J. Hydrol. 248, 216–223.
Heim, A., Gansser, A., 1939. Central Himalaya. Hindustan Publishing, Delhi.
Holmes, R.L., 1983. Computer-assisted quality control in tree-ring dating and
measurement. Tree-Ring Bull. 43, 69–78.
Horton, R.M., Mankin, J.S., Lesk, C., Coffel, E., Raymond, C., 2016. A review of recent
advances in research on extreme heat events. Curr. Clim. Change Rep. 2 (4),
242–259.
IPCC, 2021. Summary for policymakers. In: Zhai, P., Pirani, A., Connors, S.L., P´
ean, C.,
Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., Huang, M., Leitzell, K.,
Lonnoy, E., Matthews, J.B.R., Maycock, T.K., Watereld, T., Yelekçi, O., Yu, R.,
Zhou, B. (Eds.), Climate Change 2021: the Physical Science Basis. Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change [Masson-Delmotte, V. Cambridge University Press (in press).
Kattel, D.B., Yao, T., Yang, K., Tian, L., Yang, G., Joswiak, D., 2013. Temperature lapse
rate in complex mountain terrain on the southern slope of the central Himalayas.
Theor. Appl. Climatol. 113 (3), 671–682.
Khan, A., Ahmed, M., Gaire, N.P., Iqbal, J., Siddiqui, M.F., Khan, A., et al., 2021. Tree-
ring-based temperature reconstruction from the western Himalayan region in
northern Pakistan since 1705 CE. Arabian J. Geosci. 14, 1–12.
Kraaijenbrink, P.D.A., Bierkens, M.F.P., Lutz, A.F., Immerzeel, W.W., 2017. Impact of a
global temperature rise of 1.5 degrees Celsius on Asia’s glaciers. Nature 549,
257–260.
Krusic, P.J., Cook, E.R., Dukpa, D., Putnam, A.E., Rupper, S., Schaefer, J., 2015. Six
hundred thirty-eight years of summer temperature variability over the Bhutanese
Himalaya. Geophys. Res. Lett. 42, 2988–2994.
Kumar, K.R., Kumar, K.K., Pant, G.B., 1994. Diurnal asymmetry of surface temperature
trends over India. Geophys. Res. Lett. 21 (8), 677–680.
Kumar, A., Verma, A., Dobhal, D.P., Mehta, M., Kesarwani, K., 2014. Climatic control on
extreme sediment transfer from Dokriani Glacier during monsoon, Garhwal
Himalaya (India). J. Earth Syst. Sci. 123, 109–120.
Levy, B., Patz, J., 2015. Climate Change and Public Health. Oxford University Press.
Liang, E., Shao, X., Qin, N., 2008. Tree-ring based summer temperature reconstruction
for the source region of the Yangtze River on the Tibetan Plateau. Global Planet.
Change 61, 313–320.
Malik, R., Rossi, S., Sukumar, R., 2020. Variations in the timing of different phenological
stages of cambial activity in Abies pindrow (Royle) along an elevation gradient in the
north-western Himalaya. Dendrochronologia 59, 125660.
Mall, R.K., Chaturvedi, M., Singh, N., Bhatla, R., Singh, R.S., Gupta, A., Niyogi, D., 2021.
Evidence of asymmetric change in diurnal temperature range in recent decades over
different agro-climatic zones of India. Int. J. Climatol. 41 (4), 2597–2610.
Mann, M.E., Lees, J.M., 1996. Robust estimation of background noise and signal
detection in climatic time series. Clim. Change 33, 409–445.
Meehl, G.A., Tebaldi, C., 2004. More intense, more frequent, and longer lasting heat
waves in the 21
st
century. Science 305, 994–997.
Mondal, A., Khare, D., Kundu, S., 2015. Spatial and temporal analysis of rainfall and
temperature trend of India. Theor. Appl. Climatol. 122 (1), 143–158.
Negi, S.S., 2000. Himalayan Forests and Forestry. Indus Publishing.
Neukom, R., Gergis, J., Karoly, D., et al., 2014. Inter-hemispheric temperature variability
over the past millennium. Nat. Clim. Change 4, 362–367. https://doi.org/10.1038/
nclimate2174.
Norris, J., Carvalho, L.M.V., Jones, C., Cannon, F., 2019. Deciphering the contrasting
climatic trends between the central Himalaya and Karakoram with 36 years of WRF
simulations. Clim. Dynam. 52, 159–180.
Ohmura, A., 2012. Enhanced temperature variability in high-altitude climate change.
Theor. Appl. Climatol. 110 (4), 499–508.
Pages 2k Consortium, 2013. Continental-scale temperature variability during the past
two millennia. Nat. Geosci. 6, 339–346.
Pal, I., Al-Tabbaa, A., 2010. Long-term changes and variability of monthly extreme
temperatures in India. Theor. Appl. Climatol. 100, 45–46. https://doi.org/10.1007/
s00704-009-0167.
Pepin, N., Bradley, R.S., Diaz, H.F., Baraer, M., Caceres, E.B., Forsythe, N., Fowler, H.,
Greenwood, G., Hashmi, M.Z., Liu, X.D., Miller, J.R., Ning, L., Ohmura, A.,
Palazzi, E., Rangwala, I., Sch¨
oner, W., Severskiy, I., Shahgedanova, M., Wang, M.B.,
T. Rastogi et al.

Quaternary International 664 (2023) 33–41
41
Williamson, S.N., Yang, D.Q., 2015. Elevation-dependent warming in mountain
regions of the world. Nat. Clim. Change. https://doi.org/10.1038/NCLIMATE2563.
Phadtare, N.R., 2000. Sharp decrease in summer monsoon strength 4000–3500 cal yr BP
in the Central Higher Himalaya of India based on pollen evidence from alpine peat.
Quat. Res. 53 (1), 122–129.
Pratap, B., Dobhal, D.P., Mehta, M., Bhambri, R., 2015. Influence of debris cover and
altitude on glacier surface melting: a case study on Dokriani Glacier, central
Himalaya, India. Ann. Glaciol. 56, 9–16.
Pratap, B., Dobhal, D.P., Bhambri, R., Mehta, M., Tewari, V.C., 2016. Four decades of
glacier mass balance observations in the Indian Himalaya. Reg. Environ. Change 16,
643–658.
Pyrgou, A., Santamouris, M., Livada, I., 2019. Spatiotemporal analysis of diurnal
temperature range: effect of urbanization, cloud cover, solar radiation, and
precipitation. Climate 7 (7), 89.
Ram, S., 2012. Tree growth–climate relationships of conifer trees and reconstruction of
summer season Palmer Drought Severity Index (PDSI) at Pahalgam in Srinagar,
India. Quat. Int. 254, 152–158.
Rinn, F., 2003. TSAPWin: Time Series Analysis and Presentation for Dendrochronology
and Related Applications−Frank Rinn (Heidelberg, Germany).
Sano, M., Furuta, F., Kobayashi, O., Sweda, T., 2005. Temperature variations since the
mid-18th century for western Nepal, as reconstructed from tree-ring width and
density of Abies spectabilis. Dendrochronologia 23, 83–92.
Shah, N.C., Joshi, M.C., 1971. An ethnobotanical study of the Kumaon region of India.
Econ. Bot. 25 (4), 414–422. http://www.jstor.org/stable/4253291.
Shah, S.K., Pandey, U., Mehrotra, N., Wiles, G.C., Chandra, R., 2019. A winter
temperature reconstruction for the Lidder Valley, Kashmir, Northwest Himalaya
based on tree-rings of Pinus wallichiana. Clim. Dynam. 53, 4059–4075.
Sharma, K.P., Moore, B., Vorosmarty, C.J., 2000. Anthropogenic, climatic, and
hydrologic trends in the Kosi basin, Himalaya. Clim. Change 47 (1), 141–165.
Shrestha, A.B., Wake, C., Mayewski, P., Dibb, J., 1999. Maximum temperature trends in
the Himalaya and vicinity: an analysis based on temperature records from Nepal for
the period 1971–94. J. Clim. 12, 2775–2786.
Singh, J., Yadav, R.R., 2005. Spring precipitation variations over the western Himalaya,
India, since AD 1731 as deduced from tree rings. J. Geophys. Res. Atmos. 110 (D1).
Singh, J., Park, W.K., Yadav, R.R., 2006. Tree-ring-based hydrological records for
western Himalaya, India, since AD 1560. Clim. Dynam. 26, 295–303.
Singh, J., Yadav, R.R., Wilmking, M., 2009. A 694-year tree-ring based rainfall
reconstruction from Himachal Pradesh, India. Clim. Dynam. 33, 1149.
Singh, J., Yadav, R.R., 2014. February–June temperature variability in western
Himalaya, India, since AD 1455. Quat. Int. 349, 98–104.
Singh, J., Singh, N., Chauhan, P., Yadav, R.R., Br¨
auning, A., Mayr, C., Rastogi, T., 2019.
Tree-ring δ18O records of abating June–July monsoon rainfall over the Himalayan
region in the last 273 years. Quat. Int. 532, 48–56.
Singh, N., Shekhar, M., Singh, J., Gupta, A.K., Br¨
auning, A., Mayr, C., Singhal, M., 2021.
Central Himalayan tree-ring isotopes reveal increasing regional heterogeneity and
enhancement in ice mass loss since the 1960s. Cryosphere 15, 95–112.
Singh, P., Haritashya, U.K., Kumar, N., 2004. Seasonal changes in meltwater storage and
drainage characteristics of the Dokriani Glacier, Garhwal Himalayas (India). Nord.
Hydrol 35, 15–29.
Sonali, P., Kumar, D.N., Nanjundiah, R.S., 2017. Intercomparison of CMIP5 and CMIP3
simulations of the 20
th
century maximum and minimum temperatures over India and
detection of climatic trends. Theor. Appl. Climatol. 128 (1–2), 465–489.
Stokes, M.A., Smiley, T.L., 1968. An Introduction to Tree-Ring Dating (Reprinted 1996).
Univ. of Ariz. Press, Tucson.
Teskey, R., Wertin, T., Bauweraerts, I., Ameye, M., McGuire, M.A., Steppe, K., 2015.
Responses of tree species to heat waves and extreme heat events. Plant Cell Environ.
38 (9), 1699–1712.
Thapa, U.K., Shah, S.K., Gaire, N.P., Bhuju, D.R., Bhattacharyya, A., Thagunna, G.S.,
2013. Inuence of climate on radial growth of Abies pindrow in Western Nepal
Himalaya. Banko Janakari 23, 14–19.
Thapa, U.K., Shah, S.K., Gaire, N.P., Bhuju, D.R., 2015. Spring temperatures in the far-
western Nepal Himalaya since AD 1640 reconstructed from Picea smithiana tree-ring
widths. Clim. Dynam. 45, 2069–2081.
Thayyen, R.J., Gergan, J.T., Dobhal, D.P., 2005. Monsoonal control on glacier discharge
and hydrograph characteristics, a case study of Dokriani Glacier, Garhwal Himalaya,
India. J. Hydrol. 306, 37–49.
Thayyen, R.J., Gergan, J.T., Dobhal, D.P., 2007. Role of glaciers and snow cover on
headwater river hydrology in monsoon regime—micro-scale study of Din Gad
catchment, Garhwal Himalaya, India. Curr. Sci. 376–382.
Thayyen, R.J., Gergan, J.T., 2010. Role of glaciers in watershed hydrology: a preliminary
study of a " Himalayan catchment". Cryosphere 4, 115–128.
Trenberth, K.E., 1976. Spatial and temporal variations of the southern oscillation. Q. J. R.
Meteorol. Soc. 102, 639–653.
Valdiya, K.S., 1998. Dynamic Himalaya. Universities Press.
Wigley, T.M.L., Briffa, K.R., Jones, P.D., 1984. On the average value of correlated time
series with applications in dendroclimatology and hydrometeorology. J. Clim. Appl.
Meteorol. 23, 201–213.
Wilson, R., Anchukaitis, K., Briffa, K.R., Büntgen, U., Cook, E., D’Arrigo, R., Davi, N.,
Esper, J., Frank, D., Gunnarson, B., Hegerl, G., Helama, S., Klesse, S., Krusic, P.J.,
Linderholm, H.W., Myglan, V., Osborn, T.J., Rydval, M., Schneider, L., Schurer, A.,
Wiles, G., Zhang, P., Zorita, E., 2016. Last millennium northern hemisphere summer
temperatures from tree rings: Part I: the long term context. Quat. Sci. Rev. 134, 1–18.
Worm, B., Lotze, H.K., 2021. Marine biodiversity and climate change. In: Climate
Change. Elsevier, pp. 445–464.
Xu, J., Grumbine, R., Shrestha, A., Eriksson, M., Yang, X., Wang, Y., Wilkes, A., 2009. The
melting Himalayas: cascading effects of climate change on water, biodiversity, and
livelihoods. Conserv. Biol. 23, 520–530.
Xu, G., Liu, X., Zhang, Q., Zhang, Q., Hudson, A., Trouet, V., 2019. Century-scale
temperature variability and onset of industrial-era warming in the Eastern Tibetan
Plateau. Clim. Dynam. 53 (7), 4569–4590.
Yadav, R.R., 2011. Long-term hydroclimatic variability in monsoon shadow zone of
western Himalaya, India. Clim. Dynam. 36, 1453–1462.
Yadav, R.R., Park, W.K., Bhattacharyya, A., 1997. Dendroclimatic reconstruction of
April–May temperature uctuations in the western Himalaya of India since AD 1698.
Quat. Res. 48, 187–191.
Yadav, R.R., Park, W.K., Bhattacharyya, A., 1999. Spring-temperature variations in
western Himalaya, India, as reconstructed from tree-rings: AD 1390-1987. Holocene
9, 85–90.
Yadav, R.R., Park, W.K., 2000. Precipitation reconstruction using ring-width chronology
of Himalayan cedar from western Himalaya: preliminary results. J. Earth Syst. Sci.
109 (3), 339–345.
Yadav, R.R., Park, W.K., Singh, J., Dubey, B., 2004. Do the western Himalayas defy
global warming? Geophys. Res. Lett. 31 (17).
Yadav, R.R., Misra, K.G., Kotlia, B.S., Upreti, N., 2014. Premonsoon precipitation
variability in Kumaon Himalaya, India over a perspective of~ 300 years. Quat. Int.
325, 213–219.
Yang, B., Kang, X., Br¨
auning, A., Liu, J., Qin, C., Liu, J., 2010. A 622-year regional
temperature history of southeast Tibet derived from tree rings. Holocene 20,
181–190.
Zhu, H.F., Shao, X.M., Yin, Z.Y., Huang, L., 2011. Early summer temperature
reconstruction in the eastern Tibetan Plateau since AD 1440 using tree-ring width of
Sabina tibetica. Theor. Appl. Climatol. 106, 45–53.
T. Rastogi et al.