Introduction
The current uncertainties concerning the glacial shrinkage in the Himalayas
are mainly attributed to a lack of measurements, both of the glaciers and of
climatic forcing agents (e.g., Bolch et al., 2012). Recent results underline
the need for a fine-scale investigation, especially at high altitude, to
better model the hydrological dynamics in this area. However, there are few
high elevation weather stations in the world where the glaciers are located
(Tartari et al., 2009). This can be attributed to the remote location of
glaciers, the rugged terrain, and a complex political situation, all of
which make physical access difficult (Bolch et al., 2012). As a consequence
of the remoteness and difficulty in accessing many high elevation sites
combined with the complications of operating automated weather stations
(AWSs) at these altitudes, long-term measurements are challenging (Vuille,
2011). However, nearly all global climate models report increased
sensitivity to warming at high elevations (e.g., Rangwala and Miller, 2012),
while observations are less clear (Pepin and Lundqist, 2008). Moreover,
changes in the timing or amount of precipitation are much more ambiguous and
difficult to detect, and there is no clear evidence of significant changes
in total precipitation patterns in most mountain regions (Vuille, 2011).
The need for a fine-scale investigation is particularly evident on the south
slope of Mt. Everest (central southern Himalaya, CH-S) as it is one of the
heavily glaciated parts of the Himalaya (Salerno et al., 2012; Thakuri et
al., 2014). Nevertheless, these glaciers have the potential to build up
moraine-dammed lakes storing large quantities of water, which are
susceptible to GLOFs (glacial lake outburst floods) (e.g., Salerno et al.,
2012; Fujita et al., 2013). Gardelle et al. (2011) noted that this region is
most characterized by glacial lakes in the Hindu Kush Karakorum Himalaya.
Recently, Thakuri et al. (2014) noted that the Mt. Everest glaciers
experienced an accelerated shrinkage in the last 20 years (1992–2011),
as underlined by an upward shift of the snow line altitude (SLA) with a
velocity almost 3 times greater than the previous period (1962–1992).
Furthermore Bolch et al. (2011) and Nuimura et al. (2012) found a higher
mass loss rate during the last decade (2000–2010). However, to date, there
are no continuous meteorological time series able to clarify the causes of
the melting process to which the glaciers of these slopes are subjected.
In this context, since the early 1990s, PYRAMID Observatory Laboratory
(5050 m a.s.l.) was created by the Ev-K2-CNR Committee (www.evk2cnr.org).
This observatory is located
at the highest elevation at which weather data have ever been collected in
the region and thus represents a valuable data set with which to investigate
the climate change in CH-S (Tartari et al., 2002; Lami et al., 2010).
However, the remoteness and the harsh conditions of the region over the
years have complicated the operations of the AWSs, obstructing long-term
measurements from a unique station.
In this paper, we mainly explore the small-scale climate variability of the
south slopes of Mt. Everest by analyzing the minimum, maximum, and mean air
temperature (T) and liquid precipitation (Prec) time series reconstructed
from seven AWSs located from 2660 to 5600 m a.s.l. over the last couple of
decades (1994–2013). Moreover, we complete this analysis with all existing
weather stations located on both sides of the Himalayan range (Koshi Basin)
for the same period. In general, this study has the ultimate goal of linking
the climate change patterns observed at high elevation with the glacier
responses over the last 20 years, during which a more rapid glacier
shrinkage process occurred in the region of investigation.
(a) Location of the study area in the Himalaya, where the
abbreviations WH, CH, EH represents the western, central and eastern Himalaya,
respectively (the suffixes -N and -S indicate the northern and southern
slopes). (b) Focused map on the spatial distribution of all meteorological
stations used in this study, where KO and DK stand for the Koshi and Dudh
Koshi Basins, respectively; SNP represents the Sagarmatha National Park. (c) Hypsometric
curve of SNP (upper DK Basin) and altitudinal glacier distribution. Along
this curve, the locations of meteorological stations belonging to PYRAMID
Observatory Laboratory are presented.
Region of investigation
The current study is focused on the Koshi (KO) Basin which is located in the
eastern part of central Himalaya (CH) (Yao et al., 2012; Thakuri et al.,
2014). To explore possible differences in the surroundings of Mt. Everest,
we decided to consider the north and south parts of CH (with the suffixes -N
and -S, respectively) separately (Fig. 1a). The KO River (58 100 km2
of the basin) originates in the Tibetan Plateau (TP) and the Nepali
highlands. The area considered in this study is within the latitudes of
27 and 28.5∘ N and longitudes of 85.5 and
88∘ E. The altitudinal gradient of this basin is the highest in
the world, ranging from 77 to 8848 m a.s.l., i.e. Mt. Everest. We subdivide
the KO Basin into the northern side (KO-N), belonging to the CH-N, and
southern side (KO-S), belonging to the CH-S. The southern slopes of Mt. Everest
are part of the Sagarmatha (Everest) National Park (SNP) (Fig. 1b),
where the small-scale climate variability at high elevation is investigated.
The SNP is the world's highest protected area, with over 30 000 tourists in
2008 (Salerno et al., 2010a, 2013). The park area (1148 km2),
extending from an elevation of 2845–8848 m a.s.l., covers the
upper Dudh Koshi (DK) Basin (Manfredi et al., 2010; Amatya et al., 2010).
Land-cover classification shows that almost one-third of the territory is
characterized by glaciers and ice cover (Salerno et al., 2008; Tartari et
al., 2008), while less than 10 % of the park area is forested (Bajracharya
et al., 2010; Salerno et al., 2010b). The SNP presents a broad range of
bioclimatic conditions with three main bioclimatic zones: the zone of alpine
scrub; the upper alpine zone, which includes the upper limit of vegetation
growth; and the Arctic zone, where no plants can grow (UNEP and WCMC, 2008).
Figure 1c shows the glacier distribution along the hypsometric curve of the
SNP. We observe that the glacier surfaces are distributed from 4300 m to
above 8000 m a.s.l., with more than 75 % of the glacier surfaces lying
between 5000 m and 6500 m a.s.l. The 2011 area-weighted mean elevation of
the glaciers was 5720 m a.s.l. (Thakuri et al., 2014). These
glaciers are identified as the summer accumulation-type fed mainly by summer
Prec from the South Asian monsoon system, whereas the winter Prec caused by
the mid-latitude westerly wind is minimal (Yao et al., 2012). The prevailing
direction of the monsoons is S-N and SW-NE (e.g., Ichiyanagi et al., 2007).
The climate is influenced by the monsoon system because the area is located
in the subtropical zone with nearly 90 % of the annual Prec falling in the
months of June–September (this study). Heavy autumn and winter snowfalls
can occur in association with tropical cyclones and westerly disturbances,
respectively, and snow accumulation can occur at high elevations at all
times of the year (Benn et al., 2012). Bollasina et al. (2011) have demonstrated
the presence of well-defined local circulatory systems in the Khumbu Valley
(SNP). The local circulation is dominated by a system of mountain and valley
breezes. The valley breeze blows (approximately 4 m s-1) from the south
every day from sunrise to sunset throughout the monsoon season, pushing the
clouds that bring Prec northward.
Data
Weather stations at high elevation
The first automatic weather station (named hereafter AWS0) at
5050 m a.s.l. near PYRAMID Observatory Laboratory (Fig. 1c), was established in
October 1993, it has run continuously all year round (Bertolani et al., 2000). The
station, operating in extreme conditions, had recorded long-term
ground-based temperature and temperature data which are considered valid
until December 2005. Due to the obsolescence of technology, the station was
disposed of in 2006. A new station (named hereafter AWS1) was installed just
a few tens of meters away from AWS0 and has been operating since October 2000.
Other stations were installed in the following years in the upper DK
Basin in the Khumbu Valley (Table 1). In 2008, the network included sixth
monitoring points, including the highest weather station of the world,
located at South Col of Mt. Everest (7986 m a.s.l.). The locations of all
stations are presented in Fig. 1b. We can observe in Fig. 1c that this
meteorological network well represents the climatic conditions of the SNP
glaciers: AWS0 and AWS1 (5035 m a.s.l.) characterize the glacier fronts
(4870 m a.s.l.), AWS4 (5600 m a.s.l.) represents the mean elevation of
glaciers in the area (5720 m a.s.l.), and AWS5, the surface station at South
Col (7986 m a.s.l.), characterizes the highest peaks (8848 m a.s.l.).
List of surface stations belonging to PYRAMID Observatory
Laboratory network located along the south slopes of Mt. Everest (upper DK Basin).
Station
Location
Latitude
Longitude
Elevation
Sampling
Data Availability
% of daily missing data
ID
∘ N
∘ E
m a.s.l.
Frequency
From
To
Air Temperature
Precipitation
AWS3
Lukla
27.70
86.72
2660
1 h
2 Nov 2004
31 Dec 2012
23
20
AWSN
Namche
27.80
86.71
3570
1 h
27 Oct 2001
31 Dec 2012
21
27
AWS2
Pheriche
27.90
86.82
4260
1 h
25 Oct 2001
31 Dec 2013
15
22
AWS0
Pyramid
27.96
86.81
5035
2 h
1 Jan 1994
31 Dec 2005
19
16
AWS1
Pyramid
27.96
86.81
5035
1 h
1 Jan 2000
31 Dec 2013
10
21
ABC
Pyramid
27.96
86.82
5079
1 h
1 Mar 2006
31 Dec 2011
5
1
AWS4
Kala Patthar
27.99
86.83
5600
10 min
1 Jan 2009
31 Dec 2013
28
38
AWS5
South Col
27.96
86.93
7986
10 min
1 May 2008
31 Dec 2011
39
100
All stations, except AWS5 (only T), record at least T and Prec. This data set
presents some gaps (listed in Table 1) as a consequence of the complications
of operating AWS at these altitudes. The list of measured variables for each
station and relevant data can be downloaded from
http://geonetwork.evk2cnr.org/. Data processing and quality checks are
performed according to the international standards of the WMO (World
Meteorological Organization).
Mean monthly cumulated precipitation subdivided into
snowfall and rainfall and minimum, maximum, and mean temperature at
5050 m a.s.l. (reference period 1994–2013). The bars represent the
standard deviation.
The Prec sensors at these locations are conventional heated tipping buckets
which may not fully capture the solid Prec. Therefore, solid Prec is
probably underestimated, especially in winter. However, in order to know the
magnitude of the possible underestimation of the solid phase, we compared
the monthly mean Prec of the reconstructed PYRAMID series (1994–2013 period)
with the Prec of a station located downstream at 2619 m a.s.l. (Chaurikhark,
ID 1202), (Fig. 1b, Table 2) which presents monthly mean temperature above
0 ∘C even during the winter and thus a high prevalence of liquid
Prec also during these months. This comparison, supported by the elevated
correlation existing between the monthly Prec of the two stations, shows a
slight underestimation of the PYRAMID snow (about 3 ± 1 % of total
annual precipitation registered at PYRAMID, see Supplement Sect. 3 for
more details). Therefore, having much reduced the underestimation, we decided
not to manipulate data. However the trends hereafter reported refer mainly to the liquid phase of Prec. In this regard, according to both Fujita
and Sakai (2014) and field observations (Ueno et al., 1994), the
precipitation phase has been taken into account assuming that the
probability of snowfall and rainfall depends on mean daily air temperature,
using as thresholds - as proposed by the aforementioned authors -0 and
4 ∘C, respectively. In Fig. 2 we first of all
observe that at 5050 m a.s.l. 90 % of precipitation is concentrated during
June–September and that the probability of snowfall is very low (4 %),
considering that the mean daily temperature during these months is above
0 ∘C. On a yearly basis, this probability reaches 20 % of the
annual cumulated precipitation.
List of ground weather stations located in the Koshi
Basin and descriptive statistics of the Sen's slopes for minimum,
maximum, and mean air temperatures and total precipitation for the
1994–2012 period. The annual mean air temperature, the total annual
mean precipitation, and the percentage of missing daily values is also
reported. Level of significance.
Air Temperature
Precipitation
ID
Station Name
Latitude
Longitude
Elevation
Annual mean
Missing values
MinT trend
MaxT Trend
MeanT trend
Annual total
Missing values
Prec trend
∘N
∘N
m a.s.l.
∘C
%
∘C a-1
∘C a-1
∘C a-1
mm
%
mm a-1
KO-S (NEPAL)
1024
DHULIKHEL
27.61
85.55
1552
17.1
2
-0.012
0.041
0.026
1036
PANCHKHAL
27.68
85.63
865
21.4
10
0.038
0.051b
0.038a
1191
10
-25.0b
1058
TARKE GHYANG
28.00
85.55
2480
3669
10
-21.9
1101
NAGDAHA
27.68
86.10
850
1369
3
-1.4
1103
JIRI
27.63
86.23
2003
14.4
1
0.013
0.020
0.014a
2484
4
6.6
1202
CHAURIKHARK
27.70
86.71
2619
2148
2
1.3
1206
OKHALDHUNGA
27.31
86.50
1720
17.6
2
-0.017
0.042
0.000
1786
3
-5.1
1210
KURULE GHAT
27.13
86.41
497
1017
2
-23.4a
1211
KHOTANG BAZAR
27.03
86.83
1295
1324
4
15.9
1222
DIKTEL
27.21
86.80
1623
1402
6
10.4
1301
NUM
27.55
87.28
1497
4537
6
-54.3c
1303
CHAINPUR (EAST)
27.28
87.33
1329
19.1
0
-0.127b
0.024
-0.064a
1469
0
-1.1
1304
PAKHRIBAS
27.05
87.28
1680
16.7
0
-0.005
0.036b
0.015
1540
4
-3.7
1307
DHANKUTA
26.98
87.35
1210
20.0
0
-0.002
0.153d
0.071d
942
6
-9.2a
1314
TERHATHUM
27.13
87.55
1633
18.2
10
0.033
0.066a
0.049b
1052
6
-13.1a
1317
CHEPUWA
27.46
87.25
2590
2531
5
-41.9b
1322
MACHUWAGHAT
26.96
87.16
158
1429
6
-22.9a
1403
LUNGTHUNG
27.55
87.78
1780
2347
1
2.6
1405
TAPLEJUNG
27.35
87.66
1732
16.6
1
0.060b
0.085c
0.071c
1966
3
-11.6
1419
PHIDIM
27.15
87.75
1205
21.2
7
0.047b
0.082c
0.067c
1287
2
-13.6b
MEAN
27.33
87.00
1587
17.9
2
0.003
0.060a
0.029a
1527
4
-11.1
PYRAMID
27.96
86.81
5035
-2.4
0
0.072d
0.009
0.044b
449
0
-13.7d
KO-S (NEPAL)
DINGRI
28.63
87.08
4,302
3.5
0
0.037a
0.041a
0.037b
309
0
-0.1
NYALAM
28.18
85.97
3,811
4.1
0
0.032a
0.036a
0.036a
616
0
-0.2
MEAN
28.41
86.53
4,057
3.8
0.1
0.034a
0.039a
0.037b
463
0
-0.1
a p-value = 0.1, b p-value = 0.05,
c p-value = 0.01, and d p-value = 0.001.
Other weather stations at lower altitude in the Koshi Basin
In KO-S Basin (Nepal), the stations are operated by the Department of
Hydrology and Meteorology (DHM) (www.dhm.gov.np/). For daily T and Prec, we
selected 10 stations for T and 19 stations for Prec considering both the
length of the series and the monitoring continuity (< 10 % of
missing daily data). The selected stations cover an elevation range between
158 and 2619 m a.s.l. (Table 3). In KO-N Basin (TP, China), the number of
ground weather stations (operated by the Chinese Academy of Science (CAS)),
selected with the same criteria mentioned above, is considerably smaller,
just two, but these stations have a higher elevation (4302 m a.s.l. for the
Dingri station and 3811 m a.s.l. for the Nyalam station).
The quality insurance of these meteorological data is ensured considering
that they are used as part of global and regional networks including for
instance APHRODITE (Asian Precipitation–Highly Resolved Observational Data
Integration Towards Evaluation of Water Resources) (Yasutomi et al., 2011)
and GHCN (Global Historical Climatology Network) (Menne et al., 2012).
Descriptive statistics of the Sen's slopes on a
seasonal basis for minimum, maximum, and mean air temperatures and total
precipitation of weather stations located in the Koshi Basin for the
1994–2012 period. The Nepali and Tibetan stations are aggregated as mean
values. Level of significance.
Annual and seasonal temperature trends are expressed as ∘C yr-1.
Annual precipitation trend is expressed as mm yr-1, while
the seasonal precipitation trends are in mm (4 months) yr-1.
Location
Minimum Temperature
Maximum Temperature
Mean Temperature
Total Precipitation
Pre-
Monsoon
Post-
Annual
Pre-
Monsoon
Post-
Annual
Pre-
Monsoon
Post-
Annual
Pre-
Monsoon
Post-
Annual
SOUTHER KOSHI
BASIN (KO-S, NEPAL)
0.012
-0.005
-0.001
0.003
0.076a
0.052
0.069a
0.060a
0.043
0.020
0.030
0.030a
0.8
-8.6
-2.5
-11.1
PYRAMID
(NEPAL)
0.067a
0.041a
0.151d
0.072d
0.024
-0.028
0.049
0.009
0.035
0.015
0.124c
0.044c
-2.5c
-9.3c
-1.4c
-13.7d
NORTHERN KOSHI
BASIN (KO-N,TIBET)
0.042a
0.019
0.086b
0.034a
0.023
0.030
0.071b
0.039a
0.042a
0.013
0.084b
0.037b
2.2
0.4
-3.3b
-0.1
(a p-value = 0.1, b p-value = 0.05,
c p-value = 0.01, and d p-value = 0.001).
Methods
We define the pre-monsoon, monsoon, and post-monsoon seasons as the months
from February to May, June to September, and October to January,
respectively. The minT, maxT, and meanT are calculated as the minimum,
maximum, and mean daily air temperature. For total precipitation (Prec), we
calculate the mean of the cumulative precipitation for the analyzed period.
Reconstruction of the daily temperature and precipitation time series at
high elevation
The two stations named AWS0 and AWS1 in the last 20 years, considering
the extreme weather conditions of this area, present a percentage of missing
daily values of approximately 20 % (Table 1). The other stations
(hereafter named secondary stations) were used here for infilling the gaps
according to a priority criteria based on the degree of correlation among
data. AWS1 was chosen as the reference station given the length of the time
series and that it is currently still operating. Therefore, our
reconstruction (hereafter named PYRAMID) is referred to an elevation of
5035 m a.s.l.
The selected infilling method is a simple regression analysis based on
quantile mapping (e.g., Déqué, 2007; Themeßl et al., 2012). This
regression method has been preferred to more complex techniques, such as the
fuzzy rule-based approach (Abebe et al., 2000) or the artificial neural
networks (Abudu et al., 2010; Coulibaly and Evora, 2007), considering the
peculiarity of this case study. In fact, all stations are located in the
same valley (Khumbu Valley). This aspect confines the variance among the
stations to the altitudinal gradient of the considered variable (T or Prec),
which can be easily reproduced by the stochastic link created by the
quantile mapping method. In case all stations registered a simultaneous gap,
we apply a multiple imputation technique (Schneider, 2001) that uses some
other proxy variables to fill the remaining missing data. Details on the
reconstruction procedure and the computation of the associated uncertainty
are provided in Supplement Sect. 1.
The trends analysis: the sequential Mann–Kendall test
The Mann–Kendall (MK) test (Kendall, 1975) is widely adopted to assess
significant trends in hydro-meteorological time series (e.g., Carraro et
al., 2012a, b; Guyennon et al., 2013). This test is non-parametric, thereby less sensitive to extreme sample values, and is independent of the
hypothesis about the nature of the trend, whether linear or not. The MK test
verifies the assumption of the stationarity of the investigated series by
ensuring that the associated normalized Kendall's tau coefficient, μ(τ), is included within the confidence interval for a given
significance level (for α=5 %, the μ(τ) is below
-1.96 and above 1.96). In the sequential form (seqMK) (Gerstengarbe and
Werner, 1999), μ(τ) is calculated for each element of the sample.
The procedure is applied forward starting from the oldest values
(progressive) and backward starting from the most recent values
(retrograde). If no trend is present, the patterns of progressive and
retrograde μ(τ) vs. time (i.e. years) present several
crossing points, while a unique crossing period allows the approximate
location of the starting point of the trend (e.g., Bocchiola and Diolaiuti, 2010).
In this study, the seqMK is applied to monthly vectors. Monitoring the
seasonal non-stationarity, the monthly progressive μ(τ) is
reported with a pseudo colour code, where the warm colours represent the
positive slopes and cold colours the negative ones. Colour codes associated
with values outside of the range (-1.96 to 1.96) possess darker tones to
highlight the trend significance (Salerno et al., 2014). Moreover, to
monitor the overall non-stationarity of the time series, both the
progressive and the retrograde μ(τ) at the annual scale are
reported. We used the Sen's slope proposed by Sen (1968) as a robust linear
regression allowing the quantification of the potential trends revealed by
the seqMK (e.g., Bocchiola and Diolaiuti, 2010). The significance level is
established for p<0.05. We define a slight significance for p<0.10.
The uncertainty associated with the Sen's slopes (1994–2013)
is estimated through a Monte Carlo uncertainty analysis (e.g., James and
Oldenburg, 1997), described in detail in Supplement Sect. 1.
Temperature and precipitation monthly time series
(1994–2013) reconstructed at high elevations of Mt. Everest (PYRAMID):
minimum (a), maximum (b), and mean temperature (c), and precipitation (d).
Uncertainty at 95 % is presented as a grey bar. The red lines represents the
robust linear fitting of the time series characterized by the associated
Sen's slope. According to Dytham (2011), the intercepts are calculated by
taking the slopes back from every observation to the origin. The intercepts
used in here represent the median values of the intercepts calculated for every
point (Lavagnini et al., 2011). For precipitation the linear fitting
refers to the right axis.
Results
Trend analysis at high elevation
Figure 3 shows the reconstructed PYRAMID time series for minT, maxT, meanT,
and Prec resulting from the overall infilling process explained in
Supplement Sect. 1. Figure 4 analyzes the monthly trends of T and Prec
from 1994 to 2013 for PYRAMID.
Trend analysis for (a) minimum, (b) maximum, and (c) mean
air temperatures and (d) total precipitation in the upper DK Basin. The top
graph of each meteorological variable shows the monthly trend (dark line)
and uncertainty due to the reconstruction process (grey bars). The central
grid displays the results of the sequential Mann–Kendall (seqMK) test applied
at the monthly level. On the left, the colour bar represents the normalized
Kendall's tau coefficient μ (τ). The colour tones below -1.96
and above 1.96 are significant (α=5 %). On the right, the
monthly Sen's slopes and the relevant significance levels for the 1994–2013
period (∘ p-value = 0.1, * p-value = 0.05, ** p-value = 0.01,
and *** p-value = 0.001). The bottom graph plots the progressive (black line)
and retrograde (dotted line) μ (τ) applied on the annual scale. On the right, the annual
Sen's slope is shown for the 1994–2013 period. MATLAB®
script is available at http://www.irsa.cnr.it/Docs/Code/MSeqMK.m.
Minimum air temperature (minT)
November (+0.17 ∘C yr-1, p<0.01) and December
(+0.21 ∘C yr-1, p<0.01) present the highest
increasing trend; i.e. both of these 2 months experienced about an even
+4 ∘C increase over 20 years (Fig. 4a). In general, the post- and
pre-monsoon periods experience higher and more significant increases than
during the monsoon. In particular, we note the significant and consistent
increase of minT of April (+0.10 ∘C yr-1, p<0.05).
At the annual scale, the bottom graph shows a progressive μ(τ) trend parallel to the retrograde μ(τ) one for the
entire analyzed period, i.e. a continuous tendency of minT to rise, which
becomes significant in 2007, when the progressive μ(τ) assumes
values above +1.96. On the right, the Sen's slope completes the analysis,
illustrating that minT is increasing at annual level by +0.072 ± 0.011 ∘C yr-1,
p<0.001, i.e. +1.44 ± 0.22 ∘C over 20 years.
Maximum air temperature (maxT)
The post- and pre-monsoon months show larger increases in maxT, but with
lower magnitudes and significance than we observe for minT (Fig. 4b). The
highest increases for this variable occurs also for maxT in April, November
and December. Less expected is the decrease of maxT in May (-0.08 ∘C yr-1,
p<0.05) and during the monsoon months from
June to August (-0.05 ∘C yr-1, p<0.1). On the
annual scale, the bottom graph shows a continuous crossing of the
progressive and retrograde μ(τ) trends until 2007, i.e. a
general stationary condition. From 2007 until 2010, the trend significantly
increased, while 2012 and 2013 register a decrease, bringing the progressive
μ(τ) near the stationary condition. In fact, on the right, the
Sen's slope confirms that maxT is at annual level stationary over the 20
years (+0.009 ± 0.012 ∘C yr-1, p>0.1).
Mean air temperature (meanT)
Figure 4c, as expected, presents intermediate conditions for meanT in
respect to minT and maxT. All months, except May and the monsoon months from
June and August, register a positive trend (more or less significant).
December presents the highest, more significant increasing trend (+0.17 ∘C yr-1,
p<0.01), while April shows the highest, more significant increase (p<0.10) during the pre-monsoon
period. On the annual scale, the bottom graph shows that the progressive
μ(τ) trend has always increased since 2000 and that it becomes
significant beginning in 2008. On the right, the Sen's slope concludes this
analysis, showing that meanT has been significantly increasing by
+0.044 ± 0.008 ∘C yr-1, p<0.05, i.e. +0.88 ± 0.16∘C
over 20 years.
Total precipitation (Prec)
In the last years, all cells are blue; i.e. we observe for all months an
overall and strongly significant decreasing trend of Prec (Fig. 4d). In
general, the post- and pre-monsoon periods experience more significant
decreases, although the monsoon months (June–September) register the main
Prec losses (e.g. August registers a Prec loss of even -4.6 mm yr-1). On
the annual scale, the bottom graph shows a continuous decreasing progressive
μ(τ) trend since 2000 that becomes significant beginning in 2005.
On the right, the Sen's slope notes that the decreasing Prec trend is
strongly high and significant at annual level (-13.7 ± 2.4 mm yr-1, p<0.001).
The precipitation reduction is mainly due to a reduction in intensity
(cumulative precipitation for week). However, during the early and late
monsoon, a reduction in duration (number of we days for week) is evident (see further details in Supplement Sect. 2).
Trend analysis in the Koshi Basin
Table 2 provides the descriptive statistics of the Sen's slopes for minT,
maxT, meanT, and Prec for the 1994–2013 period for the Koshi Basin. The
stations located on the two sides of the Himalayan range are listed
separately. For the southern ones (KO-S), we observe that for minT less than
half of the stations experience an increasing trend and just three are
significant with p<0.1. In general, the minT on the southern side
can be defined as stationary (+0.003 ∘C yr-1). Conversely,
the maxT shows a decidedly non-stationary condition. All stations present an
increasing trend, and even 6 of the 10 are rising significantly with
at least p<0.1. The mean trend is +0.060 ∘C yr-1
(p<0.10). Similarly, the meanT shows a substantial
increase. Also in this case, 6 of the 10 stations are rising significantly with at least p<0.1. The mean trend is
+0.029 ∘C yr-1 (p<0.10). Regarding Prec, we observe that on the
KO-S, 14 of the 19 stations present a downward trend. Among them, eight
decrease significantly with at least p<0.1. The mean trend is
-11.1 mm yr-1; i.e. we observe a decreasing of 15 % (222 mm) of
precipitation fallen in the basin during the 1994–2013 period (1527 mm on
average).
The two stations located on the northern ridge (KO-N) show a singularly
slight significant rise for minT (+0.034 ∘C yr-1, p<0.10
on average) and for maxT (+0.039 ∘C yr-1, p<0.10
on average), recording a consequent mean increase of meanT
equal to +0.037 ∘C yr-1, p<0.05. As for Prec, we
observe that on the KO-N, both stations maintain stationary conditions
(-0.1 mm yr-1).
Table 3 provides the descriptive statistics of the Sen's slopes on a
seasonal base. The stations analyzed here are the same as those considered
in Table 2. We begin our description with PYRAMID, already analyzed in
detail in Fig. 4. We confirm with this seasonal grouping that the main and
significant increases of minT, maxT, and meanT are completely concentrated
during the post-monsoon period (e.g., +0.124 ∘C yr-1, p<0.01
for meanT). The pre-monsoon period experienced a slighter and
not significant increase (e.g., +0.035 ∘C yr-1, p>0.1
for meanT). In general, during the monsoon period, T is
much more stationary for all three variables (e.g.,
+0.015 ∘C yr-1, p>0.1 for meanT). Considering the other KO-S
stations, the main increasing and significant trends of meanT occurred
during the pre-monsoon (+0.043 ∘C yr-1) and post-monsoon
(+0.030 ∘C yr-1) season, while the increase during the
monsoon is slighter (+0.020 ∘C yr-1). The KO-N stations
confirm that the main increasing trend of meanT occurred outside the monsoon
period that is stationary (+0.013 ∘C yr-1).
As for Prec, PYRAMID and the other KO-S stations show that the magnitude of
the Sen's slopes is higher during the monsoon season (-9.3 and
-8.6 mm yr-1, respectively), when precipitation is more abundant. The
relatively low snowfall phase of monsoon Prec at PYRAMID (as specified
above) makes the decreasing trend observed during the summer more robust
than the annual one as devoid of the undervaluation of snowfall (albeit slight, as demonstrated above (3±1 %)). The northern stations show
slight significant decreasing Prec during the winter (-3.3 mm yr-1,
p<0.05).
Lapse rates of (a) total annual precipitation in the Koshi
Basin for the last 10 years (2003–2012) and (b) mean annual air temperature.
The daily missing data threshold is set to 10 %. Only stations
presenting at least 5 years of data (black points) are considered to create
the regressions (the bars represent 2 standard deviations). Grey points
indicate the stations presenting less than 5 years of data.
Lapse rates in the southern Koshi Basin
Air temperature gradient
This study, aiming to create a connection between the climate drivers and
cryosphere in the Koshi Basin, which presents the highest altitudinal
gradient of the world (77–8848 m a.s.l.), offers a unique opportunity to
calculate T and Prec lapse rates before analyzing their spatial trends. It
is worth noting that the T lapse rate is one of the most important variables
for modelling meltwater runoff from a glacierized basin using the T-index
method (Hock, 2005; Immerzeel et al., 2014). It is also an important
variable for determining the form of Prec and its distribution
characteristics (e.g., Hock, 2005). Figure 5b presents the lapse rate of the
annual meanT in the KO Basin (Nepal) along the altitudinal range of well
over 7000 m (865–7986 m a.s.l.). We found an altitudinal gradient of
-0.60 ∘C (100 m)-1 on the annual scale with a linear trend
(r2=0.98, p<0.001). It is known that up to altitudes of
approximately 8–17 km a.s.l. in the lower regions of the atmosphere, T
decreases with altitude at a fairly uniform rate (Washington and Parkinson,
2005). Considering that the lapse rate is mainly affected by the moisture
content of the air (Washington and Parkinson, 2005), we calculated the
seasonal gradients (not shown here). We found a dry lapse rate of
-0.65 ∘C (100 m)-1 (r2=0.99, p<0.001) during
the pre-monsoon season when AWS1 registers a mean relative humidity of
62 %. A saturated lapse rate during the monsoon season is -0.57 ∘C
(100 m)-1 (r2=0.99, p<0.001) with a
mean relative humidity of 96 %. During the post-monsoon period, we found a
lapse rate equal to that registered during the monsoon: -0.57 (100 m)-1
(r2=0.98, p<0.001) even if the relative humidity is
decidedly lower in these months (44 %). Kattel et al. (2013) explain this
anomalous low post-monsoon lapse rate as the effect of strong radiative
cooling in winter.
Precipitation gradient
The relationship of Prec with elevation helps in providing a realistic
assessment of water resources and hydrological medelling of mountainous
regions (Barros et al., 2004). In recent years, the spatial variability of
Prec has received attention because the mass losses of the Himalayan
glaciers can be explained with an increased variability in the monsoon
system (e.g., Yao et al., 2012; Thakuri et al., 2014).
Figure 5a shows the altitudinal gradient for the total annual Prec in the
Koshi Basin. We observe a clear rise in Prec with elevation until
approximately 2500 m a.s.l., corresponding to the Tarke Ghyang station (code
1058), registering an annual mean of 3669 mm (mean for the 2004–2012
period). A linear approximation (r = 0.83, p<0.001) provides a
rate of +1.16 mm m-1. At higher elevations, we observe an exponential
decrease (aebx, with a = 21 168 mm m-1 and
b = -9 × 10-4 m-1, where x is the elevation expressed as m a.s.l.)
until observing a minimum of 132 mm (years 2009 and 2013) for the Kala
Patthar station (AWS4) at 5600 m a.s.l., although, as specified above, at
these altitudes the contribution of winter snowfall could be slightly
underestimated. The changing point between the two gradients can be
reasonably assumed at approximately 2500 m a.s.l., considering that the
stations here present the highest interannual variability, belonging in this
way, depending on the year, to the linear increase or to the exponential
decrease. The clear outlier along the linear gradient is the Num station
(1301) located at 1497 m a.s.l., which recorded 4608 mm of precipitation.
This station has been excluded for the linear approximation because, as
reported by Montgomery and Stolar (2006), the station is located in the Arun
Valley, which acts as a conduit for northward transport of monsoonal
precipitation. The result is that local precipitation within the gorge of
the Arun River is several times greater than in surrounding areas.
Some previous studies of the Himalayas have considered orographic effects on
Prec (Singh and Kumar, 1997; Ichiyanagi et al., 2007). Ichiyanagi et al. (2007),
using all available Prec stations operated by DHM, of which
< 5 % of stations are located over 2500 m and just one station is
over 4000 m a.s.l., observed that in the CH-S region, the annual Prec
increases with altitude below 2000 m a.s.l. and decreases for elevations
ranging between 2000 and 3500 m a.s.l., but with no significant gradient. A
broad picture of the relationship between Prec and topography in the
Himalayas can be derived from the precipitation radar onboard the Tropical
Rainfall Measuring Mission (TRMM). Some authors found an increasing trend
with elevation characterized by two distinct maxima along two elevation
bands (950 and 2100 m a.s.l.). The second maximum is much higher than the
first, and it is located along the lesser Himalayas. Over these elevations,
the annual distribution follows an approximate exponentially decreasing
trend (Bookhagen and Burbank, 2006).
Spatial distribution of the Sen's slopes in the Koshi Basin
for minimum (a), maximum (b), and mean (c) air temperature
and (d) total precipitation for the 1994–2013 period. Data are reported in Table 2.
Physically, we can interpret the Prec gradient of Fig. 5a considering that
when the humid air masses coming from the Bay of Bengal collide with the
orographic barrier, heavy convections induce huge quantity of rain below
2500 m a.s.l. The topographic barrier of the Himalayan mountain range
causes the mechanical lift of the humid air, the cooling of the air column,
the condensation and the consequent rainfall. The further increase in relief
induces a depletion of the moisture content resulting in a severe reduction
of Prec at higher altitudes. Our study, based on ground stations, confirms
the general Prec gradient detected with the TRMM microwave observations,
even if we did not identify a marked double maximum Prec peak as observed
generally for the whole central Himalaya by Bookhagen and Burbank (2006). In
fact these author report for our specific case study (profiles 14 and 15 of
their Fig. 1b), a single step increase in relief associated with a single
Prec maximum.5.4 Spatial distribution of air temperature and precipitation
trends in the Koshi Basin
Figure 6 presents the spatial distribution of the Sen's slopes in the Koshi
Basin for minT (Fig. 6a), maxT (Fig. 6b), meanT (Fig. 6c), and Prec (Fig. 6d)
during the 1994–2013 period. The relevant data are reported in Table 2.
The Chainpur (east) station shows T trends in contrast with the other
stations (see also Table 2); therefore, we consider this station as a local
anomaly and do not discuss it further in the following sections.
Regarding minT, we observe an overall stationary condition on KO-S, as
noted above. The only two stations showing a significant increasing trend
are both located in the east. The high elevation stations (PYRAMID and both
those located on the north ridge) differ from the general pattern of the
southern basin by showing a significant increasing trend. Even for maxT, we
observe a higher increase in the southeastern basin. The central and western
parts of the KO-S seem to be more stationary. PYRAMID follows this
stationary pattern, while the northern stations (KO-N) show large and
significant increases. As a consequence, meanT shows increasing trends for
all the Koshi Basin, especially on the southeast and northern sides.
The decrease of precipitation in the southern Koshi Basin presents a quite
homogeneous pattern from which the highly elevated PYRAMID is not excluded.
The pattern is different on the north ridge, where it is stationary.
Discussion
Temperature trends of the Koshi Basin compared to the regional
pattern
The trend analysis carried out in this study for the last 2 decades on
KO-S shows full consistency with the pattern of change (shown in the
following) occurring in these regions over the last 3 decades in terms
of a higher increase in maxT (+0.060 ∘C yr-1) than in minT
(+0.003 ∘C yr-1), a seasonal pattern (more pronounced
during the pre- and post-monsoon months), and the magnitudes of the trends
(e.g., the meanT trend is +0.030 ∘C yr-1). Therefore, at
low elevations of KO-S, we observe an acceleration of warming in the recent
years compared to the rate of change reported by Kattel and Yao (2013) and
Shrestha et al. (1999) in the previous decades.
At regional level, Kattel and Yao (2013) analyzed the annual minT, maxT, and
meanT trends from stations ranging from 1304 m to 2566 m a.s.l. in CH-S
(corresponding to all stations in Nepal) during the 1980–2009 period. They
found that the magnitude of warming is higher for maxT (+0.065 ∘C yr-1),
while minT (+0.011 ∘C yr-1) exhibits larger
variability, such as positive, negative or no change; meanT was found to
increase at an intermediate rate of +0.038 ∘C yr-1. These
authors extended some time series and confirmed the findings of Shrestha et
al. (1999) that, analyzing the 1971–1994 period, found a maxT increase of
+0.059 ∘C yr-1 for all of Nepal. Furthermore, warming in
the winter was more pronounced compared to other seasons in both studies.
These results are consistent with the pattern reported in WH (e.g.,
Bhutiyani et al., 2007; Shekhar et al., 2010), in EH, and in the rest of
India (e.g., Pal and Al-Tabbaa, 2010) for the last 3 decades.
The trend analysis carried out in this study for the last 2 decades on
KO-N agrees with the regional studies (shown in the following) regarding both the considerable increase of minT (+0.034 ∘C yr-1) and
the seasonal consistency of trends, related to all three T variables,
outside the monsoon months. However, we observe that in recent years, maxT
is increasing more than the rest of the TP (+0.039 ∘C yr-1).
In general we observed an increase of meanT (0.037 ∘C yr-1)
comparable to that reported by Yang et al. (2012) (0.031 ∘C yr-1) in the 1971–2007 period.
At regional level, on the TP, the warming of minT is more prominent than
that of maxT (e.g., Liu et al., 2006; Liu et al., 2009). In particular, for
stations above 2000 m a.s.l. during the 1961–2003 period, Liu et al. (2006)
found that minT trends were consistently greater (+0.041 ∘C yr-1)
than those of maxT (+0.018 ∘C yr-1), especially
in the winter and spring months. Yang et al. (2012), focusing their analysis
on CH-N (which corresponds to the southern TP) in a more recent period
(1971–2007), showed a significant increase of +0.031 ∘C yr-1
for meanT. Yang et al. (2006) analyzed five stations located in a
more limited area of CH-N: the northern side of Mt. Everest (therefore,
including the two stations also considered in this study) from 1971 to 2004.
The warming is observed to be influenced more markedly by the minT increase.
In summary, PYRAMID shares the higher T trends outside the monsoon period.
However, in contrast with studies located south of the Himalayan ridge,
which observed a prevalence of maxT increase, PYRAMID experienced a
consistent minT increase (+0.072 ∘C yr-1 for PYRAMID vs.
+0.003 ∘C yr-1 for KO-S stations), while the maxT increase
is decidedly weaker (+0.009 ∘C yr-1 for PYRAMID
vs. +0.060 ∘C yr-1 for KO-S stations). The remarkable minT trend of
PYRAMID is higher, but more similar to the pattern of change commonly
described on the TP, in particular in CH-N, and also in this study
(+0.072 ∘C yr-1 for PYRAMID vs. +0.034 ∘C yr-1 for
KO-N stations), while the maxT increase is weaker (+0.009 ∘C yr-1
for PYRAMID vs. +0.039 ∘C yr-1 for KO-N stations).
Elevation dependency of minimum (a), maximum (b), and mean
(c) air temperatures with the Sen's slopes for the 1994–2013 period. The
circle indicates stations with less than 10 % of missing daily data, and
the star indicates stations showing a trend with p-value < 0.1. The
red marker represents the trend and the associated uncertainty (2 standard
deviations) referred to the reconstructed time series for the AWS1 station
(PYRAMID). Data are reported in Table 2.
Elevation dependency of temperature trends
Figure 7 shows T trends in the KO Basin for minT, meanT, and maxT relative
to the elevation during the 1994–2013 period. No linear pattern emerges.
However, we can observe the minT trend of the three stations located at
higher altitude (PYRAMID and KO-N stations), which increases more than that
of the lower stations (Fig. 7a, see also Table 2). Reviewing the most recent
studies in the surroundings, we found that they are quite exclusively
located on CH-N. These studies often show contradictory elevation
dependencies (Rangwala and Miller, 2012). A recent study by You et al. (2010)
did not find any significant elevation dependency in the warming
rates of meanT between 1961 and 2005. However, considering mostly the same
stations, Liu et al. (2009) found that the warming rates for minT were
greater at higher elevations. Observations from CH-S are much rarer.
Shrestha et al. (1999) found elevation dependency in the rate at which maxT
were increasing in the Nepali Himalayas (CH-S), with higher rates at higher
elevations, but this study exclusively considered stations under 3000 m a.s.l.
Furthermore we did not find for the Koshi Basin any significant elevation
dependency in the weakening rates of Prec.
Precipitation trends of the Koshi Basin compared to the regional
pattern
As will be detailed in the following, different from the north side of Mt.
Everest and from the general TP, we confirm the general monsoon weakening on
the KO-S, observing a substantial Prec decrease of 15 % (-11.1 mm yr-1,
-222 mm), but that is not significant for all stations. At PYRAMID, the
annual loss is relatively comparable with that of the KO-S (-13.7 mm yr-1,
-273 mm), but at these high elevations, as we observed in Table 2,
the weather is much more drier (449 and 1527 mm, respectively). Therefore,
the fractional loss is more than 3 times (-52 %) that of the KO-S.
Considering that the decreasing trend observed during the summer is more
robust than the annual one (see above), the fractional loss of Prec during
the monsoon is -47 %, which means that currently, on average, the
precipitation at PYRAMID is the half of what it was 20 years ago.
At regional level, Turner and Annamalai (2012), using the all-India rainfall
data based on a weighted mean of 306 stations, observed a negative
precipitation trend since the 1950s in South Asia. According to Yao et
al. (2012), using the Global Precipitation Climatology Project (GPCP) data,
there is strong evidence that precipitation from 1979 to 2010 decreased even
in the Himalayas. In eastern CH-S, where the Koshi Basin is located, they
estimated a loss of 173 mm, showing a real decreasing trend starting from
the early 1990s (mean value between grid 9 and 11 in Fig. S18 of their
paper).
On the TP, the observed pattern of change is opposite that of the monsoon
weakening described by the authors cited above. Liu et al. (2010) described
an increase in precipitation in CH-N for the period of the 1980s to 2008. Su
et al. (2006) described a marked precipitation increase in the Yangtze River
Basin (eastern CH-N). In a similar way to the T analysis, Yang et al. (2006)
considered five stations located on the northern side of Mt. Everest
(therefore, including the two stations also considered in this study) from
1971 to 2004 and observed an increasing, but not significant Prec trend. The
higher stationarity we observed is confirmed since 1971 for the two KO-N
stations considered in this study.
Mechanisms responsible for temperature warming and precipitation
weakening
According to Rangwala and Miller (2012), there are a number of mechanisms
that can cause enhanced warming rates at high elevation, and they often have
strong seasonal dependency. These mechanisms arise from either elevation-based differential changes in climate drivers, such as snow cover, clouds,
specific humidity, aerosols, and soil moisture, or differential
sensitivities of surface warming to changes in these drivers at different
elevations. This study does not aim to either realize a comprehensive review
or to demonstrate the causes that could have led to the climate change
pattern observed at PYRAMID, but our intent here is just to note the recent
hypotheses advanced in the literature that fit with our observations for the
region of investigation.
Snow/ice albedo is one of the strongest feedbacks in the climate system
(Rangwala and Miller, 2012). Increases in minT are possible if decreases in
snow cover are accompanied by increases in soil moisture and surface
humidity, which can facilitate a greater diurnal retention of the daytime
solar energy in the land surface and amplify the longwave heating of the
land surface at night (Rangwala et al., 2012). For the Tibetan Plateau,
Rikiishi and Nakasato (2006) found that the length of the snow cover season
declined at all elevations between 1966 and 2001. Moreover, minT can be
enhanced by nighttime increases in cloud cover. However, assessing changes
in clouds and quantifying cloud feedbacks will remain challenging in the
near term. For the Tibetan Plateau, Duan and Wu (2006) found that low level
nocturnal cloud cover increased over the TP between 1961 and 2003 and that
these increases explain part of the observed increases in minT.
The maxT increase observed here during April (p<0.05 in 2011, Fig. 4b)
fits with the warming reported by Pal and Al-Tabbaa (2010) which
observed that within the pre-monsoon season only April shows significant
changes in maxT in all Indian regions and WH (1901–2003 period). According
to Ramanathan et al. (2007), Gautam et al. (2010) argued that the observed
warming during the pre-monsoon period (April–June) can be ascribed not only
to the global greenhouse warming, but also to the solar radiation absorption
caused by the large amount of aerosol (mineral dust mixed with other
carbonaceous material) transported over the Gangetic-Himalayan region. As
recently reported by Marinoni et al. (2013), April represents the month for
which the transport of absorbing carbonaceous aerosol (i.e. black carbon) is
maximized in our region of investigation (Khumbu Valley). Regarding this, Putero et al. (2013) show evidence for a possible influence of open fire
occurrence in South Asia particularly abundant during this period of the year.
However the significant decreasing of maxT observed in May (p<0.05)
and the slight significant decreasing during the monsoon months from
June to August (p<0.10) appear to deviate from the scenario
proposed for April. In this respect it should be kept in mind that the
radioactive dynamical interactions of aerosol with the monsoon cycle are
extremely complex and different processes can interact with each other. As
an instance, as reported by Qian et al. (2011), the deposition of absorbing
aerosol on snow and the snow albedo feedback processes can play a prominent
role in Himalayas and TP inducing large radioactive flux changes and surface
temperature perturbation.
Recent studies associate the precipitation decrease over India during the
second half of 20th century (e.g., Ramanathan et al., 2005; Lau and
Kim, 2006) to the significant tropospheric warming over the tropical area
from the Indian Ocean to the western Pacific (e.g., Wu, 2005), while
westerlies are strengthening (Zhao et al., 2012). Other authors (e.g.,
Bollasina et al., 2011) attribute the monsoon weakening to human-influenced
aerosol emissions. In fact an increase of aerosols over South Asia has been
well documented (Ramanathan et al., 2005; Lau and Kim, 2006) and climate
model experiments suggest that sulfate aerosol may significantly reduce
monsoon precipitation (Mitchell and Johns, 1997). Despite a historical
weakening of the monsoon circulation, most studies project an increase of
the seasonal monsoon rainfall under global warming. Regarding this Levy II
et al. (2013) find that the dramatic emission reductions (35–80 %) in
anthropogenic aerosols and their precursors projected by a representative
concentration pathway (RCP) 4.5 (Moss et al., 2010) result an increasing
trend by the second half of the 21st century in South Asia and in particular
over the Himalaya (Palazzi et al., 2013).
Linkage between the temperature increases and altitudinal
glacier distribution. The 0 °C isotherms corresponding to the mean
monthly minimum and maximum temperature are plotted for the 1994 and 2013
years according the observed T trends and lapse rates.
Linking climate change patterns observed at high elevation with glacier
responses
Impact of temperature increase
Air temperature and precipitation are the two factors most commonly related
to glacier fluctuations. However, there still exists a seasonal gap in order
to explain the shrinking of summer accumulation-type glaciers (typical of
CH) due to large temperature increases observed in the region during winter
(Ueno and Aryal, 2008), as is the case for the south slopes of Mt. Everest.
Furthermore, in this study we noted a slightly significant decline in summer
maxT and stationary meanT. The real increase of T has been observed for
minT, but given the mean elevation of glaciers (5695 m a.s.l. in 1992) and
the mean elevation range of glacier fronts (4568–4817 m a.s.l. in 1992, mean
4817 m a.s.l., 249 m of standard deviation: sd) (Thakuri et al., 2014),
this increase for minT can be most likely considered ineffective for melting
processes, since T is still less than 0 ∘C. This inference can be
ascertained analyzing Fig. 8, created in order to link temperature
increases and altitudinal glacier distribution (data from Thakuri et al.,
2014). The 0 ∘C isotherms, corresponding to the mean monthly minT
and maxT, are plotted for 1994 and 2013. The elevation of each 0 ∘C
isotherm is calculated according to the accurate lapse rates computation
carried out in this study and the observed monthly T trends. We can note
that in 1994 the 0 ∘C isotherm for minT reached the elevation
band characterizing the glacier fronts only from June to September. However, 20 years later, the upward movement of the 0 ∘C isotherm is modest
(+92 m) during these months, compared to the huge but ineffective rise for
melting processes (downstream from the glacier fronts) of December–November
(even +854 m). The maxT has obviously a greater potential impact on
glaciers. In fact the 0 ∘C isotherm for of all months except
January and February crosses the elevation bands within which the glacier
fronts are located ever since 1994. In this regard, we observe that only
April (+224 m), December (+212 m), and November (+160 m) experienced
an upward of the 0 ∘C isotherm able to enhance the melting
processes, but only close to the glaciers fronts. We therefore point out
that the impact caused by the increased temperature occurring in April most
likely plays an important role not only in relation to this case study, but
also at the level of the Himalayan range. In fact, as mentioned above, Pal
and Al-Tabbaa (2010), observed that within the pre-monsoon season, only
April showed significant changes in maxT in all Indian regions and WH
(1901–2003 period).
Impact of precipitation decrease
Regarding the precipitation, in this study we noted a strong and
significant decreasing Prec trend for all months, corresponding to a
fractional loss of 47 % during the monsoon season which indicates that, on
average, the precipitation at PYRAMID is currently half of what it was
20 years ago. This climate change pattern confirms and clarifies the
observation of Thakuri et al. (2014), who noted that the southern Mt. Everest
glaciers experienced a shrinkage acceleration over the last 20
years (1992–2011), as underlined by an upward shift of SLA with a velocity
almost 3 times greater than the previous period (1962–1992). The
authors, without the support of climatic data, proposed the hypothesis that
Mt. Everest glaciers are shrinking faster since the early 1990s mainly as a
result of a weakening of precipitation over the last decades. In fact they
observed a double upward shift in the SLA of the largest glaciers
(south-oriented and with a higher altitude accumulation zone): a clear
signal of a significant decrease in accumulation. Wagnon et al. (2013) have
recently reached the same conclusion, but also in this case without the
support of any climatic studies. Bolch et al. (2011) and Nuimura et al. (2012)
registered a higher mass loss rate during the last decade
(2000–2010).
Furthermore Quincey et al. (2009) and Peters et al. (2010) observed lower
glacier flow velocity in the region over the last decades. Many studies
highlight how the present condition of ice stagnation of glaciers in the Mt
Everest region, and in general in CH-S, is attributable to low flow velocity
generated by generally negative mass balances (Bolch et al., 2008; Quincey
et al., 2009; Scherler et al., 2011). Our observations allow attributing the
lower glacier flow velocity to lower accumulation due to weaker
precipitation, which can thus be considered the main climatic factor driving
the current ice stagnation of tongues. In this regard we need to keep in
mind that changes in velocity are among the main triggers for the formation
of supraglacial and proglacial lakes (Salerno et al., 2012; Quincey et al.,
2009), which we know to be susceptible to GLOFs.
Trend analysis of annual probability of snowfall on total
cumulated precipitation. The red lines represents the robust linear fitting
of the time series characterized by the associated Sen's slope (more details
in the caption of Fig. 3).
Trend analysis of annual probability of snowfall
Figure 9 analyzes how the changes observed for the meanT at PYRAMID have
affected the probability of snowfall on total cumulated annual precipitation
in the last 20 years. The increase of meanT observed outside the monsoon
period, when the precipitation is almost completely composed by snow
(Fig. 2), brought a significant decrease of solid phase (+0.7 % yr-1,
p<0.05). Extending this analysis to the elevation bands
characterizing the glaciers distribution (see Fig. 8), through the
temperature lapse rate calculated here, we observe that at the level of the
mean glaciers (5695 m a.s.l.) the probability of snowfall is stationary
(+0.04 % yr-1), while it decreases at the mean elevation of SLAs
(5345 m a.s.l. in 1992, Thakuri et al., 2014), but not significantly
(-0.38 % yr-1, p>0.1). The reduction becomes significant at
lower altitudes. In particular, at the mean elevation of glacier fronts
(4817 m a.s.l.) the probability of snowfall is -0.56 % yr-1
(p<0.05); i.e. at these altitudes the probability of snow on annual
base is currently 11 % (p<0.05) less than 20 years ago. We
can conclude this analysis summarizing that a significant change in
precipitation phase has occurred close to the terminal portions of glaciers,
corresponding broadly to the glaciers ablation zones (around 10 %,
p<0.5), while the lower temperature of the upper glaciers zones has
so far guaranteed a stationary condition.
Conclusions
Most relevant studies on temperature trends were conducted on the Tibetan
Plateau, the Indian subcontinent (including the WH) and the Upper Indus
Basin, while studies on the mountainous regions along the southern slope of
the central Himalayas in Nepal (CH-S) are limited. Although Shrestha et
al. (1999) analyzed the maximum temperature trends over Nepal during the period
1971–1994, studies on recent temperature trends over CH-S are still lacking
and, before this study, completely absent as regards high elevation. This
paper addresses seasonal variability of minimum, maximum, and mean
temperatures and precipitation at high elevation on the southern slopes of
Mt. Everest. Moreover, we complete this analysis with data from all the
existing weather stations located on both sides of the Himalayan range
(Koshi Basin) for the 1994–2013 period, during which a more rapid glacier mass
loss occurred.
At high elevation on the southern slopes of Mt. Everest, we observed the
following.
The main increases in air temperature are almost completely concentrated during
the post-monsoon months. The pre-monsoon period experienced a slighter and insignificant
increase, while the monsoon season is generally stationary. This seasonal temperature
change pattern is shared with the entire Koshi Basin, and it is also observed in the
regional studies related to the northern and southern slopes of the Himalayan range.
Surprisingly, above 5000 m a.s.l. the maximum temperature decreases significantly
in May and slightly during the monsoon months from June to August.
The minimum temperature increased much more than the maximum temperature.
This remarkable minimum temperature trend is more similar to the pattern of
change commonly described on the Tibetan Plateau and confirmed in this study
in the northern Koshi Basin. However, this trend is in contrast with studies
located south of the Himalayan ridge. As proved by this study, the southern
Koshi Basin experienced a prevalence of maximum temperature increases. No
linear pattern emerges in the elevation dependency of temperature trends.
We only observed higher minimum temperature trends at higher altitudes.
The total annual precipitation has considerably decreased. The annual
rate of decrease above 5000 m a.s.l. is similar to the one at lower altitudes
on the southern side of the Koshi Basin, but the drier conditions of this remote
environment make the fractional loss relatively more consistent. The
precipitation at high elevation during the monsoon period is currently
half of what it was 20 years ago. These observations confirm the
monsoon weakening observed by previous studies in India and even in the
Himalayas since the early 1980s. As opposed to the northern side of the
Koshi Basin that shows in this study certain stability, as positive or
stationary trends have been observed by previous studies on the TP and
more specifically in northern central Himalaya.
There is a significantly lower probability of snowfall in the glaciers
ablation zones, while the lower temperature of the upper glaciers zones have
so far guaranteed a stationary condition.
In general, this study contributes to change the perspective on how the
climatic driver (temperature vs. precipitation) led the glacier responses in
the last 20 years.
Without demonstrating the causes that could have led to the climate change
pattern observed at the PYRAMID, we simply note the recent literature on
hypotheses that are in accordance with our observations.
In conclusion, we have here observed that weather stations at low elevations
are not able to suitably describe the climate changes occurring above 5000 m a.s.l.
and thus correctly interpret the impact observed on the cryosphere.
This consideration stresses the great importance of long-term ground
measurements at high elevation.