CO 2 variability, >5 ppm, is observed during winter, while it
is reduced nearly by half during the summer (Tiwari et al.
2014). Unlike the long-term Mauna Loa observational
records, the Indian GHGs record, as mentioned earlier, is
short. Bose et al. (2014) developed a fractionation model and
used it to reconstruct the CO 2 variability using carbon isotopic analysis of tree rings from a western Himalayan region.
These authors demonstrated that the tree ring-derived CO 2
record for the last one hundred year had a good match with
the ice core records of CO 2 variability. Considering these
observational records, we attempt to make a composite
diagram of the CRI and the SNG CO 2 records to examine the
regional features of the CO 2 variability in the Indian context.
Figure 4.2 shows the CO 2 concentration variability
approximately for the timescale of 1992–2013 with an
intermittent gap from October 2002 to July 2009.
The CRI record is shown in cyan, while the blue bars
represent the variability for the SNG site. The CRI and the
SNG data show close resemblance, though a small difference
is apparent. The CO 2 amplitude in the case of the SNG site is
slightly higher than the CRI site. In case of CRI, the sampling was done once in two months, whereas in case of
SNG, the sampling was done on weekly time interval. Since
samples collected at lower temporal resolution impart a
smoothing effect, the variability is expected to be subdued in
the case of the CRI site compared to the SNG site which had
a sampling frequency of about eight times higher than the
CRI site. Nevertheless, we may assume the combined dataset
as a representative of the CO 2 for this region and since there
is no other known similar observational dataset available
from the Indian region, we may call it as the “Indian CO 2
record.”
When this record is compared with the Mauna Loa CO 2
(MLO) variability (see Fig. 4.2), one distinct feature is
apparent. The Indian record shows higher amplitude, which
has also been discussed earlier and illustrated in Fig. 4.1. In
the earliest record, during the 1990s and 2000s, the CRI CO 2
data also show slightly larger amplitude than the MLO
record. But, in the second phase of measurement during the
early 2010s, this amplitude in the case of the Indian record
shows an increasing trend. The amplitudes are larger than
that observed during the 1990s and 2000s, and much larger
than the Mauna Loa record. As mentioned earlier, Bhattacharya et al. (2009) also measured d
13 C of CO 2 during
1993–2002 and observed a strong inverse correlation
between CO 2 and d
13 C. This anti-correlation suggests a land
biospheric control rather than the oceanic modulation on the
seasonal behavior of CO 2 in this region. But according to a
recent study, high-frequency (1 Hz) measurements of CO 2
and CH 4 concentrations using a laser-based GHG analyzer at
SNG reveal that the oceanic emission of CO 2 and/or background CO 2 transported from the distant marine environment may also be playing a moderate role in determining the
seasonal pattern of CO 2 in this region (Metya et al. 2020).
Though the marine influence needs to be further investigated
to ascertain its role, the increasing amplitude may be
attributed to enhanced land biospheric activities. It is
important to mention that Barlow et al. (2015) observed
similar behavior at a high latitude region, namely Barrow
(71.3° N, 156.6° W) in Alaska. They argue that the observed
change in amplitude is partially due to an increase in “peak
respiration” (contributes to increase the maxima in CO 2
value) and a larger increase in “peak uptake” (contributes to
extend the minima in CO 2 value). Since respiration and
photosynthetic uptake are by and large driven by vegetation,
an increased vegetation cover seems to be the most likely
reason for increased CO 2 amplitude. One of the reasons
could be that the vegetation and/or the forest cover are
probably increasing, at least in the western part of India. In
the last twenty years, the surface characteristics may have
changed significantly, and interestingly, the forest cover has
increased in India (MoEFCC 2015, 2018). On the contrary,
some studies show that the Indian forest cover has been
decreasing, at least for the last couple of decades (Meiyappan et al. 2017). Needless to say that an accurate estimate of
Indian forest cover and its characteristic behavior for different climatic zones/geographical locations in terms of its
carbon sequestration potential are essential in order to better
understand the carbon dynamics. For example, the Northeast
Indian region is characterized by high forest cover (ca. 64%;
Jain et al. 2013) but its carbon dynamical characteristics are
quite different from the rest of the country. Firstly, it shows a
very different characteristic in terms of the timing of the
maximum CO 2 uptake on a seasonal scale compared to the
rest of India (discussed later), and secondly it shows a large
CO 2 variability. Deb Burman et al. (2017) reported large
amplitude of the CO 2 concentration in a deciduous forest at
Kaziranga in Assam (380–460 ppm) during the year of
2016. Though one year of data may not be generalized as
representative values, it may be indicative of distinct spatial
characteristics of CO 2 seasonal pattern of different ecosystems in India. Analysis of such records provides useful
information on the amplitude and phase of the time series by
means of spectral decomposition technique (Barlow et al.
2015). Additionally, tree-ring based measurement of radiocarbon activity of the atmospheric air on sub-seasonal
timescale would provide information about the fossil fuel
component (Chakraborty et al. 2008) of the CO 2 emission
and may help to partition the CO 2 emitted by the agricultural
practices (Berhanu et al. 2017).
4.2.2 Methane (CH 4 )
Methane is the second most important anthropogenic GHG
after atmospheric CO 2 (on the scale of radiative forcing,
4 Observations and Modeling of GHG Concentrations and Fluxes …
77
is reduced nearly by half during the summer (Tiwari et al.
2014). Unlike the long-term Mauna Loa observational
records, the Indian GHGs record, as mentioned earlier, is
short. Bose et al. (2014) developed a fractionation model and
used it to reconstruct the CO 2 variability using carbon isotopic analysis of tree rings from a western Himalayan region.
These authors demonstrated that the tree ring-derived CO 2
record for the last one hundred year had a good match with
the ice core records of CO 2 variability. Considering these
observational records, we attempt to make a composite
diagram of the CRI and the SNG CO 2 records to examine the
regional features of the CO 2 variability in the Indian context.
Figure 4.2 shows the CO 2 concentration variability
approximately for the timescale of 1992–2013 with an
intermittent gap from October 2002 to July 2009.
The CRI record is shown in cyan, while the blue bars
represent the variability for the SNG site. The CRI and the
SNG data show close resemblance, though a small difference
is apparent. The CO 2 amplitude in the case of the SNG site is
slightly higher than the CRI site. In case of CRI, the sampling was done once in two months, whereas in case of
SNG, the sampling was done on weekly time interval. Since
samples collected at lower temporal resolution impart a
smoothing effect, the variability is expected to be subdued in
the case of the CRI site compared to the SNG site which had
a sampling frequency of about eight times higher than the
CRI site. Nevertheless, we may assume the combined dataset
as a representative of the CO 2 for this region and since there
is no other known similar observational dataset available
from the Indian region, we may call it as the “Indian CO 2
record.”
When this record is compared with the Mauna Loa CO 2
(MLO) variability (see Fig. 4.2), one distinct feature is
apparent. The Indian record shows higher amplitude, which
has also been discussed earlier and illustrated in Fig. 4.1. In
the earliest record, during the 1990s and 2000s, the CRI CO 2
data also show slightly larger amplitude than the MLO
record. But, in the second phase of measurement during the
early 2010s, this amplitude in the case of the Indian record
shows an increasing trend. The amplitudes are larger than
that observed during the 1990s and 2000s, and much larger
than the Mauna Loa record. As mentioned earlier, Bhattacharya et al. (2009) also measured d
13 C of CO 2 during
1993–2002 and observed a strong inverse correlation
between CO 2 and d
13 C. This anti-correlation suggests a land
biospheric control rather than the oceanic modulation on the
seasonal behavior of CO 2 in this region. But according to a
recent study, high-frequency (1 Hz) measurements of CO 2
and CH 4 concentrations using a laser-based GHG analyzer at
SNG reveal that the oceanic emission of CO 2 and/or background CO 2 transported from the distant marine environment may also be playing a moderate role in determining the
seasonal pattern of CO 2 in this region (Metya et al. 2020).
Though the marine influence needs to be further investigated
to ascertain its role, the increasing amplitude may be
attributed to enhanced land biospheric activities. It is
important to mention that Barlow et al. (2015) observed
similar behavior at a high latitude region, namely Barrow
(71.3° N, 156.6° W) in Alaska. They argue that the observed
change in amplitude is partially due to an increase in “peak
respiration” (contributes to increase the maxima in CO 2
value) and a larger increase in “peak uptake” (contributes to
extend the minima in CO 2 value). Since respiration and
photosynthetic uptake are by and large driven by vegetation,
an increased vegetation cover seems to be the most likely
reason for increased CO 2 amplitude. One of the reasons
could be that the vegetation and/or the forest cover are
probably increasing, at least in the western part of India. In
the last twenty years, the surface characteristics may have
changed significantly, and interestingly, the forest cover has
increased in India (MoEFCC 2015, 2018). On the contrary,
some studies show that the Indian forest cover has been
decreasing, at least for the last couple of decades (Meiyappan et al. 2017). Needless to say that an accurate estimate of
Indian forest cover and its characteristic behavior for different climatic zones/geographical locations in terms of its
carbon sequestration potential are essential in order to better
understand the carbon dynamics. For example, the Northeast
Indian region is characterized by high forest cover (ca. 64%;
Jain et al. 2013) but its carbon dynamical characteristics are
quite different from the rest of the country. Firstly, it shows a
very different characteristic in terms of the timing of the
maximum CO 2 uptake on a seasonal scale compared to the
rest of India (discussed later), and secondly it shows a large
CO 2 variability. Deb Burman et al. (2017) reported large
amplitude of the CO 2 concentration in a deciduous forest at
Kaziranga in Assam (380–460 ppm) during the year of
2016. Though one year of data may not be generalized as
representative values, it may be indicative of distinct spatial
characteristics of CO 2 seasonal pattern of different ecosystems in India. Analysis of such records provides useful
information on the amplitude and phase of the time series by
means of spectral decomposition technique (Barlow et al.
2015). Additionally, tree-ring based measurement of radiocarbon activity of the atmospheric air on sub-seasonal
timescale would provide information about the fossil fuel
component (Chakraborty et al. 2008) of the CO 2 emission
and may help to partition the CO 2 emitted by the agricultural
practices (Berhanu et al. 2017).
4.2.2 Methane (CH 4 )
Methane is the second most important anthropogenic GHG
after atmospheric CO 2 (on the scale of radiative forcing,
4 Observations and Modeling of GHG Concentrations and Fluxes …
77