1
1.1.2 Key Scientific Issues
2
Climate over the Indian subcontinent has varied signifi3
cantly in the past century in response to natural variations
4
(e.g. Box 1.2 on the variability of the ISM) and anthro5
pogenic forcing (see Box 1.3). In recent times, there has
6
been considerable progress in understanding the influence of
7
anthropogenic climate change over the Indian subcontinent,
8
particularly the regional monsoon.
9
State-of-the-art climate models project a continuation of
10
anthropogenic global warming and associated climate
11
change during the twenty-first century, the impacts of which
12
have profound implications for India. Yet, there remain
13
substantial knowledge gaps with regard to climate projec14
tions, particularly at smaller spatial and temporal scales. For
15
instance, CMIP5 simulations of historical and future chan16
ges in the monsoon rainfall exhibit wide variations across
17
the Indian region (Sperber et al. 2013; Turner and Anna18
malai 2012), posing difficulties for policy making. Likewise,
19
it is necessary to reduce the range among climate models
20
projections of future changes in Indian Ocean warming,
21
regional sea-level rise, tropical cyclone activity, weather and
22
climate extremes, changes in the Himalayan snow cover,
23
etc. It is essential to deepen our understanding of the science
24
of climate change, improve the representation of key pro25
cesses in climate models (e.g. clouds, aerosol–cloud inter26
actions, vegetation–atmosphere feedbacks, etc.) and also
27
build human capacity to address these challenges.
28
Efforts in these directions have already begun in India.
29
One such initiative is the development of an Earth System
30
Model (IITM-ESM) at the Centre for Climate Change
31
Research (CCCR) in the Indian Institute of Tropical Mete32
orology (Swapna et al. 2018). A brief discussion of the
33
IITM-ESM is provided in the following section.
35
Box 1.2: Indian Summer Monsoon Variability
36
The ISM also exhibits a rich variety of natural varia37
tions on different timescales ranging across sub38
seasonal/ intra-seasonal, interannual (year-to-year),
39
multi-decadal and centennial timescales, which are
40
evident from instrumental records and paleoclimate
41
reconstructions (e.g. Turner and Annamalai 2012;
42
Sinha et al. 2015). The sub-seasonal/intra-seasonal
43
variability of the ISM is dominated by active and
44
break monsoon spells (e.g. Rajeevan et al. 2010) and
45
the interannual variability is associated with excess or
46
deficient seasonal monsoon rainfall over India (e.g.
47
Pant and Parthasarathy 1981). The interannual and
48
decadal timescale variations in the ISM rainfall are
49
known to have links with the tropical Pacific, Indian
50
and Atlantic oceans, particularly with climate drivers
51
such as the El Nino/Southern Oscillation (ENSO),
52
Indian Ocean Dipole (IOD), Equatorial Indian Ocean
53
Oscillation (EQUINOO), Pacific Dedacal Oscillation
54
À
(PDO), etc. The reader is referred to Chap. 3 for more
55
À
details on the ISM variability and associated
56
À
teleconnections.
57
58
59
61
À
Box 1.3 Anthropogenic Drivers of Climate
62
À
Change
63
À
Changes in the atmospheric concentration of GHGs,
64
À
aerosols and LULC are the key anthropogenic drivers
65
À
of global climate change.
66
À
The global atmospheric carbon dioxide concentra67
À
tion has increased from an average of 280 ppm in the
68
À
pre-industrial period to over 407 ppm in 2018 (https://
69
À
scripps.ucsd.edu/programs/keelingcurve/), contribut70
À
ing a radiative forcing (RF) of about 2.1 W/m
2 at the
71
À
top of the atmosphere.
72
À
Unlike GHGs, which are well-mixed in the atmo73
À
sphere, the concentration of anthropogenic aerosols in
74
À
the atmosphere exhibits large spatio-temporal variability
75
À
and complex interactions with clouds and snow, giving
76
À
rise to uncertainties in the estimation of the aerosol RF.
77
À
The IPCC AR5 estimated the globally averaged total
78
À
aerosol effective radiative forcing (excluding black car79
À
bon on snow and ice) to be in the range −1.9 to
80
À
−0.1 W/m
2
. Over India, the direct aerosol RF is esti81
À
mated to range from −15 to +8 W/m
2 at the top of the
82
À
atmosphere and −49 to −31 W/m
2 at the surface (Nair
83
À
et al. 2016) (Chap. 5). The implications are that aerosol
84
À
RF can be significantly larger than the GHG forcing at
85
À
regional scales and large gradients in the aerosol RF can
86
À
significantly perturb the regional climate system.
87
À
The IPCC AR5 reported a globally averaged RF
88
À
due to anthropogenic changes in LULC to be about
89
À
−0.2 W/m
2 although it was anticipated that this esti90
À
mate may be revised downwards with emerging
91
À
research. As with aerosols, there are large spatio92
À
temporal variations in RF due to LULC changes at
93
À
regional scales.
94
À
Despite the uncertainties in the estimation of RF
95
À
due to anthropogenic aerosol and LULC changes,
96
À
there is high confidence that they have offset a sub97
À
stantial portion of the effect of GHGs on both tem98
À
perature and precipitation (IPCC AR5).
99
100
À
1.1.3 IITM-ESM: A Climate Modelling Initiative
101
À
from India
102
À
The Coupled Model Intercomparison Project (CMIP) orga103
À
nized under the auspices of the World Climate Research
104
À
Programme (WCRP) forms the basis of the climate projec105
À
tions in the IPCC Assessment Reports. The CMIP experi106
À
ments have evolved over six phases (Meehl et al. 2000;
4
R. Krishnan et al.
1.1.2 Key Scientific Issues
2
Climate over the Indian subcontinent has varied signifi3
cantly in the past century in response to natural variations
4
(e.g. Box 1.2 on the variability of the ISM) and anthro5
pogenic forcing (see Box 1.3). In recent times, there has
6
been considerable progress in understanding the influence of
7
anthropogenic climate change over the Indian subcontinent,
8
particularly the regional monsoon.
9
State-of-the-art climate models project a continuation of
10
anthropogenic global warming and associated climate
11
change during the twenty-first century, the impacts of which
12
have profound implications for India. Yet, there remain
13
substantial knowledge gaps with regard to climate projec14
tions, particularly at smaller spatial and temporal scales. For
15
instance, CMIP5 simulations of historical and future chan16
ges in the monsoon rainfall exhibit wide variations across
17
the Indian region (Sperber et al. 2013; Turner and Anna18
malai 2012), posing difficulties for policy making. Likewise,
19
it is necessary to reduce the range among climate models
20
projections of future changes in Indian Ocean warming,
21
regional sea-level rise, tropical cyclone activity, weather and
22
climate extremes, changes in the Himalayan snow cover,
23
etc. It is essential to deepen our understanding of the science
24
of climate change, improve the representation of key pro25
cesses in climate models (e.g. clouds, aerosol–cloud inter26
actions, vegetation–atmosphere feedbacks, etc.) and also
27
build human capacity to address these challenges.
28
Efforts in these directions have already begun in India.
29
One such initiative is the development of an Earth System
30
Model (IITM-ESM) at the Centre for Climate Change
31
Research (CCCR) in the Indian Institute of Tropical Mete32
orology (Swapna et al. 2018). A brief discussion of the
33
IITM-ESM is provided in the following section.
35
Box 1.2: Indian Summer Monsoon Variability
36
The ISM also exhibits a rich variety of natural varia37
tions on different timescales ranging across sub38
seasonal/ intra-seasonal, interannual (year-to-year),
39
multi-decadal and centennial timescales, which are
40
evident from instrumental records and paleoclimate
41
reconstructions (e.g. Turner and Annamalai 2012;
42
Sinha et al. 2015). The sub-seasonal/intra-seasonal
43
variability of the ISM is dominated by active and
44
break monsoon spells (e.g. Rajeevan et al. 2010) and
45
the interannual variability is associated with excess or
46
deficient seasonal monsoon rainfall over India (e.g.
47
Pant and Parthasarathy 1981). The interannual and
48
decadal timescale variations in the ISM rainfall are
49
known to have links with the tropical Pacific, Indian
50
and Atlantic oceans, particularly with climate drivers
51
such as the El Nino/Southern Oscillation (ENSO),
52
Indian Ocean Dipole (IOD), Equatorial Indian Ocean
53
Oscillation (EQUINOO), Pacific Dedacal Oscillation
54
À
(PDO), etc. The reader is referred to Chap. 3 for more
55
À
details on the ISM variability and associated
56
À
teleconnections.
57
58
59
61
À
Box 1.3 Anthropogenic Drivers of Climate
62
À
Change
63
À
Changes in the atmospheric concentration of GHGs,
64
À
aerosols and LULC are the key anthropogenic drivers
65
À
of global climate change.
66
À
The global atmospheric carbon dioxide concentra67
À
tion has increased from an average of 280 ppm in the
68
À
pre-industrial period to over 407 ppm in 2018 (https://
69
À
scripps.ucsd.edu/programs/keelingcurve/), contribut70
À
ing a radiative forcing (RF) of about 2.1 W/m
2 at the
71
À
top of the atmosphere.
72
À
Unlike GHGs, which are well-mixed in the atmo73
À
sphere, the concentration of anthropogenic aerosols in
74
À
the atmosphere exhibits large spatio-temporal variability
75
À
and complex interactions with clouds and snow, giving
76
À
rise to uncertainties in the estimation of the aerosol RF.
77
À
The IPCC AR5 estimated the globally averaged total
78
À
aerosol effective radiative forcing (excluding black car79
À
bon on snow and ice) to be in the range −1.9 to
80
À
−0.1 W/m
2
. Over India, the direct aerosol RF is esti81
À
mated to range from −15 to +8 W/m
2 at the top of the
82
À
atmosphere and −49 to −31 W/m
2 at the surface (Nair
83
À
et al. 2016) (Chap. 5). The implications are that aerosol
84
À
RF can be significantly larger than the GHG forcing at
85
À
regional scales and large gradients in the aerosol RF can
86
À
significantly perturb the regional climate system.
87
À
The IPCC AR5 reported a globally averaged RF
88
À
due to anthropogenic changes in LULC to be about
89
À
−0.2 W/m
2 although it was anticipated that this esti90
À
mate may be revised downwards with emerging
91
À
research. As with aerosols, there are large spatio92
À
temporal variations in RF due to LULC changes at
93
À
regional scales.
94
À
Despite the uncertainties in the estimation of RF
95
À
due to anthropogenic aerosol and LULC changes,
96
À
there is high confidence that they have offset a sub97
À
stantial portion of the effect of GHGs on both tem98
À
perature and precipitation (IPCC AR5).
99
100
À
1.1.3 IITM-ESM: A Climate Modelling Initiative
101
À
from India
102
À
The Coupled Model Intercomparison Project (CMIP) orga103
À
nized under the auspices of the World Climate Research
104
À
Programme (WCRP) forms the basis of the climate projec105
À
tions in the IPCC Assessment Reports. The CMIP experi106
À
ments have evolved over six phases (Meehl et al. 2000;
4
R. Krishnan et al.