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86
SI
198–208
Journal of Coastal Research
Coconut Creek, Florida
2019
____________________
DOI: 10.2112/ SI86-030.1 received 7 March 2019; accepted in
revision 31 May 2019.
*Corresponding author: zachariapu@gmail.com
©Coastal Education and Research Foundation, Inc. 2019
Climatic Projections of Indian Ocean During 2030, 2050, 2080 with
Implications on Fisheries Sector
Paruthiyazhath Joshy Akhiljith†, Vazhamattom Benjamin Liya †, Girindran Rojith†,
Parayapanal Ulahannan Zacharia†*, George Grinson†, Sudhakaran Ajith†,
Puthenthara Madhusoodanan Lakshmi†, Valiyakath Hussain Sajna†, and Thayyil Valappil Sathianandan†
ABSTRACT
Akhiljith, P.J.; Liya, V.B.; Rojith, G.; Zacharia, P.U.; Grinson, G.; Ajith, S.; Lakshmi, P.M.; Sajna, V.H., and
Sathianandan, T.V., 2019. Climatic projections of Indian ocean during 2030, 2050, 2080 with implications on
fisheries sector. In: Jithendran, K.P.; Saraswathy, R.; Balasubramanian, C.P.; Kumaraguru Vasagam, K.P.;
Jayasankar, V.; Raghavan, R.; Alavandi, S.V., and Vijayan, K.K. (eds.), BRAQCON 2019: World Brackishwater
Aquaculture Conference. Journal of Coastal Research, Special Issue No. 86, pp. 198-208. Coconut Creek (Florida),
ISSN 0749-0208.
Climatic projections are essential to frame resilient strategies towards futuristic impacts of climate changes on fish
species and habitat. The present study projects the variations of climatic variables such as Sea Surface Temperature
(SST), Sea Surface Salinity (SSS), Sea Level Rise (SLR), Precipitation (Pr), and pH along the Indian Ocean. Climate
projections for 2030, 2050 and 2080 were obtained as MIROC-ESM-CHEM, CMIP5 model output for each
Representative Concentration Pathways (RCP) scenarios. Each climatic variable was assessed for any change against
the reference year of 2015. The RCP scenarios showed an increasing trend for SLR and SST while a decreasing trend
for SSS and pH. The study focuses on assessing the impacts of projected variations on marine and aquaculture
system. The climate model projections show that the SST during 2080 is likely to rise by 0.69oC for the lowest
emissions scenario and 2.6oC for the highest emissions scenario. Elevated temperature disturbs the homeostasis of
fish and subjects to physiological stress in the habitat resulting in mortality. These thermal limits can predict
distributional changes of marine species in response to climate change. Projections showed no significant changes in
the pattern of precipitation. Changes in sea level rise and sea surface salinity reduce water quality, spawning and seed
availability, increased disease incidence and damage to freshwater aquaculture system by salinization of
groundwater. The results show that variation in SST and pH have a potential impact on marine fisheries while SSS,
SLR, Precipitation affects the aquaculture systems. The synergic effects of climatic variations are found to have
negative implications on capture fisheries as well as aquaculture system and are elucidated through this work.
ADDITIONAL INDEX WORDS: Adaptive strategies, aquaculture, climate change, fisheries, RCP, temperature.
INTRODUCTION
Fisheries and aquaculture play a key role to ensure
nutritional security to millions around the globe and reports are
available on implications on the global fisheries revenues (Lam
et al., 2016). Climate change has significant impacts on stock
composition, aquatic habitat and socio-economics of the
fisheries sector (Barange et al., 2018) and futuristic projections
is of necessity to attain climatic resilience. Many aquaculture
systems around the world is predicted to be critically impacted
by climate change through its effect on species physiology viz.,
changes in growth rate, reproductive output and disease
susceptibility as well as farming practices viz. changes to farm
locations, infrastructure and husbandry) (Brander, 2007;
Cochrane et al., 2009; Hobday, Poloczanska, and Matear, 2008).
Climate model projections by Intergovernmental panel on
climate change in its 2014 report (IPCC, 2014), indicated that
the global surface temperature during the 21st century is likely
to rise a further 0.3 to 1.7°C for their lowest emissions scenario
and 2.6 to 4.8°C for the highest emissions scenario. IPCC (2007)
has projected global annual sea level increase of 8 to 25 cm and
SST increase of 0.8 to 2.5°C by 2050. A new set of coordinated
climate modelling experiments through Coupled Model
Intercomparison Project Phase 5 (CMIP5) was designed to
simulate the possible effects of future climate change under the
scenarios known as the Representative Concentration Pathways
(RCPs) (Moss et al., 2010). The RCPs include a stringent
mitigation scenario (RCP 2.6), two intermediate scenarios (RCP
4.5 and RCP 6.0) and one scenario with very high Green House
Gases (GHG) emissions (RCP 8.5) (IPCC, 2014).
Observations of oceanographic variables are of significance
to analyse impacts and forecast on the fisheries catch. Changes
in the ocean volume are observed in recent past which is mainly
due to the increased SST, and this rise has led to variations in
the Mean Sea Level (MSL) differently in different regions
(Chowdhury and Behera, 2015). Rainfall is an important climate
factor that affects salinity and temperature, causing a change in
sea level (Perigaud, McCreary, and Zhang, 2003). It is reported
that sea surface pH has declined by about 0.1 pH unit since pre-
industrial times (Bindoff, and McDougall, 2000). Sea surface
salinity (SSS) is highly related to local evaporation and
†ICAR-Central Marine Fisheries
Research Institute, Kochi, Kerala, India
www.JCRonline.org
www.cerf-jcr.org
Impacts on Fisheries Based on Climatic Projections of the Indian Ocean 199
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Journal of Coastal Research, Special Issue No. 86, 2019
precipitation in the global ocean and river discharges in the
coastal region. Monsoon currents dominate the water exchange
between the Arabian Sea and the Bay of Bengal, with high-
salinity water to the Bay of Bengal in summer or conversely
low-salinity water to the Arabian Sea in winter (Jensen, 2003;
Zhang, Du, and Qu, 2016).
Over the last few decades, numerous studies have been
carried out to investigate future climate change impacts on the
oceanographic variables (Bopp et al., 2013; Dueri, Bopp, and
Maury, 2014; Hoegh-Guldberg et al., 2014; Roxy et al., 2016),
focused mostly on climatic projections with General Circulation
Models (GCM).
In this study, the possible model projections were analyzed
for five oceanographic variables such as sea surface temperature
(SST), Sea Surface Salinity (SSS), Sea Level Rise (SLR),
Precipitation (Pr), and pH for Indian Ocean in 2030, 2050 and
2080 based on a suitable CMIP5 model in all RCP scenarios.
METHODS
The study region lies between latitude 0o N-30o N and
longitude 50o E-100o E of the Indian Ocean. The reference year
2015 is selected for comparing against the average change of
variables in 2030, 2050 and 2080 in all RCP scenarios.
Models and Data
The output databases of MIROC-ESM-CHEM from CMIP5
were selected for the climatic projections in the Indian Ocean
region under different RCP scenarios. The selected models
include both 20th-century climate simulations and 21st-century
climate projections under RCP 2.6, RCP 4.5, RCP 6.0 and RCP
8.5 scenarios. A detailed description of each RCP scenarios is
provided in Table 1 (IPCC, 2007; Wayne, 2013). For RCP 2.6,
the lowest of the RCPs, the total radiative forcing has a peak at
approximately 3 W m–2 around the year 2050 and declines
thereafter. RCP 4.5 is a stabilization scenario, with the total
radiative forcing rising until the year 2070 and with stable
concentrations (without an overshoot pathway to 4.5 W m–2)
after the year 2070. RCP 8.5 is a continuously rising radiative
forcing pathway in which the radiative forcing levels by the end
of the 21st century are approximately 8.5 W m-2.
Yearly pH values are projected for 2010-2100 whereas SST,
SSS, SLR and Pr values are projected monthly (January-
December) for 2030, 2050 and 2080. Arabian Sea (AS) and Bay
of Bengal (BoB) are two important areas of analysis in the
Indian Ocean since each variable has its unique variations in
these regions. In order to analysis the variations of these
variables in AS and BoB spatial analysis of these variables are
performed, and for better analysis our study area is further
divided into four region, Northern Arabian Sea (NAS), Southern
Arabian Sea (SAS), Northern Bay of Bengal (NBoB) and
Southern Bay of Bengal (SBoB).
Salinity at level 1 out of 44 levels was used as sea surface
salinity in this study and sea level rise is based on sea surface
height above the ‘geoid’ (A model of global mean sea level that
is used to measure precise surface elevations). Precipitation is
the rainfall flux over ice-free ocean over the sea, computed as
the total mass of liquid water falling as liquid rain into the ice-
free portion of the ocean divided by the area of the ocean portion
of the grid cell.
RESULTS
The quantitative changes in each oceanographic parameter
have been elucidated for the future time slices of 2030, 2050 and
2080, which have implications on the formulation of climatic
resilience strategies for fisheries sector related to the Indian
Ocean.
Projected Temperature Changes
Monthly projections for SST indicate a continuous rise in all
RCP scenarios (Figure 1a). In each of the three-time slices, RCP
2.6 generally experiences the least warming, whereas RCP 8.5 is
associated with the highest warming, with RCP 4.5 and RCP 6.0
representing the moderate warming scenarios. In April as per
RCP 2.6 SST increases to 0.54oC by 2080 relative to 2015
whereas it increases to 2.4oC in RCP 8.5. As per scenario
projection, in RCP 2.6 SST will increase to 0.69oC by 2080
compared to 2015 whereas an increase of 1.42oC in RCP 4.5,
1.54oC in RCP 6.0 and 2.6oC in RCP 8.5 for the same year.
It is observed that BoB shows continuous warming since the
start of the twentieth, and warming is prominent in SAS and
SBoB compared to NAS and NBoB (Figure 1b). SST shows
higher values compared to AS, which shows a rising trend.
Comparing RCP 2.6 and RCP 8.5 scenarios, significant changes
have been seen in 2080 in RCP 8.5 with all the four regions
nearly reaching warming of 30-32oC while in RCP 2.6 only SAS
and SBoB show the above trend. The incessant warming for
over 2080 has led to the SSTs of the four regions reaching high
SST values (30-32oC) for RCP 8.5 (Figure 1b).
Sea Level Rise (SLR)
The study shows that in RCP 2.6, sea level may rise to 12.7
cm by 2080, 7.27 cm by 2050 and 2.64 cm by 2030 compared to
2015, whereas, in RCP 8.5 scenario, the sea level may rise to
19.1 cm by 2080, 9.37 cm by 2050 and 5.23 cm by 2030 (Figure
2a). Northern Bay of Bengal experiences higher rise in sea level
compared to the other three regions (Figure 2b). In each
scenario, a significant gradual rise in sea level occurs from 2015
to 2080 in all regions in which NBoB and SAS show a
prominent rise. NAS exhibit least variation in rise compared to
the other three regions.
Sea Surface Salinity (SSS)
Monthly projections for SSS show an overall decreasing
trend in 2030, 2050 and 2080 (Figure 3a). The study projects
that sea surface salinity will decrease to 0.49 psu by 2080, 0.39
psu by 2050 and 0.34 psu in 2030 compared to 2015 in RCP 2.6
scenario, whereas it may fall to 0.75 psu by 2080, 0.49 psu by
2050 and 0.14 psu in 2030 in RCP 8.5 scenario. The SSS shows
higher values in NAS compared to the other three regions, but it
shows a decreasing trend from 2015 to 2080 in all scenarios.
The results during 2080, at RCP 8.5 show a significant decrease
in SSS with respect to the RCP 2.6 scenario. Arabian Sea
experienced anomalous higher salinity than the BOB region
(Figure 3b).
Precipitation (Pr)
There are no significant variations in the general pattern of
rainfall over the seasons but variation occurs in the amount of
precipitation or extreme precipitation events in 2030, 2050 and
200 Akhiljith et al.
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Journal of Coastal Research, Special Issue No. 86, 2019
Figure 1. SST projections for RCP scenarios (a) Monthly mean and (b) Spatial analysis.
Impacts on Fisheries Based on Climatic Projections of the Indian Ocean 201
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Journal of Coastal Research, Special Issue No. 86, 2019
Figure 2. SLR projections for RCP scenarios (a) Monthly mean and (b) Spatial analysis.
202 Akhiljith et al.
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Journal of Coastal Research, Special Issue No. 86, 2019
Figure 3. SSS projections for RCP scenarios (a) Monthly mean and (b) Spatial analysis.
Impacts on Fisheries Based on Climatic Projections of the Indian Ocean 203
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Journal of Coastal Research, Special Issue No. 86, 2019
Figure 4. Precipitation projections for RCP scenarios (a) Monthly mean and (b) Spatial analysis.
204 Akhiljith et al.
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Journal of Coastal Research, Special Issue No. 86, 2019
2080 (Figure 4a). The study projects that in RCP 8.5 scenario,
increase in Pr by 0.26 mm/day by 2080, 0.28 mm/day by 2050
and fall of 0.194 mm/day by 2030 compared to 2015 mean
precipitation whereas in RCP 2.6 scenario a change in rising of
0.6 mm/day by 2080, 0.26 mm/day by 2050 and 0.16 mm/day
by 2030 is projected. Higher rate of precipitation occurs in SAS
and SBoB compared to NAS and NBoB, where NAS shows the
least variation in precipitation (Figure 4b). Precipitation shows
no gradual trend of increase or decrease, but southern regions
get the maximum amount of precipitation.
pH
An alarming trend is observed in RCP 8.5 scenario, as pH
may fall to 7.77 in 2080 from 8.01 in 2015 (Figure 5a). In RCP
2.6 pH may decrease to 0.04 units by 2080, 0.041 units by 2050
and 0.03unit in 2030 compared to 2015 whereas pH falls to
0.242 units by 2080, 0.11 units by 2050 and 0.05 units by 2030
in RCP 8.5 scenario. The pH shows a declining trend in four
regions in all scenarios in which BoB show high variations
compared to SA (Figure 5b). Northern Arabian Sea shows the
least variations whereas SBoB shows high variations. In RCP
8.5, pH varies from 8.1 units to 7.8 units in 2080 in NAS
whereas in SBoB it changes from 7.9-8 units to 7.7-7.8 units.
Table 1. Description of RCP scenarios.
RCP
Scenarios
Description
RCP 2.6
Radiative forcing reaches 3.1 W/m2 before it returns to
2.6 W/m2 by 2100. To reach such forcing levels,
ambitious greenhouse gas emissions reductions would
be required over time.
RCP 4.5
Radiative forcing is stabilized shortly after year 2100,
consistent with a future with relatively ambitious
emissions reductions.
RCP 6.0
Radiative forcing is stabilized shortly after year 2100,
which is consistent with the application of a range of
technologies and strategies for reducing greenhouse gas
emissions.
RCP 8.5
This RCP is consistent with a future with no policy
changes to reduce emissions. Characterized by
increasing greenhouse gas emissions that lead to high
greenhouse gas concentrations over time.
Table 2. Predictive values of Climatic variables in 2030, 2050 and 2080.
Climatic
variables
RCP
scenario
Predictive values
2030
2050
2080
SST
2.6
+0.43oC
+0.60oC
+0.69oC
8.5
+0.59oC
+1.3oC
+2.6oC
SLR
2.6
+2.64cm
+7.27cm
12.7cm
8.5
5.23cm
+9.37cm
19.1cm
SSS
2.6
-0.34psu
-0.39psu
-0.49psu
8.5
0.14psu
-0.49psu
-0.75psu
Pr
2.6
+0.16mm/day
+0.26mm/day
+0.6mm/day
8.5
-0.194mm/day
+0.28mm/day
+0.26mm/day
pH
2.6
-0.03unit
-0.041unit
-0.04unit
8.5
-0.05unit
-0.11unit
-0.242unit
DISCUSSION
The study provides a quantitative evaluation of the predictive
changes of oceanographic variables and their trend (in all RCP
scenarios for better and worst condition analysis) for a general
understanding of changes and their effects in the Indian Ocean.
The current study points out an increasing trend in SST of
0.69oC to 2.6oC in 2080 relative to 2015 in the Indian Ocean.
The Indian Ocean warms because it takes up much of the
additional heat that accumulates in the Earth system due to
increasing GHG concentrations. Major contributing factors that
lead to the variation of SST in the Indian Ocean are
anthropogenic forcing, GHG effects, El Niño events, Indian
Ocean dipole, monsoonal winds and La Niña events (Roxy et
al., 2014; Yoo, Yang, and Ho, 2006). Apart from El Niño
events, the Indian Ocean SSTs are influenced by a prominent
mode of variability called the Indian Ocean Dipole
(Gnanaseelan, Roxy, and Deshpande, 2017). Weakening
monsoon winds are responsible for the increased surface
warming over the Indian Ocean during the monsoon season
(Gnanaseelan, Roxy, and Deshpande, 2017). Monthly
comparison analysis of SST and Pr in 2030, 2050 and 2080
implies maximum SST rise occurs in April, May, June and July,
and increasing rainfall in May, June, July and August shows a
linear relationship of SST and Pr that enhanced the agreement
with (Gnanaseelan, Roxy, and Deshpande, 2017) that warming
in the Indian ocean increases rainfall over the Indian Ocean. The
difference in temperature change of BoB and AS seen in Figure
2b indicates increased stratification due to high river discharge
and precipitation in BoB and also increased the mixing process
in AS due to strong winds.
The study indicates a declining trend of SSS over the Indian
Ocean in the near (2030), middle (2050) and long term (2080).
Establishing relationships between the decreasing trends of SSS
in the entire Indian Ocean is not reliable because, in the tropical
Indian Ocean, the SSS shows significant spatial distribution,
featuring an east–west contrast and significant SSS variations at
equatorial and southern Indian Ocean (Du and Zhang, 2015).
Zhang, Du, and Qu (2016) identified a salinity dipole mode in
the tropical Indian Ocean, termed S-IOD, a pattern of
interannual SSS variability in the Indian Ocean. The current
study points out a variation of SSS of 0.49 psu to 0.75 psu in
2080 relative to 2015 in the Indian Ocean; these variations are
driven by various factors such as SST, Pr, evaporation, Indian
Ocean dipole, La Niña, El Niño, river discharge, monsoonal
winds and coastal upwelling. SSS show lesser values in BoB
than AS, which may be due to the higher river discharge and
precipitation in BoB and also the higher evaporation in AS.
North-south gradient of surface salinity is positive in the
Arabian Sea while it is negative in the Bay of Bengal (Parekh et
al., 2016). An increasing trend in sea level rise and precipitation
in the Indian Ocean, in the long run, could be a reason for the
declining salinity trend in the long run.
Sea level shows a gradual rise with a minimum of 12.7 cm to
a maximum of 19.1 cm by 2080 relative to 2015 with peak rise
in May to August indicating the direct impact of the increase in
SST, Pr over the same period. Increase in SST has a significant
effect on increasing ocean volume (Chowdhury and Behera,
2015) also both inter annual and decadal variability of El Niño-
Southern Oscillation (ENSO) and Indian Ocean Dipole are
expected to force considerable sea level variations in the Indian
ocean (Unnikrishnan, Nidheesh, and Lengaigne, 2015). As 90%
of the heats evolved in the atmosphere due to global warming
Impacts on Fisheries Based on Climatic Projections of the Indian Ocean 205
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Journal of Coastal Research, Special Issue No. 86, 2019
Figure 5. pH projections for RCP scenarios (a) Yearly mean and (b) Spatial analysis.
206 Akhiljith et al.
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Journal of Coastal Research, Special Issue No. 86, 2019
are accumulated in the ocean, the ocean heat storage is a major
factor for the mean sea-level rise (Mimura, 2013). Another
possible cause for this sea-level rise acceleration may be the
Himalayan glacier melt and thermal expansion of the ocean
(Unnikrishnan, Nidheesh, and Lengaigne, 2015). The increase in
trend of SLR in BoB compared to AS indicates the rise in, SST,
river discharge and precipitation in the NBoB compared to other
regions. Coastal upwelling bears much significance in Indian
marine and coastal ecosystem.
The summer rainfall (June-September) in the northern Indian
ocean shows an increasing trend from 1979 to 2005 (Yang et al.,
2013), the similar increasing trend is seen in future scenarios in
which maximum precipitation indicated from June-September in
all scenarios in 2030, 2050 and 2080 analysis. No significant
seasonal monsoon drift is observed in our study, but it shows
variations in 2030, 2050 and 2080 under RCP 8.5 whereas it
shows a gradual increase in RCP 2.6. As previously stated,
precipitation, SST and salinity are interrelated; rainfall
anomalies significantly influence salinity, dynamics, and
temperature in the Indian Ocean, but have a narrow effect on sea
level compared to wind-driven changes (Perigaud, McCreary,
and Zhang, 2003). The Indian summer monsoon is strongly
correlated with the relationships between El Niño-Southern
Oscillation (ENSO) and the Indian Ocean climate (Yang et al.,
2013). Figure 5b indicates a similar trend in which SAS and
SBoB experience greater rainfall compared to the other two
regions. Larger moisture flux convergence resulting from the
warming Indian Ocean can lead to the intensification of the
mean rainfall (Yang et al., 2013).
The study projects a decreasing trend in pH value, an
indication of increasing ocean acidification from 0.04 units to
0.24 units by 2080 relative. pH level will be less than 8.0 in
2030 in all scenario projection whereas it will be below 7.95 in
2050 and below 7.9 in 2080 in all scenarios except RCP 2.6,
which shows a stable projection of pH in 2030, 2050 and 2080
relative to the stringent climate policy implementation and GHG
reduction. This model prediction of pH is supported by the study
results of (Bhadury, 2015; Rashid, Hoque, and Akter, 2013). pH
is estimated to have dropped from a mean of 8.2 at pre-industrial
CO2 levels to a current mean of 8.1, or by about 0.1 pH units,
and further increases in atmospheric CO2 (Bopp et al., 2013).
The dissolved inorganic carbon (DIC) increase play the most
important role in elevating surface water pCO2 and decreasing
pH in the tropical Indian ocean (Xue et al., 2014). The pH value
of the sea water and bicarbonate has a positive relationship,
whereas it is slightly linked with the amount of salinity (Rashid,
Hoque, and Akter, 2013). Organisms like shellfish, oyster and
coral are the most vulnerable to ocean acidification (Rashid,
Hoque, and Akter, 2013) such that increasing acidity of seawater
would affect the formation of exoskeleton of the reefs, given
their central importance in the marine ecosystem, as the loss of
coral reefs is likely to have several ramifications
(Vivekanandan, 2010).
The variation of SST in Indian seas during the 40 years from
1976 to 2015 revealed that it has increased by 0.602°C along
northeast India, by 0.597°C along northwest India, by 0.690°C
along southeast India and by 0.819°C along southwest India
(Zacharia et al., 2016). Several studies have documented that
such change in temperature affects the spawning (Sims et al.,
2004) recruitment (Köster et al., 2009; Rijnsdorp et al., 1992)
distribution and availability of fishes (Hare et al., 2016; Perry et
al., 2005). The effects of environmental variables on the
distribution and abundance of oil sardine and mackerel
populations along the southwest coast of India have been well
documented (Krishnakumar and Bhat, 2008).
The Indian Ocean is a typical monsoon region, and the
monsoon rainfall is crucial to the social and economic activities
of local residents (Yang et al., 2013). Peak spawning season in
many fishes has been found to coincide with monsoon rains,
particularly along the west coast of India (Qasim, 1973). Marine
and riverine species that rely to a great extent on the timing and
pattern of annual rain cycles for different biological processes
are likely to be more vulnerable to climate-induced changes
(Zacharia et al., 2016). Changes in sea surface pH may have
adverse effects on marine organisms. For instance, lower
carbonate ion concentrations reduce calcification for many
calcifying organisms (Hofmann et al., 2010), while very high
CO2 levels impose physiological stress on organisms (Pörtner et
al., 2014). It is therefore imperative that, impact analysis of
oceanographic variable for future climate change are identified,
thereby allowing researchers, managers and stakeholders to
optimally allocate financial and human resources to address the
key challenges and develop adaptation ad mitigation strategies.
CONCLUSIONS
Analysis of possible changes of oceanographic variables is
very important for the future assessment of impact studies and
adaptation planning of marine and coastal ecosystems in the
Indian Ocean. From the present study, it is clear that climate
variability occurs across all temporal and spatial scales. The
intention was to demonstrate the importance of the short term,
medium and long term climatic projections for five
oceanographic variables, causes of variations and their effects
on Indian marine and aquaculture sector from a suitable CMIP5
model for 2030, 2050 and 2080 for four RCP scenarios. This
study brings out a few significant results/observations mainly;
SST and SLR projections show a significant rising trend in
2030, 2050 and 2080 as per the RCP 8.5 scenario.
Oceanographic variable values in the AS and BoB have
increased substantially in 2030, 2050 and 2080 in NAS, SAS,
NBoB and SBoB, whereas, SSS and pH shows a declining trend.
Precipitation shows an increase in three periods relative to 2015
in RCP 2.6 and RCP 8.5 scenarios. Nevertheless, a significant
rising trend was not observed. Information on climate scenario
projections and their induced impacts will help the policy
makers to devise proper adaptation planning. Multiple models of
CMIP5 and RCP based projections have to be analyzed further
in future for the Indian Ocean for 2030, 2050 and 2080 in order
to assess the quality of projection, reasons for variation and
effects in each oceanographic variables.
ACKNOWLEDGMENTS
The work is carried out by financial support from UNDP-
GEF-MoEFCC. We thank Director, ICAR-CMFRI for providing
facilities and support for carrying out the study. We also thank
the World Climate Research Programme’s Working Group on
Coupled Modeling, responsible for CMIP and the climate model
MIROC-ESM-CHEM developed by Atmosphere and Ocean
Impacts on Fisheries Based on Climatic Projections of the Indian Ocean 207
_________________________________________________________________________________________________
Journal of Coastal Research, Special Issue No. 86, 2019
Research Institute (The University of Tokyo), National Institute
for Environmental Studies, and Japan Agency for Marine-Earth
Science and Technology for making available their model
outputs for use in the study.
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