Download Modelling the Impact of Climate Change on Forest

MID-CAREER TRAINING (MCT) FOR IFS OFFICERS (PHASE-IV) SECOND CYCLE
Modelling the Impact of
Climate Change on Forest
Ecosystems
Dr. Rajiv Kumar Chaturvedi
National Environmental Sciences Fellow
Indian Institute of Science
Bangalore
IPCC, 2007
CLIMATE CHANGE (CC): AN INTRODUCTION
CDIAC, 2011
CLIMATE CHANGE AND FOREST SECTOR
Forests are a critical sector for climate change science and policy
1.
2.
Forests store carbon
- The world’s forests store about 1640 GtC, of which 1104 GtC is stored
in soils while 536 GtC is stored in biomass
A sink/ source to GHG
- Accounted for 12% of global GHG emissions in 2008
- Globally LULUCF sector is estimated to have a mitigation potential
of 13.8 GtCO2e/yr (4.06 GtC/yr)) by 2030 at carbon prices ≤ 100
US$/tCO2e
3.
Vulnerable to the impacts of Climate Change
- Forests being a climate-dependent living community are highly
vulnerable to the impacts of climate change
STATE OF INDIAN FORESTS
HOW MUCH CARBON DO INDIAN FORESTS
HOLD?
12000
8000
6000
4000
Biomas s
IBIS 1975
2005
2005
1995
1994
1986
1986
0
1980
2000
1880
C stock in Mt
10000
Source: 1880- Richard
and Flint. 1994; 1980 Richard and Flint. 1994;
1986 - Ravindranath et
al. 1997; 1986 - Chhabra
et al. 2004; 1994 Haripriya 2003; 1995 Kishwan et al. 2009,
2005 - FAO 2005, 2005 Kishwan et al. 2009;
Chaturvedi et al 2011
Soil Organic Carbon
Uncertainty of Carbon stock estimates
Chaturvedi et al., 2008 in Intl. Journ. For. Rev.
MITIGATION POTENTIAL UNDER DIFFERENT
POLICY SCENARIOS
Mitigation Potential (MtCO2)
10000
8000
6000
4000
2000
Baseline-scenario
Scenario-2020
Scenario-2030
Incremental_2020
2050
2048
2046
2044
2042
2040
2038
2036
2034
2032
2030
2028
2026
2024
2022
2020
2018
2016
2014
2012
2010
0
Incremental_2030
Indian forests can sequester an additional of 1.8 to 3.2 GtCO2e over 2010-2030
period (=0.5-0.9 GtC)
Chaturvedi et al. 2010 in Carbon Mgmt.
8000
7000
6000
5000
4000
3000
2000
1000
IRADe-AA
McKinsey
TERI-Poznan
NCAER-CGE
TERI-MoEF
Baseline-scenario
Scenario-2020
Scenario-2030
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
0
2010
Total annual GHG emission projections
and forestry offset potential (MtCO2eq)
ANNUAL GHG EMISSION PROJECTIONS FOR INDIA AND
HOW MUCH OF IT FOREST SECTOR CAN MITIGATE
Rapid afforestation could mitigate up to 9% of India’s average national emissions over the
2010-2030 period
Chaturvedi et al. 2010 in Carbon Mgmt.
VULNERABILITY OF FORESTS TO
CLIMATE CHANGE
• Forests are exposed to the climatic factors such as
heat and water stress
• CC could affect the forest range, forest type
distribution, NPP, SOC, biodiversity and the forest
based ecosystem services.
• Observations across the World suggest that climate
change is causing many species to shift their
geographical ranges, distributions, and phenologies at
faster rates than previously thought (Michelle et al
2012, Chen et al., 2011).
OBSERVED IMPACTS
• Zhu et al (2012) analyzed the long term inventory data of 92
species collected from more than 43000 forest plots in 31 US
states and demonstrated that in this part of the World climate
change is occurring more rapidly than the trees can adapt, with
59% of tree species showing signs that their geographic ranges
are contracting from both North and South.
• This suggests that trees are finding it difficult to adapt even to
the current rate of climate change, increased rates of climate
change in future will further stress the plant communities
World-wide.
• Observations also suggest that plants are moving their ranges
not only in response to temperature changes but also to
changes in rainfall patterns. Ex- in California vascular plants
have exhibited a significant downward shift in altitude in
response to changes in water balance (Crimmins et al., 2011)
OBSERVED IMPACTS IN INDIA
• A study by Telwala et al (2013) based on extensive
field sampling and historical data estimated the
vegetation shift patterns in 124 endemic species in the
Eastern Himalayan state of Sikkim, over the period
1849-1850 to 2007-2010.
• They estimated that 87% of these species show
geographical range shifts in response to observed
warming experiencing a mean upward displacement
rate of 27.53±22.04 meters per decade.
• They conclude that the "present-day plant assemblages
and community structure in the Himalaya is
substantially different from the last century and is,
therefore, in a state of flux under the impact of
warming".
MANAGING INDIAN FORESTS IN THE
FACE OF CC VULNERABILITY
• Observations alone can not guide forest
management and policy due to inertia of the
climate system and lagged system (Forests)
response to climate stresses
• Hence, projection of climate impacts on forest
ecosystems are required to assist forest
management and policy
Tools for projecting the impacts of
climate change on forests
Statistical
Models
Deterministic
Models
Biogeochemistry
Models
Equilibrium/
Static Models
Bio-geography
Model
Dynamic Model
Most Advanced tool for impact assessment
(Fishling et al., 2007)
A TYPICAL DGVM ARCHITECTURE
Climate data
TYPICAL DATA REQUIREMENTS AND
TYPICAL OUTPUTS
Input
1. Monthly mean cloudiness (%)
2. Minimum temp ever recorded at
that location minus avg temp of
coldest month (C)
3. Monthly mean precipitation rate
(mm/day)
4. Monthly mean relative humidity
(%)
5. Percentage of sand (%)
6. Percentage of clay (%)
7. Monthly mean temperature (C)
8. Topography (m)
9. Monthly mean temperature range
(C)
10. Initial vegetation types
11. Mean "wet" days per month days
12. Monthly mean wind speed
Output
1.Total soil carbon
2. Average evapo-transpiration
3. Fractional cover of canopies
4. Leaf area index
5. Average soil temperature
6. NPP
7. Total soil nitrogen
8. Average sensible heat flux
9. Height of vegetation canopies
10. Vegetation types – IBIS Classification
11. Total carbon from exchange of CO2
THE CLIMATE DATA
EVOLUTION OF RCP SCENARIOS AND THE CMIP5
MODELS: SEQUENTIAL VS PARALLEL PROCESS
SocioEconomic
Scenario
•Population
•GDP
•Energy
•Industry
•………
1997
Emissions
Scenario
•GHG
•Aerosols
•LUC
•Atmos.
Concns.
•Carbon
Cycle
•Atmos.
Chemistry
Climate
Model
Scenarios
IVA studies
•Temperature
•Precipitation
•Humidity
•Soil Moisture
•Extremes
•…………
•Broad range of
forcing 2100
•Shape of
radiative forcing
over time
Defining RCPs
•GHGs
•Aerosols
•LUC
2008
New Socio-Economic
Scenario (Vuln.
Storylines*)
•Adaptation
•Mitigation
•Stabilization
•Overshoots
•…………..
Climate Scenario
• Near-term (2035)
• Long-term (2100+)
2009
•Coastal zones
•Water Res.
•Food Security
•Forests
•Infrastructure
•……………..
SRES: Sequential
approach
CMIP3
Experiment/ AR4
Models
2007??
2000
General
Characteristics
Radiative forcing
Radiative
forcing
scenario
2010
Integration of
climate and
Socio-Economic
scenarios
• Integrated
scenarios
• Pattern scaling
• Downscaling of
climate and socioeconomic
scenarios
• ………….
2011
2012
IVA Studies
• IVA studies
• Climate change
feedbacks
• Model
development
2013
RCPs: Parallel
approach
CMIP5
Experiment
Moss et al., 2010
GtC/Yr
30
RCP 8.5
25
20
RCP 6.0
15
10
RCP 4.5
5
RCP 2.6
0
2100
2090
2080
2070
2060
2050
2040
2030
2020
2010
2005
2000
-5
LATEST EARTH SYSTEM MODELS BASED ON RCP SCENARIOS
S. .
No.
1
Model
Modeling Center (or Group)
CCSM4
2
CSIRO-Mk3.6
3
4
5
GISS-E2-R
HadGEM2-ES
IPSL-CM5A-LR
6
MIROC-ESM
7
MIROC-ESM-CHEM
8
MIROC5
9
10
11
MRI-CGCM3
NorESM1-M
BCC-CSM1.1
National Center for Atmospheric Research, USA
Commonwealth Scientific and Industrial Research Organization in
collaboration with Queensland Climate Change Centre of
Excellence, Australia
NASA Goddard Institute for Space Studies, USA
Met Office Hadley Centre, UK
Institut Pierre-Simon Laplace, France
Japan Agency for Marine-Earth Science and Technology, The
University of Tokyo), and National Institute for Environmental
Studies
Japan Agency for Marine-Earth Science and Technology, The
University of Tokyo), and National Institute for Environmental
Studies
The University of Tokyo, National Institute for Environmental
Studies, and Japan Agency for Marine-Earth Science and
Technology
Meteorological Research Institute, Japan
Norwegian Climate Centre
Beijing Climate Center, China Meteorological Administration
12
CESM1(CAM5)
13
Resolution (lat) – Resolution (lon)
deg
– deg
0.942
1.250
1.895
1.875
2.022
1.250
1.895
2.517
1.875
3.750
2.857
2.813
2.857
2.813
1.417
1.406
1.132
1.895
1.125
2.500
2.812
2.812
Community Earth System Model Contributors
0.937
1.250
FIO-ESM
The First Institute of Oceanography, SOA, China
2.812
2.812
14
GFDL-CM3
NOAA Geophysical Fluid Dynamics Laboratory
2.000
2.500
15
GFDL-ESM2G
NOAA Geophysical Fluid Dynamics Laboratory
2.000
2.500
16
17
18
GFDL-ESM2M
HadGEM2-AO
NorESM1-ME
NOAA Geophysical Fluid Dynamics Laboratory
Met Office Hadley Centre, UK
Norwegian Climate Centre
2.000
1.241
1.875
2.500
1.875
2.500
How reliable are CMIP5 model
projections for India?
VALIDATION OF CMIP5 CLIMATE PROJECTIONS FOR INDIA: A
TAYLOR DIAGRAM APPROACH
Chaturvedi et al., 2012
CMIP5 MODEL
ENSEMBLE
REASONALBLY
PROJECTS THE
SPATIAL
DISTRIBUTION OF
INDIA’S OBSERVED
CLIMATE
Chaturvedi et al., 2012
CLIMATE CHANGE PROJECTIONS FOR INDIA USING
CMIP5 MODELS AND THE NEW RCP SCENARIOS
Baseline = 1961-1990
Chaturvedi et al., 2012
Precipitation projections for India
and their reliability
Baseline = 1961-1990
Chaturvedi et al., 2012
PROJECTED CHANGE IN THE FREQUENCY OF EXTREME
RAINFALL DAYS FOR FUTURE DECADES BASED ON MIROCESM-CHEM MODEL FOR RCP SCENARIO 4.5
Chaturvedi et al., 2012
Validation of IBIS Model
MODEL VALIDATION – VEG TYPE CHANGE
1.Tropical wet evergreen forests,2.Tropical semi evergreen forests,
3.Tropical moist decidious forest, 4.Tropical dry decidious forest,
5.Tropical thorny/scrub forests, 6.Tropical dry evergreen
forest,7.Littoral and swampy forest, 8.Subtropical broad -leaved hill
forests, 9.Subtropical pine forests, 10.Sub-tropical dry evergreen
forests, 11.Montane wet temperate forests, 12.Himalayan wet/ moist
temperate forests, 13.Himalayan dry temperate forests, 14.Sub-alpine
forests, 15.Moist alpine, 16.Dry alpine
1: tropical evergreen forest / woodland, 2: tropical deciduous
forest / woodland, 3. temperate evergreen broadleaf forest /
woodland, 4: temperate evergreen conifer forest / woodland, 5:
temperate deciduous forest / woodland, 6: boreal evergreen
forest / woodland, 7: boreal deciduous forest / woodland, 8:
mixed forest / woodland, 9: savanna, 10: grassland / steppe, 11:
dense shrubland, 12: open shrubland, 13: tundra, 14: desert, 15.
polar desert / rock / ice
Chaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change
Current vegetation as simulated by IBIS
and observed using LISS III satellite data
of 2006
BASELINE AS SIMULATED BY IBIS
IBIS Baseline
Vegetation Type as per
interpretation of satellite data
DS
DE
GR
OS
RI
TE
TD
TU
Row
Totals
Kappa
0.7981
DS
1
0
0
0
0
0
0
0
DE
0
4
0
0
0
0
0
0
1
4
GR OS RI TE TD TU
0
0 0 0
1
0
0
0 2 0
0
0
3
1 0 0
0
0
3 14 2 0
0
0
3
0 29 0
0
0
1
0 0 7
0
0
0
0 0 0
1
0
0
0 9 0
0
28
10
15 42
DS-Dense Shrubland
GR-Grassland
RI-Rock / Ice
TD-Tropical Deciduous Forest
7
2
28
Column
Totals
2
6
4
19
32
8
1
37
109
DE-Desert
OS-Open Shrubland
TE- Temperate Evergreen Conifer Forest
TU-Tundra
MODEL VALIDATION - NPP
R2 = 0.63
Model generated current NPP (kgC/m2) compared with the remote-sensing-derived
mean NPP data from 1982 to 2006
Chaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change
MODEL VALIDATION - SOC
We find that mean from both the sources is approximately 5 kg/m2 over all of India (mean
of IBIS = 4.98 Kg/m2 & mean of IGBP = 4.7 Kg/m2). However, interestingly enough we find
IBIS simulated outputs to be more divergent (standard deviation = 4.27; Max = 20.83; Min
= 0.13) than IGBP estimates (Standard deviation = 1.33; Max = 11; Min = 1.8).
Chaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change
MODEL VALIDATION - SOC
Forested sites were found to have higher soil organic carbon with an average of 97 tonnes /ha
compared (with a standard deviation of 19.8 tC/ha) to Non-forested patches with an average of 64
tonnes/ ha (with a standard deviation of 27.2 tC/ha). The average Soil Organic Carbon in the
region was found to be 78.15 tonnes C/ha (S.D =29.2) as compared to 89.13 tonnes C/ha as
predicted IBIS for that particular grid.
Chaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change
39% of the forest grids
likely change under A2
scenario by 2085
causing loss of C stock
and biodiversity
1 = stable grids
2=forest grids
undergoing
change
Chaturvedi et al.
2011 in Miti. Adap.
Strat. Glob. Change
IMPACT OF CLIMATE CHANGE ON NPP
Chaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change
The effect of climate change on the NPP of forested grids, by 2085 under A2 scenario. The values shown are the percentage change of
NPP, compared to the baseline year.
IMPACT OF CC ON SOIL ORGANIC CARBON
Chaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change
The effect of climate change on the SOC of forested grids, by 2085 under A2 scenario. The values shown are the percentage change of
SOC, compared to the baseline year
IMPACT RESULTS FROM CMIP5 MODELS
IMPACT RESULTS FROM CMIP5 MODELS
IMPACT RESULTS FROM CMIP5 MODELS
LIMITATIONS OF THE IMPACT ASSESSMENT
MODELS
25
A Hypothetical depiction
RCP 2.6
RCP 4.5
Vegetation carbon (GtC)
20
RCP 6.0
15
RCP 8.5
Mean of the 4 RCPs
Known Unknowns
10
Unknown Unknowns
Extreme events
5
Tipping elements
0
2006
2016
2026
2036
2046
2056
2066
2076
2086
2096
CONCEPT OF VULNERABILITY
Exposure
Adaptive Capacity
Sensitivity
Potential
Impact
Actual
impact
VULNERABILITY TRADE-OFFS
SOME EXAMPLES OF THE ‘WIN-WIN’
ADAPTATION PRACTICES
• Anticipatory planting of species
– along latitude and altitude
– promote assisted natural regeneration
• Promote mixed species forestry
– species adapted to different temperature tolerance regimes
• Develop and implement fire protection and management practices
• Adopt suitable thinning, sanitation and other silvicultural practices
• Promote in situ and ex situ conservation of genetic diversity
• Develop drought and pest resistance in commercial tree species
• Develop and adopt sustainable forest management practices
• Expand Protected Areas and link them wherever possible to promote
migration
• Conserve forests and reduce forest fragmentation to enable species
migration
• Adoption of energy efficient fuelwood cooking devices to reduce
pressure on forests
LIMITS TO ADAPTATION
• Ecosystems including forests as well as humanity
can adapt to small to moderate climate fluctuations
(i.e <2 deg C warming)
• Beyond 2 deg C adaptation will be difficult,
dangerous and uncertain
RECORD RISE IN FOSSIL FUEL
EMISSIONS
30
GtC/Yr
Fossil Fuel based emissions
25
20
15
10
5
9.5
Gt C/Yr
RCP 6.0
9
RCP 4.5
8.5
RCP 2.6
2100
2090
2080
2070
2060
2050
2040
2030
2020
2010
2005
-5
2000
0
Fossil Fuel based emissions
RCP 8.5
8
Actual emissions
7.5
7
6.5
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
TEMPERATURE PROJECTIONS FOR INDIA
30
GtC/Yr
25
20
15
10
5
9.5
2100
2090
2080
2070
2060
2050
2040
2030
2020
2010
2005
-5
2000
0
Gt C/Yr
RCP 6.0
9
RCP 4.5
8.5
RCP 2.6
RCP 8.5
8
Actual emissions
7.5
7
6.5
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
Chaturvedi et al 2012
Humanity is on a dangerous path –
immediate mitigation is essential for
successful adaptation
Mahatma Gandhi’s Announcement of a Design
Competition
Courtesy: Prof. Anil
Gupta, IIMA
Present value about Rs. 10
Crore. Do we lack the
resources? Why do we live
with the problems
unsolved for so long ?
Thank you for your attention