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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. 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