Modelling the Impact of Climate Change on Forest Ecosystems

Transcription

1 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

2 IPCC, 2007 CLIMATE CHANGE (CC): AN INTRODUCTION CDIAC, 2011

3 CLIMATE CHANGE AND FOREST SECTOR Forests are a critical sector for climate change science and policy 1. 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 2. A sink/ source to GHG - Accounted for 12% of global GHG emissions in 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

4 STATE OF INDIAN FORESTS

5 C stock in Mt IBIS Chaturvedi et al., 2008 in Intl. Journ. For. Rev HOW MUCH CARBON DO INDIAN FORESTS HOLD? Source: Richard and Flint. 1994; Richard and Flint. 1994; Ravindranath et al. 1997; Chhabra et al. 2004; Haripriya 2003; Kishwan et al. 2009, FAO 2005, Kishwan et al. 2009; Chaturvedi et al 2011 Biomas s Soil Organic Carbon Uncertainty of Carbon stock estimates

6 Mitigation Potential (MtCO2) MITIGATION POTENTIAL UNDER DIFFERENT POLICY SCENARIOS Baseline-scenario Scenario-2020 Scenario-2030 Incremental_2020 Incremental_2030 Indian forests can sequester an additional of 1.8 to 3.2 GtCO2e over period (= GtC) Chaturvedi et al in Carbon Mgmt.

7 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 IRADe-AA McKinsey TERI-Poznan NCAER-CGE TERI-MoEF Baseline-scenario Scenario-2020 Scenario-2030 Rapid afforestation could mitigate up to 9% of India s average national emissions over the period Chaturvedi et al in Carbon Mgmt.

8 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).

9 OBSERVED IMPACTS Zhu et al (2012) analyzed the long term inventory data of 92 species collected from more than 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)

10 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 to 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".

11 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

12 Tools for projecting the impacts of climate change on forests Statistical Models Deterministic Models Biogeochemistry Models Bio-geography Model Equilibrium/ Static Models Dynamic Model Most Advanced tool for impact assessment (Fishling et al., 2007)

13 A TYPICAL DGVM ARCHITECTURE

14 Climate data TYPICAL DATA REQUIREMENTS AND TYPICAL OUTPUTS Input Output 1. Monthly mean cloudiness (%) 1.Total soil carbon 2. Minimum temp ever recorded at 2. Average evapo-transpiration that location minus avg temp of coldest month (C) 3. Monthly mean precipitation rate 3. Fractional cover of canopies (mm/day) 4. Monthly mean relative humidity 4. Leaf area index (%) 5. Percentage of sand (%) 5. Average soil temperature 6. Percentage of clay (%) 6. NPP 7. Monthly mean temperature (C) 7. Total soil nitrogen 8. Topography (m) 8. Average sensible heat flux 9. Monthly mean temperature range 9. Height of vegetation canopies (C) 10. Initial vegetation types 10. Vegetation types IBIS Classification 11. Mean "wet" days per month days 11. Total carbon from exchange of CO2 12. Monthly mean wind speed

15 THE CLIMATE DATA

16 Radiative forcing EVOLUTION OF RCP SCENARIOS AND THE CMIP5 MODELS: SEQUENTIAL VS PARALLEL PROCESS Socio- Economic Scenario Population GDP Energy Industry Emissions Scenario GHG Aerosols LUC Radiative forcing scenario Atmos. Concns. Carbon Cycle Atmos. Chemistry Climate Model Scenarios Temperature Precipitation Humidity Soil Moisture Extremes IVA studies Coastal zones Water Res. Food Security Forests Infrastructure.. SRES: Sequential approach CMIP3 Experiment/ AR4 Models ?? General Characteristics Broad range of forcing 2100 Shape of radiative forcing over time Defining RCPs GHGs Aerosols LUC New Socio-Economic Scenario (Vuln. Storylines*) Adaptation Mitigation Stabilization Overshoots.. Climate Scenario Near-term (2035) Long-term (2100+) Integration of climate and Socio-Economic scenarios Integrated scenarios Pattern scaling Downscaling of climate and socioeconomic scenarios. IVA Studies IVA studies Climate change feedbacks Model development RCPs: Parallel approach CMIP5 Experiment Moss et al., 2010

17 30 25 GtC/Yr RCP RCP RCP RCP

18 LATEST EARTH SYSTEM MODELS BASED ON RCP SCENARIOS S.. No. Model Modeling Center (or Group) Resolution (lat) deg Resolution (lon) deg 1 CCSM4 National Center for Atmospheric Research, USA CSIRO-Mk3.6 Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence, Australia 3 GISS-E2-R NASA Goddard Institute for Space Studies, USA HadGEM2-ES Met Office Hadley Centre, UK IPSL-CM5A-LR Institut Pierre-Simon Laplace, France MIROC-ESM Japan Agency for Marine-Earth Science and Technology, The University of Tokyo), and National Institute for Environmental Studies 7 MIROC-ESM-CHEM Japan Agency for Marine-Earth Science and Technology, The University of Tokyo), and National Institute for Environmental Studies 8 MIROC5 The University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology 9 MRI-CGCM3 Meteorological Research Institute, Japan NorESM1-M Norwegian Climate Centre BCC-CSM1.1 Beijing Climate Center, China Meteorological Administration CESM1(CAM5) Community Earth System Model Contributors FIO-ESM The First Institute of Oceanography, SOA, China GFDL-CM3 NOAA Geophysical Fluid Dynamics Laboratory GFDL-ESM2G NOAA Geophysical Fluid Dynamics Laboratory GFDL-ESM2M NOAA Geophysical Fluid Dynamics Laboratory HadGEM2-AO Met Office Hadley Centre, UK NorESM1-ME Norwegian Climate Centre

19 How reliable are CMIP5 model projections for India?

20 VALIDATION OF CMIP5 CLIMATE PROJECTIONS FOR INDIA: A TAYLOR DIAGRAM APPROACH Chaturvedi et al., 2012

21 CMIP5 MODEL ENSEMBLE REASONALBLY PROJECTS THE SPATIAL DISTRIBUTION OF INDIA S OBSERVED CLIMATE Chaturvedi et al., 2012

22 CLIMATE CHANGE PROJECTIONS FOR INDIA USING CMIP5 MODELS AND THE NEW RCP SCENARIOS Baseline = Chaturvedi et al., 2012

23 Chaturvedi et al., 2012 Precipitation projections for India and their reliability Baseline =

24

25 PROJECTED CHANGE IN THE FREQUENCY OF EXTREME RAINFALL DAYS FOR FUTURE DECADES BASED ON MIROC- ESM-CHEM MODEL FOR RCP SCENARIO 4.5 Chaturvedi et al., 2012

26 Validation of IBIS Model

27 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 in Miti. Adap. Strat. Glob. Change

28 Current vegetation as simulated by IBIS and observed using LISS III satellite data of 2006

29 BASELINE AS SIMULATED BY IBIS

30

31 IBIS Baseline Vegetation Type as per interpretation of satellite data DS DE GR OS RI TE TD TU Column Totals DS DE GR OS RI TE TD TU Row Totals Kappa DS-Dense Shrubland DE-Desert GR-Grassland OS-Open Shrubland RI-Rock / Ice TE- Temperate Evergreen Conifer Forest TD-Tropical Deciduous Forest TU-Tundra

32 MODEL VALIDATION - NPP R 2 = 0.63 Model generated current NPP (kgc/m 2 ) compared with the remote-sensing-derived mean NPP data from 1982 to 2006 Chaturvedi et al in Miti. Adap. Strat. Glob. Change

33 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 in Miti. Adap. Strat. Glob. Change

34 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 tonnes C/ha (S.D =29.2) as compared to tonnes C/ha as predicted IBIS for that particular grid. Chaturvedi et al in Miti. Adap. Strat. Glob. Change

35 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 in Miti. Adap. Strat. Glob. Change

36 IMPACT OF CLIMATE CHANGE ON NPP Chaturvedi et al 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.

37 IMPACT OF CC ON SOIL ORGANIC CARBON Chaturvedi et al 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

38 IMPACT RESULTS FROM CMIP5 MODELS

39 IMPACT RESULTS FROM CMIP5 MODELS

40 IMPACT RESULTS FROM CMIP5 MODELS

41 Vegetation carbon (GtC) LIMITATIONS OF THE IMPACT ASSESSMENT MODELS A Hypothetical depiction RCP 2.6 RCP 4.5 RCP RCP 8.5 Mean of the 4 RCPs Known Unknowns Unknown Unknowns Extreme events Tipping elements

42 CONCEPT OF VULNERABILITY Exposure Sensitivity Adaptive Capacity Potential Impact Actual impact

43 VULNERABILITY TRADE-OFFS

44 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

45 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

46 RECORD RISE IN FOSSIL FUEL EMISSIONS GtC/Yr Fossil Fuel based emissions Gt C/Yr RCP 6.0 RCP 4.5 RCP 2.6 RCP 8.5 Actual emissions Fossil Fuel based emissions

47 TEMPERATURE PROJECTIONS FOR INDIA 30 GtC/Yr Gt C/Yr RCP 6.0 RCP 4.5 RCP 2.6 RCP 8.5 Actual emissions Chaturvedi et al

48 Humanity is on a dangerous path immediate mitigation is essential for successful adaptation

49 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?

50 Thank you for your attention