Modelling the Impact of Climate Change on Forest Ecosystems

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Modelling the Impact of Climate Change on Forest Ecosystems

Dr. Rajiv Kumar Chaturvedi National Environmental Sciences FellowIndian Institute of Science BangaloreMID-CAREER TRAINING (MCT) FOR IFS OFFICERS (PHASE-IV) - SECOND CYCLE

1CLIMATE CHANGE (CC): AN INTRODUCTION

IPCC, 2007

CDIAC, 20112So conclude that at least two degree of warming in inevitable in best case scenario however it may increase depending on how the world responds to the challenge.Update this figureCLIMATE CHANGE AND FOREST SECTOR Forests are a critical sector for climate change science and policy

Forests store carbon - The worlds 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 change33Atmospheric build up of GHGs, especially CO2 constitutes a central theme in Climate Change Science. Forests are unique in the sense that they act as a source of CO2 emission as well as a sink, moreover being a living systems they are themselves vulnerable to the impacts of CC as well. STATE OF INDIAN FORESTS4HOW MUCH CARBON DO INDIAN FORESTS HOLD?

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 2011Uncertainty of Carbon stock estimatesChaturvedi et al., 2008 in Intl. Journ. For. Rev.

5MITIGATION POTENTIAL UNDER DIFFERENT POLICY SCENARIOS

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.6ANNUAL GHG EMISSION PROJECTIONS FOR INDIA AND HOW MUCH OF IT FOREST SECTOR CAN MITIGATE

Rapid afforestation could mitigate up to 9% of Indias average national emissions over the 2010-2030 periodChaturvedi et al. 2010 in Carbon Mgmt.7VULNERABILITY OF FORESTS TO CLIMATE CHANGEForests are exposed to the climatic factors such as heat and water stressCC 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).

8OBSERVED IMPACTSZhu 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 INDIAA 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 27.5322.04 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 VULNERABILITYObservations alone can not guide forest management and policy due to inertia of the climate system and lagged system (Forests) response to climate stressesHence, projection of climate impacts on forest ecosystems are required to assist forest management and policyTools for projecting the impacts of climate change on forestsStatistical ModelsDynamic ModelDeterministic ModelsBio-geography ModelBiogeochemistry ModelsEquilibrium/Static ModelsMost Advanced tool for impact assessment (Fishling et al., 2007)

A TYPICAL DGVM ARCHITECTURETYPICAL DATA REQUIREMENTS AND TYPICAL OUTPUTS Input Output1. Monthly mean cloudiness (%)Total soil carbon 2. Minimum temp ever recorded at that location minus avg temp of coldest month (C) 2. Average evapo-transpiration 3. Monthly mean precipitation rate (mm/day)3. Fractional cover of canopies4. Monthly mean relative humidity (%) 4. Leaf area index5. Percentage of sand (%)5. Average soil temperature6. Percentage of clay (%)6. NPP7. Monthly mean temperature (C)7. Total soil nitrogen8. Topography (m)8. Average sensible heat flux9. Monthly mean temperature range (C)9. Height of vegetation canopies10. Initial vegetation types10. Vegetation types IBIS Classification11. Mean "wet" days per month days11. Total carbon from exchange of CO2 12. Monthly mean wind speedClimate dataTHE CLIMATE DATASRES: Sequential approachCMIP3 Experiment/ AR4 ModelsRCPs: Parallel approachCMIP5 ExperimentMoss et al., 2010EVOLUTION OF RCP SCENARIOS AND THE CMIP5 MODELS: SEQUENTIAL VS PARALLEL PROCESSGeneral Characteristics

Broad range of forcing 2100Shape of radiative forcing over timeDefining RCPs

GHGsAerosolsLUCRadiative forcingNew Socio-Economic Scenario (Vuln. Storylines*)

AdaptationMitigationStabilizationOvershoots..Climate Scenario

Near-term (2035) Long-term (2100+)

Integration of climate and Socio-Economic scenarios

Integrated scenarios Pattern scaling Downscaling of climate and socio-economic scenarios .IVA Studies

IVA studies Climate change feedbacks Model development200820092010201120122013Socio-Economic Scenario

PopulationGDPEnergyIndustryEmissions Scenario

GHGAerosolsLUCRadiative forcing scenario

Atmos. Concns.Carbon CycleAtmos. Chemistry

Climate Model Scenarios

TemperaturePrecipitationHumiditySoil MoistureExtremesIVA studies

Coastal zonesWater Res.Food SecurityForestsInfrastructure..19972007??2000* Consistent with RCPs and Independent of RCPs1617LATEST EARTH SYSTEM MODELS BASED ON RCP SCENARIOS

S. . No.ModelModeling Center (or Group)Resolution (lat) degResolution (lon) deg1CCSM4National Center for Atmospheric Research, USA0.9421.2502CSIRO-Mk3.6Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence, Australia1.8951.8753GISS-E2-RNASA Goddard Institute for Space Studies, USA2.0222.5174HadGEM2-ESMet Office Hadley Centre, UK1.2501.8755IPSL-CM5A-LRInstitut Pierre-Simon Laplace, France1.8953.7506MIROC-ESMJapan Agency for Marine-Earth Science and Technology, The University of Tokyo), and National Institute for Environmental Studies2.8572.8137MIROC-ESM-CHEMJapan Agency for Marine-Earth Science and Technology, The University of Tokyo), and National Institute for Environmental Studies2.8572.8138MIROC5The University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology1.4171.4069MRI-CGCM3Meteorological Research Institute, Japan1.1321.12510NorESM1-MNorwegian Climate Centre1.8952.50011BCC-CSM1.1Beijing Climate Center, China Meteorological Administration2.8122.81212CESM1(CAM5)Community Earth System Model Contributors0.9371.25013FIO-ESMThe First Institute of Oceanography, SOA, China2.8122.81214GFDL-CM3NOAA Geophysical Fluid Dynamics Laboratory2.0002.50015GFDL-ESM2GNOAA Geophysical Fluid Dynamics Laboratory2.0002.50016GFDL-ESM2MNOAA Geophysical Fluid Dynamics Laboratory2.0002.50017HadGEM2-AOMet Office Hadley Centre, UK1.2411.87518NorESM1-MENorwegian Climate Centre1.8752.50018How reliable are CMIP5 model projections for India?19VALIDATION OF CMIP5 CLIMATE PROJECTIONS FOR INDIA: A TAYLOR DIAGRAM APPROACH

Chaturvedi et al., 201220

CMIP5 MODEL ENSEMBLE REASONALBLY PROJECTS THE SPATIAL DISTRIBUTION OF INDIAS OBSERVED CLIMATEChaturvedi et al., 201221CLIMATE CHANGE PROJECTIONS FOR INDIA USING CMIP5 MODELS AND THE NEW RCP SCENARIOS

Baseline = 1961-1990Chaturvedi et al., 201222Precipitation projections for India and their reliability

Baseline = 1961-1990Chaturvedi et al., 2012

23

24PROJECTED 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., 201225Validation of IBIS ModelMODEL 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 / iceChaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change2727Current vegetation as simulated by IBIS and observed using LISS III satellite data of 2006

BASELINE AS SIMULATED BY IBIS29

Vegetation Type as per interpretation of satellite dataDSDEGROSRITETDTUColumn TotalsIBIS BaselineDS100000102DE040020006GR003100004OS00314200019RI00302900032TE001007008TD000000101TU00009002837Row Totals141015427228109DS-Dense ShrublandDE-DesertGR-GrasslandOS-Open ShrublandRI-Rock / IceTE- Temperate Evergreen Conifer ForestTD-Tropical Deciduous ForestTU-TundraKappa0.7981MODEL VALIDATION - NPP

Model generated current NPP (kgC/m2) compared with the remote-sensing-derived mean NPP data from 1982 to 2006R2 = 0.63Chaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change3232MODEL VALIDATION - SOCWe 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. Change333333MODEL 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. Change343434

39% of the forest grids likely change under A2 scenario by 2085 causing loss of C stock and biodiversity1 = stable grids

2=forest grids undergoing changeChaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change3535

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 CLIMATE CHANGE ON NPPChaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change3636

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 yearIMPACT OF CC ON SOIL ORGANIC CARBONChaturvedi et al. 2011 in Miti. Adap. Strat. Glob. Change3737IMPACT RESULTS FROM CMIP5 MODELS

IMPACT RESULTS FROM CMIP5 MODELS

IMPACT RESULTS FROM CMIP5 MODELSKnown UnknownsMean of the 4 RCPsUnknown UnknownsExtreme eventsTipping elementsLIMITATIONS OF THE IMPACT ASSESSMENT MODELSA Hypothetical depiction CONCEPT OF VULNERABILITYExposureSensitivityPotential ImpactActual impactAdaptive CapacityVULNERABILITY TRADE-OFFS

Adaptive CapacitysensitivityExposure

sensitivityExposureAdaptive capacitySOME EXAMPLES OF THE WIN-WIN ADAPTATION PRACTICESAnticipatory planting of speciesalong latitude and altitudepromote assisted natural regenerationPromote mixed species forestry species adapted to different temperature tolerance regimesDevelop and implement fire protection and management practicesAdopt suitable thinning, sanitation and other silvicultural practicesPromote in situ and ex situ conservation of genetic diversityDevelop drought and pest resistance in commercial tree speciesDevelop and adopt sustainable forest management practicesExpand Protected Areas and link them wherever possible to promote migrationConserve forests and reduce forest fragmentation to enable species migrationAdoption of energy efficient fuelwood cooking devices to reduce pressure on forests

LIMITS TO ADAPTATIONEcosystems including forests as well as humanity can adapt to small to moderate climate fluctuations (i.e