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Bioenergy Australia Conference , November 25-26, 2013, Hunter Valley, Australia

Climate impacts of bioenergy: Beyond GHGs Ryan M. Bright 1 , Rasmus Astrup 2 , Anders H. Strømman 1 , Clara Ant ón-Fernández 2 , Maria Kvalevåg 3 , Francesco Cherubini 1 1 Industrial Ecology Program, Norwegian University of Science & Technology (NTNU), Trondheim, Norway

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Bioenergy Australia Conference , November 25-26, 2013, Hunter Valley, Australia

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  1. Climate impacts of bioenergy: Beyond GHGs Ryan M. Bright1, Rasmus Astrup2, Anders H. Strømman1, Clara Antón-Fernández2 , Maria Kvalevåg3, Francesco Cherubini1 1Industrial Ecology Program, Norwegian University of Science & Technology (NTNU), Trondheim, Norway 2Norwegian Forest and Landscape Institute, Ås, Norway 3Center for International Climate & Environmental Research – Oslo (CICERO), Norway Bioenergy Australia Conference, November 25-26, 2013, Hunter Valley, Australia

  2. Climate – Ecosystem Dynamics • Terrestrial ecosystems and climate are closely coupled systems • Land cover – especiallythetype ofvegetation – affectsclimate due to variation in albedo, soil water, surfaceroughness, theamountofleafarea from which heat can be exchanged, and rootingdepth • In addition to GHGs, a change in land cover will thus perturb climate by influencing the fluxes of energy, water vapor, and momentum exchanged with the atmosphere Source: G. Bonan, Ecological Climatology (2008)

  3. Land SurfaceBiogeophysics • Albedolargelydeterminestheamount of netradiation (Rn, i.e., availableenergy) at thesurfacegettingpartitionedintolatent heat, sensible heat, or a ground heat Rn = (1-α)SW↓ + (LW↓ - LW↑) = H + λE + G Source: G. Bonan, Ecological Climatology (2008)

  4. SurfaceBiogeophysics • Changes in albedo (i.e., from vegetationchange) lead to an externalforcing at thesurface and top-of-atmosphere. This candirectlyaffectlocal and global climate. • The netlocalclimateeffect (nearsurface temps.) will be determined by theefficiencywithwhichtheremainingnetradiation is partitionedintosensible, latent, and ground heat fluxes via convective and conductive heat transfer. This is governedlargely by surfaceroughness, plant physiology, and hydrology. Source: G. Bonan, Ecological Climatology (2008)

  5. Land SurfaceBiogeophysics and Hydrology • Apart from climate, plant physiologygovernshydrologicalprocesseslike transpiration (rootingdepth, leafstomata) and evaporation (canopyinterception/LAI) and in turn thepartitioning of turbulent heat fluxesintolatent heat andsensible heat. Source: G. Bonan, Ecological Climatology (2008)

  6. Land SurfaceBiogeophysics: Roughness • Surfaceroughness is mostlydetermined by vegetationheightwhich transfers momentum to thesurfacefacilitatingconvectivesensible heat and water vapor (latent heat) exchange from thesurface to theatmosphere. Rn = (1-α)SW↓ + (LW↓ - LW↑) = H + λE + G Source: G. Bonan, Ecological Climatology (2008)

  7. Mesoscalecirculations • Changes in vegetationpropertiescaninfluencemesoscalecirculationpatterns (and regional climate) • Example: Cross-section of a dry patch of grass (black bar) surrounded by wetforestson a summer day • Hot dry air abovethegrass is forcedupward; cool, moist air abovetheforestssubsides Source: G. Bonan, Ecological Climatology (2008)

  8. Selected Case Studies

  9. Analysis of observed biogeophysical contributions to localclimate (near-surface temps) due to LUC/LCC (~2 Mha) ontheBraziliancerrado • From naturalvegetation crop/pasture • From crop/pasture  sugarcane • Biogeophysical effectsconsidered: • Evapotranspiration • Surface albedo

  10. MODIS Observations • Nat. veg.  Crop = +∆T; Crop  Sugarcane = -∆T • Nat. veg.  Crop = -∆ET; Crop  Sugarcane = + ∆ET • Nat. veg.  Crop = +∆alb; Crop  Sugarcane = +∆alb Source: Loarie et al., NatureClimate Change (2011)

  11. Study Insights • Conversion of naturalvegetation to crop/pasturewarmsthecerrado by an average of ~1.6 C • Conversion of crop/pasture to sugarcanecoolsthe region by an average of ~0.9 C • Both land cover types arewarmerthannaturalvegetation • Evapotranspiration dominates biogeophysical (direct) drivers of localclimate in the region • ETNat. Veg. > ETSugarcane > ETCrop/pasture • Biofuel policy implications? • Discourage area expansionintonaturalvegetation areas (deforestation); promotelocalcropsubstitution (crop/pasture sugarcane) instead • Biophysicalfactorsareimportant: theycaneithercounter of enhanceclimatebenefits of bioenergy Source: Loarie et al., NatureClimate Change (2011)

  12. Climatemodelingsimulation of replacingannualcropswithperennialcrops in the U.S. Midwest for bioenergy (~84 Mha) • Biogeochemicalfactors: • Life-cycleGHGs from crop and transportation biofuel (EtOH) production • Displaced fossil fuelemissions in transport sector • Biogeophysical factors: • ∆ Surface albedo • ∆ Evapotranspiration

  13. A: Perennials minus annuals • B: Same as A, but albedo of perennials = annuals • C: Same as A, butrootingdepth of perennialsincreased to 2 m Source: Georgescu et al., PNAS (2011)

  14. Study Insights • Local and regional cooling from enhanced evapotranspiration • Local, regional, and global cooling from highersurface albedo • Albedo impactsaloneare ~ 6 times greaterthanannualbiogeochemicaleffects from offsetting fossil fueluse • Resultsdemonstratethat a thoroughevaluation of costs and benefits of bioenergy-related LUC/LCC must includepotentialimpactsonthesurfaceenergy and H2O balance to comprehensivelyaddressimportantconcerns for local, regional, and global climatechange Source: Georgescu et al., PNAS (2011)

  15. Analysis of biogeophysical climate drivers in managed boreal forests of Norway (observation-based) • Clearcut vs. Matureconiferous stands • Clearcut vs. Deciduous stands • Decidous vs. Coniferous stands • Analysis of direct global climateimpacts of alternative forestmanagement scenarios: carboncycle + albedo dynamics (empiricalmodeling-based)

  16. Mircoclimate: Biogeophysical Contributions • ∆Temperaturebetween a matureconiferous and: (a) a clear-cut stand (b) a deciduous stand • Contributions from ∆Albedo(green) dominate 6-yr. mean∆Temp. • Clear-cut and deciduous stands arecoolerthanconiferous stands Source: Bright et al., Global Change Biology (2013)

  17. Global climate: Including albedo 2010 climate RCP 4.5 ∆NEE RCP 8.5 • In a scenario in which: • Harvestintensitiesareincreased (e.g., for bioenergy) • Harvested conifer stands areallowed to naturallyregeneratewith native deciduousspecies • ∆Albedo(blue) offsets ∆NEE (CO2, red) = netmedium- & long-term global climatecooling • Impactsoutsidethemanagedforest landscape wereexcluded • Butcarbon-cycle – climateimpacts from fossil fuelsubstitutionwith bioenergy arelikelybeneficial • Including albedo changesacrossthe forested landscape is necessary to avoidsub-optimalclimate policy in boreal regions Net RCP 8.5 ∆Albedo RCP 4.5 2010 climate Source: Bright et al., Global Change Biology (2013)

  18. Included albedo changedynamics in theevaluation of several prominent global forest bioenergy valuechains • From land use (forestmanagement), not LUC/LCC • Changes in forest albedo alongonerotationcycle • Life cycleperspective • Attributional (no land usebaseline/counterfactuals, no system expansion/avoidedemissioncredits) • Metric: Global WarmingPotential (GWP), TH = 20, 100, & 500 years • Bioenergy products: Heat & TransportationFuels

  19. Characterized global directclimateimpacts (per MJ woodfuelcombusted) • Harvesting forests in regions withseasonalsnow cover = high+∆albedo • +∆albedoeffects = climatecooling (blue bars), offsets directbiogenic CO2 and life-cycle fossil GHGs • Net cooling for all TH’s for ”CA” (Canada) case Source: Cherubini, Bright, et al., Env. Res. Letters (2012)

  20. How to measure? • Coupledclimatemodels (land + atmosphere; land + atmosphere + ocean) • Georgescu et al. (2011) • Directobservation (satelliteimagery, i.e., MODIS, MERIS, SPOT-VEGETATION, Landsat 7) • Loarie et al. (2011) • Hybrid approaches (satelliteimagery + simple climatemodels/metrics) • Brightet al. (2011; 2012; 2013); Cherubini et al. (2012) • It is possible to adaptexistingclimatemetricssuch as GWP or GTP for albedo • Bright et al. (2012, 2013); Cherubini et al. (2012)

  21. Summary & Conclusions • Climateimpactassessments of bioenergy areoftenincompletewithouttheinclusion of biogeophysical dimensions • Particularlyimpacts at thelocal and regional scale • Biogeophysical climateconsiderationsare more relevant to consider: • Whenthere is LUC/LCC (i.e., de-/afforestation, crop-switching) • In managedforestecosystems (i.e., time afterharvestdisturbance) • Standardizedmethodologies and metrics do not yetexist • Climateprofile of bioenergy? It’s all about land use • Howwemanageour land to procurebiomass for bioenergy dictatesclimateimpacts/benefits, overwhelmslife-cycleemissionimpacts • Carbon sinks  global climate • Biogeophysics and hydrologylocal and global climate

  22. ThankYou. • G. Bonan (2008), EcologicalClimatology – Concepts and Applications, 2nd Edition, Cambridge University Press, Cambridge, U.K. & New York, USA • M. Georgescu et al. (2011), Directclimateeffects of perennial bioenergy crops in the United States, PNAS, doi:10.1073/pnas.1008779108 • S. Loarie et al. (2011), Directimpactsonlocalclimate of sugar-caneexpansion in Brazil, Nature ClimateChange, doi:10.1038/nclimate1067 • R. Bright et al. (2013), Climatechangeimplications of shiftingforestmanagementstrategy in a boreal forestecosystem of Norway, Global ChangeBiology, doi: 10.1111/gcb.12451 • F. Cherubini et al. (2012), Site-specific global warmingpotentials of biogenic CO2 for bioenergy: contributions from carbonfluxes and albedo dynamics, Environmental Research Letters, doi: 10.1088/1748-9326/7/4/045902 • R. Bright et al. (2011), Radiative forcingimpacts of boreal forestbiofuels: A scenario study for Norway in light of albedo, Environmental Science & Technology, doi: 10.1021/es201746b • R. Bright et al. (2012), Climateimpacts of bioenergy: Inclusion of carboncycle and albedo dynamics in lifecycleimpactassessment, EnvironmentalImpactAssessmentReview, doi: 10.1016/j.eiar.2012.01.002 More info:ryan.m.bright@ntnu.no

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