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Quantifying the risk of Amazon forest 'dieback' from climate and land-use change

Quantifying the risk of Amazon forest 'dieback' from climate and land-use change. Ben Poulter Swiss Federal Research Institute WSL in collaboration with the Marie Curie Greencycles RTN and the Potsdam Institute for Climate Impact Research (PIK). Outline. Drivers of Amazon forest dieback

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Quantifying the risk of Amazon forest 'dieback' from climate and land-use change

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  1. Quantifying the risk of Amazon forest 'dieback' from climate and land-use change Ben Poulter Swiss Federal Research Institute WSL in collaboration with the Marie Curie Greencycles RTN and the Potsdam Institute for Climate Impact Research (PIK)

  2. Outline • Drivers of Amazon forest dieback • Understanding of Amazon forest ecology • Modeling uncertainty of tropical forest dynamics • Modeling drivers and their synergies • Managing uncertainty LSCE / CEA

  3. Sitch et al. 2008 Booth et al. in rev. Cox et al. 2004 Cox et al. 2004 Salazar et al. 2007 i. drivers of Amazon forest dieback • Climate change • Reduced precipitation & increasing temperature • Dieback of forest & enhanced reduction in precip. via convective precipitation • Replicated with perturbed physics ensemble • Agreement between models • Spatio-temporal variability • Climate scenario dependent Unresolved: What are climate and ecological mechanisms driving forest dieback? What is likelihood of climate driven forest dieback? LSCE / CEA

  4. Rammankutty et al. 2007 Loarie et al. 2009 Soares et al. 2006 i. drivers of Amazon forest dieback 1. Climate What are climatic & ecological mechanisms driving forest dieback? What is likelihood of climate driven forest dieback? • Deforestation • Arc of deforestation • Future deforestation linked to connectedness and access • Estimating C-emissions is challenging Unresolved: Spatial pattern is predictable Intensity of deforestation linked global economic teleconnections Tracking fate of carbon remains challenging LSCE / CEA

  5. Morton et al. 2008 Morton et al. 2008 Aragao et al. 2007 i. drivers of Amazon forest dieback 1. Climate What are climatic & ecological mechanisms driving forest dieback? What is likelihood of climate driven forest dieback? 2. Deforestation Spatial pattern is predictable Intensity of deforestation linked global economic teleconnections Tracking fate of carbon remains challenging • Fire • Deforestation related • human ignitions • micro-climate • Climate amplifies • ~100% biomass consumption LSCE / CEA

  6. Nepstad 2008 i. drivers of Amazon forest dieback 1. Climate What are climatic & ecological mechanisms driving forest dieback? What is likelihood of climate driven forest dieback? 2. Deforestation Spatial pattern is predictable Intensity of deforestation linked global economic teleconnections Tracking fate of carbon remains challenging 3. Fire Linked to climate and deforestation Strong feedback on forest degradation Synergies How will interactions affect spatio-temporal dynamics of Amazon forest dieback? Is there information in the spatial temporal pattern of uncertainties useful for biodiveristy protection, REDD, etc.? LSCE / CEA

  7. Outline • Drivers of Amazon forest dieback • Understanding of Amazon forest ecology • Modeling uncertainty of tropical forest dynamics • Modeling drivers and their synergies • Is reducing uncertainty possible? LSCE / CEA

  8. IPCC AR4 2007 Li et al. 2006 Malhi et al. 2009 ii. understanding of Amazon forest ecology • Climate • GCM model disagreement • Model-obs. disagreement LSCE / CEA

  9. Myneni et al. 2007 Phillips et al. 1998 Phillips et al. 2009 ii. understanding of Amazon forest ecology • Aboveground processes • Biomass • Increasing • Radiation (Hashimoto et al. 2009) • CO2 • Disturbance (Gloor et al. 2010) • Sensitivity to drought • Canopy processes • Dynamic phenology • Sustained by deep soils • Resilient to drought • Not resilient to drought LSCE / CEA

  10. ii. understanding of Amazon forest ecology Experiment 1 • Tested robustness of seasonal cycle to increasing data quality (BISE filter, QA/QC filters) • EVI and LAI seasonality sensitive to atmospheric contamination Poulter and Cramer, 2009 LSCE / CEA

  11. Saleska et al. 2003 Saleska et a. 2007 Ecosystem models get seasonal cycle wrong ii. understanding of Amazon forest ecology • Proposed mechanisms sustaining seasonal forest dynamics: • Deep soils and roots (18 m; Nepstad et al. 1994) • Maintain GPP during dry season (Saleska et al. 2003) • Green up is an anticipatory response to light (Myneni et al. 2007) • Wet tropical forests are radiation limited (Nemani et al. 2004) LSCE / CEA

  12. Poulter et al. 2009 ii. understanding of Amazon forest ecology Experiment 2 • Tested relative effects of: • deep soils / roots and, • dynamic 'anticipatory' tropical phenology • Using the LPJ DGVM • Dry season length gradient Poulter et al. 2009 Stockli et al. 2008 LSCE / CEA

  13. LPJml Dynamic Vegetation Model Climate,Soil, CO2 AET • 10 plant functional types • competition, mortality, establishment • fire (globfirm) • photosynthesis: coupled C and H2O cycles • C allocation (funct. and struct. relations) • Carbon pools: 4 in vegetation, 4 in litter/soil • Full hydrology Ci Transformed by process modules into Space & Time Loops AET sapwood heartwood Ci crown area leaves LAI height stem diameter C budget, H2O Budget, Vegetation Composition 0-50 cm 50-150 cm fine roots LSCE / CEA

  14. High X% X% Fraction of Photosynthetic Available Radiation (FPAR) Low Low High Leaf Area Index (LAI) ii. understanding of Amazon forest ecology • Deep soils required to maintain dry season GPP • Dynamic LAI not required (fpar saturation, dynamic Vcmax) modis gpp = grey triangles shallow soil = black triangles/squares deep soil = black diamonds/circles dynamic phen = black circles/squares Poulter et al. 2009 LSCE / CEA

  15. Outline • Drivers of Amazon forest dieback • Understanding of Amazon forest ecology • Modeling uncertainty of tropical forest dynamics • Modeling drivers and their synergies • Managing uncertainty LSCE / CEA

  16. Set included 42 parameters and evaluated against eddy flux data (1000 sets). For example: Soil depth Rooting distribution Respiration Q10 Maximum transpiration Minimum conductance … 20 parameters identified as important for determining variability of key outputs and used for basinwide runs (400 sets) Soil depth Rooting distribution Respiration Q10 Maximum transpiration Minimum conductance … Random sample Latin hypercube Poulter et al. 2010 iii. modeling uncertainty of tropical forest dynamics Experiment 3 • Identify sources of uncertainty for projecting climate impacts in Amazon Basin • Identify key parameters and their spatial influence • Partition uncertainty between vegetation model and climate projection • Methods • LPJml DGVM • Latin Hypercube Analysis • Ensemble of GCM models (8) • SRES A2 storyline • Variance partitioning following Hawkins et al. 2009 LSCE / CEA

  17. iii. modeling uncertainty of tropical forest dynamics Experiment 3 • GCM model selection provided range of precipitation (+/-) and temperature projections (+/++) • Benchmarking • Compared to flux towers and biomass data • Parameter sets resulting in unrealistic outcomes removed • Site comparison did not include local effects (floodplain, management history) LSCE / CEA

  18. iii. modeling uncertainty of tropical forest dynamics • Change in aboveground C-stocks • -16 to +30 Pg C change • Change in forest cover • -13 to +2% increase • Parameters • Initial PFT composition influential • via competitive parameters (TO, alpha) • - Establishment - recovery • Soil depth - water access • Rooting depth: • >> roots in upper layer less water access LSCE / CEA

  19. West Amazonia East Amazonia iii. modeling uncertainty of tropical forest dynamics • Combining parameter uncertainty with GCM uncertainty: • - Climate projection main source of uncertainty • Variance partitioning • IV important ~10-20 yrs • Spatial variability in importance of GCM uncertainty • - Signal to noise ratio < 1 in E. Amazonia, greater than 1 in W. Amazonia until ~2060 LSCE / CEA

  20. Outline • Drivers of Amazon forest dieback • Understanding of Amazon forest ecology • Modeling uncertainty of tropical forest dynamics • Modeling drivers and their synergies • Managing uncertainty LSCE / CEA

  21. iv. modeling drivers and their synergies Experiment 4 • Coupled land-use dynamics with LPJml • New deforestation-fire function added to GlobFirm • NOAA-12 hot pixels • Scalar modifies area burnt-fire season length • As deforestation increases, longer fire season length… • Ensembles/factorial approach • 9 GCM models (SRES A2) • (no climate feedback) • 2 deforestation scenarios (based on Soares et al. 2005) • 40% reduction by 2050 • Interpolated to 2100 assuming today's conservation areas LSCE / CEA

  22. iv. modeling drivers and their synergies • Current NBP • -0.49 to -0.12 PgC a-1 • Future NBP (2100) • -0.40 to 0.97 PgC a-1 • Change in carbon stocks • - Climate change / CO2 : -16 to +33 PgC • + fire : -19 to +33 PgC • + deforestation : -40 to + 12 PgC - • Previous studies • Soares - 32 PgC loss from deforestation • Cox - 35 PgC loss from climate change • Low agreement between climate projections: • - 37% agreement in sign of NBP change in 2100 • Linear climate response, with increasing importance of synergies with more extreme climate change LSCE / CEA

  23. Outline • Drivers of Amazon forest dieback • Understanding of Amazon forest ecology • Modeling uncertainty of tropical forest dynamics • Modeling drivers and their synergies • Managing uncertainty LSCE / CEA

  24. v. Managing uncertainty • “…Where there are threats of serious or irreversible damage, lack of full scientific certainty should not be used as a reason for postponing such measures” UNFCCC 1992 • Risk management of tropics • Spatio-temporal dimensions • Model developments • Canopy dynamics • Acclimation • Photosynthesis • Respiration • PFT diversity • Hydrology • Hydraulic lift • Deep soils/roots • Climate importance Cox and Stephenson 2007 LSCE / CEA

  25. Questions? • Email: poulter@wsl.ch • Papers… • Poulter B, Aragao L, Heinke J, et al.(2010a) Net biome production of the Amazon Basin in the 21st Century. Global Change Biology, doi: 10.1111/j.1365-2486.2009.02064.x. • Poulter B, Cramer W (2009a) Satellite remote sensing of tropical forest canopies and their seasonal dynamics. International Journal of Remote Sensing,30, 6575-6590. • Poulter B, Hattermann F, Hawkins E, et al. (2010b) Robust dynamics of Amazon dieback to climate change with perturbed ecosystem model parameters. Global Change Biology, doi: 10.1111/j.1365-2486.2009.02157.x. • Poulter B, Heyder U, Cramer W (2009b) Modelling the sensitivity of the seasonal cycle of GPP to dynamic LAI and soil depths in tropical rainforests. Ecosystems,12, 517-533. • Acknowledgements • Wolfgang Cramer, Andrew Friend, Ursula Heyder, Fred Hatterman, Soenke Zaehle, Ed Hawkins, Stephen Sitch, Greencycles RTN LSCE / CEA

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