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Quantifying the risk of Amazon forest 'dieback' from climate and land-use change Ben Poulter Swiss Federal Research Institute WSL in collaboration with.

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Presentation on theme: "Quantifying the risk of Amazon forest 'dieback' from climate and land-use change Ben Poulter Swiss Federal Research Institute WSL in collaboration with."— Presentation transcript:

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 June 7/8 2010LSCE / CEA2 Outline Drivers of Amazon forest dieback Understanding of Amazon forest ecology Modeling uncertainty of tropical forest dynamics Modeling drivers and their synergies Managing uncertainty

3 June 7/8 2010LSCE / CEA3 i. drivers of Amazon forest dieback Cox et al Climate change 1.Reduced precipitation & increasing temperature 2.Dieback of forest & enhanced reduction in precip. via convective precipitation 3.Replicated with perturbed physics ensemble 4.Agreement between models 1.Spatio-temporal variability 2.Climate scenario dependent Booth et al. in rev. Cox et al Sitch et al Salazar et al Unresolved: What are climate and ecological mechanisms driving forest dieback? What is likelihood of climate driven forest dieback?

4 June 7/8 2010LSCE / CEA4 i. drivers of Amazon forest dieback 1. Climate What are climatic & ecological mechanisms driving forest dieback? What is likelihood of climate driven forest dieback? 1.Deforestation 1.Arc of deforestation 2.Future deforestation linked to connectedness and access 3.Estimating C-emissions is challenging Loarie et al Soares et al Unresolved: Spatial pattern is predictable Intensity of deforestation linked global economic teleconnections Tracking fate of carbon remains challenging Rammankutty et al. 2007

5 June 7/8 2010LSCE / CEA5 Morton et al 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 1.Fire 1.Deforestation related 1.human ignitions 2.micro-climate 2.Climate amplifies 3.~100% biomass consumption Aragao et al Morton et al. 2008

6 June 7/8 2010LSCE / CEA6 i. drivers of Amazon forest dieback Nepstad 2008 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.? 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

7 June 7/8 2010LSCE / CEA7 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?

8 June 7/8 2010LSCE / CEA8 IPCC AR ii. understanding of Amazon forest ecology Li et al Climate 1.GCM model disagreement 2.Model-obs. disagreement Malhi et al. 2009

9 June 7/8 2010LSCE / CEA9 Phillips et al ii. understanding of Amazon forest ecology 1.Aboveground processes 1.Biomass 1.Increasing 1.Radiation (Hashimoto et al. 2009) 2.CO2 3.Disturbance (Gloor et al. 2010) 2.Sensitivity to drought 2.Canopy processes 1.Dynamic phenology 1.Sustained by deep soils 2.Resilient to drought 3.Not resilient to drought Phillips et al Myneni et al. 2007

10 June 7/8 2010LSCE / CEA10 Poulter and Cramer, 2009 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

11 June 7/8 2010LSCE / CEA11 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) Saleska et a Saleska et al Ecosystem models get seasonal cycle wrong

12 June 7/8 2010LSCE / CEA12 Experiment 2 Tested relative effects of: –deep soils / roots and, –dynamic 'anticipatory' tropical phenology –Using the LPJ DGVM –Dry season length gradient ii. understanding of Amazon forest ecology Stockli et al Poulter et al. 2009

13 June 7/8 2010LSCE / CEA13 Transformed by process modules into Climate, Soil, CO 2 C budget, H 2 O Budget, Vegetation Composition  10 plant functional types  competition, mortality, establishment  fire (globfirm)  photosynthesis: coupled C and H 2 O cycles  C allocation (funct. and struct. relations)  Carbon pools: 4 in vegetation, 4 in litter/soil  Full hydrology AET CiCi CiCi crown area height fine roots leaves LAI sapwood heartwood 0-50 cm cm stem diameter Space & Time Loops LPJml Dynamic Vegetation Model

14 June 7/8 2010LSCE / CEA14 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 Leaf Area Index (LAI) LowHigh Low High Fraction of Photosynthetic Available Radiation (FPAR) X% Poulter et al. 2009

15 June 7/8 2010LSCE / CEA15 Outline Drivers of Amazon forest dieback Understanding of Amazon forest ecology Modeling uncertainty of tropical forest dynamics Modeling drivers and their synergies Managing uncertainty

16 June 7/8 2010LSCE / CEA16 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 Latin hypercube Random sample 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 … Poulter et al. 2010

17 June 7/8 2010LSCE / CEA17 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)

18 June 7/8 2010LSCE / CEA18 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

19 June 7/8 2010LSCE / CEA19 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 East Amazonia West Amazonia

20 June 7/8 2010LSCE / CEA20 Outline Drivers of Amazon forest dieback Understanding of Amazon forest ecology Modeling uncertainty of tropical forest dynamics Modeling drivers and their synergies Managing uncertainty

21 June 7/8 2010LSCE / CEA21 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

22 June 7/8 2010LSCE / CEA22 iv. modeling drivers and their synergies Current NBP to PgC a -1 Future NBP (2100) to 0.97 PgC a-1 Change in carbon stocks - Climate change / CO 2 : -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

23 June 7/8 2010LSCE / CEA23 Outline Drivers of Amazon forest dieback Understanding of Amazon forest ecology Modeling uncertainty of tropical forest dynamics Modeling drivers and their synergies Managing uncertainty

24 June 7/8 2010LSCE / CEA24 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 Cox and Stephenson 2007 importance

25 June 7/8 2010LSCE / CEA25 Questions ? – 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: /j x. –Poulter B, Cramer W (2009a) Satellite remote sensing of tropical forest canopies and their seasonal dynamics. International Journal of Remote Sensing, 30, –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: /j 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, Acknowledgements –Wolfgang Cramer, Andrew Friend, Ursula Heyder, Fred Hatterman, Soenke Zaehle, Ed Hawkins, Stephen Sitch, Greencycles RTN


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