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100608_Poulter_Amazon.ppt - OPeNDAPDODS-IPSL

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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 the Marie Curie Greencycles RTN and the
        Potsdam Institute for Climate Impact Research (PIK)
                 Outline
• Drivers of Amazon forest dieback
• Understanding of Amazon forest ecology
• Modeling uncertainty of tropical forest
  dynamics
• Modeling drivers and their synergies
• Managing uncertainty



June 7/8 2010       LSCE / CEA              2
  i. drivers of Amazon forest dieback
1.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

Unresolved:
What are climate and ecological mechanisms
driving forest dieback?
What is likelihood of climate driven forest           Cox et al. 2004
dieback?                                                Sitch et al. 2008
                                                       Booth et al. in rev.
                                                         Cox et al. 2004



                                                     Salazar et al. 2007

 June 7/8 2010                          LSCE / CEA            3
  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

Unresolved:
Spatial pattern is predictable
Intensity of deforestation linked global economic                   Rammankutty et al. 2007

teleconnections                                                           Loarie et al. 2009
Tracking fate of carbon remains challenging                               Soares et al. 2006




June 7/8 2010                               LSCE / CEA                           4
 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                                      Morton et al. 2008


                                                                        Morton et al. 2008
                                                                         Aragao et al. 2007




June 7/8 2010                                LSCE / CEA                        5
 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.?
                                                                    Nepstad 2008




June 7/8 2010                                  LSCE / CEA                      6
                 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?



June 7/8 2010       LSCE / CEA              7
        ii. understanding of Amazon forest
                      ecology
1.Climate
  1. GCM model disagreement
  2. Model-obs. disagreement



                                                             IPCC AR4 2007




                                            Li et al. 2006
                                            Malhi et al. 2009
June 7/8 2010                  LSCE / CEA                       8
        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
                                                      Phillips et al. 2009
      2. Resilient to drought                                 Phillips 2007
                                                         Myneni et al.et al. 1998

      3. Not resilient to drought


June 7/8 2010                            LSCE / CEA                9
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
June 7/8 2010                       LSCE / CEA               10
         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 al. 2003


     Ecosystem models get seasonal cycle wrong


                                                                        Saleska et a. 2007
 June 7/8 2010                                 LSCE / CEA                         11
         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




                                                         Poulter et al. 2009



                                                       Stockli et al. 2008
June 7/8 2010                             LSCE / CEA            12
                   LPJml Dynamic Vegetation Model
     Climate, Soil, CO2




                                     10 plant functional types
             process modules into




                                     competition, mortality, establishment
               Transformed by




                                     fire (globfirm)
Time Loops
 Space &




                                     photosynthesis: coupled C and H2O cycles
                                     C allocation (funct. and struct. relations)
                                     Carbon pools: 4 in vegetation, 4 in litter/soil
                                                                                                 crown area
                                     Full hydrology                                                   leaves


                                                                                                       LAI
                                                              AET           AET
                                                                                        height
                                                                           Ci          stem          sapwood
                                                            Ci                                       heartwood
   C budget, H2O Budget,                                                             diameter
   Vegetation Composition
      June 7/8 2010                                       LSCE / CEA                  0-50 cm                   13
                                                                                                       fine roots
                                                                                    50-150 cm
                                       ii. understanding of Amazon forest
                                                     ecology
                       •            Deep soils required to maintain dry
                                    season GPP
                       •            Dynamic LAI not required (fpar
                                    saturation, dynamic Vcmax)
                             High
                                                  X%
Available Radiation (FPAR)
Fraction of Photosynthetic




                                                              X%




                             Low
                                     Low                             High
                                                                     modis gpp = grey triangles
                                             Leaf Area Index (LAI)   shallow soil = black triangles/squares
                                                                     deep soil = black diamonds/circles
                                                                                                              Poulter et al. 2009
                                                                     dynamic phen = black circles/squares

                       June 7/8 2010                                          LSCE / CEA                                    14
                 Outline
• Drivers of Amazon forest dieback
• Understanding of Amazon forest ecology
• Modeling uncertainty of tropical forest
  dynamics
• Modeling drivers and their synergies
• Managing uncertainty



June 7/8 2010       LSCE / CEA              15
iii. modeling uncertainty of tropical forest
                dynamics
                                                     Set included 42 parameters and
Experiment 3                                         evaluated against eddy flux data
                                                                                             Random sample

•   Identify sources of uncertainty for              (1000 sets).
    projecting climate impacts in
                                                     For example:
    Amazon Basin                                                   Soil depth
     –   Identify key parameters and their spatial                 Rooting distribution
         influence
                                                                   Respiration Q10
     –   Partition uncertainty between vegetation                  Maximum transpiration
         model and climate projection
                                                                   Minimum conductance
                                                                   …                         Latin hypercube
•   Methods                                          20 parameters identified as
     –   LPJml DGVM                                  important for determining variability
                                                     of key outputs and used for
     –   Latin Hypercube Analysis
                                                     basinwide runs (400 sets)
     –   Ensemble of GCM models (8)                                Soil depth
     –   SRES A2 storyline                                         Rooting distribution
     –   Variance partitioning following Hawkins                   Respiration Q10
         et al. 2009                                               Maximum transpiration
                                                                   Minimum conductance
                                                                   …
                                                                                              Poulter et al. 2010



June 7/8 2010                                   LSCE / CEA                                                16
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)




June 7/8 2010                           LSCE / CEA   17
 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



 June 7/8 2010                      LSCE / CEA   18
 iii. modeling uncertainty of tropical forest
                 dynamics
                                                               East Amazonia   West Amazonia




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




 June 7/8 2010                                    LSCE / CEA                                   19
                 Outline
• Drivers of Amazon forest dieback
• Understanding of Amazon forest ecology
• Modeling uncertainty of tropical forest
  dynamics
• Modeling drivers and their synergies
• Managing uncertainty



June 7/8 2010       LSCE / CEA              20
    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




June 7/8 2010                                                   LSCE / CEA   21
 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


 June 7/8 2010                                   LSCE / CEA   22
                 Outline
• Drivers of Amazon forest dieback
• Understanding of Amazon forest ecology
• Modeling uncertainty of tropical forest
  dynamics
• Modeling drivers and their synergies
• Managing uncertainty



June 7/8 2010       LSCE / CEA              23
                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
                                importance




         • Respiration
     – PFT diversity
     – Hydrology
         • Hydraulic lift
         • Deep soils/roots
     – Climate                                                          Cox and Stephenson 2007


June 7/8 2010                                LSCE / CEA                               24
• 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




June 7/8 2010                                        LSCE / CEA                                                            25

				
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