quantifying the risk of amazon forest 'dieback' from climate and land-use change ben...
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Quantifying the risk of Amazon forest 'dieback' from climate and
land-use change
Ben Poulter
Swiss Federal Research Institute WSLin collaboration with the Marie Curie Greencycles RTN and the
Potsdam Institute for Climate Impact Research (PIK)
June 7/8 2010 LSCE / CEA 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
June 7/8 2010 LSCE / CEA 3
i. drivers of Amazon forest dieback
Cox et al. 2004
1.Climate change1. Reduced precipitation & increasing
temperature2. Dieback of forest & enhanced reduction
in precip. via convective precipitation3. Replicated with perturbed physics
ensemble4. Agreement between models
1. Spatio-temporal variability2. Climate scenario dependent
Booth et al. in rev.
Cox et al. 2004
Sitch et al. 2008
Salazar et al. 2007
Unresolved:What are climate and ecological mechanisms driving forest dieback?What is likelihood of climate driven forest dieback?
June 7/8 2010 LSCE / CEA 4
i. drivers of Amazon forest dieback1. ClimateWhat are climatic & ecological mechanisms driving forest dieback?What is likelihood of climate driven forest dieback?
1.Deforestation1. Arc of deforestation2. Future deforestation linked to
connectedness and access3. Estimating C-emissions is
challenging
Loarie et al. 2009Soares et al. 2006
Unresolved:Spatial pattern is predictableIntensity of deforestation linked global economic teleconnectionsTracking fate of carbon remains challenging
Rammankutty et al. 2007
June 7/8 2010 LSCE / CEA 5
Morton et al. 2008
i. drivers of Amazon forest dieback1. ClimateWhat are climatic & ecological mechanisms driving forest dieback?What is likelihood of climate driven forest dieback?
2. DeforestationSpatial pattern is predictableIntensity of deforestation linked global economic teleconnectionsTracking fate of carbon remains challenging
1.Fire1. Deforestation related
1. human ignitions2. micro-climate
2. Climate amplifies3. ~100% biomass consumption
Aragao et al. 2007
Morton et al. 2008
June 7/8 2010 LSCE / CEA 6
i. drivers of Amazon forest dieback
Nepstad 2008
SynergiesHow 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. ClimateWhat are climatic & ecological mechanisms driving forest dieback?What is likelihood of climate driven forest dieback?
2. DeforestationSpatial pattern is predictableIntensity of deforestation linked global economic teleconnectionsTracking fate of carbon remains challenging
3. FireLinked to climate and deforestationStrong feedback on forest degradation
June 7/8 2010 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?
June 7/8 2010 LSCE / CEA 8
IPCC AR4 2007
ii. understanding of Amazon forest ecology
Li et al. 2006
1.Climate1. GCM model disagreement2. Model-obs. disagreement
Malhi et al. 2009
June 7/8 2010 LSCE / CEA 9
Phillips et al. 1998
ii. understanding of Amazon forest ecology
1.Aboveground processes1. Biomass
1. Increasing1.Radiation (Hashimoto et al. 2009)
2.CO23.Disturbance (Gloor et al. 2010)
2. Sensitivity to drought
2. Canopy processes1. Dynamic phenology
1.Sustained by deep soils2. Resilient to drought3. Not resilient to drought
Phillips et al. 2009Myneni et al. 2007
June 7/8 2010 LSCE / CEA 10Poulter 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
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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. 2007
Saleska et al. 2003
Ecosystem models get seasonal cycle wrong
June 7/8 2010 LSCE / CEA 12
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. 2008
Poulter et al. 2009
Poulter et al. 2009
June 7/8 2010 LSCE / CEA 13
Tra
nsf
orm
ed b
y p
roce
ss m
od
ule
s in
toClimate, Soil, CO2
C budget, H2O Budget,Vegetation Composition
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
AET
Ci
AET
Ci
crown area
height
fine roots
leaves
LAI
sapwoodheartwood
0-50 cm50-150 cm
stemdiameter
Spa
ce &
T
ime
Loop
s
LPJml Dynamic Vegetation Model
June 7/8 2010 LSCE / CEA 14
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 trianglesshallow soil = black triangles/squaresdeep soil = black diamonds/circlesdynamic phen = black circles/squares
Leaf Area Index (LAI)
Low HighLow
High
Fra
ctio
n o
f P
ho
tosy
nth
etic
A
vaila
ble
Rad
iati
on
(F
PA
R)
X%
X%
Poulter et al. 2009
June 7/8 2010 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
June 7/8 2010 LSCE / CEA 16
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
Latin hypercube
Random sampleSet included 42 parameters and evaluated against eddy flux data (1000 sets).
For example:Soil depthRooting distributionRespiration Q10Maximum transpirationMinimum conductance…
20 parameters identified as important for determining variability of key outputs and used for basinwide runs (400 sets)
Soil depthRooting distributionRespiration Q10Maximum transpirationMinimum conductance…
Poulter et al. 2010
June 7/8 2010 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)
June 7/8 2010 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
June 7/8 2010 LSCE / CEA 19
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
June 7/8 2010 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
June 7/8 2010 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
June 7/8 2010 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
June 7/8 2010 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
June 7/8 2010 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 Cox and Stephenson 2007
imp
ort
an
ce
June 7/8 2010 LSCE / CEA 25
• Questions?– Email: [email protected]
• 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