Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze
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Transcript of Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze
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Paul R. MoorcroftDavid Medvigy, Stephen Wofsy, J. William Munger, M. Dietze
Harvard University
Developing a predictive science of the biosphere
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- we now have models that make predictions for the long-term responses of terrestrial ecosystems to climate change.
- but are they predictive?
carbon flux: land-air global mean temperature
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- existing ‘big-leaf’ dynamic terrestrial biosphere models (DGVMs) are interesting, but largely unconstrained hypotheses for the effects of climate variability and change on terrestrial ecosystems.
- models are fundamental to inference about the state of carbon cycle because the predictions of interest are at scales larger than those at which most measurements are made.
atm
osph
eric
C
O2 m
eas.
satellite observations(leaf phenology, soil
moisture)
Can
opy
CO
2 &
H2O
flux
es.
forest inventories (vegetation dynamics)
spatial scale 1m2
1000km2
100km2
10km2
1km2 earth
decades
years
months
hours
time
scal
e
- as a result, scaling is a key issue
(Moorcroft 2006)
Aircraft measurementsof CO2 & H2O fluxes
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Ecosystem Demography Model (ED2)
ha (~10-2 km2)
(Moorcroft et al. 2001,Medvigy et al. 2006)
of plant type i
mortality growth
waternitrogencarbon
recruitment
~ 15 m
leaf carbon fluxes
evapo-transpiration
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carb
on
up
take
(N
EE
tC h
a-1
y-1)
Harvard Forest LTER ecosystem measurements
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- initialize with observed stand structure
- model forced with climatology and radiation observed at Harvard Forest meteorological station.
ED2 biosphere model
Atmospheric Grid Cell
ED-2 model fitting at Harvard Forest (42oN, -72oW)
- 2 year model fit (1995 & 1996), in which model was constrained against:
- hourly, monthly and yearly GPP and Rtotal - hourly ET - above-ground growth & mortality of deciduous & coniferous trees
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optimizedinitialobserved
= optimization period
Improved predictability at Harvard Forest: 10-yr simulations (1992-2001)
Net Carbon Fluxes (NEP)
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Improved predictability at Harvard Forest: 10-yr patterns of tree growth and mortality (1992-2001)
observedinitial optimized
growth
mortality
= optimization period
GPP
respiration (ra + rh )
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Improved predictability at Harvard Forest: 10-yr simulations (1992-2001)
observedinitial optimized = optimization period
conifers hardwoods
mor
tali
tygr
owth
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Vegetation model optimization: results
model parameters are generally well-constrained: average coefficient of variation: 17%
(= 95% confidence interval)
(-85,
+160)
Change in goodness of fit: 450 log-likelihood (l) units (sig level: l= 20)
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HowlandForest
Harvard Forest
Howland Forest (45oN, -68o W)
Howland forest Composition:
growth
observedinitial optimized
net carbon fluxes (NEP)
(no changes in any of the model parameters)
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Gross Primary Productivity (tC ha-1 mo-1 )
conifer basal area increment (tC ha-1 mo-1 )
hardwood basal area increment (tC ha-1 mo-1 )
Improved predictability at Howland Forest: 5-yr simulations (1996-2000)
=> model improvement is general, not site-specific
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Regional Simulations
- climate drivers : ECMWF reanalysis dataset
- stand composition & harvesting rates: US Forest Service & Quebec
forest inventory 1985 - 1995
- again, no change in any of the model parameters
Harvard Forest
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initial
Regional decadal-scale dynamics of above-ground biomass growth (tC/ha/yr)
observed optimized
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Conclusions: Developing a predictive science of the biosphere
structured biosphere models such as ED2 can be parameterized & tested against field measurements yielding a model with accurate:• canopy-scale carbon & water fluxes • tree-level growth & mortality dynamics (the processes that govern long-term vegetation change)
capture observed regional scale variation in ecosystem dynamics without the need for site-specific parameters or tuning (scale accurately in space).
capture short-term & long-term vegetation dynamics(scale accurately in time).
Able to demonstrate that:
shown that it is possible to develop terrestrial biosphere models that not only make predictions about the future of ecosystems but are also truly predictive.
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optimization site
Ameriflux site
Future Directions:
North American Carbon Plan (NACP): expanding to sub-continental scale.
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Biosphere-atmosphere feedbacks Amazonia
(Cox et al 2000)
Amazonian deforestation predicted to change South American climate
(Shukla et al 1990)
Change in Annual Precipitation (mm)
Santarem Flux tower
(3oS, -55oW)
Forest Inventory:
Predicted collapse of the Amazon forests in response to rising CO2
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Collaborators: Steve Wofsy, Bill Munger, Roni Avissar, Bob Walko, D. Hollinger, Andrew Richardson
Lab: Marco Albani, David Medvigy, Daniel Lipsitt, M. Dietze
Acknowledgements
References:Moorcroft et al. 2001. Ecological Monographs 74:557-586. Hurtt et al. 2002. PNAS 99:1389-1394.Albani & Moorcroft (2006) Global Change Biology 12:2370-2390Moorcroft (2006) Trends in Ecology and Evolution 21:400-407Medvigy et al. (2007) Global Change Biology (in review)
Funding: National Science Foundation Department of EnergyNational Aeronautics and Space Administration
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Soil decomposition model
temperature sensitivity f(T) soil moisture sensitivity f()
rela
tive
de
com
posi
tion
ra
te
optimized
initial
3-box biogeochemistry model (fast, structural & slow C pools)