Paul R. Moorcroft

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Developing a predictive science of the biosphere
Paul R. Moorcroft David Medvigy, Stephen Wofsy,
J. William Munger, M. Dietze
Harvard University
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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?
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(Moorcroft 2006)
- 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.
- as a result, scaling is a key issue
forest inventories (vegetation dynamics)
decades
atmospheric CO2 meas.
years
time scale
satellite observations (leaf phenology,
soil moisture)
Canopy CO2 H2O fluxes.
months
Aircraft measurements of CO2 H2O fluxes
hours
spatial scale
1m2
1000km2
100km2
10km2
1km2
earth
- 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.
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(Moorcroft et al. 2001, Medvigy et al. 2006)
Ecosystem Demography Model (ED2)
evapo-transpiration
leaf carbon fluxes
mortality
recruitment
ha (10-2 km2)
water nitrogen carbon
15 m
of plant type i
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Harvard Forest LTER ecosystem measurements
carbon uptake (NEE tC ha-1 y-1)
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ED-2 model fitting at Harvard Forest (42oN,
-72oW)
- initialize with observed stand structure
Atmospheric Grid Cell
ED2 biosphere model
- model forced with climatology and radiation
observed at Harvard Forest meteorological station.
- 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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Improved predictability at Harvard Forest 10-yr
simulations (1992-2001)
Net Carbon Fluxes (NEP)
optimized
initial
observed
optimization period
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Improved predictability at Harvard Forest 10-yr
patterns of tree growth and mortality (1992-2001)
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Improved predictability at Harvard Forest 10-yr
simulations (1992-2001)
conifers
hardwoods
growth
observed
initial
optimized
optimization period
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Vegetation model optimization results
Change in goodness of fit 450 log-likelihood
(Dl) units (sig level Dl 20)
( 95 confidence interval)
model parameters are generally well-constrained
average coefficient of variation 17
(-85, 160)
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Howland Forest (45oN, -68o W)
Howland forest Composition
Howland Forest
Harvard Forest
(no changes in any of the model parameters)
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Improved predictability at Howland Forest 5-yr
simulations (1996-2000)
Gross Primary Productivity (tC ha-1 mo-1 )
gt model improvement is general, not
site-specific
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Regional Simulations
Harvard Forest

• stand composition harvesting rates US Forest
  Service Quebec
• forest inventory 1985 - 1995

• climate drivers ECMWF reanalysis dataset

- again, no change in any of the model parameters
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Regional decadal-scale dynamics of above-ground
biomass growth (tC/ha/yr)
observed
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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)

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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Future Directions
North American Carbon Plan (NACP) expanding to
sub-continental scale.
Ameriflux site
optimization site
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Biosphere-atmosphere feedbacks Amazonia
(Cox et al 2000)
Predicted collapse of the Amazon forests in
response to rising CO2
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Acknowledgements
Lab Marco Albani, David Medvigy, Daniel Lipsitt,
M. Dietze
Collaborators Steve Wofsy, Bill Munger, Roni
Avissar, Bob Walko, D. Hollinger, Andrew
Richardson
References Moorcroft et al. 2001. Ecological
Monographs 74557-586. Hurtt et al. 2002. PNAS
991389-1394. Albani Moorcroft (2006) Global
Change Biology 122370-2390 Moorcroft (2006)
Trends in Ecology and Evolution
21400-407 Medvigy et al. (2007) Global Change
Biology (in review)
Funding National Science Foundation
Department of Energy National Aeronautics
and Space Administration
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Soil decomposition model
initial
3-box biogeochemistry model (fast, structural
slow C pools)
optimized
temperature sensitivity f(T)
soil moisture sensitivity f(q)
relative decomposition rate
(q)