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Paul R. Moorcroft David 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. atmospheric CO 2 meas. satellite observations (leaf phenology, soil moisture) Canopy CO 2 & H 2 O fluxes. forest inventories (vegetation dynamics) spatial scale 1m 2 1000km 2 100km 2 10km 2 1km 2 earth decades years months hours time scale - as a result, scaling is a key issue (Moorcroft 2006) Aircraft measurements of CO 2 & H 2 O fluxes
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Ecosystem Demography Model (ED2) ha (~10 -2 km 2 ) (Moorcroft et al. 2001, Medvigy et al. 2006) of plant type i mortality growth water nitrogen carbon recruitment ~ 15 m leaf carbon fluxes evapo- transpir ation
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carbon uptake (NEE tC ha -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 (42 o N, -72 o W) - 2 year model fit (1995 & 1996), in which model was constrained against: - hourly, monthly and yearly GPP and R total - 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) observedinitialoptimized growth mortality = optimization period GPP respiration (r a + r h )
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Improved predictability at Harvard Forest: 10-yr simulations (1992-2001) observedinitialoptimized = optimization period conifers hardwoods mortality growth
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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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Howland Forest Harvard Forest Howland Forest (45 o N, -68 o W) Howland forest Composition: growth observedinitialoptimized 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 (3 o S, -55 o W) Forest Inventory: Predicted collapse of the Amazon forests in response to rising CO 2
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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 12:2370-2390 Moorcroft (2006) Trends in Ecology and Evolution 21:400-407 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 temperature sensitivity f(T) soil moisture sensitivity f( ) relative decomposition rate optimized initial 3-box biogeochemistry model (fast, structural & slow C pools)
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