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Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze

Developing a predictive science of the biosphere. Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze. Harvard University. carbon flux: land-air. global mean temperature.

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Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze

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  1. Developing a predictive science of the biosphere Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze Harvard University

  2. carbon flux: land-air global mean temperature - we now have models that make predictions for the long-term responses of terrestrial ecosystems to climate change. - but are they predictive?

  3. (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 1km2 10km2 100km2 1000km2 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.

  4. (Moorcroft et al. 2001, Medvigy et al. 2006) growth Ecosystem Demography Model (ED2) evapo-transpiration leaf carbon fluxes mortality recruitment ha (~10-2 km2) water nitrogen carbon ~ 15 m of plant type i

  5. Harvard Forest LTER ecosystem measurements carbon uptake (NEE tC ha-1 y-1)

  6. 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

  7. Improved predictability at Harvard Forest: 10-yr simulations (1992-2001) Net Carbon Fluxes (NEP) observed initial optimized = optimization period

  8. growth GPP mortality respiration (ra + rh ) = optimization period initial observed optimized Improved predictability at Harvard Forest: 10-yr patterns of tree growth and mortality (1992-2001)

  9. mortality Improved predictability at Harvard Forest: 10-yr simulations (1992-2001) hardwoods conifers growth initial observed optimized = optimization period

  10. 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)

  11. Howland Forest (45oN, -68o W) growth net carbon fluxes (NEP) initial observed optimized Howland forest Composition: Howland Forest Harvard Forest (no changes in any of the model parameters)

  12. 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) Gross Primary Productivity (tC ha-1 mo-1 ) => model improvement is general, not site-specific

  13. 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

  14. optimized initial Regional decadal-scale dynamics of above-ground biomass growth (tC/ha/yr) observed

  15. Able to demonstrate that: capture short-term & long-term vegetation dynamics (scale accurately in time). capture observed regional scale variation in ecosystem dynamics without the need for site-specific parameters or tuning (scale accurately in space). 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.

  16. Future Directions: North American Carbon Plan (NACP): expanding to sub-continental scale. Ameriflux site optimization site

  17. (Shukla et al 1990) Santarem Flux tower (3oS, -55oW) Amazonian deforestation predicted to change South American climate Change in Annual Precipitation (mm) Forest Inventory: Biosphere-atmosphere feedbacks Amazonia (Cox et al 2000) Predicted collapse of the Amazon forests in response to rising CO2

  18. 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 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

  19. 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)

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