HR: 14:55h
AN: B53D-05    [Abstracts]
TI: Remote sensing data assimilation for a prognostic model of vegetation phenology
AU: * Stockli, R
EM: stockli@atmos.colostate.edu
AF: Department of Atmospheric Science, Colorado State University, Fort Colllins, CO 80523, United States
AU: * Stockli, R
EM: stockli@atmos.colostate.edu
AF: NASA Earth Observatory, Goddard Space Flight Center, Greenbelt, MD 20771, United States
AU: * Stockli, R
EM: stockli@atmos.colostate.edu
AF: Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, ZH 8092, Switzerland
AU: Rutishauser, T
EM: rutis@giub.unibe.ch
AF: Institute of Geography, University of Bern, Bern, BE 3012, Switzerland
AU: Ahrends, H
EM: hella.ahrends@giub.unibe.ch
AF: Institute of Geography, University of Bern, Bern, BE 3012, Switzerland
AU: Denning, S
EM: denning@atmos.colostate.edu
AF: Department of Atmospheric Science, Colorado State University, Fort Colllins, CO 80523, United States
AU: Thornton, P
EM: thornton@ucar.edu
AF: Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80305, United States
AB: Since vegetation and climate interact dynamically, a two-way coupling of vegetation phenology will lead to a better representation of the terrestrial water and carbon cycle in weather forecasts and climate predictions. We investigate four existing prognostic phenology schemes for use in global climate simulations: The C/N and DGVM scheme as part of the Community Land Model Version 3 (NCAR), the GSI scheme as part of the Simple Biosphere Model Version 2.5 (CSU/ETH), and the Joint UK Land Environment Simulator (UK Metoffice). We document their highly variable performance in various climatic environments. Our aim is to provide a vegetation phenology scheme which can prognose a more realistic seasonal-to- interannual variability of transpiring leaves in response to climatic forcings on a global scale. To achieve this goal, MODIS-derived phenological states are assimilated into the above (existing) phenology schemes by use of the Ensemble Kalman Filter. Through simultaneous states and parameter estimation the large uncertainty of biome- dependent phenological parameters (e.g. minimum temperature required for leaf-out) can be reduced. However, this step involves a careful quantification of uncertainties in the cloud- and aerosol-contaminated satellite data. Next we show how the constrained parameter set improves the seasonal variability of predicted phenological states for a global range of ecosystem types in various climate zones. Interannual variability of a extra-tropical climatic region is validated by use of a reconstructed start-of-season dataset covering deciduous temperate broadleaf vegetation in the Swiss lowland plateau. The currently sparse coverage of long and consistent intra- seasonal validation datasets for such models justifies new autonomous ground-based observational networks and reconstruction of phenological states from proxy datasets. Upon completion of this NASA Energy and Water Cycle sponsored research project a generally-applicable prognostic phenology scheme including a satellite data assimilatoin framework will be provided to the climate modeling community. It will be suitable for two applications: a) processing a global phenological reanalysis dataset (assimilation mode) b) predicting phenological states in future climate simulations (prognostic mode)
DE: 0416 Biogeophysics
DE: 0426 Biosphere/atmosphere interactions (0315)
DE: 0466 Modeling
DE: 0480 Remote sensing
DE: 3315 Data assimilation
SC: Biogeosciences [B]
MN: 2007 Fall Meeting