HR: 10:35h
AN: B52B-02    [Abstracts]
TI: Regional Carbon Dioxide Simulations Using Coupled Stochastic Time-Inverted Lagrangian Transport, Weather Forecast and Research, and Vegetation Photosynthesis and Respiration Models
AU: * Eluszkiewicz, J
EM: jel@aer.com
AF: Atmospheric and Environmental Research, Inc., 131 Hartwell Avenue, Lexington, MA 02421, United States
AU: Nehrkorn, T
EM: tnehrkor@aer.com
AF: Atmospheric and Environmental Research, Inc., 131 Hartwell Avenue, Lexington, MA 02421, United States
AU: Wofsy, S C
EM: swofsy@deas.harvard.edu
AF: School of Engineering and Applied Science, Harvard University, 29 Oxford Street, Cambridge, MA 02138, United States
AU: Matross, D M
EM: dmatross@nature.berkeley.edu
AF: Department of Environmental Science, Policy, and Management University of California Berkeley, 151 Hilgard Hall, Berkeley, CA 94720, United States
AU: Gerbig, C
EM: cgerbig@bgc-jena.mpg.de
AF: Max-Planck-Institut für Biogeochemie, Hans-Knoell-Str. 10, Jena, D-07745, Germany
AU: Lin, J C
EM: jcl@uwaterloo.ca
AF: Department of Earth & Environmental Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
AU: Freitas, S R
EM: sfreitas@cptec.inpe.br
AF: Divisão de Modelagem e Desenvolvimento Centro de Previsão de Tempo e Estudos Climáticos - INPE, Rodovia Presidente Dutra, km 39, Cachoeira Paulista, SP 12630, Brazil
AU: Longo, M
EM: mlongo@fas.harvard.edu
AF: School of Engineering and Applied Science, Harvard University, 29 Oxford Street, Cambridge, MA 02138, United States
AU: Andrews, A
EM: Arlyn.Andrews@noaa.gov
AF: NOAA Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305, United States
AU: Peters, W
EM: Wouter.Peters@noaa.gov
AF: NOAA Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305, United States
AB: Transport errors are known to be a large source of uncertainty in the inverse ("top-down") carbon flux estimates on global, continental, and regional scales. With a view to reducing these errors, we have configured the Stochastic Time-Inverted Lagrangian Transport (STILT) model to be driven by meteorological fields from the Weather Forecast and Research (WRF) model. Simulations of tower-based and airborne CO₂ measurements have been performed using the STILT/WRF model coupled to the Vegetation Photosynthesis and Respiration Model (VPRM). The use of WRF meteorology leads to superior model performance compared with standard meteorological products and the combined STILT/WRF/VPRM model produces promising simulations of both ground-based and airborne CO₂ data collected during spring and summer of 2004, especially in daytime conditions. Nevertheless, persistent simulation biases are evident as well, particularly with regard to the nighttime CO₂ build-up, the morning transition to convective conditions, and the overall high bias in the airborne simulations. A limited model inter- comparison study, comparing STILT simulations driven by the WRF model against those driven by another mesoscale atmospheric model, the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) model, has revealed that, while model transport ensembles can ameliorate the most obvious simulation deficiencies, the overall improvement is not large. This is reflected in the fact that the model-to-model differences are smaller than between an individual transport model and observations, pointing to systematic errors in the simulated transport and the yet-to-be-quantified contribution from errors in the biosphere model. Overall, the STILT/WRF/VPRM offers a powerful tool for continental and regional scale carbon flux estimates. However, the best hope of reducing transport uncertainties hampering these estimates lies with a model ensemble approach involving an interchange of both transport and biosphere models.
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0426 Biosphere/atmosphere interactions (0315)
DE: 0428 Carbon cycling (4806)
DE: 0466 Modeling
SC: Biogeosciences [B]
MN: 2007 Fall Meeting