References

1. a) Approach Process models for GPP, RA and RH are formulated, with effects of disturbance on growth and respiration: Model parameters are initialized using point-mode comparisons with Biome-BGC output. For validation and optimization, we couple the process models to the Stochastic Time-Inverted Lagrangian Transport (STILT) model (LIN et al., 2003), which determines the relative influence of each part of the region surrounding the observation site on the measured concentration data. This approach builds on high-resolution remote sensing products (e.g. Landsat, MODIS) as well as re-analysis meteorological datasets (EDAS-40, Daymet) to drive the models, thus modeled results are completely independent of local measurements. b) c) The ORCA West Coast Regional Project - Use of Top-Down Modeling in a Regional Carbon Budget Approach to Estimate Gross Carbon Fluxes for Oregon-California Mathias Göckede1, Julie M. Styles2, David P. Turner1, Beverly E. Law1 1Department of Forest Science, Oregon State University; 2South Australian Research and Development Institute, Primary Industries Resources and South Australia (SARDI/PIRSA) RESULTS Motivation The ORCA project aims to determine the regional carbon balance of Oregon and northern California (see also poster by LAW et al., this meeting), with particular focus on the effect of disturbance history and climate variability on carbon sources and sinks. ORCA provides a regional test of the overall NACP strategy by demonstrating bottom-up and model-data fusion approaches to derive regional to continental carbon balances. The ORCA model-data fusion component presented here focuses on optimizing simple biosphere process models for gross primary production (GPP), autotrophic respiration (RA) and heterotrophic respiration (RH), using CO2 concentration time series filtered by atmospheric transport modeling. Figure 2: Comparison of hourly CO2 concentrations measured at the Metolius intermediate pine site with results obtained by the ORCA top-down modeling approach. Guideline has been fit by eye to emphasize underlying trends. Model validation and optimization The STILT model simulates the trajectories of a fluid ensemble backward in time, yielding the so-called source area or footprint of the measurement. Coupling STILT to a process model allows approximation of the ‘loading’ (NEE > 0) or ‘unloading’ (NEE < 0) of the particles with CO2 (e.g. GERBIG et al., 2003). In the same way, anthropogenic influence can be estimated by using fossil fuel inventories as the source. Adding advected CO2 as lateral boundary conditions produces a simulated time series of CO2 concentration, which can be compared to measured time series that have been corrected for the fossil fuel signal. Process model parameters will be optimized by minimizing the differences between simulated and observed time series. Figure 1: Time series of CO2 budget components, obtained by coupling STILT footprints to source/sink models or datasets. Figure 3: Time series of modeled and measured CO2 concen-trations at the Metolius intermediate pine site. All results shown in Figs. 1-3 have been obtained for a 6-day trial period at the Metolius intermediate pine AmeriFlux site (44.27N, 121.33W, 1310m asl). The timeframe was chosen because of a prevailing zonal flow from the West, which minimized uncertainties due to transport and advection. Fig. 1 demonstrates that while biogenic processes dominate the daily CO2 concentration dynamics (amplitudes of up to 20 ppm per day), fossil fuel emissions and changing background concentrations have to be considered in this modeling approach. Fossil fuel emissions (source: M. Fischer, LBNL, Berkeley) generally have a weak influence in this remote region, but could accumulate to >2ppm for different wind directions. For background CO2 (source: N. Parazoo, CSU, Fort Collins) changes of up to 4ppm were observed even for this ideal timeframe, with differences expected to rise when trajectories start further inland. Daily CO2 dynamics (Fig.3) could be captured well for some days, while a too strong morning buildup resulted in a large offset on 5/28-29. Fig.2 indicates higher uncertainties with increasing absolute CO2 level, pro-bably caused by incorrect boundary layer dynamics during stable strati-fication. The observed offset can be attributed to infrequent calibration. • Summary / Conclusion • Process model/STILT coupling produces reasonable CO2 dynamics- High uncertainties during stable stratification at night • Absolute concentrations might be incorrect, calibration issue • Overall product promising, general trends well captured • Next steps • Concentrate on daytime, use dynamics instead of concentrations • Use additional background CO2 information (NOAA CarbonTracker) • Optimize process model based on modeling results • Refine meteorological fields by application of mesoscale models References Gerbig, C, Lin, JC, Wofsy, SC, Daube, BC, Andrews, AE, Stephens, BB, Bakwin, PS, Grainger, CA (2003) Toward constraining regional-scale fluxes of CO2 with atmospheric observations over a continent: 2. Analysis of COBRA data using a receptor-oriented framework. J Geophys Res – Atmos 108 (D24), 4757, doi:10.1029/2003JD003770 Lin, JC, Gerbig, C, Wofsy, SC, Andrews, AE, Daube, BC, Davis, KJ, Grainger, CA (2003) A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model. J Geophys Res – Atmos 108 (D16), 4493, doi:10.1029/2002JD003161 Corresponding author Mathias Göckede Oregon State University, Department of Forest Science 321 Richardson Hall, Corvallis, OR 97331 Phone: 541-737-9956, Fax: 541-737-1393 email: mathias.goeckede@oregonstate.edu