1. T Jing M. Chen 1, Baozhang Chen 1, Gang Mo 1, and Doug Worthy 2 1 Department of Geography, University of Toronto, 100 St. George Street, Toronto, Ontario, Canada M5S 3G3 2 Air Quality Research Division, Environment Canada, Toronto, Ontario, Canada M3H 5T4 Landscape-level Carbon Flux Estimates from Atmospheric CO 2 Concentration Measurements and Remote Sensing-based Footprint Integration 1. Introduction Carbon fluxes at the landscape level (~10 4 km 2 ), a critical upscaling step from site to region, are estimated using two independent methods: (1) retrieving daily gross primary productivity (GPP) and ecosystem respiration from the diurnal CO 2 variation pattern (Chen et al., GRL, 33: L10803, 2006), and (2) spatially explicit hourly carbon cycle modeling based on remote sensing and then integrating the daily flux field with a concentration footprint function depending on wind and atmospheric stability. Results from these two methods are compared against eddy covariance measurements representing a small area within the footprint. These comparisons so far have been made for a 28-m tower at an old black spruce site near Candle Lake (53.98717  N, 105.11779  W), Saskatchewan, Canada. Similar approaches are being used for three more towers in Canada and the Wisconsin tall tower at USA. Fig. 2. Land cover map surrounding the tower site indicated by T. Fig. 1. Black spruce site (the original BOREAS-SOBS) with a 28 m tower near Candle Lake, Saskatchewan, Canada. 2. Sites and Measurements 2.1 Study site and footprint area: The research site is located at (53.987  N, 105.118  W) and 100 km NE of Prince Albert, Saskatchewan (Fig. 1). It is referred to as Southern Old Black Spruce (SOBS). As shown in Fig. 2, the concentration footprint area of the tower is heterogeneous. The dominant land cover type is conifer forest within 100 km of the tower, while the south-east area (>180 km far from the tower ) is dominated by grass or crop covers. The concentration footprints extend to these cover types (Fig. 4). 2.2 CO 2 mixing ratio and eddy covariance flux measurements: Half-hourly net CO 2 flux and other meteorological variables at this site were measured. CO 2 concentration was also measured at both of 28 m and 20 m heights according to WMO (Global Atmospheric Watch) with an accuracy of 0.1 ppm. Fig. 3. An example of measured and modeled CO 2 mixing ratio in July, 2003. Also shown is the modeled CO 2 mixing ratio with GPP=0 at daytime. Triangles indicate the times of sunrise and sunset. 3. Modeling Methodologies 3.1 Planetary boundary layer budgeting The combined BEPS-VDS model (Boreal Ecosystem Productivity Simulator [Liu et al., 2002] and the Vertical Diffusion Scheme [Chen et al., 2004]) is employed in this study. As shown in Fig. 3, the simulated values generally follow closely the measured values in the diurnal cycle, while the simulated CO 2 with GPP=0 increases considerably from the measured CO 2. This increase is expected as the carbon uptake by photosynthesis is set to zero. Physically, the hourly 3.2 Remote sensing-based footprint integration The total regional flux captured by the CO 2 sensor on a tower is the weighted sum of the upwind footprint source areas (Ω), GPP= ΣGPP i  W i, where W i is the relative contribution of the footprint function for pixel i within the whole footprint area. GPP i for pixel i is calculated using BEPS driven by remote sensing, climate and soil data. W i = f i /Σf i, where the footprint function f i (pixel i with x,y coordinates; x is in the daily mean wind direction and y is in the cross wind direction) is computed using a model: F i (x,y,z m -z 0 )=D y (x,y)D z (x,z m )/U(x), where D y and D z are the crosswind and vertical concentration distribution functions, respectively, and U(x) is the effective speed of plume advection. They are dependant on standard surface-layer scaling parameters and based on an analytical solution of a dispersion equation in the Eulerian co-ordinate system (modified after Schmid and Oke, 1990). differences in CO 2 (  C i, in ppm) between the measured and simulated (with GPP=0) cases reflects the accumulated reduction of CO 2 by GPP. Assuming that this reduction is uniform in the mixed layer, the simulated mixed layer height (z i ) and the average dry air density (ρ air ) can then be used to estimate the time-integrated (since sunrise) GPP per unit surface area. The daily total GPP then equals, where SR is the hour of sunrise and SS is sunset (Chen et al. 2006). 4. Results and Analysis Four sets of GPP values are produced: (1) GPP derived from the eddy covariance flux measurements at the tower site, which is considered to be the basis for comparison; (2) GPP modeled by BEPS for the site (one pixel); (3) GPP retrieved from CO 2 concentration measured at the site; and (4) GPP modeled by BEPS for the region after footprint integration. The goal of the intercomparison of these GPP results is to see if concentration-derived fluxes are reliable. This goal is achieved through two steps: (1) testing if BEPS agrees with site measurements, and (2) if GPP derived from CO 2 concentration agrees with regional estimates by BEPS. The seasonal variations of these four sets of GPP are shown in Fig. 6 with encouraging similar temporal patterns. The site level GPP values from EC measurements and from BEPS agree well (Fig. 7), suggesting that BEPS produces a small bias (4%) and high accuracy (76%) on daily basis. The landscape-level GPP estimates also agree well (Fig. 8), showing a 6% bias in the concentration-derived GPP relative to BEPS estimates with a r 2 values of 0.74. The landscape-level GPP estimates are about 25% smaller than the site-level estimates because the tower site is one of the most productive areas within the footprint, e.g. the low productivity areas of grassland and cropland had effects on the CO 2 concentration measured at the tower and these effects are effectively included in both BEPS modeling and planetary boundary layer budgeting. These results demonstrate that CO 2 concentration measurements at flux tower sites, even though made at the same heights as EC instruments, can provide information for much larger footprints for upscaling purposes. High-precision, hourly CO 2 concentration measurements at EC towers are encouraged. Fig. 4. Daily concentration footprint for (a) August 11, 2003, (b) August 24, 2003, and (3) integrated for the whole year of 2003 for CO 2 measurements at 28 m at the black spruce site. Concentration footprints are 2-3 orders larger than flux footprints. b Fig. 5. Gross primary productivity (GPP) modeled by BEPS for an 800 km  800 km area around the tower site ( ) for (a) August 11, 2003, (b) August 24, 2003. References: Chen, B., J. M. Chen, J. Liu, D. Chan, K. Higuchi, and A. Shashkov (2004), A Vertical Diffusion Scheme to estimate the atmospheric rectifier effect, J. Geophys. Res. 109, D04306, doi:10.1029/2003JD003925. Chen, J. M., B. Chen, K. Higuchi, J. Liu, D. Chan, D. Worthy, P. Tans, and T. A. Black (2006), Boreal ecosystems sequestered more carbon in warmer years, Geophysical Research Letters 33, L10803. Liu, J. et al. (2002), Net primary productivity mapped for Canada at 1-km resolution, Global Ecology & Biogeography, 11, 115-129 Schmid HP and Oke TR (1990), A model to estimate the source area contributing to turbulent exchange in the surface layer over patchy terrain. Q. J. R. Meteorol. Soc, 116, 965-988. T Fig. 6. Seasonal variations of GPP at the site level and landscape level. Landscape-level estimates from CO 2 concentration and from BEPS after footprint integration are smaller than the site level estimates from EC data and BEPS because of non- productive areas within the footprint (Fig. 5) Fig. 7. Site-level GPP comparison. Fig. 8. Landscape-level GPP comparison. Acknowledgement: This research was supported by the Canadian Foundation for Climate and Atmospheric Sciences. This work is part of the Fluxnet Canada Research Network. The EC data used in this study were collected by Any Black of the University of British Columbia and Alan Barr of Environment Canada Poster presented at the North America Carbon Cycle open science meeting, Colorado Spring, January 23-26, 2007 T ac b T a T b