COBRA CO 2 data (tower, aircraft) Fossil fuel CO 2 and CO inventories RAMS, GOES assimilated fields (T, wind, sunlight) STILT “measured” vegetation  CO 2 AMERIFLUX/FCRN flux & conc’n biosphere flux VPM Optimally- constrained regional fluxes +functional responses IGBP veg + MODIS (EVI, LSWI) Lateral CO 2 and CO boundary condition: remote observations (CMDL) fossil fuel CO 2 advected CO 2 modeled vegetation  CO 2 Optimizati on ( i ) INPUTSPRODUCT Influence (Footprints) RECEPTOR-ORIENTED FRAMEWORK FOR LARGE SCALE CO 2 FLUXES Fig. 1. Schematic diagram of the analysis framework for regional and continental CO 2 fluxes a 5b

Altitude [km ASL] Cumulative Distance [km] Altitude [km ASL] 11 June 2004 AM Pm PBL PM residual layer Figure 2. ( left panels ) RAMS mixed-layer hts (Z i ) for 1700 UT (1200 EST) for New England and E. Canada, 10 June 2004 (upper ) and 11 June 2004 (lower ). ( middle panels ) CO 2 concentration cross sections in early morning ( upper ) and afternoon, 11 June, observed on the red sections of the flight track ( right panels ). The cross sections followed the movement of the airmass as given by STILT. Note the draw down of CO 2 during the day, and the excellent validation of Z i in BRAMS both for the residual layer and the afternoon PBL. 11 June UT (1400 EDT) Fig. 3. Observations and environmental forcing from Day 163 (11 June 2004). ( left ) B- RAMS PBL mass flux, showing northwesterly flow. (center) GOES-E visible image, showing the cloud field and flow of cool, dry, cloud-free air to Maine. ( right, top to bottom ) Fig. 4a Fluxes of CO 2 at Howland, concentrations at 25 and 100 m at Argyle, and hourly concentration differences at Argyle; flux and gradient reversals are synchronized at UT, 1-2 hrs before sunset, and likewise 1-2 hours after sunrise. Fig. 4b ( left ) CO 2 land vs sea. ( upper ) Hourly CO 2 (ppm) from Harvard Forest (grey), midday data (black), and 10-day means of midday data (blue); data from Bermuda (orange) and Mauna Loa (red) for comparison. Vertical lines mark the dates for flux reversal. ( center ) Difference between Harvard Forest midday and Bermuda, color coded by sign of sthe surface flux (emission  ; uptake  ). ( lower ) 24-hr mean tower fluxes mmole m -2 s -1 ). ( right, expanded scale )The contrast between surface and overlying CO 2 using CMDL aircraft data over Harvard Forest (dashed line, center panel); note that CO 2 observed at Bermuda and directly over the tower are very similar. Data sources: Harvard Forest, Barford et al., 2001, updated; Bermuda and aircraft, Tom Conway, CMDL.

(b) Upstream CO 2 [ppmv] Downstream CO 2 [ppmv] Upstream CO [ppbv]Downstream CO [ppbv] 0 (c)(d) CO 2 Flux [  mole/m 2 /s] Forest Cropland Fossil Fuel Observed Optimized Tot Modeled Fig. 5. The framework applied to a Pseudo-Lagrangian experiment over North Dakota (Aug. 2000). Two cross sections were flown at 1400UT and 2100UT on 02 Aug. 2002, about 40 km apart, following the mean air travel. (a) Observed upstream and downstream tracer cross-sections from the ND experiment, showing CO 2 and CO. Flight paths are shown in grey. The origin refers to the mean horizontal position of the aircraft during the sampling of the cross-section. The x -axis represents the horizontal location along the first principal component of the aircraft locations. Note the validation of the predicted air flow by tracking the CO maximum in the PBL, which was due to a forest fire plume originating in Canada, and the evident removal of CO 2 from the PBL.. (c) The modeled CO 2 fluxes attributed to fossil fuel combustion (red dashed), forest (green), and cropland (orange), with Bayesian error estimates. This example sampled mostly cropland (spring wheat). (d) The total constrained biospheric CO 2 flux (black dashed) — sum of the separate components in (c), the optimized flux after Bayesian inverse analysis of the GSB (blue dashed), and the observed biospheric flux derived from dividing a simple difference of the vertically integrated CO 2 inventories (upstream-downstream, solid black ) by the elapsed time [ Lin et al. 2004]. The derived fluxes represent the average daytime uptake for CO 2 (8 am to 3 pm local standard time). As might have been expected, the uptake magnitude by wheat in North Dakota was notably smaller than the prior (corn, black dashed line) from AmeriFlux. (a)

respiration combust. photosynthesis CO 2 CO log 10 > ppm/(  mol-m -2 s -1 ) Fig. 7. Harvard Forest tower data analyzed using STILT (using the same assimilated meteorological fields that drive the biosphere model) to compute the “influence function” (response [ppm] at the aircraft or tower caused by unit flux at a given time upstream. The optimized GSB flux model from COBRA-2000 was used without adjustment to reconstruct CO and CO 2 data observed at Harvard Forest., and to attribute CO 2 concentration changes to ecosystem processes ( above ) and to changing footprint areas ( upper left ) during the synoptic progression from northerly to westerly flow. 24h NEE [  mol/(m 2 s)] GOES-8 Visible 8/18/ GMT Figure 6. Examples of the framework applied to large-scale aircraft data. ( upper right ) Time integrated footprints from STILT for two continental racetrack patterns flown in COBRA-2003, computed using the EDAS assimilation. There were 176 vertical profiles in 36 flights over 82 hours of flight time. ( upper left ) Clouds (visible, GOES-8) over eastern N. America on 18 Aug ( lower ) Mean 24-hour NEE for the US using the GSB parameters derived for the northern transect of COBRA-2000 using the framework. Note the strong influence of cloudiness on C exchange fluxes, and the release of CO 2 in the drought-affected vegetation of the southern US [from Gerbig et al. 2003b].

Respiration = α r + (β r × (EVI-EVI min )/(EVI max -EVI min )) Climate data GPP = (α × T scalar × W scalar × P scalar ) × FAPAR PAV × PAR Surface Reflectance: MODIS LSWIEVI Tower Data Validation A priori W scalar = (1 + LSWI)/(1 + LSWI max ) ; P scalar = (1 + LSWI)/2 Fig. 8. Schematic of the VPRM [ after Xiao et al., 2004 ] (upper ) R in terms of EVI anomaly, giving the spatial variation of biomass with the prior calibrated to flux tower data. ( lower ) GPP in terms of FAPAR (=EVI) and LSWI. EVI and LSWI are derived from MODIS data, resolved seasonally and averaged W accounts for the effect of water stress, and P for phenology. MODIS EVI GOES shortwave veg. type IGBP VPRM NEP Fig. 10. VPRM example. The VPRM combines data from MODIS (EVI and LSWI, upper left ), GOES ( lower left ), and IGBP (to be replaced by SYNMAP) ( lower right ) to produce hourly or daily NEP over the study region ( upper right ). Results are shown for Maine and S. E. Canada for day 153 (01 June) in MJ/m 2 /day Fig. 9. Model (lines) and observed (points) 8-day mean daily GPP ( left ) and R ( right ) [units: gC/m 2 /day], for 2002 at Howland Forest. The model fit optimized 3 parameters in the VPRM (2 for GEE, 1 for respiration, see Fig. 6) for data from , using MODIS time series data (average of 9 1x1 km squares, reported every 8 days) for 9 vegetation types, each with data from eddy tower in the center. These data are tabulated for numerous EOS Land Validation Core Sites (including Harvard and Howland Forests) at gC/m 2 /d