4.6 Article

Toward constraining regional-scale fluxes of CO2 with atmospheric observations over a continent:: 1.: Observed spatial variability from airborne platforms -: art. no. 4756

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2002JD003018

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regional carbon flux; spatial variability of CO2

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We analyze the spatial variability of CO2 measurements from aircraft platforms, including extensive observations acquired over North America during the CO2 Budget and Rectification Airborne (COBRA) study in 2000. The COBRA data set is unique in its dense spatial coverage and extensive profiling in the lower atmosphere. Strong signatures of CO2 fluxes at the land surface were observed in the active and relic mixed layers of the atmosphere (up to similar to20 ppm gradients). Free tropospheric CO2 exhibited significantly less variability except in areas affected by convective transport. Statistical analyses of the COBRA data indicate that CO2 mixed-layer averages can be determined from vertical profiles with an accuracy of approximately +/-0.2 ppm, limited by atmospheric variance. Analysis of the associated representation error suggests that models require horizontal resolution smaller than similar to30 km to fully resolve spatial variations of atmospheric CO2 in the boundary layer over the continent. To provide a global context for these data, we analyzed the GLOBALVIEW marine boundary layer (MBL) reference CO2. Comparison of the MBL reference with extensive aircraft data extending over 20 years, covering the whole troposphere over the northern Pacific, shows significant seasonal biases of up to 2 ppm in the free troposphere, indicating that the MBL reference is a suitable boundary condition only for some applications. The spatial variability of CO2 revealed by the COBRA-2000 calls for a suitable analysis framework to derive regional and continental fluxes, presented in a companion paper. The problem requires boundary conditions constrained by both surface and upper tropospheric observations and constraints on terrestrial fluxes that exploit the information content of the highly variable CO2 distribution over land.

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