ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-6-539-2006 What can tracer observations in the continental boundary layer tell us about surface-atmosphere fluxes? Gerbig C. 1 Lin J. C. 2 3 Munger J. W. 2 Wofsy S. C. 2 Max-Planck-Institute for Biogeochemistry, P.O. Box 10 01 64, 07701 Jena, Germany Dept. of Earth and Planetary Sciences and Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA now at: Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO 80523-1371, USA 22 02 2006 6 2 539 554 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/6/539/2006/acp-6-539-2006.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/6/539/2006/acp-6-539-2006.pdf

We analyze the potential for inferring spatially resolved surface fluxes from atmospheric tracer observations within the mixed layer, such as from monitoring towers, using a receptor oriented transport model (Stochastic Time-Inverted Lagrangian Transport model - STILT) coupled to a simple biosphere in which CO<sub>2</sub> fluxes are represented as functional responses to environmental drivers (radiation and temperature). Transport and biospheric fluxes are coupled on a dynamic grid using a polar projection with high horizontal resolution (~20 km) in near field, and low resolution far away (as coarse as 2000 km), reducing the number of surface pixels without significant loss of information. To test the system, and to evaluate the errors associated with the retrieval of fluxes from atmospheric observations, a pseudo data experiment was performed. A large number of realizations of measurements (pseudo data) and a priori fluxes were generated, and for each case spatially resolved fluxes were retrieved. Results indicate strong potential for high resolution retrievals based on a network of tall towers, subject to the requirement of correctly specifying the a priori uncertainty covariance, especially the off diagonal elements that control spatial correlations. False assumptions about the degree to which the uncertainties in the a priori fluxes are spatially correlated may lead to a strong underestimation of uncertainties in the retrieved fluxes, or, equivalently, to biased retrievals. The framework presented here, however, allows a conservative choice of the off diagonal elements that avoids biasing the retrievals.