Introduction The fraction of future fossil fuel CO 2 emissions that will remain in the atmosphere depends in part on how much CO 2 will be taken up and released by terrestrial environments. There is currently a poor understanding of the processes responsible for regional land surface fluxes of CO 2, a prerequisite for optimizing climate predictions. Regional-scale atmospheric inverse models can be used to study the processes controlling CO 2 surface fluxes. Inverse models have been applied to correct surface flux estimates based on the differences between modeled and observed CO 2 concentrations. It is often unclear whether the corrections should be associated with photosynthesis, respiration, or other processes. Carbonyl sulfide (OCS) is a new tracer of photosynthesis [Montzka and Tans, 2004] that is shown here to provide a crucial link between the flux corrections and processes. Assimilation Framework A regional atmospheric transport model and atmospheric concentration observations are synthesized to obtain optimal estimates of surface fluxes using the four dimensional variational (4D-Var) data assimilation technique (Fig.2). Observations of CO 2, OCS, and related species are made from the NASA DC8 aircraft during daytime flights over North America in July/August The STEM mesoscale transport model provides the atmospheric transport and 4D-Var data assimilation [Carmichael et al., 2003]. The optimal surface fluxes are obtained by minimizing the cost function, where y is the vector of observed concentration, h(c) is the vector of modeled concentrations, E f is the correction factor applied to surface fluxes, and B and O are the model and observation error matrices. Acknowledgments We thank Wouter Peters and Ian Baker for providing TM5 boundary conditions and SiB biosphere surface fluxes respectively. Funding for this project was provided by the NASA Earth System Science Fellowship program. Results The July 25 th flight, has highly correlated (r 2 = 0.92) observations of CO 2 and OCS, providing an opportunity for simultaneous assimilation. The model CO 2 concentrations overestimate the observations at each low altitude pass (Fig. 3). These low altitude errors are strongly influenced by errors in the model surface fluxes and can be corrected with the inverse model. The errors could be due to underestimates of the photosynthesis sink and overestimates of the respiration and fossil fuel sources. Which surface fluxes should be corrected? First, we assimilate only CO 2 observations and correct the model errors by calculating scaling factors for the photosynthesis surface sink. The average RMS error is reduced by 50% (Fig. 4) with scaling factors that increase the photosynthesis sink upstream of the low altitude flight passes (Fig. 5). Conclusions The goal of regional-scale carbon science is to understand terrestrial biosphere processes in order to predict future surface fluxes. Simultaneous analysis of carbon dioxide and carbonyl sulfide provides a needed link between model error and land surface processes. The results of an assimilation with aircraft observations presented here lead to a number of conclusion concerning CO 2 data assimilation: 1) With a CO 2 -only data assimilation framework it is possible for model errors due to large overestimates in respiration surface fluxes to be interpreted as underestimates in photosynthesis fluxes. 2) The correlation of CO 2 errors and OCS errors can be used to guide the inverse model towards correcting model errors by modifying photosynthesis fluxes verses other fluxes. 3) The simultaneous assimilation can be achieved by applying CO 2 photosynthesis land surface fluxes to both CO 2 and OCS modeled species. The results shown here are promising for further studies of the ICARTT data set that will simultaneously recover photosynthesis and respiration surface fluxes with an added constraint of OCS for the photosynthesis flux. These results are also promising for future studies of OCS surface fluxes that will separate the influence of plant uptake of OCS from soil uptake of OCS. Simultaneous assimilation of carbon dioxide and carbonyl sulfide observations for regional surface flux optimization 1 Campbell, J.E., 1 Stanier, C.O., 1 Carmichael, G.R., 2 Blake, N.J., 3 Vay, S.A., 3 Choi, Y.H., 1 Chai, T., 1 Tang, Y., 1 Mena-Carrasco, M., 1 Schnoor, J.L. 1 Center for Global and Regional Env’l. Research, University of Iowa, 2 Dept. of Chemistry, University of California, Irvine, 3 NASA Langley Literature cited Campbell J.E., Carmichael G.R., Tang Y.H., Chai T., Vay S.A., Choi Y.H., Sachse G.W., Singh H.B., Schnoor, J.L., Stanier C.O. (In Press), Analysis of anthropogenic CO 2 signal in ICARTT observations using a regional chemical transport model and its adjoint. Tellus B. Carmichael, G.R., Daescu, D.N., Sandu, A., Chai, T. (2003), Computational aspects of chemical data assimilation into atmospheric models, in Computational Science: ICCS Lect. Notes Comput. Sci., vol. 4, pp 26—278, Springer, New York. Montzka S. A., Tans P. P. (2004), Can carbonyl sulfide help constrain gross vegetative fluxes of carbon dioxide?, Eos Trans. AGU Fall Meet. Suppl., Abstract B21E-04. Fig. 2. Process diagram for regional atmospheric inversion including the forward model (black), assimilation model (red) and airborne observations (blue). For further information Please contact Elliott Campbell at (319) CO 2 OCS Fig. 1. The dominant land fluxes of CO 2 are photosynthesis and respiration while OCS uptake is influenced by a process linked to photosynthesis. Mesoscale Transport (STEM) Mesoscale Meteorology (MM5) Surface Flux (SiB and Kettle) Global Meteorology (NCEP FNL) Global Transport (ECMWF/TM5 ) Data Assimilation (STEM 4D-Var) Observations CO 2 (Vay et al) OCS (Blake et al) Flux Correction Scaling Factor Fig. 4. Time series for CO 2 observations (black), model concentrations (orange), and model concentrations corrected by the inverse model (blue). Although the CO 2 -only data assimilation shows excellent reduction of the model error, it may provide the right answer for the wrong reasons. Comparison of observation and model results for CO 2 and OCS provide evidence of when the model concentration error (Fig. 3) is due to photosynthesis flux error or other errors. This information can be used to determine which surface fluxes should be modified by the inverse model and ultimately, which surface flux processes are constrained by data assimilation. For the July 25 th flight, the CO 2 and OCS errors are highly correlated (r 2 = 0.86) for the second and third low altitude passes (20hr and 21hr) but not correlated for the first low altitude pass (Fig. 6). Because the photosynthesis surface fluxes are used to drive the CO 2 model and the OCS model (in addition to non-related fluxes) the correlated errors are likely due to errors in the photosynthesis flux. Thus, the error in the plant uptake should be corrected for the second two low altitude passes but not for the first pass. When the observations of CO 2 and OCS are included in the cost function (equation 1) the errors of both species will constrain the improvements calculated by the scaling factors. The corrected model results show that the simultaneous assimilation successfully prevents the photosynthesis surface flux from being increased for the first low altitude pass (Fig. 7). Overall there is less of a reduction in error in the second two low altitude passes due to error in the fixed ratio that converts photosynthesis fluxes into OCS plant uptake. If photosynthesis fluxes cannot be corrected to improve the model error in the first low altitude pass then what process is responsible? Reduction of the CO 2 fossil fuel and respiration sources could also provide a solution. The fossil fuel source has very small errors in this air mass as indicated by alternative methods for estimating fossil fuel CO 2 (Campbell et al., In Press). The respiration flux is likely the source of error because: The first low altitude pass is sensitive to changes in the respiration because the observation is made earlier in the day. The modeled respiration concentrations during this low altitude pass are higher than at any other point on the ICARTT flight paths. When the inverse model is used to estimate scaling factors for the respiration surface fluxes, the average RMS error is reduced. Fig. 3 July 25 th ICARTT flight path altitudes (exaggerated height of bars) and CO 2 error as observed – modeled concentrations (color of bars). CO 2 Error (ppm) Fig. 5. Optimal surface flux scaling factors (shaded squares) that adjust the photosynthetic modeled flux in each surface grid cell. Colored dots are the initial observed to model concentration error. Fig. 6. Model errors (observed - modeled concentrations) for CO 2 and OCS. Fig. 7. CO 2 and OCS observations (black), model concentrations (orange), and model concentrations corrected during simultaneous assimilation (blue).