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Progress in Modeling Global Atmospheric CO2 Fluxes and Transport

S. Randy Kawa, NASA Goddard Space Flight Center, stephan.r.kawa@nasa.gov (Presenting)
A. Scott Denning, Colorado State University, denning@atmos.colostate.edu
G. James Collatz, NASA Goddard Space Flight Center, jcollatz@biome2.gsfc.nasa.gov
David J. Erickson, Oak Ridge National Laboratory, ericksondj@ornl.gov

Progress in better determining CO2 sources and sinks will almost certainly rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. Use of advanced data requires improved modeling and analysis capability. Here we seek to develop and integrate improved formulations for 1) atmospheric transport, 2) terrestrial uptake and release, 3) biomass and 4) fossil fuel burning, and 5) observational data analysis including inverse calculations. The transport modeling is based on meteorological data assimilation analysis from the Goddard Modeling and Assimilation Office. Use of assimilated met data enables model comparison to CO2 and other observations across wide range of scales of variability. In this presentation we focus on the short end of the temporal variability spectrum: hourly to synoptic to seasonal. Using CO2 fluxes at varying temporal resolution from the Transcom-C model intercomparison exercise, we examine the model’s ability to simulate CO2 variability in comparison to observations at different times, locations, and altitudes. We find that the model can resolve much of the variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The influence of key process representations is inferred. The high degree of fidelity in these simulations leads us to anticipate incorporation of real-time, highly resolved observations into a multidisciplinary carbon cycle data assimilation system that will reduce uncertainty in the terrestrial CO2 sink and lead toward credible, tested predictive models of climate and carbon needed for informed policy decisions.

Presentation Type:  Poster

Abstract ID: 7

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