HR: 0800h
AN: B51C-0227    [Abstracts]
TI: Analysis of Factors Controlling Interannual Variations in Atmospheric CO2 During 1997-2004
AU: * Kasibhatla, P
B51C-0227 AF: Duke University, Nicholas School of the Environment and Earth Sciences, Durham, NC 27708 United States
AU: Li, Q
B51C-0227 AF: Duke University, Nicholas School of the Environment and Earth Sciences, Durham, NC 27708 United States
AU: Randerson, J
B51C-0227 AF: University of California Irvine, Department of Earth System Science, Irvine, CA 92697 United States
AU: van der Werf, G
B51C-0227 AF: Vrije Universiteit Amsterdam, Faculty of Earth & Life Sciences, Amsterdam, 1081 HV Netherlands
AU: Giglio, L
B51C-0227 AF: Science Systems and Applications, Inc., NASA Goddard Space Flight Center, Code 923, Greenbelt, MD 20771 United States
AU: Collatz, J
B51C-0227 AF: NASA Goddard Space Flight Center, Code 923, Greenbelt, MD 20771 United States
AU: Miller, J
B51C-0227 AF: NOAA/CMDL, Carbon Cycle Greenhouse Gases Group, Boulder, CO 80305 United States
AU: Conway, T
B51C-0227 AF: NOAA/CMDL, Carbon Cycle Greenhouse Gases Group, Boulder, CO 80305 United States
AU: Novelli, P
B51C-0227 AF: NOAA/CMDL, Carbon Cycle Greenhouse Gases Group, Boulder, CO 80305 United States
AB: Measurements of surface atmospheric CO2 concentrations show that atmospheric CO2 growth rates vary significantly from year to year. Understanding the driving mechanisms of these interannual growth rate variations is important in terms of predicting future levels of atmospheric CO2. In this study, we investigate the relative contributions of interannual variations in terrestrial net primary production, heterotrophic respiration, and fire emissions to interannual variations in atmospheric CO2 during the 1997-2004 period. The geographical and temporal distribution of C fluxes associated with each of these processes is first derived using an updated version of the CASA biogeochemical cycle model that uses multiple satellite datasets as constraints. The CASA fluxes are then used to drive an atmospheric chemical transport model to calculate the resulting atmospheric CO2 concentration anomalies arising from each process. Finally, an inverse modeling methodology using atmospheric CO2 measurements from the NOAA/CMDL network, as well as atmospheric CO measurements from the same network as an additional constraint on fire C emissions, is applied to derive optimal estimates of the geographical and temporal distribution of C flux anomalies associated with each process.
DE: 0426 Biosphere/atmosphere interactions (0315)
DE: 0428 Carbon cycling (4806)
SC: Biogeosciences [B]
MN: Fall Meeting 2005