OPS 3-8
Long-term observations and analysis for an integrated carbon observing system

Tuesday, August 11, 2015
Exhibit Hall, Baltimore Convention Center
Beverly E. Law, Forest Ecosystems & Society, Oregon State University, Corvallis, OR
Logan T. Berner, Forest Ecosystems & Society, Oregon State University, Corvallis, OR
Zhenlin Yang, Forest Ecosystems & Society, Oregon State University, Corvallis, OR
Andres Schmidt, Forest Ecosystems & Society, Oregon State University, Corvallis, OR
Background/Question/Methods: A globally integrated carbon observation and analysis system is needed to improve fundamental understanding of the global carbon cycle, to improve projection of future changes, and to verify effectiveness of policies aimed to reduce greenhouse gas emissions and increase carbon sequestration. Building on existing networks, an integrated carbon observation system requires expanding and sustaining observations of terrestrial ecosystem fluxes, air-sea exchange, atmospheric trace gases and diagnostics, and anthropogenic emissions. A major challenge is to bring remote sensing measurements to a level of long-term consistency of observations and accuracy in order to be incorporated in models. 

Results/Conclusions: Multiple synthesis and analysis approaches have been developed and applied with observations from the long-term AmeriFlux network and tall towers. For example, a data-driven approach was used to upscale eddy covariance flux data to the continent by integration flux observations, meteorology, stand age, aboveground biomass and a proxy for canopy nitrogen concentrations as well as remote sensing observations from a variety of products from MODIS satellite sensors (Xiao et al. 2014).  Here, the main source of interannual variability in carbon fluxes were drought and disturbances. In addition, the accuracy of predicted fluxes can be improved using inverse modeling with atmospheric CO2 from tall towers, aircraft, and satellites. In land models such as CLM4.5, parameterization with canopy observations for dominant forest species in place of plant functional types improves model sensitivity to drought and disturbances. Thus, integration of observations in a variety of modeling frameworks can reduce uncertainty in projections of status and trends in carbon cycling. Consistent long-term observations and reducing uncertainty are essential for the scale of policy-relevance.