A13L-3340:
A Global Synthesis Inversion Analysis of Recent Variability in Natural CO2 Fluxes Using Gosat and in Situ Observations
Monday, 15 December 2014
James S Wang1,2, Stephan R Kawa1 and George James Collatz1, (1)NASA Goddard Space Flight Ctr, Greenbelt, MD, United States, (2)Universities Space Research Association Columbia, Columbia, MD, United States
Abstract:
About one-half of the CO2 emissions from fossil fuel combustion and deforestation accumulates in the atmosphere, where it contributes to global warming. The rest is taken up by vegetation and the ocean. The precise contribution of the two, and the location and year-to-year variability of the CO2 sinks are, however, not well understood. We use a batch Bayesian inversion approach to deduce the global spatiotemporal distributions of CO2 fluxes during 2009-2010. For prior constraints, we utilize fluxes from the CASA-GFED model of the terrestrial biosphere and biomass burning driven by satellite observations and interannually varying meteorology. We also use measurement-based ocean flux estimates, and fixed fossil CO2 emissions. Here, we present results from our inversions that incorporate column CO2 measurements from the GOSAT satellite (ACOS retrieval, filtered and bias-corrected) and in situ observations (individual flask and afternoon-average continuous observations) to estimate fluxes in 108 regions over 8-day intervals. Relationships between fluxes and atmospheric concentrations are derived using the PCTM atmospheric transport model run at 2° x 2.5° (latitude/longitude) resolution driven by meteorology from the MERRA reanalysis. We evaluate the posterior CO2 concentrations using independent aircraft and other data sets. The optimized fluxes generally resemble those from other inversion systems using different techniques, for example indicating a net terrestrial biospheric CO2 sink, and a shift in the sink from tropics to northern high latitudes when going from an in-situ-only inversion to a GOSAT inversion. We show that in this inversion framework, GOSAT provides better flux estimates in most regions with its greater spatial coverage, but we also discuss impacts of possible remaining biases in the data.