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Time-dependent inversion estimates of global biomass-burning CO emissions using Measurement of Pollution in the Troposphere (MOPITT) measurements

  • Author(s): Arellano, Avelino F
  • Kasibhatla, Prasad S
  • Giglio, Louis
  • van der Werf, Guido R
  • Randerson, James T
  • Collatz, G. James
  • et al.
Abstract

 We present an inverse-modeling analysis of CO emissions using column CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) instrument and a global chemical transport model (GEOS-CHEM). We first focus on the information content of MOPITT CO column retrievals in terms of constraining CO emissions associated with biomass burning and fossil fuel/biofuel use. Our analysis shows that seasonal variation of biomass-burning CO emissions in Africa, South America, and Southeast Asia can be characterized using monthly mean MOPITT CO columns. For the fossil fuel/biofuel source category the derived monthly mean emission estimates are noisy even when the error statistics are accurately known, precluding a characterization of seasonal variations of regional CO emissions for this source category. The derived estimate of CO emissions from biomass burning in southern Africa during the June–July 2000 period is significantly higher than the prior estimate (prior, 34 Tg; posterior, 13 Tg). We also estimate that emissions are higher relative to the prior estimate in northern Africa during December 2000 to January 2001 and lower relative to the prior estimate in Central America and Oceania/Indonesia during April–May and September–October 2000, respectively. While these adjustments provide better agreement of the model with MOPITT CO column fields and with independent measurements of surface CO from National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory at background sites in the Northern Hemisphere, some systematic differences between modeled and measured CO fields persist, including model overestimation of background surface CO in the Southern Hemisphere. Characterizing and accounting for underlying biases in the measurement model system are needed to improve the robustness of the top-down estimates.

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