Direct top‐down estimates of biomass burning CO emissions using TES and MOPITT versus bottom‐up GFED inventory. Issue 14 (29th July 2013)

• Record Type: Journal Article
• Authors: Pechony, Olga; Shindell, Drew T.; Faluvegi, Greg
• Is Part Of: Journal of geophysical research. Volume 118:Issue 14(2013)
• Page Start: 8054
• Page End: 8066
• Publication Date: 2013-07-29
• Subjects: Carbon monoxide -- Biomass burning emissions -- MOPITT -- TES -- GFED -- Climate model; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/jgrd.50624
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract : [1] In this study, we utilize near‐simultaneous observations from two sets of multiple satellite sensors to segregate Tropospheric Emission Spectrometer (TES) and Measurements of Pollution in the Troposphere (MOPITT) CO observations over active fire sources from those made over clear background. Hence, we obtain direct estimates of biomass burning CO emissions without invoking inverse modeling as in traditional top‐down methods. We find considerable differences between Global Fire Emissions Database (GFED) versions 2.1 and 3.1 and satellite‐based emission estimates in many regions. Both inventories appear to greatly underestimate South and Southeast Asia emissions, for example. On global scales, however, CO emissions in both inventories and in the MOPITT‐based analysis agree reasonably well, with the largest bias (30%) found in the Northern Hemisphere spring. In the Southern Hemisphere, there is a one‐month shift between the GFED and MOPITT‐based fire emissions peak. Afternoon tropical fire emissions retrieved from TES are about two times higher than the morning MOPITT retrievals. This appears to be both a real difference due to the diurnal fire activity variations, and a bias due to the scarcity of TES data. Key Points: Near‐simultaneous fire and CO satellite observations used to estimate fire CO Satellite CO estimates and GFED 2 and 3 show large regional differences Global agreement is reasonable with largest biases in Northern Hemisphere spring