Interannual variability of carbon monoxide emission estimates over South America from 2006 to 2010
Hooghiemstra, P.B. ; Krol, M.C. ; Leeuwen, T.T. van; Werf, G.R. van der; Novelli, P.C. ; Deeter, M.N. ; Aben, I. ; Rockmann, T. - \ 2012
Journal of Geophysical Research: Atmospheres 117 (2012). - ISSN 2169-897X
variational data assimilation - land-use change - climate-change - co emissions - amazon deforestation - brazilian amazon - fire emissions - model tm5 - mopitt - inversion
We present the first inverse modeling study to estimate CO emissions constrained by both surface and satellite observations. Our 4D-Var system assimilates National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) surface and Measurements Of Pollution In The Troposphere (MOPITT) satellite observations jointly by fitting a bias correction scheme. This approach leads to the identification of a positive bias of maximum 5 ppb in MOPITT column-averaged CO mixing ratios in the remote Southern Hemisphere (SH). The 4D-Var system is used to estimate CO emissions over South America in the period 2006-2010 and to analyze the interannual variability (IAV) of these emissions. We infer robust, high spatial resolution CO emission estimates that show slightly smaller IAV due to fires compared to the Global Fire Emissions Database (GFED3) prior emissions. South American dry season (August and September) biomass burning emission estimates amount to 60, 92, 42, 16 and 93 Tg CO/yr for 2006 to 2010, respectively. Moreover, CO emissions probably associated with pre-harvest burning of sugar cane plantations in Sao Paulo state are underestimated in current inventories by 50-100%. We conclude that climatic conditions (such as the widespread drought in 2010) seem the most likely cause for the IAV in biomass burning CO emissions. However, socio-economic factors (such as the growing global demand for soy, beef and sugar cane ethanol) and associated deforestation fires, are also likely as drivers for the IAV of CO emissions, but are difficult to link directly to CO emissions.