JULY 2002: Interannual and Seasonal Variability of Biomass Burning Emissions Constrained by Satellite Observations

Click on each image to enlarge

Figure 1: Average biomass burning emission rate (Tg CO month-1) for the globe, Northern Hemisphere, Southern Hemisphere, Indonesia and Malaysia, Brazil, southern Africa, Southeast Asia, northern Africa, Central America and Mexico, and northern South America.

Figure 2: Time series of annual emission rates (Tg CO/yr) by region for 1979-2000. The contribution of each region is "stacked" on the contribution of each region plotted below it, so that the line for India represents the total global emission rate. The annual emissions attributed to "other" regions include northern South America, Australia, the Continental U.S., Europe, etc.

A method was developed to estimate seasonal and interannual variation of biomass burning for use in global chemical transport models. The average seasonal variation was estimated from 4 years of Along Track Scanning Radiometer (ATSR) and 1-2 years of Advanced Very High Resolution Radiometer (AVHRR) World Fire Atlases (fire-counts detected by satellite) (Figure 1). We used the Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) data product as a surrogate to estimate interannual variability in biomass burning for six regions: Southeast Asia, Indonesia and Malaysia, Brazil, Central America and Mexico, Canada and Alaska, and Asiatic Russia (Figure 2). The AI data set covers the period from 1979 to the present with an interruption in satellite observations from mid-1993 to mid-1996; this data gap was filled where possible with area burned estimates in the literature for different regions. Between August 1996 and July 2000 the ATSR fire-counts provided specific locations of emissions and a record of interannual variability throughout the world. Fires in Indonesia and Malaysia contribute significantly to global interannual variability. No trend is apparent in global biomass burning emissions of CO over the last two decades, but there is significant interannual variability (Figure 2). This work was led by Bryan Duncan and Randall Martin and a full account is given in Duncan et al. [2002].