[3] During the fall of 2006, human‐set fires and dry conditions from a moderate El Niño resulted in significant biomass burning emissions over Indonesia [e.g., Field et al ., 2009 ] with CO emissions from the burning approximately 4–6 times larger than the previous year as discussed in Logan et al . [ 2008 ]. Here we use tropospheric CH 4 and CO measurements from the Aura Tropospheric Emission Spectrometer (TES) satellite instrument [ Worden et al ., 2012 ] along with new CO profiles, with near‐surface sensitivity, from the Measurements of Pollution in the Troposphere (MOPITT) instrument and the Goddard Earth Observing System (GEOS)‐Chem global chemistry and transport model to quantify CH 4 emissions from these fires and how the fires affected tropical tropospheric methane concentrations.

[2] Recent studies suggest that wet tropical conditions from La Niña result in increased wetland emissions, whereas dry tropical conditions from El Niño result in decreased wetland emissions [ Hodson et al ., 2011 ], and that this El Niño/La Niña (or ENSO) driven variability could partly explain the recent methane increase since 2006 [e.g., Dlugokencky et al ., 2009 ]. Variations in tropical fire emissions potentially counterbalance the change in emissions from tropical wetlands, as dry conditions resulting in decreased wetland emissions can be favorable toward increasing fire emissions [ Bousquet et al ., 2006 ; Dlugokencky et al ., 2009 ; Andreae and Merlet , 2001 ]. Model‐based methane estimates from fires can have large uncertainties because of the spatial and temporal variability of fire emissions, primarily derived from satellite‐derived burnt area measurements, as well as the dependency of the fire CH 4 /CO 2 emission factors (g/kg dry matter burned) on fuel type and combustion phase [e.g., Van Der Werf et al ., 2010 ]. Furthermore, the resulting estimates of atmospheric methane from fires are not well corroborated by the surface network, primarily located in “background” locations where smoke plumes are typically diluted prior to measurement [ Dlugokencky et al ., 2009 ]. Reflected sunlight‐based satellite measurements of atmospheric methane have not been used to quantify methane from fires because of scattering from aerosols in smoke plumes or sparse sampling of plumes [ Frankenberg et al ., 2011 ; Ross et al ., 2013 ].

[5] Figure 2 (partly adapted from Worden et al . [ 2013 ]) shows model and data distributions of methane and CO for September, October, and November of 2006. A weighted least squares linear fit for the data distributions is calculated based upon TES observations errors. An empirical estimate of the “error” in the GEOS‐Chem slope is calculated by the scatter in the GEOS‐Chem distribution as discussed in Worden et al . [ 2013 ]. Based on in situ data [e.g., Van der Werf et al ., 2010 ], we expect that an emission ratio near 0.11 (ppb/ppb) is representative of tropical fires, whereas larger values for the emission ratios indicate larger contribution from other sources (e.g., wetlands) relative to the emitted CO. Worden et al . [ 2013 ] (and supporting information ) shows that this distribution is primarily explained by the Indonesian fire emissions. A larger slope during November indicates that the CH 4 emissions from the fires, relative to other sources, are lower. The larger scatter and lower correlation of the observed CH 4 /CO data in September suggests that non‐biomass burning sources contribute to the observed free tropospheric methane.

[4] Figure 1 shows the near‐global distribution of methane and carbon monoxide in October 2006, observed by TES and simulated by the GEOS‐Chem global chemistry and transport model (e.g., supporting information) [ Pickett‐Heaps et al ., 2011 ]. Details on the quality flags, bias corrections, sensitivities, and uncertainties of the TES satellite data and comparisons to the GEOS‐Chem model are discussed in the supporting information and in Worden et al . [ 2013 ]. In summary the CH 4 and CO data have approximately 1% or better precision and 0.5% or better accuracy after a constant bias correction is made to the data. These infrared‐based estimates of methane have little sensitivity to aerosols from fires because aerosol optical depth from fires inversely depends on wavelength. GEOS‐Chem model (2° × 2.5° spatial and 4 h time resolution) profiles corresponding to the best time and space match with each TES profile are used for the comparison. The TES‐averaging kernels and a priori GEOS‐Chem model constraint are applied to corresponding GEOS‐Chem model profiles to account for the TES retrieval regularization and vertical resolution. Although the TES IR data is primarily sensitive to methane in the free troposphere, the volume‐mixing ratios of both model and adjusted TES profiles are then averaged over the whole troposphere to mitigate retrieval errors in the data and transport errors in the model. Figure 1 shows that the Indonesian fires make a clear impact on regional CO distributions, with observed enhancements up to 300+ ppb (see also Figure 2 b). Because methane is long lived, the effect of the Indonesian fires on atmospheric methane is not readily apparent relative to the global distribution but can be observed by differencing the TES October 2006 and October 2005 CH 4 estimates (Figure 1 , top right).

3 Estimate of Methane Emissions From Indonesian Peat Fires

[6] Previous efforts to reconcile satellite and model atmospheric composition estimates have been stymied by large uncertainties in the a priori model emissions and emission ratios, errors in modeled vertical transport, and the vertical sensitivity and uncertainties of the satellite data [e.g., Kopacz et al., 2010; Liu et al., 2010; Jiang et al., 2013]. For methane, disentangling the role of fire emissions from nearby wetland or anthropogenic emissions on observed atmospheric concentrations can also be challenging [e.g., Bousquet et al., 2006; Worden et al., 2013]. For these reasons, we use new methane data from TES (as well as CO to identify air parcels affected by the fires) along with new profile CO estimates from MOPITT to provide separate methane emission estimates from the fires and evaluation of these errors. optimal estimation (OE) is used to quantify emissions using these satellite data and the GEOS‐Chem model. In the OE inverse framework, data and model are compared in a cost function that depends on errors in the data and prior knowledge of the emissions. Iterations of model emissions are performed until this cost function reaches a minimum.

[7] We first estimate total methane emissions from Indonesia for September, October, and November of 2006 using the TES data and the OE approach described in Jones et al. [2003] and supporting information. This approach quantifies emissions from large‐scale land masses. Satellite data uncertainties and emissions from other sources are the dominant error terms calculated using this approach. These errors are random and therefore describe the precision of the estimate. We find total Indonesian fire emissions for the October through November 2006 time period are approximately 4.5 Tg ± 4% (Table 1 “Updated Biomass Burning” row), surprisingly consistent with the model estimate that is based on a burnt area estimate, the estimate of carbon from burnt area, and CH 4 /CO 2 emissions ratios. However, the TES‐based estimate depends on knowledge of the non‐biomass burning Indonesian emissions (Table 1) and the errors on these emissions are not well known. The good agreement between model and data in the CH 4 /CO distributions (Figure 2) indicates that we can evaluate the impact of this error by quantifying CO emissions. Errors in modeled vertical transport also directly propagate to the emissions estimate [e.g., Jones et al., 2003; Kopacz et al., 2010; Jiang et al., 2013]. To evaluate these two error sources, we provide a separate, corroborating estimate of the Indonesian biomass burning emissions and its errors using MOPITT multispectral CO profiles that can distinguish near‐surface CO from free tropospheric CO (for land scenes with less than 5% cloud contamination) and hence provide increased sensitivity to the fire emissions of CO and its subsequent vertical transport.

Table 1. CH 4 Emissions Used for 2006 GEOS‐Chem Model Estimates for September Through November 2006, Updated With TES CH4 Observations Indonesia 2006 CH 4 Emissions CH 4 (Tg/month) Emission Type September October November 3 Month Total A priori Total 2.39 4.06 1.67 8.12 A priori Biomass Burning 1.12 2.77 0.39 4.28 A priori Wetlands 0.67 0.69 0.68 2.04 A priori Other 0.60 0.60 0.60 1.80 Updated Total 2.23 ± 0.09 3.86 ± 0.09 2.24 ± 0.13 8.33 ± 0.18 Updated Biomass Burning 0.96 ± 0.09 2.57 ± 0.09 0.96 ± 0.13 4.49 ± 0.18 Updated CH 4 from CO 0.84 2.58 0.70 4.12

[8] We use a 4‐D variational approach (supporting information) [Kopacz et al., 2010; Jiang et al., 2011, 2013] to quantify global CO emissions during the fall of 2006 using the MOPITT V5J profiles (supporting information) [Deeter et al., 2013]. This approach allows for emissions estimates with substantially increased spatial resolution to take advantage of the near‐surface sensitivity of the CO profiles for estimating the spatial variability of the fire emissions. However, precision errors are not calculated due to computational cost. Errors in modeled vertical transport affect the modeled vertical distribution of CO and hence CH 4 . Emissions estimates based on the MOPITT CO profiles are therefore directly affected by errors in vertical transport. In principal, use of the MOPITT total column data to quantify emissions is less sensitive to vertical transport errors because the mass of the total column must be preserved in the model regardless of where convection places the corresponding air parcels. On the other hand, the MOPITT column‐based estimates will preferentially weigh the CO values in the boundary layer over land, while over ocean, only the thermal channel contributes, with highest sensitivity in the midtroposphere [Worden et al., 2010]. We can empirically evaluate how the combination of vertical sensitivity in the satellite data and vertical transport error in the model affects the emissions estimates by comparing the results of the inversion using the MOPITT profile against an inversion using the total column. Figure 3 (left) shows the vertical structure of the mean relative bias across the tropics and subtropics, averaged from 20°S to 20°N, between the modeled and a priori CO fields (supporting information). The a priori shows a large positive bias in the middle troposphere corresponding to the equatorial Eastern Pacific Ocean, the Atlantic Ocean, and the Indian Ocean. These horizontal biases are due to continental outflow as discussed in Liu et al. [2010] and are significantly reduced in the a posteriori simulations with both the profile and column inversions. However, residual vertical gradients exist over all regions that are consistent with vertical transport biases in the model [Liu et al., 2010; Jiang et al., 2013] and which cannot be corrected in the inversion of the emissions.

Figure 3 Open in figure viewer PowerPoint Fractional (color bar) residuals between Model and MOPITT CO profiles averaged between 20°S and 20°N. The x axis is longitude and the y axis is pressure (hPa). (left column) residuals before the (middle column) profile‐based inversion and (right column) column‐based inversion. Indonesia is near 110°E longitude.

[9] As shown in Figure 3, emissions estimates from the profile‐based inversion have the smallest residuals between the MOPITT profile data and GEOS‐Chem model for September and October. Both approaches yield acceptable results for November with the profile‐based estimate biased high over Indonesia and the column‐based estimate biased low. Using both results for November and the profile‐based results for September and October (Table 2), along with the a priori emission ratio of 0.061 g/g (0.106 ppb/ppb) for CH 4 /CO suggests a range of CH 4 emissions of 4.1 to 5 Tg (Table 1, “Updated CH 4 from CO” row).

[10] The uncertainty in the CH 4 /CO emission ratio also affects this estimate. From Andreae and Merlet [2001] we expect an approximate 35% error in the CH 4 /CO emission ratio for a single fire and consequently a smaller error when the fires are aggregated over region and a month. Although the observed and modeled CH 4 /CO distributions (Figure 2) agree within the errors of the actual CH 4 /CO ratio, the difference suggests that the model emission ratio could be biased low by 13% based on the October distributions in Figure 2. Assuming these errors are not random, the spread in the CH 4 estimate could range from 3.5 to 5 Tg, which spans the estimate using the TES CH 4 data. As discussed earlier, this range now incorporates the error terms from data, transport, and knowledge error in nearby non‐biomass‐burning emissions. For these reasons we report a value of 4.25 ± 0.75 Tg for these El Nino driven fires (Table 2).

Table 2. CO Emissions, Updated With MOPITT V5J CO Profiles and Columns Indonesia 2006 CO (Tg) Emissions Emission Type September October November 3 Month Total A priori 19.3 45.2 7.5 85.4 Update (CO Profiles) 13.7 42.3 23.4 79.4 Update (CO columns) 12.5 27.9 11.5 51.9