http://www.gmes-atmosphere.eu) project is a European initiative to achieve a complete and consistent view of the atmospheric composition, and to establish connections between chemical composition and weather forecasts, which in future may improve weather forecasts (Zhang, 2008, Grell and Baklanov, 2

l.7. : "analyze": In my understanding, the paper discusses the ability of MACC to forecast rather than to analyze large-scale pollution episodes. The reviewer is correct that “analyze” is misleading in this context. It should be understood as a reference to the assimilation system, which provides an “analysis” of the atmospheric composition. We now change this to: This extreme event is used to evaluate the ability of the global MACC (.) atmospheric composition forecasting system to provide analyses of large-scale pollution episodes. Introduction, p. 31853. The statement: "a range of observations were used in various studies to characterize the tropospheric composition during this episode : : :" may be understood such that all of the four mentioned studies are purely observational (in contrast to the study by Huijnen et al.), what is not true. The respective paragraph should be revised and extended to allow a reader to get a better and more accurate idea about the previous studies of the same episode. We have revised the introduction accordingly: A range of observations were used in various studies to characterize the tropospheric composition during this episode, including in-situ data for fine and coarse aerosol mass (PM2.5 and PM10, van Donkelaar et al., 2011), aerosol optical thickness (Chubarova et al., 2011) ozone (O3) and carbon monoxide (CO) (Konovalov et al., 2011, Elansky et al., 2011), CO total columns (Yurganov et al., 2011), as well as space-based information of CO total columns from Atmospheric Infrared Sounder (AIRS), and aerosol optical depth (AOD) and single-scattering albedo from Ozone Monitoring Instrument (OMI) (Witte et al., 2011, Mei et al., 2011). In addition, it had been shown before that fire episodes can be analyzed by the Infrared Atmospheric Sounding Interferometer (IASI) CO (Turquety et al., 2009) as well as nitrogen dioxide (NO2) and formaldehyde (HCHO) observations from the OMI and Scanning Imaging Absorption SpectroMeter for Atmospheric ChartographY (SCIAMACHY) instruments (e.g., Mebust et al., 2011; Stavrakou et al., 2009). Model studies assessing estimates of emissions for CO (Konovalov et al., 2011, Yurganov et al., 2011, Fokeeva et al., 2011), and aerosol (van Donkelaar et al., 2011, Kaiser et al., 2012) have been reported for the 2010 Russian fires. p. 31857, l. 4,5: " It applies the same 60 level vertical discretization as the IFS, but the horizontal resolution is 3 lon x 2 lat, globally". Is the horizontal resolution of IFS different? Yes, as specified on p. 31856, l 23, IFS runs on T159, which is approx. 1.2 deg. p. 31859, l. 20. "23 percent": Where this critical value is taken from (reference)? This value was based on the assumption that water stress occurs below 0.5 θsat , the volumetric soil moisture at saturation. Here θsat = 0.472 m/m for loamy soils, see, e.g., the IFS manual, http://www.ecmwf. int/research/ifsdocs/CY37r2/IFSPart4.pdf , and references therein. We acknowledge that the actual value of 0.5 θsat is rather arbitrary, and change the text accordingly to: . the soil wetness was below the critical level of 23%, i.e. half the volumetric soil moisture at saturation, where water stress in vegetation is assumed to take place. p. 31860, l. 18-21: "Furthermore, the CO and HCHO emissions are much higher than the monthly-mean GFEDv3.1 emissions. This is mainly caused by the different predominant soil type maps used in GFEDv3.1 and GFASv1.0". Can the authors justify the last statement, or is it simply a guess? GFEDv3.1 represents organic soil content in parts of Russia but only for the calculation of the dry matter combustion rate; the emission factors for peat are not applied outside tropical peat regions (van der Werf et al., 2010). GFASv1.0 applies emission factors for peat across all tropical and Russian peat land cover types. The finding of larger CO and HCHO emissions and lower NOx and BC emissions in GFAS than in GFED is consistent with the differences in respective emission factors for peat and agricultural fires. We have updated the manuscript accordingly. p. 31860: "the CO and HCHO emissions are much higher than the monthly-mean GFEDv3.1 emissions": It is mentioned on the page 31858 that the conversion factor was derived with a linear regression between the observed fire radiative energy and the dry matter burned in the GFEDv3.1 inventory. Do these facts imply that GFAS estimates are much smaller than the GFEDv3.1 data in some other regions. Are so large differences between GFAS and GFEDv3.1 typical, or these region and event are exceptional? Please comment. If the conversion factor had been as a ratio of global budgets then compensations in other regions and events could be expected. However, the approach of a linear regression has been chosen for the very reason of minimising such compensations. This leads, for example, to overall slightly higher emission because of the lower detection threshold of GFAS, which is not compensated in any of the more active fire regions. Further differences occur because the land cover map of GFED is finer than the one of GFAS (500m vs 0.5deg). On a global multi-annual average, the CO emission of GFAS are 6% larger than the ones of GFED. On a continental scale it may be larger or smaller by typically 20% and in the region east of Moscow with variable fires and the dedicated peat emission factors of GFAS, it is typically twice as large in GFAS than in GFED. For more details, see Tab. 5 and Fig. 7 of Kaiser et al. BG 2012. For clarity we now include the following comment in the description of GFAS: . on a global, multi-annual average, the CO emissions of GFASv1.0 are 6% larger than GFEDv3.1, on a continental scale differences are typically 20%. p. 31860, l. 28: "The CO emissions are _25% higher than Konovalov et al. (2011)" According to Table 3 of the reviewed paper, CO emissions from fires in the considered regions during July and August are _ 13.3 Tg, while according to Table 4 in Konovalov et al. (2011) the fires in European Russia emitted about 12.8 Tg of CO in the same period. That is, the difference is actually less than 4 percent. This is an encouraging agreement. However, the considered regions are not quite identical. Please correct the discussion accordingly (including the conclusions). ibid: ": : : higher than Konovalov et al. (2011)" => ": : : higher than in Konovalov et al. (2011)" We interpret Table 4 of Konovalov et al. (2011) as reporting total fire emissions over European Russia of 9.7 Tg CO, of which 3.2 Tg is attributed to peat fires. This appears to be different to the reviewer's interpretation, who added the two numbers. So we think the statement of ~25% higher emissions as found in the current study compared to Konovalov et al. is correct. p. 31862, l. 28: "The RMSE for D+3 in run Assim-GFAS is slightly worse than the one in run Assim": I would not say that the difference between 0.77 and 0.52 (that is, almost 50 percent) is really a "slight" difference. The reviewer is correct. We remove ‘slightly’ p. 31866, l. 16, 17. : "key spatial patterns are captured by the model, such as the northsouth gradient in O3 columns over Western Russia". What are the other "key spatial patterns" captured by the model? We change this to: . the large-scale spatial patterns are captured by the model, including the north-south gradient in O3 columns over western Russia. p. 31866, l. 23: "This is larges for run CNT" => "It is largest for the run CNT"