Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: a case study in northern sub-Saharan African region

The WRF-Chem model version 3.5 is used in this study (Fast et al 2006, Grell et al 2005). The model configuration is similar to the one used in Ge et al (2013) and Yang et al (2013), as well as their treatments of OC/BC optical properties. The gas-phase chemistry is based on the Regional Acid Deposition Model, version2 (RADM2) photochemical mechanism (Stockwell et al 1990). The aerosol modules are the Modal Aerosol Dynamics Model for Europe (MADE) (Ackermann et al 1998). The radiative schemes used are the two-stream multi-band Goddard model Chou et al (1998) with ozone climatology for shortwave (SW) and the RRTM scheme (Mlawer et al 1997) for longwave. The smoke direct radiative effect is considered in the radiative calculations, but aerosol indirect effect (e.g., aerosol-cloud microphysical interaction) is not turned on in the model for this study. To facilitate the study of smoke semi-direct effect on the cloud and resultant change of cloud radiative effects (CREs), we introduced in these two radiative transfer codes the extra outputs for downwelling and upwelling radiative fluxes at the TOA and SFC (ground surface) for both clear sky and all sky conditions.

In this study, based on Lambert conformal projection, a double-nested grid configuration is used over the northern sub-Saharan African (NSSA) region: the coarse outer domain has horizontal resolutions of 81 km × 81 km with total 130 × 85 grid points; the fine inner domain has 259 × 133 points with 27 km grid spacing. The lower left corners for these two domains are (21.88°S, 29.42°W) and (13.24°S, 16.55°W), respectively. The inner domain can be roughly delineated in figure 1. Similar to Yang et al (2012), no transboundary transport of chemical species into the model domain is considered.

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A total of seven smoke inventories are used independently to specify the BC and OC emissions: (1) FLAMBE (Fire Locating and Modeling of Burning Emissions inventory; Reid et al 2009), (2) FINNv1.0 (Fire Inventory from NCAR version 1.0; Wiedinmyer et al 2011), (3) GFEDv3.1 (Global Fire Emissions Database version 3.1; van der Werf et al 2010), (4) FEER-SEVIRIv1.0 (Fire Energetics and Emissions Research version 1.0 using fire radiative power (FRP) measurements from the geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI); Roberts and Wooster 2008, Ichoku and Ellison 2013), (5) GFASv1.0 (Global Fire Assimilation System version 1.0; Kaiser et al 2012), (6) GBBEP-Geo (NESDIS Global Biomass Burning Emissions Product; Zhang et al 2012), and (7) QFEDv2.4 (Quick Fire Emissions Dataset version 2.4; Darmenov and da Silva 2013). The key specifics of these emission algorithms are listed in table 1. For those inventories (GFED, FINNv1.0, GFAS, and QFED) that do not present the diurnal cycle of smoke emissions, we applied the diurnal profile based upon the three hourly and daily temporal variability in fire emissions derived from Geostationary Operational Environmental Satellite (GOES) and MODIS (Mu et al 2011). Finally, the emission rates are kept as constant for each hour in every three-hourly interval. FINNv1.0, GFEDv3.1, GFASv1.0, and QFEDv2.4 provide smoke emissions for different constituents (such as OC, BC, PM_2.5). For other inventories that do not specifically contain BC and OC emissions (such as FLAMBE, FEER-SEVIRIv1.0, and GBBEP-Geo), this study used the set of emission factors described by Andrea and Merlet (2001) to convert total dry matter burned to BC and OC in GBBEP-Geo, and to obtain the OC and BC emission ratios both relative to PM_2.5 and to TPM (total particulate matter, table 1). By multiplying those emission ratios with the corresponding products from the emission inventories, OC and BC emissions for FLAMBE and FEER-SEVIRIv1.0 are obtained as well.

Eight sets of simulations are performed for each month, with the first not considering any fire emissions and the rest respectively incorporating seven different emission inventories (as mentioned above). In the model, the injection height of smoke (only OC + BC) emissions is specified as 650 m above the surface (Yang et al 2013). The 1°× 1° National Center for Environmental Prediction (NCEP) 6 h Final Analysis (FNL) data are used for initializing and specifying the temporally evolving lateral boundary conditions. For all simulations, we provide a spin-up time of one week, and only data during February and November 2010 are analyzed.

Figure 1 shows the monthly-total smoke OC + BC emissions (g m⁻²; a1–a7) and monthly-mean simulated column total PM_2.5 (mg m⁻²; c1–c7) during February 2010, along with their ratios to the means among different inventories for smoke emission (b1–b7) and for PM_2.5 (d1–d7), respectively. Clearly, all inventories showed the zonal distribution of fire emissions around 0–15°N, along with some in the Congo tropical forest (about 7°S, 15°E) (figures 1(a1)–(a7)). These emissions originate primarily from the predominant human-induced fires related to agricultural, savanna, and deforestation burning in the study region (e.g., Hao and Liu (1994), Johnson et al 2008, Dami et al 2012). Confined within the 0–15°N belt are the three high smoke emission centers situated around 15°W −5°W, 0–10°E, and 15°E–30°E, which also correspondingly render three locations of high concentrations of simulated total PM_2.5 (figure 1). Hereafter, these regions are named west, middle and east regions, respectively.

Only FEER-SEVIRIv1.0 produced the strongest emissions in the middle region and far weaker emissions in the east region where the strongest emissions are commonly found for the other emission inventories. As shown in table 1, only FEER-SEVIRIv1.0 and QFEDv2.4 are based on top-down approaches that use satellite measurements of both FRP and smoke AOD. However, the FEER-SEVIRIv1.0 emissions over NSSA region are based on geostationary satellite-sensor (SEVIRI) FRP data in conjunction with FEERv1.0 smoke emission coefficients derived from MODIS FRP and near-source smoke AOD (Ichoku and Ellison 2013). Therefore, the difference between the emission inventories also in part supports the fact that the large uncertainty existing among different satellite retrievals may include the spatial and temporal variation of fires. Furthermore, a qualitative comparison of the model total column OC + BC simulations with total column AOD retrievals from MODIS on Terra and Aqua in February 2010 (figures 1(e) and (f)) show that aerosol concentrations are prominent both in the middle and east regions, with the middle region showing a slightly higher loading; indicating that the various emission inventories spatially reflect relative strengths and weaknesses in capturing emission source strengths by using different approaches.

Considering all the estimates as a whole, the overall mean and standard deviation among different smoke emission inventories are 607 ± 397 Gg, with coefficients of variation (CV defined as ratio of standard deviation and the mean of all inventories) of 65%–68% (table 2). Perhaps due to the adoption of MODIS AOD to constrain their smoke emissions, FEER-SEVIRIv1.0 and QFEDv2.4 have similar total emissions and both are a factor of 2–3 larger than GFASv1.0. The smallest emission estimated by GBBEP-Geo is likely due to its use of a constant conversion factor (0.368 ± 0.015 kg (dry mass) M J⁻¹) (Zhang et al 2012), which is four times smaller than that (1.37 kg (dry mass) M J⁻¹) used in GFASv0 (Kaiser et al 2009). Compared with GBBEP-Geo, the GFASv1.0 algorithm continues to adopt the larger land cover-specific conversion factors, leading to larger smoke emissions (Kaiser et al 2012). Furthermore, the absence of constraining GBBEP-Geo with either MODIS AOD or something similar may be another key reason for its lower emissions. As a result, GBBEP-Geo yields a factor of 8–16 smaller emissions than those indicated by FEER-SEVIRIv1.0 and QFEDv2.4. The MODIS burn-scar mapping method used by GFEDv3.1 cannot detect small fires that burn less than half of its 500 m × 500 m nominal pixel size (Giglio et al 2009). Thus, the larger emission in GFASv1.0 as compared to GFEDv3.1 is mainly due to a higher detection threshold used by the MODIS burned area observation product than those in the underlying MODIS FRP observations (Kaiser et al 2012).

Table 2.  Comparisons among domain-total smoke (OC + BC) emissions (Gg), simulated monthly-mean PM_2.5 loadings (Gg), simulated monthly-mean domain-mean AOD, simulated monthly-mean domain-mean smoke effects ^a on SWTOA_clr, SWSFC_clr (W m⁻²), T₂ and T₇₀₀ (K), respectively. The maximal smoke effects are also listed in parenthesis. The period covered is February 2010.

Variables	Smoke (OC + BC) emissions (Gg)	Simulated PM2.5 loadings (Gg)	AOD	ΔSWTOAclr (W m−2)	ΔSWSFCclr (W m−2)	ΔT2(K)	ΔT700(K)
FLAMBE	1235	188	0.034	−0.42 (−2.34)	−2.47 (−25.1)	−0.033 (−0.26)	0.017 (0.096)
FINN	495	126	0.021	−0.43 (−1.74)	−1.16 (−12.6)	−0.022 (−0.16)	0.009 (0.058)
GFED	302	128	0.022	−0.38 (−1.43)	−1.32 (−12.3)	−0.030 (−0.20)	0.010 (0.078)
FEER-SEVIRI	839	177	0.032	−0.40 (−2.43)	−2.22 (−15.6)	−0.039 (−0.33)	0.020 (0.118)
GFAS	376	123	0.021	−0.37 (−1.37)	−1.26 (−5.4)	−0.025 (−0.15)	0.009 (0.062)
GBBEP-Geo	103	95	0.015	−0.37 (−1.37)	−0.69 (−2.4)	−0.017 (−0.16)	0.003 (0.076)
QFED	898	187	0.034	−0.35 (−1.74)	−2.48 (−13.4)	−0.048 (−0.28)	0.020 (0.105)
Mean$\pm \delta$ b	607 ± 397	146${\mkern 1mu} \pm {\mkern 1mu} 37$	0.026${\mkern 1mu} \pm {\mkern 1mu} 0.008$	−0.39 ± 0.03	−1.66 ± 0.72	−0.03 ± 0.01	0.013${\mkern 1mu} \pm {\mkern 1mu} 0.006$
Range c	12	2	2.3	1	3.6	3	7
CV d	65%	25%	31%	8%	43%	33%	46%

Note: ^aAll domain-mean smoke effects shown here are only based on those grids with 95% confidence by paired samples t test. ^b $\delta$: standard deviation; ^cRange: max/min; ^dCV(coefficient of variation): $\delta /{\rm mean}$.

More spatial discrepancies can be seen in figure 1, in which the ratios of smoke emissions of OC + BC to the means among different inventories (labeled as Ratio_smoke) are shown. North-South gradients of fire emissions differ among different smoke inventories, perhaps driven by the different gradients in vegetation density, fuel type and land cover adopted in these inventories. FLAMBE usually has the largest emissions in tropical Africa with Ratio_smoke > 5. Smaller smoke emissions from FLAMBE and FINN are found in the northern parts of the NSSA than in their southern parts, while the reverse is true for GFED. GFAS and QFED show similar spatial patterns may due to the similar application of MODIS FPR observations in both (Kaiser et al 2012, Darmenov and da Silva 2013). Overall, the domain means of Ratio_smoke averaged only over the grids having fire emissions are 0.9, 0.4, 0.5, 2.6, 1.1, 0.2, and 1.3 for FLAMBE, FINN, GFED, FEER-SEVIRI, GFAS, GBBEP-Geo, and QFED, respectively. The overall mean and standard deviation of Ratio_smoke are 1.00 ± 0.81, with a CV of 81%.

Although FLAMBE, one of the bottom-up approaches, produces larger domain total emissions (1235 Gg) than even the top-down approaches, it does not have the largest domain mean of Ratio_smoke (0.9). This is because of a few very big emissions in FLAMBE. The maximum grid-cell emission of 9.09 g m⁻² for FLAMBE as compared to <3.55 g m⁻² for the other inventories outweighs its overall smaller emissions, more than FEER-SEVIRI and QFED for instance. Among the different inventories based on their daily total emissions per grid cell (figure 2(a)), FLAMBE cumulative smoke (OC + BC) emission is generally in the middle, and smaller than FEER-SEVIRI and QFED for small to medium values. It is only after including big emissions (>148 mg m⁻², i.e., log(OC + BC emission) >5) that FLAMBE becomes larger than those of the top-down approaches. This may be due to a static estimate of 62.5 ha area burned per MODIS hot spot adopted by FLAMBE. Reid et al (2009) shows how this estimate results in over-estimation of fire-affected areas in grassland and savanna landscapes, and under-estimation in densely forested landscapes.

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For the monthly-mean column loading of PM_2.5 simulated by WRF-Chem3.5, their domain means are 188, 126, 128, 177, 123, 95, and 187 Gg for FLAMBE, FINNv1.0, GFEDv3.1, FEER-SEVIRIv1.0, GFASv1.0, GBBEP-Geo and QFEDv2.4, respectively (figure 1). The overall mean and standard deviation are 146 ± 37 Gg with a CV of 25% (table 2). The PM_2.5 loadings are within a factor of 2.4 (taking the smallest one from GBBEP-Geo as base), compared with a factor of 12 difference in the emissions. Moreover, as shown in figure 1, the domain means of Ratio_PM_2.5 (i.e., the ratios of simulated PM_2.5 loadings to the means among different inventories) are 1.2, 0.9, 0.9, 1.2, 0.9, 0.7, and 1.2 for FLAMBE, FINN, GFED, FEER-SEVIRI, GFAS, GBBEP-Geo, and QFED, respectively. The CV of Ratio_PM_2.5 is 20%, much smaller than that of Ratio_smoke (81%). Hence, in terms of quantities over the study domain, the large differences in smoke (OC + BC) emissions are not reflected in the simulated loading of PM_2.5.

The results may in part reflect the nonlinearity in the source-receptor relationship. In this relationship, the atmosphere, through diffusion as well as wet and dry deposition processes, generally dampens the effect of differences in the source strength on the atmospheric loadings. This can be more clearly shown in figure 2(c). February is the beginning of the transition time from the dry to the wet season in the NSSA region. Thus, the smoke emissions in February, though much smaller than those in January, obviously decrease with time (figure 2(b)). Correspondingly, the simulated PM_2.5 loadings decrease with time as well (figure 2(c)), although there exist some peaks caused by big smoke emission events, e.g., on the 6th–7th February. The slopes of PM_2.5 decrease from −3.67 to − 4.77 to −1.39 to  − 2.18 and then to −0.30 as the smoke emissions decrease from FLAMBE, FEER-SEVIRI and QFED to FINN, GFED, and GFAS and then to GBBEP-Geo. Clearly those inventories producing larger smoke emissions and thus larger PM_2.5 loadings generally have larger damping decrements, inferring the nonlinearity in the source-receptor relationship. In other words, the differences in the total amount of emissions among various emission inventories is mainly due to the large differences in the estimate of emissions from areas of large fire concentrations (figure 2(a)). However, these seemingly large fires are probably constituted by multiples of relatively smaller fires that are very local and only persist for a few days at most. As a result, in regional and monthly averages, the effect of differences in the smoke emissions on the atmospheric loadings is largely dampened, resulting in the CVs of the domain means of PM_2.5 loadings and Ratio_PM_2.5 within a factor of 2–3. However, at the local scale where the large concentrations of the fires occur, quite different spatial patterns are still found (figure 1). For example, taking the GBBEP-Geo as base, the PM_2.5 loading differences can reach up to a factor of 16–33 in the smoke source region with the Ratio_PM_2.5 of GBBEP-Geo larger than 0.2.

The surface PM_2.5 mass concentration shows a similar contrast with respect to emission as the PM_2.5 column loading, and overall, it shows a CV of 37% in the domain and monthly averages (figure S4). In terms of Ratio_SFC_PM_2.5 (i.e., the ratios of simulated surface PM_2.5 concentration to the means among different inventories), the CV is 23%. However, again, at the local scale, a factor of up to 10–60 difference can be found in surface PM_2.5 concentrations.

Figure 3 shows monthly-mean modeled aerosol optical depth (AOD) at 550 nm, aerosol absorption optical depth (AAOD, blue contour) at 550 nm overlaid with the map of surface albedo, and the smoke radiative effects on modeled clear-sky net downwelling shortwave (SW) top of atmosphere (TOA) radiative fluxes (ΔSWTOA_clr, W m⁻²) in February 2010. Note that only statistically significant ΔSWTOA_clr values (paired samples t test with p < 5%) are shown in figure 3.

The AODs (figures 3(a1)−(a7)) present similar patterns as their corresponding PM_2.5 loadings (figures 1(c1)−(c7)). The larger the PM_2.5 loadings are, the bigger the AODs. The domain averages of AOD at 550 nm are 0.034, 0.021, 0.022, 0.032, 0.021, 0.015, and 0.034 for FLAMBE, FINN, GFED, FEER-SEVIRI, GFAS, GBBEP-Geo and QFED, respectively. Their overall mean and standard deviation are 0.026 ± 0.008, with a spread of 31%. The relative difference of 2.3 is close to that of the simulated total PM_2.5 loadings (2.0), reflecting the first-order linear relationship between aerosol mass loading and its optical depth as well.

Negative ΔSWTOA_clr are commonly found with the domain means of –0.42, –0.43, –0.38, –0.40, –0.37, –0.37, and –0.35 W m⁻² for FLAMBE, FINN, GFED, FEER-SEVIRI, GFAS, GBBEP-Geo, and QFED, respectively (figures 3(c1)−(c7)). Their mean and standard deviation are –0.39  ± 0.03 W m⁻² with a spread of 8%. This can be understood that smoke particles with low or moderate AOD values over the low surface albedo conditions (such as over the ocean and tropical forest region with albedo less than 0.15) (figures 3(b1)−(b7)) generally scatter more solar radiation back-to-space than the parts being absorbed (Wang and Christopher 2006). However, over the bright surfaces, the smoke absorption can be enhanced by strong and multiple reflection of solar radiation between the surface and smoke layer (Ge et al 2014). Hence, over the arid Saharan region (northward of 15°N with surface albedo > 0.3), positive ΔSWTOA_clr values are found. Also in smoke source regions with moderate surface albedo (0.2−0.25), AOD is larger and single scattering albedo is low, yielding larger AAOD and absorption and hence large positive ΔSWTOA_clr. The cancellation between the smoke negative and positive TOA forcings mentioned above produced only a factor of 1.2 differences in domain-means of ΔSWTOA_clr, which is far less than that in smoke emissions.

At regional scales, the surface temperature change is mainly due to the change of net downwelling SW radiative flux. We show in figure 4 the modeled net downwelling SW radiative fluxes at the SFC both under clear sky (ΔSWSFC_clr, figures 4(a1)–(a7)) and all sky (ΔSWSFC, figures 4(b1)–(b7)) conditions. To study the changes of CREs induced by smoke particles, we also show in figures 4(c1)–(c7) the modeled SW CREs at SFC (ΔSWCRE_sfc, W m⁻²). The SWCRE_sfc is defined as ${\rm SWCR}{{{\rm E}}_{{\rm SFC}}}=F_{{\rm SW},{\rm SFC}}^{{\rm all}}-F_{{\rm SW},\,{\rm SFC}}^{{\rm clr}}$, where $F$ is the net (downward minus upward) flux, ${\rm clr}$ designates clear skies, and$\;{\rm all}\;$denotes a mixture of clear and cloudy skies. Note that only statistically significant results (paired samples t with p < 5%) are shown in figure 4, which is similar to that of Ge et al (2014).

ΔSWSFC_clr generally is more significant than its counterpart at the TOA, because both absorption and scattering of smoke particles result in the extinction of radiation at the surface. Their values can be up to −25.1, −12.6, −12.3, −15.6, −5.4, −2.4 and −13.4 W m⁻² for FLAMBE, FINN, GFED, FEER-SEVIRI, GFAS, GBBEP-Geo, and QFED, respectively (figures 4(a1)–(a7)). The locations of large reductions of SWSFC_clr coincide quite well with the regions of strong smoke emissions (figures 1(a1)–(a7)), and hence also those of large AODs (figures 3(a1)–(a7)). Overall, the domain averages of ΔSWSFC_clr are −2.47, −1.16, −1.32, −2.22, −1.26, −0.69 and −2.48 W m⁻² for FLAMBE, FINN, GFED, FEER-SEVIRI, GFAS, GBBEP-Geo and QFED, respectively. Together, their uncertainty range is within a factor of 4, and their mean and standard deviation are −1.66 ± 0.72, with a CV of 43%. This uncertainty range is slightly larger than that of AOD (i.e., 2), possibly reflecting the role of multiple scattering and surface inhomogeneity in the domain averages. Moreover, quite different spatial patterns are still found, especially over the source regions. Taking ΔSWSFC_clr for example, their differences based on the different emissions compared to GBBEP-Geo can reach up to a factor of 4–19.

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During February 2010, there are two distinct synoptic regimes in the domain of interest: one, the dry region north of ∼10°N without clouds at all except at its eastern edge; and the other, the wet region south of ∼10°N with heavy clouds, especially in the ITCZ domain (not shown). As shown in figures 4(b1)–(b7), spatial distributions of ΔSWSFC over the dry region appear to be very similar to those of ΔSWSFC_clr due to the lack of clouds. In the wet region, changes in the instantaneous fields of clouds induced by smoke particles can be quite large, although most of them did not satisfy the paired samples t test at the monthly scale (figure not shown). This is mainly due to the high sensitivity of cloud fields to changes in atmospheric circulation, vertical profiles of meteorological fields, vertical stability, and other factors. Nevertheless, over the strong smoke emission regions, significant proportions of ΔSWCRE_sfc were consistently found to be positive (figures 4(c1)–(c7)), further modifying SWSFC. Positive ΔSWCRE_sfc increase SWSFC, showing a weakened smoke radiative effect.

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Finally, we examine the smoke radiative effects on temperature at 2 m (ΔT₂, K, figures 5(a1)–(a7)) and air temperature in the lower troposphere 700 hPa (ΔT₇₀₀, K, figures 5(b1)–(b7)). Note that only statistically significant differences (paired samples t test with p < 5%) are shown here. Generally speaking, due to the smoke particles' radiative effects, in the smoke emission areas, surface temperature (T₂) decreases while temperature in the lower troposphere (e.g., 700 hPa) increases. The strong reductions of T₂ are generally caused by the strong decreases in SWSFC, whereas the increases of T₇₀₀ result from the BC strong absorptions. In addition, the large changes of T₇₀₀ are generally southeast of those in T₂, coinciding with the direction of northeast trade winds. In brief, the decreases of ΔT₂ can be up to 0.26, 0.16, 0.20, 0.33, 0.15, 0.16, and 0.28 K, and ΔT₇₀₀ also show maximal increases with values of 0.096, 0.058, 0.078, 0.118, 0.062, 0.076, and 0.105 K, for FLAMBE, FINN, GFED, FEER-SEVIRI, GFAS, GBBEP-Geo and QFED, respectively. This thermal vertical structure is favorable to a stable atmosphere, and hence reduces the formation of cloud, partially explaining the generally positive changes of SWCRE_sfc in FLAMBE and QFED (figure 4). The relative small smoke effects on T₂ over the ocean are due to the ocean's large heat capacity and latent heat release, and the slight warming may be partly due to the mixing of smoke-absorption-induced warm air in the lower part of the troposphere with the air near the surface (Wang and Christopher 2006).

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Overall, the mean and standard deviation of ΔT₂ and ΔT₇₀₀ are −0.03 ± 0.01 and 0.013 ± 0.006 K, with a CV of 35% and 51%, respectively. However, we note that the absolute changes in temperature are likely affected by the model configuration in terms of OC and BC ratio, model boundary condition, parameterization schemes for cloud and boundary layers, and relative position between smoke and cloud layers (Feingold et al 2005, Wilcox 2012). While the values of these changes appear small in terms of domain averages, it should be considered that the domain is about eight times larger than the smoke source region (primarily contained within the 4°N–10°N belt). At local scales, the absolute changes of T₂ and T₇₀₀ associated with individual experiments are much larger than their counterparts in domain averages. Among all experiments, a factor of 3 and 7 differences in ΔT₂ and ΔT₇₀₀ are also found, both of which are less than that of smoke emissions (12). Moreover, as shown in figures 5(c1)–(c7) and (d1)–(d7), the domain means of Ratio_ΔT₂ (i.e., the ratios of simulated ΔT₂ to the means among different inventories) are 1.4, 0.9, 1.0, 1.6, 0.9, 0.8, and 1.6 for FLAMBE, FINN, GFED, FEER-SEVIRI, GFAS, GBBEP-Geo, and QFED, respectively, and those of Ratio_ΔT₇₀₀ (i.e., the ratios of simulated ΔT₇₀₀ to the means among different inventories) are 1.5, 0.7, 1.0, 1.9, 0.8, 0.2, and 1.9. The overall mean and standard deviation are 1.17 ± 0.35 with a CV of 30% for Ratio_ΔT₂, and 1.14 ± 0.65 with a CV of 56% for Ratio_ΔT₇₀₀. Those CV are larger than that of Ratio_PM_2.5 (<25%), probably due to the fact that only statistically significant values are considered, although they are all significantly smaller than the CV of Ratio_smoke (81%). All the numerical comparisons presented in this study are summarized in tables 2 and 3.

Table 3.  Comparisons among domain-means of Ratio_Smoke, Ratio_PM_2.5, Ratio_ΔT₂, and Ratio_ΔT₇₀₀. All ratios are compared with the mean values across all emission inventories. The period covered is February 2010.

	Ratio_Smoke	Ratio_PM2.5	Ratio_ ΔT2 a	Ratio_ ΔT700 a
FLAMBE	0.9	1.2	1.4	1.5
FINN	0.4	0.9	0.9	0.7
GFED	0.5	0.9	1.0	1.0
FEER-SEVIRI	2.6	1.2	1.6	1.9
GFAS	1.1	0.9	0.9	0.8
GBBEP-Geo	0.2	0.7	0.8	0.2
QFED	1.3	1.2	1.6	1.9
Mean$\pm \delta$	1.00 ± 0.81	1.00 ± 0.20	1.17 ± 0.35	1.14 ± 0.65
Range	13.0	1.7	2.0	9.5
CV	81%	20%	30%	56%

Note: ^aAll domain-mean smoke effects shown here are only based on those grids with 95% confidence by paired samples t test.