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  • View in gallery
    Fig. 1.

    The vertical profile of aerosol plume mass injection percent (%) in (a) Southern Hemisphere South America (SHSA), (b) Southern Hemisphere Africa (SHAF), and (c) equatorial Asia (EQAS) during their respective peak fire seasons. Peak fire season occurs in SHSA during August–October, in SHAF during July–September, and in EQAS during August–October.

  • View in gallery
    Fig. 2.

    Locations of the AERONET stations used to optimize fire emissions in each GFED region. AERONET station locations are identified with red crosses; shaded gray regions are shown to visually denote the boundaries of each GFED region.

  • View in gallery
    Fig. 3.

    (left) Comparison of E3SM-simulated and MODIS-derived aerosol optical depth (AOD) in (a) Southern Hemisphere South America (SHSA), (b) Southern Hemisphere Africa (SHAF) and (c) equatorial Asia (EQAS) during 2000–16. (right) The means (lines) and one standard deviation ranges (shading) of monthly AOD for each region over the measurement record.

  • View in gallery
    Fig. 4.

    Fire contributions to annual mean atmospheric composition during 1997–2016: (a) primary organic matter (POM) burden (mg m−2), (b) secondary organic aerosol (SOA) burden (mg m−2), (c) black carbon (BC) burden (mg m−2), and (d) aerosol optical depth (AOD). These estimates were derived by averaging the difference between the E3SM optimized fire and no fire simulations.

  • View in gallery
    Fig. 5.

    Fire impacts on Earth’s radiation budget: (a) net shortwave aerosol direct effect (ADE) at the top of the atmosphere (W m−2), (b) net shortwave aerosol indirect effect at the top of atmosphere (W m−2), (c) net shortwave aerosol direct effect at the surface (W m−2), (d) net shortwave aerosol indirect effect (AIE) at the surface (W m−2), and (e) shortwave atmospheric absorption by fire aerosols in the atmosphere (W m−2).

  • View in gallery
    Fig. 6.

    Fire impacts on near-surface climate, including (a) downwelling solar radiation at the surface (W m−2), (b) surface air temperature (°C), (c) precipitation (mm day−1), (d) relative humidity (%), (e) surface wind speed (m s−1), and (f) the fraction of diffuse shortwave radiation (%). Differences are shown between the optimized fire and no-fire E3SM simulations, and for (d) and (f) we report the absolute percentage difference for relative humidity and diffuse radiation, respectively. Areas where fire-induced changes are significant are shown in Fig. S1. These fire impacts are estimated for the period 1997–2016.

  • View in gallery
    Fig. 7.

    The annual mean cycle of fire-induced changes in aerosol optical depth (AOD; unitless), downwelling shortwave radiation (Sin; W m−2), surface air temperature (TAS; °C), precipitation (PPT; mm day−1), relative humidity (RH; %), surface wind speed (U; m s−1), evapotranspiration (ET; mm day−1), and gross primary production (GPP; g C m−2 month−1) for selected regions in the southern Amazon, central Africa, and the Maritime Continent region of tropical Asia. The exact locations of these regions are shown in Fig. S1a. The gray-shaded area represents the peak fire season (i.e., August–October for the southern Amazon and Maritime Continent region, and July–September for central Africa). The annual cycle was created by averaging monthly differences between the optimized fire and no fire simulations, and correspond to the period during 1997–2016.

  • View in gallery
    Fig. 8.

    Conceptual diagram showing the influence of fire aerosols on surface relative humidity.

  • View in gallery
    Fig. 9.

    The influence of fire aerosols on annual mean gross primary productivity (GPP) during 1997–2016. (a) GPP from the optimized fire simulation (g C m−2 yr−1), (b) fire-induced changes in GPP from the difference between the optimized fire and no-fire simulations (g C m−2 yr−1), and (c) relative change in annual GPP derived from the information shown in (a) and (b) (%).

  • View in gallery
    Fig. 10.

    (a) Global distribution of fire impacts on GPP from (top) E3SM and (bottom) as derived from a multiple linear regression model forced by key driver variables across the tropics (i.e., within the latitude band of 23°S–23°N) with units of g C m−2 yr−1. (b) A scatterplot of fire-induced changes in GPP from E3SM and from the multiple linear regression model [Eq. (2)].

  • View in gallery
    Fig. 11.

    The relative contribution of different driver variables to fire-induced changes into GPP (%) including from changes in (a) surface air temperature, (b) relative humidity, (c) downwelling solar radiation, (d) diffuse light percentage, and (e) soil water.

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The Influence of Fire Aerosols on Surface Climate and Gross Primary Production in the Energy Exascale Earth System Model (E3SM)

Li XuaDepartment of Earth System Science, University of California, Irvine, California

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Qing ZhubEarth and Environment Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California

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William J. RileybEarth and Environment Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California

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Yang ChenaDepartment of Earth System Science, University of California, Irvine, California

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Hailong WangcPacific Northwest National Laboratory, Richland, Washington

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Po-Lun MacPacific Northwest National Laboratory, Richland, Washington

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James T. RandersonaDepartment of Earth System Science, University of California, Irvine, California

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Abstract

Fire-emitted aerosols play an important role in influencing Earth’s climate, directly by scattering and absorbing radiation and indirectly by influencing cloud microphysics. The quantification of fire–aerosol interactions, however, remains challenging and subject to uncertainties in emissions, plume parameterizations, and aerosol properties. Here we optimized fire-associated aerosol emissions in the Energy Exascale Earth System Model (E3SM) using the Global Fire Emissions Database (GFED) and AERONET aerosol optical depth (AOD) observations during 1997–2016. We distributed fire emissions vertically using smoke plume heights from Multiangle Imaging SpectroRadiometer (MISR) satellite observations. From the optimization, we estimate that global fires emit 45.5 Tg yr−1 of primary particulate organic matter and 3.9 Tg yr−1 of black carbon. We then performed two climate simulations with and without the optimized fire emissions. We find that fire aerosols significantly increase global AOD by 14% ± 7% and contribute to a reduction in net shortwave radiation at the surface (−2.3 ± 0.5 W m−2). Together, fire-induced direct and indirect aerosol effects cause annual mean global land surface air temperature to decrease by 0.17° ± 0.15°C, relative humidity to increase by 0.4% ± 0.3%, and diffuse light fraction to increase by 0.5% ± 0.3%. In response, GPP declines by 2.8 Pg C yr−1 as a result of large positive drivers (decreases in temperature and increases in humidity and diffuse light), nearly cancelling out large negative drivers (decreases in shortwave radiation and soil moisture). Our analysis highlights the importance of fire aerosols in modifying surface climate and photosynthesis across the tropics.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Li Xu, lxu16@uci.edu

Abstract

Fire-emitted aerosols play an important role in influencing Earth’s climate, directly by scattering and absorbing radiation and indirectly by influencing cloud microphysics. The quantification of fire–aerosol interactions, however, remains challenging and subject to uncertainties in emissions, plume parameterizations, and aerosol properties. Here we optimized fire-associated aerosol emissions in the Energy Exascale Earth System Model (E3SM) using the Global Fire Emissions Database (GFED) and AERONET aerosol optical depth (AOD) observations during 1997–2016. We distributed fire emissions vertically using smoke plume heights from Multiangle Imaging SpectroRadiometer (MISR) satellite observations. From the optimization, we estimate that global fires emit 45.5 Tg yr−1 of primary particulate organic matter and 3.9 Tg yr−1 of black carbon. We then performed two climate simulations with and without the optimized fire emissions. We find that fire aerosols significantly increase global AOD by 14% ± 7% and contribute to a reduction in net shortwave radiation at the surface (−2.3 ± 0.5 W m−2). Together, fire-induced direct and indirect aerosol effects cause annual mean global land surface air temperature to decrease by 0.17° ± 0.15°C, relative humidity to increase by 0.4% ± 0.3%, and diffuse light fraction to increase by 0.5% ± 0.3%. In response, GPP declines by 2.8 Pg C yr−1 as a result of large positive drivers (decreases in temperature and increases in humidity and diffuse light), nearly cancelling out large negative drivers (decreases in shortwave radiation and soil moisture). Our analysis highlights the importance of fire aerosols in modifying surface climate and photosynthesis across the tropics.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Li Xu, lxu16@uci.edu

1. Introduction

Wildfires play an important role in influencing terrestrial ecosystems through modulating the carbon cycle (Bond et al. 2005; Bowman et al. 2009; Arneth et al. 2010; van der Werf et al. 2017; Brando et al. 2020; Li 2020), redistributing nutrients (Chen et al. 2010; Mahowald 2011; Pellegrini et al. 2018), and modifying near-surface climate by means of biophysical impacts (Chambers et al. 2005; Li 2020) and aerosol emissions (Ward et al. 2012; Clark et al. 2015; Grandey et al. 2016; Jiang et al. 2016; Jiang et al. 2020). At a global scale, wildfires have burned about 500 Mha yr−1 over the past several decades with long-term negative trends in savanna ecosystems driven by agricultural expansion (Andela et al. 2017) and positive trends in many temperate and boreal ecosystems, which have been attributed, in part, to warming and drying during the summer fire season (Abatzoglou and Williams 2016; Whitman et al. 2019; Williams et al. 2019). To assess how these fire trends are influencing terrestrial surface climate and gross primary production (GPP) requires an improved understanding of contemporary fire impacts on aerosol optical depth, aerosol interactions with radiation and cloud microphysics, and the response of atmospheric dynamics and the land surface to these forcings.

The full impact of fire-emitted aerosols on the climate system depends on several processes, many of which are uncertain, or poorly constrained by observations. The total amount of fire carbon emissions is about 2.2 Pg C yr−1 (Ito and Penner 2005; van der Werf et al. 2017), yet these estimates may underestimate the full impact of fires on atmospheric composition because of the difficulty of satellites in measuring the location, size, and emissions from small fires (Randerson et al. 2012; Chuvieco et al. 2019; Roteta et al. 2019). Emission factors for biomass burning aerosols, including primary organic carbon (OC) and black carbon (BC), and secondary organic aerosol (SOA) precursor gases are also undersampled for many important fire types during representative environmental conditions. Recent observations of organic carbon aerosol emission factors from Indonesian peat fires (Stockwell et al. 2016; Jayarathne et al. 2018; Wiggins et al. 2018; Wooster et al. 2018), for example, are considerably higher than earlier measurements from laboratory studies, and these new measurements are not yet integrated into several global inventories. Bond et al. (2013), combining estimates of fire emissions with other aerosol sources, showed that wildfires globally contributed to more than one-third of black carbon emissions and about two-thirds of primary organic aerosol emissions. This attribution is consistent with a recent study showing that fires are the largest source of primary carbonaceous aerosol globally (Andreae 2019).

Once released into the atmosphere, fire aerosols influence climate by means of several different mechanisms, owing to their different chemical and optical properties. Primary-emitted organic matter, secondary organic aerosol, and sulfate are highly scattering aerosols, reflecting incoming solar radiation back into space and thereby cooling the surface (Penner et al. 1992; Penner et al. 1998; Ghan and Schwartz 2007). Black carbon, in contrast, absorbs solar radiation, simultaneously contributing to atmospheric heating and surface cooling. Black carbon deposition on snow and ice further contributes to warming by reducing surface albedo and accelerating melting (Flanner et al. 2007). Fire aerosols also indirectly affect climate by modifying cloud microphysical properties, serving as cloud condensation nuclei or ice nuclei and therefore changing cloud droplet sizes. These aerosol-induced cloud microphysical effects influence cloud albedo (Twomey 1974) and lifetime (Albrecht 1989). The direct effects at the top of atmosphere from fire-emitted organic and black carbon aerosols were found to largely cancel each other out at a global scale, leading to a net global mean forcing of 0.0 ± 0.2 W m−2 (Bond et al. 2013; Tosca et al. 2013; Jiang et al. 2016; Jiang et al. 2020).

Fire-emitted aerosols, through their direct radiative effects and interactions with cloud microphysics, influence near-surface climate including air temperature, precipitation, relative humidity, and wind speed. However, there are large uncertainties in previous estimations of fire aerosol effects on surface climate. Jones et al. (2007) reported that increasing fire aerosol emissions from the preindustrial period to the present has cooled global near-surface air temperatures by about 0.25°C. Tosca et al. (2013) found that contemporary fire aerosols reduced global mean surface air temperature by about 0.13°C and reduced precipitation over high biomass burning regions in Africa and South America. Fire aerosols, particularly fire-emitted BC, likely increase vertical stratification within the troposphere, which, in turn, may reduce convection and precipitation (Ackerman et al. 2000; Andreae and Rosenfeld 2008). In the southern Amazon, fire-emitted aerosols have been found to drive increases in stratification, enhance the dry season circulation pattern, and lengthen the dry season (Zhang et al. 2009). Hodnebrog et al. (2016) found that a main driver of the observed decline in dry season precipitation in Southern Hemisphere Africa originated from a positive trend in fire emissions. Baro et al. (2017) reported that the presence of fire aerosols over Russia reduced surface winds and surface air temperature during individual fire events, leading to the development of a more stable planetary boundary layer. Fire aerosols suppress the cloud formation in Amazon forest when absorbing aerosol such as black carbon is embedded in the cloud layer (Feingold et al. 2005; Koren et al. 2005) and have an influence on subsequent precipitation. Fire aerosols have potential to change near-surface relative humidity through their influence on surface air temperature, evapotranspiration, and boundary layer heights. Relative humidity near the surface may increase by as much as 10% in response to high concentrations of scattering aerosols or decrease in response to absorbing aerosols (Yu et al. 2002). Atmospheric aerosols, including fire aerosols, not only reduce the downward shortwave radiation at surface but also enhance the diffuse fraction (Mahowald 2011).

Through their effects on near-surface climate, fire aerosols influence terrestrial ecosystem gross primary production (GPP) (Li et al. 2014; Yue and Unger 2018; Li 2020; Zou et al. 2020), which is the largest land carbon flux. The impact of aerosols on GPP could be either positive or negative, depending upon the magnitude of aerosol loading and its impact on surface temperature, diffuse radiation fraction, and other meteorological variables (Gu et al. 2002; Mercado et al. 2009; Doughty et al. 2010; Rap et al. 2015; Keppel-Aleks and Washenfelder 2016; Yue et al. 2017; Yue and Unger 2018; Li 2020). Using tower observations, Doughty et al. (2010) found that smoke aerosols increase net ecosystem carbon uptake in an Amazon rain forest primarily by diffusing more light and partly due to lower canopy temperatures. Oliveira et al. (2007) also reported increases in forest productivity under a moderately thick smoke loading during the dry season over the Amazon basin. Chen and Zhuang (2014) found that atmospheric aerosols, including aerosols from fires, enhanced global GPP by 4.9 Pg C yr−1, primarily as a consequence of increases in the fraction of diffuse light. Yue and Unger (2018) found that fire aerosols contributed to global GPP increases by 0.05 ± 0.30 Pg C yr−1, when fire impacts on diffuse light and surface ozone concentrations were considered.

Here we use the Energy Exascale Earth System Model (E3SM) (Golaz et al. 2019) to explore the radiative effects of fire aerosols and their influence on near-surface climate and gross primary production. First, we optimized fire-associated aerosol emissions from the Global Fire Emissions Database version 4 (GFED4s) by constraining E3SM modeled aerosol optical depths with observations from the Aerosol Robotic Network (AERONET) (Giles et al. 2019). We then derived vertical profiles of smoke plume mass fraction from Multiangle Imaging SpectroRadiometer (MISR) plume-height observations (val Martin et al. 2018; Zhu et al. 2018), and distributed the fire emitted aerosols to different model layers accordingly. With the optimized emissions and injection vertical profiles, we performed two global simulations using E3SM with and without fire emissions. The data and methods are described in detail in section 2, followed by the results given in section 3. Discussions are presented in section 4 and the conclusions are summarized in section 5.

2. Data and methods

a. Original GFED4s fire emissions

We used primary carbonaceous (i.e., black carbon and organic matter) and sulfate aerosol emissions and precursor gas emissions for secondary organic aerosols and SO2 from the Global Fire Emissions Database, version 4, including small fires (GFED4s) (van der Werf et al. 2017), as a set of a priori emissions in E3SM for our aerosol optical depth (AOD) optimization. The GFED4s emissions inventory covered the period from 1997 to 2016 at 0.25° spatial resolution and a monthly time resolution. GFED4s computes carbon emissions from the MODIS burned area (Giglio et al. 2018), with adjustments for small fires (Randerson et al. 2012), along with model estimates of fuel consumption from below- and aboveground biomass along with surface litter pools. Pool-specific estimates of combustion completeness vary as a function of fuel type and time-evolving environmental conditions. GFED4s uses separate emission factors for six biomes (i.e., savannas and grassland, boreal forest, temperate forest, tropical deforestation, peatland, and agriculture), drawing upon estimates from the synthesis by Akagi et al. (2011) and several additional studies.

Total fire emissions (averaged over 1997–2016) of primary organic matter (POM), secondary organic aerosol precursor gases (SOAG), black carbon (BC), and SO2 in the globe and in 14 GFED regions are reported in Table 1. At a global scale, mean emissions during this time period were 21.8 Tg yr−1 for POM, 5.4 Tg yr−1 for SOAG, 1.8 Tg yr−1 for BC, and 2.2 Tg yr−1 for SO2. Following the method described for the MOZART-4 model by Emmons et al. (2010), we derived monthly secondary organic aerosol precursor gases (SOAG) from fire-emitted volatile organic compounds (VOCs), which includes isoprene, terpenes, and three lumped species using the GFED4s emission factors. The lumped species represent alkanes and alkenes with four or more carbon atoms, which are respectively referred to as BIGALK and BIGENE, and aromatic compounds, which are referred to as TOLUENE. The SOA mass yields from the VOCs were assumed to be 6.0% for isoprene, 37.5% for terpenes, 7.5% for BIGALK, 7.5% for BIGENE, and 22.5% for TOLUENE, similar to the CAM5-MAM3 model described by Liu et al. (2012). We assumed primary sulfate aerosol emissions were in the form of ammonium bisulfate with total mass estimated as 2.5% of fire emissions of SO2 gas (Liu et al. 2012).

Table 1.

Fire emissions scaling factors in each GFED region averaged over 1997–2016. Original and optimized fire emissions of primary organic matter (POM), secondary organic aerosol precursor gas (SOAG), black carbon (BC), and gaseous SO2 are also shown. The global value in the column of the fire emission scalar is the average of all the regional values; other columns show the emission sums across regions. See Fig. 2 for the expansion and location of each region.

Table 1.

b. MISR-derived constraints on the vertical distribution of fire aerosol injection

We derived separate vertical profiles for the injection of fire aerosol mass for each of 14 continental-scale regions using an scheme developed by Zhu et al. (2018) from MISR observations (Val Martin et al. 2018). The original injection fractions have a 2.0° × 2.5° spatial resolution, a monthly input frequency, and a 29-layer vertical resolution; these data can be accessed through the supplementary material provided by Zhu et al. (2018). To create a climatology for E3SM (and reduce noise during low-fire periods), we developed a mean injection profile during the 3-month peak fire season for each of the 14 continental regions that are same as those used in the GFED4s inventory (van der Werf et al. 2017). The fire peak season was determined by identifying the month of maximum emissions from the 20-yr time series of GFED4s carbon emissions during 1997–2016. Figure 1 shows examples of the MISR-derived vertical profiles for three major tropical biomass burning regions during their respective fire peak seasons. Aerosol injection extends from surface through a height of 6 km in South America and South Africa and is lower in equatorial Asia. Compared with the default injection profiles in E3SM (Dentener et al. 2006), the MISR-derived parameterizations inject more fire-emitted aerosol above the boundary layer.

Fig. 1.
Fig. 1.

The vertical profile of aerosol plume mass injection percent (%) in (a) Southern Hemisphere South America (SHSA), (b) Southern Hemisphere Africa (SHAF), and (c) equatorial Asia (EQAS) during their respective peak fire seasons. Peak fire season occurs in SHSA during August–October, in SHAF during July–September, and in EQAS during August–October.

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

c. Energy Exascale Earth System Model

We used the Energy Exascale Earth System Model version 1 (E3SMv1) (Golaz et al. 2019) to simulate fire aerosol processes and their influence on near-surface climate and gross primary production in land with and without landscape fire emissions. We configured E3SM following the Atmospheric Model Intercomparison Project (AMIP) project (Gates et al. 1999), where the evolution of the atmosphere and land are simulated by the E3SM Atmosphere Model (EAM) version 1 (EAMv1) (Rasch et al. 2019) and the E3SM Land Model (ELM) version 1 (ELMv1, based on the Community Land Model version 4.5 with new features in representing land biogeochemistry) (Zhu and Riley 2015; Zhu et al. 2019; Burrows et al. 2020).

For our analysis, we used a configuration of E3SM that has about a 1° × 1° horizontal resolution and 72 vertical layers extending from Earth’s surface to about 0.1 hPa. The E3SM atmospheric model uses a spectral element dynamical core (Taylor and Fournier 2010; Dennis et al. 2012) on a cubed-sphere computation grid. The aerosol module used in E3SM is an updated four-mode version of the modal aerosol module (MAM4) (Liu et al. 2016) with improved treatment of secondary organic aerosols (Shrivastava et al. 2015; Lou et al. 2020), sea spray aerosols (Burrows et al. 2014), and scavenging, transport, microphysics, and numerics as summarized in Wang et al. (2020).

We performed two sets of E3SM simulations to examine the impacts of fire aerosols on surface climate and GPP (Table 2). First, we optimized fire aerosol emissions in E3SM by conducting two initial simulations with and without GFED4s fire emissions during 1997–2016 (denoted as fire and no fire simulations, respectively) and comparing model estimates of AOD with observations from AERONET (see section 2d). Second, we assessed fire impacts on climate with two longer 100-yr simulations, respectively with and without the optimized fire emissions from the previous step.

Table 2.

Summary of simulation experiments in this study.

Table 2.

In all simulations we used the same nonfire aerosol sources, including monthly anthropogenic emissions of primary organic matter, black carbon, sulfate, and SO2 using the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) emission inventory for the year 2005 (Lamarque et al. 2010) and biogenic and fossil fuel sources of secondary organic aerosol precursor gases for the year 2000 (Shrivastava et al. 2015; Lou et al. 2020). The fire and nonfire aerosol emissions are prescribed as monthly means at each model grid cell and are linearly interpolated in time between the centers of each month. Global mean emissions of primary organic matter, secondary organic aerosol precursor gases, black carbon, and SO2 gas during 1997–2016 from fire and nonfire sources used in this work are reported in Table S1 in the online supplemental material. Emissions of dust and sea salt aerosols were calculated online as a function of wind speed and temperature. Greenhouse gas concentrations were held constant at levels from 2006. Sea surface temperatures (SSTs) were prescribed as the ocean surface boundary condition in all of the simulations using the monthly mean Hadley Center sea surface temperature dataset merged with version 2 of the National Oceanic and Atmospheric Administration (NOAA) weekly optimum interpolation sea surface temperature analysis (Hurrell et al. 2008). The SSTs varied monthly over the 1997–2016 period to synchronously match the SST-induced interannual variations in atmospheric dynamics with observed variability in fire emissions from the GFED inventory.

The optimized fire simulation consisted of five repeating, consecutive cycles of optimized fire emissions coinciding with five repeating cycles of time-varying SSTs spanning 1997–2016. A second no-fire simulation was identical in all respects with respect to forcing and initial conditions but had fire emissions set equal to zero. We removed the first 20 years from the optimized fire and no-fire simulations to remove spinup effects on surface hydrology and biogeochemistry and report differences between the remaining 80 years of each simulation.

d. Optimizing fire emissions

E3SM estimates of AOD derived from the original GFED4s emissions inventory exhibited a low bias, particularly in high biomass burning regions during the peak fire season. This low bias is a well-documented phenomenon that is not unique to E3SM or GFED4s (Pan et al. 2020). Several factors likely contribute to the low bias in the bottom-up inventory, including an underestimation of emissions from small, short-lived fires and the use of emission factors that may underestimate POM emissions from smoldering combustion phases (Stockwell et al. 2016; Jayarathne et al. 2018; Wiggins et al. 2018).

To develop a more realistic representation of fire aerosol impacts on surface climate and GPP, we performed a simple optimization of emissions in each GFED region to bring the model AOD in better agreement with the AERONET measurements (version 3; https://aeronet.gsfc.nasa.gov/), drawing upon the approach first described by Johnston et al. (2012). This optimization step was performed after implementing the MISR injection height parameterization described in section 2b. Using monthly time series of AOD from the no fire and original fire simulations during 1997–2016, we solved the following regression equation for α and β parameters at each AERONET site:
AODAERONET=β×AODNoFire+α×(AODFireAODNoFire),
where AODNoFire is the E3SM AOD time series from the no-fire simulation and AODFire is the fire simulation with the original GFED4s emissions (and all other forcing agents). In each GFED region, we selected AERONET sites in which fire aerosols were an important driver of AOD variability. These sites were identified as sites where Eq. (1) significantly improved the model fit over a model forced solely with emissions from the no-fire simulation (with significance defined as a p value less than 0.05 reported from an ANOVA test). For developing regional estimates of α, we further restricted our analysis to AERONET sites where at least 60% of the data variance was explained by the E3SM fire simulation. The locations of AERONET sites in each GFED region that met this criterion are shown in Fig. 2, and the number of sites and months of observations in each region are reported in Table S2. We then used the mean value of α computed across the different AERONET stations within a GFED region to adjust the fire emissions for the optimized fire simulation. We required a minimum of five AERONET stations in each region (each meeting the requirement described above) to adjust the regional fire emissions. Central America (CEAM), Northern Hemisphere South America (NHSA), and Northern Hemisphere Africa (NHAF) did not meet this requirement. For these regions we applied the emission optimization factor from a nearby region with a similar composition of plant functional types. Specifically, we applied the optimization factor from Southern Hemisphere South America (SHSA) to NHSA and CEAM, and the optimization factor from Southern Hemisphere Africa (SHAF) to NHAF.
Fig. 2.
Fig. 2.

Locations of the AERONET stations used to optimize fire emissions in each GFED region. AERONET station locations are identified with red crosses; shaded gray regions are shown to visually denote the boundaries of each GFED region.

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

In important tropical biomass burning regions with good AERONET coverage, GFED4s consistently underestimated AOD (Table 1). For SHSA, SHAF, equatorial Asia (EQAS), and Australia (AUST), the mean optimization factors were 2.79, 2.46, 2.94, and 1.80, respectively. Globally, optimized aerosol emissions from fire increased by about a factor of 2, from 21.8 to 45.5 Tg yr−1 for POM, from 5.4 to 10.6 Tg yr−1 for SOAG, from 1.8 to 3.9 Tg yr−1 for BC, and from 2.2 to 4.6 Tg yr−1 for SO2.

As an independent assessment of the optimization, we compared AOD simulated with the optimized emissions in E3SM estimates from the monthly level-3 MODIS product (collection 6.1; https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD08_M3) (Sayer et al. 2014). Optimization of aerosol emissions using AERONET yielded considerable improvement in model performance when compared with independent MODIS observations for SHSA, SHAF, and EQAS (Fig. 3), although in all three regions the optimized fire simulation shows a low bias at the end of the fire season. At the global scale, the AERONET optimization improved agreement with MODIS observations [diagnosed using root-mean-square error (RMSE)] in 10 out of 14 continental regions. The mean improvement in RMSE at a global scale, including ocean regions, was about 8%.

Fig. 3.
Fig. 3.

(left) Comparison of E3SM-simulated and MODIS-derived aerosol optical depth (AOD) in (a) Southern Hemisphere South America (SHSA), (b) Southern Hemisphere Africa (SHAF) and (c) equatorial Asia (EQAS) during 2000–16. (right) The means (lines) and one standard deviation ranges (shading) of monthly AOD for each region over the measurement record.

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

e. Impacts of fire on GPP

GPP, defined as the integral of carbon assimilated during photosynthesis by all plants (and canopy layers) within an ecosystem per unit of ground surface area per unit time (Joiner et al. 2018; Badgley et al. 2019), is a key flux regulating the strength of carbon-concentration and carbon-climate feedbacks (Arora et al. 2020). In E3SM, fire aerosols influence GPP through multiple pathways, including by modifying solar radiation, diffuse light, surface air temperature, relative humidity, wind speed, and precipitation. To understand how the spatial pattern of GPP in E3SM responds to fire-induced changes in near-surface climate, we use a multivariate linear regression model to assess the importance of different climate driver variables. In our regression model we initially considered more than 10 climate variables and found that not all of the variables contributed to explaining the spatial pattern of fire-induced changes in GPP. We then refined the regression model by removing one variable at a time until all the selected variables in the final regression model were statistically significant (with a confidence interval of 95%). The final regression model was found to be a function of fire-induced anomalies in surface air temperature, relative humidity, downward shortwave radiation at surface, diffuse radiation fraction, and surface soil moisture. The goal of the regression was to quantify in a simple way how much of the variance in the spatial pattern of annual mean GPP change (∆GPP) due to fires can be explained by fire-induced annual mean changes in surface climate variables such as surface air temperature (∆Ts), relative humidity (∆RH), downwelling solar radiation (∆Sin), diffuse radiation fraction (∆DRF), and surface soil water content (∆SWCs) across tropical regions between 23°S and 23°N. The multivariate linear regression model is described by
ΔGPP=aΔTs(x)+bΔRH(x)+cΔSin(x)+dΔDRF(x)+eΔSWCs(x)+ε
where x refers to a land grid cell located within the tropical regions between 23°S and 23°N. Then, we used the regression model to quantify the importance of different driver variables to the spatial pattern of the GPP response. To do this, we isolated each term in the model by holding the other climate variable changes constant (at zero) in a stepwise analysis. Using annual mean GPP maps from E3SM, we computed the GPP contribution of driver variables listed in Eq. (2) and attempted to separate the influence of each of these individual climate drivers using a spatial linear regression analysis.

3. Results

a. Spatial distribution of fire aerosol concentrations and radiative effects

Fire contributions to annual mean aerosol column mass integrals and AOD are highest over tropical Africa, the southern Amazon, and Indonesia (Fig. 4). The largest column burdens for fire-emitted POM and BC occur in southern Africa (Figs. 4a,c), whereas the SOA burden shows a maximum over equatorial Asia (Fig. 4b). Fire aerosols significantly impact AOD widely across the tropics between 23°S and 23°N with annual mean levels exceeding 0.2 in central and southern Africa (Fig. 4d). During the fire season, mean AOD exceeds 0.4 on all three tropical continents, and during high fire years in South America and equatorial Asia, AOD often exceeds a value of 1 (Fig. 3), indicating that more than 63% (>1 − e−1) of visible light is either scattered or absorbed before reaching the surface. These high AOD values are consistent with past work showing that El Niño triggers a predictable cascade of fire activity across different tropical continents (Chen et al. 2017) and measurements from field campaigns in high biomass burning regions (Haywood et al. 2003; Eck et al. 2013). Our model simulations show that, globally, fires account for 84% ± 23% of the total POM burden, 12% ± 4% of the total SOA burden, and 65% ± 10% of the total BC burden (Table 3). These estimates are broadly consistent with fire contributions to their respective global emissions budget (Table S1). All together, these fire aerosols contribute to an enhancement of global mean AOD by 14% ± 7%.

Fig. 4.
Fig. 4.

Fire contributions to annual mean atmospheric composition during 1997–2016: (a) primary organic matter (POM) burden (mg m−2), (b) secondary organic aerosol (SOA) burden (mg m−2), (c) black carbon (BC) burden (mg m−2), and (d) aerosol optical depth (AOD). These estimates were derived by averaging the difference between the E3SM optimized fire and no fire simulations.

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

Table 3.

Summary of the global atmospheric composition and radiation response to fire aerosols using optimized GFED4s emissions during 1997–2016. The value after the ± sign represents one standard deviation, which was computed using four consecutive 20-yr cycles from E3SM.

Table 3.

The direct and indirect radiative effects of carbonaceous aerosols are complicated. Primary organic matter and secondary organic aerosols from fires are mainly scattering aerosols, increasing the reflectance of shortwave radiation at the top of the atmosphere and reducing downward shortwave radiation flux at the surface. Black carbon from fires, in contrast, contributes mostly to atmospheric heating, yet may also reduce solar radiation at the surface by absorbing and scattering solar radiation in the atmosphere. Figure 5 shows the spatial distribution of fire-induced aerosol direct and indirect effects at the top of the atmosphere and at the surface and the pattern of atmospheric absorption. The global average net fire aerosol direct effect at the surface is about −1.5 ± 0.3 W m−2 (Fig. 5c) as a consequence of aerosol scattering and absorption throughout the troposphere and stratosphere. We note that the fire aerosol direct and indirect effects reported in this study refer to the instantaneous radiative flux change in net solar radiation resulting from the presence of fire aerosols in the atmosphere during the contemporary era, similar to aerosol direct effect as clarified in Heald et al. (2014). The fire aerosol indirect effect, from the influence of fire aerosols on cloud properties, contributes to a decrease in surface radiation (−1.0 ± 0.3 W m−2), with the strongest regional impact collocated with the influence of the direct effect in central and western Africa (Fig. 5d). Atmospheric absorption from fire-emitted aerosols (mainly BC) is about 1.7 ± 0.4 W m−2 (Fig. 5e). At a global scale, the combined direct and indirect fire aerosol effects reduce net shortwave radiation at the surface by −2.3 ± 0.5 W m−2 (Table 3). At the surface fire aerosols have a small positive contribution of about 0.15 ± 0.12 W m−2 through their influence on surface albedo (Table 3). The net aerosol direct effect at TOA contributes to a small positive global forcing (0.25 ± 0.09 W m−2), particularly over the ocean off the west coasts of Africa and South America (Fig. 5a). In these regions, carbonaceous aerosol layers are lofted above marine stratocumulus cloud decks that have a high albedo, maximizing the effect of aerosol absorption. Over land, cloud cover is lower and carbonaceous aerosol layers more frequently occur within or below clouds. Thus, the scattering and atmospheric absorption effects of fire aerosols often cancel each other out, leading to a near-zero net direct aerosol effect at the top of the atmosphere above many land areas. These findings are consistent with previous work (Sakaeda et al. 2011; Tesfaye et al. 2014). The indirect effect of fire aerosols contributes to a negative shortwave forcing at the top of the atmosphere (−1.0 ± 0.3 W m−2) that is about a factor of 4 larger in magnitude than the aerosol direct effect at the top of the atmosphere. The aerosol indirect effect has the strongest regional impacts located off the west coasts of Africa and South America, where marine stratocumulus clouds are persistent (Fig. 5b).

Fig. 5.
Fig. 5.

Fire impacts on Earth’s radiation budget: (a) net shortwave aerosol direct effect (ADE) at the top of the atmosphere (W m−2), (b) net shortwave aerosol indirect effect at the top of atmosphere (W m−2), (c) net shortwave aerosol direct effect at the surface (W m−2), (d) net shortwave aerosol indirect effect (AIE) at the surface (W m−2), and (e) shortwave atmospheric absorption by fire aerosols in the atmosphere (W m−2).

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

b. The influence of fire aerosols on surface climate over land

The combination of direct and indirect aerosol effects from fires reduces downwelling solar radiation at surface, with a global mean decrease over land of 3.1 ± 0.7 W m−2 (Table 4) and regional declines exceeding 30 W m−2 in central Africa (Fig. 6). The reductions in surface downward shortwave radiation, combined with atmospheric heating, contribute to a cascade of changes in surface climate. Over the global land surface, fire aerosols cause surface air temperature to significantly decrease by 0.17° ± 0.15°C, surface relative humidity to significantly increase by 0.4% ± 0.3%, and the diffuse shortwave fraction to significantly increase by 0.5% ± 0.3% (Table 4), with stronger impacts where and when biomass burning emissions are high (Figs. 6 and 7; see also Figs. S1 and S2 in the online supplemental material). The highest regional increase of the fire-induced diffuse radiation is about 2% in Southern Hemisphere Africa (Table 5). Significant decreases in wind speed and planetary boundary layer height also occur globally and in many high biomass burning regions (Table 4, Fig. S1). The precipitation response over land is more variable, although significant regional decreases occur across the Amazon, Northern Hemisphere South America, and Central America (Fig. 6c; see also Fig. S1). The fire-induced decreases in precipitation are spatially coincident with a positive aerosol indirect effect at the top of the atmosphere and at the surface (Figs. 5b,d), which is in accordance with the reduction in cloud fractions and thereby cloud albedo over these regions. The largest regional reduction of precipitation is about 0.2 mm day−1 in the Northern Hemisphere South America (Table 5).

Table 4.

Summary of the simulated land climate variables response to fire aerosols averaged during 1997–2016. The value after the ± sign represents one standard deviation, which was computed using four consecutive 20-yr cycles from E3SM. (Note that the quantities represent the area-weighted global mean over land areas from 60°S to 80°N and thus exclude Antarctica and parts of northern Greenland.)

Table 4.
Fig. 6.
Fig. 6.

Fire impacts on near-surface climate, including (a) downwelling solar radiation at the surface (W m−2), (b) surface air temperature (°C), (c) precipitation (mm day−1), (d) relative humidity (%), (e) surface wind speed (m s−1), and (f) the fraction of diffuse shortwave radiation (%). Differences are shown between the optimized fire and no-fire E3SM simulations, and for (d) and (f) we report the absolute percentage difference for relative humidity and diffuse radiation, respectively. Areas where fire-induced changes are significant are shown in Fig. S1. These fire impacts are estimated for the period 1997–2016.

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

Fig. 7.
Fig. 7.

The annual mean cycle of fire-induced changes in aerosol optical depth (AOD; unitless), downwelling shortwave radiation (Sin; W m−2), surface air temperature (TAS; °C), precipitation (PPT; mm day−1), relative humidity (RH; %), surface wind speed (U; m s−1), evapotranspiration (ET; mm day−1), and gross primary production (GPP; g C m−2 month−1) for selected regions in the southern Amazon, central Africa, and the Maritime Continent region of tropical Asia. The exact locations of these regions are shown in Fig. S1a. The gray-shaded area represents the peak fire season (i.e., August–October for the southern Amazon and Maritime Continent region, and July–September for central Africa). The annual cycle was created by averaging monthly differences between the optimized fire and no fire simulations, and correspond to the period during 1997–2016.

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

Table 5.

Summary of the simulated regional average climate variable response to fire aerosols. ∆AOD, ∆TAS, ∆PPT, ∆Sin, and ∆Diffuse radiation denote changes in aerosol optical depth, surface air temperature, precipitation, downward solar radiation, and diffuse radiation percentage, respectively. The last two columns represent the total GPP changes and respective percentage changes in each region. See Fig. 2 for the expansion and location of each region.

Table 5.

At least two separate mechanisms may contribute to fire-induced increases in surface relative humidity (Fig. 8). First, fire aerosols reduce solar radiation, causing a significant decline in surface air temperature and thus saturation water vapor pressure. For the same specific humidity, this will have the effect of raising relative humidity and is illustrated in the top pathway shown in Fig. 8. At the same time, the decreases in surface shortwave radiation reduce sensible heat fluxes. Decreases in turbulent sensible heat fluxes reduce wind speed (Fig. 6e), planetary boundary layer (PBL) turbulence and the PBL height (Fig. S1, Table 4). Lower planetary boundary layer heights, in turn, are likely to reduce entrainment of dry air from the free troposphere, thus increasing surface specific humidity in fire-affected regions (Fig. S1). The decreases in surface turbulence also contribute to a decrease in surface wind speeds (Table 4, Fig. 6e). Decreases in surface wind speeds may also increase the residence times of air near the surface over continental interior regions, allowing for the air packet to pick up more water vapor content from evapotranspiration (ET), even though the ET flux declines in some regions (Fig. 7). This second mechanism is illustrated with the bottom pathway shown in Fig. 8. Together, both the decrease in saturation water vapor and increase in water vapor content likely contribute to the increase in relative humidity caused by fire aerosols, particularly over high biomass burning regions in Africa and South America. The increases in relative humidity caused by fire aerosols are important because, as described below, they have a strong positive effect on GPP. Also, during the fire season, they may contribute to negative feedbacks to additional fire emissions through their impact on fuel moisture levels.

Fig. 8.
Fig. 8.

Conceptual diagram showing the influence of fire aerosols on surface relative humidity.

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

c. Effects of fire aerosols on GPP

The influence of fire aerosols on the spatial pattern of GPP is highly heterogeneous in spaces, with decreases across many areas in Northern Hemisphere Africa, the northeastern coast of South America, and the Maritime Continent islands of tropical Asia (Fig. 7) and increases across areas in southern Africa, the southern Amazon, and central North America (Fig. 9). At a global scale, areas with positive and negative fire aerosol impacts largely cancel each other, yielding a small net decline in GPP of 2.8 ± 1.5 Pg C yr−1 (Table 4). This decline represents a 2.4% reduction in global GPP, which is estimated by E3SM to be 117 Pg C yr−1 (Table 4). At a regional scale, the fire-associated GPP decreases in almost all GFED regions with the greatest reduction (>1 Pg C yr−1 or 8%) in Northern Hemisphere Africa and a moderate reduction (about 3%–5%) in Central America, Northern Hemisphere South America, and Southern Hemisphere Africa (Table 5).

Fig. 9.
Fig. 9.

The influence of fire aerosols on annual mean gross primary productivity (GPP) during 1997–2016. (a) GPP from the optimized fire simulation (g C m−2 yr−1), (b) fire-induced changes in GPP from the difference between the optimized fire and no-fire simulations (g C m−2 yr−1), and (c) relative change in annual GPP derived from the information shown in (a) and (b) (%).

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

The complex spatial pattern of the GPP response shown in Fig. 9 is a consequence of varying fire aerosol impacts on key environmental controls that regulate photosynthesis, including available light, temperature, relative humidity, diffuse light fraction, and moisture availability (Collatz et al. 1991). To better understand the interplay between these drivers in different regions, we examined the seasonal pattern of these drivers for three regions with substantially different annual mean GPP responses (Fig. 7). In the southern Amazon, which has a small positive GPP response, fire aerosols cause high levels of surface cooling and a large positive humidity response, but only moderate declines in solar radiation. In contrast, in central Africa, which has a large negative GPP response, fire aerosols contribute to moderate levels of surface cooling, but large declines in solar radiation. For Maritime Continent islands in tropical Asia, a small negative GPP response is associated with low levels of fire-induced cooling and a weak humidity response, likely as a consequence of buffering effects on climate from nearby ocean areas. In tropical Asia, fire impacts on near-surface climate are concentrated during relatively brief but intense intervals associated with strong El Niño events (Fig. S2).

With the set of five variables given in Eq. (2), the linear regression model is able to explain about 61% of the spatial variance of GPP response to fires across the tropics, and is able to capture many of the features visible in E3SM (Fig. 10a). When analyzing the importance of different driver variables in shaping the spatial pattern of the GPP response, we found that downwelling shortwave radiation at the surface is the single most important variable in driving the GPP response to fire aerosols, which contributes to a decrease of about 9% in tropical GPP (Table 6). For every 1 W m−2 decline in downwelling shortwave radiation at the surface from fire aerosol effects, tropical GPP decreases by about 23 g C m−2 yr−1. Decreases in surface soil moisture contribute an additional 0.3% decline in tropical GPP with a heterogeneous spatial pattern (Fig. 11) that reflects regional differences in fire aerosol impacts on the balance between precipitation and evapotranspiration.

Fig. 10.
Fig. 10.

(a) Global distribution of fire impacts on GPP from (top) E3SM and (bottom) as derived from a multiple linear regression model forced by key driver variables across the tropics (i.e., within the latitude band of 23°S–23°N) with units of g C m−2 yr−1. (b) A scatterplot of fire-induced changes in GPP from E3SM and from the multiple linear regression model [Eq. (2)].

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

Table 6.

The importance of climate variables contributing to fire-induced changes in gross primary production in tropics.

Table 6.
Fig. 11.
Fig. 11.

The relative contribution of different driver variables to fire-induced changes into GPP (%) including from changes in (a) surface air temperature, (b) relative humidity, (c) downwelling solar radiation, (d) diffuse light percentage, and (e) soil water.

Citation: Journal of Climate 34, 17; 10.1175/JCLI-D-21-0193.1

The negative impacts of fire aerosols on GPP associated with downward shortwave radiation and soil moisture are largely offset by other mechanisms leading to positive responses in near-surface climate. For example, fire-induced increases in relative humidity cause tropical GPP to increase by 5.6%; at the level of an individual grid cell, a 1% increase in the absolute value of relative humidity causes tropical GPP to increase by 149 g C m−2 yr−1. This relationship likely emerges as a consequence of relative humidity impacts on stomatal conductance and water use efficiency in ELM. Fire-induced surface cooling further enhances tropical GPP through a set of separate but related mechanisms. Notably, lower surface air temperature increase the carboxylation efficiency of the rubisco enzyme relative to its oxygenation function and further contribute to improvements in water use efficiency (Collatz et al. 1991). The net effect of these enhancements, as revealed by the regression model, leads to a GPP gain of about 170 g C m−2 yr−1 for every 1°C decrease in annual mean surface air temperature. Across the tropics as a whole, the fire-induced cooling effect causes a 1.5% increase in GPP.

Increases in diffuse light are known to reduce canopy shading of interior leaves, yielding higher light use efficiencies and high levels of canopy GPP following volcanic eruptions and downwind of cities (Farquhar and Roderick 2003; Gu et al. 2003). In ELM this effect is captured through explicit representation of shaded and sunlit leaves. In other words, an increase of diffuse radiation could enhance the sunlit fraction of total leaf area (and simultaneously reduce the shaded leaf area) and thus lead to higher GPP. Fire aerosol-driven increases in the fraction of diffuse radiation in E3SM cause GPP to increase across the tropics by 0.7%. For every 1% increase in diffuse radiation, GPP is predicted to increase locally by 9.6 g C m−2 yr−1.

In summary, the positive effects of fire-induced changes in relative humidity, surface air temperature, and diffuse radiation fraction on GPP nearly cancel out the negative effects of changing solar radiation and soil moisture across the tropics as whole (Table 6). This analysis highlights the complexity of the GPP response and the importance of using a fully coupled atmosphere–land model to explore these interactions. Figure 11 illustrates the relative contribution of different climate driver variables to the fire-induced changes into GPP (%), considering the product of the magnitude of the near-surface climate change and scalar regression coefficient relative to the annual mean E3SM simulated GPP from the optimized fire simulation in the tropics (shown in Fig. 9a). This analysis reveals that both the positive and negative drivers of the fire aerosol–induced GPP response are strongest over central Africa (Fig. S2).

4. Discussion

a. Fire aerosol radiative effects and impacts on surface climate

Previous modeling studies have assessed the impacts of fire aerosols on Earth’s radiation budget (Ward et al. 2012; Tosca et al. 2013; Heald et al. 2014; Veira et al. 2015; Grandey et al. 2016; Jiang et al. 2016; Jiang et al. 2020; Zou et al. 2020) and their subsequent influence on surface climate, including surface air temperature and precipitation (Tosca et al. 2013; Jiang et al. 2016; Jiang et al. 2020; Zou et al. 2020). A comparison of our estimates with reports from previous modeling studies is shown in Table 7. For the aerosol direct effect at the top of the atmosphere, many previous estimates are nearly zero, reflecting nearly equal contributions (in magnitude) of radiation absorption by black carbon and aerosol scattering and reflection by organic aerosols. These estimates vary between −0.2 W m−2 reported by Veira et al. (2015) and 0.25 W m−2 reported in this study. Five out of nine modeling studies using (or tracing heritage to) the Community Atmosphere Model show a small positive aerosol direct effect at top of the atmosphere, whereas four other atmospheric models show a small negative effect. Some of the variation among models may stem from differences in the model treatments of aerosol transport and removal processes that largely impact the spatial distribution of carbonaceous aerosols (Wang et al. 2013), and parameterizations describing the mixing state, absorptivity, and hygroscopicity of black carbon and organic matter. Using a coupling Earth system model, Veira et al. (2015) show that the fire aerosol direct radiative effect at top of atmosphere has relatively low sensitivity to emissions injection height and conclude that differences between models instead are caused by differences in the atmospheric model treatment of carbonaceous aerosols. In this context, important optical and physical parameterizations for black carbon and organic aerosols include refractive indices, water uptake parameters, aging processes, size distributions, and mixing processes, as well as the relative amounts of black and organic carbon in fire emissions. The varying degrees of the absorptivity of fire aerosols were also identified in Brown et al. (2021). At the surface, there is consensus among models that the aerosol direct effect from wildfires reduces net shortwave radiation, with an effect that varies between −1.3 and −1.6 W m−2 across models. Studies that report fire aerosol indirect effects find a stronger negative forcing at the top of the atmosphere, and our estimate of −0.98 W m−2 falls within the −0.7 to −1.6 W m−2 range of previous estimates.

Table 7.

Comparison of the simulated global fire aerosol radiative effects (ADE: aerosol direct effect; AIE: aerosol indirect effect) and surface climate response to fire aerosols with previous studies.

Table 7.

With decreases in surface shortwave radiation from both direct and indirect effects, fire aerosols contribute to surface cooling and a slowdown of the hydrological cycle on a global basis. Our estimates of a surface air temperature reduction of 0.17° ± 0.15°C and a mean annual precipitation decrease of −0.04 ± 0.04 mm day−1 falls within the range of previous reports summarized in Table 7.

b. Evidence for a negative feedback on fire emissions

Fuel moisture levels play an important role in regulating savanna fire fuel consumption, fire spread rates, and the likelihood of nighttime extinction (Randerson et al. 2012; Hodnebrog et al. 2016; van der Werf et al. 2017). Here we show that by cooling the surface and reducing planetary boundary layer heights (Table 4), fire aerosols increase surface relative humidity (Fig. 6d). For fine dead fuel classes, such as surface litter and dry grasses, the higher levels of relative humidity are likely to rapidly equilibrate with the moisture levels in the biomass, and thus fire aerosol effects on surface climate are likely to influence fire behavior. For example, in the Amazon, relative humidity increases by more than 3% during the dry season (Fig. 7) and by more than 6% during high fire years (Fig. S2). These aerosol-driven increases in relative humidity may reduce fire emissions during periods of intense drought, contributing to a negative feedback on fire emissions. The higher relative humidity, lower temperatures, and increases in diffuse light may also act as a shield to protect nearby intact forests from drought extremes, including drought-induced tree mortality (Phillips et al. 2010). Although less certain, fire aerosols may also reduce precipitation in other areas, contributing to a positive feedback in Sumatra and Borneo (Tosca et al. 2011; Hodzic and Duvel 2018), the Amazon (Zhang et al. 2009; Spracklen and Garcia-Carreras 2015), Southern Hemisphere South America (Thornhill et al. 2018), and southern Africa (Hodnebrog et al. 2016).

c. GPP response

Overall the net effect of fire aerosols on GPP in E3SM is relatively small at a global scale because the impacts from several strong positive and negative drivers cancel one another out. Increases in surface cooling and relative humidity enhance GPP whereas decreases in light availability reduce GPP. Our estimate of a small fire-induced decrease in global GPP is consistent with the magnitude of previous estimates summarized in Table 8, with four global modeling studies showing a negative effect and a single study showing a small positive effect. Variation in the GPP response among studies is likely a consequence of model-to-model differences in the representation of photosynthesis, atmospheric model configuration, and experimental design, including, for example, whether the model solely considers fire aerosol effects on the radiation budget or allows for dynamical atmospheric responses to aerosol direct and indirect effects. More work is needed to systematically assess the drivers of the model-to-model differences; in this context, the spatial regression analysis presented here may be of use in comparing GPP responses to climate drivers within and across models.

Table 8.

Comparison of the simulated global fire-induced GPP changes with previous studies.

Table 8.

The fire-induced increase in regional GPP in the southern Amazon from E3SM is consistent with measurements (Oliveira et al. 2007; Doughty et al. 2010) and previous modeling studies (Rap et al. 2015; Li 2020), and occurs because enhancements from cooling and increases in humidity and diffuse light more than compensate for moderate decreases in downward solar radiation (Fig. 7). Fire-induced decreases in GPP in central Africa from E3SM agree with the results reported by Li (2020) because the magnitude of fire-induced surface cooling is weaker compared to other regions, while the magnitude of the shortwave radiation decline is considerably larger (Fig. 7).

5. Conclusions

Based on an optimized set of fire-associated trace gases and aerosol emissions derived from the GFED4s dataset and AERONET aerosol optical depth observations, here we examined fire aerosol effects on global surface climate and GPP using the E3SM model. The optimization approach required nearly doubling BC and particulate organic matter aerosol sources from GFED4s to match AERONET observations. With the optimized fire emissions, fire aerosols contribute to nearly two-thirds of the global BC aerosol burden, over 80% of the primary organic matter aerosol burden, and about 12% of the secondary organic aerosol burden. As a result, fire aerosols account for about 14% of global annual mean AOD, and more closely match MODIS satellite observations in the tropics during 1997–2016, particularly during the peak fire season in high biomass burning regions.

Through direct and indirect aerosol effects, fires significantly reduce shortwave radiation reaching the surface and consequently lead to a decrease in surface air temperature. The largest regional reductions in surface air temperature occurred in Southern Hemisphere Africa with declines of more than 0.6°C. Surface cooling and atmospheric heating stratifies the global atmosphere and slows the hydrological cycle, with global annual mean precipitation decreasing by 0.04 mm day−1. Surface relative humidity increases in most fire regions, as a consequence of increases in water vapor content from trapping of evapotranspiration in a shallower boundary layer and decreases in the saturation vapor pressure associated with decreases in surface air temperature.

In E3SM, the GPP responses to fire-induced changes in near-surface climate show considerable regional heterogeneity. To understand how spatial and temporal GPP variations respond to changes in near-surface climate, we used a multivariate linear regression model to attribute fire-induced GPP changes to different climate drivers. This analysis revealed that fire aerosol effects on surface shortwave radiation and soil moisture reduce GPP, but these effects are largely offset in many areas by surface cooling and increases in relatively humidity and diffuse light. The net impact of all these drivers contributes to a small GPP enhancement in the southwestern Amazon but a decline in central Africa, highlighting important and distinct fire–radiation–climate–productivity interactions in different tropical regions.

Wildfires play a critical role in influencing surface ozone concentrations in the tropics (Anderson et al. 2016; Yue and Unger 2018). In this context, an important next step is to couple fire impacts on atmospheric chemistry within an Earth system model to assess the combined impacts of aerosol and ozone on the surface radiation budget and GPP (Ainsworth et al. 2012; Lombardozzi et al. 2013). Fires also play an important role in influencing phosphorus and nitrogen availability in ecosystems (Mahowald et al. 2008; Myriokefalitakis et al. 2016), and these interactions may contribute to either positive or negative effects on regional GPP. While aerosols from combustion sources are likely to be reduced as the international community moves toward renewable energy sources, aerosol emissions from land use and land cover change may increase (Ward et al. 2012), as highlighted by recent increases in deforestation fires within the Amazon (Brando et al. 2020). Ultimately, integration of fire impacts on atmospheric composition and ecosystem processes, including vegetation dynamics that regulate the carbon sink (Arora and Melton 2018), is needed to develop a comprehensive understanding of how future changes in fire regimes will feed back on the climate system.

Acknowledgments

This research was supported by the U.S. Department of Energy’s Biological and Environmental Research (BER) division of the Office of Science. This funding included support from the Earth System Model Development Program under Grant DE-SC0021302. Additional support was provided by the Office of Science’s Regional and Global Model Analysis program through the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA; including contract DEAC02-05CH11231) and by the Earth System Model Development Program’s support of E3SM and the “Enabling Aerosol–cloud interactions at GLobal convection-permitting scalES (EAGLES)” project (74358). HW acknowledges support from the E3SM project. The Pacific Northwest National Laboratory (PNNL) is operated for DOE by Battelle Memorial Institute under Contract DE-AC05-76RL01830.

Data availability statement

The fire emission data are publicly available from the Global Fire Emission Database version 4s (GFED4s; https://www.globalfiredata.org/). The gridded MISR plume height observations are available in the supplement information in Zhu et al. (2018). The AERONET measurements (version 3) are available at http://aeronet.gsfc.nasa.gov. The level-3 monthly MODIS product (Collection 6.1) is publicly available at https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD08_M3. The E3SM simulations are available on request from the corresponding author.

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