Impacts of global open-fire aerosols on direct radiative, cloud and surface-albedo effects simulated with CAM5

Abstract

Aerosols from open-land fires could significantly perturb the global radiation balance and induce climate change. In this study, Community Atmosphere Model version 5 (CAM5) with prescribed daily fire aerosol emissions is used to investigate the spatial and seasonal characteristics of radiative effects (REs, relative to the case of no fires) of open-fire aerosols including black carbon (BC) and particulate organic matter (POM) from 2003 to 2011. The global annual mean RE from aerosol-radiation interactions (REari) of all fire aerosols is 0.16±0.01Wm^-2 (1σ uncertainty), mainly due to the absorption of fire BC (0.25±0.01Wm^-2), while fire POM induces a small effect (-0.05 and 0.04±0.01Wm^-2 based on two different methods). Strong positive REari is found in the Arctic and in the oceanic regions west of southern Africa and South America as a result of amplified absorption of fire BC above low-level clouds, in general agreement with satellite observations. The global annual mean RE due to aerosol-cloud interactions (REaci) of all fire aerosols is -0.70±0.05Wm^-2, resulting mainly from the fire POM effect (-0.59±0.03Wm^-2). REari (0.43±0.03Wm^-2) and REaci (-1.38±0.23Wm^-2) in the Arctic are stronger than in the tropics (0.17±0.02 and -0.82±0.09Wm^-2 for REari and REaci), although the fire aerosol burden is higher in the tropics. The large cloud liquid water path over land areas and low solar zenith angle of the Arctic favor the strong fire aerosol REaci (up to -15Wm^-2) during the Arctic summer. Significant surface cooling, precipitation reduction and increasing amounts of low-level cloud are also found in the Arctic summer as a result of the fire aerosol REaci based on the atmosphere-only simulations. The global annual mean RE due to surface-albedo changes (REsac) over land areas (0.03±0.10Wm^-2) is small and statistically insignificant and is mainly due to the fire BC-in-snow effect (0.02Wm^-2) with the maximum albedo effect occurring in spring (0.12Wm^-2) when snow starts to melt.

Atmos. Chem. Phys., 16, 1480514824, 2016 www.atmos-chem-phys.net/16/14805/2016/ doi:10.5194/acp-16-14805-2016 Author(s) 2016. CC Attribution 3.0 License.

Yiquan Jiang1,2, Zheng Lu2, Xiaohong Liu2, Yun Qian3, Kai Zhang3, Yuhang Wang4, and Xiu-Qun Yang1

1CMA-NJU Joint Laboratory for Climate Prediction Studies, Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, China

2Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming, USA

3Pacic Northwest National Laboratory, Richland, Washington, USA

4School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA Correspondence to: Xiaohong Liu ([email protected])

Received: 24 February 2016 Published in Atmos. Chem. Phys. Discuss.: 1 April 2016 Revised: 30 October 2016 Accepted: 8 November 2016 Published: 29 November 2016

Abstract. Aerosols from open-land res could signicantly perturb the global radiation balance and induce climate change. In this study, Community Atmosphere Model version 5 (CAM5) with prescribed daily re aerosol emissions is used to investigate the spatial and seasonal characteristics of radiative effects (REs, relative to the case of no res) of open-re aerosols including black carbon (BC) and particulate organic matter (POM) from 2003 to 2011. The global annual mean RE from aerosolradiation interactions (REari) of all re aerosols is 0.16 0.01 W m2

(1 uncertainty), mainly due to the absorption of re BC(0.25 0.01 W m2), while re POM induces a small ef

fect (0.05 and 0.04 0.01 W m2 based on two differ

ent methods). Strong positive REari is found in the Arctic and in the oceanic regions west of southern Africa and South America as a result of amplied absorption of re BC above low-level clouds, in general agreement with satellite observations. The global annual mean RE due to aerosolcloud interactions (REaci) of all re aerosols is

0.70 0.05 W m2, resulting mainly from the re POM ef

fect (0.59 0.03 W m2). REari (0.43 0.03 W m2) and

REaci (1.38 0.23 W m2) in the Arctic are stronger than

in the tropics (0.17 0.02 and 0.82 0.09 W m2 for

REari and REaci), although the re aerosol burden is higher in the tropics. The large cloud liquid water path over land areas and low solar zenith angle of the Arctic favor the strong re aerosol REaci (up to 15 W m2) during the Arc

tic summer. Signicant surface cooling, precipitation reduction and increasing amounts of low-level cloud are also found

Impacts of global open-re aerosols on direct radiative, cloud and surface-albedo effects simulated with CAM5

in the Arctic summer as a result of the re aerosol REaci based on the atmosphere-only simulations. The global annual mean RE due to surface-albedo changes (REsac) over land areas (0.03 0.10 W m2) is small and statistically in

signicant and is mainly due to the re BC-in-snow effect(0.02 W m2) with the maximum albedo effect occurring in spring (0.12 W m2) when snow starts to melt.

1 Introduction

Open res or biomass burning of living and dead vegetation are an integral component of the Earths system and have signicant impacts on the carbon cycle (Ciais et al., 2013) and the climate (Bowman et al., 2009; Keywood et al., 2011; Liu et al., 2014; Sommers et al., 2014; Voulgarakis and Field, 2015). On one hand, open res can perturb the climate system by emitting greenhouse gases and aerosols (Kaiser et al., 2012; Wiedinmyer et al., 2011). On the other hand, climate states and variabilities can play a critical role in determining the occurrence frequency and intensity of open res (Marlon et al., 2009; van der Werf et al., 2008; Westerling et al., 2006; Bistinas et al., 2014). However, much is unknown regarding the feedback mechanisms between open re and climate interactions (Carslaw et al., 2010; Liu et al., 2014). A qualication of radiative forcing of re aerosols as conducted in this study is the rst step to reducing these uncertainties.

Particles emitted from open res can exert signicant perturbations to the climate system by scattering and absorbing

Published by Copernicus Publications on behalf of the European Geosciences Union.

14806 Y. Jiang et al.: Impacts of global open-re aerosols

the solar radiation in the atmosphere (direct effect) (Carslaw et al., 2010) and by changing the surface albedo when they are deposited on the snow and ice (surface-albedo effect) (Flanner et al., 2007; Quinn et al., 2008; Randerson et al., 2006; Qian et al., 2011, 2015). In addition, open re or smoke particles can modify the cloud properties, precipitation efciency and the hydrological cycle by changing the atmospheric thermal structure (semi-direct effect) (Koch and Del Genio, 2010; Andreae et al., 2004) or acting as cloud condensation nuclei (CCN) (indirect effects) (Andreae and Rosen-feld, 2008; Qian et al., 2009; Lu and Sokolik, 2013).

The radiative effect (RE) (Boucher and Tanr, 2000) and radiative forcing (RF) (Forster et al., 2007; Myhre et al., 2013a) are typical metrics used to assess and compare anthropogenic and natural drivers of climate change. The aerosol RE represents the instantaneous radiative impact of atmospheric particles on the Earths energy balance (Heald et al., 2014). RF is calculated as the change of RE between two different periods, e.g., the pre-industrial and the present-day times (Heald et al., 2014; Liu et al., 2007), based on the aerosol and precursor gas emissions in the two periods (Dentener et al., 2006; Lamarque et al., 2010).

RF from aerosolradiation interactions (RFari) involving biomass burning aerosols has been estimated since the IPCC second Assessment Report (AR2). Based on the Aerosol Comparisons between Observations and Models (AeroCom)Phase II simulations (Bond et al., 2013; Myhre et al., 2013b), RFari of biomass burning aerosols in the IPCC Fifth Assessment Report (AR5) is estimated to be 0.0 W m2 (ranging from 0.20 to 0.20 W m2), and the RFari of biomass burn

ing black carbon (BC) and primary organic matter (POM) have values with opposite signs (i.e., 0.10 and 0.10 W m2,

respectively).

There are also some studies that estimated the RE from aerosolradiation interactions (REari) involving re aerosols by comparing the simulation with re emissions to the simulation with no re emissions. For example, using the NCAR Community Atmosphere Model version 4 (CAM4) with a bulk aerosol module, Tosca et al. (2013) reported that the top-of-atmosphere (TOA) REari from global biomass burning aerosols is 0.18 0.10 W m2, averaged for the period

of 19972009. Ward et al. (2012) estimated the REari from biomass burning aerosols in the pre-industrial (for the year 1850), present-day (for the year 2000) and future time periods (for the year 2100), and found that the biomass burning aerosol REari for the year 2000 is 0.13 and 0.27 W m2 for

all-sky and clear-sky conditions, respectively.

RE from aerosolcloud interactions (REaci) of biomass burning aerosols can be comparable in magnitude or of an even stronger magnitude than the REari (Liu et al., 2014).With a global aerosol-climate model, the REaci of biomass burning aerosols was estimated to range from 1.74 to 1.00 W m2 for the year 2000 in Ward et al. (2012). The semi-direct radiative effect of biomass burning aerosols is not independently assessed in IPCC reports. The magnitude was

reported to be about 7.0 W m2 in the southern American biomass burning regions by examining the radiative ux difference with and without the biomass burning aerosol effect on clouds (Liu, 2005).

The RF or RE due to surface-albedo changes (RFsac or REsac) of BC from open res and other sources has been estimated in previous studies. For biomass burning emissions with a strong (1998) and weak (2001) boreal re year, RE of re BC-in-snow was estimated to be 0.011 and 0.006 W m2, respectively (Flanner et al., 2007). Randerson et al. (2006) reported that BC from a boreal forest re deposited on snow and sea ice introduced a global annual mean RE of 8 5 W m2 of burned area in the rst year when the re

happened. A summary of BC-in-snow forcing/effect can be found in Bond et al. (2013). They reported that the present-day RE of re BC-in-snow ranges from 0.006 to 0.02 W m2 based on previous studies (Jacobson, 2004; Rypdal et al., 2009; Skeie et al., 2011; Hansen et al., 2005; Flanner et al., 2007, 2009; Koch et al., 2009).

Biomass burning aerosols can have signicant impacts on global and regional precipitation and atmospheric circulation. With the change of re emissions from 1860 to 2000, Jones et al. (2007) found that biomass burning aerosols decrease the global near-surface air temperature by about0.25 C when considering the feedbacks of sea surface temperature (SST) in the model. As shown in Tosca et al. (2013), the direct and semi-direct effects of biomass burning aerosols reduce the precipitation near the equator and weaken the Hadley circulation. With a regional climate model, Zhang et al. (2009) found that biomass burning aerosols may warm and stabilize the lower troposphere and thus reinforce the dry season rainfall pattern in southern Amazonia. The absorption of shortwave radiation by biomass burning BC could increase the vertical stratication and inhibit both the cloud formation and precipitation (Ackerman et al., 2000; Tosca et al., 2014). In contrast, biomass burning aerosols could invigorate the convective clouds (Andreae et al., 2004; Koren et al., 2005) through suppressing warm rain processes in the convection and enhance the latent heat release at higher levels (Andreae and Rosenfeld, 2008).

Although there have been many studies quantifying the RE of re aerosols, further investigation is still needed as the current estimations of re aerosol RE are still associated with large uncertainties (e.g., Myhre and Samset, 2015;Chakrabarty et al., 2014). The REs of co-emitted re POM vs. BC are even less clear. In this study, we estimate the present day (from 2003 to 2011) open-re aerosol REs (including REari, REaci and REsac) using the NCAR Community Atmosphere Model version 5.3 (CAM5) with the 4-mode version of the modal aerosol module (MAM4). We use two methods to calculate the REari of re aerosols (total, BC only and POM only). One method estimates the REari based on different model simulations (Ghan, 2013), and the other one calculates the REari directly through multiple diagnostic radiation calls in a single simulation. The spatial and sea-

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four lognormal modes: Aitken, accumulation, coarse and primary carbon mode (Liu et al., 2016). An additional primary carbon mode is included in MAM4 on the top of MAM3 to explicitly treat the microphysical aging of primary carbonaceous aerosols (POM and BC) in the atmosphere. POM and BC in MAM4 are emitted in the primary carbon mode instead of directly in the accumulation mode as in MAM3. MAM4 signicantly increases the BC and POM concentrations in the remote regions (e.g., over oceans and the Arctic) due to reduced wet scavenging of POM and BC in the primary carbon mode with a lower hygroscopicity than in the accumulation mode. The increase is relatively small in the land source regions (Liu et al., 2016).

2.2 Experimental design

CAM5 was run with the nite volume dynamics core in a resolution of 0.9 latitude by 1.25 longitude and 30 vertical levels. The model was run for the time period of year 2003 to 2011 (i.e., for 9 years) with prescribed monthly SST and sea ice. The year 2003 was run twice and the rst year simulation was used as a model spin-up. Global Fire Emissions Database version 3.1 (GFED 3.1) daily emissions (Giglio et al., 2013) for BC, POM and sulfur dioxide (SO2) from 2003 to 2011 are prescribed, and the vertical distribution of re emissions is based on the AeroCom protocol (Dentener et al., 2006). Anthropogenic aerosol and precursor gas emissions are from the IPCC AR5 data set (Lamarque et al., 2010). We performed our control experiment (FIRE) with the GFED re emissions turned on and a sensitivity experiment (NOFIRE) with the re emissions turned off. Differences between FIRE and NOFIRE experiments are used to calculate the REs and atmospheric effects of biomass burning aerosols on temperature and precipitation. Two additional experiments, NOFIREBC and NOFIREPOM, were respectively performed with re BC and POM emissions turned off.Differences between the control (FIRE) and these two experiments represent the contributions from biomass burning of BC and POM. Other forcings (e.g., SST, greenhouse gases) of all these experiments are kept the same. We performed ten ensemble members for each of these experiments. Furthermore, we performed the other experiment (FIRE_BBFFBF) using the modied CAM5 model that separately predicts the BC and POM from biomass burning (BB), fossil fuel (FF) and biofuel (BF) sources, while other model features are kept the same as the FIRE experiment. A summary of all the experiments in this study can be found in Table 1.

2.3 Methods of calculating re aerosol radiative effects

The REs of all re aerosols, re BC and re POM are calculated from the differences in TOA shortwave uxes ([Delta1]F ) between the FIRE experiment and the three other experiments (NOFIRE, NOFIREBC and NOFIREPOM, respectively). All the atmospheric variables (including temperature,

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Y. Jiang et al.: Impacts of global open-re aerosols 14807

sonal characteristics of re aerosol REs, and the impacts on

the global precipitation and temperature are discussed.

Compared to earlier studies of re aerosol REs (Tosca et al., 2013; Ward et al., 2012), a number of improvements are made in this study. First, a higher model horizontal resolution at 0.9 by 1.25 is used vs. 1.9 by 2.5 . The higher resolution allows for more efcient transport of aerosols from the sources to remote regions (Ma et al., 2013, 2014). Model resolution has also been shown to be important for aerosol REaci (Ma et al., 2015). Second, the latest CAM5 model with MAM4 is used. MAM4 with an additional primary carbon mode explicitly treats the microphysical aging of primary carbonaceous aerosols (POM/BC) in the atmosphere. MAM4 has higher BC and POM burdens over the earlier 3-mode version of MAM (MAM3) in the remote regions by 30 % (Liu

et al., 2016). Third, daily instead of monthly re emissions are used, which allows the model to consider the effect of fast changes in the re emission ux on local atmospheric conditions. It is expected that, using the monthly mean emission ux, the model cannot consider the effect of extremely strong res, thus it might underestimate the re aerosol REs for such cases. Finally, a new methodology (Ghan, 2013) is used to more accurately diagnose the REs of re aerosols.Central to this method is that the REari must be calculated in the presence of clouds (i.e., under the all-sky condition), and the REaci must be calculated under the condition of no aerosol effects on radiation. With the radiative forcing decomposition of this method, REsac can also be quantied.

The paper is organized as follows. Section 2 introduces the model and experiments. Section 3 describes the methods of diagnosing the re aerosol REs. Section 4 presents the model results of re aerosol REs and impacts on global and regional surface temperature and precipitation. Conclusions and discussion are given in Sect. 5.

2 Model, experimental design and aerosol radiative effect method

2.1 Model

In our study, we use the Community Earth System Model (CESM) version 1.2, with the Community Atmosphere Model version 5.3 (CAM5.3) (Neale et al., 2012) coupled with the Community Land Model version 4 (CLM4) (Oleson et al., 2010). The Snow, Ice, and Aerosol Radiative model (SNICAR) (Flanner and Zender, 2005) is turned on in the simulations to diagnose the biomass burning BC-in-snow effect. CAM5 includes several major updates in its physics parameterizations compared to previous CAM versions. A two-moment stratiform cloud microphysics scheme is included in CAM5 to predict both the mass and number mixing ratios of cloud liquid and cloud ice (Morrison and Gettelman, 2008). MAM4, which was updated from MAM3 (Liu et al., 2012), includes aerosol mass and number mixing ratios in

14808 Y. Jiang et al.: Impacts of global open-re aerosols

Table 1. Numerical experiments and associated re aerosol emissions in each experiment.

Experiment Ensembles Fire Fire Fire BC POM SO2

FIRE 10 On On On NOFIRE 10 Off Off Off NOFIREBC 10 Off On On NOFIREPOM 10 On Off On FIRE_BBFFBF 1 On On On

precipitation and circulation) are allowed to adjust in the experiments. However, with SST and sea ice prescribed in these experiments, only the rapid adjustments are taken into account. Thus, the effective radiative effects are actually calculated in this study.

[Delta1]Fre aero = Fre Fnore (1) [Delta1]Fre bc = Fre Fnorebc (2)

[Delta1]Fre pom = Fre Fnorepom (3)

The total TOA shortwave ux change can be broken into the REari, REaci and REsac. The aerosol REaci results from both the aerosol effect on clouds (i.e., acting as CCN) and the aerosol semi-direct effect on clouds (i.e., affecting the atmospheric states due to absorbing aerosols). We adopt the method by Ghan (2013) to separate the REari, REaci and REsac from the total effects of all re aerosols, re BC and re POM, respectively. The method is summarized as follows. Fclean is the radiative ux at TOA calculated from a diagnostic radiation call in the same control simulations, but neglecting the scattering and absorption of solar radiation by aerosols. Fclean,clear is the clear-sky radiative ux at TOA calculated from the same diagnostic radiation call, but neglecting scattering and absorption by both clouds and aerosols.

[Delta1]F = [Delta1](F Fclean) +[Delta1](Fclean Fclean,clear)

(REari) (REaci)

+[Delta1]Fclean,clear(REsac) (4)

In the method above, REaci includes both aerosol indirect and semi-direct effects. The re BC has a much weaker indirect effect due to its lower mass burden and lower hygroscopicity compared to re POM (Koch et al., 2011). Thus, the re aerosol semi-direct effect can be approximately represented by the REaci of re BC. The re aerosol indirect effect can be estimated as the difference between the re aerosol REaci and semi-direct effect. With the sea ice prescribed in these experiments, the radiative effect of re aerosols on sea ice albedo is not considered in REsac.

We undertake another method to estimate the re aerosol REari from the experiment (FIRE_BBFFBF). With explicit predictions of re POM and re BC in FIRE_BBFFBF, the

Figure 1. Seasonal variation of GFED monthly re (a) organic carbon (OC) and (b) black carbon (BC) emissions (Tg C month1) averaged for the period of year 2003 to 2011 in the global, tropical (25 S to 25 N) and the Arctic (60 to 90 N) regions.

REari of re BC and re POM are estimated by two diagnostic radiation calls, each time neglecting the scattering and absorption of solar radiation of re BC and re POM.This more direct method is named BBFFBF, and the REari of re BC and re POM will be compared with those from the method by Ghan (2013). The re BC-in-snow effect is calculated from SNICAR, and compared with the REsac estimated from Ghan (2013).

3 Results

3.1 Simulation of biomass burning aerosols

The biomass burning BC and POM from forest, grass and agriculture res are signicant contributors to the total BC and POM emissions. Figure 1 shows the seasonal variation of GFED re emissions (including forest, grass and agriculture res) in the global, tropical (25 S to 25 N), and the Arctic (60 to 90 N) regions. Global re emission is the largest during the boreal summer as well as in the boreal autumn (September/October) when it is the re season in the tropical regions of the Southern Hemisphere (SH). The tropical re emission contributes the most to the annual global re emission (80 % for BC and 85 % for OC), compared to other

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Y. Jiang et al.: Impacts of global open-re aerosols 14809

Figure 2. Comparison of modeled seasonal variations of aerosol optical depth (AOD) for the period of 20032011 with observations for the same period from the AERONET sites. The upper, middle and bottom panels represent the sites in southern Africa, South America and the Arctic. The vertical bars are 1 variabilities for the modeled and observed AOD.

regions. The Arctic is the other important re region, where the emission maximum is found during the boreal summer. In the boreal summer, the OC emission in the Arctic regions is about 50 % of that in the tropical region. The BC emission in the Arctic is much smaller than in the tropical regions even in the boreal summer re season. The dominant re type in the SH tropics is deforestation, savanna and grassland res, while that in the Arctic is the forest res. The OC to BC ratio (OC / BC) of forest res is almost 3 times higher than that of deforestation, savanna and grassland res (van der Werf et al., 2010). This is because for forest res, most of the emissions come from the smoldering phase of burning, which has a higher OC to BC ratio. For deforestation, savanna and grassland res, the emissions come mainly from the aming phase of burning, which yields a lower OC to BC ratio.

Figure S1 in the Supplement shows the latitudinal and longitudinal distributions of vertically integrated concentrations (column burdens) of BC and POM from BB, FF and BF sources based on the FIRE_BBFFBF experiment. The BC and POM from the BB source are mainly distributed in the tropical and subtropical regions (southern Africa, South America and Southeast Asia) and in the middle to high latitudes (North of 45 N) of the Northern Hemisphere (NH)

(northeast Asia, Alaska and Canada). The largest column

burdens of biomass burning aerosols are located in southern

Africa and the adjacent oceanic areas (1.5 and 20 mg m2 for

BC and POM, respectively). The biomass burning aerosols are important aerosol species in the Arctic regions and contribute up to 53 and 86 % of the total burdens of BC and POM respectively in the Arctic (from 60 to 90 N). In comparison, the maximum column burdens of fossil fuel BC and POM are found in East Asia, southern Asia, western Europe and North America. The maximum column burdens of biofuel BC and POM occur in East Asia, southern Asia and central Africa. The biofuel and fossil fuel sources are dominant contributors to BC and POM in East Asia and southern Asia. In other regions of the world, biomass burning is the primary source of BC and POM. Globally, biomass burning contributes 41 and 70 % to the total burdens of BC and POM, respectively. Biomass burning can also emit SO2. However, it only contributes 3 % to the total global sulfate burden (gure not

shown), so only radiative effects of biomass burning POM and BC are discussed in this study.

The simulated aerosol optical depth (AOD) and single-scattering albedo (SSA) (including aerosols from all sources) are validated with observations from the AErosol RObotic NETwork (AERONET, http://aeronet.gsfc.nasa.gov

Web End =http://aeronet.gsfc.nasa.gov ) at sites signicantly affected by biomass burning activity in southern Africa, South America and the Arctic regions, as shown in

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14810 Y. Jiang et al.: Impacts of global open-re aerosols

and CuiabaMiranda is most obvious in September and October (the re season), which may be attributed to the underestimation of re emissions. However, the modeled AOD is higher than the observations before the re season for Alta Floresta and Rio Branco, which could be due to the overestimation of re emission in this period. The simulated SSA in South America ranges mostly between 0.87 and 0.95 and matches the observations reasonably well (Fig. 3df). The modeled SSA is too low during the re season and exhibits too strong a seasonality. It implies that the model underestimation of scattering aerosols (e.g., POM) may be more severe than of BC during the re season.

In the Arctic, small AOD (less than 0.3) and large SSA (larger than 0.9) are observed for the three sites. The ob-served large SSA in the re season (boreal summer) is consistent with the high OC / BC ratio of re emissions in the Arctic (Fig. 1). The model signicantly underestimates the observed AOD in the Arctic in both re and nonre seasons. The underestimation of AOD can be due to (1) the underestimation of re emissions in the NH high latitudes (e.g., Stohl et al., 2013) and/or fossil fuel emissions in Asia (e.g., Cohen and Wang, 2014), (2) the excessive scavenging of aerosols during their transport from the NH midlatitude industrial regions by liquid-phase clouds (Wang et al., 2013a) and (3) the coarse horizontal resolution ( 100 km) of the model (Ma et

al., 2014). Although MAM4 increases the column burdens of POM and BC by up to 40 % in many remote regions com-

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Figure 3. Same as Fig. 2, but for the comparison of single-scattering albedo (SSA) at 550 nm.

Figs. 2 and 3 (see Fig. S2 in the Supplement for the site locations). The AERONET AOD and SSA data are averaged for the years from 2003 to 2011 to match the simulation period, although there are missing AERONET data for some periods. We note that Tosca et al. (2013) and Ward et al. (2012) applied scaling factors (from 1 to 3 varying by regions) to re emissions to improve modeled AOD magnitudes, whereas here we do not apply any such scaling. In southern Africa, modeled monthly AOD agrees with observations within a factor of 2 for the three sites (Fig. 2ac). The underestimation of AOD is found in the tropical site (Mongu) (Fig. 2a) during the boreal autumn (the re season). The simulated AOD in the two other sites (Skukuza and Ascension Island) is generally consistent with observations in both the magnitude and seasonal trend. The simulated SSA in southern Africa ranges between 0.75 and 0.95 and generally matches the ob-served SSA magnitude and seasonal cycle in the two land sites (Mongu and Skukuza) (Fig. 3ab). However, an overestimation of SSA is found in the oceanic site (Ascension Island) (Fig. 3c). The reason for this overestimation of SSA and thus the underestimation of absorption AOD (AAOD) is unclear and could be due to the model not treating the absorption enhancement of aged re BC during its transport.

The simulated AOD in South America is generally consistent with observations within a factor of 2 (Fig. 2df). The seasonal variation of simulated AOD generally matches the observations. The underestimation of AOD in Alta Floresta

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Figure 4. Annual mean radiative effect due to aerosolradiation interactions (REari) (W m2) averaged over the period of 20032011 due to (a) all re aerosols, (c) re BC and (e) re POM estimated with the method of BBFFBF (left panels) and with the method by Ghan (2013) (b, d, f in the right panels). The plus signs in (b, d, f) denote the regions where the radiative effect estimated with Ghan (2013) is statistically signicant at the 0.05 level.

pared to MAM3, it still underestimates the surface BC concentrations in the Arctic (Liu et al., 2016). The modeled SSA in the Arctic is lower than the observations, which implies that the simulation of AAOD is better than of AOD and the underestimation of nonabsorbing aerosols (e.g., sulfate and POM) in the Arctic may be more severe than of BC.

3.2 Radiative effect due to aerosolradiation interactions

The annual mean REari of all re aerosols (including BC,

POM and sulfate) estimated with the method of BBFFBF and with the method by Ghan (2013) is shown in Fig. 4ab. The re sulfate is not included in the calculation of REari of all re aerosols with the method of BBFFBF. Its effect is minor since the global annual mean burden of re sulfate (0.09 mg m2) is much smaller than of re POM(1.25 mg m2), but both are light scattering. The statistical signicance of REari estimated with the Ghan (2013) method over the interannual variability and ensemble member diversity is shown in Fig. 4 (and also later gures). The REari of all re aerosols from the two methods agree with each other very well. Thus, we will report the REari

of all re aerosols using the Ghan (2013) method below.

The global annual mean REari of all re aerosols is positive (0.16 0.01 W m2), which indicates a warming ef

fect from all re aerosols. The REari is positive on the globe except in some land areas (e.g., southern Africa, South America, the Great Lakes, northern Canada and Eastern Siberia). The maximum positive REari is located in ocean areas west of southern Africa ( 5.0 W m2) and South Amer

ica ( 1.5 W m2). Positive REari up to 1 W m2 is found in

the Arctic (60 to 90 N). The different signs of REari between land and ocean areas of southern Africa and South America result from the differences in cloud fraction and cloud liquid water path (LWP) between land and ocean regions. In the re season (AugustSeptemberOctober) of the SH tropical regions, cloud fraction and cloud LWP over the land areas (10 % and 20 g m2, respectively) are much smaller than those over the adjacent ocean areas (70 % and 70 g m2). The biomass burning aerosols are transported above the low-level stratocumulus clouds, and when biomass burning BC resides above clouds, its absorption of solar radiation is signicantly enhanced due to the reection of solar radiation by underlying clouds (Abel et al., 2005; Zhang et al., 2016).

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14812 Y. Jiang et al.: Impacts of global open-re aerosols

Figure 5. (a) SeptemberOctoberNovember (SON) mean re aerosol radiative effects due to aerosolradiation interactions (REari) (W m2) for the period of 20032011 over the southeast Atlantic Ocean due to all re aerosols. Panels (b, c) are the same as (a), but for the above-cloud aerosol REari for the period of 20072011 estimated using Aqua/MODIS and Terra/MODIS products (Zhang et al., 2014).

A comparison of modeled REari in the boreal autumn (SeptemberOctoberNovember) over the South Atlantic Ocean with satellite observations is shown in Fig. 5. The observed above-cloud aerosol REari is calculated with the method by Zhang et al. (2014) using the Aqua/MODIS and Terra/MODIS products. The observed above-cloud aerosol REari over southeastern Atlantic Ocean is 312 W m2, with higher values near the coasts. The simulated REari agrees better with Aqua/MODIS-observed REari than with Terra/MODIS in both the magnitude and spatial pattern. REari estimated from Terra/MODIS (morning time) is stronger than REari estimated from Aqua/MODIS (afternoon time) due to the larger amount of underlying cloud in the morning (Min and Zhang, 2014). Over South America during the re season (August to September), the clear-sky re aerosol REari is estimated to be 5.2 W m2 by

Sena and Artaxo (2015), which is larger than our model result (2.1 W m2). This is consistent with the underestima

tion of modeled AOD in South America compared to the

AERONET data (Fig. 2).

The seasonal variation of REari of all re aerosols with the

Ghan (2013) method is shown in Fig. S3 in the Supplement. The REari has a maximum (1.13 W m2) in the boreal summer (JuneJulyAugust, JJA) over the Arctic regions, partially due to the low solar zenith angles there. The maximum positive REari in the tropical regions occurs in the boreal summer and autumn (September, October and November, SON) during the re season of southern Africa and South America. The REari reaches a positive maximum in Southeast Asia during the re season in March, April and May (MAM).

The REari of re BC is shown in Fig. 4cd. The re BC

REari calculated from the two methods are similar in magnitudes and spatial patterns, and there is much less noise with the BBFFBF method. The global annual mean re BC REari is about 0.25 0.01 W m2 and positive over the globe (the

regions with negative values in Fig. 4d are in general not statistically signicant). Unlike all re aerosols, re BC generates a positive forcing in the land regions of southern Africa and South America, and the amplication effect of low-level

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Y. Jiang et al.: Impacts of global open-re aerosols 14813

tic (by 0.05 mg m2) compared to the FIRE experiment.

Thus, one should be careful when using the Ghan (2013) method to diagnose the radiative forcing of a single component within co-emitted aerosols. The REari of re POM is negative across most of the globe. However, positive forcing can be found over oceanic regions west of southern Africa and South America, the North Pacic Ocean and the Polar regions, where large amounts of low-level cloud, sea ice or land ice exist. The multiple scatterings between the above-cloud re POM and low-level clouds or between the re POM and the Earths bright surface with high albedos could reduce the amount of solar radiation reected by these low-level clouds and bright surfaces in the case without the re POM (Zhang et al., 2016). With the BBFFBF method, the sum of REari from re POM and re BC (i.e., 0.20 W m2) is larger than that of all re aerosols (0.15 W m2). It reects the nonlinear interactions among different aerosol components (Ghan et al., 2012). For example, re POM and water on internally mixed re BC particles enhance solar absorption by re BC.The nonlinearity is stronger with the Ghan (2013) method.

3.3 Radiative effect due to aerosolcloud interactions

The annual mean REaci due to all re aerosols, re BC and re POM are shown in Fig. 6. The REaci diagnosed with the Ghan (2013) method includes both aerosol indirect and semi-direct effects. The re aerosol semi-direct effect (to be discussed below) is much smaller (0.04 0.03 W m2 on the

global mean) than the indirect effect, and the REaci is mostly from the re aerosol indirect effect. The global annual mean REaci of all re aerosols is 0.70 0.05 W m2 (Fig. 6a).

In the tropical regions, the strong negative REaci is located in the adjacent ocean areas of southern Africa, South America and Australia, with the maximum REaci of 8.0 W m2

over the South Atlantic Ocean. The strong negative REaci also occurs in the Arctic (60 to 90 N). The REaci in Eastern

Siberia, Alaska and Canada is as large as 6.0 W m2.

The re BC has a weak indirect effect by acting as CCN, but can reduce the amount of cloud through its semi-direct effect. The REaci of re BC (Fig. 6b) can approximate the re BC semi-direct effect with a small global annual mean value of 0.04 0.03 W m2. However, a stronger positive

effect can be found in the western Pacic (3.0 W m2) and Arctic regions (1.0 W m2). The global annual mean REaci of re POM is 0.59 0.03 W m2 (Fig. 6c) and dominates

the cloud effect of all re aerosols. The sum of REaci from re BC and POM (0.62 0.03 W m2) is smaller in mag

nitude than from all re aerosols (0.70 0.05 W m2) due

to the nonlinear interactions of re BC and re POM (Jiang et al., 2013) as well as the negative REaci of re sulfate. As an example of the nonlinear interactions, the internal mixing of re POM and re BC by all re aerosols enhances the cloud droplet number concentration in comparison to the sum of cloud droplet number concentrations from individual re POM and re BC (Jiang et al., 2013).

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Figure 6. Annual mean radiative effects due to aerosolcloud interactions (REaci) (W m2) averaged over the period of 20032011 due to (a) all re aerosols, (b) re BC and (c) re POM. The plus signs denote the regions where the radiative effect is statistically signicant at the 0.1 level.

clouds on re BC positive forcing can be clearly seen in southern Africa and the adjacent Atlantic Ocean.

The global annual mean REari values of re POM from the two methods somewhat differ from each other (Fig. 4ef). The BBFFBF method gives a small negative value (0.05 W m2), while the Ghan (2013) method shows a

small positive value (0.04 0.01 W m2). The difference is

mainly in the Arctic regions where the positive forcing from Ghan (2013) is larger than from the BBFFBF method. This is because the removal of re POM emissions in the NOFIREPOM experiment affects the properties of aerosol particles within which co-emitted re BC is internally mixed with re POM, causing a decrease of BC burden in the Arc-

14814 Y. Jiang et al.: Impacts of global open-re aerosols

Figure 7. Seasonal variation of radiative effects of all re aerosols due to aerosolcloud interactions (REaci) (W m2) for the period of 20032011 for (a) DecemberJanuaryFebruary (DJF), (b) MarchAprilMay (MAM), (c) JuneJulyAugust (JJA) and (d) September OctoberNovember (SON). The plus signs denote the regions where the radiative effect is statistically signicant at the 0.05 level.

The seasonal variation of all re aerosol REaci is shown in Fig. 7. The maximum of re aerosol REaci is in the boreal summer (i.e., the re season in NH), located in the NH high latitudes (60 to 90 N). The largest summer REaci is found in the land areas and is as large as 15 W m2. The re aerosol

REaci in the tropical regions is most signicant in the boreal summer (up to 15 W m2) and autumn (up to 10 W m2)

over the ocean areas. The different spatial distributions of re aerosol REaci in the NH high latitudes and in the tropics result from the difference in cloud distribution between the two regions. During the re season the cloud LWP over the land areas in the NH middle and high latitudes is 3 times larger than over the ocean areas in the tropics. Larger cloud LWP favors the stronger REaci, because a larger LWP associated with warm cloud and rain processes favors the aerosol indirect effect of slowing down the autoconversion of cloud water to rain (Ghan et al., 2012; Jiang et al., 2015). Meanwhile, in the NH high latitudes, the lower solar zenith angle in the boreal summer favors the stronger REaci. Like the re aerosol REari, the smallest re aerosol REaci occurs in the boreal spring.

Seasonal variations of zonal mean re aerosol REari,

REaci, cloud LWP, amount of low-level (from surface to 750 hPa) cloud and vertically integrated (burden) concentrations of re POM and re BC are shown in Fig. 8. The seasonal variation of re BC and re POM burdens is largest in the SH low latitudes (from 30 S to 0 N) and NH mid-

dle and high latitudes (50 to 90 N). A distinct feature of these two areas is that the maximum re BC burden in NH(0.3 mg m2) is much lower than in SH (0.8 mg m2), while the maximum POM burdens in these two areas are comparable. Interestingly, the REari is larger in the boreal summer in NH than in the boreal autumn in SH, although the re BC burden is much lower in the NH summer. It is mainly due to the larger amount of low cloud in the NH high latitudes, which enhances the absorption of re BC. The maximum REari in the NH summer is found near the North Pole (70 to 90 N) and not around 60 N where the re aerosol burden is highest. The REaci of re aerosols is about 3 times larger in the boreal summer in NH than in the boreal autumn in SH, although the burden of re POM in NH is comparable to that in SH. The larger cloud LWP in the NH summer around 4070 N favors the stronger REaci there.

3.4 Surface-albedo effect

Here we compare the modeled BC-in-snow (BCS) concentrations with observation data collected from multiple eld campaigns over the Arctic (Doherty et al., 2010) and northern China (Wang et al., 2013b; Qian et al., 2014). Figure 9a shows the simulated (from FIRE and NOFIRE experiments) and observed BCS concentrations as a function of latitude. The range of observed BCS concentrations is between 1 and 200 ng g1 in the Arctic and between 50 and 2000 ng g1 in

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Y. Jiang et al.: Impacts of global open-re aerosols 14815

Figure 8. Time-latitude cross sections of zonal mean and monthly (a) vertically integrated concentrations (mg m2) of re BC and (b) re POM, (c) cloud liquid water path (LWP, in g m2), (d) low-level cloud cover (CLDLOW, in %), (e) radiative effect due to aerosolradiation interactions (REari, in W m2) and (f) radiative effect due to aerosolcloud interactions (REaci, in W m2) of all re aerosols.

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14816 Y. Jiang et al.: Impacts of global open-re aerosols

10 40 45 50 55 60 65 70 75 80

10

10

a

b

10

10

BCintopsnowlayer(ngg)

BCintopsnowlayer(ngg)

10

10

10

10

10

10

Observation Fire-on Fire-off

Northern China Arctic

10

Latitude N

Observation

Fire-off

Fire-on

Observation

Fire-off

Fire-on

Figure 9. Evaluation of CAM5-simulated black carbon (BC) concentration for the period of 20032011 (in ng g1) in the top snow layer against observations in the Arctic (Doherty et al., 2010) and northern China (Wang et al., 2013b). The top snow layer ranges in thickness from 1 to 3 cm. Conguration of the two CAM5 simulations (FIRE and NOFIRE) is summarized in Table 1. Panel (a) shows the comparisons at different latitudes. The box and whisker plot in (b) shows the minimum and maximum value with the bar, the 25th and 75th percentiles with the box, the 50th percentile (i.e., median) by the bar within the box and the mean value with the dot.

northern China. Both FIRE and NOFIRE experiments capture the meridional gradient in BCS concentrations between the midlatitudes (northern China) and high latitudes (Arctic).The mean and median concentrations of BCS are both overestimated in northern China, implying high biases from the anthropogenic emissions and/or model physics (Fig. 9b). The mean and median BCS concentrations from the FIRE experiment agree slightly better with the observations than those from the NOFIRE experiment in the Arctic (Fig. 9b). This suggests that re emissions are important for BCS concentrations in the Arctic.

The annual mean REsac of all re aerosols estimated with Ghan (2013) and the re BCS effect diagnosed from SNICAR are shown in Fig. 10a. We note that the radiative effect due to BC deposition on sea ice is not considered since sea ice is prescribed in the simulations. The global annual mean REsac (0.03 0.10 W m2) is much

smaller compared to the REari and REaci. The REsac over land is maximum in spring (0.12 0.27 W m2) and win

ter (0.06 0.16 W m2). The REsac over land in summer

and autumn is very small (less than 0.01 W m2). We note that the mean REsac calculated from Ghan (2013) is much smaller than the standard deviation which resulted from the internal variability.

The annual mean re BCS effect calculated from SNICAR is shown in Fig. 10b and c. The spatial distribution of the re BCS effect is similar to the re REsac, implying that the re REsac has a signicant contribution from the re BCS effect. Averaged when only snow is present, the re BCS effect is larger (0.048 W m2). The global mean re BCS effect (with the presence of snow) can be as large as 0.06 W m2 in spring. The maximum re BCS effect (up to 1 W m2) is located in Greenland and the very northern reaches of Canada,

while in the other Arctic regions and northern China it is smaller.

The positive REsac in Siberia, North America and Canada can be a result of the BCS effect. However, the REsac in these regions is larger than the BCS effect especially in spring. The snow melting and snow depth change due to the BCS warming may induce a larger positive REsac than the albedo change due to BCS itself. The negative REsac over land can be a result of atmospheric feedbacks caused by re aerosols (Ghan, 2013).

3.5 Fire aerosol effects on shortwave radiation, global temperature and precipitation

Here, we show the annual mean net shortwave ux changes at TOA (i.e., total radiative effect), in the atmosphere and at the surface as well as changes in surface air temperature, convective and large-scale precipitation due to all re aerosols in Fig. 11 and Table 2. The global mean net shortwave ux change at TOA due to all re aerosols is

0.55 0.07 W m2, which indicates that re aerosols lead

to the reduction of shortwave ux into the Earths system. The zonal mean TOA shortwave ux reduction in the Arctic regions (1.35 1.03 W m2) is much larger than in the

tropical regions (0.66 0.09 W m2). The cooling at TOA

is mostly from re aerosol REaci. The maximum negative RE is located in the land areas of the Arctic and ocean areas of the tropics. Although the global mean total radiative effect is negative, a positive effect is found in some land areas (e.g., Africa, Greenland).

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Y. Jiang et al.: Impacts of global open-re aerosols 14817

Table 2. Global, tropics (25 S to 25 N) and Arctic (60 to 90 N) annual mean re aerosol (POM and BC) burdens, re aerosol AOD, total re aerosol radiative effect (RE) at TOA, radiative effects due to aerosolradiation interactions (REari), due to aerosolcloud interactions (REaci), surface-albedo changes (REsac) and changes in cloud liquid water path (LWP), low-level cloud cover, net solar uxes at the surface, shortwave atmospheric absorption, surface air temperature and precipitation (total, convective and large scale) due to all re aerosols. Standard deviations about the 10-ensemble means are included.

Global Tropics Arctic (25 S to 25 N) (60 to 90 N)

Fire POM burden (mg m2) 1.25 0.01 1.87 0.01 1.70 0.08

Fire BC burden (mg m2) 0.106 0.001 0.17 0.001 0.09 0.004

Fire aerosol optical depth 0.008 0.001 0.012 0.001 0.007 0.0004

Total radiative effect (W m2) 0.55 0.07 0.66 0.09 1.35 1.03

Radiative effect due to ARI (W m2) 0.16 0.01 0.17 0.017 0.43 0.028

Radiative effect due to ACI (W m2) 0.70 0.05 0.82 0.09 1.38 0.23

Radiative effect due to surface-albedo changes (over land, W m2) 0.03 0.10 0.04 0.06 0.09 0.80

Cloud liquid water path (g m2) 1.62 0.01 1.95 0.13 2.59 0.25

Low-level cloud cover (%) 0.012 0.06 0.055 0.05 0.46 0.45

Net solar ux at surface (W m2) 1.38 0.05 1.91 0.12 2.27 1.04

Shortwave atmospheric absorption (W m2) 0.83 0.03 1.25 0.04 0.92 0.05

Surface air temperature (K) 0.03 0.03 0.024 0.011 0.15 0.20

Total precipitation rate (mm day1) 0.010 0.002 0.016 0.01 0.001 0.02

Convective precipitation rate (mm day1) 0.003 0.002 0.001 0.009 0.005 0.003

Large-scale precipitation rate (mm day1) 0.007 0.002 0.015 0.003 0.004 0.019

The shortwave atmospheric absorption change in the tropical regions is larger than in the Arctic regions. It is because BC burden in the tropics (0.17 mg m2) is larger than in the

Arctic (0.09 mg m2). Strong absorption ( 8 W m2) in the

atmosphere is found in the land areas of southern Africa and South America and in the southeast Atlantic. The surface shortwave ux change in the Arctic is mostly from the TOA shortwave ux reduction due to the re aerosol REaci, while the surface shortwave ux change in the tropics is mostly due to the re BC absorption in the atmosphere.

The re aerosols lead to the reduction of the global mean surface air temperature (Ts) by 0.03 0.03 K, consistent with

the reduction of shortwave uxes at TOA and at the surface.

The largest surface cooling is found in the Arctic and tropical regions by up to 0.6 K. The cooling of the Arctic is related to the strong re aerosol REaci, while the cooling in the tropics is mainly from the surface shortwave ux reduction due to the re BC absorption. The Ts change in the ocean areas is very small since the SST is prescribed in our simulations.

The global mean total precipitation is reduced by0.010 0.002 mm day1 due to all re aerosols (Table 2).

Unlike the Ts change, the precipitation reduction in the tropics (0.016 0.01 mm day1) is much larger than in the Arc

tic (0.001 0.02 mm day1, not statistically signicant). The

reduction in the tropics is mainly from the large-scale precipitation decrease (0.015 0.003 mm day1). The net de

crease in convective precipitation is very small in the tropics (0.001 0.009 mm day1, not statistically signicant), as

the convective precipitation signicantly decreases near the equator and increases in the regions away from the equator,

partly consistent with the results of Tosca et al. (2013). The precipitation reduction in southern Africa is consistent with the recent ndings of Hodnebrog et al. (2016). The shortwave ux reduction at the surface leads to a stabilization of the atmospheric boundary layer and a suppression of convection near the equator. The strong atmospheric absorption by re BC leads to the reduction of low-level clouds and large-scale precipitation in the tropics. Both effects lead to a signicant reduction of total precipitation near the equator. The precipitation decrease in the NH high latitudes is mainly from the reduction of convective precipitation. We note that the temperature and (especially) precipitation changes reported here do not represent the complete impact of re aerosols, since the SSTs are xed in our simulations. Fully coupled atmosphere and ocean models will be used to further investigate the impact of re aerosols.

Figure 12 shows the changes of Ts, total precipitation, cloud LWP and low-level cloud cover in the boreal summer due to all re aerosols. The Ts is reduced by more than 1 K in most of land areas around 60 N. The maximum cooling (larger than 1.5 K) is found in Eastern Siberia, Alaska and Canada. A decrease of total precipitation (by about 0.2 mm day1) is found in these regions. Accompanying the surface cooling and precipitation reduction, a signicant increase of cloud LWP and low-level cloud cover is found there. This is a result of the indirect effect of re aerosols in the land areas of the Arctic (60 to 90 N). The re

POM leads to the reduction of cloud droplet effective radius and the increase of cloud droplet number concentration, con-

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14818 Y. Jiang et al.: Impacts of global open-re aerosols

In addition, the re aerosol REari and re BC-in-snow effect are diagnosed from an CESM experiment which tracks the open-re BC and POM separately from fossil fuel and biofuel sources and compared with the estimates from the Ghan (2013) method.

The BC and POM burdens from open res are largest in the tropical regions (southern Africa, South America and Southeast Asia) and in the NH middle to high latitudes (North of 45 N) (northeast Asia, Alaska and Canada). Fire aerosols contribute 41 and 70 % to the global burden of BC and POM, respectively. When being compared to the AERONET AOD and SSA data, modeled monthly AOD agrees with observations within a factor of 2 for most of the southern African and South American sites. The model underestimation of AOD is found in the South American sites near re source regions, which is most obvious in the re season (September and October). The model underestimates the observed AOD in the Arctic regions in both re and nonre seasons. The modeled SSA in southern Africa and South America is generally in agreement with observations, while the modeled SSA in the Arctic is lower.

The annual mean REari of all re aerosols is0.16 0.01 W m2 and positive over most areas except

in some land areas (e.g., southern Africa, northern Canada and Eastern Siberia). The annual maximum REari is found in the oceanic areas to the west of southern Africa(5 W m2) and South America (1.5 W m2). The positive

REari over the land regions of southern Africa and South America is smaller, although the re aerosol burdens are higher. The annual zonal mean REari in the Arctic regions can reach 0.43 0.028 W m2 and is larger than in the

tropical regions (0.17 0.017 W m2), although the re

aerosol burden is higher in the tropics. The annual mean REari of re BC is about 0.25 0.01 W m2 and posi

tive over the globe. Fire POM induces a weak negative REari globally (0.05 W m2) with the BBFFBF method

and a small positive value (0.04 0.01 W m2) with the

Ghan (2013) method. The positive REari of re POM is found over oceanic areas to the west of southern Africa and South America, North Pacic and polar regions where the low-level cloud coverage is large or the surface albedo is high.

The global annual mean REaci of all re aerosols is

0.70 0.05 W m2 and the maximum effect is located in

the ocean areas west of southern Africa and South America and land areas of the NH high latitudes. The maximum re aerosol REaci occurs in the NH high latitudes in the boreal summer, which results from the large cloud LWP over the land areas and the low solar zenith angle. Associated with the strong indirect effects of re aerosols in the Arctic summer, signicant surface cooling, precipitation reduction, and low-level cloud cover increase are found in these regions.

Modeled BCS concentrations from the FIRE experiment are evaluated against observations in northern China and the Arctic and they generally agree with the observations for the

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Figure 10. (a) Annual mean radiative effect due to surface-albedo changes (REsac, W m2) averaged over the period of 20032011 of all re aerosols over land regions, and annual mean surface effect of re BC-in-snow calculated from SNICAR averaged (b) over all times and (c) only when snow is present. The plus signs in (a) denote the regions where the radiative effect is statistically signicant at the 0.1 level.

sistent with observed re effects on clouds over Canada and

the United States (Peng et al., 2002).

4 Discussion and conclusions

Although many studies have been conducted on the re aerosols RE and RF (e.g., Bond et al., 2013; Myhre et al., 2013b; Ward et al., 2012; Tosca et al., 2013), the current estimations are still associated with large uncertainties. In this study, the re aerosol RE (including REari, REaci and RE-sac) is calculated based on a new method from Ghan (2013).

Y. Jiang et al.: Impacts of global open-re aerosols 14819

Figure 11. Annual mean net shortwave ux changes (W m2) over the period of 20032011 (a) at top of the atmosphere, (b) in the atmosphere, (c) at the surface and changes of (d) surface air temperature (Ts, K), (e) convective precipitation (mm d1) and (f) large-scale precipitation (mm d1) due to all re aerosols. The plus signs denote the regions where the change is statistically signicant at the 0.1 level.

mean and median values in the Arctic regions. The high bias of modeled BCS concentrations in northern China may not result from the re BC because differences in BCS concentrations between FIRE and NOFIRE experiments are very small in northern China. The global annual mean REsac is0.03 0.10 W m2 (statistically insignicant) with the max

imum effect in spring (0.12 W m2). The REsac is mainly due to the effect of re BC deposit on snow (0.02 W m2)

diagnosed from SNICAR with the maximum effect as large as 0.06 W m2 (when snow is present) in spring.

The re aerosols reduce the global mean surface air temperature (Ts) by 0.03 0.03 K and precipitation by

0.01 0.002 mm day1. The maximum cooling ( 1 K) due

to re aerosols occurs around 60 N in boreal summer, and a suppression of precipitation ( 0.1 mm day1) is also found

there. The strong cooling is a result of the strong indirect ef-

fects (15 W m2) in the land areas of the Arctic regions (60

to 90 N). A signicant reduction of precipitation in southern

Africa is also noticed. We note that these results are based on the simulations with xed SSTs and may not represent the full climate responses.

In our study, the global radiative effect of re aerosols is estimated from simulations performed with the 4-mode version modal aerosol module (MAM4) (Liu et al., 2016), daily re emissions with prescribed vertical emission proles, and higher model resolution (0.9 by 1.25 ) compared to earlier modeling studies of re aerosols (Tosca et al., 2013; Ward et al., 2012). In their studies, the GFED re aerosol emissions were increased by a factor of 13 depending on regions matching the observed AOD. In our study, we do not apply the scaling factor to the re aerosol emissions. Our global annual mean REari of re aerosols (0.16 0.01 W m2) is,

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14820 Y. Jiang et al.: Impacts of global open-re aerosols

Figure 12. Changes in (a) surface air temperature (Ts, in K), (b) total precipitation (mm d1), (c) cloud liquid water path (LWP, in g m2) and (d) low-level cloud cover (CLDLOW, in %) due to all re aerosols in the boreal summer (JJA) averaged for the period of 20032011. The plus signs denote the regions where the change is statistically signicant at the 0.1 level.

however, close to 0.18 W m2 in Tosca et al. (2013) and0.13 W m2 in Ward et al. (2012). The similar re aerosol REari from our study, which has smaller re emissions than these previous studies can result from (1) the use of MAM4 in our study, which more realistically represents the external/internal mixing of BC with other soluble aerosol species;(2) the more accurate estimation of REari of re aerosols in the presence of low-level clouds with the method by Ghan (2013) and (3) the inclusion of vertical emissions of re aerosols, which allows more efcient transport of re aerosols from sources. The REaci due to re aerosols in our study (0.70 0.05 W m2) is smaller than 1.64 W m2

in Ward et al. (2012) due to the lower re POM emissions used in this study compared to Ward et al. (2012).

We note that there are limitations and uncertainties with our study. The model still underestimates observed AODs (mostly within a factor of 2) at the sites predominantly inuenced by biomass burning aerosols during the re season, which implies that the re aerosol RE can be stronger than estimated in this study. The RE estimates of re POM and re BC with the Ghan (2013) approach may not be accurate due to the internal mixing of co-emitted re components (POM and BC). In our simulations, sea ice is prescribed, thus the re BC effect on sea ice albedo is not considered. The brown carbon component of POM (Feng et al., 2013) is not treated

in the current CESM model, which may result in an underestimation of atmospheric absorption of re aerosols.

5 Data availability

The re emission data were obtained from the Global Fire Emissions Database (GFED, http://www.globalfiredata.org

Web End =http://www.globalredata.org ). The AERONET data were obtained from http://aeronet.gsfc.nasa.gov

Web End =http://aeronet.gsfc. http://aeronet.gsfc.nasa.gov

Web End =nasa.gov . Model outputs are available on request from the corresponding author. The source codes and model setups needed to repeat all simulations are also available upon request.

The Supplement related to this article is available online at http://dx.doi.org/10.5194/acp-16-14805-2016-supplement

Web End =doi:10.5194/acp-16-14805-2016-supplement .

Acknowledgements. This work is supported by the Ofce of Science of the US Department of Energy (DOE) as the NSFDOE-USDA Joint Earth System Modeling (EaSM) Program, the National Key Basic Research Program (973 Program) of China under Grant No. 2010CB428504, and the National Natural Science Foundation of China (NSFC) under Grant No. 41505062.The Pacic Northwest National Laboratory is operated for the

Atmos. Chem. Phys., 16, 1480514824, 2016 www.atmos-chem-phys.net/16/14805/2016/

Y. Jiang et al.: Impacts of global open-re aerosols 14821

DOE by the Battelle Memorial Institute under contract DEAC05-76RL01830. The authors would like to acknowledge the use of computational resources (ark:/85065/d7wd3xhc) at the NCAR-Wyoming Supercomputing Center provided by the National Science Foundation and the State of Wyoming, and supported by NCARs Computational and Information Systems Laboratory. The re emission data were obtained from the Global Fire Emissions Database (GFED, http://www.globalfiredata.org

Web End =http://www.globalredata.org ). The AERONET data were obtained from http://aeronet.gsfc.nasa.gov

Web End =http://aeronet.gsfc.nasa.gov . We thank Xiangjun Shi for the help with processing the AERONET data.

Edited by: K. TsigaridisReviewed by: two anonymous referees

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