Abstract

The terrestrial biosphere is an important source of natural aerosol. Natural aerosol sources alter climate, but are also strongly controlled by climate, leading to the potential for natural aerosol–climate feedbacks. Here we use a global aerosol model to make an assessment of terrestrial natural aerosol–climate feedbacks, constrained by observations of aerosol number. We find that warmer-than-average temperatures are associated with higher-than-average number concentrations of large (>100 nm diameter) particles, particularly during the summer. This relationship is well reproduced by the model and is driven by both meteorological variability and variability in natural aerosol from biogenic and landscape fire sources. We find that the calculated extratropical annual mean aerosol radiative effect (both direct and indirect) is negatively related to the observed global temperature anomaly, and is driven by a positive relationship between temperature and the emission of natural aerosol. The extratropical aerosol–climate feedback is estimated to be −0.14 W m−2 K−1 for landscape fire aerosol, greater than the −0.03 W m−2 K−1 estimated for biogenic secondary organic aerosol. These feedbacks are comparable in magnitude to other biogeochemical feedbacks, highlighting the need for natural aerosol feedbacks to be included in climate simulations.

Main

The terrestrial biosphere regulates atmospheric composition and climate by altering the exchange of energy, water and trace gases between the surface and atmosphere1. The terrestrial biosphere is an important source of natural aerosols2 from vegetation fires and biogenic volatile organic compounds (BVOCs), which can form secondary organic aerosol (SOA)3. These natural sources can dominate ambient aerosol in tropical4,5,6,7, temperate8,9 and boreal10 environments. Atmospheric aerosol alters the Earth’s climate by absorbing and scattering radiation (the direct radiative effect, DRE) and through altering the albedo of clouds (the first aerosol indirect effect, AIE)11. Because natural aerosol constitutes a major fraction of ambient aerosol it can have important radiative effects12,13,14,15. The physical and biological processes that control natural aerosol sources are highly sensitive to climate2. For example, changes to climate drive large changes in fire16,17,18, BVOC19 and dust20 emissions. These interactions between natural aerosol and the climate create the potential for natural aerosol–climate feedbacks.

A number of natural aerosol–climate feedbacks have been proposed. The first proposed, and perhaps best known, involves ocean biology and the emission of di-methylsulfide21. Terrestrial aerosol–climate feedbacks have also been suggested1. Warmer temperatures drive increased BVOC emissions and increased SOA concentrations, which lead to a negative radiative effect and a cooling impact on climate22. Warmer temperatures also lead to increased fires and associated aerosol emissions16 with impacts on climate. Observations of increased aerosol concentrations with increasing ambient temperatures have been attributed to these interactions23,24. However, the magnitude of natural aerosol feedbacks has rarely been assessed, although large projected changes in natural aerosol under a warming climate suggest that they could be substantial2. Here we explore the potential magnitude of aerosol feedbacks for two terrestrial natural aerosol sources with important climate impacts12: biogenic SOA and landscape fire aerosol.

Exploring natural aerosol–temperature interactions

To explore the potential for natural aerosol–climate feedbacks, we analysed long-term measurements of aerosol number made at 11 continental locations (Supplementary Fig. 1) mostly across Northern Hemisphere mid-latitudes24. We used the number concentration of particles with a dry diameter larger than 100 nm (N 100) as a proxy for concentrations of cloud condensation nuclei (CCN)25. Particles with dry diameters larger than 100 nm are also able to scatter radiation efficiently in the atmosphere.

At most of these locations, N 100 is positively related to local surface temperature (Supplementary Fig. 2) as reported previously24. We find that a global aerosol microphysics model26 (see Methods) reproduces this observed relationship well (Supplementary Fig. 2). To further explore the relationship between surface temperature and N 100, we de-seasonalized both variables. Figure 1 shows the N 100 anomaly as a function of the anomaly in local surface temperature. In summer we find that most locations exhibit a strong positive relationship between the temperature anomaly and anomaly in N 100, with little or no relationship in winter. This means that in summer, days that are warmer than average typically have higher than average N 100. The global model, analysed in the same way as the observations, reproduces the observed relationships (Fig. 1). The summertime mean observed sensitivity between N 100 and temperature, calculated across all observation stations, is +51.3 ± 5.9 cm−3 K−1 (linear regression based on 500 bootstrap samples) and is well reproduced by the model (+43.5 ± 4.2 cm−3 K−1). The relative anomaly in particle number shows a similar relationship that is also well reproduced by the model (Supplementary Fig. 3). This suggests that the model captures the processes responsible for driving the observed temperature–aerosol relationships.

Fig. 1: The relationship between particle number anomaly and temperature anomaly.
Fig. 1

The particle number anomaly is calculated for N 100. ad, Winter (a,b) and summer (c,d) relationships are shown for both observations (a,c) and the model (b,d). Each observation is represented by a point (left), whereas the lines represent the median N 100 anomaly per 2 K temperature anomaly bin. Different locations are indicated by the different coloured lines. All stations except one are in the Northern Hemisphere, where winter is December to February and summer is July to August. For the one station in the Southern Hemisphere we show July to August as winter and December to February as summer.

The observed relationship between temperature and aerosol number could be due to interactions between natural aerosol sources and climate. However, it could also be due to processes that are unrelated to natural aerosol sources. For example, warmer temperatures could be associated with lower rainfall, and therefore reduced aerosol loss via wet deposition. Or warmer temperatures could be related to the transport of southerly air masses towards the measurement locations, bringing more polluted air with high aerosol concentrations.

To help interpret the observed relationships, we analysed multiple simulations from the atmospheric aerosol model in which we individually switch off interannual variability in natural aerosol emissions or meteorology (see Methods). At all locations, we find that the simulated relationship between the N 100 anomaly and the temperature anomaly breaks down (the sensitivity of N 100 to temperature reduced to +5.0 ± 1.6 cm−3 K−1) when we remove the interannual variability in simulated meteorology (that is, the simulation uses repeating 1997 meteorological data). This suggests that meteorology is an important mechanism driving the observed relationship between the anomalies in N 100 and temperature. Simulations where we remove the interannual variability in natural aerosol emissions have less impact on the simulated relationship between N 100 and temperature, with the sensitivity reducing to +41.9 ± 4.2 cm−3 K−1 for fixed fire and +40.9 ± 4.0 cm−3 K−1 for fixed BVOC emissions (Supplementary Fig. 4). We also find that if we increase the simulated SOA formation from BVOCs (by a factor of five) the relationship between N 100 and temperature further breaks down at warm temperature anomalies (Supplementary Fig. 4). This demonstrates that the relationship between N 100 and temperature is sensitive to the treatment of SOA in the model and suggests that this treatment is adequately represented in the control simulation. Overall our analysis suggests that although meteorology is the dominant driver of the observed relationships between temperature and aerosol number, variability in natural aerosol emission also contributes. Our realistic simulation of the observed relationships between aerosol and temperature suggests that our model treatment of fire emissions and SOA14 are adequate to simulate the main interactions that are important for this study.

Interannual variability in aerosol radiative effects

Using the multi-annual simulations of global aerosol, we explore how natural aerosol sources alter the interannual variability in the top-of-atmosphere aerosol radiative effect. We focus on the aerosol DRE and first AIE, also known as the cloud albedo effect11. Other interactions between aerosol and cloud are likely, but are highly uncertain27. Over the period 1997–2007, the global annual mean DRE has a standard deviation of 0.025 W m−2 whereas the AIE has a standard deviation of 0.017 W m−2 (Supplementary Fig. 5). In this control simulation, the year-to-year variability in fire emissions are prescribed from the Global Fire Emissions Dataset version 3 (GFED3)28 and BVOC emissions are calculated using MEGAN version 2.129. To isolate the contribution of different aerosol sources to this variability in the aerosol radiative effect we individually switch off natural aerosol–climate couplings (see Methods). We then use the difference between the control simulation and the simulation where the interannual variability of the natural aerosol source has been switched off to calculate the variability caused by each natural aerosol source. Figure 2 shows the interannual variability in the simulated aerosol radiative effect due to variability in biogenic SOA and fire emissions. Variability in fire aerosol causes interannual variability in both the DRE and AIE of greater than 0.5 W m−2 over and downwind of regions of tropical and boreal fires. Interannual variability in biogenic SOA causes smaller variability in the radiative effect, with variability of up to 0.2 W m−2 over the southeast United States and tropical forest regions. Landscape fires have also been shown to control interannual variability in regional surface carbonaceous aerosol concentrations9 and aerosol optical depth30.

Fig. 2: Interannual variability in aerosol radiative effects.
Fig. 2

ad, DRE (a,b) and AIE (c,d) for biogenic SOA (a,c) and fire emissions (b,d). The standard deviation in the global annual mean radiative effect over the period 1997–2007 is shown in each panel.

Figure 2 shows that some of the largest simulated radiative impacts caused by variability in natural aerosol are in the tropics. However, our understanding of atmospheric composition and emissions of natural aerosol in the tropics is still poor. There are very few long-term studies of aerosol size distribution in the tropical atmosphere and none of our 11 stations are in the tropics (defined here as 20° S to 20° N). The tropics are thought to be the dominant source of both BVOC29 and fire28 emissions. However, there have been few studies of BVOC emissions in the tropics. A study in the Amazon, confirmed the importance of temperature, light and leaf phenology in driving BVOC emissions but also suggested our mechanistic understanding of BVOC emissions in the tropics is still limited31. The chemical composition of monoterpene emissions in the tropics may also vary with temperature, with reactive isomers being enriched at high temperatures32,33 with potential consequences for SOA. For these reasons we focus on the extratropical (>20° S and >20° N) radiative effect, where there is less uncertainty in BVOC emissions and we have observations to constrain the sensitivity of the aerosol model to natural aerosol.

We explored the control on the variability in the aerosol radiative effect over the period 1997–2007 (see Methods). We find that there is a negative correlation between the global (land and ocean) surface temperature anomaly and the anomaly in extratropical annual mean DRE (Pearson’s r = −0.74, P < 0.01) and AIE (r = −0.52, P < 0.1) (Supplementary Table 1). Figure 3 shows the anomaly in the radiative effect due to variability in biogenic SOA and fire aerosol as a function of the anomaly in annual global temperature (see Methods). We find an even stronger negative correlation between the global temperature anomaly and the anomaly in the extratropical annual mean radiative effect from biogenic SOA, both for the DRE (r = −0.76, P < 0.01) and AIE (r = −0.71, P < 0.01). Simulated emissions of BVOC are strongly controlled by temperature29; we find a strong positive correlation between the annual extratropical BVOC emission and the global temperature anomaly (monoterpene r = 0.78; isoprene r = 0.79) (Supplementary Fig. 7). Warmer-than-average years drive increased BVOC emissions leading to increased formation of SOA, which results in a stronger negative DRE and AIE.

Fig. 3: The relationship between aerosol radiative effect anomaly and global temperature anomaly.
Fig. 3

a,b, Anomaly in DRE (a) and AIE (b) due to variability in biogenic SOA (blue) and fire aerosol (red) as a function of global temperature anomaly. Symbols show the results for the extratropics (>20° N and >20° S). Linear fits are shown for the extratropics (solid line) and at the global scale (dashed line). The correlation (r) between the radiative effect and temperature anomaly are shown in each panel. Temperature anomalies are calculated relative to a 1971–2000 climatology.

We also simulate a negative correlation between the global temperature anomaly and the anomaly in the extratropical radiative effect from fire, both for the DRE (r = −0.5, P < 0.1) and AIE (r = −0.51, P < 0.05). We find a positive correlation between the annual extratropical particulate emission from landscape fires and the global temperature anomaly (r = 0.39) (Supplementary Fig. 7). The correlation between the temperature anomaly and the radiative effect from fires is weaker compared with BVOC, due to this weaker correlation between temperature and fire emission. Global fire activity is governed by a complex suite of climate, natural and human ignition sources and available fuel18. Although years with warm temperature anomalies are associated with greater fire emissions, other climate variables such as rainfall and relative humidity are also important18,34.

Diagnosing natural aerosol–climate feedbacks

We used the relationship between the radiative effect and global temperature anomaly to estimate the aerosol radiative feedback (λ) for the different natural aerosol sources, following the framework of previous work1 (see Methods). In this framework climate feedbacks are expressed in common units of W m−2 K−1 and are shown in Fig. 4. We estimate that fire results in an extratropical direct aerosol radiative feedback of −0.09 ± 0.06 W m−2 K−1 and an extratropical indirect aerosol radiative feedback of −0.06 ± 0.03 W m−2 K−1. We estimate a smaller radiative feedback due to biogenic SOA, with an extratropical direct radiative feedback of −0.02 ± 0.01 W m−2 K−1 and an extratropical indirect aerosol radiative feedback of −0.007 ± 0.002 W m−2 K−1.

Fig. 4: Simulated natural aerosol feedback.
Fig. 4

Values are shown for biogenic SOA (blue) and fire aerosol (red). The solid bars show the extratropical feedback (>20° N and >20° S), and the dashed bars show the global feedback. The error bars show the standard error in the estimated feedback (based on 500 bootstrap samples).

Figure 4 also shows our estimates of the global radiative feedbacks. The global aerosol feedback for fire aerosol is similar to that calculated for the extratropics. In contrast, the global biogenic SOA feedback is about double the strength of that calculated in the extratropics (Fig. 4). We note that we have no observational constraint for the natural aerosol feedback in the tropics, and so these global estimates are unconstrained. Long-term observations of BVOC emissions and aerosol concentrations in the tropics are urgently needed.

The stronger fire aerosol radiative feedback compared with the biogenic SOA feedback is primarily due to the stronger interannual variability of fire emissions compared with BVOCs. The coefficient of variation (standard deviation divided by the mean) for global particulate emission from fire is 19.6% (Supplementary Fig. 7 and Methods). The simulated coefficient of variation for BVOC emissions is substantially smaller both for isoprene emissions (2.9%) and for monoterpene emissions (2.4%). Our simulated interannual variability in BVOC emissions and biogenic SOA matches previous work35. The absolute variability in both BVOC and particulate emissions from fire is greatest in the tropics, but the extratropics exhibit greater fractional variability (Supplementary Figs. 6 and 7).

We find that the direct radiative feedback is stronger than the indirect radiative feedback for both natural aerosol sources. This behaviour is particularly true for biogenic SOA where the direct aerosol feedback is more than a factor of three greater than the indirect aerosol feedback. We note that our estimated direct aerosol feedback for fires will depend on the net DRE of fire aerosol which is uncertain36. The relatively weak aerosol indirect feedback for biogenic SOA is due to the AIE being relatively insensitive to emission of BVOC12,14. The global biogenic SOA feedback from the aerosol indirect effect that we estimate here (−0.013 ± 0.002 W m−2 K−1) is similar to the global mean value of −0.01 W m−2 K−1 inferred from selected observations24.

Our estimate of natural aerosol–climate feedbacks is applicable for the present day and may be different in future or past climates. Climate change and increased atmospheric carbon dioxide concentrations will alter the amount and type of vegetation37,38, leading to variation in both BVOC29 and fire emissions39,40. Changes in environmental factors in a warming climate may lead to stressed vegetation and additional BVOC emissions, potentially creating stronger couplings between vegetation, aerosol and climate41. Increased CO2 concentrations may alter BVOC emissions29, thereby changing biogenic SOA and associated feedbacks. Feedbacks can also be directly altered by human activity. Land-use change and land management have altered BVOC and fire emissions since pre-human times42. Anthropogenic aerosol suppresses natural aerosol–climate interactions43, meaning natural aerosol–climate feedbacks may strengthen with future reductions in anthropogenic aerosol emissions. Additional feedbacks between the biosphere, BVOC, fire emissions and climate that operate through precipitation and soil moisture are possible, but are not included here.

The strength of natural aerosol feedbacks is comparable in magnitude to a range of other biogeochemical feedbacks1 and is opposite in sign to the global snow–albedo feedback44, which has been estimated as +0.1 W m−2 K−1. Our findings suggest that natural aerosol–climate feedbacks may play a role in moderating the net temperature response to CO2-driven or other external forcings, and should be included in fully coupled simulations of the past and future climate.

Methods

Observations

We used long-term observations of N 100 and surface temperature from 11 surface stations. The observations are as described in a previous study24. We de-seasonalized both N 100 and temperature through subtracting the long-term monthly mean from the original data. We calculated the sensitivity of N 100 to the surface temperature between an anomaly of −10 K and +10 K.


Aerosol model

We used the TOMCAT chemical transport model coupled to the GLOMAP-mode aerosol microphysics model26 to simulate the distribution of atmospheric aerosol over the period 1997–2007. Fire emissions were from GFEDv3, based on burned area, active fire detections and plant productivity from the MODerate resolution Imaging Spectroradiometer (MODIS)28. Emissions of isoprene and monoterpenes were calculated using MEGANv2.129 in the Community Land Model (CLMv4.5). Emissions depend on the distribution of vegetation, CO2 concentration, solar radiation, temperature and moisture. Anthropogenic aerosol emissions and precursors were from the MACCity dataset45. Other natural aerosol and aerosol precursor emissions include oceanic DMS emissions calculated using a sea–air transfer velocity46 and a sea surface concentration database47, sea-spray emissions48 and volcanic sulphur emissions49. GLOMAP was forced with ERA-Interim analyses from the European Centre for Medium Range Weather Forecasts (ECMWF). We use offline oxidant concentrations from the TOMCAT chemical transport model. Here GLOMAP has a horizontal resolution of 2.8° × 2.8° and 31 vertical levels between the surface and 10 hPa. The model simulates aerosol component mass and number concentration (two-moment modal) in seven lognormal modes: hygroscopic nucleation, Aitken, accumulation, coarse, and non-hygroscopic Aitken, accumulation and coarse modes. The modal version of the model matches a more computationally expensive sectional scheme50. SOA is formed from the oxidation of monoterpenes and isoprene and is treated as described in previous work14. The oxidation products of monoterpenes are able to participate in new particle formation51 and growth whereas the oxidation products of isoprene contribute only to condensational growth. A control simulation where emissions and meteorology varied according to simulation year was compared against simulations where one specific emission or process was fixed to 1997 values. These were: (1) anthropogenic emissions, (2) biogenic VOC emissions, (3) landscape fire emissions and (4) ERA-Interim fields. All simulations were run for the period 1997–2007.


Radiation model

Top-of-atmosphere, all-sky aerosol radiative effects were calculated using the Suite of Community Radiative Transfer codes (SOCRATES)52. We calculated the DRE and the AIE resulting from changes to cloud droplet number concentration. Full details are provided in ref. 14.

Aerosol radiative effects were calculated for all five aerosol model simulations that are described above. The global annual mean radiative effect was calculated for each simulation. The anomaly in the global annual mean radiative effect was calculated with respect to the start year of the simulation (1997). We then calculated global annual mean radiative effect anomaly for each emission or process as the difference in global mean radiative effect anomaly between the control simulation and the simulation where that process had been fixed to 1997 values. The sum of radiative effect anomalies from the four simulations agreed with the radiative effect anomaly from the control simulation to within 2%.


Climate feedback

Global annual temperature anomalies (ΔT) are from the National Oceanic and Atmospheric Administration (NOAA). We used the NOAA Merged Land Ocean Global Surface Temperature Analysis dataset (NOAAGlobalTemp v4.0.1)53, a spatially gridded (5° × 5°) global surface temperature dataset. Temperature anomalies were calculated over land and ocean with respect to the 1971 to 2000 climatology. The Pearson’s correlation (r) between the change in the top-of-atmosphere radiative effect (ΔRE) and ΔT was calculated for each simulation.

We calculate the climate feedback (λ) following previous work1. Climate feedbacks, expressed in units of W m−2 K−1, are calculated for each natural aerosol as λ = ΔRE/ΔT), determined from the gradient of the best fit line between ΔRE and ΔT. Uncertainty in the calculated feedback is estimated using a bootstrapping approach, based on 500 bootstrap samples.


Code availability

A request for the code used to generate these results can be made via http://www.ukca.ac.uk/wiki/index.php/Main_Page


Data availability

The NOAA Merged Land Ocean Global Surface Temperature Analysis Dataset (NOAAGlobalTemp v4.0.1) is available online (https://www.ncdc.noaa.gov/data-access/marineocean-data/noaa-global-surface-temperature-noaaglobaltemp). Data from our model simulations are available upon request.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We acknowledge support from the Natural Environment Research Council (NE/K015966/1), EU Horizon 2020 (SC5-01-2014; grant agreement no 641816) and the Academy of Finland Centre of Excellence (grant nos 1118615 and 272041). We would like to thank the providers of measurement data for ref. 24. This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk).

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Affiliations

  1. Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK

    • C. E. Scott
    • , S. R. Arnold
    •  & D. V. Spracklen
  2. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    • S. A. Monks
  3. Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, USA

    • S. A. Monks
  4. Department of Physics, University of Helsinki, Helsinki, Finland

    • A. Asmi
    •  & P. Paasonen
  5. International Institute for Applied Systems Analysis, Laxenburg, Austria

    • P. Paasonen

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Contributions

All authors contributed to the research design. C.E.S. and S.A.M. performed model simulations. A.A. and P.P. provided observational data. C.E.S., D.V.S. and S.R.A. analysed the data. All authors contributed to scientific discussions and helped to write the manuscript.

Competing interests

The authors declare no competing financial interests.

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Correspondence to C. E. Scott or D. V. Spracklen.

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https://doi.org/10.1038/s41561-017-0020-5

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