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Influence of biogenic emissions from boreal forests on aerosol–cloud interactions

Abstract

Boreal forest acts as a carbon sink and contributes to the formation of secondary organic aerosols via emission of aerosol precursor compounds. However, these influences on the climate system are poorly quantified. Here we show direct observational evidence that aerosol emissions from the boreal forest biosphere influence warm cloud microphysics and cloud–aerosol interactions in a scale-dependent and highly dynamic manner. Analyses of in situ and ground-based remote-sensing observations from the SMEAR II station in Finland, conducted over eight months in 2014, reveal substantial increases in aerosol load over the forest one to three days after aerosol-poor marine air enters the forest environment. We find that these changes are consistent with secondary organic aerosol formation and, together with water-vapour emissions from evapotranspiration, are associated with changes in the radiative properties of warm, low-level clouds. The feedbacks between boreal forest emissions and aerosol–cloud interactions and the highly dynamic nature of these interactions in air transported over the forest over timescales of several days suggest boreal forests have the potential to mitigate climate change on a continental scale. Our findings suggest that even small changes in aerosol precursor emissions, whether due to changing climatic or anthropogenic factors, may substantially modify the radiative properties of clouds in moderately polluted environments.

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Fig. 1: Time evolution of the particle number size distribution.
Fig. 2: Time evolution of the particle mass concentration and optical properties.
Fig. 3: Cloud-related variables.
Fig. 4: Schematic representation of processes affecting aerosols and clouds during an air mass transport over boreal forests.

Data availability

Measurement data for the analysis and figures in this study are archived on the Zenodo repository (https://doi.org/10.5281/zenodo.5645340). The SMEAR II data are available through the AVAA portal (smear.avaa.csc.fi). The ground-based data used in this article are generated by the Atmospheric Radiation Measurement (ARM) user facility and are made available from the ARM Data Discovery website (https://adc.arm.gov/discovery/) as follows: ceilometer data (CEIL) from https://doi.org/10.5439/1181954, dual-channel microwave radiometer (MWR) from https://doi.org/10.5439/1046211, high-spectral-resolution lidar (HSRL) from https://doi.org/10.5439/1025200, optical rain gauge (MET) from https://doi.org/10.5439/1786358 and W-band cloud radar (MWACR) from https://doi.org/10.5439/1150242. The products derived from the ground-based remote-sensing data used in this article (target classification, cloud fraction and liquid water content) are generated by the European Research Infrastructure for the observation of Aerosol, Clouds and Trace Gases (ACTRIS) and are available from the ACTRIS Data Centre using the following link: https://hdl.handle.net/21.12132/2.c85c6a6c2bc348f8. Source data are provided with this paper.

Code availability

The codes for time-over-land calculations are available from the authors upon request. The CCN retrieval package60 can be obtained upon request from Z. Yue (yue_zhiguo@163.com). Automated Mapping of Convective Clouds (AMCC) Thermodynamical, Microphysical and CCN Properties from SNPP/VIIRS.

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Acknowledgements

The work was supported by Academy of Finland via Center of Excellence in Atmospheric Sciences (project no. 272041), Flagship programme for Atmospheric and Climate Competence Center (ACCC, 337549, 337552, 337550) and grants 317380, 320094 and 334792, 328290, 302958, 325656, 316114, 325647, 325681 and 341271, European Research Council Advanced Grants (227463-ATMNUCLE, 742206-ATM-GTP,) and Starting Grants (638703-COALA, 714621-GASPARCON), the Arena for the gap analysis of the existing Arctic Science Co-Operations (AASCO) funded by Prince Albert Foundation contract no. 2859, and ‘Quantifying carbon sink, CarbonSink+ and their interaction with air quality’ INAR project funded by Jane and Aatos Erkko Foundation. This work was partly supported by the Office of Science (BER), US Department of Energy via BAECC (Petäjä, DE-SC0010711), BAECC-SNEX (Moisseev), European Commission via projects, FORCeS, ACTRIS, ACTRIS-TNA, ACTRIS2, ACTRIS-IMP, BACCHUS, eLTER, ICOS, PEGASOS and Nordforsk via Cryosphere–Atmosphere Interactions in a Changing Arctic Climate, CRAICC. The BAECC-SNEX was also supported by NASA Global Precipitation Measurement (GPM) Mission ground validation programme. The deployment of AMF2 to Hyytiälä was enabled and supported by ARM. Argonne National Laboratory’s work was supported by the US Department of Energy, Assistant Secretary for Environmental Management, Office of Science and Technology, under contract DE-AC02-06CH11357. The ground-based data used in this study were obtained from the Atmospheric Radiation Measurement (ARM) user facility, managed by the Office of Biological and Environmental Research for the US Department of Energy Office of Science. We acknowledge ACTRIS for providing the products derived from the ground-based data in this study, which were produced by the Finnish Meteorological Institute and are available for download from https://cloudnet.fmi.fi/. The authors gratefully acknowledge AMF2, SMEAR2 and the BAECC community for their support in initiating the BAECC campaign, its implementation, operation, data analysis and interpretation.

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Correspondence to T. Petäjä.

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Extended data

Extended Data Fig. 1 Statistics on events, non-events and undefined days as a function of time over land in the studied transport sector.

Shorter air mass transport times over the boreal forest favor atmospheric new particle formation, whereas non-event days become more frequent at longer air mass transport times over land.

Source data

Extended Data Fig. 2 Organic aerosol composition as a function of time over land.

A two-factor Positive Matrix Factorization (PMF50) solution performed with Source Finder (SoFi68) hints towards a large contribution of low-volatility oxygenated organic aerosol (LV-OOA) to the total organic loading. The oxidized CO2+ fragment contributes greatly to the LV-OOA mass concentration indicating a high degree of oxidation69. The semi-volatility oxygenated organic aerosol (SV-OOA) shows slightly lower loading compared to LV-OOA. We acknowledge that the PMF solution presented here only gives a rough estimate of the OA factors since also other factors, such as hydrocarbon-like organic aerosol (HOA) and biomass burning organic aerosol (BBOA) can contribute to the total organic loading. However, previous studies suggest that their contribution to the total organic aerosol is minor at SMEAR II as shown in Crippa et al70. Moreover, their finer separation would not change the LV-OOA loading due to the minor CO2+ ion fragment contribution to the HOA and BBOA mass spectra.

Source data

Extended Data Fig. 3 Backscatter fraction as a function of time over land.

The fraction of radiation scattered in the backward direction determined with the nephelometer for the in-situ aerosol decreases as a function of time over land in the studied transport sector. The figure shows that the aerosol particles grow to larger sizes and thus scatter less into the backward direction as the air masses reside longer over the boreal forest region.

Source data

Extended Data Fig. 4 In-situ determined CCN activation diameters as a function of time over land.

The critical CCN activation diameters at water vapor supersaturations (Sc) of 0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 % as a function of time over land in the studied transport sector. Compared with sub-100 nm particles, the sub-population of particles able to act as CCN at Sc = 0.1% shows a notable increase Dcr as a function of time over land. This feature can be explained by a combination of two things: 1) these particles are aged, possibly originating from anthropogenic sources, making them relatively hygroscopic when entering the boreal forest region, 2) accumulation of rather non-hygroscopic organic vapors into these particles decreases their hygroscopicity with increasing transport times over the boreal forest.

Source data

Extended Data Fig. 5 Ground-based remote sensing determined water vapour mixing ratios and column integrated liquid water path as a function of time over land.

a. Specific humidity and b. cloud liquid path (LWP) as a function of time over land in the studied transport sector. The data for time over land < 75 h are used in the fitting and the two red points are removed from the fit as outliers. The shaded areas show 25th and 75th percentiles that illustrate variability of measurements contributing to the averaged LWP for a given time over land and are consistent with the approach applied to creation of all other figures in the study.

Source data

Extended Data Fig. 6 Satellite derived CCN concentration along a selected trajectory.

The trajectory arrived to Hyytiälä from the clean sector during August 17, 2014.

Source data

Extended Data Fig. 7 Cloud fraction and precipitation as a function of time over land.

a. Mean cloud fraction as a function of time over land in the studied transport sector. b. Precipitation accumulated in the hour following trajectory arrival to the station as a function of time over land in the studied transport sector. There is an outlier at (78 h; 1 mm) not shown in the figure, corresponding to a single heavy rain event.

Source data

Extended Data Fig. 8 Time evolution of the total particle mass concentration and organic aerosol mass concentration in different air mass sectors.

The same as Fig. 2a, but a. clean and b. polluted air mass transport sectors.

Source data

Extended Data Fig 9 Time evolution of the particle number size distribution.

The same as Fig. 1c, but a. the clean and b. polluted air mass transport sectors.

Source data

Extended Data Fig 10 Cloud-related variables.

The same as Fig. 3a, except for a. the clean and b. polluted air mass transport sectors.

Source data

Supplementary information

Supplementary Information

Supplementary Table 1

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Source Data Fig. 1

Statistical Source Data for Fig. 1.

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Statistical Source Data for Fig. 2.

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Statistical Source Data for Fig. 3.

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Statistical Source Data for Extended Data Fig. 1.

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Statistical Source Data for Extended Data Fig. 5.

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Statistical Source Data for Extended Data Fig. 6.

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Statistical Source Data for Extended Data Fig. 9.

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Statistical Source Data for Extended Data Fig. 10.

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Petäjä, T., Tabakova, K., Manninen, A. et al. Influence of biogenic emissions from boreal forests on aerosol–cloud interactions. Nat. Geosci. 15, 42–47 (2022). https://doi.org/10.1038/s41561-021-00876-0

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