Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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


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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

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 ( The SMEAR II data are available through the AVAA portal ( 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 ( as follows: ceilometer data (CEIL) from, dual-channel microwave radiometer (MWR) from, high-spectral-resolution lidar (HSRL) from, optical rain gauge (MET) from and W-band cloud radar (MWACR) from 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: 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 ( Automated Mapping of Convective Clouds (AMCC) Thermodynamical, Microphysical and CCN Properties from SNPP/VIIRS.


  1. Boucher, O. et al. in Climate Change 2013: The Physical Science Basis (eds T. Stocker et al.) Ch. 7 (Cambridge Univ. Press, 2013).

  2. Rosenfeld, D. et al. Global observations of aerosol–cloud–precipitation–climate interactions. Rev. Geophys. 52, 750–808 (2014).

    Article  Google Scholar 

  3. Twohy, C. H. et al. Impacts of aerosol particles on the microphysical and radiative properties of stratocumulus clouds over the southeast Pacific Ocean. Atmos. Chem. Phys. 13, 2541–2562 (2013).

    Article  Google Scholar 

  4. Goren, T. & Rosenfeld, D. Extensive closed cell marine stratocumulus downwind of Europe–a large aerosol cloud mediated radiative effect or forcing? J. Geophys. Res. Atmos. 120, 6098–6116 (2015).

    Article  Google Scholar 

  5. Liu, Y. et al. Analysis of aerosol effects on warm clouds over the Yangtze River Delta from multi-sensor satellite observations. Atmos. Chem. Phys. 17, 5623–5641 (2017).

    Article  Google Scholar 

  6. Malavelle, F. F. et al. Strong constraints on aerosol–cloud interactions from volcanic eruptions. Nature 546, 485–491 (2017).

    Article  Google Scholar 

  7. Lu, Z. et al. Biomass smoke from southern Africa can significantly enhance the brightness of stratocumulus over the southeastern Atlantic Ocean. Proc. Natl Acad. Sci. USA 115, 2924–2929 (2018).

    Article  Google Scholar 

  8. Ross, A. D. et al. Exploring the first aerosol indirect effect over Southeast Asia using a 10-year collocated MODIS, CALIOP, and model dataset. Atmos. Chem. Phys. 18, 12747–12764 (2018).

    Article  Google Scholar 

  9. Gryspeerdt, E. et al. Constraining the aerosol influence on cloud liquid water path. Atmos. Chem. Phys. 19, 5331–5347 (2019).

    Article  Google Scholar 

  10. Mülmentstädt, J. et al. Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes. Sci. Adv. 6, eaaz6733 (2020).

    Google Scholar 

  11. Bellouin, N. et al. Bounding global aerosol radiative forcing of climate change. Rev. Geophys. (2020).

  12. Carslaw, K. S. et al. Large contribution of natural aerosols to uncertainty in indirect forcing. Nature 503, 67–71 (2013).

    Article  Google Scholar 

  13. Bonan, G. P. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

    Article  Google Scholar 

  14. Kulmala, M. et al. Direct observations of atmospheric nucleation. Science 339, 943–946 (2013).

    Article  Google Scholar 

  15. Paasonen, P. et al. Warming-induced increase in aerosol number concentration likely to moderate climate change. Nat. Geosci. 6, 438–442 (2013).

    Article  Google Scholar 

  16. Kerminen, V.-M. et al. Atmospheric new particle formation and growth: review of field observations. Environ. Res. Lett. 13, 103003 (2018).

    Article  Google Scholar 

  17. Tunved, P. et al. High natural aerosol loading over boreal forests. Science 312, 261–263 (2006).

    Article  Google Scholar 

  18. Riipinen, I. et al. The contribution of organics to atmospheric nanoparticle growth. Nat. Geosci. 5, 453–458 (2012).

    Article  Google Scholar 

  19. Ehn, M. et al. A large source of low-volatility secondary organic aerosol. Nature 506, 476–479 (2014).

    Article  Google Scholar 

  20. Tröstl, J. et al. The role of low-volatility organic compounds in initial particle growth in the atmosphere. Nature 533, 527–533 (2016).

    Article  Google Scholar 

  21. Pierce, J. R., Westerveld, D. M., Atwood, S. A., Barne, E. A. & Leaitch, W. R. New-particle formation, growth and climate-relevant particle production in Egbert, Canada: analysis of 1 year of size-distribution observations. Atmos. Chem. Phys. 14, 8647–8663 (2014).

    Article  Google Scholar 

  22. Spracklen, D. V., Bonn, B. & Carslaw, K. S. Boreal forests, aerosol and the impacts on clouds and climate. Philos. Trans. R. Soc. Lond. A. (2008).

  23. Scott, C. E. et al. The direct and indirect radiative effects of biogenic secondary organic aerosol. Atmos. Chem. Phys. 14, 447–470 (2014).

    Article  Google Scholar 

  24. Riuttanen, L., Hulkkonen, M., Dal Maso, M., Junninen, H. & Kulmala, M. Trajectory analysis of atmospheric transport of fine particles, SO2, NOx and O3 to the SMEAR II station in Finland in 1996–2008. Atmos. Chem. Phys. 13, 2153–2164 (2013).

    Article  Google Scholar 

  25. Petäjä, T. et al. BAECC, a field campaign to elucidate the impact of biogenic aerosols on clouds and climate. Bull. Am. Met. Soc. 97, 1909–1928 (2016).

    Article  Google Scholar 

  26. Hari, P. & Kulmala, M. Station for measuring ecosystem–atmosphere relations (SMEAR II). Boreal Environ. Res. 10, 315–322 (2005).

    Google Scholar 

  27. Petäjä, T. et al. Effects of SO2 oxidation on ambient aerosol growth in water and ethanol vapours. Atmos. Chem. Phys. 5, 767–779 (2005).

    Article  Google Scholar 

  28. Aiken, A. C. et al. O/C and OM/OC ratios of primary, secondary, and ambient organic aerosols with high-resolution time-of-flight aerosol mass spectrometry. Environ. Sci. Technol. 42, 4478–4485 (2008).

    Article  Google Scholar 

  29. Yatavelli, R. L. N. et al. Estimating the contribution of organic acids to Northern Hemispheric continental organic aerosol. Geophys. Res. Lett. (2015).

  30. Äijälä, M. et al. Constructing a data-driven receptor model for organic and inorganic aerosol: a synthesis analysis of eight mass spectrometric data sets from a boreal forest site. Atmos. Chem. Phys. 19, 3645–3672 (2019).

    Article  Google Scholar 

  31. Eloranta, E. E. in Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere (ed. Weitkamp, C.) Ch. 5 (Springer-Verlag, 2005).

  32. Brenquier, J. L., Pawlowska, H. & Schüller, L. Cloud microphysical and radiative properties for parameterization and satellite monitoring of the indirect effect of aerosol on climate. J. Geophys. Res. 108, 8632 (2003).

    Article  Google Scholar 

  33. Gettelman, A. Putting the clouds back in aerosol–cloud interactions. Atmos. Chem. Phys. 15, 12397–12411 (2015).

    Article  Google Scholar 

  34. Rosenfeld, D. et al. Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers. Proc. Nat. Acad. Sci. USA 113, 5828–5834 (2016).

    Article  Google Scholar 

  35. Hoppel, W. A., Frick, G. M. & Larson, R. E. Effect of nonprecipitating clouds on the aerosol size distribution. Geophys. Res. Lett. 13, 125–128 (1986).

    Article  Google Scholar 

  36. Covert, D. S., Kapustin, V. N., Bates, T. S. & Quinn, P. K. Physical properties of marine boundary layer aerosol particles of the mid-Pacific in relation to sources and meteorological transport. J. Geophys. Res. 101, 6919–6930 (1996).

    Article  Google Scholar 

  37. Andronache, C. Estimated variability of below-cloud removal by rainfall for observed aerosol size distributions. Atmos. Chem. Phys. 3, 131–143 (2003).

    Article  Google Scholar 

  38. Pryor, S. C., Joerger, J. M. & Sullivan, R. C. Empirical estimates of size-resolved precipitation scavenging coefficients for ultrafine particles. Atmos. Environ. 143, 133–138 (2016).

    Article  Google Scholar 

  39. Bollasina, M. A., Ming, Y. & Ramaswamy, V. Anthropogenic aerosols and the weakening of the South Asian summer monsoon. Science 334, 502–505 (2011).

    Article  Google Scholar 

  40. Coopman, Q., Garrett, T. J., Finch, D. P. & Riedi, J. High sensitivity of Arctic liquid clouds to long-range anthropogenic aerosol transport. Geophys. Res. Lett. 45, 372–381 (2018).

    Article  Google Scholar 

  41. Brient, F. & Bony, S. Interpretation of the positive cloud feedback predicted by a climate model under global warming. Clim. Dyn. 40, 2415–2431 (2013).

    Article  Google Scholar 

  42. Teuling, A. J. et al. Observational evidence for cloud cover enhancement over western European forests. Nat. Comm. 8, 14065 (2017).

    Article  Google Scholar 

  43. Jokinen, V. & Mäkelä, J. M. Closed loop arrangement with critical orifice for DMA sheath/excess flow system. J. Aerosol Sci. 28, 643–648 (1997).

    Article  Google Scholar 

  44. Petäjä, T. Science Plan: Biogenic Aerosols—Effects on Clouds and Climate (BAECC) DOE/SC-ARM-13-024 (DOE, 2013).

  45. Aalto, P. et al. Physical characterization of aerosol particles during nucleation events. Tellus B 53, 344–358 (2001).

    Article  Google Scholar 

  46. Wiedensohler, A. et al. Mobility particle size spectrometers: harmonization of technical standards and data structure to facilitate high quality long-term observations of atmospheric particle number size distributions. Atmos. Meas. Tech. 5, 657–685 (2012).

    Article  Google Scholar 

  47. Anderson, T. L. & Ogren, J. A. Determining aerosol radiative properties using the TSI 3563 integrating nephelometer. Aerosol Sci. Technol. 29, 57–69 (1998).

    Article  Google Scholar 

  48. Paramonov, M. et al. A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network. Atmos. Chem. Phys. 15, 11999–12009 (2015).

    Article  Google Scholar 

  49. Ng, N. L. et al. An aerosol chemical speciation monitor (ACSM) for routine monitoring of the composition and mass concentrations of ambient aerosol. Aerosol Sci. Technol. 45, 780–794 (2011).

    Article  Google Scholar 

  50. Paatero, P. & Tapper, U. Positive matrix factorization: a non‐negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111–126 (1994).

    Article  Google Scholar 

  51. Bambha, R. et al. High Spectral Resolution Lidar (HSRL). (ARM, 2014).

  52. Goldsmith, J. High Spectral Resolution Lidar (HSRL) Instrument Handbook DOE/SC-ARM-TR-157 (DOE, 2016).

  53. Balloon-Borne Sounding System (SONDEWNPN). (ARM, 2015).

  54. Kyrouac, J. & Shi, Y. Surface Meteorological Instrumentation (MET). (ARM, 2014).

  55. Morris, V., Zhang, D., & Ermold, B. Ceilometer (CEIL): Cloud-Base Heights. (ARM, 2014).

  56. Lindenmaier, I. et al. Marine W-Band (95GHz) ARM Cloud Radar. (ARM, 2014).

  57. Cadeddu, M. Microwave Water Radiometer (MWR): Water Liq, and Vapor along Line of Sight (LOS) Path. (ARM, 2014).

  58. Cloud Profiling Products: Classification, Liquid water Content, Categorize; 2014-02-02 to 2014-09-09. (ACTRIS, 2017).

  59. Illingworth, A. J. et al. Cloudnet: continuous evaluation of cloud profiles in seven operational models using ground-based observations. Bull. Am. Met. Soc. 88, 883–898 (2007).

    Article  Google Scholar 

  60. Yue, Z. et al. Automated mapping of convective clouds (AMCC) thermodynamical, microphysical and CCN Properties from SNPP/VIIRS satellite data. J. Appl. Met. Clim. (2019).

  61. Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modelling system. Bull. Am. Met. Soc. 96, 2059–2077 (2015).

    Article  Google Scholar 

  62. Kanamitsu, M. Description of NMC global data assimilation and forecast system. Weather Forecast. 4, 335–342 (1989).

    Article  Google Scholar 

  63. Kyrö, E.-M. et al. Trends in new particle formation in Eastern Lapland, Finland: effect of decreasing sulfur emissions from Kola Peninsula. Atmos. Chem. Phys. 14, 4383–4396 (2014).

    Article  Google Scholar 

  64. Frisch, S., Shupe, M., Djalalova, I., Feingold, G. & Poellot, M. The retrieval of stratus cloud droplet effective radius with cloud radars. J. Atmos. Ocean. Tech. 19, 835–842 (2002).

    Article  Google Scholar 

  65. Sarna, K. & Russchenberg, H. W. J. Ground-based remote sensing scheme for monitoring aerosol–cloud interactions. Atmos. Meas. Tech. 9, 1039–1050 (2016).

    Article  Google Scholar 

  66. Falconi, M. T., von Lerber, A., Ori, D., Marzano, F. S. & Moisseev, D. Snowfall retrieval at X, Ka and W bands: consistency of backscattering and microphysical properties using BAECC ground-based measurements. Atmos. Meas. Tech. 11, 3059–3079 (2018).

    Article  Google Scholar 

  67. Kollias, P., Puigdomènech Treserras, B. & Protat, A. Calibration of the 2007–2017 record of ARM cloud radar observations using CloudSat. Atmos. Meas. Tech. 12, 4949–4964 (2019).

  68. Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U. & Prévôt, A. S. H. SoFi, an IGOR-based interface for the efficient use of the generalized multilinear engine (ME-2) for the source apportionment: ME-2 application to aerosol mass spectrometer data. Atmos. Meas. Tech. 6, 3649–3661 (2013).

    Article  Google Scholar 

  69. Canagaratna, M. R. et al. Elemental ratio measurements of organic compounds using aerosol mass spectrometry: characterization, improved calibration, and implications. Atmos. Chem. Phys. 15, 253–272 (2015).

    Article  Google Scholar 

  70. Crippa, M. et al. Organic aerosol components derived from 25 AMS datasets across Europe using a newly developed ME-2 based source apportionment strategy. Atmos. Chem. Phys. 14, 6159–6176 (2014).

    Article  Google Scholar 

Download references


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 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.

Author information

Authors and Affiliations


Corresponding author

Correspondence to T. Petäjä.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review information

Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Tamara Goldin, Simon Harold.

Additional information

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

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

Source data

Source Data Fig. 1

Statistical Source Data for Fig. 1.

Source Data Fig. 2

Statistical Source Data for Fig. 2.

Source Data Fig. 3

Statistical Source Data for Fig. 3.

Source Data Extended Data Fig. 1

Statistical Source Data for Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Statistical Source Data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Statistical Source Data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Statistical Source Data for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Statistical Source Data for Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Statistical Source Data for Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Statistical Source Data for Extended Data Fig. 7.

Source Data Extended Data Fig. 8

Statistical Source Data for Extended Data Fig. 8.

Source Data Extended Data Fig. 9

Statistical Source Data for Extended Data Fig. 9.

Source Data Extended Data Fig. 10

Statistical Source Data for Extended Data Fig. 10.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing