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Synergistic HNO3–H2SO4–NH3 upper tropospheric particle formation

  • New particle formation in the upper free troposphere is a major global source of cloud condensation nuclei (CCN)1,2,3,4. However, the precursor vapours that drive the process are not well understood. With experiments performed under upper tropospheric conditions in the CERN CLOUD chamber, we show that nitric acid, sulfuric acid and ammonia form particles synergistically, at rates that are orders of magnitude faster than those from any two of the three components. The importance of this mechanism depends on the availability of ammonia, which was previously thought to be efficiently scavenged by cloud droplets during convection. However, surprisingly high concentrations of ammonia and ammonium nitrate have recently been observed in the upper troposphere over the Asian monsoon region5,6. Once particles have formed, co-condensation of ammonia and abundant nitric acid alone is sufficient to drive rapid growth to CCN sizes with only trace sulfate. Moreover, our measurements show that these CCN are also highly efficient ice nucleating particles—comparable to desert dust. Our model simulations confirm that ammonia is efficiently convected aloft during the Asian monsoon, driving rapid, multi-acid HNO3–H2SO4–NH3 nucleation in the upper troposphere and producing ice nucleating particles that spread across the mid-latitude Northern Hemisphere.
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Author:Mingyi WangORCiD, Mao Xiao, Barbara BertozziORCiDGND, Guillaume MarieORCiD, Birte RörupORCiD, Benjamin SchulzeORCiD, Roman Bardakov, Xu-Cheng HeORCiD, Jiali ShenORCiD, Wiebke ScholzORCiD, Ruby MartenORCiD, Lubna DadaORCiDGND, Rima BaalbakiORCiD, Brandon Lopez, Houssni LamkaddamORCiD, Hanna E. ManninenORCiD, Antonio AmorimORCiD, Farnoush Ataei, Pia Bogert, Zoé BrasseurORCiD, Lucía Caudillo, Louis-Philippe De Menezes, Jonathan DuplissyORCiD, Annica M. L. EkmanORCiD, Henning FinkenzellerORCiD, Loïc Gonzalez Carracedo, Manuel GranzinORCiD, Roberto Guida, Martin HeinritziORCiDGND, Victoria Hofbauer, Kristina HöhlerORCiD, Kimmo Korhonen, Jordan E. KrechmerORCiD, Christoph Andreas KürtenORCiDGND, Katrianne LehtipaloORCiDGND, Naser G. A. MahfouzORCiD, Vladimir MakhmutovORCiD, Dario MassabòORCiD, Serge Mathot, Roy L. MauldinORCiD, Bernhard MentlerORCiD, Tatjana MüllerORCiD, Antti OnnelaORCiD, Tuukka PetäjäORCiD, Maxim PhilippovORCiD, Ana A. Piedehierro, Andrea PozzerORCiD, Ananth Ranjithkumar, Meredith SchervishORCiD, Siegfried SchobesbergerORCiDGND, Mario SimonORCiD, Yuri Stozhkov, António Tomé, Nsikanabasi Silas UmoORCiD, Franziska VogelORCiDGND, Robert WagnerORCiDGND, Dongyu S. Wang, Stefan K. WeberORCiD, André WeltiORCiD, Yusheng Wu, Marcel Zauner-WieczorekORCiDGND, Mikko SipiläORCiD, Paul M. WinklerORCiD, Armin HanselORCiD, Urs BaltenspergerORCiDGND, Markku KulmalaORCiDGND, Richard C. FlaganORCiD, Joachim CurtiusORCiD, Ilona RiipinenORCiD, Hamish GordonORCiD, Jos LelieveldORCiDGND, Imad El HaddadORCiDGND, Rainer VolkamerORCiDGND, Douglas R. WorsnopORCiD, Theodoros ChristoudiasORCiD, Jasper KirkbyORCiD, Ottmar MöhlerORCiDGND, Neil McPherson DonahueORCiDGND
URN:urn:nbn:de:hebis:30:3-632653
DOI:https://doi.org/10.1038/s41586-022-04605-4
ISSN:1476-4687
Parent Title (English):Nature
Publisher:Nature Publ. Group
Place of publication:London [u.a.]
Document Type:Article
Language:English
Date of Publication (online):2022/05/18
Date of first Publication:2022/05/18
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2023/03/08
Tag:Atmospheric chemistry; Atmospheric science; Climate change
Volume:605
Page Number:23
First Page:483
Last Page:489
Note:
The full dataset shown in the figures is publicly available at https://doi.org/10.5281/zenodo.5949440
Note:
We thank the European Organization for Nuclear Research (CERN) for supporting CLOUD with important technical and financial resources. This research has received funding from the US National Science Foundation (nos. AGS-1801574, AGS-1801897, AGS-1602086, AGS-1801329, AGS-2132089 and AGS-1801280), the European Union’s Horizon 2020 programme (Marie Skłodowska-Curie ITN no. 764991 ‘CLOUD-MOTION’), the European Commission, H2020 Research Infrastructures (FORCeS, no. 821205), the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie no. 895875 ‘NPF-PANDA’), a European Research Council (ERC) project ATM-GTP contract (no. 742206), an ERC-CoG grant INTEGRATE (no. 867599), the Swiss National Science Foundation (nos. 200021_169090, 200020_172602 and 20FI20_172622), the Academy of Finland ACCC Flagship (no. 337549), the Academy of Finland Academy professorship (no. 302958), the Academy of Finland (nos. 1325656, 316114 and 325647), Russian MegaGrant project ‘Megapolis – heat and pollution island: interdisciplinary hydroclimatic, geochemical and ecological analysis’ (application reference 2020-220-08-5835), Jane and Aatos Erkko Foundation ‘Quantifying carbon sink, CarbonSink+ and their interaction with air quality’ INAR project, Samsung PM2.5 SRP, Prince Albert Foundation ‘the Arena for the gap analysis of the existing Arctic Science Co-Operations (AASCO)’ (no. 2859), the German Federal Ministry of Education and Research (CLOUD-16 project nos. 01LK1601A and 01LK1601C), the Knut and Alice Wallenberg Foundation Wallenberg Academy Fellows project AtmoRemove (no. 2015.0162), the Portuguese Foundation for Science and Technology (no. CERN/FIS-COM/0014/2017) and the Technology Transfer Project N059 of the Karlsruhe Institute of Technology (KIT). The FIGAERO-CIMS was supported by a Major Research Instrumentation (MRI) grant for the US NSF AGS-1531284, as well as the Wallace Research Foundation. The computations by R.Bardakov were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Center (NSC).
Note:
Correspondence to Neil M. Donahue: nmd@andrew.cmu.edu
Note:
The EMAC (ECHAM/MESSy) model is continuously further developed and applied by a consortium of institutions. The use of MESSy
and access to the source code is licensed to all affiliates of institutions that are members of the MESSy Consortium. Institutions can
become a member of the MESSy Consortium by signing the MESSy
Memorandum of Understanding. More information can be found on
the MESSy Consortium website (https://www.messy-interface.org).
All code modifications presented in this paper will be included in the
next version of MESSy. The TOMCAT model (http://homepages.see.
leeds.ac.uk/~lecmc/tomcat.html) is a UK community model. It is available to UK (or NERC-funded) researchers who normally access the
model on common facilities or who are helped to install it on their local
machines. As it is a complex research tool, new users will need help to
use the model optimally. We do not have the resources to release and
support the model in an open way. Any potential user interested in
the model should contact Martyn Chipperfield. The model updates
described in this paper are included in the standard model library. The
cloud trajectories model is publicly available at https://doi.org/10.5281/
zenodo.5949440. Codes for conducting the analysis presented in this
paper can be obtained by contacting the corresponding author, Neil
M. Donahue (nmd@andrew.cmu.edu).
HeBIS-PPN:508616980
Institutes:Geowissenschaften / Geographie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International

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