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Published May 19, 2022 | Supplemental Material + Published
Journal Article Open

Synergistic HNO₃–H₂SO₄–NH₃ upper tropospheric particle formation

Abstract

New particle formation in the upper free troposphere is a major global source of cloud condensation nuclei (CCN). 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 region. 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.

Additional Information

© 2022 Nature Publishing Group. Received 28 July 2021; Accepted 02 March 2022; Published 18 May 2022. 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). I.R. thanks the Max Planck Society for a sabbatical award. M.W. thanks Siebel Scholars Foundation for financial support. Data availability: The full dataset shown in the figures is publicly available at https://doi.org/10.5281/zenodo.5949440. Source data are provided with this paper. Code availability: 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). Contributions: M.W., B.B., J.K. and N.M.D. planned the experiments. M.W., B.B., G.M., B.R., B.S., X.-C.H., J.S., W.S., R.M., B.L., H.L., H.E.M., F.A., P.B., Z.B., L.C., L.-P.D.M., J.D., H.F., L.G.C., M.G., R.G., V.H., A.K., K.L., V.M., D.M., S.M., R.L.M., B.M., T.M., A.O., T.P., M.P., A.A.P., A.P., M.Simon, Y.S., A.T., N.S.U., F.V., R.W., D.S.W., S.K.W., A.W., Y.W., M.Z.-W., M.Sipilä, P.M.W., A.H., U.B., M.K., R.C.F., J.C., R.V., I.E.-H., J.K., K.K. O.M., S.S. and N.M.D. prepared the CLOUD facility or measuring instruments. M.W., B.B., G.M., B.R., B.S., X.-C.H., J.S., W.S., R.M., B.L., H.E.M., A.A., L.C., L.G.C., M.G., M.H., V.H., J.E.K., N.G.A.M., D.M., R.L.M., B.M., A.R., M.Schervish, M.Simon, A.T., N.S.U., F.V., D.S.W., S.K.W., A.W., M.Z.-W., P.M.W., J.K. and K.K. collected the data. M.W., M.X., B.B., G.M., B.R., B.S., R.Bardakov, J.S., W.S., L.D., R.Baalbaki, B.L., D.S.W., S.K.W., A.W., I.R., T.C. and N.M.D. analysed the data. M.W., M.X., B.B., R.Bardakov, X.-C.H., J.S., W.S., R.M., L.D., R.Baalbaki., B.L., H.L., H.E.M., A.M.L.E., H.F., M.H., K.H., A.K., N.S.U., R.W., A.W., A.H., U.B., M.K., R.C.F., J.C., R.V., I.R., H.G., J.L., I.E.-H., D.R.W., T.C., J.K., O.M., S.S. and N.M.D. contributed to the scientific discussion. M.W., B.B., R.Bardakov, W.S., R.M., B.L., H.L., K.H., A.K., U.B., R.C.F., J.C., R.V., I.R., H.G., J.L., I.E.-H., T.C., J.K., O.M. and N.M.D. wrote the manuscript. The authors declare no competing interests. Peer review information: Nature thanks Bernd Kärcher and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023