The study of atmospheric ice-nucleating particles via microfluidically generated droplets

Ice-nucleating particles (INPs) play a significant role in the climate and hydrological cycle by triggering ice formation in supercooled clouds, thereby causing precipitation and affecting cloud lifetimes and their radiative properties. However, despite their importance, INP often comprise only 1 in 10³–10⁶ ambient particles, making it difficult to ascertain and predict their type, source, and concentration. The typical techniques for quantifying INP concentrations tend to be highly labour-intensive, suffer from poor time resolution, or are limited in sensitivity to low concentrations. Here, we present the application of microfluidic devices to the study of atmospheric INPs via the simple and rapid production of monodisperse droplets and their subsequent freezing on a cold stage. This device offers the potential for the testing of INP concentrations in aqueous samples with high sensitivity and high counting statistics. Various INPs were tested for validation of the platform, including mineral dust and biological species, with results compared to literature values. We also describe a methodology for sampling atmospheric aerosol in a manner that minimises sampling biases and which is compatible with the microfluidic device. We present results for INP concentrations in air sampled during two field campaigns: (1) from a rural location in the UK and (2) during the UK’s annual Bonfire Night festival. These initial results will provide a route for deployment of the microfluidic platform for the study and quantification of INPs in upcoming field campaigns around the globe, while providing a benchmark for future lab-on-a-chip-based INP studies

Dataset associated with 'Contributions of biogenic material to the atmospheric ice-nucleating particle population in North Western Europe'

Results from the study used to create figures in pape

Contribution of feldspar and marine organic aerosols to global ice nucleating particle concentrations

Ice-nucleating particles (INPs) are known to affect the amount of ice in mixed-phase clouds, thereby influencing many of their properties. The atmospheric INP concentration changes by orders of magnitude from terrestrial to marine environments, which typically contain much lower concentrations. Many modelling studies use parameterizations for heterogeneous ice nucleation and cloud ice processes that do not account for this difference because they were developed based on INP measurements made predominantly in terrestrial environments without considering the aerosol composition. Errors in the assumed INP concentration will influence the simulated amount of ice in mixed-phase clouds, leading to errors in top-of-atmosphere radiative flux and ultimately the climate sensitivity of the model. Here we develop a global model of INP concentrations relevant for mixed-phase clouds based on laboratory and field measurements of ice nucleation by K-feldspar (an ice-active component of desert dust) and marine organic aerosols (from sea spray). The simulated global distribution of INP concentrations based on these two species agrees much better with currently available ambient measurements than when INP concentrations are assumed to depend only on temperature or particle size. Underestimation of INP concentrations in some terrestrial locations may be due to the neglect of INPs from other terrestrial sources. Our model indicates that, on a monthly average basis, desert dusts dominate the contribution to the INP population over much of the world, but marine organics become increasingly important over remote oceans and they dominate over the Southern Ocean. However, day-to-day variability is important. Because desert dust aerosol tends to be sporadic, marine organic aerosols dominate the INP population on many days per month over much of the mid-and high-latitude Northern Hemisphere. This study advances our understanding of which aerosol species need to be included in order to adequately describe the global and regional distribution of INPs in models, which will guide ice nucleation researchers on where to focus future laboratory and field work

A major combustion aerosol event had a negligible impact on the atmospheric ice-nucleating particle population

Clouds containing supercooled water are important for both climate and weather, but our knowledge of which aerosol particle types nucleate ice in these clouds is far from complete. Combustion aerosols have strong anthropogenic sources and if these aerosol types were to nucleate ice in clouds they might exert a climate forcing. Here, we quantified the atmospheric ice-nucleating particle (INP) concentrations during the UK’s annual Bonfire Night celebrations, which are characterised by strong anthropogenic emissions of combustion aerosol. We used three immersion mode techniques covering more than six orders of magnitude in INP concentration over the temperature range from −10 °C to homogeneous freezing. We found no observable systematic change in the INP concentration on three separate nights, despite more than a factor of 10 increase in aerosol number concentrations, up to a factor of 10 increase in PM10 concentration and more than a factor of 100 increase in black carbon (BC) mass concentration relative to pre-event levels. This implies that BC and other combustion aerosol such as ash did not compete with the INPs present in the background air. Furthermore, the upper limit of the ice-active site surface density, ns(T), of BC was shown to be consistent with several other recent laboratory studies, showing a very low ice-nucleating activity of BC. We conclude that combustion aerosol particles similar to those emitted on Bonfire Night are at most of secondary importance for the INP population relevant for mixed-phase clouds in typical mid-latitude terrestrial locations

The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation

Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of ?3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution (? 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from ? ?40% at 12 km to ? ?3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.The ETH and UIBK team acknowledges PRACE for awarding access to Piz Daint at Swiss National Supercomputing Center (CSCS, Switzerland), and the Federal Office for Meteorology and Climatology MeteoSwiss, the Swiss National Supercomputing Centre (CSCS), and ETH Zürich for their contributions to the development of the GPU-accelerated version of COSMO. The funding for their research was provided by the Swiss National Sciences Foundation through the Sinergia Grant CRSII2_154486 ’crCLIM’. The ETH team, MZ, CNRM IPSL, ICTP, SMHI, Met-Office, DMI, CMCC, HZG, KNMI acknowledge funding from the HORIZON 2020 EUCP (European Climate Prediction System) project (https://www.eucp-project.eu, grant agreement No. 776613). The RegCM simulations by the ICTP have been completed thanks to the support of the Consorzio Interuniversitario per il Calcolo Automatico dell’Italia Nord Orientale (CINECA) super-computing center (Bologna, Italy). ICTP team acknowledge the CETEMPS, University of L’Aquila, for allowing access to the Italian database of precipitation which GRIPHO is based on. EK and SK acknowledge the GRNET HPC-ARIS infrastructure (project pr003005) and the AUTH-IT scientific center for their support. MT acknowledge that computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF), and further acknowledge the funding of the German Research Foundation (DFG) through grant nr. 401857120. HT and DM acknowledge the projects HighEnd:Extremes, EASICLIM, and reclip:convex, funded by the Austrian Climate Research Programme (ACRP) of the Klima- und Energiefonds (nos. B368608, KR16AC0K13160, and B769999, respectively) and the Vienna Scientific Cluster (VSC) (projects 70992 and 71193). HT, DM and KG gratefully acknowledge the computing time granted through JARA (project JJSC39) and the John von Neumann Institute for Computing (NIC) (project HKA19) at the Jülich Supercomputing Centre. AL-G acknowledges support by the Spanish government through grant BES-2016-078158 and MINECO/FEDER co-funded project MULTI-SDM (CGL2015-66583-R). UCAN simulations have been carried out on the Altamira Supercomputer at the Instituto de Física de Cantabria (IFCA, CSIC-UC), member of the Spanish Supercomputing Network. SS and TL gratefully acknowledge the support of the Norwegian Environment Agency and their basic funding support of NORCE’s Climate Services strategic project. Their simulations were performed on resources provided by UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway. JM and JF gratefully acknowledge the support by the Spanish government R+D programme through grant INSIGNIA (CGL2016-79210-R), co-funded by the ERDF/FEDER. The UHOH team and JM are also thankful for the support of the German Science Foundation (DFG) through project FOR 1695. The UHOH simulations were carried out using the computational resources received from the supercomputing center HLRS in Stuttgart, Germany. IPSL’s work was granted access to the HPC resources of TGCC under the allocations 2018-A0030106877 and 2019-A0030106877 made by GENCI. EB and BA thank the Hessian Competence Center for High Performance Computing. The CICERO team was funded through the Norwegian Research Council project HYPRE (grant no. 243942) and acknowledges computing resources from Notur (NN9188K). EJK gratefully acknowledges funding from the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). All authors gratefully acknowledge the WCRP-CORDEX-FPS on Convective phenomena at high resolution over Europe and the Mediterranean (FPSCONV-ALP-3) and the research data exchange infrastructure and services provided by the Jülich Supercomputing Centre, Germany, as part of the Helmholtz Data Federation initiative