2021, Hartmann, Markus, Gong, Xianda, Kecorius, Simonas, van Pinxteren, Manuela, Vogl, Teresa, Welti, André, Wex, Heike, Zeppenfeld, Sebastian, Herrmann, Hartmut, Wiedensohler, Alfred, Stratmann, Frank

Ice-nucleating particles (INPs) initiate the primary ice formation in clouds at temperatures above ca. -38gC and have an impact on precipitation formation, cloud optical properties, and cloud persistence. Despite their roles in both weather and climate, INPs are not well characterized, especially in remote regions such as the Arctic. We present results from a ship-based campaign to the European Arctic during May to July 2017. We deployed a filter sampler and a continuous-flow diffusion chamber for offline and online INP analyses, respectively. We also investigated the ice nucleation properties of samples from different environmental compartments, i.e., the sea surface microlayer (SML), the bulk seawater (BSW), and fog water. Concentrations of INPs (NINP) in the air vary between 2 to 3 orders of magnitudes at any particular temperature and are, except for the temperatures above -10gC and below -32gC, lower than in midlatitudes. In these temperature ranges, INP concentrations are the same or even higher than in the midlatitudes. By heating of the filter samples to 95gC for 1ĝ€¯h, we found a significant reduction in ice nucleation activity, i.e., indications that the INPs active at warmer temperatures are biogenic. At colder temperatures the INP population was likely dominated by mineral dust. The SML was found to be enriched in INPs compared to the BSW in almost all samples. The enrichment factor (EF) varied mostly between 1 and 10, but EFs as high as 94.97 were also observed. Filtration of the seawater samples with 0.2ĝ€¯μm syringe filters led to a significant reduction in ice activity, indicating the INPs are larger and/or are associated with particles larger than 0.2ĝ€¯μm. A closure study showed that aerosolization of SML and/or seawater alone cannot explain the observed airborne NINP unless significant enrichment of INP by a factor of 105 takes place during the transfer from the ocean surface to the atmosphere. In the fog water samples with -3.47gC, we observed the highest freezing onset of any sample. A closure study connecting NINP in fog water and the ambient NINP derived from the filter samples shows good agreement of the concentrations in both compartments, which indicates that INPs in the air are likely all activated into fog droplets during fog events. In a case study, we considered a situation during which the ship was located in the marginal sea ice zone and NINP levels in air and the SML were highest in the temperature range above -10gC. Chlorophyll a measurements by satellite remote sensing point towards the waters in the investigated region being biologically active. Similar slopes in the temperature spectra suggested a connection between the INP populations in the SML and the air. Air mass history had no influence on the observed airborne INP population. Therefore, we conclude that during the case study collected airborne INPs originated from a local biogenic probably marine source. © Author(s) 2021.

2019, Kecorius, Simonas, Vogl, Teresa, Paasonen, Pauli, Lampilahti, Janne, Rothenberg, Daniel, Wex, Heike, Zeppenfeld, Sebastian, van Pinxteren, Manuela, Hartmann, Markus, Henning, Silvia, Gong, Xianda, Welti, Andre, Kulmala, Markku, Stratmann, Frank, Herrmann, Hartmut, Wiedensohler, Alfred

In a warming Arctic the increased occurrence of new particle formation (NPF) is believed to originate from the declining ice coverage during summertime. Understanding the physico-chemical properties of newly formed particles, as well as mechanisms that control both particle formation and growth in this pristine environment, is important for interpreting aerosol-cloud interactions, to which the Arctic climate can be highly sensitive. In this investigation, we present the analysis of NPF and growth in the high summer Arctic. The measurements were made on-board research vessel Polarstern during the PS106 Arctic expedition. Four distinctive NPF and subsequent particle growth events were observed, during which particle (diameter in a range 10-50 nm) number concentrations increased from background values of approx. 40 up to 4000 cm-3. Based on particle formation and growth rates, as well as hygroscopicity of nucleation and the Aitken mode particles, we distinguished two different types of NPF events. First, some NPF events were favored by negative ions, resulting in more-hygroscopic nucleation mode particles and suggesting sulfuric acid as a precursor gas. Second, other NPF events resulted in less-hygroscopic particles, indicating the influence of organic vapors on particle formation and growth. To test the climatic relevance of NPF and its influence on the cloud condensation nuclei (CCN) budget in the Arctic, we applied a zero-dimensional, adiabatic cloud parcel model. At an updraft velocity of 0.1 m s-1, the particle number size distribution (PNSD) generated during nucleation processes resulted in an increase in the CCN number concentration by a factor of 2 to 5 compared to the background CCN concentrations. This result was confirmed by the directly measured CCN number concentrations. Although particles did not grow beyond 50 nm in diameter and the activated fraction of 15-50 nm particles was on average below 10 %, it could be shown that the sheer number of particles produced by the nucleation process is enough to significantly influence the background CCN number concentration. This implies that NPF can be an important source of CCN in the Arctic. However, more studies should be conducted in the future to understand mechanisms of NPF, sources of precursor gases and condensable vapors, as well as the role of the aged nucleation mode particles in Arctic cloud formation. © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.

2022, Schimmel, Willi, Kalesse-Los, Heike, Maahn, Maximilian, Vogl, Teresa, Foth, Andreas, Garfias, Pablo Saavedra, Seifert, Patric

In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. We present VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), a retrieval based on deep convolutional neural networks (CNNs) mapping radar Doppler spectra to the probability of the presence of cloud droplets (CD). The training of the CNN was realized using the Cloudnet processing suite as supervisor. Once trained, VOODOO yields the probability for CD directly at Cloudnet grid resolution. Long-term predictions of 18 months in total from two mid-latitudinal locations, i.e., Punta Arenas, Chile (53.1 S, 70.9 W), in the Southern Hemisphere and Leipzig, Germany (51.3 N, 12.4 E), in the Northern Hemisphere, are evaluated. Temporal and spatial agreement in cloud-droplet-bearing pixels is found for the Cloudnet classification to the VOODOO prediction. Two suitable case studies were selected, where stratiform, multi-layer, and deep mixed-phase clouds were observed. Performance analysis of VOODOO via classification-evaluating metrics reveals precision > 0.7, recall ≈ 0.7, and accuracy ≈ 0.8. Additionally, independent measurements of liquid water path (LWP) retrieved by a collocated microwave radiometer (MWR) are correlated to the adiabatic LWP, which is estimated using the temporal and spatial locations of cloud droplets from VOODOO and Cloudnet in connection with a cloud parcel model. This comparison resulted in stronger correlation for VOODOO (≈ 0.45) compared to Cloudnet (≈ 0.22) and indicates the availability of VOODOO to identify CD beyond lidar attenuation. Furthermore, the long-term statistics for 18 months of observations are presented, analyzing the performance as a function of MWR-LWP and confirming VOODOO's ability to identify cloud droplets reliably for clouds with LWP > 100 g m-2. The influence of turbulence on the predictive performance of VOODOO was also analyzed and found to be minor. A synergy of the novel approach VOODOO and Cloudnet would complement each other perfectly and is planned to be incorporated into the Cloudnet algorithm chain in the near future.

2019, Wendisch, Manfred, Macke, Andreas, Ehrlich, André, Lüpkes, Christof, Mech, Mario, Chechin, Dmitry, Dethloff, Klaus, Velasco, Carola Barrientos, Bozem, Heiko, Brückner, Marlen, Clemen, Hans-Christian, Crewell, Susanne, Donth, Tobias, Dupuy, Regis, Ebell, Kerstin, Egerer, Ulrike, Engelmann, Ronny, Engler, Christa, Eppers, Oliver, Gehrmann, Martin, Gong, Xianda, Gottschalk, Matthias, Gourbeyre, Christophe, Griesche, Hannes, Hartmann, Jörg, Hartmann, Markus, Heinold, Bernd, Herber, Andreas, Herrmann, Hartmut, Heygster, Georg, Hoor, Peter, Jafariserajehlou, Soheila, Jäkel, Evelyn, Järvinen, Emma, Jourdan, Olivier, Kästner, Udo, Kecorius, Simonas, Knudsen, Erlend M., Köllner, Franziska, Kretzschmar, Jan, Lelli, Luca, Leroy, Delphine, Maturilli, Marion, Mei, Linlu, Mertes, Stephan, Mioche, Guillaume, Neuber, Roland, Nicolaus, Marcel, Nomokonova, Tatiana, Notholt, Justus, Palm, Mathias, van Pinxteren, Manuela, Quaas, Johannes, Richter, Philipp, Ruiz-Donoso, Elena, Schäfer, Michael, Schmieder, Katja, Schnaiter, Martin, Schneider, Johannes, Schwarzenböck, Alfons, Seifert, Patric, Shupe, Matthew D., Siebert, Holger, Spreen, Gunnar, Stapf, Johannes, Stratmann, Frank, Vogl, Teresa, Welti, André, Wex, Heike, Wiedensohler, Alfred, Zanatta, Marco, Zeppenfeld, Sebastian

Clouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, and surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) aircraft campaign and the Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and Aerosol (PASCAL) ice breaker expedition. Both observational studies were performed in the framework of the German Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC) project. They took place in the vicinity of Svalbard, Norway, in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft; the icebreaker Research Vessel (R/V) Polarstern; an ice floe camp including an instrumented tethered balloon; and the permanent ground-based measurement station at Ny-Ålesund, Svalbard, were employed to observe Arctic low- and mid-level mixed-phase clouds and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above the R/V Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.

2019, Kalesse, Heike, Vogl, Teresa, Paduraru, Cosmin, Luke, Edward

In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time-height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols - Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume. © 2019 Copernicus GmbH. All rights reserved.