2020, Costa-Surós, Montserrat, Sourdeval, Odran, Acquistapace, Claudia, Baars, Holger, Carbajal Henken, Cintia, Genz, Christa, Hesemann, Jonas, Jimenez, Cristofer, König, Marcel, Kretzschmar, Jan, Madenach, Nils, Meyer, Catrin I., Schrödner, Roland, Seifert, Patric, Senf, Fabian, Brueck, Matthias, Cioni, Guido, Engels, Jan Frederik, Fieg, Kerstin, Gorges, Ksenia, Heinze, Rieke, Kumar Siligam, Pavan, Burkhardt, Ulrike, Crewell, Susanne, Hoose, Corinna, Seifert, Axel, Tegen, Ina, Quaas, Johannes

Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth's changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model), to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than fivefold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used, aiming at the detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which a large variety of cloud regimes was present over the selected domain of central Europe. It is first demonstrated that the aerosol fields used in the model are consistent with corresponding satellite aerosol optical depth retrievals for both 1985 (perturbed) and 2013 (reference) conditions. In comparison to retrievals from groundbased lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, the detection and attribution process was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud base altitude and cloud lifetime, the aerosol response was small compared to their natural vari ability. Also, large uncertainties in satellite and ground-based observations make the detection and attribution difficult for these quantities. An exception to this is the fact that at a large liquid water path value (LWP > 200 g m-2), the control simulation matches the observations, while the perturbed one shows an LWP which is too large. The model simulations allowed for quantifying the radiative forcing due to aerosol-cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol-cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region and aerosol perturbation-2:6 W m-2. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of-0:8 W m-2 © 2020 Author(s).

2022, Ganske, Anette, Heil, Angelika, Lammert, Andrea, Kretzschmar, Jan, Quaas, Johannes

Scientific data should be published in a way so that other scientists can benefit from these data, enabling further research. The FAIR Data Principles are defining the basic prerequisite for a good data publication: data should be Findable, Accessible, Interoperable, and Reusable. Increasingly, research communities are developing discipline-specific data publication standards under consideration of the FAIR Data Principles. A very comprehensive yet strict data standard has been developed for the climate model output within the Climate Model Intercomparison Project (CMIP), which largely builds upon the Climate and Forecast Metadata Conventions (CF conventions). There are, however, many areas of atmospheric modelling where data cannot be standardised according to the CMIP data standard because, e.g., the data contain specific variables which are not covered by the CMIP standard. Furthermore, fulfilling the strict CMIP data standard for smaller Model Intercomparison Projects (MIPs) requires much effort (in time and manpower) and hence the outcome of these MIPs often remains non-standardised. For innovative model diagnostics, preexisting standards are also not flexible enough. For that reason, the ATMODAT standard, a quality guideline for atmospheric model data, was created. The ATMODAT standard defines a set of requirements that aim at ensuring the high reusability of atmospheric model data publications. The requirements include the use of the netCDF file format, the application of the CF conventions, rich and standardised file metadata, and the publication of the data with a DataCite DOI. Additionally, a tool for checking the conformity of data and metadata to this standard, the atmodat data checker, was developed and is available on GitHub under an open licence. By using the more flexible ATMODAT standard, the publication of standardised datasets is simplified for smaller MIPs. This standardisation process is presented as an example using the data of an aerosol-climate model from the AeroCOM MIP. Furthermore, the landing pages of ATMODAT-compliant data publications can be highlighted with the EASYDAB logo. EASYDAB (Earth System Data Branding) is a newly developed quality label for carefully curated and highly standardised data publications. The ATMODAT data standardisation can easily be transferred to data from other disciplines and contribute to their improved reusability.

2021, Stevens, Bjorn, Bony, Sandrine, Farrell, David, Ament, Felix, Blyth, Alan, Fairall, Christopher, Karstensen, Johannes, Quinn, Patricia K., Speich, Sabrina, Acquistapace, Claudia, Aemisegger, Franziska, Crewell, Susanne, Cronin, Timothy, Cui, Zhiqiang, Cuypers, Yannis, Daley, Alton, Damerell, Gillian M., Dauhut, Thibaut, Deneke, Hartwig, Desbios, Jean-Philippe, Dörner, Steffen, Albright, Anna Lea, Donner, Sebastian, Douet, Vincent, Drushka, Kyla, Dütsch, Marina, Ehrlich, André, Emanuel, Kerry, Emmanouilidis, Alexandros, Etienne, Jean-Claude, Etienne-Leblanc, Sheryl, Faure, Ghislain, Bellenger, Hugo, Feingold, Graham, Ferrero, Luca, Fix, Andreas, Flamant, Cyrille, Flatau, Piotr Jacek, Foltz, Gregory R., Forster, Linda, Furtuna, Iulian, Gadian, Alan, Galewsky, Joseph, Bodenschatz, Eberhard, Gallagher, Martin, Gallimore, Peter, Gaston, Cassandra, Gentemann, Chelle, Geyskens, Nicolas, Giez, Andreas, Gollop, John, Gouirand, Isabelle, Gourbeyre, Christophe, de Graaf, Dörte, Caesar, Kathy-Ann, de Groot, Geiske E., Grosz, Robert, Güttler, Johannes, Gutleben, Manuel, Hall, Kashawn, Harris, George, Helfer, Kevin C., Henze, Dean, Herbert, Calvert, Holanda, Bruna, Chewitt-Lucas, Rebecca, Ibanez-Landeta, Antonio, Intrieri, Janet, Iyer, Suneil, Julien, Fabrice, Kalesse, Heike, Kazil, Jan, Kellman, Alexander, Kidane, Abiel T., Kirchner, Ulrike, Klingebiel, Marcus, de Boer, Gijs, Körner, Mareike, Kremper, Leslie Ann, Kretzschmar, Jan, Krüger, Ovid, Kumala, Wojciech, Kurz, Armin, L'Hégaret, Pierre, Labaste, Matthieu, Lachlan-Cope, Tom, Laing, Arlene, Delanoë, Julien, Landschützer, Peter, Lang, Theresa, Lange, Diego, Lange, Ingo, Laplace, Clément, Lavik, Gauke, Laxenaire, Rémi, Le Bihan, Caroline, Leandro, Mason, Lefevre, Nathalie, Denby, Leif, Lena, Marius, Lenschow, Donald, Li, Qiang, Lloyd, Gary, Los, Sebastian, Losi, Niccolò, Lovell, Oscar, Luneau, Christopher, Makuch, Przemyslaw, Malinowski, Szymon, Ewald, Florian, Manta, Gaston, Marinou, Eleni, Marsden, Nicholas, Masson, Sebastien, Maury, Nicolas, Mayer, Bernhard, Mayers-Als, Margarette, Mazel, Christophe, McGeary, Wayne, McWilliams, James C., Fildier, Benjamin, Mech, Mario, Mehlmann, Melina, Meroni, Agostino Niyonkuru, Mieslinger, Theresa, Minikin, Andreas, Minnett, Peter, Möller, Gregor, Morfa Avalos, Yanmichel, Muller, Caroline, Musat, Ionela, Forde, Marvin, Napoli, Anna, Neuberger, Almuth, Noisel, Christophe, Noone, David, Nordsiek, Freja, Nowak, Jakub L., Oswald, Lothar, Parker, Douglas J., Peck, Carolyn, Person, Renaud, George, Geet, Philippi, Miriam, Plueddemann, Albert, Pöhlker, Christopher, Pörtge, Veronika, Pöschl, Ulrich, Pologne, Lawrence, Posyniak, Michał, Prange, Marc, Quiñones Meléndez, Estefanía, Radtke, Jule, Gross, Silke, Ramage, Karim, Reimann, Jens, Renault, Lionel, Reus, Klaus, Reyes, Ashford, Ribbe, Joachim, Ringel, Maximilian, Ritschel, Markus, Rocha, Cesar B., Rochetin, Nicolas, Hagen, Martin, Röttenbacher, Johannes, Rollo, Callum, Royer, Haley, Sadoulet, Pauline, Saffin, Leo, Sandiford, Sanola, Sandu, Irina, Schäfer, Michael, Schemann, Vera, Schirmacher, Imke, Hausold, Andrea, Schlenczek, Oliver, Schmidt, Jerome, Schröder, Marcel, Schwarzenboeck, Alfons, Sealy, Andrea, Senff, Christoph J., Serikov, Ilya, Shohan, Samkeyat, Siddle, Elizabeth, Smirnov, Alexander, Heywood, Karen J., Späth, Florian, Spooner, Branden, Stolla, M. Katharina, Szkółka, Wojciech, de Szoeke, Simon P., Tarot, Stéphane, Tetoni, Eleni, Thompson, Elizabeth, Thomson, Jim, Tomassini, Lorenzo, Hirsch, Lutz, Totems, Julien, Ubele, Alma Anna, Villiger, Leonie, von Arx, Jan, Wagner, Thomas, Walther, Andi, Webber, Ben, Wendisch, Manfred, Whitehall, Shanice, Wiltshire, Anton, Jacob, Marek, Wing, Allison A., Wirth, Martin, Wiskandt, Jonathan, Wolf, Kevin, Worbes, Ludwig, Wright, Ethan, Wulfmeyer, Volker, Young, Shanea, Zhang, Chidong, Zhang, Dongxiao, Jansen, Friedhelm, Ziemen, Florian, Zinner, Tobias, Zöger, Martin, Kinne, Stefan, Klocke, Daniel, Kölling, Tobias, Konow, Heike, Lothon, Marie, Mohr, Wiebke, Naumann, Ann Kristin, Nuijens, Louise, Olivier, Léa, Pincus, Robert, Pöhlker, Mira, Reverdin, Gilles, Roberts, Gregory, Schnitt, Sabrina, Schulz, Hauke, Siebesma, A. Pier, Stephan, Claudia Christine, Sullivan, Peter, Touzé-Peiffer, Ludovic, Vial, Jessica, Vogel, Raphaela, Zuidema, Paquita, Alexander, Nicola, Alves, Lyndon, Arixi, Sophian, Asmath, Hamish, Bagheri, Gholamhossein, Baier, Katharina, Bailey, Adriana, Baranowski, Dariusz, Baron, Alexandre, Barrau, Sébastien, Barrett, Paul A., Batier, Frédéric, Behrendt, Andreas, Bendinger, Arne, Beucher, Florent, Bigorre, Sebastien, Blades, Edmund, Blossey, Peter, Bock, Olivier, Böing, Steven, Bosser, Pierre, Bourras, Denis, Bouruet-Aubertot, Pascale, Bower, Keith, Branellec, Pierre, Branger, Hubert, Brennek, Michal, Brewer, Alan, Brilouet, Pierre-Etienne, Brügmann, Björn, Buehler, Stefan A., Burke, Elmo, Burton, Ralph, Calmer, Radiance, Canonici, Jean-Christophe, Carton, Xavier, Cato Jr., Gregory, Charles, Jude Andre, Chazette, Patrick, Chen, Yanxu, Chilinski, Michal T., Choularton, Thomas, Chuang, Patrick, Clarke, Shamal, Coe, Hugh, Cornet, Céline, Coutris, Pierre, Couvreux, Fleur

The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

2020, Gasnke, Anette, Kraft, Angelina, Kaiser, Amandine, Heydebreck, Daniel, Lammert, Andrea, Höck, Heinke, Thiemann, Hannes, Voss, Vivien, Grawe, David, Leitl, Bernd, Schlünzen, K. Heinke, Kretzschmar, Jan, Quaas, Johannes

Within the AtMoDat project (Atmospheric Model Data), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. The ATMODAT standard includes concrete recommendations related to the maturity, publication and enhanced FAIRness of atmospheric model data. The suggestions include requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine readable landing pages are a core element of this standard, and should hold and present discipline-specific metadata on simulation and variable level. This standard is an updated and translated version of "Bericht über initialen Kernstandard und Kurationskriterien des AtMoDat Projektes (v2.4)

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.