Global modelling of ice nucleating particles and their effects on cirrus clouds

Atmospheric ice nucleating aerosol particles (INPs) can influence the climate system by modifying cloud properties and consequently the Earth’s radiation budget. However, these aerosol-cloud interactions and their effects on the global scale, especially regarding cirrus clouds, are still only poorly understood, and subject to large uncertainties. In this thesis INP-induced modifications of cirrus clouds are analysed by employing the atmospheric chemistry general circulation model EMAC, including the aerosol microphysics submodel MADE3. To facilitate the analysis of INP-effects, model improvements and developments with respect to the representation of ice nucleating particles are applied. Besides mineral dust and soot aerosols, additional types of INPs are implemented, i.e. crystalline ammonium sulfate and glassy organic particles. A global climatology of the different INP-types is presented and global effects on cirrus coud properties and the radiative balance are quantified. Sensitivities with respect to the freezing efficiency of INPs, to the vertical velocities during cloud formation, and the effects of anthropogenic INPs are analysed.Eisbildende Aerosolpartikel in der globalen Atmosphäre, auch Eiskerne genannt, können durch Beeinflussung von Wolkeneigenschaften und damit verbundenen Modifikationen des Strahlungshaushaltes wichtige Klimaänderungen bewirken. Aerosol-Wolken-Wechselwirkungen, besonders im Hinblick auf Zirruswolken, sind allerdings mit großen Unsicherheiten behaftet. In dieser Dissertation werden Eiskern-Effekte auf Zirruswolken und Strahlung mithilfe des globalen Klima-Chemie Modells EMAC analysiert, welches das Aerosol-Mikrophysik Modul MADE3 beinhaltet. Zur Analyse der globalen Eiskerneffekte wird die Modelldarstellung eisbildender Aerosole weiterentwickelt und, neben Mineralstaub und Ruß, um zusätzliche Eiskerntypen erweitert: kristallines Ammoniumsulfat und hochviskose organische Partikel. Eine globale Klimatologie der verschiedenen Eiskern-Typen wird präsentiert und globale Effekte auf Zirruswolken-Eigenschaften und Strahlungsbilanz werden quantifiziert. Sensitivitäten im Hinblick auf die Gefriereigenschaften der Eiskerne, die Vertikalgeschwindigkeiten während der Wolkenbildung und die Effekte anthropogener Eiskerne werden analysiert

Global modelling of ice nucleating particles and their effects on cirrus clouds

Atmospheric ice nucleating aerosol particles (INPs) can influence the climate system by modifying cloud properties and consequently the Earth’s radiation budget. However, these aerosol-cloud interactions and their effects on the global scale, especially regarding cirrus clouds, are still only poorly understood, and subject to large uncertainties. In this thesis INP-induced modifications of cirrus clouds are analysed by employing the atmospheric chemistry general circulation model EMAC, including the aerosol microphysics submodel MADE3. To facilitate the analysis of INP-effects, model improvements and developments with respect to the representation of ice nucleating particles are applied. Besides mineral dust and soot aerosols, additional types of INPs are implemented, i.e. crystalline ammonium sulfate and glassy organic particles. A global climatology of the different INP-types is presented and global effects on cirrus coud properties and the radiative balance are quantified. Sensitivities with respect to the freezing efficiency of INPs, to the vertical velocities during cloud formation, and the effects of anthropogenic INPs are analysed.Eisbildende Aerosolpartikel in der globalen Atmosphäre, auch Eiskerne genannt, können durch Beeinflussung von Wolkeneigenschaften und damit verbundenen Modifikationen des Strahlungshaushaltes wichtige Klimaänderungen bewirken. Aerosol-Wolken-Wechselwirkungen, besonders im Hinblick auf Zirruswolken, sind allerdings mit großen Unsicherheiten behaftet. In dieser Dissertation werden Eiskern-Effekte auf Zirruswolken und Strahlung mithilfe des globalen Klima-Chemie Modells EMAC analysiert, welches das Aerosol-Mikrophysik Modul MADE3 beinhaltet. Zur Analyse der globalen Eiskerneffekte wird die Modelldarstellung eisbildender Aerosole weiterentwickelt und, neben Mineralstaub und Ruß, um zusätzliche Eiskerntypen erweitert: kristallines Ammoniumsulfat und hochviskose organische Partikel. Eine globale Klimatologie der verschiedenen Eiskern-Typen wird präsentiert und globale Effekte auf Zirruswolken-Eigenschaften und Strahlungsbilanz werden quantifiziert. Sensitivitäten im Hinblick auf die Gefriereigenschaften der Eiskerne, die Vertikalgeschwindigkeiten während der Wolkenbildung und die Effekte anthropogener Eiskerne werden analysiert

Global modelling of ice nucleating particles and their effects on cirrus clouds

Atmospheric ice nucleating aerosol particles (INPs) can influence the climate system by modifying cloud properties and consequently the Earth's radiation budget. However, these aerosol-cloud interactions and their effects on the global scale, especially regarding cirrus clouds, are still only poorly understood, and subject to large uncertainties. In this thesis INP-induced modifications of cirrus clouds are analysed by employing the atmospheric chemistry general circulation model EMAC, including the aerosol microphysics submodel MADE3. To facilitate the analysis of INP-effects, model improvements and developments with respect to the representation of ice nucleating particles are applied. Besides mineral dust and soot aerosols, additional types of INPs are implemented, i.e. crystalline ammonium sulfate and glassy organic particles. A global climatology of the different INP-types is presented and global effects on cirrus coud properties and the radiative balance are quantified. Sensitivities with respect to the freezing efficiency of INPs, to the vertical velocities during cloud formation, and the effects of anthropogenic INPs are analysed

Exploring the uncertainties in the aviation soot-cirrus effect

A global aerosol–climate model, including a twomoment cloud microphysical scheme and a parametrization for aerosol-induced ice formation in cirrus clouds, is applied in order to quantify the impact of aviation soot on natural cirrus clouds. Several sensitivity experiments are performed to assess the uncertainties in this effect related to (i) the assumptions on the ice nucleation abilities of aviation soot, (ii) the representation of vertical updrafts in the model, and (iii) the use of reanalysis data to relax the model dynamics (the socalled nudging technique)

An aerosol classification scheme for global simulations using the K-means machine learning method

The K-means machine learning algorithm is applied to climatological data of seven aerosol properties from a global aerosol simulation using EMAC-MADE3. The aim is to partition the aerosol properties across the global atmosphere in specific aerosol regimes; this is done mainly for evaluation purposes. K-means is an unsupervised machine learning method with the advantage that an a priori definition of the aerosol classes is not required. Using K-means, we are able to quantitatively define global aerosol regimes, so-called aerosol clusters, and explain their internal properties and their location and extension. This analysis shows that aerosol regimes in the lower troposphere are strongly influenced by emissions. Key drivers of the clusters' internal properties and spatial distribution are, for instance, pollutants from biomass burning and biogenic sources, mineral dust, anthropogenic pollution, and corresponding mixtures. Several continental clusters propagate into oceanic regions as a result of long-range transport of air masses. The identified oceanic regimes show a higher degree of pollution in the Northern Hemisphere than over the southern oceans. With increasing altitude, the aerosol regimes propagate from emission-induced clusters in the lower troposphere to roughly zonally distributed regimes in the middle troposphere and in the tropopause region. Notably, three polluted clusters identified over Africa, India, and eastern China cover the whole atmospheric column from the lower troposphere to the tropopause region. The results of this analysis need to be interpreted taking the limitations and strengths of global aerosol models into consideration. On the one hand, global aerosol simulations cannot estimate small-scale and localized processes due to the coarse resolution. On the other hand, they capture the spatial pattern of aerosol properties on the global scale, implying that the clustering results could provide useful insights for aerosol research. To estimate the uncertainties inherent in the applied clustering method, two sensitivity tests have been conducted (i) to investigate how various data scaling procedures could affect the K-means classification and (ii) to compare K-means with another unsupervised classification algorithm (HAC, i.e. hierarchical agglomerative clustering). The results show that the standardization based on sample mean and standard deviation is the most appropriate standardization method for this study, as it keeps the underlying distribution of the raw data set and retains the information of outliers. The two clustering algorithms provide similar classification results, supporting the robustness of our conclusions. The classification procedures presented in this study have a markedly wide application potential for future model-based aerosol studies

Investigation of global aerosol regime developments from pre-industrial times to the future

The temporal development of the global distribution of aerosol regimes, i.e. areas of specific aerosol characteristics, is investigated based on global aerosol simulations performed for six time slices, including pre-industrial (1750, 1850) and present-day conditions (2015), as well as future (2050) following the Shared Socioeconomic Pathways SSP3-7.0, SSP2-4.5 and SSP1-1.9. The aerosol characteristics are evaluated in terms of aerosol regimes by combining the information about multiple aerosol properties within a cluster analysis. The present-day case serves as a reference where aerosol regimes are identified via the unsupervised machine learning technique K-means. In addition, a supervised machine learning technique (Random Forest) is applied on the basis of the aerosol regime classification criteria gained from the reference case. This allows for a consistent identification of comparable aerosol regimes across the different time slices. With this approach we analyze, for instance, which present-day aerosol regimes could have been present in the past and how they might develop in the future. These analyses are conducted for three atmospheric altitude layers with a special focus on the lower troposphere. After conducting a primary classification, we also implement a secondary sub-classification to assess the fine structure of the identified primary regimes. The results are further complemented by an in-depth analysis of the emissions of primary aerosol species and aerosol precursor gases for the different time slices in selected regions, including information about the respective contributions from different emission sectors. Details will be presented on a poster

Coupling aerosols to (cirrus) clouds in the global EMAC-MADE3 aerosol–climate model

A new cloud microphysical scheme including a detailed parameterization for aerosol-driven ice formation in cirrus clouds is implemented in the global ECHAM/MESSy Atmospheric Chemistry (EMAC) chemistry–climate model and coupled to the third generation of the Modal Aerosol Dynamics model for Europe adapted for global applications (MADE3) aerosol submodel. The new scheme is able to consistently simulate three regimes of stratiform clouds – liquid, mixed-, and ice-phase (cirrus) clouds – considering the activation of aerosol particles to form cloud droplets and the nucleation of ice crystals. In the cirrus regime, it allows for the competition between homogeneous and heterogeneous freezing for the available supersaturated water vapor, taking into account different types of ice-nucleating particles, whose specific ice-nucleating properties can be flexibly varied in the model setup. The new model configuration is tuned to find the optimal set of parameters that minimizes the model deviations with respect to observations. A detailed evaluation is also performed comparing the model results for standard cloud and radiation variables with a comprehensive set of observations from satellite retrievals and in situ measurements. The performance of EMAC-MADE3 in this new coupled configuration is in line with similar global coupled models and with other global aerosol models featuring ice cloud parameterizations. Some remaining discrepancies, namely a high positive bias in liquid water path in the Northern Hemisphere and overestimated (underestimated) cloud droplet number concentrations over the tropical oceans (in the extratropical regions), which are both a common problem in these kinds of models, need to be taken into account in future applications of the model. To further demonstrate the readiness of the new model system for application studies, an estimate of the anthropogenic aerosol effective radiative forcing (ERF) is provided, showing that EMAC-MADE3 simulates a relatively strong aerosol-induced cooling but within the range reported in the Intergovernmental Panel on Climate Change (IPCC) assessments

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Rest of authors: Decky Junaedi, Robert R. Junker, Eric Justes, Richard Kabzems, Jeffrey Kane, Zdenek Kaplan, Teja Kattenborn, Lyudmila Kavelenova, Elizabeth Kearsley, Anne Kempel, Tanaka Kenzo, Andrew Kerkhoff, Mohammed I. Khalil, Nicole L. Kinlock, Wilm Daniel Kissling, Kaoru Kitajima, Thomas Kitzberger, Rasmus Kjøller, Tamir Klein, Michael Kleyer, Jitka Klimešová, Joice Klipel, Brian Kloeppel, Stefan Klotz, Johannes M. H. Knops, Takashi Kohyama, Fumito Koike, Johannes Kollmann, Benjamin Komac, Kimberly Komatsu, Christian König, Nathan J. B. Kraft, Koen Kramer, Holger Kreft, Ingolf Kühn, Dushan Kumarathunge, Jonas Kuppler, Hiroko Kurokawa, Yoko Kurosawa, Shem Kuyah, Jean-Paul Laclau, Benoit Lafleur, Erik Lallai, Eric Lamb, Andrea Lamprecht, Daniel J. Larkin, Daniel Laughlin, Yoann Le Bagousse-Pinguet, Guerric le Maire, Peter C. le Roux, Elizabeth le Roux, Tali Lee, Frederic Lens, Simon L. Lewis, Barbara Lhotsky, Yuanzhi Li, Xine Li, Jeremy W. Lichstein, Mario Liebergesell, Jun Ying Lim, Yan-Shih Lin, Juan Carlos Linares, Chunjiang Liu, Daijun Liu, Udayangani Liu, Stuart Livingstone, Joan Llusià, Madelon Lohbeck, Álvaro López-García, Gabriela Lopez-Gonzalez, Zdeňka Lososová, Frédérique Louault, Balázs A. Lukács, Petr Lukeš, Yunjian Luo, Michele Lussu, Siyan Ma, Camilla Maciel Rabelo Pereira, Michelle Mack, Vincent Maire, Annikki Mäkelä, Harri Mäkinen, Ana Claudia Mendes Malhado, Azim Mallik, Peter Manning, Stefano Manzoni, Zuleica Marchetti, Luca Marchino, Vinicius Marcilio-Silva, Eric Marcon, Michela Marignani, Lars Markesteijn, Adam Martin, Cristina Martínez-Garza, Jordi Martínez-Vilalta, Tereza Mašková, Kelly Mason, Norman Mason, Tara Joy Massad, Jacynthe Masse, Itay Mayrose, James McCarthy, M. Luke McCormack, Katherine McCulloh, Ian R. McFadden, Brian J. McGill, Mara Y. McPartland, Juliana S. Medeiros, Belinda Medlyn, Pierre Meerts, Zia Mehrabi, Patrick Meir, Felipe P. L. Melo, Maurizio Mencuccini, Céline Meredieu, Julie Messier, Ilona Mészáros, Juha Metsaranta, Sean T. Michaletz, Chrysanthi Michelaki, Svetlana Migalina, Ruben Milla, Jesse E. D. Miller, Vanessa Minden, Ray Ming, Karel Mokany, Angela T. Moles, Attila Molnár V, Jane Molofsky, Martin Molz, Rebecca A. Montgomery, Arnaud Monty, Lenka Moravcová, Alvaro Moreno-Martínez, Marco Moretti, Akira S. Mori, Shigeta Mori, Dave Morris, Jane Morrison, Ladislav Mucina, Sandra Mueller, Christopher D. Muir, Sandra Cristina Müller, François Munoz, Isla H. Myers-Smith, Randall W. Myster, Masahiro Nagano, Shawna Naidu, Ayyappan Narayanan, Balachandran Natesan, Luka Negoita, Andrew S. Nelson, Eike Lena Neuschulz, Jian Ni, Georg Niedrist, Jhon Nieto, Ülo Niinemets, Rachael Nolan, Henning Nottebrock, Yann Nouvellon, Alexander Novakovskiy, The Nutrient Network, Kristin Odden Nystuen, Anthony O'Grady, Kevin O'Hara, Andrew O'Reilly-Nugent, Simon Oakley, Walter Oberhuber, Toshiyuki Ohtsuka, Ricardo Oliveira, Kinga Öllerer, Mark E. Olson, Vladimir Onipchenko, Yusuke Onoda, Renske E. Onstein, Jenny C. Ordonez, Noriyuki Osada, Ivika Ostonen, Gianluigi Ottaviani, Sarah Otto, Gerhard E. Overbeck, Wim A. Ozinga, Anna T. Pahl, C. E. Timothy Paine, Robin J. Pakeman, Aristotelis C. Papageorgiou, Evgeniya Parfionova, Meelis Pärtel, Marco Patacca, Susana Paula, Juraj Paule, Harald Pauli, Juli G. Pausas, Begoña Peco, Josep Penuelas, Antonio Perea, Pablo Luis Peri, Ana Carolina Petisco-Souza, Alessandro Petraglia, Any Mary Petritan, Oliver L. Phillips, Simon Pierce, Valério D. Pillar, Jan Pisek, Alexandr Pomogaybin, Hendrik Poorter, Angelika Portsmuth, Peter Poschlod, Catherine Potvin, Devon Pounds, A. Shafer Powell, Sally A. Power, Andreas Prinzing, Giacomo Puglielli, Petr Pyšek, Valerie Raevel, Anja Rammig, Johannes Ransijn, Courtenay A. Ray, Peter B. Reich, Markus Reichstein, Douglas E. B. Reid, Maxime Réjou-Méchain, Victor Resco de Dios, Sabina Ribeiro, Sarah Richardson, Kersti Riibak, Matthias C. Rillig, Fiamma Riviera, Elisabeth M. R. Robert, Scott Roberts, Bjorn Robroek, Adam Roddy, Arthur Vinicius Rodrigues, Alistair Rogers, Emily Rollinson, Victor Rolo, Christine Römermann, Dina Ronzhina, Christiane Roscher, Julieta A. Rosell, Milena Fermina Rosenfield, Christian Rossi, David B. Roy, Samuel Royer-Tardif, Nadja Rüger, Ricardo Ruiz-Peinado, Sabine B. Rumpf, Graciela M. Rusch, Masahiro Ryo, Lawren Sack, Angela Saldaña, Beatriz Salgado-Negret, Roberto Salguero-Gomez, Ignacio Santa-Regina, Ana Carolina Santacruz-García, Joaquim Santos, Jordi Sardans, Brandon Schamp, Michael Scherer-Lorenzen, Matthias Schleuning, Bernhard Schmid, Marco Schmidt, Sylvain Schmitt, Julio V. Schneider, Simon D. Schowanek, Julian Schrader, Franziska Schrodt, Bernhard Schuldt, Frank Schurr, Galia Selaya Garvizu, Marina Semchenko, Colleen Seymour, Julia C. Sfair, Joanne M. Sharpe, Christine S. Sheppard, Serge Sheremetiev, Satomi Shiodera, Bill Shipley, Tanvir Ahmed Shovon, Alrun Siebenkäs, Carlos Sierra, Vasco Silva, Mateus Silva, Tommaso Sitzia, Henrik Sjöman, Martijn Slot, Nicholas G. Smith, Darwin Sodhi, Pamela Soltis, Douglas Soltis, Ben Somers, Grégory Sonnier, Mia Vedel Sørensen, Enio Egon Sosinski Jr, Nadejda A. Soudzilovskaia, Alexandre F. Souza, Marko Spasojevic, Marta Gaia Sperandii, Amanda B. Stan, James Stegen, Klaus Steinbauer, Jörg G. Stephan, Frank Sterck, Dejan B. Stojanovic, Tanya Strydom, Maria Laura Suarez, Jens-Christian Svenning, Ivana Svitková, Marek Svitok, Miroslav Svoboda, Emily Swaine, Nathan Swenson, Marcelo Tabarelli, Kentaro Takagi, Ulrike Tappeiner, Rubén Tarifa, Simon Tauugourdeau, Cagatay Tavsanoglu, Mariska te Beest, Leho Tedersoo, Nelson Thiffault, Dominik Thom, Evert Thomas, Ken Thompson, Peter E. Thornton, Wilfried Thuiller, Lubomír Tichý, David Tissue, Mark G. Tjoelker, David Yue Phin Tng, Joseph Tobias, Péter Török, Tonantzin Tarin, José M. Torres-Ruiz, Béla Tóthmérész, Martina Treurnicht, Valeria Trivellone, Franck Trolliet, Volodymyr Trotsiuk, James L. Tsakalos, Ioannis Tsiripidis, Niklas Tysklind, Toru Umehara, Vladimir Usoltsev, Matthew Vadeboncoeur, Jamil Vaezi, Fernando Valladares, Jana Vamosi, Peter M. van Bodegom, Michiel van Breugel, Elisa Van Cleemput, Martine van de Weg, Stephni van der Merwe, Fons van der Plas, Masha T. van der Sande, Mark van Kleunen, Koenraad Van Meerbeek, Mark Vanderwel, Kim André Vanselow, Angelica Vårhammar, Laura Varone, Maribel Yesenia Vasquez Valderrama, Kiril Vassilev, Mark Vellend, Erik J. Veneklaas, Hans Verbeeck, Kris Verheyen, Alexander Vibrans, Ima Vieira, Jaime Villacís, Cyrille Violle, Pandi Vivek, Katrin Wagner, Matthew Waldram, Anthony Waldron, Anthony P. Walker, Martyn Waller, Gabriel Walther, Han Wang, Feng Wang, Weiqi Wang, Harry Watkins, James Watkins, Ulrich Weber, James T. Weedon, Liping Wei, Patrick Weigelt, Evan Weiher, Aidan W. Wells, Camilla Wellstein, Elizabeth Wenk, Mark Westoby, Alana Westwood, Philip John White, Mark Whitten, Mathew Williams, Daniel E. Winkler, Klaus Winter, Chevonne Womack, Ian J. Wright, S. Joseph Wright, Justin Wright, Bruno X. Pinho, Fabiano Ximenes, Toshihiro Yamada, Keiko Yamaji, Ruth Yanai, Nikolay Yankov, Benjamin Yguel, Kátia Janaina Zanini, Amy E. Zanne, David Zelený, Yun-Peng Zhao, Jingming Zheng, Ji Zheng, Kasia Ziemińska, Chad R. Zirbel, Georg Zizka, Irié Casimir Zo-Bi, Gerhard Zotz, Christian Wirth.Max Planck Institute for Biogeochemistry; Max Planck Society; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; International Programme of Biodiversity Science (DIVERSITAS); International Geosphere-Biosphere Programme (IGBP); Future Earth; French Foundation for Biodiversity Research (FRB); GIS ‘Climat, Environnement et Société'.http://wileyonlinelibrary.com/journal/gcbhj2021Plant Production and Soil Scienc