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Geoscientific Model Development
An interactive open-access journal of the European Geosciences Union
Journal topic
- About
- Editorial board
- Articles
- Special issues
- Highlight articles
- Manuscript tracking
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IF 5.240
5.768CiteScore
8.9SNIP 1.713
SJR 3.18
index 71h5-index 51
Abstracted/indexed
Abstracted/indexed
Volume 7, issue 4
Geosci. Model Dev., 7, 1733–1766, 2014
https://doi.org/10.5194/gmd-7-1733-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-1733-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 7, 1733–1766, 2014
https://doi.org/10.5194/gmd-7-1733-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-1733-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Model description paper 20 Aug 2014
Model description paper | 20 Aug 2014
Development of two-moment cloud microphysics for liquid and ice within the NASA Goddard Earth Observing System Model (GEOS-5)
D. Barahona et al.
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Jie Gong, Xiping Zeng, Dong L. Wu, S. Joseph Munchak, Xiaowen Li, Stefan Kneifel, Davide Ori, Liang Liao, and Donifan Barahona
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-256, https://doi.org/10.5194/acp-2020-256, 2020
Preprint under review for ACP
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This work provides a novel view of using polarized passive microwave measurements to study the interlinked cloud-convection-precipitation process. The magnitude of differences between polarized radiance measurements are found linked to ice microphysics (crystal shape, size, orientation and density), mesoscale dynamic and thermodynamic structures, and surface precipitation. We conclude that passive sensors with multiple polarized channel pairs may serve as substitutes for spaceborn radar.
Donifan Barahona
Atmos. Chem. Phys., 18, 17119–17141, https://doi.org/10.5194/acp-18-17119-2018, https://doi.org/10.5194/acp-18-17119-2018, 2018
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This work develops a model for ice formation mediated by particles immersed within droplets. Ice nucleation is not only enhanced by the modification of the thermodynamic properties of the vicinal water but is also inhibited by decreased water mobility near the particle. The ice nucleation rate is thus determined by competing kinetic and thermodynamic factors during ice formation. A new regime where ice nucleation is mediated mainly by kinetics instead of thermodynamics is discovered.
Sara Bacer, Sylvia C. Sullivan, Vlassis A. Karydis, Donifan Barahona, Martina Krämer, Athanasios Nenes, Holger Tost, Alexandra P. Tsimpidi, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 11, 4021–4041, https://doi.org/10.5194/gmd-11-4021-2018, https://doi.org/10.5194/gmd-11-4021-2018, 2018
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The complexity of ice nucleation mechanisms and aerosol--ice interactions makes their representation still challenging in atmospheric models. We have implemented a comprehensive ice crystal formation parameterization in the global chemistry-climate model EMAC to improve the representation of ice crystal number concentrations. The newly implemented parameterization takes into account processes which were previously neglected by the standard version of the model.
Sylvia C. Sullivan, Ricardo Morales Betancourt, Donifan Barahona, and Athanasios Nenes
Atmos. Chem. Phys., 16, 2611–2629, https://doi.org/10.5194/acp-16-2611-2016, https://doi.org/10.5194/acp-16-2611-2016, 2016
Short summary
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We use the adjoint model of a cirrus parameterization to quantify sources of crystal variability for various ice-nucleating spectra and output from CAM5.
The sensitivities can be directly linked to nucleation regime and
efficiency of various INP.
The lab-based spectrum calculates much higher INP efficiencies than field-based ones, owing to aerosol surface properties.
The sensitivity to temperature tends to be low, due to the compensating effects of temperature on INP spectrum parameters.
D. Barahona
Atmos. Chem. Phys., 15, 13819–13831, https://doi.org/10.5194/acp-15-13819-2015, https://doi.org/10.5194/acp-15-13819-2015, 2015
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This paper describes the process of the transfer of water molecules between liquid and the ice during the early stages of ice formation. Using concepts of nonreversible thermodynamics, it is shown that the activation energy can be defined in terms of the bulk self-diffusivity of water and the probability of interface transfer. The application of this model to classical nucleation theory shows good agreement of measured nucleation rates with experimental results for temperatures as low as 190K.
D. Barahona
Atmos. Chem. Phys., 14, 7665–7680, https://doi.org/10.5194/acp-14-7665-2014, https://doi.org/10.5194/acp-14-7665-2014, 2014
Y. C. Sud, D. Lee, L. Oreopoulos, D. Barahona, A. Nenes, and M. J. Suarez
Geosci. Model Dev., 6, 57–79, https://doi.org/10.5194/gmd-6-57-2013, https://doi.org/10.5194/gmd-6-57-2013, 2013
Ifayoyinsola Ibikunle, Andreas Beyersdorf, Pedro Campuzano-Jost, Chelsea Corr, John D. Crounse, Jack Dibb, Glenn Diskin, Greg Huey, Jose-Luis Jimenez, Michelle J. Kim, Benjamin A. Nault, Eric Scheuer, Alex Teng, Paul O. Wennberg, Bruce Anderson, James Crawford, Rodney Weber, and Athanasios Nenes
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-501, https://doi.org/10.5194/acp-2020-501, 2020
Preprint under review for ACP
Short summary
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Analysis of observations over South Korea during the NASA/NIER
KORUS-AQ field campaign show that aerosol is fairly acidic (mean pH 2.43 ± 0.68). Aerosol formation is always sensitive to HNO3 levels, especially in highly polluted regions, while it is only exclusively sensitive to NH3 in some rural/remote regions. Nitrate levels accumulate because dry deposition velocity is low. HNO3 reductions achieved by NOx controls can be the most effective PM reduction strategy for all conditions observed.
Shunliu Zhao, Matthew G. Russell, Amir Hakami, Shannon L. Capps, Matthew D. Turner, Daven K. Henze, Peter B. Percell, Jaroslav Resler, Huizhong Shen, Armistead G. Russell, Athanasios Nenes, Amanda J. Pappin, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Charles O. Stanier, and Tianfeng Chai
Geosci. Model Dev., 13, 2925–2944, https://doi.org/10.5194/gmd-13-2925-2020, https://doi.org/10.5194/gmd-13-2925-2020, 2020
Yilin Chen, Huizhong Shen, Jennifer Kaiser, Yongtao Hu, Shannon L. Capps, Shunliu Zhao, Amir Hakami, Jhih-Shyang Shih, Gertrude K. Pavur, Matthew D. Turner, Daven K. Henze, Jaroslav Resler, Athanasios Nenes, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Tianfeng Chai, Lieven Clarisse, Pierre-François Coheur, Martin Van Damme, and Armistead G. Russell
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-523, https://doi.org/10.5194/acp-2020-523, 2020
Preprint under review for ACP
Short summary
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Ammonia (NH3) emissions can exert adverse impacts on air quality and ecosystem well-being. NH3 emission inventories are viewed as highly uncertain. Here we optimize the NH3 emission estimates in the U.S. using an air quality model and NH3 measurements from the IASI satellite instruments. The optimized NH3 emissions are much higher than the National Emission Inventory estimates in April. The optimized NH3 emissions improved model performance when evaluated against independent observation.
Jens Redemann, Robert Wood, Paquita Zuidema, Sarah J. Doherty, Bernadette Luna, Samuel E. LeBlanc, Michael S. Diamond, Yohei Shinozuka, Ian Y. Chang, Rei Ueyama, Leonhard Pfister, Ju-me Ryoo, Amie N. Dobracki, Arlindo M. da Silva, Karla M. Longo, Meloë S. Kacenelenbogen, Connor J. Flynn, Kristina Pistone, Nichola M. Knox, Stuart J. Piketh, James M. Haywood, Paola Formenti, Marc Mallet, Philip Stier, Andrew S. Ackerman, Susanne E. Bauer, Ann M. Fridlind, Gregory R. Carmichael, Pablo E. Saide, Gonzalo A. Ferrada, Steven G. Howell, Steffen Freitag, Brian Cairns, Brent N. Holben, Kirk D. Knobelspiesse, Simone Tanelli, Tristan S. L'Ecuyer, Andrew M. Dzambo, Ousmane O. Sy, Greg M. McFarquhar, Michael R. Poellot, Siddhant Gupta, Joseph R. O'Brien, Athanasios Nenes, Mary E. Kacarab, Jenny P. S. Wong, Jennifer D. Small-Griswold, Kenneth L. Thornhill, David Noone, James R. Podolske, K. Sebastian Schmidt, Peter Pilewskie, Hong Chen, Sabrina P. Cochrane, Arthur J. Sedlacek, Timothy J. Lang, Eric Stith, Michal Segal-Rozenhaimer, Richard A. Ferrare, Sharon P. Burton, Chris A. Hostetler, David J. Diner, Steven E. Platnick, Jeffrey S. Myers, Kerry G. Meyer, Douglas A. Spangenberg, Hal Maring, and Lan Gao
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-449, https://doi.org/10.5194/acp-2020-449, 2020
Preprint under review for ACP
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Southern Africa produces significant biomass burning emissions whose impacts on regional and global climate are poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a five-year NASA investigation designed to study the key processes that determine these climate impacts. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project, the data set it produced, and the most important initial findings.
Georgia Sotiropoulou, Etienne Vignon, Gillian Young, Hugh Morrison, Sebastian J. O'Shea, Thomas Lachlan-Cope, Alexis Berne, and Athanasios Nenes
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-328, https://doi.org/10.5194/acp-2020-328, 2020
Preprint under review for ACP
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Summer clouds have a significant impact on the radiation budget of the Antarctic surface and thus on ice-shelf melting. However these are poorly represented in climate models due to errors in their microphysical structure, including the number of ice crystals that they contain. We show that break-up from ice-particle collisions can substantially magnify the ice crystal number concentration with significant implications for surface radiation. This process is currently missing in climate models.
Lanxiadi Chen, Chao Peng, Athanasios Nenes, Wenjun Gu, Hanjing Fu, Xing Jian, Huanhuan Zhang, Guohua Zhang, Jianxi Zhu, Xinming Wang, and Mingjin Tang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-442, https://doi.org/10.5194/acp-2020-442, 2020
Preprint under review for ACP
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We investigated hygroscopic properties of a number of mineral dust particles in a quantitative manner, via measuring the sample mass at different relative humidities. The robust and comprehensive data obtained would significantly improve our knowledge of hygroscopicity of mineral dust and its impacts on atmospheric chemistry and climate.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-279, https://doi.org/10.5194/acp-2020-279, 2020
Preprint under review for ACP
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Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models, and requires satellite data for better quantification. The review summarizes the challenges in properly doing so, and outlines avenues for progress towards a better use of aerosol retrievals, and better retrievals of droplet concentrations.
Jie Gong, Xiping Zeng, Dong L. Wu, S. Joseph Munchak, Xiaowen Li, Stefan Kneifel, Davide Ori, Liang Liao, and Donifan Barahona
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-256, https://doi.org/10.5194/acp-2020-256, 2020
Preprint under review for ACP
Short summary
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This work provides a novel view of using polarized passive microwave measurements to study the interlinked cloud-convection-precipitation process. The magnitude of differences between polarized radiance measurements are found linked to ice microphysics (crystal shape, size, orientation and density), mesoscale dynamic and thermodynamic structures, and surface precipitation. We conclude that passive sensors with multiple polarized channel pairs may serve as substitutes for spaceborn radar.
Havala O. T. Pye, Athanasios Nenes, Becky Alexander, Andrew P. Ault, Mary C. Barth, Simon L. Clegg, Jeffrey L. Collett Jr., Kathleen M. Fahey, Christopher J. Hennigan, Hartmut Herrmann, Maria Kanakidou, James T. Kelly, I-Ting Ku, V. Faye McNeill, Nicole Riemer, Thomas Schaefer, Guoliang Shi, Andreas Tilgner, John T. Walker, Tao Wang, Rodney Weber, Jia Xing, Rahul A. Zaveri, and Andreas Zuend
Atmos. Chem. Phys., 20, 4809–4888, https://doi.org/10.5194/acp-20-4809-2020, https://doi.org/10.5194/acp-20-4809-2020, 2020
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Acid rain is recognized for its impacts on human health and ecosystems, and programs to mitigate these effects have had implications for atmospheric acidity. Historical measurements indicate that cloud and fog droplet acidity has changed in recent decades in response to controls on emissions from human activity, while the limited trend data for suspended particles indicate acidity may be relatively constant. This review synthesizes knowledge on the acidity of atmospheric particles and clouds.
Ari Laaksonen, Jussi Malila, and Athanasios Nenes
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-202, https://doi.org/10.5194/acp-2020-202, 2020
Preprint under review for ACP
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Aerosol particles containing black carbon are ubiquitos in the atmosphere, and originate from combustion processes. We examine their capability to act as condensation centers for water vapor. We make use of published experimental data sets for different types of black carbon particles, ranging from very pure particles to particles that contain both black carbon and water soluble organic matter, and show that a recently developed theory reproduces most of the experimental results.
Athanasios Nenes, Spyros N. Pandis, Maria Kanakidou, Armistead Russell, Shaojie Song, Petros Vasilakos, and Rodney J. Weber
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-266, https://doi.org/10.5194/acp-2020-266, 2020
Preprint under review for ACP
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Ecosystems and air quality are affected by the dry deposition of inorganic reactive nitrogen (Nr, the sum of ammonium and nitrate). Its large variability is driven by the large difference in deposition velocity of N when in the gas or particle phase. Here we show that aerosol liquid water and acidity, by affecting gas-particle partitioning, modulate the dry deposition velocity of NH3, HNO3 and Nr worldwide. These effects explain the rapid accumulation of nitrate aerosol during haze events.
Athanasios Nenes, Spyros N. Pandis, Rodney J. Weber, and Armistead Russell
Atmos. Chem. Phys., 20, 3249–3258, https://doi.org/10.5194/acp-20-3249-2020, https://doi.org/10.5194/acp-20-3249-2020, 2020
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We show that aerosol acidity (pH) and liquid water content naturally emerge as previously ignored parameters that drive particulate matter formation in the atmosphere, and its sensitivity to emissions of ammonia and nitric acid. The simple framework presented is easily applied to ambient measurements or model output, and it provides the
chemical regimeof PM sensitivity to ammonia and nitric acid availability.
Aikaterini Bougiatioti, Athanasios Nenes, Jack J. Lin, Charles A. Brock, Joost de Gouw, Jin Liao, Ann M. Middlebrook, and Andre Welti
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-225, https://doi.org/10.5194/acp-2020-225, 2020
Preprint under review for ACP
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The number concentration of droplets in clouds in the summertime southeastern United State is influenced by aerosol variations but limited by the strong competition for supersaturated water vapor. Concurrent variations in vertical velocity magnifies the response of cloud droplet number to aerosol increases by up to a factor of 5. Omitting the covariance omit the covariance of vertical velocity with aerosol number may therefore bias estimates of the cloud albedo effect from aerosols.
Mary Kacarab, K. Lee Thornhill, Amie Dobracki, Steven G. Howell, Joseph R. O'Brien, Steffen Freitag, Michael R. Poellot, Robert Wood, Paquita Zuidema, Jens Redemann, and Athanasios Nenes
Atmos. Chem. Phys., 20, 3029–3040, https://doi.org/10.5194/acp-20-3029-2020, https://doi.org/10.5194/acp-20-3029-2020, 2020
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We find that extensive biomass burning aerosol plumes from southern Africa can profoundly influence clouds in the southeastern Atlantic. Concurrent variations in vertical velocity, however, are found to magnify the relationship between boundary layer aerosol and the cloud droplet number. Neglecting these covariances may strongly bias the sign and magnitude of aerosol impacts on the cloud droplet number.
Maria Zakoura, Stylianos Kakavas, Athanasios Nenes, and Spyros N. Pandis
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-1146, https://doi.org/10.5194/acp-2019-1146, 2020
Preprint under review for ACP
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The dependence of aerosol acidity on particle size, location and altitude over Europe during a summertime period is investigated. Differences of up to 1–4 pH units are predicted between sub- and super-micron particles in northern and southern Europe. Particles of all sizes become increasingly acidic with altitude (0.5–2 pH units decrease over 2.5&tinsp;km). The size-dependent pH differences carry important implications for pH-sensitive processes in the aerosol.
Arnaldo Negron, Natasha DeLeon-Rodriguez, Samantha M. Waters, Luke D. Ziemba, Bruce Anderson, Michael Bergin, Konstantinos T. Konstantinidis, and Athanasios Nenes
Atmos. Chem. Phys., 20, 1817–1838, https://doi.org/10.5194/acp-20-1817-2020, https://doi.org/10.5194/acp-20-1817-2020, 2020
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Airborne biological particles impact human health, cloud formation, and ecosystems, but few techniques are available to characterize their atmospheric abundance. Combining a newly developed high-volume sampling/flow cytometry technique together with an laser-induced fluorescence instrument, we detect a highly dynamic bioaerosol community over urban Atlanta, composed of pollen, fungi, and bacteria with low and high nucleic acid content.
Georgia Sotiropoulou, Sylvia Sullivan, Julien Savre, Gary Lloyd, Thomas Lachlan-Cope, Annica M. L. Ekman, and Athanasios Nenes
Atmos. Chem. Phys., 20, 1301–1316, https://doi.org/10.5194/acp-20-1301-2020, https://doi.org/10.5194/acp-20-1301-2020, 2020
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Arctic clouds constitute a large source of uncertainty in predictions of future climate. Observations indicate that the number concentration of cloud ice crystals exceeds the concentration of aerosols that can act as ice-nucleating particles (INPs). We show that ice multiplication due to mechanical break-up upon collisions between the few primary ice crystals (formed from INPs) can explain the discrepancy. Including a description of the process in climate models can improve cloud representation.
Edward Gryspeerdt, Johannes Mülmenstädt, Andrew Gettelman, Florent F. Malavelle, Hugh Morrison, David Neubauer, Daniel G. Partridge, Philip Stier, Toshihiko Takemura, Hailong Wang, Minghuai Wang, and Kai Zhang
Atmos. Chem. Phys., 20, 613–623, https://doi.org/10.5194/acp-20-613-2020, https://doi.org/10.5194/acp-20-613-2020, 2020
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Aerosol radiative forcing is a key uncertainty in our understanding of the human forcing of the climate, with much of this uncertainty coming from aerosol impacts on clouds. Observation-based estimates of the radiative forcing are typically smaller than those from global models, but it is not clear if they are more reliable. This work shows how the forcing components in global climate models can be identified, highlighting similarities between the two methods and areas for future investigation.
Michael A. Battaglia Jr., Rodney J. Weber, Athanasios Nenes, and Christopher J. Hennigan
Atmos. Chem. Phys., 19, 14607–14620, https://doi.org/10.5194/acp-19-14607-2019, https://doi.org/10.5194/acp-19-14607-2019, 2019
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The effects of water-soluble organic carbon (WSOC) on aerosol pH were characterized for aqueous-phase particles containing a mixture of inorganics and organics. The ISORROPIA-II and E-AIM models were used in conjunction with AIOMFAC to quantify the effect of organics on aerosol pH through (1) changes to the aerosol liquid water content and (2) changes to the hydrogen ion activity coefficient. The study included both organic acids and nonacids, at RH levels ranging from 70 to 90 %.
Eleni Marinou, Matthias Tesche, Athanasios Nenes, Albert Ansmann, Jann Schrod, Dimitra Mamali, Alexandra Tsekeri, Michael Pikridas, Holger Baars, Ronny Engelmann, Kalliopi-Artemis Voudouri, Stavros Solomos, Jean Sciare, Silke Groß, Florian Ewald, and Vassilis Amiridis
Atmos. Chem. Phys., 19, 11315–11342, https://doi.org/10.5194/acp-19-11315-2019, https://doi.org/10.5194/acp-19-11315-2019, 2019
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We assess the feasibility of ground-based and spaceborne lidars to retrieve profiles of cloud-relevant aerosol concentrations and ice-nucleating particles. The retrieved profiles are in good agreement with airborne in situ measurements. Our methodology will be applied to satellite observations in the future so as to provide a global 3D product of cloud-relevant properties.
Lei Lin, Andrew Gettelman, Yangyang Xu, Chenglai Wu, Zhili Wang, Nan Rosenbloom, Susan C. Bates, and Wenjie Dong
Geosci. Model Dev., 12, 3773–3793, https://doi.org/10.5194/gmd-12-3773-2019, https://doi.org/10.5194/gmd-12-3773-2019, 2019
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Here we evaluate the performance of the Community Atmosphere Model version 6 (CAM6) released in 2018, with the default 1º horizontal resolution and a higher-resolution simulation (approximately 0.25º), against various precipitation observational datasets over Asia. With the prognostic treatment of precipitation processes (which is missing in CAM5) and the new microphysics module, CAM6 is able to better simulate climatological mean and extreme precipitation over Asia.
George S. Fanourgakis, Maria Kanakidou, Athanasios Nenes, Susanne E. Bauer, Tommi Bergman, Ken S. Carslaw, Alf Grini, Douglas S. Hamilton, Jill S. Johnson, Vlassis A. Karydis, Alf Kirkevåg, John K. Kodros, Ulrike Lohmann, Gan Luo, Risto Makkonen, Hitoshi Matsui, David Neubauer, Jeffrey R. Pierce, Julia Schmale, Philip Stier, Kostas Tsigaridis, Twan van Noije, Hailong Wang, Duncan Watson-Parris, Daniel M. Westervelt, Yang Yang, Masaru Yoshioka, Nikos Daskalakis, Stefano Decesari, Martin Gysel-Beer, Nikos Kalivitis, Xiaohong Liu, Natalie M. Mahowald, Stelios Myriokefalitakis, Roland Schrödner, Maria Sfakianaki, Alexandra P. Tsimpidi, Mingxuan Wu, and Fangqun Yu
Atmos. Chem. Phys., 19, 8591–8617, https://doi.org/10.5194/acp-19-8591-2019, https://doi.org/10.5194/acp-19-8591-2019, 2019
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Effects of aerosols on clouds are important for climate studies but are among the largest uncertainties in climate projections. This study evaluates the skill of global models to simulate aerosol, cloud condensation nuclei (CCN) and cloud droplet number concentrations (CDNCs). Model results show reduced spread in CDNC compared to CCN due to the negative correlation between the sensitivities of CDNC to aerosol number concentration (air pollution) and updraft velocity (atmospheric dynamics).
Jenny P. S. Wong, Maria Tsagkaraki, Irini Tsiodra, Nikolaos Mihalopoulos, Kalliopi Violaki, Maria Kanakidou, Jean Sciare, Athanasios Nenes, and Rodney J. Weber
Atmos. Chem. Phys., 19, 7319–7334, https://doi.org/10.5194/acp-19-7319-2019, https://doi.org/10.5194/acp-19-7319-2019, 2019
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Biomass burning is a major source of light-absorbing organic species in atmospheric aerosols, and it can play an important role in climate and atmospheric chemistry. Through a combination of laboratory experiments and field observations, this work demonstrated that the light absorption properties of aged biomass burning organic aerosols are dominated by high-molecular-weight compounds. In addition, we found that total hydrated sugars may be a robust tracer for aged biomass burning aerosols.
Panayiotis Kalkavouras, Aikaterini Bougiatioti, Nikos Kalivitis, Iasonas Stavroulas, Maria Tombrou, Athanasios Nenes, and Nikolaos Mihalopoulos
Atmos. Chem. Phys., 19, 6185–6203, https://doi.org/10.5194/acp-19-6185-2019, https://doi.org/10.5194/acp-19-6185-2019, 2019
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We study how new particle formation (NPF) events affect clouds throughout the year at a ground site in the E Mediterranean. Using a new tools and evaluation metrics, NPF is found to affect only evening and nocturnal clouds by modestly increasing droplet number by 7 to 12 %. A conventional analysis based on CCN concentration at prescribed supersaturation levels or aerosol size can considerably bias the perceived influence of NPF events on regional clouds, the hydrological cycle, and climate.
Edward Gryspeerdt, Tom Goren, Odran Sourdeval, Johannes Quaas, Johannes Mülmenstädt, Sudhakar Dipu, Claudia Unglaub, Andrew Gettelman, and Matthew Christensen
Atmos. Chem. Phys., 19, 5331–5347, https://doi.org/10.5194/acp-19-5331-2019, https://doi.org/10.5194/acp-19-5331-2019, 2019
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The liquid water path (LWP) is the strongest control on cloud albedo, such that a small change in LWP can have a large radiative impact. By changing the droplet number concentration (Nd) aerosols may be able to change the LWP, but the sign and magnitude of the effect is unclear. This work uses satellite data to investigate the relationship between Nd and LWP at a global scale and in response to large aerosol perturbations, suggesting that a strong decrease in LWP at high Nd may be overestimated.
Nønne L. Prisle, Jack J. Lin, Sara Purdue, Haisheng Lin, J. Carson Meredith, and Athanasios Nenes
Atmos. Chem. Phys., 19, 4741–4761, https://doi.org/10.5194/acp-19-4741-2019, https://doi.org/10.5194/acp-19-4741-2019, 2019
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We measure surface activity and cloud-forming potential of pollenkitt, an organic mixture coating pollen grains. Cloud droplet formation is affected through both surface tension and bulk depletion, with a consistent particle size-dependent signature. We observe nonideal solution effects in pollenkitt mixtures with ammonium sulfate salt. Our results suggest sensitivity of general water interactions, including cloud formation by pollen and their fragments, to both atmospheric humidity and aging.
Joseph A. Finlon, Greg M. McFarquhar, Stephen W. Nesbitt, Robert M. Rauber, Hugh Morrison, Wei Wu, and Pengfei Zhang
Atmos. Chem. Phys., 19, 3621–3643, https://doi.org/10.5194/acp-19-3621-2019, https://doi.org/10.5194/acp-19-3621-2019, 2019
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A new approach describing the relationship between ice crystal mass (m) and dimension (D) is derived, characterizing it as a set of
equally realizableparameters based on the natural variability in cloud conditions observed by aircraft over the Great Plains. Results from this approach address shortcomings of microphysical parameterization schemes and remote sensing retrievals that employ a single m–D relation for a given ice species or environment.
Hongyu Guo, Athanasios Nenes, and Rodney J. Weber
Atmos. Chem. Phys., 18, 17307–17323, https://doi.org/10.5194/acp-18-17307-2018, https://doi.org/10.5194/acp-18-17307-2018, 2018
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Overprediction of fine-particle ammonium-sulfate molar ratios (R) by thermodynamic models is suggested as evidence for organic aerosol limiting the condensation of ammonia onto particles, with significant impacts on aerosol chemistry. We find that the effects of small amounts of salt and dust, combined with measurement artifacts, explain the discrepancy in R. These results are highly insensitive to mixing state. This means that aerosol predictions are much more robust than thought before.
Donifan Barahona
Atmos. Chem. Phys., 18, 17119–17141, https://doi.org/10.5194/acp-18-17119-2018, https://doi.org/10.5194/acp-18-17119-2018, 2018
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This work develops a model for ice formation mediated by particles immersed within droplets. Ice nucleation is not only enhanced by the modification of the thermodynamic properties of the vicinal water but is also inhibited by decreased water mobility near the particle. The ice nucleation rate is thus determined by competing kinetic and thermodynamic factors during ice formation. A new regime where ice nucleation is mediated mainly by kinetics instead of thermodynamics is discovered.
Sylvia C. Sullivan, Christian Barthlott, Jonathan Crosier, Ilya Zhukov, Athanasios Nenes, and Corinna Hoose
Atmos. Chem. Phys., 18, 16461–16480, https://doi.org/10.5194/acp-18-16461-2018, https://doi.org/10.5194/acp-18-16461-2018, 2018
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Ice crystal formation in clouds can occur via thermodynamic nucleation, but also via mechanical collisions between pre-existing crystals or co-existing droplets. When descriptions of this mechanical ice generation are implemented into the COSMO weather model, we find that the contributions to crystal number from thermodynamic and mechanical processes are of the same order. Mechanical ice generation also intensifies differences in precipitation intensity between dynamic and quiescent regions.
Stelios Myriokefalitakis, Akinori Ito, Maria Kanakidou, Athanasios Nenes, Maarten C. Krol, Natalie M. Mahowald, Rachel A. Scanza, Douglas S. Hamilton, Matthew S. Johnson, Nicholas Meskhidze, Jasper F. Kok, Cecile Guieu, Alex R. Baker, Timothy D. Jickells, Manmohan M. Sarin, Srinivas Bikkina, Rachel Shelley, Andrew Bowie, Morgane M. G. Perron, and Robert A. Duce
Biogeosciences, 15, 6659–6684, https://doi.org/10.5194/bg-15-6659-2018, https://doi.org/10.5194/bg-15-6659-2018, 2018
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The first atmospheric iron (Fe) deposition model intercomparison is presented in this study, as a result of the deliberations of the United Nations Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP; http://www.gesamp.org/) Working Group 38. We conclude that model diversity over remote oceans reflects uncertainty in the Fe content parameterizations of dust aerosols, combustion aerosol emissions and the size distribution of transported aerosol Fe.
Sara Bacer, Sylvia C. Sullivan, Vlassis A. Karydis, Donifan Barahona, Martina Krämer, Athanasios Nenes, Holger Tost, Alexandra P. Tsimpidi, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 11, 4021–4041, https://doi.org/10.5194/gmd-11-4021-2018, https://doi.org/10.5194/gmd-11-4021-2018, 2018
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The complexity of ice nucleation mechanisms and aerosol--ice interactions makes their representation still challenging in atmospheric models. We have implemented a comprehensive ice crystal formation parameterization in the global chemistry-climate model EMAC to improve the representation of ice crystal number concentrations. The newly implemented parameterization takes into account processes which were previously neglected by the standard version of the model.
Petros Vasilakos, Armistead Russell, Rodney Weber, and Athanasios Nenes
Atmos. Chem. Phys., 18, 12765–12775, https://doi.org/10.5194/acp-18-12765-2018, https://doi.org/10.5194/acp-18-12765-2018, 2018
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In this work, we investigated the role of emission reductions on aerosol acidity and particulate nitrate. We found that models exhibit positive biases in pH predictions, attributed to very high levels of crustal elements (Mg, Ca, K) in model simulations, which in turn led to an increasing aerosol pH trend over the past decade and allowed nitrate to become an important component of aerosol, which is inconsistent with the measurements, highlighting the importance of accurate pH prediction.
Hongyu Guo, Rene Otjes, Patrick Schlag, Astrid Kiendler-Scharr, Athanasios Nenes, and Rodney J. Weber
Atmos. Chem. Phys., 18, 12241–12256, https://doi.org/10.5194/acp-18-12241-2018, https://doi.org/10.5194/acp-18-12241-2018, 2018
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Reduction in ammonia has been proposed as a way to lower fine particle mass and improve air quality, but gas-phase ammonia is linked to agricultural productivity. We assess the feasibility of ammonia control at a variety of locations through an aerosol thermodynamic analysis. We show that aerosol response to ammonia control is highly nonlinear and only becomes effective when ambient particle pH drops below approximately 3. Particle pH is a relevant aerosol air quality parameter.
Theodora Nah, Hongyu Guo, Amy P. Sullivan, Yunle Chen, David J. Tanner, Athanasios Nenes, Armistead Russell, Nga Lee Ng, L. Gregory Huey, and Rodney J. Weber
Atmos. Chem. Phys., 18, 11471–11491, https://doi.org/10.5194/acp-18-11471-2018, https://doi.org/10.5194/acp-18-11471-2018, 2018
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We present measurements from a field study conducted in an agriculturally intensive region in the southeastern US during the fall of 2016 to investigate how NH3 affects particle acidity and SOA formation via gas–particle partitioning of semi-volatile organic acids. For this study, higher NH3 concentrations relative to what has been measured in the region in previous studies had minor effects on PM1 organic acids and their influence on the overall organic aerosol and PM1 mass concentrations.
Evangelia Kostenidou, Eleni Karnezi, James R. Hite Jr., Aikaterini Bougiatioti, Kate Cerully, Lu Xu, Nga L. Ng, Athanasios Nenes, and Spyros N. Pandis
Atmos. Chem. Phys., 18, 5799–5819, https://doi.org/10.5194/acp-18-5799-2018, https://doi.org/10.5194/acp-18-5799-2018, 2018
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The volatility distribution of organic aerosol (OA) and its sources during the Southern Oxidant and Aerosol Study (SOAS) was estimated. The volatility distribution of all components covered a wide range including both semi-volatile and low-volatility components. The oxygen content of the factors can be combined with their estimated volatility and hygroscopicity to provide a better view of their physical properties.
Julia Schmale, Silvia Henning, Stefano Decesari, Bas Henzing, Helmi Keskinen, Karine Sellegri, Jurgita Ovadnevaite, Mira L. Pöhlker, Joel Brito, Aikaterini Bougiatioti, Adam Kristensson, Nikos Kalivitis, Iasonas Stavroulas, Samara Carbone, Anne Jefferson, Minsu Park, Patrick Schlag, Yoko Iwamoto, Pasi Aalto, Mikko Äijälä, Nicolas Bukowiecki, Mikael Ehn, Göran Frank, Roman Fröhlich, Arnoud Frumau, Erik Herrmann, Hartmut Herrmann, Rupert Holzinger, Gerard Kos, Markku Kulmala, Nikolaos Mihalopoulos, Athanasios Nenes, Colin O'Dowd, Tuukka Petäjä, David Picard, Christopher Pöhlker, Ulrich Pöschl, Laurent Poulain, André Stephan Henry Prévôt, Erik Swietlicki, Meinrat O. Andreae, Paulo Artaxo, Alfred Wiedensohler, John Ogren, Atsushi Matsuki, Seong Soo Yum, Frank Stratmann, Urs Baltensperger, and Martin Gysel
Atmos. Chem. Phys., 18, 2853–2881, https://doi.org/10.5194/acp-18-2853-2018, https://doi.org/10.5194/acp-18-2853-2018, 2018
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Collocated long-term observations of cloud condensation nuclei (CCN) number concentrations, particle number size distributions and chemical composition from 12 sites are synthesized. Observations cover coastal environments, the Arctic, the Mediterranean, the boreal and rain forest, high alpine and continental background sites, and Monsoon-influenced areas. We interpret regional and seasonal variability. CCN concentrations are predicted with the κ–Köhler model and compared to the measurements.
Sylvia C. Sullivan, Corinna Hoose, Alexei Kiselev, Thomas Leisner, and Athanasios Nenes
Atmos. Chem. Phys., 18, 1593–1610, https://doi.org/10.5194/acp-18-1593-2018, https://doi.org/10.5194/acp-18-1593-2018, 2018
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Ice multiplication (IM) processes can have a profound impact on cloud and precipitation development but are poorly understood. Here we study whether a lower limit of ice nuclei exists to initiate IM. The lower limit is found to be extremely low (0.01 per liter or less). A counterintuitive but profound conclusion thus emerges: IM requires cloud formation around a thermodynamic
sweet spotand is sensitive to fluctuations in cloud condensation nuclei concentration alone.
Peter A. Bogenschutz, Andrew Gettelman, Cecile Hannay, Vincent E. Larson, Richard B. Neale, Cheryl Craig, and Chih-Chieh Chen
Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, https://doi.org/10.5194/gmd-11-235-2018, 2018
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This paper compares results of developmental versions of a widely used climate model. The simulations only differ in the choice of how to model the sub-grid-scale physics in the atmospheric model. This work is novel because it is the first time that a particular physics option has been tested in a fully coupled climate model. Here, we demonstrate that this physics option has the ability to produce credible coupled climate simulations, with improved metrics in certain fields.
Bethan White, Edward Gryspeerdt, Philip Stier, Hugh Morrison, Gregory Thompson, and Zak Kipling
Atmos. Chem. Phys., 17, 12145–12175, https://doi.org/10.5194/acp-17-12145-2017, https://doi.org/10.5194/acp-17-12145-2017, 2017
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Aerosols influence cloud and precipitation by modifying cloud droplet number concentrations (CDNCs). We simulate three different types of convective cloud using two different cloud microphysics parameterisations. The simulated cloud and precipitation depends much more strongly on the choice of microphysics scheme than on CDNC. The uncertainty differs between types of convection. Our results highlight a large uncertainty in cloud and precipitation responses to aerosol in current models.
Khairunnisa Yahya, Timothy Glotfelty, Kai Wang, Yang Zhang, and Athanasios Nenes
Geosci. Model Dev., 10, 2333–2363, https://doi.org/10.5194/gmd-10-2333-2017, https://doi.org/10.5194/gmd-10-2333-2017, 2017
Petros Vasilakos, Yong-Ηa Kim, Jeffrey R. Pierce, Sotira Yiacoumi, Costas Tsouris, and Athanasios Nenes
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-96, https://doi.org/10.5194/gmd-2017-96, 2017
Revised manuscript not accepted
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Radioactive charging can significantly impact the way radioactive aerosols behave, and as a result their lifetime, but such effects are neglected in predictive model studies of radioactive plumes. We extend a well-established model that simulates the evolution of atmospheric particulate matter to account for radioactive charging effects in an accurate and computationally efficient way. It is shown that radioactivity can strongly impact the deposition patterns of aerosol.
Hongyu Guo, Jiumeng Liu, Karl D. Froyd, James M. Roberts, Patrick R. Veres, Patrick L. Hayes, Jose L. Jimenez, Athanasios Nenes, and Rodney J. Weber
Atmos. Chem. Phys., 17, 5703–5719, https://doi.org/10.5194/acp-17-5703-2017, https://doi.org/10.5194/acp-17-5703-2017, 2017
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Fine particle pH is linked to many environmental impacts by affecting particle concentration and composition. Predicted Pasadena, CA (CalNex campaign), PM1 pH is 1.9 and PM2.5 pH 2.7, the latter higher due to sea salts. The model predicted gas–particle partitionings of HNO3–NO3−, NH3–NH4+, and HCl–Cl− are in good agreement, verifying the model predictions. A summary of contrasting locations in the US and eastern Mediterranean shows fine particles are generally highly acidic, with pH below 3.
Vlassis A. Karydis, Alexandra P. Tsimpidi, Sara Bacer, Andrea Pozzer, Athanasios Nenes, and Jos Lelieveld
Atmos. Chem. Phys., 17, 5601–5621, https://doi.org/10.5194/acp-17-5601-2017, https://doi.org/10.5194/acp-17-5601-2017, 2017
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The importance of mineral dust for cloud droplet formation is studied by considering the adsorption activation of insoluble dust particles and the thermodynamic interactions between mineral cations and inorganic anions. This study demonstrates that a comprehensive treatment of the CCN activity of mineral dust and its chemical and thermodynamic interactions with inorganic species by chemistry climate models is important to realistically account for aerosol–chemistry–cloud–climate interaction.
Chenglai Wu, Xiaohong Liu, Minghui Diao, Kai Zhang, Andrew Gettelman, Zheng Lu, Joyce E. Penner, and Zhaohui Lin
Atmos. Chem. Phys., 17, 4731–4749, https://doi.org/10.5194/acp-17-4731-2017, https://doi.org/10.5194/acp-17-4731-2017, 2017
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This study utilizes a novel approach to directly compare the CAM5-simulated cloud macro- and microphysics with the collocated HIPPO observations for the period of 2009 to 2011. The model cannot capture the large spatial variabilities of observed RH, which is responsible for much of the model missing low-level warm clouds. A large portion of the RH bias results from the discrepancy in water vapor. The model underestimates the observed number concentration and ice water content.
Andrew Gettelman, Chih-Chieh Chen, Mark Z. Jacobson, Mary A. Cameron, Donald J. Wuebbles, and Arezoo Khodayari
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-218, https://doi.org/10.5194/acp-2017-218, 2017
Revised manuscript not accepted
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Aviation emissions create several impacts on climate. Condensation trails (contrails) are aviation produced cirrus clouds. Aircraft also emit aerosols, including soot (black carbon) and sulfate. Analyses of the climate effects of 2050 aviation emissions have been conducted with two coupled Chemistry Climate Models (CCMs) including experiments with coupled ocean models.
Alexandra Tsekeri, Vassilis Amiridis, Franco Marenco, Athanasios Nenes, Eleni Marinou, Stavros Solomos, Phil Rosenberg, Jamie Trembath, Graeme J. Nott, James Allan, Michael Le Breton, Asan Bacak, Hugh Coe, Carl Percival, and Nikolaos Mihalopoulos
Atmos. Meas. Tech., 10, 83–107, https://doi.org/10.5194/amt-10-83-2017, https://doi.org/10.5194/amt-10-83-2017, 2017
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The In situ/Remote sensing aerosol Retrieval Algorithm (IRRA) provides vertical profiles of aerosol optical, microphysical and hygroscopic properties from airborne in situ and remote sensing measurements. The algorithm is highly advantageous for aerosol characterization in humid conditions, employing the ISORROPIA II model for acquiring the particle hygroscopic growth. IRRA can find valuable applications in aerosol–cloud interaction schemes and in validation of active space-borne sensors.
Havala O. T. Pye, Benjamin N. Murphy, Lu Xu, Nga L. Ng, Annmarie G. Carlton, Hongyu Guo, Rodney Weber, Petros Vasilakos, K. Wyat Appel, Sri Hapsari Budisulistiorini, Jason D. Surratt, Athanasios Nenes, Weiwei Hu, Jose L. Jimenez, Gabriel Isaacman-VanWertz, Pawel K. Misztal, and Allen H. Goldstein
Atmos. Chem. Phys., 17, 343–369, https://doi.org/10.5194/acp-17-343-2017, https://doi.org/10.5194/acp-17-343-2017, 2017
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We use a chemical transport model to examine how organic compounds in the atmosphere interact with water present in particles. Organic compounds themselves lead to water uptake, and organic compounds interact with water associated with inorganic compounds in the rural southeast atmosphere. Including interactions of organic compounds with water requires a treatment of nonideality to more accurately represent aerosol observations during the Southern Oxidant and Aerosol Study (SOAS) 2013.
Panayiotis Kalkavouras, Elissavet Bossioli, Spiros Bezantakos, Aikaterini Bougiatioti, Nikos Kalivitis, Iasonas Stavroulas, Giorgos Kouvarakis, Anna P. Protonotariou, Aggeliki Dandou, George Biskos, Nikolaos Mihalopoulos, Athanasios Nenes, and Maria Tombrou
Atmos. Chem. Phys., 17, 175–192, https://doi.org/10.5194/acp-17-175-2017, https://doi.org/10.5194/acp-17-175-2017, 2017
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Concentrations of chemically and size-resolved submicron aerosol particles along with concentrations of gases and meteorological variables were measured at Santorini and Finokalia (central and southern Aegean Sea) during the Etesians. Particle nucleation bursts were recorded. The NPF can double CCN number (at 0.1 % supersaturation), but the resulting strong competition for water vapor in cloudy updrafts decreases maximum supersaturation by 14 % and augments the potential droplet number by 12 %.
Carsten Warneke, Michael Trainer, Joost A. de Gouw, David D. Parrish, David W. Fahey, A. R. Ravishankara, Ann M. Middlebrook, Charles A. Brock, James M. Roberts, Steven S. Brown, Jonathan A. Neuman, Brian M. Lerner, Daniel Lack, Daniel Law, Gerhard Hübler, Iliana Pollack, Steven Sjostedt, Thomas B. Ryerson, Jessica B. Gilman, Jin Liao, John Holloway, Jeff Peischl, John B. Nowak, Kenneth C. Aikin, Kyung-Eun Min, Rebecca A. Washenfelder, Martin G. Graus, Mathew Richardson, Milos Z. Markovic, Nick L. Wagner, André Welti, Patrick R. Veres, Peter Edwards, Joshua P. Schwarz, Timothy Gordon, William P. Dube, Stuart A. McKeen, Jerome Brioude, Ravan Ahmadov, Aikaterini Bougiatioti, Jack J. Lin, Athanasios Nenes, Glenn M. Wolfe, Thomas F. Hanisco, Ben H. Lee, Felipe D. Lopez-Hilfiker, Joel A. Thornton, Frank N. Keutsch, Jennifer Kaiser, Jingqiu Mao, and Courtney D. Hatch
Atmos. Meas. Tech., 9, 3063–3093, https://doi.org/10.5194/amt-9-3063-2016, https://doi.org/10.5194/amt-9-3063-2016, 2016
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In this paper we describe the experimental approach, the science goals and early results of the NOAA SENEX campaign, which was focused on studying the interactions between biogenic and anthropogenic emissions to form secondary pollutants.
During SENEX, the NOAA WP-3D aircraft conducted 20 research flights between 27 May and 10 July 2013 based out of Smyrna, TN. The SENEX flights included day- and nighttime flights in the Southeast as well as flights over areas with intense shale gas extraction.
Chih-Chieh Chen and Andrew Gettelman
Atmos. Chem. Phys., 16, 7317–7333, https://doi.org/10.5194/acp-16-7317-2016, https://doi.org/10.5194/acp-16-7317-2016, 2016
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The impact of aviation emissions through 2050 is simulated by a comprehensive global climate model. Four different future emission scenarios of the same flight tracks are considered. The results reveal that the global radiative forcing of contrail cirrus is positive and can increase by a factor of 7 in 2050 from the 2006 level. The aviation aerosols can produce negative forcing, mainly over the oceans, and increase by a factor of 4 in 2050 from the 2006 level.
Christopher R. Hoyle, Clare S. Webster, Harald E. Rieder, Athanasios Nenes, Emanuel Hammer, Erik Herrmann, Martin Gysel, Nicolas Bukowiecki, Ernest Weingartner, Martin Steinbacher, and Urs Baltensperger
Atmos. Chem. Phys., 16, 4043–4061, https://doi.org/10.5194/acp-16-4043-2016, https://doi.org/10.5194/acp-16-4043-2016, 2016
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A simple statistical model to predict the number of aerosols which activate to form cloud droplets in warm clouds has been established, based on regression analysis of data from the high-altitude site Jungfraujoch. It is found that cloud droplet formation at the Jungfraujoch is predominantly controlled by the number concentration of aerosol particles. A statistical model based on only the number of particles larger than 80nm can explain 79 % of the observed variance in droplet numbers.
Shipeng Zhang, Minghuai Wang, Steven J. Ghan, Aijun Ding, Hailong Wang, Kai Zhang, David Neubauer, Ulrike Lohmann, Sylvaine Ferrachat, Toshihiko Takeamura, Andrew Gettelman, Hugh Morrison, Yunha Lee, Drew T. Shindell, Daniel G. Partridge, Philip Stier, Zak Kipling, and Congbin Fu
Atmos. Chem. Phys., 16, 2765–2783, https://doi.org/10.5194/acp-16-2765-2016, https://doi.org/10.5194/acp-16-2765-2016, 2016
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The variation of aerosol indirect effects (AIE) in several climate models is investigated across different dynamical regimes. Regimes with strong large-scale ascent are shown to be as important as stratocumulus regimes in studying AIE. AIE over regions with high monthly large-scale surface precipitation rate contributes the most to the total aerosol indirect forcing. These results point to the need to reduce the uncertainty in AIE in different dynamical regimes.
Sylvia C. Sullivan, Ricardo Morales Betancourt, Donifan Barahona, and Athanasios Nenes
Atmos. Chem. Phys., 16, 2611–2629, https://doi.org/10.5194/acp-16-2611-2016, https://doi.org/10.5194/acp-16-2611-2016, 2016
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We use the adjoint model of a cirrus parameterization to quantify sources of crystal variability for various ice-nucleating spectra and output from CAM5.
The sensitivities can be directly linked to nucleation regime and
efficiency of various INP.
The lab-based spectrum calculates much higher INP efficiencies than field-based ones, owing to aerosol surface properties.
The sensitivity to temperature tends to be low, due to the compensating effects of temperature on INP spectrum parameters.
Colin M. Zarzycki, Kevin A. Reed, Julio T. Bacmeister, Anthony P. Craig, Susan C. Bates, and Nan A. Rosenbloom
Geosci. Model Dev., 9, 779–788, https://doi.org/10.5194/gmd-9-779-2016, https://doi.org/10.5194/gmd-9-779-2016, 2016
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This paper highlights the sensitivity of simulated tropical cyclone climatology to the choice of ocean coupling grid in high-resolution climate simulations. When computations of atmosphere–ocean interactions are carried out on the coarser grid in the system, key quantities such as surface wind drag and heat fluxes are incorrectly calculated. In the case of a coarser ocean grid, significantly stronger cyclone winds result, due to misaligned frictional vectors in the atmospheric dynamical core.
D. Barahona
Atmos. Chem. Phys., 15, 13819–13831, https://doi.org/10.5194/acp-15-13819-2015, https://doi.org/10.5194/acp-15-13819-2015, 2015
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This paper describes the process of the transfer of water molecules between liquid and the ice during the early stages of ice formation. Using concepts of nonreversible thermodynamics, it is shown that the activation energy can be defined in terms of the bulk self-diffusivity of water and the probability of interface transfer. The application of this model to classical nucleation theory shows good agreement of measured nucleation rates with experimental results for temperatures as low as 190K.
P. H. Lauritzen, J. T. Bacmeister, P. F. Callaghan, and M. A. Taylor
Geosci. Model Dev., 8, 3975–3986, https://doi.org/10.5194/gmd-8-3975-2015, https://doi.org/10.5194/gmd-8-3975-2015, 2015
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This paper documents the NCAR global model topography generation software. The software generates elevation and related data for global atmospheric models based in GTOPO30 or GMTED2010/MODIS source data.
K. Thayer-Calder, A. Gettelman, C. Craig, S. Goldhaber, P. A. Bogenschutz, C.-C. Chen, H. Morrison, J. Höft, E. Raut, B. M. Griffin, J. K. Weber, V. E. Larson, M. C. Wyant, M. Wang, Z. Guo, and S. J. Ghan
Geosci. Model Dev., 8, 3801–3821, https://doi.org/10.5194/gmd-8-3801-2015, https://doi.org/10.5194/gmd-8-3801-2015, 2015
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This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that is implemented in CAM v5.3. We show mean climate and tropical variability results from global simulations. The model has a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. We also show estimation of computational expense and sensitivity to number of subcolumns.
A. Gettelman
Atmos. Chem. Phys., 15, 12397–12411, https://doi.org/10.5194/acp-15-12397-2015, https://doi.org/10.5194/acp-15-12397-2015, 2015
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Aerosols affect cloud properties, and the radiative effects of clouds. Human emissions of aerosol particles and precursors may alter the radiative effects of clouds. This is generally a cooling effect that offsets other warming effects of human emissions of gases. Simulating these aerosol effects on clouds are highly dependent on the formulation of the microphysical (cloud droplet scale) processes. This work uses model simulations to show these effects are large, and depend on certain processes.
A. Molod, L. Takacs, M. Suarez, and J. Bacmeister
Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, https://doi.org/10.5194/gmd-8-1339-2015, 2015
T. Eidhammer, H. Morrison, A. Bansemer, A. Gettelman, and A. J. Heymsfield
Atmos. Chem. Phys., 14, 10103–10118, https://doi.org/10.5194/acp-14-10103-2014, https://doi.org/10.5194/acp-14-10103-2014, 2014
M. S. Johnston, S. Eliasson, P. Eriksson, R. M. Forbes, A. Gettelman, P. Räisänen, and M. D. Zelinka
Atmos. Chem. Phys., 14, 8701–8721, https://doi.org/10.5194/acp-14-8701-2014, https://doi.org/10.5194/acp-14-8701-2014, 2014
D. Barahona
Atmos. Chem. Phys., 14, 7665–7680, https://doi.org/10.5194/acp-14-7665-2014, https://doi.org/10.5194/acp-14-7665-2014, 2014
C.-C. Chen and A. Gettelman
Atmos. Chem. Phys., 13, 12525–12536, https://doi.org/10.5194/acp-13-12525-2013, https://doi.org/10.5194/acp-13-12525-2013, 2013
A. Gettelman, H. Morrison, C. R. Terai, and R. Wood
Atmos. Chem. Phys., 13, 9855–9867, https://doi.org/10.5194/acp-13-9855-2013, https://doi.org/10.5194/acp-13-9855-2013, 2013
J.-I. Yano, M. Bister, Ž. Fuchs, L. Gerard, V. T. J. Phillips, S. Barkidija, and J.-M. Piriou
Atmos. Chem. Phys., 13, 4111–4131, https://doi.org/10.5194/acp-13-4111-2013, https://doi.org/10.5194/acp-13-4111-2013, 2013
Y. C. Sud, D. Lee, L. Oreopoulos, D. Barahona, A. Nenes, and M. J. Suarez
Geosci. Model Dev., 6, 57–79, https://doi.org/10.5194/gmd-6-57-2013, https://doi.org/10.5194/gmd-6-57-2013, 2013
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Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0
An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1
Representing model uncertainty for global atmospheric CO2 flux inversions using ECMWF-IFS-46R1
Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0)
Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign
Dynamic Anthropogenic activitieS impacting Heat emissions (DASHv1.0): Development and evaluation
WRF-Chem v3.9 simulations of the East Asian dust storm in May 2017: modeling sensitivities to dust emission and dry deposition schemes
Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
Configuration and intercomparison of deep learning neural models for statistical downscaling
A Mass- and Energy-Conserving Framework for Using Machine Learning to Speed Computations
Modelling the mineralogical composition and solubility of mineral dust in the Mediterranean area with CHIMERE 2017r4
The first Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL1
The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory
Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1
Can machine learning improve the model representation of TKE dissipation rate in the boundary layer for complex terrain?
Hindcasting and forecasting of regional methane from coal mine emissions in the Upper Silesian Coal Basin using the online nested global regional chemistry–climate model MECO(n) (MESSy v2.53)
Verification of the regional atmospheric model CCLM v5.0 with conventional data and lidar measurements in Antarctica
Prediction of source contributions to urban background PM10 concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0 – Part 1: The country contributions
Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite
Towards the closure of momentum budget analyses in the WRF (v3.8.1) model
PAMTRA 1.0: A Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere
Coupling aerosols to (cirrus) clouds in the global EMAC-MADE3 aerosol–climate model
Local fractions – a method for the calculation of local source contributions to air pollution, illustrated by examples using the EMEP MSC-W model (rv4_33)
An intercomparison of tropospheric ozone reanalysis products from CAMS, CAMS interim, TCR-1, and TCR-2
PM2.5 ∕ PM10 ratio prediction based on a long short-term memory neural network in Wuhan, China
FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 1: Model physics and numerics
Overview of the PALM model system 6.0
Description and evaluation of the UKCA stratosphere–troposphere chemistry scheme (StratTrop vn 1.0) implemented in UKESM1
Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues
EXPLUME v1.0: a model for personal exposure to ambient O3 and PM2.5
Uncertainties in climate change projections covered by the ISIMIP and CORDEX model subsets from CMIP5
HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 2: The bottom–up module
Global aerosol simulations using NICAM.16 on a 14-km grid spacing for a climate study: Improved and remaining issues relative to a lower-resolution model
Simulation of extreme heat waves with empirical importance sampling
Arianna Valmassoi, Jimy Dudhia, Silvana Di Sabatino, and Francesco Pilla
Geosci. Model Dev., 13, 3179–3201, https://doi.org/10.5194/gmd-13-3179-2020, https://doi.org/10.5194/gmd-13-3179-2020, 2020
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Irrigation affects the atmosphere and models are required to understand its full impact. However, there is no agreed procedure to describe irrigation within regional models. The present study introduces three new methods to integrate this process into the models and validates it for the Po Valley in northern Italy. All the tests done show that the results are improved with the new irrigation techniques when compared against some measures (e.g., temperature, potential evapotranspiration).
Dai Koshin, Kaoru Sato, Kazuyuki Miyazaki, and Shingo Watanabe
Geosci. Model Dev., 13, 3145–3177, https://doi.org/10.5194/gmd-13-3145-2020, https://doi.org/10.5194/gmd-13-3145-2020, 2020
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A new data assimilation system with a 4D local ensemble transform Kalman filter for the whole neutral atmosphere is developed using a T42L124 general circulation model. A conventional observation dataset and bias-corrected satellite temperature data are assimilated. After the improvements of the forecast model, the assimilation parameters are optimized. The minimum optimal number of ensembles is also examined. Results are evaluated using the reanalysis data and independent radar observations.
Dominik Fröhlich and Andreas Matzarakis
Geosci. Model Dev., 13, 3055–3065, https://doi.org/10.5194/gmd-13-3055-2020, https://doi.org/10.5194/gmd-13-3055-2020, 2020
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In the frame of the project "MOSAIK - Model-based city planning and application in climate change", a biometeorology module was implemented into the Parallelized Large-Eddy Simulation Model (PALM). It is comprised of methods for the estimation of three popular thermal indices for the assessment of human thermal comfort or thermal stress. Results of the new module are compared to those by the SkyHelios model as well as the version of the indices published in VDI guideline 3787, part 2.
Solène Turquety, Laurent Menut, Guillaume Siour, Sylvain Mailler, Juliette Hadji-Lazaro, Maya George, Cathy Clerbaux, Daniel Hurtmans, and Pierre-François Coheur
Geosci. Model Dev., 13, 2981–3009, https://doi.org/10.5194/gmd-13-2981-2020, https://doi.org/10.5194/gmd-13-2981-2020, 2020
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Biomass burning emissions are a major source of trace gases and aerosols that need to be accounted for in air quality assessment and forecasting. The APIFLAME model presented in this paper allows the calculation of these emissions based on merged satellite observations at hourly time steps and kilometer scales. Implementing emissions in a chemistry transport model allows realistic simulations of fire plumes as illustrated for wildfires in Portugal in August 2016 using the CHIMERE model.
Shunliu Zhao, Matthew G. Russell, Amir Hakami, Shannon L. Capps, Matthew D. Turner, Daven K. Henze, Peter B. Percell, Jaroslav Resler, Huizhong Shen, Armistead G. Russell, Athanasios Nenes, Amanda J. Pappin, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Charles O. Stanier, and Tianfeng Chai
Geosci. Model Dev., 13, 2925–2944, https://doi.org/10.5194/gmd-13-2925-2020, https://doi.org/10.5194/gmd-13-2925-2020, 2020
Gan Luo, Fangqun Yu, and Jonathan M. Moch
Geosci. Model Dev., 13, 2879–2903, https://doi.org/10.5194/gmd-13-2879-2020, https://doi.org/10.5194/gmd-13-2879-2020, 2020
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This work improved pH calculation for cloud, rain, and wet surfaces, fraction of cloud available for aqueous-phase chemistry, rainout efficiencies for various types of cloud, empirical washout by rain and snow, and wet surface uptake in GEOS-Chem v12.6.0. We compared simulated mass concentrations of aerosol precursors and aerosols with surface monitoring networks, Arctic sites, and ATom observations, and showed that the model results with the updated wet processes agree better for most species.
Laura D. Fowler, Mary C. Barth, and Kiran Alapaty
Geosci. Model Dev., 13, 2851–2877, https://doi.org/10.5194/gmd-13-2851-2020, https://doi.org/10.5194/gmd-13-2851-2020, 2020
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The cloud liquid and ice water path and precipitation simulated with the Model for Prediction Across Scales are compared against satellite data over the tropical Pacific Ocean. Uniform and variable-resolution experiments using scale-aware convection schemes produce strong biases between simulated and observed diagnostics. Results underscore the importance of evaluating clouds, their optical properties, and radiation budget in addition to precipitation in mesh refinement global simulations.
Alessandro Cotronei and Thomas Slawig
Geosci. Model Dev., 13, 2783–2804, https://doi.org/10.5194/gmd-13-2783-2020, https://doi.org/10.5194/gmd-13-2783-2020, 2020
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We converted the radiation part of the atmospheric model ECHAM to single-precision arithmetic, using a step-by-step change in all modules. A small code portion still requires higher precision. The generated code can be easily changed from double to single precision and vice versa. The quality of the output of the single-precision version is comparable to observational data and the one of the original code. The runtime was reduced by 40 %, and the energy consumption could also be decreased.
Carlos A. F. Marques, Martinho Marta-Almeida, and José M. Castanheira
Geosci. Model Dev., 13, 2763–2781, https://doi.org/10.5194/gmd-13-2763-2020, https://doi.org/10.5194/gmd-13-2763-2020, 2020
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A free software package for the computation of the 3-D normal modes of an hydrostatic atmosphere is presented. This software performs the computations in isobaric coordinates and was developed for two user-friendly languages: MATLAB and Python. The software can be used to expand the global atmospheric circulation onto the 3-D normal modes.
Ingrid Super, Hugo A. C. Denier van der Gon, Michiel K. van der Molen, Stijn N. C. Dellaert, and Wouter Peters
Geosci. Model Dev., 13, 2695–2721, https://doi.org/10.5194/gmd-13-2695-2020, https://doi.org/10.5194/gmd-13-2695-2020, 2020
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Understanding urban CO2 fluxes is increasingly important to support emission reduction policies. In this work we extended an existing framework for emission verification to increase its suitability for urban areas and source-sector-based decision-making by using a dynamic emission model. We find that the dynamic emission model provides a better understanding of emissions at small scales and their uncertainties. By including co-emitted species, emission can be related to specific source sectors.
Fangqun Yu, Alexey B. Nadykto, Gan Luo, and Jason Herb
Geosci. Model Dev., 13, 2663–2670, https://doi.org/10.5194/gmd-13-2663-2020, https://doi.org/10.5194/gmd-13-2663-2020, 2020
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Secondary particles formed via nucleation have important implications for air quality and climate. Here we describe nucleation rate lookup tables for four different nucleation mechanisms that can be readily used in chemistry transport and climate models. The nucleation rates predicted have been assessed against state-of-the-art laboratory measurements. The lookup tables cover a wide range of key parameters controlling binary, ternary, and ion-mediated nucleation in the Earth's atmosphere.
Jessica M. Tomaszewski and Julie K. Lundquist
Geosci. Model Dev., 13, 2645–2662, https://doi.org/10.5194/gmd-13-2645-2020, https://doi.org/10.5194/gmd-13-2645-2020, 2020
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Wind farms can briefly impact the nearby environment by reducing wind speeds and mixing warmer air down to the surface. The wind farm parameterization (WFP) in the Weather Research and Forecasting (WRF) model is a tool that numerically simulates wind farms and these meteorological impacts. We highlight the importance of choice in model settings and find that sufficiently fine vertical and horizontal grids with turbine turbulence are needed to accurately simulate wind farm meteorological impacts.
Georgy Ayzel, Tobias Scheffer, and Maik Heistermann
Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, https://doi.org/10.5194/gmd-13-2631-2020, 2020
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In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time.
Erik H. Hoffmann, Roland Schrödner, Andreas Tilgner, Ralf Wolke, and Hartmut Herrmann
Geosci. Model Dev., 13, 2587–2609, https://doi.org/10.5194/gmd-13-2587-2020, https://doi.org/10.5194/gmd-13-2587-2020, 2020
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A condensed multiphase halogen and DMS chemistry mechanism for application in chemical transport models has been developed and applied by 2D simulations to explore multiphase marine chemistry above the pristine open ocean. The model simulations have demonstrated the ability of the mechanism in studying aerosol cloud processing effects in the marine atmosphere. First 2D simulations have shown significant differences in the DMS processing under convective and stratiform cloud conditions.
Sam J. Silva, Colette L. Heald, and Alex B. Guenther
Geosci. Model Dev., 13, 2569–2585, https://doi.org/10.5194/gmd-13-2569-2020, https://doi.org/10.5194/gmd-13-2569-2020, 2020
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Simulating the influence of the biosphere on atmospheric chemistry has traditionally been computationally intensive. We describe a surrogate canopy physics model parameterized using a statistical learning technique and specifically designed for use in large-scale chemical transport models. Our surrogate model reproduces a more detailed model to within 10 % without a large computational demand, improving the process representation of biosphere–atmosphere exchange.
Lu Shen, Daniel J. Jacob, Mauricio Santillana, Xuan Wang, and Wei Chen
Geosci. Model Dev., 13, 2475–2486, https://doi.org/10.5194/gmd-13-2475-2020, https://doi.org/10.5194/gmd-13-2475-2020, 2020
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Chemical mechanisms in air quality models tend to get more complicated with time, reflecting both increasing knowledge and the need for greater scope. This objectively improves the models but increases the computational burden. In this work, we present an approach that can reduce the computational cost of chemical integration by 30–40 % while maintaining an accuracy better than 1 %. It retains the complexity of the full mechanism where it is needed and preserves full diagnostic information.
Shelley C. van der Graaf, Richard Kranenburg, Arjo J. Segers, Martijn Schaap, and Jan Willem Erisman
Geosci. Model Dev., 13, 2451–2474, https://doi.org/10.5194/gmd-13-2451-2020, https://doi.org/10.5194/gmd-13-2451-2020, 2020
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Chemical transport models (CTMs) are important tools to determine the fate of reactive nitrogen (Nr) emissions. The parameterization of the surface–atmosphere exchange in CTMs is often only linked to fixed, land-use-dependent values. In this paper, we present an approach to derive more realistic, dynamic leaf area index (LAI) and roughness length (z0) input maps using multiple satellite products. We evaluate the effect on Nr concentration and deposition fields modelled in the LOTOS-EUROS CTM.
Michael Jähn, Gerrit Kuhlmann, Qing Mu, Jean-Matthieu Haussaire, David Ochsner, Katherine Osterried, Valentin Clément, and Dominik Brunner
Geosci. Model Dev., 13, 2379–2392, https://doi.org/10.5194/gmd-13-2379-2020, https://doi.org/10.5194/gmd-13-2379-2020, 2020
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Emission inventories of air pollutants and greenhouse gases are widely used as input for atmospheric chemistry transport models. However, the pre-processing of these data is both time-consuming and requires a large amount of disk storage. To overcome this issue, a Python package has been developed and tested for two different models. There, the inventory is projected to the model grid and scaling factors are provided. This approach saves computational time while remaining numerically equivalent.
Joe R. McNorton, Nicolas Bousserez, Anna Agustí-Panareda, Gianpaolo Balsamo, Margarita Choulga, Andrew Dawson, Richard Engelen, Zak Kipling, and Simon Lang
Geosci. Model Dev., 13, 2297–2313, https://doi.org/10.5194/gmd-13-2297-2020, https://doi.org/10.5194/gmd-13-2297-2020, 2020
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To infer carbon emissions from observations using atmospheric models, detailed knowledge of uncertainty is required. The uncertainties associated with models are often estimated because they are difficult to attribute. Here we use a state-of-the-art weather model to assess the impact of uncertainty in the wind fields on atmospheric concentrations of carbon dioxide. These results can be used to help quantify the uncertainty in estimated carbon emissions from atmospheric observations.
Stephan Rasp
Geosci. Model Dev., 13, 2185–2196, https://doi.org/10.5194/gmd-13-2185-2020, https://doi.org/10.5194/gmd-13-2185-2020, 2020
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Subgrid parameterizations are largely responsible for uncertainties in climate models. Recently, several studies tried to improve the representation of subgrid processes by learning parameterization directly from high-resolution modeling data. In this paper, the current state of the art of this research direction is summarized, and an algorithm is proposed to combat major problems with existing approaches, namely instabilities and biases.
Li Pan, HyunCheol Kim, Pius Lee, Rick Saylor, YouHua Tang, Daniel Tong, Barry Baker, Shobha Kondragunta, Chuanyu Xu, Mark G. Ruminski, Weiwei Chen, Jeff Mcqueen, and Ivanka Stajner
Geosci. Model Dev., 13, 2169–2184, https://doi.org/10.5194/gmd-13-2169-2020, https://doi.org/10.5194/gmd-13-2169-2020, 2020
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Compared to anthropogenic emissions, emissions from wildfires are largely uncontrolled and unpredictable. Quantitatively describing wildfire emissions and their contributions to air pollution remains a substantial challenge for air quality forecasting efforts. In this study, we test the wildfire calculation algorithm used by the National Air Quality Forecasting Capability (NAQFC) by comparison with ground, satellite and flight measurements during the Southeast Nexus (SENEX) field experiment.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-52, https://doi.org/10.5194/gmd-2020-52, 2020
Revised manuscript accepted for GMD
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Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local and larger scales urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, socio-economic factors and in response to environmental conditions.
Yi Zeng, Minghuai Wang, Chun Zhao, Siyu Chen, Zhoukun Liu, Xin Huang, and Yang Gao
Geosci. Model Dev., 13, 2125–2147, https://doi.org/10.5194/gmd-13-2125-2020, https://doi.org/10.5194/gmd-13-2125-2020, 2020
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Dust aerosol can impact many processes of the Earth system, but large uncertainties still remain in dust simulations. In this study, we investigated dust simulation sensitivity to two dust emission schemes and three dry deposition schemes using WRF-Chem. An optimal combination of dry deposition scheme and dust emission scheme has been identified to best simulate the dust storm in comparison with observation. Our results highlight the importance of dry deposition schemes for dust simulation.
Luke M. Western, Zhe Sha, Matthew Rigby, Anita L. Ganesan, Alistair J. Manning, Kieran M. Stanley, Simon J. O'Doherty, Dickon Young, and Jonathan Rougier
Geosci. Model Dev., 13, 2095–2107, https://doi.org/10.5194/gmd-13-2095-2020, https://doi.org/10.5194/gmd-13-2095-2020, 2020
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Assessments of greenhouse gas emissions using atmospheric measurements and meteorological models, or
top-downmethods, are important to verify national inventories or produce a stand-alone estimate where no inventory exists. We present a novel top-down method to estimate emissions. This approach uses a fast method called an integrated nested Laplacian approximation to estimate how these emissions are correlated with other emissions in different locations and at different times.
Jorge Baño-Medina, Rodrigo Manzanas, and José Manuel Gutiérrez
Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, https://doi.org/10.5194/gmd-13-2109-2020, 2020
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In this study we intercompare different deep learning topologies for statistical downscaling purposes. As compared to the top-ranked methods in the largest-to-date downscaling intercomparison study, our results better predict the local climate variability. Moreover, deep learning approaches can be suitably applied to large regions (e.g., continents), which can therefore foster the use of statistical downscaling in flagship initiatives such as CORDEX.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-83, https://doi.org/10.5194/gmd-2020-83, 2020
Revised manuscript accepted for GMD
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Large air quality and climate models calculate different physical and chemical phenomena in separate operators within the overall model, some of which are computationally intensive. Machine learning tools can memorize the behavior of these operators and replace them, but the replacements must still obey physical laws, like conservation principles. This work derives a mathematical framework for machine learning replacements that conserves properties, such as mass or energy, to machine precision.
Laurent Menut, Guillaume Siour, Bertrand Bessagnet, Florian Couvidat, Emilie Journet, Yves Balkanski, and Karine Desboeufs
Geosci. Model Dev., 13, 2051–2071, https://doi.org/10.5194/gmd-13-2051-2020, https://doi.org/10.5194/gmd-13-2051-2020, 2020
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Modelling of mineral dust is often done using one single mean species. In this study, differentiated mineral species with their chemical composition are implemented in the CHIMERE regional chemistry-transport model by using global databases. Simulations are carried out to quantify the realism and gain of such mineralogy.
Mike Bush, Tom Allen, Caroline Bain, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Humphrey Lean, Adrian Lock, James Manners, Marion Mittermaier, Cyril Morcrette, Rachel North, Jon Petch, Chris Short, Simon Vosper, David Walters, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, https://doi.org/10.5194/gmd-13-1999-2020, 2020
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In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against.
Mariko Oue, Aleksandra Tatarevic, Pavlos Kollias, Dié Wang, Kwangmin Yu, and Andrew M. Vogelmann
Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, https://doi.org/10.5194/gmd-13-1975-2020, 2020
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We developed the Cloud-resolving model Radar SIMulator (CR-SIM) capable of apples-to-apples comparisons between the multiwavelength, zenith-pointing, and scanning radar and multi-remote-sensing (radar and lidar) observations and the high-resolution atmospheric model output. Applications of CR-SIM as a virtual observatory operator aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements.
Thomas Schwitalla, Kirsten Warrach-Sagi, Volker Wulfmeyer, and Michael Resch
Geosci. Model Dev., 13, 1959–1974, https://doi.org/10.5194/gmd-13-1959-2020, https://doi.org/10.5194/gmd-13-1959-2020, 2020
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Performing seasonal simulations on horizontal grid resolutions of a few kilometres over the entire globe remains challenging. We demonstrate the added value of simulating large-scale patterns and feedbacks at 3 km resolution compared to a coarser-resolution forecast using the WRF numerical weather model on a latitude-belt domain. Results show an improvement of cloud coverage in the tropics, better representation of teleconnection, and improvements of precipitation patterns in different regions.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-16, https://doi.org/10.5194/gmd-2020-16, 2020
Revised manuscript accepted for GMD
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While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models are inaccurate, especially in complex terrain. In this study, we leverage the potential of machine learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε, and a total elimination of its average bias.
Anna-Leah Nickl, Mariano Mertens, Anke Roiger, Andreas Fix, Axel Amediek, Alina Fiehn, Christoph Gerbig, Michal Galkowski, Astrid Kerkweg, Theresa Klausner, Maximilian Eckl, and Patrick Jöckel
Geosci. Model Dev., 13, 1925–1943, https://doi.org/10.5194/gmd-13-1925-2020, https://doi.org/10.5194/gmd-13-1925-2020, 2020
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Based on the global and regional chemistry–climate model system MECO(n), we implemented a forecast system to support the planning of measurement campaign research flights with chemical weather forecasts. We applied this system for the first time to provide 6 d forecasts in support of the CoMet 1.0
campaign targeting methane emitted from coal mining ventilation shafts in the Upper Silesian Coal Basin in Poland. We describe the new forecast system and evaluate its forecast skill.
Rolf Zentek and Günther Heinemann
Geosci. Model Dev., 13, 1809–1825, https://doi.org/10.5194/gmd-13-1809-2020, https://doi.org/10.5194/gmd-13-1809-2020, 2020
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We used the climate model CCLM to simulate the Weddell Sea region with a resolutions of 15 and 5 km. By adjusting the turbulence parametrization a warm bias over the Antarctic Plateau was removed. For sea ice we found a temperature bias around +/-1 K and a wind speed bias of 1 m s-1. Comparisons of radio soundings showed a bias around zero and a RMSE of 1–2 K for temperature and 3–4 m s-1 for wind speed. Comparison with wind Doppler lidar yielded almost no bias and a RMSE of ca. 2 m s-1.
Matthieu Pommier, Hilde Fagerli, Michael Schulz, Alvaro Valdebenito, Richard Kranenburg, and Martijn Schaap
Geosci. Model Dev., 13, 1787–1807, https://doi.org/10.5194/gmd-13-1787-2020, https://doi.org/10.5194/gmd-13-1787-2020, 2020
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The EMEP and LOTOS-EUROS models comprise the operational source contribution prediction system for the European cities within the Copernicus Atmosphere Monitoring Service (CAMS). This study presents a first evaluation of this system, with hourly resolution, by focusing on one PM10 episode in December 2016, dominated by the influence of domestic emissions. It shows that the system provides valuable information on the composition and contributions of different countries to PM10.
Scot M. Miller, Arvind K. Saibaba, Michael E. Trudeau, Marikate E. Mountain, and Arlyn E. Andrews
Geosci. Model Dev., 13, 1771–1785, https://doi.org/10.5194/gmd-13-1771-2020, https://doi.org/10.5194/gmd-13-1771-2020, 2020
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New observations of greenhouse gases from satellites and aircraft provide an unprecedented window into global carbon sources and sinks. However, these new datasets also present enormous computational challenges due to the sheer number of observations. In this article, we discuss the challenges of estimating greenhouse gas source and sinks using very large atmospheric datasets and evaluate several strategies for overcoming these challenges.
Ting-Chen Chen, Man-Kong Yau, and Daniel J. Kirshbaum
Geosci. Model Dev., 13, 1737–1761, https://doi.org/10.5194/gmd-13-1737-2020, https://doi.org/10.5194/gmd-13-1737-2020, 2020
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Budget analysis helps to quantify the contribution of different terms in a selected prognostic equation within a numerical simulation. However, it is well acknowledged that non-negligible errors generally exist if such equations are analyzed in model post-processing. Here, we develop an inline budget retrieval method within the WRF model to give a high-accuracy budget closure. We also compare the inline and post-processed budgets to investigate the potential sources of errors in the latter.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-356, https://doi.org/10.5194/gmd-2019-356, 2020
Revised manuscript accepted for GMD
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The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in python and FORTRAN for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in-situ observation or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Mattia Righi, Johannes Hendricks, Ulrike Lohmann, Christof Gerhard Beer, Valerian Hahn, Bernd Heinold, Romy Heller, Martina Krämer, Michael Ponater, Christian Rolf, Ina Tegen, and Christiane Voigt
Geosci. Model Dev., 13, 1635–1661, https://doi.org/10.5194/gmd-13-1635-2020, https://doi.org/10.5194/gmd-13-1635-2020, 2020
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A new cloud microphysical scheme is implemented in the global EMAC-MADE3 aerosol model and evaluated. The new scheme features a detailed parameterization for aerosol-driven ice formation in cirrus clouds, accounting for the competition between homogeneous and heterogeneous ice formation processes. The comparison against satellite data and in situ measurements shows that the model performance is in line with similar global coupled models featuring ice cloud parameterizations.
Peter Wind, Bruce Rolstad Denby, and Michael Gauss
Geosci. Model Dev., 13, 1623–1634, https://doi.org/10.5194/gmd-13-1623-2020, https://doi.org/10.5194/gmd-13-1623-2020, 2020
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We present a new method for individually quantifying the contributions from different sources to local air pollution. The method can be used to distinguish the sources of local air pollution for any position in one single model simulation and thus to provide detailed maps of the origin of the pollutants. Hence, it can be used for time-critical operational services by providing scientific information as input for local policy decisions on air pollution abatement.
Vincent Huijnen, Kazuyuki Miyazaki, Johannes Flemming, Antje Inness, Takashi Sekiya, and Martin G. Schultz
Geosci. Model Dev., 13, 1513–1544, https://doi.org/10.5194/gmd-13-1513-2020, https://doi.org/10.5194/gmd-13-1513-2020, 2020
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We present the evaluation and intercomparison of global tropospheric ozone reanalyses that have been produced in recent years. Such reanalyses can be used to assess the current state and variability of tropospheric ozone.
The reanalyses show overall good agreements with independent ground and ozone-sonde observations for the diurnal, synoptical, seasonal, and interannual variabilities, with generally improved performances for the updated reanalyses.
Xueling Wu, Ying Wang, Siyuan He, and Zhongfang Wu
Geosci. Model Dev., 13, 1499–1511, https://doi.org/10.5194/gmd-13-1499-2020, https://doi.org/10.5194/gmd-13-1499-2020, 2020
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This paper presents a composite prediction system designed to improve the accuracy and applicability of PM2.5 / PM10 predictions. Based on remote sensing images, the aerosol optical thickness was obtained and corrected. Then, we selected PM2.5 / PM10-related factors from meteorological factors and air pollutants and compared the effects of several intelligent models in different prediction patterns. The results showed that the LSTM model had significant advantages in accuracy and stability.
Arnau Folch, Leonardo Mingari, Natalia Gutierrez, Mauricio Hanzich, Giovanni Macedonio, and Antonio Costa
Geosci. Model Dev., 13, 1431–1458, https://doi.org/10.5194/gmd-13-1431-2020, https://doi.org/10.5194/gmd-13-1431-2020, 2020
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This paper presents FALL3D-8.0, the latest version release of an open-source code with a track record of 15+ years and a growing number of users in the volcanological and atmospheric communities. The code, originally conceived for atmospheric dispersal and deposition of tephra particles, has been extended to model other types of particles, aerosols and radionuclides. This paper details the FALL3D-8.0 model physics and the numerical implementation of the code.
Björn Maronga, Sabine Banzhaf, Cornelia Burmeister, Thomas Esch, Renate Forkel, Dominik Fröhlich, Vladimir Fuka, Katrin Frieda Gehrke, Jan Geletič, Sebastian Giersch, Tobias Gronemeier, Günter Groß, Wieke Heldens, Antti Hellsten, Fabian Hoffmann, Atsushi Inagaki, Eckhard Kadasch, Farah Kanani-Sühring, Klaus Ketelsen, Basit Ali Khan, Christoph Knigge, Helge Knoop, Pavel Krč, Mona Kurppa, Halim Maamari, Andreas Matzarakis, Matthias Mauder, Matthias Pallasch, Dirk Pavlik, Jens Pfafferott, Jaroslav Resler, Sascha Rissmann, Emmanuele Russo, Mohamed Salim, Michael Schrempf, Johannes Schwenkel, Gunther Seckmeyer, Sebastian Schubert, Matthias Sühring, Robert von Tils, Lukas Vollmer, Simon Ward, Björn Witha, Hauke Wurps, Julian Zeidler, and Siegfried Raasch
Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, https://doi.org/10.5194/gmd-13-1335-2020, 2020
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In this paper, we describe the PALM model system 6.0. PALM is a Fortran-based turbulence-resolving code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. During the last years, PALM has been significantly improved and now offers a variety of new components that are especially designed to simulate the urban atmosphere at building-resolving resolution.
Alexander T. Archibald, Fiona M. O'Connor, Nathan Luke Abraham, Scott Archer-Nicholls, Martyn P. Chipperfield, Mohit Dalvi, Gerd A. Folberth, Fraser Dennison, Sandip S. Dhomse, Paul T. Griffiths, Catherine Hardacre, Alan J. Hewitt, Richard S. Hill, Colin E. Johnson, James Keeble, Marcus O. Köhler, Olaf Morgenstern, Jane P. Mulcahy, Carlos Ordóñez, Richard J. Pope, Steven T. Rumbold, Maria R. Russo, Nicholas H. Savage, Alistair Sellar, Marc Stringer, Steven T. Turnock, Oliver Wild, and Guang Zeng
Geosci. Model Dev., 13, 1223–1266, https://doi.org/10.5194/gmd-13-1223-2020, https://doi.org/10.5194/gmd-13-1223-2020, 2020
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Here we present a description and evaluation of the UKCA stratosphere–troposphere chemistry scheme (StratTrop vn 1.0) implemented in the UK Earth System Model (UKESM1). UKCA StratTrop represents a substantial step forward compared to previous versions of UKCA. We show here that it is fully suited to the challenges of representing interactions in a coupled Earth system model and identify key areas and components for future development that will make it even better in the future.
Kyunghwa Lee, Jinhyeok Yu, Sojin Lee, Mieun Park, Hun Hong, Soon Young Park, Myungje Choi, Jhoon Kim, Younha Kim, Jung-Hun Woo, Sang-Woo Kim, and Chul H. Song
Geosci. Model Dev., 13, 1055–1073, https://doi.org/10.5194/gmd-13-1055-2020, https://doi.org/10.5194/gmd-13-1055-2020, 2020
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For the purpose of providing reliable and robust air quality predictions, an operational air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2) by preparing the initial conditions for model simulations via data assimilation using satellite- and ground-based observations. The performance of the developed air quality prediction system was evaluated using ground in situ data during the KORUS-AQ campaign period.
Myrto Valari, Konstandinos Markakis, Emilie Powaga, Bernard Collignan, and Olivier Perrussel
Geosci. Model Dev., 13, 1075–1094, https://doi.org/10.5194/gmd-13-1075-2020, https://doi.org/10.5194/gmd-13-1075-2020, 2020
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To understand how atmospheric pollution affects human health, we need to know the inhaled dose of pollutants. We develop a model that follows the individuals of a population during their daily activities and estimates pollutant concentration levels in the ambient air. We show that certain practices, such as biking in the city, expose people to PM2.5 concentration levels higher than the WHO recommendations. We also show that living in green buildings will significantly decrease exposure to ozone.
Rui Ito, Hideo Shiogama, Tosiyuki Nakaegawa, and Izuru Takayabu
Geosci. Model Dev., 13, 859–872, https://doi.org/10.5194/gmd-13-859-2020, https://doi.org/10.5194/gmd-13-859-2020, 2020
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The model performance and the coverage of the uncertainty in the climate changes were investigated for the ensembles of CMIP5 models used in ISIMIP2b and CORDEX programs. We found both programs selected models that acceptably reproduced the historical climate. Also, the global common ensemble (ISIMIP2b) has difficulty in capturing the uncertainty in two variables at the regional scale, whereas the region-specific ensemble (CORDEX) overcomes the difficulty by applying a properly large ensemble.
Marc Guevara, Carles Tena, Manuel Porquet, Oriol Jorba, and Carlos Pérez García-Pando
Geosci. Model Dev., 13, 873–903, https://doi.org/10.5194/gmd-13-873-2020, https://doi.org/10.5194/gmd-13-873-2020, 2020
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Emission inventories are a key input to numerical systems that simulate air quality. In this paper, we present an open-source tool intended for the computation of high-resolution anthropogenic emissions for air quality modelling. Emissions are estimated using detailed methods that combine local activity and emission factors along with meteorological data. Specific results are presented for Spain. Nevertheless, the model is designed so that it can be applicable to any European country or region.
Daisuke Goto, Yousuke Sato, Hisashi Yashiro, Kentaroh Suzuki, Eiji Oikawa, Rei Kudo, Takashi M. Nagao, and Teruyuki Nakajima
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-34, https://doi.org/10.5194/gmd-2020-34, 2020
Revised manuscript accepted for GMD
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We executed a global aerosol model over 3 years with the finest grid size in the world. The results elucidated that global annual averages of parameters associated with the aerosols were generally comparable to those obtained from a low-resolution model (LRM), but spatio-temporal variabilities of the aerosol components and their associated parameters provided better results and closer to the observations than those from the LRM. This study clarified the advantages of the high-resolution model.
Pascal Yiou and Aglaé Jézéquel
Geosci. Model Dev., 13, 763–781, https://doi.org/10.5194/gmd-13-763-2020, https://doi.org/10.5194/gmd-13-763-2020, 2020
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This paper presents an adaptation of a method of "importance sampling" to simulate large ensembles of extreme heat waves (i.e., the most extreme heat waves that could be), given a fixed returned period. We illustrate how this algorithm works for European heat waves and investigate the atmospheric features of such ensembles of events. We argue that such an algorithm can be used to simulate other types of events, including cold spells or prolonged episodes of precipitation.
Cited articles
Abdul-Razzak, H. and Ghan, S.: A parameterization of aerosol activation, 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000.
Arakawa, A.: The cumulus parameterization problem: Past, present, and future, J. Climate, 17, 2493–2525, 2004.
Bacmeister, J., Eckermann, S., Tsias, A., Carslaw, K., and Peter, T.: Mesoscale temperature fluctuations induced by a spectrum of gravity waves: A comparison of parameterizations and their impact on stratospheric microphysics, J. Atmos. Sci., 56, 1913–1924, 1999.
Bacmeister, J., Suarez, M., and Robertson, F. R.: Rain Reevaporation, Boundary Layer, Convection Interactions, and Pacific Rainfall Patterns in an AGCM, J. Atmos. Sci., 63, 3383–3403, https://doi.org/10.1175/JAS3791.1, 2006.
Baker, B. A.: On the Role of Phoresis in Cloud Ice Initiation, J. Atmos. Sci., 48, 1545–1548, https://doi.org/10.1175/1520-0469(1991)048<1545:OTROPI>2.0.CO;2, 1991.
Barahona, D.: On the ice nucleation spectrum, Atmos. Chem. Phys., 12, 3733–3752, https://doi.org/10.5194/acp-12-3733-2012, 2012.
Barahona, D. and Nenes, A.: Parameterization of cloud droplet formation in large scale models: including effects of entrainment, J. Geophys. Res., 112, D16026, https://doi.org/10.1029/2007JD008473, 2007.
Barahona, D. and Nenes, A.: Parameterization of cirrus formation in large scale models: Homogeneous nucleation, J. Geophys. Res., 113, D11211, https://doi.org/10.1029/2007JD009355, 2008.
Barahona, D. and Nenes, A.: Parameterizing the competition between homogeneous and heterogeneous freezing in cirrus cloud formation – monodisperse ice nuclei, Atmos. Chem. Phys., 9, 369–381, https://doi.org/10.5194/acp-9-369-2009, 2009a.
Barahona, D. and Nenes, A.: Parameterizing the competition between homogeneous and heterogeneous freezing in cirrus cloud formation – monodisperse ice nuclei, Atmos. Chem. Phys., 9, 369–381, https://doi.org/10.5194/acp-9-369-2009, 2009b.
Barahona, D. and Nenes, A.: Dynamical states of low temperature cirrus, Atmos. Chem. Phys., 11, 3757–3771, https://doi.org/10.5194/acp-11-3757-2011, 2011.
Barahona, D., Rodriguez, J., and Nenes, A.: Sensitivity of the global distribution of cirrus Ice crystal concentration to heterogeneous freezing, J. Geophys. Res., 15, D23213, https://doi.org/10.1029/2010JD014273, 2010a.
Barahona, D., West, R. E. L., Stier, P., Romakkaniemi, S., Kokkola, H., and Nenes, A.: Comprehensively accounting for the effect of giant CCN in cloud activation parameterizations, Atmos. Chem. Phys., 10, 2467–2473, https://doi.org/10.5194/acp-10-2467-2010, 2010b.
Barkstrom, B.: The earth radiation budget experiment (ERBE), B. Am. Meteorol. Soc., 65, 1170–1185, 1984.
Blackadar, A. K.: The Vertical Distribution of Wind and Turbulent Exchange in a Neutral Atmosphere, J. Geophys. Res., 67, 3095–3102, 1962.
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011.
Burrows, S. M., Hoose, C., Pöschl, U., and Lawrence, M. G.: Ice nuclei in marine air: biogenic particles or dust?, Atmos. Chem. Phys., 13, 245–267, https://doi.org/10.5194/acp-13-245-2013, 2013.
Chiriaco, M., Chepfer, H., Minnis, P., Haeffelin, M., Platnick, S., Baumgardner, D., Dubuisson, P., McGill, M., Noël, V., Pelon, J., Spangenberg, D., Sun-Mack, S., and Wind, G.: Comparison of CALIPSO-like, LaRC, and MODIS retrievals of ice-cloud properties over SIRTA in France and Florida during CRYSTAL-FACE, J. Appl. Meteorol. Clim., 46, 249–272, 2007.
Choi, Y., Lindzen, R., Ho, C., and Kim, J.: Space observations of cold-cloud phase change, P. Natl. Acad. Sci., 107, 11211–11216, 2010.
Chou, M.-D. and Suarez, M.: An efficient thermal infrared radiation parameterization for use in general circulation models, Vol. 3 of NASA Tech. Memorandum 104606, NASA Goddard Space Flight Center, Greenbelt, MD, USA, 1994.
Chou, M.-D., Suarez, M., Ho, C.-H., Yan, M.-H., and Lee, K.-T.: A Solar Radiation Model for Use in Climate Studies, J. Atmos. Sci., 49, 762–772, 1992.
Colarco, P., da Silva, A., Chin, M., and Diehl, T.: Online simulations of global aerosol distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based aerosol optical depth, J. Geophys. Res., 115, D14207, https://doi.org/10.1029/2009JD012820, 2010.
Conant, W. C., VanReken, T., Rissman, T., Varutbangkul, V., Jonsson, H., Nenes, A., Jimenez, J., Delia, A., Bahreini, R., Roberts, G., Flagan, R., and Seinfeld, J. H.: Aerosol-cloud drop concentration closure in warm clouds, J. Geophys. Res., 109, D13204, https://doi.org/10.1029/2003JD004324, 2004.
Cziczo, D. J., Froyd, K. D., Hoose, C., Jensen, E. J., Diao, M., Zondlo, M. A., Smith, J. B., Twohy, C. H., and Murphy, D. M.: Clarifying the Dominant Sources and Mechanisms of Cirrus Cloud Formation, Science, 340, 1320–1324, 2013.
Dean, S. M., Flowerdew, J., Lawrence, B. N., and Eckermann, S. D.: Parameterisation of orographic cloud dynamics in a GCM, Clim. Dynam., 28, 581–597, https://doi.org/10.1007/s00382-006-0202-0, 2007.
Del Genio, A., Yao, M., Kovari, W., and Lo, K.: A prognostic cloud water parameterization for global climate models, J. Climate, 9, 270–304, 1996.
Del Genio, A. D., Kovari, W., Yao, M.-S., and Jonas, J.: Cumulus Microphysics and Climate Sensitivity, J. Climate, 18, 2376–2387, https://doi.org/10.1175/JCLI3413.1, 2005.
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting global atmospheric ice nuclei distributions and their impacts on climate, P. Natl. Acad. Sci., 107, 11217–11222, https://doi.org/10.1073/pnas.0910818107, 2010.
Devasthale, A. and Thomas, M. A.: Sensitivity of Cloud Liquid Water Content Estimates to the Temperature-Dependent Thermodynamic Phase: A Global Study Using CloudSat Data, J. Climate, 25, 7297–7307, 2012.
Diehl, T., Heil, A., Chin, M., Pan, X., Streets, D., Schultz, M., and Kinne, S.: Anthropogenic, biomass burning, and volcanic emissions of black carbon, organic carbon, and SO2 from 1980 to 2010 for hindcast model experiments, Atmos. Chem. Phys. Discuss., 12, 24895–24954, https://doi.org/10.5194/acpd-12-24895-2012, 2012.
Eliasson, S., Buehler, S. A., Milz, M., Eriksson, P., and John, V. O.: Assessing observed and modelled spatial distributions of ice water path using satellite data, Atmos. Chem. Phys., 11, 375–391, https://doi.org/10.5194/acp-11-375-2011, 2011.
Ferrier, S. B.: A Double-Moment Multiple-Phase Four-Class Bulk Ice Scheme. Part I: Description, J. Atmos. Sci., 51, 249–280, https://doi.org/10.1175/1520-0469(1994)051<0249:ADMMPF>2.0.CO;2, 1994.
Fornea, A. P., Brooks, S. D., Dooley, J. B., and Saha, A.: Heterogeneous freezing of ice on atmospheric aerosols containing ash, soot, and soil, J. Geophys. Res., 114, D13201, https://doi.org/10.1029/2009JD011958, 2009.
Fountoukis, C. and Nenes, A.: Continued development of a cloud droplet formation parameterization for global climate models, J. Geophys. Res., 110, D11212, https://doi.org/10.1029/2004JD005591, 2005.
Fountoukis, C., Nenes, A., Meskhidze, N., Bahreini, R., Conant, W. C., Jonsson, H., Murphy, S., Sorooshian, A., Varutbangkul, V., Brechtel, F., Flagan, R., and Seinfeld, J. H.: Aerosol-cloud drop concentration closure for clouds sampled during the International Consortium for Atmospheric Reseach on Transport and Transformation 2004 campaign, J. Geophys. Res., 112, D10S30, https://doi.org/10.1029/2006JD007272, 2007.
Frank, W. M. and Cohen, C.: Simulation of Tropical Convective Systems. Part I: A Cumulus Parameterization, J. Atmos. Sci., 44, 3787–3799, https://doi.org/10.1175/1520-0469(1987)044<3787:SOTCSP>2.0.CO;2, 1987.
Gayet, J., Ovarlez, J., Shcherbakov, V., Ström, J., Schumann, U., Minikin, A., Auriol, F., Petzold, A., and Monier, M.: Cirrus cloud microphysical and optical properties at southern and northern midlatitudes during the INCA experiment, J. Geophys. Res., 109, D20206, https://doi.org/10.1029/2004JD004803, 2004.
Gettelman, A. and Kinnison, D. E.: The global impact of supersaturation in a coupled chemistry-climate model, Atmos. Chem. Phys., 7, 1629–1643, https://doi.org/10.5194/acp-7-1629-2007, 2007.
Gettelman, A., Fetzer, E. J., Eldering, A., and Irion, F. W.: The global distribution of supersaturation in the upper troposphere from the Atmospheric Infrared Sounder, J. Climate, 19, 6089–6103, 2006.
Gettelman, A., Morrison, H., and Ghan, S. J.: A New Two-Moment Bulk Stratiform Cloud Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3). Part II: Single-Column and Global Results, J. Climate, 21, 3660–3679, https://doi.org/10.1175/2008JCLI2116.1, 2008.
Gettelman, A., Liu, X., Ghan, S. J., Morrison, H., Park, S., Conley, A. J., Klein, S. A., Boyle, J., Mitchell, D. L., and Li, J.-L. F.: Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model, J. Geophys. Res., 115, D18216, https://doi.org/10.1029/2009JD013797, 2010.
Gettelman, A., Liu, X., Barahona, D., Lohmann, U., and Chen, C.: Climate impacts of ice nucleation, J. Geophys. Res., 117, D20201, https://doi.org/10.1029/2012JD017950, 2012.
Gierens, K., Schumann, U., Helten, M., Smit, H., and Marenco, A.: A distribution law for relative humidity in the upper troposphere and lower stratosphere derived from three years of MOZAIC measurements, Ann. Geophys., 17, 1218–1226, https://doi.org/10.1007/s00585-999-1218-7, 1999.
Golaz, J.-C., Salzmann, M., Donner, L. J., Horowitz, L. W., Ming, Y., and Zhao, M.: Sensitivity of the Aerosol Indirect Effect to Subgrid Variability in the Cloud Parameterization of the GFDL Atmosphere General Circulation Model AM3, J. Climate, 24, 3145–3160, https://doi.org/10.1175/2010JCLI3945.1, 2010.
Gregory, D.: Estimation of entrainment rate in simple models of convective clouds, Q. J. Roy. Meteorol. Soc., 127, 53–72, 2001.
Gultepe, I. and Isaac, G.: The relationship between cloud droplet and aerosol number concentrations for climate models, Int. J. Climatol., 16, 941–946, 1996.
Guo, H., Liu, Y., Daum, P., Senum, G., and Tao, W.: Characteristics of vertical velocity in marine stratocumulus: comparison of large eddy simulations with observations, Environ. Res. Lett., 3, 045020, https://doi.org/10.1088/1748-9326/3/4/045020, 2008.
Haag, W. and Kärcher, B.: The impact of aerosols and gravity waves on cirrus at midlatitudes, J. Geophys. Res., 109, D12202, https://doi.org/10.1029/2004JD004579, 2004.
Han, Q., Rossow, W. B., Chou, J., and Welch, R. M.: Global variation of column droplet concentration in low-level clouds, Geophys. Res. Lett., 25, 1419–1422, https://doi.org/10.1029/98GL01095, 1998.
Hendricks, J., Kärcher, B., and Lohmann, U.: Effects of ice nuclei on cirrus clouds in a global climate model, J. Geophys. Res., 116, D18206, https://doi.org/10.1029/2010JD015302, 2011.
Herzog, A. and Vial, F.: A study of the dynamics of the equatorial lower stratosphere by use of ultra-long-duration balloons, J. Geophys. Res., 106, 22745–22761, 2001.
Heymsfield, A. J., Bansemer, A., Field, P. R., Durden, S. L., Stith, J. L., Dye, J. E., Hall, W., and Grainger, C. A.: Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitating Clouds: Results from In Situ Observations in TRMM Field Campaigns., J. Atmos. Sci., 59, 3457–3491, https://doi.org/10.1175/1520-0469(2002)059<3457:OAPOPS>2.0.CO;2, 2002.
Heymsfield, A. J., van Zadelhoff, G.-J., Donovan, D. P., Fabry, F., Hogan, R. J., and Illingworth, A. J.: Refinements to Ice Particle Mass Dimensional and Terminal Velocity Relationships for Ice Clouds. Part II: Evaluation and Parameterizations of Ensemble Ice Particle Sedimentation Velocities, J. Atmos. Sci., 64, 1068–1088, https://doi.org/10.1175/JAS3900.1, 2007.
Hoose, C., Kristjánsson, J., and Burrows, S.: How important is biological ice nucleation in clouds on a global scale?, Environ. Res. Lett., 5, 024009, https://doi.org/10.1088/1748-9326/5/2/024009, 2010.
Hu, Y., Rodier, S., Xu, K.-M., Sun, W., Huang, J., Lin, B., Zhai, P., and Josset, D.: Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements, J. Geophys. Res., 115, D00H34, https://doi.org/10.1029/2009JD012384, 2010.
Huffman, G., Adler, R., Arkin, P., Chang, A., Ferraro, R., Gruber, A., Janowiak, J., McNab, A., Rudolf, B., and Schneider, U.: The global precipitation climatology project (GPCP) combined precipitation dataset, B. Am. Meteorol. Soc., 78, 5–20, 1997.
IPCC: Climate change 2007: the physical basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA., 2007.
Jensen, E. and Pfister, L.: Transport and freeze-drying in the tropical tropopause layer, J. Geophys. Res., 109, D02207, https://doi.org/10.1029/2003JD004022, 2004.
Jensen, E. J., Pfister, L., Bui, T.-P., Lawson, P., and Baumgardner, D.: Ice nucleation and cloud microphysical properties in tropical tropopause layer cirrus, Atmos. Chem. Phys., 10, 1369–1384, https://doi.org/10.5194/acp-10-1369-2010, 2010.
Jensen, E. J., Pfister, L., and Bui, T. P.: Physical processes controlling ice concentrations in cold cirrus near the tropical tropopause, J. Geophys. Res., 117, D11205, https://doi.org/10.1029/2011JD017319, 2012.
Joos, H., Spichtinger, P., Gayet, J., and Minikin, A.: Orographic cirrus in the global climate model ECHAM5, J. Geophys. Res., 113, https://doi.org/10.1029/2007JD009605, 2008.
Joos, H., Spichtinger, P., and Lohmann, U.: Influence of a future climate on the microphysical and optical properties of orographic cirrus clouds in ECHAM5, J. Geophys. Res., 115, D19129, https://doi.org/10.1029/2010JD013824, 2010.
Kärcher, B. and Burkhardt, U.: A cirrus scheme for global circulation models, Q. J. Roy. Meteorol. Soc., 134, 1439–1461, https://doi.org/10.1002/Qj.301, 2008.
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud formation: homogeneous freezing including effects or aerosol size, J. Geophys. Res., 107, 4698, https://doi.org/10.1029/2001JD001429, 2002.
Kärcher, B. and Ström, J.: The roles of dynamical variability and aerosols in cirrus cloud formation, Atmos. Chem. Phys., 3, 823–838, https://doi.org/10.5194/acp-3-823-2003, 2003.
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based parameterization of cirrus cloud formation for use in global atmospheric models, J. Geophys. Res., 111, D01205, https://doi.org/10.1029/2005JD006219, 2006.
Kaufman, Y. J. and Koren, I.: Smoke and pollution aerosol effect on cloud cover, Science, 313, 655–658, 2006.
Kay, J., Hillman, B., Klein, S., Zhang, Y., Medeiros, B., Pincus, R., Gettelman, A., Eaton, B., Boyle, J., Marchand, R., and Ackerman, A.: Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators, J. Climate, 25, 5190–5207, 2012.
Khain, A., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., and Krugliak, H.: Notes on the state-of-the-art numerical modeling of cloud microphysics, Atmos. Res., 55, 159–224, https://doi.org/10.1016/S0169-8095(00)00064-8, 2000.
Khairoutdinov, M. and Kogan, Y.: A new cloud physics parameterization in a Large-Eddy simulation model of marine stratocumulus, Mon. Weather Rev., 128, 229–243, 2000.
King, M., Menzel, W., Kaufman, Y., Tanre, D., Gao, B.-C., Platnick, S., Ackerman, S., Remer, L., Pincus, R., and Hubanks, P.: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS, IEEE T. Geosci. Remote, 41, 442–458, https://doi.org/10.1109/TGRS.2002.808226, 2003.
Koop, T., Luo, B., Tslas, A., and Peter, T.: Water activity as the determinant for homogeneous ice nucleation in aqueous solutions, Nature, 406, 611–614, 2000.
Korolev, A. and Mazin, I.: Supersaturation of water vapor in clouds, J. Atmos. Sci., 60, 2957–2974, 2003.
Krämer, M., Schiller, C., Afchine, A., Bauer, R., Gensch, I., Mangold, A., Schlicht, S., Spelten, N., Sitnikov, N., Borrmann, S., de Reus, M., and Spichtinger, P.: Ice supersaturations and cirrus cloud crystal numbers, Atmos. Chem. Phys., 9, 3505–3522, https://doi.org/10.5194/acp-9-3505-2009, 2009.
Kumar, P., Nenes, A., and Sokolik, I. N.: Importance of adsorption for CCN activity and hygroscopic properties of mineral dust aerosol, Geophys. Res. Lett., 36, L24804, https://doi.org/10.1029/2009GL040827, 2009a.
Kumar, P., Sokolik, I. N., and Nenes, A.: Parameterization of cloud droplet formation for global and regional models: including adsorption activation from insoluble CCN, Atmos. Chem. Phys., 9, 2517–2532, https://doi.org/10.5194/acp-9-2517-2009, 2009b.
Ladino, L., Stetzer, O., Lüönd, F., Welti, A., and Lohmann, U.: Contact freezing experiments of kaolinite particles with cloud droplets, J. Geophys. Res., 116, D22202, https://doi.org/10.1029/2011JD015727, 2011.
Lance, S., Nenes, A., and Rissman, T.: Chemical and dynamical effects on cloud droplet number: Implication for estimates of the aerosol indirect effect, J. Geophys. Res., 109, D22208, https://doi.org/10.1029/2004JD004596, 2004.
Lance, S., Shupe, M. D., Feingold, G., Brock, C. A., Cozic, J., Holloway, J. S., Moore, R. H., Nenes, A., Schwarz, J. P., Spackman, J. R., Froyd, K. D., Murphy, D. M., Brioude, J., Cooper, O. R., Stohl, A., and Burkhart, J. F.: Cloud condensation nuclei as a modulator of ice processes in Arctic mixed-phase clouds, Atmos. Chem. Phys., 11, 8003–8015, https://doi.org/10.5194/acp-11-8003-2011, 2011.
Li, J.-L. F., Waliser, D. E., Chen, W.-T., Guan, B., Kubar, T., Stephens, G., Ma, H.-Y., Deng, M., Donner, L., Seman, C., and Horowitz, L.: An observationally based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary satellite data, J. Geophys. Res., 117, D16105, https://doi.org/10.1029/2012JD017640, 2012.
Li, J.-L. F., Lee, S., Waliser, D. E., Lee, S., Guan, B., Stephens, G., and Teixeira, J.: Assessment of Cloud Liquid Water in CMIP3, CMIP5, and Contemporary GCMs and Reanalyses with Observations, J. Geophys. Res., under review, 2014.
Lindzen, R. S.: Turbulence and stress owing to gravity wave and tidal breakdown, J. Geophys. Res., 86, 9707–9714, https://doi.org/10.1029/JC086iC10p09707, 1981.
Liu, X., Shi, X., Zhang, K., Jensen, E. J., Gettelman, A., Barahona, D., Nenes, A., and Lawson, P.: Sensitivity studies of dust ice nuclei effect on cirrus clouds with the Community Atmosphere Model CAM5, Atmos. Chem. Phys., 12, 12061–12079, https://doi.org/10.5194/acp-12-12061-2012, 2012.
Liu, Y., Daum, P., and Yum, S.: Analytical expression for the relative dispersion of the cloud droplet size distribution, Geophys. Res. Lett., 33, L02810, https://doi.org/10.1029/2005GL024052, 2006.
Liu, Y., Daum, P., Guo, H., and Peng, Y.: Dispersion bias, dispersion effect, and the aerosol-cloud conundrum, Environ. Res. Lett., 3, 045021, https://doi.org/10.1088/1748-9326/3/4/045021, 2008.
Locatelli, J. D. and Hobbs, P. V.: Fall Speeds and Masses of Solid Precipitation Particles, J. Geophys. Res., 79, 2185–2197, https://doi.org/10.1029/JC079i015p02185, 1974.
Loeb, N., Wielicki, B., Doelling, D., Smith, G., Keyes, D., Kato, S., Manalo-Smith, N., and Wong, T.: Toward optimal closure of the Earth's top-of-atmosphere radiation budget, J. Climate, 22, 748–766, 2009.
Lohmann, U.: Global anthropogenic aerosol effects on convective clouds in ECHAM5-HAM, Atmos. Chem. Phys., 8, 2115-2131, https://doi.org/10.5194/acp-8-2115-2008, 2008.
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005, 2005.
Lohmann, U., Spichtinger, P., Jess, S., Peter, T., and Smit, H.: Cirrus cloud formation and ice supersaturated regions in a global climate model, Environ. Res. Lett., 3, 045022; https://doi.org/10.1088/1748-9326/3/4/045022, 2008.
Louis, J. F., Weill, A., and Vidal-Madjar, D.: Dissipation length in stable layers, Bound.-La. Meteorol., 25, 229–243, https://doi.org/10.1007/BF00119538, 1983.
Manabe, S., Smagorinsky, J., and Strickler, R. F.: Simulated climatology of a general circulation model with a hydrologic cycle 1, Mon. Weather Rev., 93, 769–798, 1965.
Marcolli, C., Gedamke, S., Peter, T., and Zobrist, B.: Efficiency of immersion mode ice nucleation on surrogates of mineral dust, Atmos. Chem. Phys., 7, 5081–5091, https://doi.org/10.5194/acp-7-5081-2007, 2007.
Marshall, J. S. and Palmer, W. M. K.: The distribution of raindrops with size, J. Meteorol., 5, 165–166, 1948.
McFarlane, N. A.: The Effect of Orographically Excited Gravity Wave Drag on the General Circulation of the Lower Stratosphere and Troposphere, J. Atmos. Sci., 44, 1775–1800, https://doi.org/10.1175/1520-0469(1987)044<1775:TEOOEG>2.0.CO;2, 1987.
McFarquhar, G. and Heymsfield, A.: Parameterization of tropical cirrus ice crystal size distributions and implications for radiative transfer: Results from CEPEX, J. Atmos. Sci., 54, 2187–2200, 1997.
Molod, A.: Constraints on the Total Water PDF in GCMs from AIRS and a High Resolution Model, J. Climate, 25, 8341–8352, 2012.
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I., and Eichmann, A.: The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna, Vol. 28 of Technical Report Series on Global Modeling and Data Assimilation, NASA Goddard Space Flight Center, Greenbelt, MD, USA, 2012.
Moorthi, S. and Suarez, M. J.: Relaxed Arakawa-Schubert – A parameterization of moist convection for general circulation models, Mon. Weather Rev., 120, 978–1002, 1992.
Morales, R. and Nenes, A.: Characteristic updrafts for computing distribution-averaged cloud droplet number, and stratocumulus cloud properties, J. Geophys. Res., 115, D18220, https://doi.org/10.1029/2009JD013233, 2010.
Morales Betancourt, R., Lee, D., Oreopoulos, L., Sud, Y. C., Barahona, D., and Nenes, A.: Sensitivity of cirrus and mixed-phase clouds to the ice nuclei spectra in McRAS-AC: single column model simulations, Atmos. Chem. Phys., 12, 10679–10692, https://doi.org/10.5194/acp-12-10679-2012, 2012.
Morrison, H. and Gettelman, A.: A New Two-Moment Bulk Stratiform Cloud Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3). Part I: Description and Numerical Tests, J. Climate, 21, 3642–3659, https://doi.org/10.1175/2008JCLI2105.1, 2008.
Morrison, H., Curry, J. A., and Khvorostyanov, V. I.: A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description, J. Atmos. Sci., 62, 1665–1677, https://doi.org/10.1175/JAS3446.1, 2005.
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012.
Murray, B. J., Broadley, S. L., Wilson, T. W., Atkinson, J. D., and Wills, R. H.: Heterogeneous freezing of water droplets containing kaolinite particles, Atmos. Chem. Phys., 11, 4191–4207, https://doi.org/10.5194/acp-11-4191-2011, 2011.
Murray, B. J., OSullivan, D., Atkinson, J., and Webb, M.: Ice nucleation by particles immersed in supercooled cloud droplets, Chem. Soc. Rev., 41, 6519–6554, 2012.
Nenes, A. and Seinfeld, J. H.: Parameterization of cloud droplet formation in global climate models, J. Geophys. Res., 108, 4415, https://doi.org/10.1029/2002JD002911, 2003.
Niemand, M., Möhler, O., Vogel, B., Vogel, H., Hoose, C., Connolly, P., Klein, H., Bingemer, H., DeMott, P., Skrotzki, J., and Leisner, T.: A particle-surface-area-based parameterization of immersion freezing on desert dust particles, J. Atmos. Sci., 69, 3077–3092, https://doi.org/10.1175/JAS-D-11-0249.1, 2012.
Peng, Y., Lohmann, U., and Leaitch, W.: Importance of vertical velocity variations in the cloud droplet nucleation process of marine stratocumulus, J. Geophys. Res., 110, D21213, https://doi.org/10.1029/2004JD004922, 2005.
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007.
Phillips, V. T., Donner, L., and Garner, S.: Nucleation processes in deep convection simulated by a cloud-system-resolving model with double-moment bulk microphysics, J. Atmos. Sci., 64, 738–761, https://doi.org/10.1175/JAS3869.1, 2007.
Phillips, V. T., DeMott, P., and Andronache, C.: An empirical parameterization of heterogeneous ice nucleation for multiple chemical species of aerosol, J. Atmos. Sci., 65, 2757–2783, https://doi.org/10.1175/2007JAS2546.1, 2008.
Phillips, V. T., Demott, P. J., Andronache, C., Pratt, K. A., Prather, K. A., Subramanian, R., and Twohy, C.: Improvements to an Empirical Parameterization of Heterogeneous Ice Nucleation and its Comparison with Observations, J. Atmos. Sci., 70, 378–409, https://doi.org/10.1175/JAS-D-12-080.1, 2013.
Pincus, R., Platnick, S., Ackerman, S., Hemler, R., and Patrick Hofmann, R.: Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators, J. Climate, 25, 4699–4720, 2012.
Platnick, S., King, M., Ackerman, S., Menzel, W., Baum, B., Riédi, J., and Frey, R.: The MODIS cloud products: Algorithms and examples from Terra, IEEE T. Geosci. Remote., 41, 459–473, 2003.
Popovitcheva, O., Kireeva, E., Persiantseva, N., Khokhlova, T., Shonija, N., Tishkova, V., and Demirdjian, B.: Effect of soot on immersion freezing of water and possible atmospheric implications, Atmos. Res., 90, 326–337, 2008.
Pruppacher, H. and Klett, J.: Microphysics of clouds and precipitation, Kluwer Academic Publishers, Boston, MA, 2nd Edn., 1997.
Putman, W. and Suarez, M.: Cloud-system resolving simulations with the NASA Goddard Earth Observing System global atmospheric model (GEOS-5), Geophys. Res. Lett., 38, L16809, https://doi.org/10.1029/2011GL048438, 2011.
Quaas, J., Ming, Y., Menon, S., Takemura, T., Wang, M., Penner, J. E., Gettelman, A., Lohmann, U., Bellouin, N., Boucher, O., Sayer, A. M., Thomas, G. E., McComiskey, A., Feingold, G., Hoose, C., Kristjánsson, J. E., Liu, X., Balkanski, Y., Donner, L. J., Ginoux, P. A., Stier, P., Grandey, B., Feichter, J., Sednev, I., Bauer, S. E., Koch, D., Grainger, R. G., Kirkevåg, A., Iversen, T., Seland, Ø., Easter, R., Ghan, S. J., Rasch, P. J., Morrison, H., Lamarque, J.-F., Iacono, M. J., Kinne, S., and Schulz, M.: Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data, Atmos. Chem. Phys., 9, 8697–8717, https://doi.org/10.5194/acp-9-8697-2009, 2009.
Raatikainen, T., Nenes, A., Seinfeld, J. H., Morales, R., Moore, R. H., Lathem, T. L., Lance, S., Padro, L. T., Lin, J. J., Cerully, K. M., Bougiatioti, A., Cozic, J., Ruehl, C. R., Chuang, P. Y., Anderson, B. E., Flagan, R. C., Jonsson, H., Mihalopoulos, N., and Smith, J. N.: Worldwide data sets constrain the water vapor uptake coefficient in cloud formation, P. Natl. Acad. Sci., 110, 3760–3764, https://doi.org/10.1073/pnas.1219591110, 2013.
Ramanathan, V., Crutzen, P., Kiehl, J., and Rosenfeld, D.: Aerosols, climate, and the hydrological cycle, Science, 294, 2119–2124, 2001.
Reale, O., Lau, W. K., Kim, K.-M., and Brin, E.: Atlantic Tropical Cyclogenetic Processes during SOP-3 NAMMA in the GEOS-5 Global Data Assimilation and Forecast System, J. Atmos. Sci., 66, 3563–3578, https://doi.org/10.1175/2009JAS3123.1, 2009.
Ren, C. and Mackenzie, A.: Cirrus parameterization and the role of ice nuclei, Q. J. Roy. Meteorol. Soc., 131, 1585–1605, 2005.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An Improved In Situ and Satellite SST Analysis for Climate, J. Climate, 15, 1609–1625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2, 2002.
Rienecker, M., Suarez, M., Todling, R., Bacmeister, J., Takacs, L., Liu, H.-C., Gu, W., Sienkiewicz, M., Koster, R., Gelaro, R., Stajner, I., and Nielsen, J.: The GEOS-5 Data Assimilation System – Documentation of Versions 5.0.1, 5.1.0, and 5.2.0., Vol. 27 of Technical Report Series on Global Modeling and Data Assimilation, NASA Goddard Space Flight Center, Greenbelt, MD, USA, 2008.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications, J. Climate, 24, 3624–3648, https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Rosenfeld, D. and Woodley, W. L.: Deep convective clouds with sustained supercooled liquid water down to −37.5 °C, Nature, 405, 440–442, https://doi.org/10.1038/35013030, 2000.
Rosenfeld, D., Lohmann, U., Raga, G. B., O'Dowd, C. D., Kulmala, M., Fuzzi, S., Reissell, A., and Andreae, M. O.: Flood or Drought: How Do Aerosols Affect Precipitation?, Science, 321, 1309–1313, https://doi.org/10.1126/science.1160606, 2008.
Rossow, W. and Schiffer, R.: Advances in understanding clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2266–2288, 1999.
Salzmann, M., Ming, Y., Golaz, J.-C., Ginoux, P. A., Morrison, H., Gettelman, A., Krämer, M., and Donner, L. J.: Two-moment bulk stratiform cloud microphysics in the GFDL AM3 GCM: description, evaluation, and sensitivity tests, Atmos. Chem. Phys., 10, 8037–8064, https://doi.org/10.5194/acp-10-8037-2010, 2010.
Sato, K.: Vertical wind disturbances in the troposphere and lower stratosphere observed by the MU radar, J. Atmos. Sci., 47, 2803–2817, 1990.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics, John Wiley and Sons, New York, NY, USA, 1998.
Slingo, J.: The development and verification of a cloud prediction scheme for the ECMWF model, Q. J. R. Meteorol. Soc., 113, 899–927, 1987.
Smith, R. N. B.: A scheme for predicting layer clouds and their water content in a general circulation model, Q. J. Roy. Meteorol. Soc., 116, 435–460, 1990.
Song, X. and Zhang, G. J.: Microphysics parameterization for convective clouds in a global climate model: Description and single-column model tests, J. Geophys. Res., 116, D02201, https://doi.org/10.1029/2010JD014833, 2011.
Spencer, R., Hood, R., and Goodman, H.: Precipitation retrieval over land and ocean with the SSM/I-Identification and characteristics of the scattering signal, J. Atm. Ocean. Tech., 6, 254–273, 1989.
Spichtinger, P. and Cziczo, D. J.: Impact of heterogeneous ice nuclei on homogeneous freezing events in cirrus clouds, J. Geophys. Res., 115, D14208, https://doi.org/10.1029/2009JD012168, 2010.
Stevens, B. and Feingold, G.: Untangling aerosol effects on clouds and precipitation in a buffered system, Nature, 461, 607–613, 2009.
Sud, Y. C. and Walker, G. K.: Microphysics of clouds with the relaxed Arakawa Schubert scheme (McRAS). Part I: Design and evaluation with GATE Phase III Data, J. Atmos. Sci., 56, 3196–3220, https://doi.org/10.1175/1520-0469(1999)056<3196:MOCWTR>2.0.CO;2, 1999.
Sud, Y. C., Lee, D., Oreopoulos, L., Barahona, D., Nenes, A., and Suarez, M. J.: Performance of McRAS-AC in the GEOS-5 AGCM: aerosol-cloud-microphysics, precipitation, cloud radiative effects, and circulation, Geosci. Model Dev., 6, 57–79, https://doi.org/10.5194/gmd-6-57-2013, 2013.
Tiedtke, M.: Representation of clouds in large-scale models, Monthly Weather Review, 121, 3040, https://doi.org/10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2, 1993.
Tompkins, A.: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover, J. Atmos. Sci., 59, 1917–1942, 2002.
Tonttila, J., O'Connor, E. J., Niemelä, S., Räisänen, P., and Järvinen, H.: Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations, Atmos. Chem. Phys., 11, 9207–9218, https://doi.org/10.5194/acp-11-9207-2011, 2011.
Twomey, S.: The influence of pollution on the shortwave cloud albedo of clouds, J. Atmos. Sci., 34, 1149–1152, 1977.
Twomey, S.: Aerosols, clouds and radiation, Atmos. Environ., 25A, 2435–2442, 1991.
Wang, M. and Penner, J. E.: Cirrus clouds in a global climate model with a statistical cirrus cloud scheme, Atmos. Chem. Phys., 10, 5449–5474, https://doi.org/10.5194/acp-10-5449-2010, 2010.
Wiacek, A., Peter, T., and Lohmann, U.: The potential influence of Asian and African mineral dust on ice, mixed-phase and liquid water clouds, Atmos. Chem. Phys., 10, 8649–8667, https://doi.org/10.5194/acp-10-8649-2010, 2010.
Wise, M. E., Baustian, K. J., Koop, T., Freedman, M. A., Jensen, E. J., and Tolbert, M. A.: Depositional ice nucleation onto crystalline hydrated NaCl particles: a new mechanism for ice formation in the troposphere, Atmos. Chem. Phys., 12, 1121–1134, https://doi.org/10.5194/acp-12-1121-2012, 2012.
Xie, P. and Arkin, P.: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs, B. Am. Meteorol. Soc., 78, 2539–2558, 1997.
Young, K. C.: The Role of Contact Nucleation in Ice Phase Initiation in Clouds, J. Atmos. Sci., 31, 768–776, https://doi.org/10.1175/1520-0469(1974)031<0768:TROCNI>2.0.CO;2, 1974.
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