Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5737-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5737-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
25 Nov 2020
Model description paper |
| 25 Nov 2020
| 25 Nov 2020
A new parameterization of ice heterogeneous nucleation coupled to aerosol chemistry in WRF-Chem model version 3.5.1: evaluation through ISDAC measurements
Setigui Aboubacar Keita
CORRESPONDING AUTHOR
ESCER Centre, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada
Eric Girard
ESCER Centre, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada
deceased, 10 July 2017
Jean-Christophe Raut
ESCER Centre, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada
Laboratoire Atmosphères, Observations Spatiales (LATMOS)/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France
Maud Leriche
ESCER Centre, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada
Laboratoire d’Aérologie (LA), CNRS, Université Paul Sabatier, Toulouse, France
now at: Laboratoire de Météorologie Physique (LaMP), CNRS, Université Clermont-Auvergne, Aubière, France
Jean-Pierre Blanchet
ESCER Centre, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada
Jacques Pelon
Laboratoire Atmosphères, Observations Spatiales (LATMOS)/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France
Tatsuo Onishi
Laboratoire Atmosphères, Observations Spatiales (LATMOS)/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France
Ana Cirisan
ESCER Centre, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada
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Antonio Donateo, Gianluca Pappaccogli, Federico Scoto, Maurizio Busetto, Francesca Lucia Lovisco, Natalie Brett, Douglas Keller, Brice Barret, Elsa Dieudonné, Roman Pohorsky, Andrea Baccarini, Slimane Bekki, Jean-Christophe Raut, Julia Schmale, Kathy S. Law, Steve R. Arnold, Gilberto Javier Fochesatto, William R. Simpson, and Stefano Decesari
EGUsphere, https://doi.org/10.5194/egusphere-2025-1366, https://doi.org/10.5194/egusphere-2025-1366, 2025
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A study in Fairbanks, Alaska, measured winter aerosol fluxes on snow. Both emission and deposition occurred, with larger particles settling faster. Weather influenced dispersion and deposition, while wind-driven turbulence enhanced deposition despite stable conditions. Results show aerosol accumulation in snow impacts pollution and snowmelt. Findings help improve aerosol models and pollution studies in cold cities.
Anderson Da Silva, Louis Marelle, Jean-Christophe Raut, Yvette Gramlich, Karolina Siegel, Sophie L. Haslett, Claudia Mohr, and Jennie L. Thomas
Atmos. Chem. Phys., 25, 5331–5354, https://doi.org/10.5194/acp-25-5331-2025, https://doi.org/10.5194/acp-25-5331-2025, 2025
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Particle sources in polar climates are unclear, affecting climate representation in models. This study introduces an evaluated method for tracking particles with backward modeling. Tests on simulated particles allowed us to show that traditional detection methods often misidentify sources. An improved method that accurately traces the origins of aerosol particles in the Arctic is presented. The study recommends using this enhanced method for better source identification of atmospheric species.
Natalie Brett, Kathy S. Law, Steve R. Arnold, Javier G. Fochesatto, Jean-Christophe Raut, Tatsuo Onishi, Robert Gilliam, Kathleen Fahey, Deanna Huff, George Pouliot, Brice Barret, Elsa Dieudonné, Roman Pohorsky, Julia Schmale, Andrea Baccarini, Slimane Bekki, Gianluca Pappaccogli, Federico Scoto, Stefano Decesari, Antonio Donateo, Meeta Cesler-Maloney, William Simpson, Patrice Medina, Barbara D'Anna, Brice Temime-Roussel, Joel Savarino, Sarah Albertin, Jingqiu Mao, Becky Alexander, Allison Moon, Peter F. DeCarlo, Vanessa Selimovic, Robert Yokelson, and Ellis S. Robinson
Atmos. Chem. Phys., 25, 1063–1104, https://doi.org/10.5194/acp-25-1063-2025, https://doi.org/10.5194/acp-25-1063-2025, 2025
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Processes influencing dispersion of local anthropogenic pollution in Arctic wintertime are investigated with Lagrangian dispersion modelling. Simulated power plant plume rise that considers temperature inversion layers improves results compared to observations (interior Alaska). Modelled surface concentrations are improved by representation of vertical mixing and emission estimates. Large increases in diesel vehicle emissions at temperatures reaching −35°C are required to reproduce observed NOx.
Gérard Ancellet, Camille Viatte, Anne Boynard, François Ravetta, Jacques Pelon, Cristelle Cailteau-Fischbach, Pascal Genau, Julie Capo, Axel Roy, and Philippe Nédélec
Atmos. Chem. Phys., 24, 12963–12983, https://doi.org/10.5194/acp-24-12963-2024, https://doi.org/10.5194/acp-24-12963-2024, 2024
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Characterization of ozone pollution in urban areas benefited from a measurement campaign in summer 2022 in the Paris region. The analysis is based on 21 d of lidar and aircraft observations. The main objective is an analysis of the sensitivity of ozone pollution to the micrometeorological processes in the urban atmospheric boundary layer and the transport of regional pollution. The paper also discusses to what extent satellite observations can track observed ozone plumes.
Thomas Lesigne, François Ravetta, Aurélien Podglajen, Vincent Mariage, and Jacques Pelon
Atmos. Chem. Phys., 24, 5935–5952, https://doi.org/10.5194/acp-24-5935-2024, https://doi.org/10.5194/acp-24-5935-2024, 2024
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Upper tropical clouds have a strong impact on Earth's climate but are challenging to observe. We report the first long-duration observations of tropical clouds from lidars flying on board stratospheric balloons. Comparisons with spaceborne observations reveal the enhanced sensitivity of balloon-borne lidar to optically thin cirrus. These clouds, which have a significant coverage and lie in the uppermost troposphere, are linked with the dehydration of air masses on their way to the stratosphere.
Lucas Pailler, Laurent Deguillaume, Hélène Lavanant, Isabelle Schmitz, Marie Hubert, Edith Nicol, Mickaël Ribeiro, Jean-Marc Pichon, Mickaël Vaïtilingom, Pamela Dominutti, Frédéric Burnet, Pierre Tulet, Maud Leriche, and Angelica Bianco
Atmos. Chem. Phys., 24, 5567–5584, https://doi.org/10.5194/acp-24-5567-2024, https://doi.org/10.5194/acp-24-5567-2024, 2024
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The composition of dissolved organic matter of cloud water has been investigated through non-targeted high-resolution mass spectrometry on only a few samples collected in the Northern Hemisphere. In this work, the chemical composition of samples collected at Réunion Island (SH) is investigated and compared to samples collected at Puy de Dôme (NH). Sampling, analysis and data treatment with the same methodology produced a unique dataset for investigating the molecular composition of clouds.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
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Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Maud Leriche, Pierre Tulet, Laurent Deguillaume, Frédéric Burnet, Aurélie Colomb, Agnès Borbon, Corinne Jambert, Valentin Duflot, Stéphan Houdier, Jean-Luc Jaffrezo, Mickaël Vaïtilingom, Pamela Dominutti, Manon Rocco, Camille Mouchel-Vallon, Samira El Gdachi, Maxence Brissy, Maroua Fathalli, Nicolas Maury, Bert Verreyken, Crist Amelynck, Niels Schoon, Valérie Gros, Jean-Marc Pichon, Mickael Ribeiro, Eric Pique, Emmanuel Leclerc, Thierry Bourrianne, Axel Roy, Eric Moulin, Joël Barrie, Jean-Marc Metzger, Guillaume Péris, Christian Guadagno, Chatrapatty Bhugwant, Jean-Mathieu Tibere, Arnaud Tournigand, Evelyn Freney, Karine Sellegri, Anne-Marie Delort, Pierre Amato, Muriel Joly, Jean-Luc Baray, Pascal Renard, Angelica Bianco, Anne Réchou, and Guillaume Payen
Atmos. Chem. Phys., 24, 4129–4155, https://doi.org/10.5194/acp-24-4129-2024, https://doi.org/10.5194/acp-24-4129-2024, 2024
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Aerosol particles in the atmosphere play a key role in climate change and air pollution. A large number of aerosol particles are formed from the oxidation of volatile organic compounds (VOCs and secondary organic aerosols – SOA). An important field campaign was organized on Réunion in March–April 2019 to understand the formation of SOA in a tropical atmosphere mostly influenced by VOCs emitted by forest and in the presence of clouds. This work synthesizes the results of this campaign.
Victoria A. Flood, Kimberly Strong, Cynthia H. Whaley, Kaley A. Walker, Thomas Blumenstock, James W. Hannigan, Johan Mellqvist, Justus Notholt, Mathias Palm, Amelie N. Röhling, Stephen Arnold, Stephen Beagley, Rong-You Chien, Jesper Christensen, Makoto Deushi, Srdjan Dobricic, Xinyi Dong, Joshua S. Fu, Michael Gauss, Wanmin Gong, Joakim Langner, Kathy S. Law, Louis Marelle, Tatsuo Onishi, Naga Oshima, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Manu A. Thomas, Svetlana Tsyro, and Steven Turnock
Atmos. Chem. Phys., 24, 1079–1118, https://doi.org/10.5194/acp-24-1079-2024, https://doi.org/10.5194/acp-24-1079-2024, 2024
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It is important to understand the composition of the Arctic atmosphere and how it is changing. Atmospheric models provide simulations that can inform policy. This study examines simulations of CH4, CO, and O3 by 11 models. Model performance is assessed by comparing results matched in space and time to measurements from five high-latitude ground-based infrared spectrometers. This work finds that models generally underpredict the concentrations of these gases in the Arctic troposphere.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Eleftherios Ioannidis, Kathy S. Law, Jean-Christophe Raut, Louis Marelle, Tatsuo Onishi, Rachel M. Kirpes, Lucia M. Upchurch, Thomas Tuch, Alfred Wiedensohler, Andreas Massling, Henrik Skov, Patricia K. Quinn, and Kerri A. Pratt
Atmos. Chem. Phys., 23, 5641–5678, https://doi.org/10.5194/acp-23-5641-2023, https://doi.org/10.5194/acp-23-5641-2023, 2023
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Remote and local anthropogenic emissions contribute to wintertime Arctic haze, with enhanced aerosol concentrations, but natural sources, which also contribute, are less well studied. Here, modelled wintertime sea-spray aerosols are improved in WRF-Chem over the wider Arctic by including updated wind speed and temperature-dependent treatments. As a result, anthropogenic nitrate aerosols are also improved. Open leads are confirmed to be the main source of sea-spray aerosols over northern Alaska.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
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This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
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Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
Meryl Wimmer, Gwendal Rivière, Philippe Arbogast, Jean-Marcel Piriou, Julien Delanoë, Carole Labadie, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 3, 863–882, https://doi.org/10.5194/wcd-3-863-2022, https://doi.org/10.5194/wcd-3-863-2022, 2022
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The effect of deep convection representation on the jet stream above the cold front of an extratropical cyclone is investigated in the global numerical weather prediction model ARPEGE. Two simulations using different deep convection schemes are compared with (re)analysis datasets and NAWDEX airborne observations. A deeper jet stream is observed with the less active scheme. The diabatic origin of this difference is interpreted by backward Lagrangian trajectories and potential vorticity budgets.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
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Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Thibault Vaillant de Guélis, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker
Atmos. Meas. Tech., 15, 1931–1956, https://doi.org/10.5194/amt-15-1931-2022, https://doi.org/10.5194/amt-15-1931-2022, 2022
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A new IIR-based cloud and aerosol discrimination (CAD) algorithm is developed using the IIR brightness temperature differences for cloud and aerosol features confidently identified by the CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduce the ambiguity in a notable fraction of
not confidentV4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.
Pamela A. Dominutti, Pascal Renard, Mickaël Vaïtilingom, Angelica Bianco, Jean-Luc Baray, Agnès Borbon, Thierry Bourianne, Frédéric Burnet, Aurélie Colomb, Anne-Marie Delort, Valentin Duflot, Stephan Houdier, Jean-Luc Jaffrezo, Muriel Joly, Martin Leremboure, Jean-Marc Metzger, Jean-Marc Pichon, Mickaël Ribeiro, Manon Rocco, Pierre Tulet, Anthony Vella, Maud Leriche, and Laurent Deguillaume
Atmos. Chem. Phys., 22, 505–533, https://doi.org/10.5194/acp-22-505-2022, https://doi.org/10.5194/acp-22-505-2022, 2022
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We present here the results obtained during an intensive field campaign conducted in March to April 2019 in Reunion. Our study integrates a comprehensive chemical and microphysical characterization of cloud water. Our investigations reveal that air mass history and cloud microphysical properties do not fully explain the variability observed in their chemical composition. This highlights the complexity of emission sources, multiphasic exchanges, and transformations in clouds.
Lilian Loyer, Jean-Christophe Raut, Claudia Di Biagio, Julia Maillard, Vincent Mariage, and Jacques Pelon
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-326, https://doi.org/10.5194/amt-2021-326, 2021
Revised manuscript not accepted
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The Arctic is facing drastic climate changes, and more observations are needed to better understand what is happening. Unfortunately observations are limited in the High Arctic. To obtain more observations, multiples buoys equipped with lidar, have been deployed in this region. This paper presents an approach to estimate the optical properties of clouds, and solar plus terrestrial energies from lidar measurements in the Arctic.
Gwendal Rivière, Meryl Wimmer, Philippe Arbogast, Jean-Marcel Piriou, Julien Delanoë, Carole Labadie, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 2, 1011–1031, https://doi.org/10.5194/wcd-2-1011-2021, https://doi.org/10.5194/wcd-2-1011-2021, 2021
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Inacurracies in representing processes occurring at spatial scales smaller than the grid scales of the weather forecast models are important sources of forecast errors. This is the case of deep convection representation in models with 10 km grid spacing. We performed simulations of a real extratropical cyclone using a model with different representations of deep convection. These forecasts lead to different behaviors in the ascending air masses of the cyclone and the jet stream aloft.
Liviu Ivănescu, Konstantin Baibakov, Norman T. O'Neill, Jean-Pierre Blanchet, and Karl-Heinz Schulz
Atmos. Meas. Tech., 14, 6561–6599, https://doi.org/10.5194/amt-14-6561-2021, https://doi.org/10.5194/amt-14-6561-2021, 2021
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Starphotometry seeks to provide accurate measures of nocturnal optical depth (OD). It is driven by a need to characterize aerosols and their radiative forcing effects during a very data-sparse period. A sub-0.01 OD error is required to adequately characterize key aerosol parameters. We found approaches for sufficiently mitigating errors to achieve the 0.01 standard. This renders starphotometry the equal of daytime techniques and opens the door to exploiting its distinct star-pointing advantages.
Didier Bruneau and Jacques Pelon
Atmos. Meas. Tech., 14, 4375–4402, https://doi.org/10.5194/amt-14-4375-2021, https://doi.org/10.5194/amt-14-4375-2021, 2021
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Taking advantage of Aeolus success and of our airborne lidar system expertise, we present a new spaceborne wind lidar design for operational Aeolus follow-on missions, keeping most of the initial lidar system but relying on a single Mach–Zehnder interferometer to relax operational constraints and reduce measurement bias. System parameters are optimized. Random and systematic errors are shown to be compliant with the initial mission requirements. In addition, the system allows unbiased retrieval.
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3253–3276, https://doi.org/10.5194/amt-14-3253-2021, https://doi.org/10.5194/amt-14-3253-2021, 2021
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The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results are presented in a companion paper (Part II).
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3277–3299, https://doi.org/10.5194/amt-14-3277-2021, https://doi.org/10.5194/amt-14-3277-2021, 2021
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The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version 3 as a result of the significant changes implemented in the version 4 algorithms, which are presented in a companion paper (Part I).
David L. A. Flack, Gwendal Rivière, Ionela Musat, Romain Roehrig, Sandrine Bony, Julien Delanoë, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 2, 233–253, https://doi.org/10.5194/wcd-2-233-2021, https://doi.org/10.5194/wcd-2-233-2021, 2021
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The representation of an extratropical cyclone in simulations of two climate models is studied by comparing them to observations of the international field campaign NAWDEX. We show that the current resolution used to run climate model projections (more than 100 km) is not enough to represent the life cycle accurately, but the use of 50 km resolution is good enough. Despite these encouraging results, cloud properties (partitioning liquid and solid) are found to be far from the observations.
Julia Maillard, François Ravetta, Jean-Christophe Raut, Vincent Mariage, and Jacques Pelon
Atmos. Chem. Phys., 21, 4079–4101, https://doi.org/10.5194/acp-21-4079-2021, https://doi.org/10.5194/acp-21-4079-2021, 2021
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Clouds remain a major source of uncertainty in understanding the Arctic climate, due in part to the lack of measurements over the sea ice. In this paper, we exploit a series of lidar profiles acquired from autonomous drifting buoys deployed in the Arctic Ocean and derive a statistic of low cloud frequency and macrophysical properties. We also show that clouds contribute to warm the surface in the shoulder seasons but not significantly from May to September.
Keun-Ok Lee, Brice Barret, Eric L. Flochmoën, Pierre Tulet, Silvia Bucci, Marc von Hobe, Corinna Kloss, Bernard Legras, Maud Leriche, Bastien Sauvage, Fabrizio Ravegnani, and Alexey Ulanovsky
Atmos. Chem. Phys., 21, 3255–3274, https://doi.org/10.5194/acp-21-3255-2021, https://doi.org/10.5194/acp-21-3255-2021, 2021
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This paper focuses on the emission sources and pathways of pollution from the boundary layer to the Asian monsoon anticyclone (AMA) during the StratoClim aircraft campaign period. Simulations with the Meso-NH cloud-chemistry model at a horizontal resolution of 15 km are performed over the Asian region to characterize the impact of monsoon deep convection on the composition of AMA and on the formation of the Asian tropopause aerosol layer during the StratoClim campaign.
Cited articles
Abbatt, J. P. D., Leaitch, W. R., Aliabadi, A. A., Bertram, A. K., Blanchet, J.-P., Boivin-Rioux, A., Bozem, H., Burkart, J., Chang, R. Y. W., Charette, J., Chaubey, J. P., Christensen, R. J., Cirisan, A., Collins, D. B., Croft, B., Dionne, J., Evans, G. J., Fletcher, C. G., Galí, M., Ghahremaninezhad, R., Girard, E., Gong, W., Gosselin, M., Gourdal, M., Hanna, S. J., Hayashida, H., Herber, A. B., Hesaraki, S., Hoor, P., Huang, L., Hussherr, R., Irish, V. E., Keita, S. A., Kodros, J. K., Köllner, F., Kolonjari, F., Kunkel, D., Ladino, L. A., Law, K., Levasseur, M., Libois, Q., Liggio, J., Lizotte, M., Macdonald, K. M., Mahmood, R., Martin, R. V., Mason, R. H., Miller, L. A., Moravek, A., Mortenson, E., Mungall, E. L., Murphy, J. G., Namazi, M., Norman, A.-L., O'Neill, N. T., Pierce, J. R., Russell, L. M., Schneider, J., Schulz, H., Sharma, S., Si, M., Staebler, R. M., Steiner, N. S., Thomas, J. L., von Salzen, K., Wentzell, J. J. B., Willis, M. D., Wentworth, G. R., Xu, J.-W., and Yakobi-Hancock, J. D.: Overview paper: New insights into aerosol and climate in the Arctic, Atmos. Chem. Phys., 19, 2527–2560, https://doi.org/10.5194/acp-19-2527-2019, 2019. a, b
Archuleta, C. M., DeMott, P. J., and Kreidenweis, S. M.: Ice nucleation by surrogates for atmospheric mineral dust and mineral dust/sulfate particles at cirrus temperatures, Atmos. Chem. Phys., 5, 2617–2634, https://doi.org/10.5194/acp-5-2617-2005, 2005. a
Atmospheric Radiation Measurement (ARM) user facility: CLDMICROPROP-51, available at: https://www.arm.gov/data/data-sources/cldmicroprop-51, last access: September 2020. a
Atkinson, D. E., Sassen, K., Hayashi, M., Cahill, C. F., Shaw, G., Harrigan, D., and Fuelberg, H.: Aerosol properties over Interior Alaska from lidar, DRUM Impactor sampler, and OPC-sonde measurements and their meteorological context during ARCTAS-A, April 2008, Atmos. Chem. Phys., 13, 1293–1310, https://doi.org/10.5194/acp-13-1293-2013, 2013. a, b
Berg, L. K., Shrivastava, M., Easter, R. C., Fast, J. D., Chapman, E. G., Liu, Y., and Ferrare, R. A.: A new WRF-Chem treatment for studying regional-scale impacts of cloud processes on aerosol and trace gases in parameterized cumuli, Geosci. Model Dev., 8, 409–429, https://doi.org/10.5194/gmd-8-409-2015, 2015. a
Bigg, E. K.: The formation of atmospheric ice crystals by the freezing of
droplets, Q. J. Roy. Meteor. Soc., 79,
510–519, https://doi.org/10.1002/qj.49707934207,
1953. a
Blanchet, J.-P. and Girard, E.: Arctic “greenhouse effect”, Nature, 371, p. 383,
https://doi.org/10.1038/371383a0, 1994. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh,
S., Sherwood, S., B., S., and Zhang, X.: Clouds and Aerosols, in: Climate
Change 2013: The Physical Science Basis, Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 2013. a
Burton, S. P., Ferrare, R. A., Hostetler, C. A., Hair, J. W., Rogers, R. R., Obland, M. D., Butler, C. F., Cook, A. L., Harper, D. B., and Froyd, K. D.: Aerosol classification using airborne High Spectral Resolution Lidar measurements – methodology and examples, Atmos. Meas. Tech., 5, 73–98, https://doi.org/10.5194/amt-5-73-2012, 2012. a
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model
with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation
and Sensitivity, Mon. Weather Rev. 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001. a
Chen, J.-P., Hazra, A., and Levin, Z.: Parameterizing ice nucleation rates using contact angle and activation energy derived from laboratory data, Atmos. Chem. Phys., 8, 7431–7449, https://doi.org/10.5194/acp-8-7431-2008, 2008. a, b
Chin, M., Rood, R. B., Lin, S.-J., Müller, J.-F., and Thompson, A. M.:
Atmospheric sulfur cycle simulated in the global model GOCART: Model
description and global properties, J. Geophys. Res.-Atmos., 105, 24671–24687, https://doi.org/10.1029/2000jd900384, 2000. a
Cirisan, A., Girard, E., Blanchet, J.-P., Keita, S., Gong, W., Irish, V., and
Bertam, A.: Modellings of the observed INP concentration during Arctic summer
campaigns, Atmosphere, 11, 916, https://doi.org/10.3390/atmos11090916, 2020. a, b, c
Connolly, P. J., Möhler, O., Field, P. R., Saathoff, H., Burgess, R., Choularton, T. W., and Gallagher, M. W.: Corrigendum to: “Studies of heterogeneous freezing by three different desert dust samples;;, Atmos. Chem. Phys., 9, 2805–2824, 2009, Atmos. Chem. Phys., 13, 10079–10080, https://doi.org/10.5194/acp-13-10079-2013, 2013. a
Cooper, W. A.: Ice Initiation in Natural Clouds, Meteor. Mon.,
43, 29–32, https://doi.org/10.1175/0065-9401-21.43.29, 1986. a
Curry, J. A., Schramm, J. L., Rossow, W. B., and Randall, D.: Overview of
Arctic Cloud and Radiation Characteristics, J. Climate, 9,
1731–1764, https://doi.org/10.1175/1520-0442(1996)009<1731:OOACAR>2.0.CO;2, 1996. a
DeMott, P. J., Meyers, M. P., and Cotton, W. R.: Parameterization and Impact
of Ice initiation Processes Relevant to Numerical Model Simulations of Cirrus
Clouds, J. Atmos. Sci., 51, 77–90,
https://doi.org/10.1175/1520-0469(1994)051<0077:PAIOII>2.0.CO;2, 1994. a
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. USA, 107, 11217–11222, https://doi.org/10.1073/pnas.0910818107,
2010. a, b, c
DeMott, P. J., Prenni, A. J., McMeeking, G. R., Sullivan, R. C., Petters, M. D., Tobo, Y., Niemand, M., Möhler, O., Snider, J. R., Wang, Z., and Kreidenweis, S. M.: Integrating laboratory and field data to quantify the immersion freezing ice nucleation activity of mineral dust particles, Atmos. Chem. Phys., 15, 393–409, https://doi.org/10.5194/acp-15-393-2015, 2015. a
Eastwood, M. L., Cremel, S., Gehrke, C., Girard, E., and Bertram, A. K.: Ice
nucleation on mineral dust particles: Onset conditions, nucleation rates and
contact angles, J. Geophys. Res., 113, D22203,
https://doi.org/10.1029/2008jd010639, 2008. a, b, c, d
Eastwood, M. L., Cremel, S., Wheeler, M., Murray, B. J., Girard, E., and
Bertram, A. K.: Effects of sulfuric acid and ammonium sulfate coatings on the
ice nucleation properties of kaolinite particles, Geophys. Res.
Lett., 36, L02811,
https://doi.org/10.1029/2008gl035997, 2009. a, b, c, d
Eckhardt, S., Quennehen, B., Olivié, D. J. L., Berntsen, T. K., and Cherian,
R.: Corrigendum to “Current model capabilities for simulating black carbon and sulfateconcentrations in the Arctic atmosphere: a multi-model evaluationusing a comprehensive measurement data set” published in Atmos. Chem. Phys., 15, 9413–9433, 2015, Atmos. Chem. Phys.,
https://doi.org/10.5194/acp-15-9413-2015-corrigendum, 2015. a
Eidhammer, T., DeMott, P. J., and Kreidenweis, S. M.: A comparison of
heterogeneous ice nucleation parameterizations using a parcel model
framework, J. Geophys. Res., 114, D06202, https://doi.org/10.1029/2008jd011095,
2009. a
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010. a, b
Fisher, J. A., Jacob, D. J., Wang, Q., Bahreini, R., Carouge, C. C., Cubison,
M. J., Dibb, J. E., Diehl, T., Jimenez, J. L., Leibensperger, E. M., Lu, Z.,
Meinders, M. B. J., Pye, H. O. T., Quinn, P. K., Sharma, S., Streets, D. G.,
van Donkelaar, A., and Yantosca, R. M.: Sources, distribution, and acidity of
sulfate–ammonium aerosol in the Arctic in winter–spring, Atmos.
Environ., 45, 7301–7318, https://doi.org/10.1016/j.atmosenv.2011.08.030, 2011. a, b, c, d, e, f
Fletcher, N. H.: The physics of rainclouds, Cambridge University Press, 1962. a
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. a
Glaccum, R. A. and Prospero, J. M.: Saharan aerosols over the tropical North
Atlantic – Mineralogy, Mar. Geol., 37, 295–321,
https://doi.org/10.1016/0025-3227(80)90107-3, 1980. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock,
W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF
model, Atmos. Environ., 39, 6957–6975,
https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a, b
Grenier, P. and Blanchet, J.-P.: Investigation of the sulphate-induced freezing
inhibition effect from CloudSat and CALIPSO measurements, J.
Geophys. Res., 115, D22205, https://doi.org/10.1029/2010jd013905, 2010. a
Grenier, P., Blanchet, J., and Muñoz‐Alpizar, R.: Study of polar thin ice
clouds and aerosols seen by CloudSat and CALIPSO during midwinter 2007,
J. Geophys. Res., 114, D09201, https://doi.org/10.1029/2008jd010927, 2009. a
Guenther, A.: Corrigendum to ”Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)” published in Atmos. Chem. Phys., 6, 3181–3210, 2006, Atmos. Chem. Phys., 7, 4327–4327, https://doi.org/10.5194/acp-7-4327-2007, 2007. a
Hoose, C. and Möhler, O.: Heterogeneous ice nucleation on atmospheric aerosols: a review of results from laboratory experiments, Atmos. Chem. Phys., 12, 9817–9854, https://doi.org/10.5194/acp-12-9817-2012, 2012. a
Hoose, C., Kristjánsson, J. E., Chen, J.-P., and Hazra, A.: A
Classical-Theory-Based Parameterization of Heterogeneous Ice Nucleation by
Mineral Dust, Soot, and Biological Particles in a Global Climate Model,
J. Atmos. Sci., 67, 2483–2503,
https://doi.org/10.1175/2010jas3425.1, 2010. a
Hung, H., Malinowski, A., and Scot, T. M.: Kinetics of Heterogeneous Ice
Nucleation on the Surfaces of Mineral Dust Cores Inserted into Aqueous
Ammonium Sulfate Particles, J. Phys. Chem. A, 107, 1296–1306,
https://doi.org/10.1021/jp021593y, 2003. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys.
Res., 113, D13103, https://doi.org/10.1029/2008jd009944, 2008. a, b
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., 2013. a
Janjić, Z. I.: The Step-Mountain Eta Coordinate Model: Further Developments
of the Convection, Viscous Sublayer, and Turbulence Closure Schemes, Mon.
Weather Rev., 122, 927–945,
https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994. a, b
Jouan, C., Pelon, J., Girard, E., Ancellet, G., Blanchet, J. P., and Delanoë, J.: On the relationship between Arctic ice clouds and polluted air masses over the North Slope of Alaska in April 2008, Atmos. Chem. Phys., 14, 1205–1224, https://doi.org/10.5194/acp-14-1205-2014, 2014. a, b, c
Kanji, Z. A. and Abbatt, J. P. D.: Ice Nucleation onto Arizona Test Dust at
Cirrus Temperatures: Effect of Temperature and Aerosol Size on Onset Relative
Humidity, Am. Chem. Soc., 114, 935–941, https://doi.org/10.1021/jp908661m,
2010. a
Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo,
D. J., and Krämer, M.: Overview of Ice Nucleating Particles, Meteor.
Mon., 58, 1.1–1.33, https://doi.org/10.1175/amsmonographs-d-16-0006.1, 2017. a
Kay, J. E., L'Ecuyer, T., Chepfer, H., Loeb, N., Morrison, A., and Cesana,
G.: Recent Advances in Arctic Cloud and Climate Research, Current Climate
Change Reports, 2, 159–169, https://doi.org/10.1007/s40641-016-0051-9, 2016. a
Keita, S., Girard, E., Raut, J.-C., Pelon, J., Blanchet, J.-P., Lemoine, O.,
and Onishi, T.: Simulating Arctic Ice Clouds during Spring Using an Advanced
Ice Cloud Microphysics in the WRF Model, Atmosphere, 10, 433,
https://doi.org/10.3390/atmos10080433, 2019. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Keita, S. A., Girard, E., Raut, J.-C., Leriche, M., Blanchet, J.-P., Pelon, J., Onishi, T., and Keita, A. C.: paper_gmd-2020-50 (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.4033654, 2020. a
Khvorostyanov, V. I. and Curry, J. A.: Critical humidities of homogeneous and
heterogeneous ice nucleation: Inferences from extended classical nucleation
theory, J. Geophys. Res., 114, D04207,
https://doi.org/10.1029/2008jd011197,
2009. a, b
Kong, F. and Yau, M. K.: An explicit approach to microphysics in MC2,
Atmosphere-Ocean, 35, 257–291, https://doi.org/10.1080/07055900.1997.9649594, 1997. a
Kulkarni, G., Sanders, C., Zhang, K., Liu, X., and Zhao, C.: Ice nucleation of
bare and sulfuric acid-coated mineral dust particles and implication for
cloud properties, J. Geophys. Res.-Atmos., 119,
9993–10 011, https://doi.org/10.1002/2014JD021567, 2014. a
Kumar, A., Marcolli, C., Luo, B., and Peter, T.: Ice nucleation activity of silicates and aluminosilicates in pure water and aqueous solutions – Part 1: The K-feldspar microcline, Atmos. Chem. Phys., 18, 7057–7079, https://doi.org/10.5194/acp-18-7057-2018, 2018. a
Kumar, A., Marcolli, C., and Peter, T.: Ice nucleation activity of silicates and aluminosilicates in pure water and aqueous solutions – Part 2: Quartz and amorphous silica, Atmos. Chem. Phys., 19, 6035–6058, https://doi.org/10.5194/acp-19-6035-2019, 2019a. a
Kumar, A., Marcolli, C., and Peter, T.: Ice nucleation activity of silicates and aluminosilicates in pure water and aqueous solutions – Part 3: Aluminosilicates, Atmos. Chem. Phys., 19, 6059–6084, https://doi.org/10.5194/acp-19-6059-2019, 2019b. a
Lawson, R. P., Woods, S., Jensen, E., Erfani, E., Gurganus, C., Gallagher, M.,
Connolly, P., Whiteway, J., Baran, A. J., May, P., Heymsfield, A., Schmitt,
C. G., McFarquhar, G., Um, J., Protat, A., Bailey, M., Lance, S., Muehlbauer,
A., Stith, J., Korolev, A., Toon, O. B., and Krämer, M.: A Review of Ice
Particle Shapes in Cirrus formed In Situ and in Anvils, J. Geophys. Res.-Atmos., 124, 10049–10090,
https://doi.org/10.1029/2018JD030122, 2019. a
Liu, X., Penner, J. E., Ghan, S. J., and Wang, M.: Inclusion of Ice
Microphysics in the NCAR Community Atmospheric Model Version 3 (CAM3),
J. Climate, 20, 4526–4547, https://doi.org/10.1175/jcli4264.1, 2007. a, b
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. a
Martin, S. T.: Phase Transitions of Aqueous Atmospheric Particles, Chem.
Rev., 100, 3403–3454, https://doi.org/10.1021/cr990034t, 2000. a
Matrosov, S. Y., Maahn, M., and de Boer, G.: Observational and Modeling Study
of Ice Hydrometeor Radar Dual-Wavelength Ratios, J. Appl.
Meteorol. Clim., 58, 2005–2017, https://doi.org/10.1175/JAMC-D-19-0018.1,
2019. a
McFarquhar, G. M., Ghan, S., Verlinde, J., Korolev, A., Strapp, J. W., Schmid,
B., Tomlinson, J. M., Wolde, M., Brooks, S. D., Cziczo, D., Dubey, M. K.,
Fan, J., Flynn, C., Gultepe, I., Hubbe, J., Gilles, M. K., Laskin, A.,
Lawson, P., Leaitch, W. R., Liu, P., Liu, X., Lubin, D., Mazzoleni, C.,
Macdonald, A.-M., Moffet, R. C., Morrison, H., Ovchinnikov, M., Shupe, M. D.,
Turner, D. D., Xie, S., Zelenyuk, A., Bae, K., Freer, M., and Glen, A.:
Indirect and Semi-direct Aerosol Campaign, B. Am.
Meteorol. Soc., 92, 183–201, https://doi.org/10.1175/2010bams2935.1, 2011. a, b, c
McFarquhar, G. M., Baumgardner, D., and Heymsfield, A. J.: Background and
Overview, Meteor. Mon., 58, v–ix,
https://doi.org/10.1175/amsmonographs-d-16-0018.1, 2017. a
Meyers, M. P., DeMott, P. J., and Cotton, W. R.: New Primary Ice-Nucleation
Parameterizations in an Explicit Cloud Model, J. Appl.
Meteorol., 31, 708–721,
https://doi.org/10.1175/1520-0450(1992)031<0708:NPINPI>2.0.CO;2, 1992. a, b, c, d
Mölders, N., Tran, H. N. Q., Quinn, P., Sassen, K., Shaw, G. E., and Kramm,
G.: Assessment of WRF/Chem to simulate sub–Arctic boundary layer
characteristics during low solar irradiation using radiosonde, SODAR, and
surface data, Atmos. Pollut. Res., 2, 283–299,
https://doi.org/10.5094/apr.2011.035, 2011. a, b, c
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, 2005a. a
Morrison, H., Curry, J. A., Shupe, M. D., and Zuidema, P.: A New Double-Moment
Microphysics Parameterization for Application in Cloud and Climate Models.
Part II: Single-Column Modeling of Arctic Clouds, J. Atmos.
Sci., 62, 1678–1693, https://doi.org/10.1175/JAS3447.1, 2005b. a
Murray, B. J., O'Sullivan, D., Atkinson, J. D., and Webb, M. E.: Ice nucleation
by particles immersed in supercooled cloud droplets, Chem. Soc. Rev., 41,
6519–6554, https://doi.org/10.1039/c2cs35200a, 2012. a, b
National Center for Atmospheric Research (NCAR) and University Corporation for Atmospheric Research (UCAR): WRF Source Codes and Graphics Software, available at: https://www2.mmm.ucar.edu/wrf/users/download/get_source.html, last access: September 2020a. a
National Center for Atmospheric Research (NCAR) and University Corporation for Atmospheric Research (UCAR): NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, available at: https://doi.org/10.5065/D6M043C6, last access: September 2020b. a
Niedermeier, D., Ervens, B., Clauss, T., Voigtländer, J., Wex, H., Hartmann,
S., and Stratmann, F.: A computationally efficient description of
heterogeneous freezing: A simplified version of the Soccer ball model,
Geophys. Res. Lett., 41, 736–741, https://doi.org/10.1002/2013gl058684, 2014. a
Panda, A. K., Mishra, B., Mishra, D., and Singh, R.: Effect of sulphuric acid
treatment on the physico-chemical characteristics of kaolin clay, Colloids
and Surface. A, 363, 98–104,
https://doi.org/10.1016/j.colsurfa.2010.04.022, 2010. a
Pant, A., Fok, A., Parsons, M. T., Mak, J., and Bertram, A. K.: Deliquescence
and crystallization of ammonium sulfate-glutaric acid and sodium
chloride-glutaric acid particles, Geophys. Res. Lett., 31, L12111,
https://doi.org/10.1029/2004GL020025, 2004. a
Pant, A., Parsons, M. T., and Bertram, A. K.: Crystallization of Aqueous
Ammonium Sulfate Particles Internally Mixed with Soot and Kaolinite:
Crystallization Relative Humidities and Nucleation Rates, J.
Phys. Chem. A, 110, 8701–8709, https://doi.org/10.1021/jp060985s, 2006. a
Parsons, M. T., Mak, J., Lipetz, S. R., and Bertram, A. K.: Deliquescence of
malonic, succinic, glutaric, and adipic acid particles, J. Geophys. Res.-Atmos., 109, D06212, https://doi.org/10.1029/2003jd004075,
2004b. a
Phillips, V. T. J., 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. a
Prenni, A. J., Petters, M. D., Kreidenweis, S. M., DeMott, P. J., and Ziemann,
P. J.: Cloud droplet activation of secondary organic aerosol, J. Geophys. Res.-Atmos., 112, D10223, https://doi.org/10.1029/2006jd007963, 2007. a
Raut, J.-C., Marelle, L., Fast, J. D., Thomas, J. L., Weinzierl, B., Law, K. S., Berg, L. K., Roiger, A., Easter, R. C., Heimerl, K., Onishi, T., Delanoë, J., and Schlager, H.: Cross-polar transport and scavenging of Siberian aerosols containing black carbon during the 2012 ACCESS summer campaign, Atmos. Chem. Phys., 17, 10969–10995, https://doi.org/10.5194/acp-17-10969-2017, 2017. a
Schoenberg Ferrier, 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. a, b, c
Schwarz, J. P., Gao, R. S., Perring, A. E., Spackman, J. R., and Fahey, D. W.:
Black carbon aerosol size in snow, Sci. Rep., 3, 1356, https://doi.org/10.1038/srep01356,
2013. a
Shantz, N. C., Gultepe, I., Andrews, E., Zelenyuk, A., Earle, M. E., Macdonald,
A. M., Liu, P. S. K., and Leaitch, W. R.: Optical, physical, and chemical
properties of springtime aerosol over Barrow Alaska in 2008, International
J. Climatol., 34, 3125–3138, https://doi.org/10.1002/joc.3898, 2014. a
Shaw, W. J., Jerry Allwine, K., Fritz, B. G., Rutz, F. C., Rishel, J. P., and
Chapman, E. G.: An evaluation of the wind erosion module in DUSTRAN,
Atmos. Environ., 42, 1907–1921, 2008. a
Shindell, D. and Faluvegi, G.: Climate response to regional radiative forcing
during the twentieth century, Nat. Geosci., 2, 294–300,
https://doi.org/10.1038/ngeo473, 2009. a
Shindell, D. T., Chin, M., Dentener, F., Doherty, R. M., Faluvegi, G., Fiore, A. M., Hess, P., Koch, D. M., MacKenzie, I. A., Sanderson, M. G., Schultz, M. G., Schulz, M., Stevenson, D. S., Teich, H., Textor, C., Wild, O., Bergmann, D. J., Bey, I., Bian, H., Cuvelier, C., Duncan, B. N., Folberth, G., Horowitz, L. W., Jonson, J., Kaminski, J. W., Marmer, E., Park, R., Pringle, K. J., Schroeder, S., Szopa, S., Takemura, T., Zeng, G., Keating, T. J., and Zuber, A.: A multi-model assessment of pollution transport to the Arctic, Atmos. Chem. Phys., 8, 5353–5372, https://doi.org/10.5194/acp-8-5353-2008, 2008. a
Sullivan, R. C., Petters, M. D., DeMott, P. J., Kreidenweis, S. M., Wex, H., Niedermeier, D., Hartmann, S., Clauss, T., Stratmann, F., Reitz, P., Schneider, J., and Sierau, B.: Irreversible loss of ice nucleation active sites in mineral dust particles caused by sulphuric acid condensation, Atmos. Chem. Phys., 10, 11471–11487, https://doi.org/10.5194/acp-10-11471-2010, 2010. a, b
Vali, G.: Interpretation of freezing nucleation experiments: singular and stochastic; sites and surfaces, Atmos. Chem. Phys., 14, 5271–5294, https://doi.org/10.5194/acp-14-5271-2014, 2014. a
Vali, G., DeMott, P. J., Möhler, O., and Whale, T. F.: Technical Note: A proposal for ice nucleation terminology, Atmos. Chem. Phys., 15, 10263–10270, https://doi.org/10.5194/acp-15-10263-2015, 2015. a, b
Welti, A., Lüönd, F., Stetzer, O., and Lohmann, U.: Influence of particle size on the ice nucleating ability of mineral dusts, Atmos. Chem. Phys., 9, 6705–6715, https://doi.org/10.5194/acp-9-6705-2009, 2009. a
Welti, A., Lüönd, F., Kanji, Z. A., Stetzer, O., and Lohmann, U.: Time dependence of immersion freezing: an experimental study on size selected kaolinite particles, Atmos. Chem. Phys., 12, 9893–9907, https://doi.org/10.5194/acp-12-9893-2012, 2012. a, b
Wheeler, M. J. and Bertram, A. K.: Deposition nucleation on mineral dust particles: a case against classical nucleation theory with the assumption of a single contact angle, Atmos. Chem. Phys., 12, 1189–1201, https://doi.org/10.5194/acp-12-1189-2012, 2012. a
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011. a, b
Wild, O., Zhu, X., and J., P. M.: Fast-J: Accurate Simulation of In- and
Below-Cloud Photolysis in Tropospheric Chemical Models, J.
Atmos. Chem., 37, 245–282, https://doi.org/10.1023/A:1006415919030, 2000. a, b
Wright, T. P. and Petters, M. D.: The role of time in heterogeneous freezing
nucleation, J. Geophys. Res.-Atmos., 118, 3731–3743,
https://doi.org/10.1002/jgrd.50365, 2013. a
Yang, F., Tan, J., Zhao, Q., Du, Z., He, K., Ma, Y., Duan, F., Chen, G., and Zhao, Q.: Characteristics of PM2.5 speciation in representative megacities and across China, Atmos. Chem. Phys., 11, 5207–5219, https://doi.org/10.5194/acp-11-5207-2011, 2011. a
Young, K. C.: A Numerical Simulation of Wintertime, Orographic Precipitation:
Part I. Description of Model Microphysics and Numerical Techniques, J. Atmos. Sci., 31, 1735–1748,
https://doi.org/10.1175/1520-0469(1974)031<1735:ANSOWO>2.0.CO;2, 1974. a
Zaveri, R. A. and Peters, L. K.: A new lumped structure photochemical mechanism
for large-scale applications, J. Geophys. Res.-Atmos.,
104, 30387–30415, https://doi.org/10.1029/1999jd900876, 1999. a
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for
Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys. Res.-Atmos., 113, D13204, https://doi.org/10.1029/2007JD008782, 2008. a, b, c
Zhang, Q., Jimenez, J. L., Worsnop, D. R., and Canagaratna, M.: A Case Study of
Urban Particle Acidity and Its Influence on Secondary Organic Aerosol,
Environ. Sci. Technol., 41, 3213–3219, https://doi.org/10.1021/es061812j,
2007.
a
Zhao, C. and Garrett, T. J.: Effects of Arctic haze on surface cloud radiative
forcing, Geophys. Res. Lett., 42, 557–564,
https://doi.org/10.1002/2014gl062015, 2015. a
