Articles | Volume 25, issue 3
https://doi.org/10.5194/acp-25-1617-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-1617-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Feb 2025
Research article |
| 05 Feb 2025
| 05 Feb 2025
Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model
Astrid B. Gjelsvik
CORRESPONDING AUTHOR
Department of Geosciences, University of Oslo, Oslo, Norway
Department of Geosciences, University of Oslo, Oslo, Norway
Tim Carlsen
Department of Geosciences, University of Oslo, Oslo, Norway
Franziska Hellmuth
Department of Geosciences, University of Oslo, Oslo, Norway
Stefan Hofer
Department of Geosciences, University of Oslo, Oslo, Norway
Zachary McGraw
Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Harald Sodemann
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Trude Storelvmo
Department of Geosciences, University of Oslo, Oslo, Norway
Nord University Business School, Nord University, Bodø, Norway
Related authors
No articles found.
Manfred Wendisch, Benjamin Kirbus, Davide Ori, Matthew D. Shupe, Susanne Crewell, Harald Sodemann, and Vera Schemann
Atmos. Chem. Phys., 25, 15047–15076, https://doi.org/10.5194/acp-25-15047-2025, https://doi.org/10.5194/acp-25-15047-2025, 2025
Short summary
Short summary
Aircraft observations of air parcels moving into and out of the Arctic are reported. From the data, heating and cooling as well as drying and moistening of the air masses along their way into and out of the Arctic could be measured for the first time. These data are used to evaluate if weather prediction models are able to accurately represent these air mass transformations. This work helps to model the future Arctic climate changes, which may have an impact for mid-latitude weather as well.
Ove W. Haugvaldstad, Dirk Olivié, Trude Storelvmo, and Michael Schulz
Atmos. Chem. Phys., 25, 13199–13219, https://doi.org/10.5194/acp-25-13199-2025, https://doi.org/10.5194/acp-25-13199-2025, 2025
Short summary
Short summary
Our study examines what would happen if desert dust in the atmosphere doubled, motivated by dust sedimentation records showing a large increase in dust levels since industrialization began. Using climate model simulations, we assess how more dust affects Earth's energy balance and rainfall. We found that models disagree on whether more dust overall cools or warms the planet. Additionally, more dust tends to reduce rainfall because it absorbs radiation and encourages the formation of ice clouds.
Lise Seland Graff, Jerry Tjiputra, Ada Gjermundsen, Andreas Born, Jens Boldingh Debernard, Heiko Goelzer, Yan-Chun He, Petra Margaretha Langebroek, Aleksi Nummelin, Dirk Olivié, Øyvind Seland, Trude Storelvmo, Mats Bentsen, Chuncheng Guo, Andrea Rosendahl, Dandan Tao, Thomas Toniazzo, Camille Li, Stephen Outten, and Michael Schulz
Earth Syst. Dynam., 16, 1671–1698, https://doi.org/10.5194/esd-16-1671-2025, https://doi.org/10.5194/esd-16-1671-2025, 2025
Short summary
Short summary
The magnitude of future Arctic amplification is highly uncertain. Using the Norwegian Earth System Model, we explore the effect of improving the representation of clouds, ocean eddies, the Greenland ice sheet, sea ice, and ozone on the projected Arctic winter warming in a coordinated experiment set. These improvements all lead to enhanced projected Arctic warming, with the largest changes found in the sea ice retreat regions and the largest uncertainty found on the Atlantic side.
Jenny Bjordal, Anthony A. Smith Jr., Henri Cornec, and Trude Storelvmo
EGUsphere, https://doi.org/10.5194/egusphere-2025-4660, https://doi.org/10.5194/egusphere-2025-4660, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Our research introduces NorESM2-DIAM, a new modelling tool that connects a climate model with an economic model to better understand how climate change affects economies. With the new model we can estimate both global and regional economic consequences – and shows that these impacts can vary a lot depending on where you are. The model is the first of its kind and a good starting point for future model development, making even better predictions possible in the future.
Daniele Zannoni, Hans Christian Steen-Larsen, Harald Sodemann, Iris Thurnherr, Cyrille Flamant, Patrick Chazette, Julien Totems, Martin Werner, and Myriam Raybaut
Atmos. Chem. Phys., 25, 9471–9495, https://doi.org/10.5194/acp-25-9471-2025, https://doi.org/10.5194/acp-25-9471-2025, 2025
Short summary
Short summary
High-resolution airborne observations reveal that mixing between the free troposphere and surface evapotranspiration flux primarily modulates the water vapor isotopic composition in the lower troposphere. Water vapor isotope structure variations occur on the scale of hundreds of meters, underlining the utility of stable isotopes for studying microscale atmospheric dynamics. This study also provides the basis for better validation of water vapor isotope remote sensing retrievals with surface observations.
Filip Severin von der Lippe, Tim Carlsen, Trude Storelvmo, and Robert Oscar David
EGUsphere, https://doi.org/10.5194/egusphere-2025-3711, https://doi.org/10.5194/egusphere-2025-3711, 2025
Short summary
Short summary
This paper investigates how clouds associated with Arctic marine cold air outbreaks (CAOs) respond to climate change. By utilizing machine learning methods and remote sensing data from the past 25 years, the study identifies trends indicating a shortening of the CAO season. This has implications for the Arctic energy balance, underscoring the importance of further investigating these clouds to understand the trajectory of future Arctic climate.
Gianluca Di Natale, Helen Brindley, Laura Warwick, Sanjeevani Panditharatne, Ping Yang, Robert Oscar David, Tim Carlsen, Sorin Nicolae Vâjâiac, Alex Vlad, Sorin Ghemulet, Richard Bantges, Andreas Foth, Martin Flügge, Reidar Lyngra, Hilke Oetjen, Dirk Schuettemeyer, Luca Palchetti, and Jonathan Murray
EGUsphere, https://doi.org/10.5194/egusphere-2025-3547, https://doi.org/10.5194/egusphere-2025-3547, 2025
Short summary
Short summary
Cirrus clouds play a vital role in regulating the energy balance of our planet. Unfortunately, these are still not completely understood representing the major source of error in the predictive performance of climate models. We show that a good consinstency between in situ measurements of cirrus cloud microphysics and remote sensing observations from ground base is achievable by simulating the emitted spectrum with the current parameterization of cirrus optical properties.
Harald Sodemann
EGUsphere, https://doi.org/10.5194/egusphere-2025-574, https://doi.org/10.5194/egusphere-2025-574, 2025
Short summary
Short summary
The WaterSip software locates regions where precipitation comes from. WaterSip evaluates of the water budget of the air masses, providing information on the conditions during evaporation, transport, and arrival at the target area. WaterSip can be easily configured and writes gridded output files. Guidance is given on where uncertainties arise using a case study, and best practices are recommended. This manuscript supports the comparison of different methods to find precipitation sources.
Tómas Zoëga, Trude Storelvmo, and Kirstin Krüger
Atmos. Chem. Phys., 25, 2989–3010, https://doi.org/10.5194/acp-25-2989-2025, https://doi.org/10.5194/acp-25-2989-2025, 2025
Short summary
Short summary
We use an Earth system model to systematically investigate the climate response to high-latitude effusive volcanic eruptions as a function of eruption season and size, with a focus on the Arctic. We find that different seasons strongly modulate the climate response, with Arctic surface warming observed in winter and cooling in summer. Additionally, as eruptions increase in terms of sulfur dioxide emissions, the climate response becomes increasingly insensitive to variations in emission strength.
Franziska Hellmuth, Tim Carlsen, Anne Sophie Daloz, Robert Oscar David, Haochi Che, and Trude Storelvmo
Atmos. Chem. Phys., 25, 1353–1383, https://doi.org/10.5194/acp-25-1353-2025, https://doi.org/10.5194/acp-25-1353-2025, 2025
Short summary
Short summary
This article compares the occurrence of supercooled liquid-containing clouds (sLCCs) and their link to surface snowfall in CloudSat–CALIPSO, ERA5, and the CMIP6 models. Significant discrepancies were found, with ERA5 and CMIP6 consistently overestimating sLCC and snowfall frequency. This bias is likely due to cloud microphysics parameterization. This conclusion has implications for accurately representing cloud phase and snowfall in future climate projections.
Huiying Zhang, Xia Li, Fabiola Ramelli, Robert O. David, Julie Pasquier, and Jan Henneberger
Atmos. Meas. Tech., 17, 7109–7128, https://doi.org/10.5194/amt-17-7109-2024, https://doi.org/10.5194/amt-17-7109-2024, 2024
Short summary
Short summary
Our innovative IceDetectNet algorithm classifies each part of aggregated ice crystals, considering both their basic shape and physical processes. Trained on ice crystal images from the Arctic taken by a holographic camera, it correctly classifies over 92 % of the ice crystals. These more detailed insights into the components of aggregated ice crystals have the potential to improve our estimates of microphysical properties such as riming rate, aggregation rate, and ice water content.
Ragnhild Bieltvedt Skeie, Magne Aldrin, Terje K. Berntsen, Marit Holden, Ragnar Bang Huseby, Gunnar Myhre, and Trude Storelvmo
Earth Syst. Dynam., 15, 1435–1458, https://doi.org/10.5194/esd-15-1435-2024, https://doi.org/10.5194/esd-15-1435-2024, 2024
Short summary
Short summary
Climate sensitivity and aerosol forcing are central quantities in climate science that are uncertain and contribute to the spread in climate projections. To constrain them, we use observations of temperature and ocean heat content as well as prior knowledge of radiative forcings over the industrialized period. The estimates are sensitive to how aerosol cooling evolved over the latter part of the 20th century, and a strong aerosol forcing trend in the 1960s–1970s is not supported by our analysis.
Andrew W. Seidl, Aina Johannessen, Alena Dekhtyareva, Jannis M. Huss, Marius O. Jonassen, Alexander Schulz, Ove Hermansen, Christoph K. Thomas, and Harald Sodemann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-293, https://doi.org/10.5194/essd-2024-293, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
ISLAS2020 set out to measure the stable water isotopic composition of Arctic moisture. By not only measuring at different sites around Ny-Ålesund, Svalbard, but also measuring at variable heights above surface level, we aim to characterize processes that produce or modify the isotopic composition. We also collect precipitation samples from sites that were typically downstream of Ny-Ålesund, so as to capture the isotopic composition during removal from the atmospheric water cycle.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Britta Schäfer, Robert Oscar David, Paraskevi Georgakaki, Julie Thérèse Pasquier, Georgia Sotiropoulou, and Trude Storelvmo
Atmos. Chem. Phys., 24, 7179–7202, https://doi.org/10.5194/acp-24-7179-2024, https://doi.org/10.5194/acp-24-7179-2024, 2024
Short summary
Short summary
Mixed-phase clouds, i.e., clouds consisting of ice and supercooled water, are very common in the Arctic. However, how these clouds form is often not correctly represented in standard weather models. We show that both ice crystal concentrations in the cloud and precipitation from the cloud can be improved in the model when aerosol concentrations are prescribed from observations and when more processes for ice multiplication, i.e., the production of new ice particles from existing ice, are added.
Idunn Aamnes Mostue, Stefan Hofer, Trude Storelvmo, and Xavier Fettweis
The Cryosphere, 18, 475–488, https://doi.org/10.5194/tc-18-475-2024, https://doi.org/10.5194/tc-18-475-2024, 2024
Short summary
Short summary
The latest generation of climate models (Coupled Model Intercomparison Project Phase 6 – CMIP6) warm more over Greenland and the Arctic and thus also project a larger mass loss from the Greenland Ice Sheet (GrIS) compared to the previous generation of climate models (CMIP5). Our work suggests for the first time that part of the greater mass loss in CMIP6 over the GrIS is driven by a difference in the surface mass balance sensitivity from a change in cloud representation in the CMIP6 models.
Ghislain Motos, Gabriel Freitas, Paraskevi Georgakaki, Jörg Wieder, Guangyu Li, Wenche Aas, Chris Lunder, Radovan Krejci, Julie Thérèse Pasquier, Jan Henneberger, Robert Oscar David, Christoph Ritter, Claudia Mohr, Paul Zieger, and Athanasios Nenes
Atmos. Chem. Phys., 23, 13941–13956, https://doi.org/10.5194/acp-23-13941-2023, https://doi.org/10.5194/acp-23-13941-2023, 2023
Short summary
Short summary
Low-altitude clouds play a key role in regulating the climate of the Arctic, a region that suffers from climate change more than any other on the planet. We gathered meteorological and aerosol physical and chemical data over a year and utilized them for a parameterization that help us unravel the factors driving and limiting the efficiency of cloud droplet formation. We then linked this information to the sources of aerosol found during each season and to processes of cloud glaciation.
Harald Sodemann, Alena Dekhtyareva, Alvaro Fernandez, Andrew Seidl, and Jenny Maccali
Atmos. Meas. Tech., 16, 5181–5203, https://doi.org/10.5194/amt-16-5181-2023, https://doi.org/10.5194/amt-16-5181-2023, 2023
Short summary
Short summary
We describe a device that allows one to produce a continuous stream of water vapour with a specified level of humidity. As a main innovation, we can mix waters with different water isotope composition. Through a series of tests we show that the performance characteristics of the device are in line with specifications. We present two laboratory applications where the device proves useful, first in characterizing instruments, and second for the analysis of water contained in stalagmites.
Casey J. Wall, Trude Storelvmo, and Anna Possner
Atmos. Chem. Phys., 23, 13125–13141, https://doi.org/10.5194/acp-23-13125-2023, https://doi.org/10.5194/acp-23-13125-2023, 2023
Short summary
Short summary
Interactions between aerosol pollution and liquid clouds are one of the largest sources of uncertainty in the effective radiative forcing of climate over the industrial era. We use global satellite observations to decompose the forcing into components from changes in cloud-droplet number concentration, cloud water content, and cloud amount. Our results reduce uncertainty in these forcing components and clarify their relative importance.
Astrid Fremme, Paul J. Hezel, Øyvind Seland, and Harald Sodemann
Weather Clim. Dynam., 4, 449–470, https://doi.org/10.5194/wcd-4-449-2023, https://doi.org/10.5194/wcd-4-449-2023, 2023
Short summary
Short summary
We study the atmospheric moisture transport into eastern China for past, present, and future climate. Hence, we use different climate and weather prediction model data with a moisture source identification method. We find that while the moisture to first order originates mostly from similar regions, smaller changes consistently point to differences in the recycling of precipitation over land between different climates. Some differences are larger between models than between different climates.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, https://doi.org/10.5194/gmd-16-1735-2023, 2023
Short summary
Short summary
Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth system models. These updates include the ability to run the scheme on graphics processing units (GPUs), changes to the numerical description of precipitation, and a correction to the ice number. There are big improvements in the computational performance that can be achieved with GPU acceleration.
Andrew W. Seidl, Harald Sodemann, and Hans Christian Steen-Larsen
Atmos. Meas. Tech., 16, 769–790, https://doi.org/10.5194/amt-16-769-2023, https://doi.org/10.5194/amt-16-769-2023, 2023
Short summary
Short summary
It is challenging to make field measurements of stable water isotopes in the Arctic. To this end, we present a modular stable-water-isotope analyzer profiling system. The system operated for a 2-week field campaign on Svalbard during the Arctic winter. We evaluate the system’s performance and analyze any potential impact that the field conditions might have had on the isotopic measurements and the system's ability to resolve isotope gradients in the lowermost layer of the atmosphere.
Julie Thérèse Pasquier, Jan Henneberger, Fabiola Ramelli, Annika Lauber, Robert Oscar David, Jörg Wieder, Tim Carlsen, Rosa Gierens, Marion Maturilli, and Ulrike Lohmann
Atmos. Chem. Phys., 22, 15579–15601, https://doi.org/10.5194/acp-22-15579-2022, https://doi.org/10.5194/acp-22-15579-2022, 2022
Short summary
Short summary
It is important to understand how ice crystals and cloud droplets form in clouds, as their concentrations and sizes determine the exact radiative properties of the clouds. Normally, ice crystals form from aerosols, but we found evidence for the formation of additional ice crystals from the original ones over a large temperature range within Arctic clouds. In particular, additional ice crystals were formed during collisions of several ice crystals or during the freezing of large cloud droplets.
Britta Schäfer, Tim Carlsen, Ingrid Hanssen, Michael Gausa, and Trude Storelvmo
Atmos. Chem. Phys., 22, 9537–9551, https://doi.org/10.5194/acp-22-9537-2022, https://doi.org/10.5194/acp-22-9537-2022, 2022
Short summary
Short summary
Cloud properties are important for the surface radiation budget. This study presents cold-cloud observations based on lidar measurements from the Norwegian Arctic between 2011 and 2017. Using statistical assessments and case studies, we give an overview of the macro- and microphysical properties of these clouds and demonstrate the capabilities of long-term cloud observations in the Norwegian Arctic from the ground-based lidar at Andenes.
Sebastian Becker, André Ehrlich, Evelyn Jäkel, Tim Carlsen, Michael Schäfer, and Manfred Wendisch
Atmos. Meas. Tech., 15, 2939–2953, https://doi.org/10.5194/amt-15-2939-2022, https://doi.org/10.5194/amt-15-2939-2022, 2022
Short summary
Short summary
Airborne radiation measurements are used to characterize the solar directional reflection of a mixture of Arctic sea ice and open-ocean surfaces in the transition zone between both surface types. The mixture reveals reflection properties of both surface types. It is shown that the directional reflection of the mixture can be reconstructed from the directional reflection of the individual surfaces, accounting for the special conditions present in the transition zone.
Sorin Nicolae Vâjâiac, Andreea Calcan, Robert Oscar David, Denisa-Elena Moacă, Gabriela Iorga, Trude Storelvmo, Viorel Vulturescu, and Valeriu Filip
Atmos. Meas. Tech., 14, 6777–6794, https://doi.org/10.5194/amt-14-6777-2021, https://doi.org/10.5194/amt-14-6777-2021, 2021
Short summary
Short summary
Warm clouds (with liquid droplets) play an important role in modulating the amount of incoming solar radiation to Earth’s surface and thus the climate. The most efficient way to study them is by in situ optical measurements. This paper proposes a new methodology for providing more detailed and reliable structural analyses of warm clouds through post-flight processing of collected data. The impact fine aerosol incorporation in water droplets might have on such measurements is also discussed.
Jonas Hamperl, Clément Capitaine, Jean-Baptiste Dherbecourt, Myriam Raybaut, Patrick Chazette, Julien Totems, Bruno Grouiez, Laurence Régalia, Rosa Santagata, Corinne Evesque, Jean-Michel Melkonian, Antoine Godard, Andrew Seidl, Harald Sodemann, and Cyrille Flamant
Atmos. Meas. Tech., 14, 6675–6693, https://doi.org/10.5194/amt-14-6675-2021, https://doi.org/10.5194/amt-14-6675-2021, 2021
Short summary
Short summary
Laser active remote sensing of tropospheric water vapor is a promising technology for enhancing our understanding of processes governing the global hydrological cycle. We investigate the potential of a ground-based lidar to monitor the main water vapor isotopes at high spatio-temporal resolutions in the lower troposphere. Using a realistic end-to-end simulator, we show that high-precision measurements can be achieved within a range of 1.5 km, in mid-latitude or tropical environments.
Yongbiao Weng, Aina Johannessen, and Harald Sodemann
Weather Clim. Dynam., 2, 713–737, https://doi.org/10.5194/wcd-2-713-2021, https://doi.org/10.5194/wcd-2-713-2021, 2021
Short summary
Short summary
High-resolution measurements of stable isotopes in near-surface vapour and precipitation show a
W-shaped evolution during a 24 h land-falling atmospheric river event in southern Norway. We distinguish contributions from below-cloud processes, weather system characteristics, and moisture source conditions during different stages of the event. Rayleigh distillation models need to be expanded by additional processes to accurately predict isotopes in surface precipitation from stratiform clouds.
Patrick Chazette, Cyrille Flamant, Harald Sodemann, Julien Totems, Anne Monod, Elsa Dieudonné, Alexandre Baron, Andrew Seidl, Hans Christian Steen-Larsen, Pascal Doira, Amandine Durand, and Sylvain Ravier
Atmos. Chem. Phys., 21, 10911–10937, https://doi.org/10.5194/acp-21-10911-2021, https://doi.org/10.5194/acp-21-10911-2021, 2021
Short summary
Short summary
To gain understanding on the vertical structure of atmospheric water vapour above mountain lakes and to assess its link to the isotopic composition of the lake water and small-scale dynamics, the L-WAIVE field campaign was conducted in the Annecy valley in the French Alps in June 2019. Based on a synergy between ground-based, boat-borne, and airborne measuring platforms, significant gradients of isotopic content have been revealed at the transitions to the lake and to the free troposphere.
Fabiola Ramelli, Jan Henneberger, Robert O. David, Johannes Bühl, Martin Radenz, Patric Seifert, Jörg Wieder, Annika Lauber, Julie T. Pasquier, Ronny Engelmann, Claudia Mignani, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys., 21, 6681–6706, https://doi.org/10.5194/acp-21-6681-2021, https://doi.org/10.5194/acp-21-6681-2021, 2021
Short summary
Short summary
Orographic mixed-phase clouds are an important source of precipitation, but the ice formation processes within them remain uncertain. Here we investigate the origin of ice crystals in a mixed-phase cloud in the Swiss Alps using aerosol and cloud data from in situ and remote sensing observations. We found that ice formation primarily occurs in cloud top generating cells. Our results indicate that secondary ice processes are active in the feeder region, which can enhance orographic precipitation.
Anna J. Miller, Killian P. Brennan, Claudia Mignani, Jörg Wieder, Robert O. David, and Nadine Borduas-Dedekind
Atmos. Meas. Tech., 14, 3131–3151, https://doi.org/10.5194/amt-14-3131-2021, https://doi.org/10.5194/amt-14-3131-2021, 2021
Short summary
Short summary
To characterize atmospheric ice nuclei, we present (1) the development of our home-built droplet freezing technique (DFT), which involves the Freezing Ice Nuclei Counter (FINC), (2) an intercomparison campaign using NX-illite and an ambient sample with two other DFTs, and (3) the application of lignin as a soluble and commercial ice nuclei standard with three DFTs. We further compiled the growing number of DFTs in use for atmospheric ice nucleation since 2000 and add FINC.
Maxi Boettcher, Andreas Schäfler, Michael Sprenger, Harald Sodemann, Stefan Kaufmann, Christiane Voigt, Hans Schlager, Donato Summa, Paolo Di Girolamo, Daniele Nerini, Urs Germann, and Heini Wernli
Atmos. Chem. Phys., 21, 5477–5498, https://doi.org/10.5194/acp-21-5477-2021, https://doi.org/10.5194/acp-21-5477-2021, 2021
Short summary
Short summary
Warm conveyor belts (WCBs) are important airstreams in extratropical cyclones, often leading to the formation of intense precipitation. We present a case study that involves aircraft, lidar and radar observations of water and clouds in a WCB ascending from western Europe across the Alps towards the Baltic Sea during the field campaigns HyMeX and T-NAWDEX-Falcon in October 2012. A probabilistic trajectory measure and an airborne tracer experiment were used to confirm the long pathway of the WCB.
Fabiola Ramelli, Jan Henneberger, Robert O. David, Annika Lauber, Julie T. Pasquier, Jörg Wieder, Johannes Bühl, Patric Seifert, Ronny Engelmann, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys., 21, 5151–5172, https://doi.org/10.5194/acp-21-5151-2021, https://doi.org/10.5194/acp-21-5151-2021, 2021
Short summary
Short summary
Interactions between dynamics, microphysics and orography can enhance precipitation. Yet the exact role of these interactions is still uncertain. Here we investigate the role of low-level blocking and turbulence for precipitation by combining remote sensing and in situ observations. The observations show that blocked flow can induce the formation of feeder clouds and that turbulence can enhance hydrometeor growth, demonstrating the importance of local flow effects for orographic precipitation.
Evelyn Jäkel, Tim Carlsen, André Ehrlich, Manfred Wendisch, Michael Schäfer, Sophie Rosenburg, Konstantina Nakoudi, Marco Zanatta, Gerit Birnbaum, Veit Helm, Andreas Herber, Larysa Istomina, Linlu Mei, and Anika Rohde
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-14, https://doi.org/10.5194/tc-2021-14, 2021
Preprint withdrawn
Short summary
Short summary
Different approaches to retrieve the optical-equivalent snow grain size using satellite, airborne, and ground-based observations were evaluated and compared to modeled data. The study is focused on low Sun and partly rough surface conditions encountered North of Greenland in March/April 2018. We proposed an adjusted airborne retrieval method to reduce the retrieval uncertainty.
Kine Onsum Moseid, Michael Schulz, Trude Storelvmo, Ingeborg Rian Julsrud, Dirk Olivié, Pierre Nabat, Martin Wild, Jason N. S. Cole, Toshihiko Takemura, Naga Oshima, Susanne E. Bauer, and Guillaume Gastineau
Atmos. Chem. Phys., 20, 16023–16040, https://doi.org/10.5194/acp-20-16023-2020, https://doi.org/10.5194/acp-20-16023-2020, 2020
Short summary
Short summary
In this study we compare solar radiation at the surface from observations and Earth system models from 1961 to 2014. We find that the models do not reproduce the so-called
global dimmingas found in observations. Only model experiments with anthropogenic aerosol emissions display any dimming at all. The discrepancies between observations and models are largest in China, which we suggest is in part due to erroneous aerosol precursor emission inventories in the emission dataset used for CMIP6.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Veit Helm, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, https://doi.org/10.5194/tc-14-3959-2020, 2020
Short summary
Short summary
The angular reflection of solar radiation by snow surfaces is particularly anisotropic and highly variable. We measured the angular reflection from an aircraft using a digital camera in Antarctica in 2013/14 and studied its variability: the anisotropy increases with a lower Sun but decreases for rougher surfaces and larger snow grains. The applied methodology allows for a direct comparison with satellite observations, which generally underestimated the anisotropy measured within this study.
Cited articles
Al-Naimi, R. and Saunders, C.: Measurements of natural deposition and condensation-freezing ice nuclei with a continuous flow chamber, Atmos. Environ., 19, 1871–1882, https://doi.org/10.1016/0004-6981(85)90012-5, 1985. a
Ansmann, A., Tesche, M., Seifert, P., Althausen, D., Engelmann, R., Fruntke, J., Wandinger, U., Mattis, I., and Müller, D.: Evolution of the ice phase in tropical altocumulus: SAMUM lidar observations over Cape Verde, J. Geophys. Res.-Atmos., 114, D17208, https://doi.org/10.1029/2008JD011659, 2009. a
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013. a
Bergeron, T.: On the physics of cloud and precipitation, Proc. 5th Assembly U.G.G.I., 17–24 September 1933, Lisbon, Portugal, 2, 156–180, 1935. 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, b, c, d
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. a, b
Boer, G., Morrison, H., Shupe, M., and Hildner, R.: Evidence of liquid dependent ice nucleation in high-latitude stratiform clouds from surface remote sensors, Geophys. Res. Lett., 38, L01803, https://doi.org/10.1029/2010GL046016, 2011. a
Bruno, O.: Distributions of supercooled liquid fraction from CALIOP V4, Zenodo [data set], https://doi.org/10.5281/zenodo.8289057, 2022. a
Bruno, O., Hoose, C., Storelvmo, T., Coopman, Q., and Stengel, M.: Exploring the Cloud Top Phase Partitioning in Different Cloud Types Using Active and Passive Satellite Sensors, Geophys. Res. Lett., 48, e2020GL089863, https://doi.org/10.1029/2020GL089863, 2021. a
Carlsen, T. and David, R. O.: Spaceborne evidence that ice-nucleating particles influence high-latitude cloud phase, Geophys. Res. Lett., 49, e2022GL098041, https://doi.org/10.1029/2022GL098041, 2022. a, b
Ceppi, P., Hartmann, D. L., and Webb, M. J.: Mechanisms of the Negative Shortwave Cloud Feedback in Middle to High Latitudes, J. Climate, 29, 139–157, https://doi.org/10.1175/JCLI-D-15-0327.1, 2016. a
Cesana, G. and Chepfer, H.: Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP, J. Geophys. Res.-Atmos., 118, 7922–7937, https://doi.org/10.1002/jgrd.50376, 2013 (data available at: https://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html#MapLowMidHighphase, last access: 30 September 2024). a
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P., Stubenrauch, C. J., and Zeng, S.: The GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP), J. Geophys. Res.-Atmos., 115, D00H16, https://doi.org/10.1029/2009JD012251, 2010. a, b
Cox, C. J. and Halliwell, D.: Basic measurements of radiation at station Alert (2004-08–2014-06), AeroCan, Wilcox, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.932867, 2021. a, b
Crameri, F.: Scientific colour maps (8.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.8409685, 2023. a
Creamean, J. M., Cross, J. N., Pickart, R., McRaven, L., Lin, P., Pacini, A., Hanlon, R., Schmale, D. G., Ceniceros, J., Aydell, T., Colombi, N., Bolger, E., and DeMott, P. J.: Ice Nucleating Particles Carried From Below a Phytoplankton Bloom to the Arctic Atmosphere, Geophys. Res. Lett., 46, 8572–8581, https://doi.org/10.1029/2019GL083039, 2019. a, b
Creamean, J. M., Barry, K., Hill, T. C. J., Hume, C., DeMott, P. J., Shupe, M. D., Dahlke, S., Willmes, S., Schmale, J., Beck, I., Hoppe, C. J. M., Fong, A., Chamberlain, E., Bowman, J., Scharien, R., and Persson, O.: Annual cycle observations of aerosols capable of ice formation in central Arctic clouds, Nat. Commun., 13, 3537, https://doi.org/10.1038/s41467-022-31182-x, 2022. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sys., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
David, R. O., Cascajo-Castresana, M., Brennan, K. P., Rösch, M., Els, N., Werz, J., Weichlinger, V., Boynton, L. S., Bogler, S., Borduas-Dedekind, N., Marcolli, C., and Kanji, Z. A.: Development of the DRoplet Ice Nuclei Counter Zurich (DRINCZ): validation and application to field-collected snow samples, Atmos. Meas. Tech., 12, 6865–6888, https://doi.org/10.5194/amt-12-6865-2019, 2019. a, b, c
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b
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., Hill, T. C. J., McCluskey, C. S., Prather, K. A., Collins, D. B., Sullivan, R. C., Ruppel, M. J., Mason, R. H., Irish, V. E., Lee, T., Hwang, C. Y., Rhee, T. S., Snider, J. R., McMeeking, G. R., Dhaniyala, S., Lewis, E. R., Wentzell, J. J. B., Abbatt, J., Lee, C., Sultana, C. M., Ault, A. P., Axson, J. L., Martinez, M. D., Venero, I., Santos-Figueroa, G., Stokes, M. D., Deane, G. B., Mayol-Bracero, O. L., Grassian, V. H., Bertram, T. H., Bertram, A. K., Moffett, B. F., and Franc, G. D.: Sea spray aerosol as a unique source of ice nucleating particles, P. Natl. Acad. Sci. USA, 113, 5797–5803, https://doi.org/10.1073/pnas.1514034112, 2016. a
English, J. M., Kay, J. E., Gettelman, A., Liu, X., Wang, Y., Zhang, Y., and Chepfer, H.: Contributions of Clouds, Surface Albedos, and Mixed-Phase Ice Nucleation Schemes to Arctic Radiation Biases in CAM5, J. Climate, 27, 5174–5197, https://doi.org/10.1175/JCLI-D-13-00608.1, 2014. a, b
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Findeisen, W.: Kolloid-meteorologische Vorgänge bei Neiderschlags-bildung, Meteorol. Z., 55, 121–133, 1938. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., M.Watanabe, Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. a, b, c, d
Geerts, B., Giangrande, S. E., McFarquhar, G. M., Xue, L., Abel, S. J., Comstock, J. M., Crewell, S., DeMott, P. J., Ebell, K., Field, P., Hill, T. C. J., Hunzinger, A., Jensen, M. P., Johnson, K. L., Juliano, T. W., Kollias, P., Kosovic, B., Lackner, C., Luke, E., Lüpkes, C., Matthews, A. A., Neggers, R., Ovchinnikov, M., Powers, H., Shupe, M. D., Spengler, T., Swanson, B. E., Tjernström, M., Theisen, A. K., Wales, N. A., Wang, Y., Wendisch, M., and Wu, P.: The COMBLE Campaign: A Study of Marine Boundary Layer Clouds in Arctic Cold-Air Outbreaks, B. Am. Meteorol. Soc., 103, E1371–E1389, https://doi.org/10.1175/BAMS-D-21-0044.1, 2022. a, b, c, d, e, f
Gettelman, A. and Morrison, H.: Advanced Two-Moment Microphysics for Global Models. Part I: Off line tests and comparisons with other schemes, J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015. a
Gettelman, A., Morrison, H., Eidhammer, T., Thayer-Calder, K., Sun, J., Forbes, R., McGraw, Z., Zhu, J., Storelvmo, T., and Dennis, J.: Importance of ice nucleation and precipitation on climate with the Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1), Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, 2023. a, b
Gjelsvik, A. B.: Ice Nucleating Particles in Arctic Clouds and Their Impact on Climate, Master's thesis, University of Oslo, http://urn.nb.no/URN:NBN:no-98231, 2022. a
Gjelsvik, A. B., David, R. O., Carlsen, T., Hellmuth, F., McGraw, Z., Hofer, S., Sodemann, H., Thurnherr, I., and Storelvmo, T.: Ice-Nucleating Particle Concentrations from the MC2/ISLAS 2021 campaign in Andenes, and NorESM2 simulations with observationally constrained INPs, Zenodo [data set], https://doi.org/10.5281/zenodo.11617774, 2024. a
Gu, M., Wang, Z., Wei, J., and Yu, X.: An assessment of Arctic cloud water paths in atmospheric reanalyses, Acta Oceanol. Sin., 40, 46–57, https://doi.org/10.1007/s13131-021-1706-5, 2021. a
Hahn, L. C., Armour, K. C., Zelinka, M. D., Bitz, C. M., and Donohoe, A.: Contributions to Polar Amplification in CMIP5 and CMIP6 Models, Front. Earth Sci., 9, 710036, https://doi.org/10.3389/feart.2021.710036, 2021. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023. a, b
Hofer, S., Hahn, L. C., Shaw, J. K., McGraw, Z. S., Bruno, O., Hellmuth, F., Pietschnig, M., Mostue, I. A., David, R. O., Carlsen, T., and Storelvmo, T.: Realistic representation of mixed-phase clouds increases projected climate warming, Commun. Earth Environ., 5, 390, https://doi.org/10.1038/s43247-024-01524-2, 2024. 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
Huang, W. T. K., Ickes, L., Tegen, I., Rinaldi, M., Ceburnis, D., and Lohmann, U.: Global relevance of marine organic aerosol as ice nucleating particles, Atmos. Chem. Phys., 18, 11423–11445, https://doi.org/10.5194/acp-18-11423-2018, 2018. a
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013. a
Iversen, T., Bentsen, M., Bethke, I., Debernard, J. B., Kirkevåg, A., Seland, Ø., Drange, H., Kristjansson, J. E., Medhaug, I., Sand, M., and Seierstad, I. A.: The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections, Geosci. Model Dev., 6, 389–415, https://doi.org/10.5194/gmd-6-389-2013, 2013. 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, b, c
Kanji, Z. A., Welti, A., Corbin, J. C., and Mensah, A. A.: Black Carbon Particles Do Not Matter for Immersion Mode Ice Nucleation, Geophys. Res. Lett., 47, e2019GL086764, https://doi.org/10.1029/2019GL086764, 2020. a
Kartverket: Andenes map, Kartverket, http://norgeskart.no (last access: 26 April 2022), 2022. a
Kawai, K., Matsui, H., and Tobo, Y.: Dominant Role of Arctic Dust With High Ice Nucleating Ability in the Arctic Lower Troposphere, Geophys. Res. Lett., 50, e2022GL102470, https://doi.org/10.1029/2022GL102470, 2023. a, b
Khanal, S., Wang, Z., and French, J. R.: Improving middle and high latitude cloud liquid water path measurements from MODIS, Atmos. Res., 243, 105033, https://doi.org/10.1016/j.atmosres.2020.105033, 2020. a
Kirkevåg, A., Iversen, T., Seland, Ø., Hoose, C., Kristjánsson, J. E., Struthers, H., Ekman, A. M. L., Ghan, S., Griesfeller, J., Nilsson, E. D., and Schulz, M.: Aerosol–climate interactions in the Norwegian Earth System Model – NorESM1-M, Geosci. Model Dev., 6, 207–244, https://doi.org/10.5194/gmd-6-207-2013, 2013. a
Kirkevåg, A., Grini, A., Olivié, D., Seland, Ø., Alterskjær, K., Hummel, M., Karset, I. H. H., Lewinschal, A., Liu, X., Makkonen, R., Bethke, I., Griesfeller, J., Schulz, M., and Iversen, T.: A production-tagged aerosol module for Earth system models, OsloAero5.3 – extensions and updates for CAM5.3-Oslo, Geosci. Model Dev., 11, 3945–3982, https://doi.org/10.5194/gmd-11-3945-2018, 2018. a, b
Knopf, D. A., Wang, P., Wong, B., Tomlin, J. M., Veghte, D. P., Lata, N. N., China, S., Laskin, A., Moffet, R. C., Aller, J. Y., Marcus, M. A., and Wang, J.: Physicochemical characterization of free troposphere and marine boundary layer ice-nucleating particles collected by aircraft in the eastern North Atlantic, Atmos. Chem. Phys., 23, 8659–8681, https://doi.org/10.5194/acp-23-8659-2023, 2023. a
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018. a
Maturilli, M.: Basic and other measurements of radiation at station Ny-Ålesund (2006-05 et seq), Alfred Wegener Institute – Research Unit Potsdam, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.914927, 2020. a, b
McArthur, L.: Baseline Surface Radiation Network (BSRN), Operations Manual Version 2.1, World Climate Research Programme, https://bsrn.awi.de/fileadmin/user_upload/bsrn.awi.de/Publications/McArthur.pdf (last access: 12 June 2024), 2005. a
McCluskey, C. S., DeMott, P. J., Ma, P.-L., and Burrows, S. M.: Numerical Representations of Marine Ice-Nucleating Particles in Remote Marine Environments Evaluated Against Observations, Geophys. Res. Lett., 46, 7838–7847, https://doi.org/10.1029/2018GL081861, 2019. a
Miller, A. J., Brennan, K. P., Mignani, C., Wieder, J., David, R. O., and Borduas-Dedekind, N.: Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard, Atmos. Meas. Tech., 14, 3131–3151, https://doi.org/10.5194/amt-14-3131-2021, 2021. a, b
Moore, K. A., Hill, T. C. J., McCluskey, C. S., Twohy, C. H., Rainwater, B., Toohey, D. W., Sanchez, K. J., Kreidenweis, S. M., and DeMott, P. J.: Characterizing Ice Nucleating Particles Over the Southern Ocean Using Simultaneous Aircraft and Ship Observations, J. Geophys. Res.-Atmos., 129, e2023JD039543, https://doi.org/10.1029/2023JD039543, 2024. a
Murray, B. J., Carslaw, K. S., and Field, P. R.: Opinion: Cloud-phase climate feedback and the importance of ice-nucleating particles, Atmos. Chem. Phys., 21, 665–679, https://doi.org/10.5194/acp-21-665-2021, 2021. a, b, c
NASA: CERES_EBAF_Ed4.1 Data Quality Summary, NASA, https://ceres.larc.nasa.gov/documents/DQ_summaries/CERES_EBAF_Ed4.1_DQS.pdf (last access: 12 June 2024), 2021. a
NASA/LARC/SD/ASDC: CERES Energy Balanced and Filled (EBAF) TOA and Surface Monthly means data in netCDF Edition 4.1, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/TERRA-AQUA/CERES/EBAF_L3B.004.1, 2019. a
Norwegian Meteorological Institute: Andøya air pressure at sea level, MET Norway [data set], https://thredds.met.no/thredds/catalog/met.no/observations/surface/87110/178/catalog.html, last access: 12 June 2021. a
Pereira Freitas, G., Adachi, K., Conen, F., Heslin-Rees, D., Krejci, R., Tobo, Y., Yttri, K. E., and Zieger, P.: Regionally sourced bioaerosols drive high-temperature ice nucleating particles in the Arctic, Nat. Commun., 14, 5997, https://doi.org/10.1038/s41467-023-41696-7, 2023. a, b
Prenni, A. J., Harrington, J. Y., Tjernström, M., DeMott, P. J., Avramov, A., Long, C. N., Kreidenweis, S. M., Olsson, P. Q., and Verlinde, J.: Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?, B. Am. Meteorol. Soc., 88, 541–550, https://doi.org/10.1175/BAMS-88-4-541, 2007. a, b
Raif, E. N., Barr, S. L., Tarn, M. D., McQuaid, J. B., Daily, M. I., Abel, S. J., Barrett, P. A., Bower, K. N., Field, P. R., Carslaw, K. S., and Murray, B. J.: High ice-nucleating particle concentrations associated with Arctic haze in springtime cold-air outbreaks, Atmos. Chem. Phys., 24, 14045–14072, https://doi.org/10.5194/acp-24-14045-2024, 2024. a, b
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Commun. Earth Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3, 2022. a, b
Riihimaki, L., Long, C., and Dutton, E. G.: Basic and other measurements of radiation at station Barrow (1992-01 et seq), NOAA Global Monitoring Laboratory, Boulder, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.959215, 2023. a, b
Rogers, D. C.: Field and laboratory studies of ice nucleation in winter orogpahic clouds, University of Wyoming, ISBN 979-8-204-35194-3, 1982. a
Šantl-Temkiv, T., Lange, R., Beddows, D., Rauter, U., Pilgaard, S., Dall'Osto, M., Gunde-Cimerman, N., Massling, A., and Wex, H.: Biogenic Sources of Ice Nucleating Particles at the High Arctic Site Villum Research Station, Environ. Sci. Technol., 53, 10580–10590, https://doi.org/10.1021/acs.est.9b00991, 2019. a
Schill, G. P., DeMott, P. J., Emerson, E. W., Rauker, A. M. C., Kodros, J. K., Suski, K. J., Hill, T. C. J., Levin, E. J. T., Pierce, J. R., Farmer, D. K., and Kreidenweis, S. M.: The contribution of black carbon to global ice nucleating particle concentrations relevant to mixed-phase clouds, P. Natl. Acad. Sci. USA, 117, 22705–22711, https://doi.org/10.1073/pnas.2001674117, 2020. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Gao, S., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moree, A., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NorESM2 source code as used for CMIP6 simulations (includes additional experimental setups, extended model documentation, automated inputdata download, restructuring of BLOM/iHAMOCC input data), Zenodo [code], https://doi.org/10.5281/zenodo.3905091, 2020a. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020b. a, b, c
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification: A research synthesis, Global Planet. Change, 77, 85–96, https://doi.org/10.1016/j.gloplacha.2011.03.004, 2011. a
Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., and Hofer, S.: Using Satellite Observations to Evaluate Model Microphysical Representation of Arctic Mixed-Phase Clouds, Geophys. Res. Lett., 49, e2021GL096191, https://doi.org/10.1029/2021GL096191, 2022. a, b, c, d
Shi, Y., Liu, X., Wu, M., Zhao, X., Ke, Z., and Brown, H.: Relative importance of high-latitude local and long-range-transported dust for Arctic ice-nucleating particles and impacts on Arctic mixed-phase clouds, Atmos. Chem. Phys., 22, 2909–2935, https://doi.org/10.5194/acp-22-2909-2022, 2022. a, b, c, d
Sotiropoulou, G., Lewinschal, A., Georgakaki, P., Phillips, V. T. J., Patade, S., Ekman, A. M. L., and Nenes, A.: Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model, J. Climate, 37, 4275–4290, https://doi.org/10.1175/JCLI-D-22-0458.1, 2024. a, b
Sprenger, M. and Wernli, H.: The LAGRANTO Lagrangian analysis tool – version 2.0, Geosci. Model Dev., 8, 2569–2586, https://doi.org/10.5194/gmd-8-2569-2015, 2015. a
Stephens, G. L.: Radiation Profiles in Extended Water Clouds. II: Parameterization Schemes, J. Atmos. Sci., 35, 2123–2132, https://doi.org/10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2, 1978. a
Storelvmo, T. and Tan, I.: The Wegener-Bergeron-Findeisen process – Its discovery and vital importance for weather and climate, Meteorol. Z., 24, 455–461, https://doi.org/10.1127/metz/2015/0626, 2015. a
Sze, K. C. H., Wex, H., Hartmann, M., Skov, H., Massling, A., Villanueva, D., and Stratmann, F.: Ice-nucleating particles in northern Greenland: annual cycles, biological contribution and parameterizations, Atmos. Chem. Phys., 23, 4741–4761, https://doi.org/10.5194/acp-23-4741-2023, 2023. a, b, c, d, e
Tan, I. and Storelvmo, T.: Evidence of Strong Contributions From Mixed-Phase Clouds to Arctic Climate Change, Geophys. Res. Lett., 46, 2894–2902, https://doi.org/10.1029/2018GL081871, 2019. a, b
Tan, I., Storelvmo, T., and Zelinka, M. D.: Observational constraints on mixed-phase clouds imply higher climate sensitivity, Science, 352, 224–227, https://doi.org/10.1126/science.aad5300, 2016. a, b
Taylor, P. C., Boeke, R. C., Boisvert, L. N., Feldl, N., Henry, M., Huang, Y., Langen, P. L., Liu, W., Pithan, F., Sejas, S. A., and Tan, I.: Process Drivers, Inter-Model Spread, and the Path Forward: A Review of Amplified Arctic Warming, Front. Earth Sci., 9, 758361, https://doi.org/10.3389/feart.2021.758361, 2022. a, b, c
Tobo, Y., Adachi, K., DeMott, P. J., Hill, T. C. J., Hamilton, D. S., Mahowald, N. M., Nagatsuka, N., Ohata, S., Uetake, J., Kondo, Y., and Koike, M.: Glacially sourced dust as a potentially significant source of ice nucleating particles, Nat. Geosci., 12, 253–258, https://doi.org/10.1038/s41561-019-0314-x, 2019. a, b
Tobo, Y., Adachi, K., Kawai, K., Matsui, H., Ohata, S., Oshima, N., Kondo, Y., Hermansen, O., Uchida, M., Inoue, J., and Koike, M.: Surface warming in Svalbard may have led to increases in highly active ice-nucleating particles, Commun. Earth Environ., 5, 516, https://doi.org/10.1038/s43247-024-01677-0, 2024. a
Toniazzo, T., Bentsen, M., Craig, C., Eaton, B. E., Edwards, J., Goldhaber, S., Jablonowski, C., and Lauritzen, P. H.: Enforcing conservation of axial angular momentum in the atmospheric general circulation model CAM6, Geosci. Model Dev., 13, 685–705, https://doi.org/10.5194/gmd-13-685-2020, 2020. a
Vali, G.: Quantitative Evaluation of Experimental Results an the Heterogeneous Freezing Nucleation of Supercooled Liquids, J. Atmos. Sci., 28, 402–409, https://doi.org/10.1175/1520-0469(1971)028<0402:QEOERA>2.0.CO;2, 1971. a, b
Vergara-Temprado, J., Murray, B. J., Wilson, T. W., O'Sullivan, D., Browse, J., Pringle, K. J., Ardon-Dryer, K., Bertram, A. K., Burrows, S. M., Ceburnis, D., DeMott, P. J., Mason, R. H., O'Dowd, C. D., Rinaldi, M., and Carslaw, K. S.: Contribution of feldspar and marine organic aerosols to global ice nucleating particle concentrations, Atmos. Chem. Phys., 17, 3637–3658, https://doi.org/10.5194/acp-17-3637-2017, 2017. a, b
Vergara-Temprado, J., Holden, M. A., Orton, T. R., O'Sullivan, D., Umo, N. S., Browse, J., Reddington, C., Baeza-Romero, M. T., Jones, J. M., Lea-Langton, A., Williams, A., Carslaw, K. S., and Murray, B. J.: Is Black Carbon an Unimportant Ice-Nucleating Particle in Mixed-Phase Clouds?, J. Geophys. Res.-Atmos., 123, 4273–4283, https://doi.org/10.1002/2017JD027831, 2018. a
Wegener, A.: Thermodynamik der Atmosphäre, Barth, Leipzig, 1911. a
Westbrook, C. D. and Illingworth, A. J.: Evidence that ice forms primarily in supercooled liquid clouds at temperatures >−27 °C, Geophys. Res. Lett., 38, L14808, https://doi.org/10.1029/2011GL048021, 2011. a
Wex, H., Huang, L., Zhang, W., Hung, H., Traversi, R., Becagli, S., Sheesley, R. J., Moffett, C. E., Barrett, T. E., Bossi, R., Skov, H., Hünerbein, A., Lubitz, J., Löffler, M., Linke, O., Hartmann, M., Herenz, P., and Stratmann, F.: Annual variability of ice-nucleating particle concentrations at different Arctic locations, Atmos. Chem. Phys., 19, 5293–5311, https://doi.org/10.5194/acp-19-5293-2019, 2019. a, b
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009. a
Xie, S., Liu, X., Zhao, C., and Zhang, Y.: Impact of ice nucleation parameterizations on CAM5 simulated arctic clouds and radiation: A sensitivity study, AIP Conf. Proc., 1527, 747–750, https://doi.org/10.1063/1.4803378, 2013. a
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. a
Yun, Y. and Penner, J. E.: An evaluation of the potential radiative forcing and climatic impact of marine organic aerosols as heterogeneous ice nuclei, Geophys. Res. Lett., 40, 4121–4126, https://doi.org/10.1002/grl.50794, 2013. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a, b, c
Zhao, X., Liu, X., Burrows, S. M., and Shi, Y.: Effects of marine organic aerosols as sources of immersion-mode ice-nucleating particles on high-latitude mixed-phase clouds, Atmos. Chem. Phys., 21, 2305–2327, https://doi.org/10.5194/acp-21-2305-2021, 2021. a
Short summary
Ice formation in clouds has a substantial impact on radiation and precipitation and must be realistically simulated in order to understand present and future Arctic climate. Rare aerosols known as ice-nucleating particles can play an important role in cloud ice formation, but their representation in global climate models is not well suited for the Arctic. In this study, the simulation of cloud phase is improved when the representation of these particles is constrained by Arctic observations.
Ice formation in clouds has a substantial impact on radiation and precipitation and must be...
Altmetrics
Final-revised paper
Preprint