Articles | Volume 25, issue 2
https://doi.org/10.5194/acp-25-1353-2025
© 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-25-1353-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2025
Research article |
| 31 Jan 2025
| 31 Jan 2025
Evaluation of biases in mid-to-high-latitude surface snowfall and cloud phase in ERA5 and CMIP6 using satellite observations
Franziska Hellmuth
CORRESPONDING AUTHOR
Department of Geosciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway
Tim Carlsen
Department of Geosciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway
Anne Sophie Daloz
Center for International Climate Research (CICERO), Gaustadalleen 21, 0349 Oslo, Norway
Robert Oscar David
Department of Geosciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway
Haochi Che
Department of Geosciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway
Trude Storelvmo
Department of Geosciences, University of Oslo, Blindernveien 31, 0371 Oslo, Norway
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Benjamin Poschlod and Anne Sophie Daloz
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Information about snow depth is important within climate research but also many other sectors, such as tourism, mobility, civil engineering, and ecology. Climate models often feature a spatial resolution which is too coarse to investigate snow depth. Here, we analyse high-resolution simulations and identify added value compared to a coarser-resolution state-of-the-art product. Also, daily snow depth extremes are well reproduced by two models.
Clemens Schwingshackl, Anne Sophie Daloz, Carley Iles, Kristin Aunan, and Jana Sillmann
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Idunn Aamnes Mostue, Stefan Hofer, Trude Storelvmo, and Xavier Fettweis
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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.
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Our study examined the interaction between atmospheric particles and moisture over the south-eastern Atlantic Ocean during the biomass burning seasons in Africa. We found that organic components of these particles play a more important role in aerosol-moisture interactions than previously expected. This discovery is important as such interactions impact radiation and climate. Current climate models might need better representations of the moisture-absorbing properties of organic aerosols.
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
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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.
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
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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.
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
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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.
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
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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.
Haochi Che, Philip Stier, Duncan Watson-Parris, Hamish Gordon, and Lucia Deaconu
Atmos. Chem. Phys., 22, 10789–10807, https://doi.org/10.5194/acp-22-10789-2022, https://doi.org/10.5194/acp-22-10789-2022, 2022
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Extensive stratocumulus clouds over the south-eastern Atlantic (SEA) can lead to a cooling effect on the climate. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). Here, we investigated the source attribution of CCN in the SEA as well as the cloud responses. Our results show that aerosol nucleation contributes most to CCN in the marine boundary layer. In terms of emissions, anthropogenic sources contribute most to the CCN and 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
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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.
Caroline Dang, Michal Segal-Rozenhaimer, Haochi Che, Lu Zhang, Paola Formenti, Jonathan Taylor, Amie Dobracki, Sara Purdue, Pui-Shan Wong, Athanasios Nenes, Arthur Sedlacek III, Hugh Coe, Jens Redemann, Paquita Zuidema, Steven Howell, and James Haywood
Atmos. Chem. Phys., 22, 9389–9412, https://doi.org/10.5194/acp-22-9389-2022, https://doi.org/10.5194/acp-22-9389-2022, 2022
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Transmission electron microscopy was used to analyze aged African smoke particles and how the smoke interacts with the marine atmosphere. We found that the volatility of organic aerosol increases with biomass burning plume age, that black carbon is often mixed with potassium salts and that the marine atmosphere can incorporate Na and Cl into smoke particles. Marine salts are more processed when mixed with smoke plumes, and there are interesting Cl-rich yet Na-absent marine particles.
Lu Zhang, Michal Segal-Rozenhaimer, Haochi Che, Caroline Dang, Arthur J. Sedlacek III, Ernie R. Lewis, Amie Dobracki, Jenny P. S. Wong, Paola Formenti, Steven G. Howell, and Athanasios Nenes
Atmos. Chem. Phys., 22, 9199–9213, https://doi.org/10.5194/acp-22-9199-2022, https://doi.org/10.5194/acp-22-9199-2022, 2022
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Widespread biomass burning (BB) events occur annually in Africa and contribute ~ 1 / 3 of global BB emissions, which contain a large family of light-absorbing organics, known as brown carbon (BrC), whose absorption of incident radiation is difficult to estimate, leading to large uncertainties in the global radiative forcing estimation. This study quantifies the BrC absorption of aged BB particles and highlights the potential presence of absorbing iron oxides in this climatically important region.
Haochi Che, Michal Segal-Rozenhaimer, Lu Zhang, Caroline Dang, Paquita Zuidema, Arthur J. Sedlacek III, Xiaoye Zhang, and Connor Flynn
Atmos. Chem. Phys., 22, 8767–8785, https://doi.org/10.5194/acp-22-8767-2022, https://doi.org/10.5194/acp-22-8767-2022, 2022
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A 17-month in situ study on Ascension Island found low single-scattering albedo and strong absorption enhancement of the marine boundary layer aerosols during biomass burnings on the African continent, along with apparent patterns of regular monthly variability. We further discuss the characteristics and drivers behind these changes and find that biomass burning conditions in Africa may be the main factor influencing the optical properties of marine boundary aerosols.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
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Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
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
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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.
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
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We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
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
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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.
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
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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
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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.
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
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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
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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.
Jim M. Haywood, Steven J. Abel, Paul A. Barrett, Nicolas Bellouin, Alan Blyth, Keith N. Bower, Melissa Brooks, Ken Carslaw, Haochi Che, Hugh Coe, Michael I. Cotterell, Ian Crawford, Zhiqiang Cui, Nicholas Davies, Beth Dingley, Paul Field, Paola Formenti, Hamish Gordon, Martin de Graaf, Ross Herbert, Ben Johnson, Anthony C. Jones, Justin M. Langridge, Florent Malavelle, Daniel G. Partridge, Fanny Peers, Jens Redemann, Philip Stier, Kate Szpek, Jonathan W. Taylor, Duncan Watson-Parris, Robert Wood, Huihui Wu, and Paquita Zuidema
Atmos. Chem. Phys., 21, 1049–1084, https://doi.org/10.5194/acp-21-1049-2021, https://doi.org/10.5194/acp-21-1049-2021, 2021
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Every year, the seasonal cycle of biomass burning from agricultural practices in Africa creates a huge plume of smoke that travels many thousands of kilometres over the Atlantic Ocean. This study provides an overview of a measurement campaign called the cloud–aerosol–radiation interaction and forcing for year 2017 (CLARIFY-2017) and documents the rationale, deployment strategy, observations, and key results from the campaign which utilized the heavily equipped FAAM atmospheric research aircraft.
Haochi Che, Philip Stier, Hamish Gordon, Duncan Watson-Parris, and Lucia Deaconu
Atmos. Chem. Phys., 21, 17–33, https://doi.org/10.5194/acp-21-17-2021, https://doi.org/10.5194/acp-21-17-2021, 2021
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The south-eastern Atlantic is semi-permanently covered by some of the largest stratocumulus clouds and is influenced by one-third of the biomass burning emissions from African fires. A UKEMS1 model simulation shows that the absorption effect of biomass burning aerosols is the most significant on clouds and radiation. The dominate cooling and rapid adjustments induced by the radiative effects of biomass burning aerosols result in an overall cooling in the south-eastern Atlantic.
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
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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
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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.
Anna J. Miller, Killian P. Brennan, Claudia Mignani, Jörg Wieder, Assaf Zipori, Robert O. David, and Nadine Borduas-Dedekind
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-361, https://doi.org/10.5194/amt-2020-361, 2020
Preprint withdrawn
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For characterizing atmospheric ice nuclei, we present (1) the development of our home-built droplet freezing technique (DFT), the Freezing Ice Nuclei Counter (FINC), (2) an intercomparison campaign using NX-illite and an ambient sample with three DFTs, and (3) the application of lignin as a soluble and commercial ice nuclei standard with four DFTs. We further compiled the growing number of DFTs in use for atmospheric ice nucleation since 2000, to which we add FINC.
Anne Sophie Daloz, Marian Mateling, Tristan L'Ecuyer, Mark Kulie, Norm B. Wood, Mikael Durand, Melissa Wrzesien, Camilla W. Stjern, and Ashok P. Dimri
The Cryosphere, 14, 3195–3207, https://doi.org/10.5194/tc-14-3195-2020, https://doi.org/10.5194/tc-14-3195-2020, 2020
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The total of snow that falls globally is a critical factor governing freshwater availability. To better understand how this resource is impacted by climate change, we need to know how reliable the current observational datasets for snow are. Here, we compare five datasets looking at the snow falling over the mountains versus the other continents. We show that there is a large consensus when looking at fractional contributions but strong dissimilarities when comparing magnitudes.
Cited articles
Abel, S. J., Boutle, I. A., Waite, K., Fox, S., Brown, P. R. A., Cotton, R., Lloyd, G., Choularton, T. W., and Bower, K. N.: The Role of Precipitation in Controlling the Transition from Stratocumulus to Cumulus Clouds in a Northern Hemisphere Cold-Air Outbreak, J. Atmos. Sci., 74, 2293–2314, https://doi.org/10.1175/JAS-D-16-0362.1, 2017. a
Ackermann, L., Danek, C., Gierz, P., and Lohmann, G.: AMOC Recovery in a Multicentennial Scenario Using a Coupled Atmosphere-Ocean-Ice Sheet Model, Geophys. Res. Lett., 47, e2019GL086810, https://doi.org/10.1029/2019GL086810, 2020. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. a
Bennartz, R., Shupe, M. D., Turner, D. D., Walden, V. P., Steffen, K., Cox, C. J., Kulie, M. S., Miller, N. B., and Pettersen, C.: July 2012 Greenland melt extent enhanced by low-level liquid clouds, Nature, 496, 83–86, https://doi.org/10.1038/nature12002, 2013. a
Bergeron, T.: Über die dreidimensional verknüpfende Wetteranalyse, Det Norske videnskapsakademi i Oslo, Geofysiske publikasjoner, 5, 1–111, Nkr 14.60, 1928. a
Bjordal, J., Storelvmo, T., Alterskjær, K., and Carlsen, T.: Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback, Nat. Geosci., 13, 718–721, https://doi.org/10.1038/s41561-020-00649-1, 2020. a
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
Boisvert, L. N., Webster, M. A., Petty, A. A., Markus, T., Bromwich, D. H., and Cullather, R. I.: Intercomparison of Precipitation Estimates over the Arctic Ocean and Its Peripheral Seas from Reanalyses, J. Climate, 31, 8441–8462, https://doi.org/10.1175/JCLI-D-18-0125.1, 2018. a, b
Bokhorst, S., Pedersen, S. H., Brucker, L., Anisimov, O., Bjerke, J. W., Brown, R. D., Ehrich, D., Essery, R. L. H., Heilig, A., Ingvander, S., Johansson, C., Johansson, M., Jónsdóttir, I. S., Inga, N., Luojus, K., Macelloni, G., Mariash, H., McLennan, D., Rosqvist, G. N., Sato, A., Savela, H., Schneebeli, M., Sokolov, A., Sokratov, S. A., Terzago, S., Vikhamar-Schuler, D., Williamson, S., Qiu, Y., and Callaghan, T. V.: Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts, Ambio, 45, 516–537, https://doi.org/10.1007/s13280-016-0770-0, 2016. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5195, variable versions: clw (20180804), tas (20180711), prsn (20180711), areacella (20180711), 2018. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020a. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Balkanski, Y., Checa-Garcia, R., Hauglustaine, D., Bekki, S., and Marchand, M.: IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.13661, variable versions: clw (20200608), tas (20200528), prsn (20200528), areacella (20200528), 2020b. 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, b
Callaghan, T. V., Johansson, M., Brown, R. D., Groisman, P. Y., Labba, N., Radionov, V., Bradley, R. S., Blangy, S., Bulygina, O. N., Christensen, T. R., Colman, J. E., Essery, R. L. H., Forbes, B. C., Forchhammer, M. C., Golubev, V. N., Honrath, R. E., Juday, G. P., Meshcherskaya, A. V., Phoenix, G. K., Pomeroy, J., Rautio, A., Robinson, D. A., Schmidt, N. M., Serreze, M. C., Shevchenko, V. P., Shiklomanov, A. I., Shmakin, A. B., Sköld, P., Sturm, M., Woo, M.-k., and Wood, E. F.: Multiple Effects of Changes in Arctic Snow Cover, AMBIO, 40, 32–45, https://doi.org/10.1007/s13280-011-0213-x, 2011. 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
Carlsen, T., Birnbaum, G., Ehrlich, A., Freitag, J., Heygster, G., Istomina, L., Kipfstuhl, S., Orsi, A., Schäfer, M., and Wendisch, M.: Comparison of different methods to retrieve optical-equivalent snow grain size in central Antarctica, The Cryosphere, 11, 2727–2741, https://doi.org/10.5194/tc-11-2727-2017, 2017. a
Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and de Boer, G.: Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39, L20804, https://doi.org/10.1029/2012GL053385, 2012. a, b, c
Cesana, G., Waliser, D. E., Jiang, X., and Li, J.-L. F.: Multimodel evaluation of cloud phase transition using satellite and reanalysis data, J. Geophys. Res.-Atmos., 120, 7871–7892, https://doi.org/10.1002/2014JD022932, 2015. a, b, c, d
Chen, H., Sun, J., and Lin, W.: Anthropogenic influence would increase intense snowfall events over parts of the Northern Hemisphere in the future, Environ. Res. Lett., 15, 114022, https://doi.org/10.1088/1748-9326/abbc93, publisher: IOP Publishing, 2020. a, b, c, d
CIRA: 2C-SNOW-PROFILE: CloudSat Snow Profile, Colorado State University, Cooperative Institute for Research in the Atmosphere (CIRA), https://www.cloudsat.cira.colostate.edu/data-products/2c-snow-profile, last access: 31 March 2018. a
CIRA: 2B-CLDCLASS-LIDAR: Cloud Classification and Cloud Properties from Lidar, Colorado State University, Cooperative Institute for Research in the Atmosphere (CIRA), https://www.cloudsat.cira.colostate.edu/data-products/2b-cldclass-lidar, last access: 24 February 2019a. a
CIRA: ECMWF Auxiliary Data for CloudSat, Colorado State University, Cooperative Institute for Research in the Atmosphere (CIRA), https://www.cloudsat.cira.colostate.edu/data-products/ecmwf-aux, last access: 25 September 2019b. a
Copernicus Climate Change Service (C3S): ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) [data set], variables downloaded: Total column cloud liquid water, Total column rain water, 2m temperature, Mean snowfall rate, retrieved May 2023, https://doi.org/10.24381/cds.adbb2d47, 2018. a, b
Costa, A., Meyer, J., Afchine, A., Luebke, A., Günther, G., Dorsey, J. R., Gallagher, M. W., Ehrlich, A., Wendisch, M., Baumgardner, D., Wex, H., and Krämer, M.: Classification of Arctic, midlatitude and tropical clouds in the mixed-phase temperature regime, Atmos. Chem. Phys., 17, 12219–12238, https://doi.org/10.5194/acp-17-12219-2017, 2017. a
Crameri, F.: Scientific colour maps, 1.4, Zenodo [code], https://doi.org/10.5281/zenodo.8409685, 2018. a
Cucchi, M., Amici, A., Mavel, V., Bergeron, C., Brookshaw, A., Comyn-Platt, E., and Cagnazzo, C.: Daily statistics calculated from ERA5 data, https://datastore.copernicus-climate.eu/documents/app-c3s-daily-era5-statistics/C3S_Application-Documentation_ERA5-daily-statistics-v2.pdf (last access: 12 October 2022), 2021. a
Daloz, A. S., Mateling, M., L'Ecuyer, T., Kulie, M., Wood, N. B., Durand, M., Wrzesien, M., Stjern, C. W., and Dimri, A. P.: How much snow falls in the world's mountains? A first look at mountain snowfall estimates in A-train observations and reanalyses, The Cryosphere, 14, 3195–3207, https://doi.org/10.5194/tc-14-3195-2020, 2020. a, b, c, d
Danco, J. F., DeAngelis, A. M., Raney, B. K., and Broccoli, A. J.: Effects of a Warming Climate on Daily Snowfall Events in the Northern Hemisphere, J. Climate, 29, 6295–6318, https://doi.org/10.1175/JCLI-D-15-0687.1, 2016. a, b
Danek, C., Shi, X., Stepanek, C., Yang, H., Barbi, D., Hegewald, J., and Lohmann, G.: AWI AWI-ESM1.1LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.9328, variable versions: clw (20200401), tas (20200401), prsn (20200401), areacella (20200401), 2020. a
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
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
Douville, H., Raghavan, K., Renwick, J., Allan, R., Arias, P., Barlow, M., Cerezo-Mota, R., Cherchi, A., Gan, T., Gergis, J., Jiang, D., Khan, A., Pokam Mba, W., Rosenfeld, D., Tierney, J., and Zolina, O.: Chapter 8: Water Cycle Changes, 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., 1055–1210, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781009157896.010, 2023. a
Edel, L., Claud, C., Genthon, C., Palerme, C., Wood, N., L’Ecuyer, T., and Bromwich, D.: Arctic Snowfall from CloudSat Observations and Reanalyses, J. Climate, 33, 2093–2109, https://doi.org/10.1175/JCLI-D-19-0105.1, 2020. a, b, c, d
Ehrlich, A., Wendisch, M., Bierwirth, E., Gayet, J.-F., Mioche, G., Lampert, A., and Mayer, B.: Evidence of ice crystals at cloud top of Arctic boundary-layer mixed-phase clouds derived from airborne remote sensing, Atmos. Chem. Phys., 9, 9401–9416, https://doi.org/10.5194/acp-9-9401-2009, 2009. a
Eisenberg, D. and Warner, K. E.: Effects of Snowfalls on Motor Vehicle Collisions, Injuries, and Fatalities, Am. J. Public Health, 95, 120–124, https://doi.org/10.2105/AJPH.2004.048926, 2005. a
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, b
Findeisen, W.: Die kolloidmeteorologischen Vorgänge bei der Niederschlagsbildung (Colloidal meteorological processes in the formation of precipitation), Meteorol. Z., 121–133, https://doi.org/10.1127/metz/2015/0675, translated and edited by: Volken, E., Giesche, A. M., and Brönnimann, S., Meteorol. Z., 24 (2015), Schweizerbart'sche Verlagsbuchhandlung, 1938. a
Fletcher, J. K., Mason, S., and Jakob, C.: A Climatology of Clouds in Marine Cold Air Outbreaks in Both Hemispheres, J. Climate, 29, 6677–6692, https://doi.org/10.1175/JCLI-D-15-0783.1, 2016. a
Forbes, R. and Tompkins, A.: An improved representation of cloud and precipitation, ECMWF Newsletter, https://doi.org/10.21957/NFGULZHE, 2011. a
Forbes, R., Tompkins, A., and Untch, A.: A new prognostic bulk microphysics scheme for the IFS, ECMWF Technical Memorandum, 649, https://www.ecmwf.int/sites/default/files/elibrary/2011/9441-new-prognostic-bulk-microphysics-scheme-ifs.pdf (last access: 2 February 2022), 2011. a
Forbes, R. M. and Ahlgrimm, M.: On the Representation of High-Latitude Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model, Mon. Weather Rev., 142, 3425–3445, https://doi.org/10.1175/MWR-D-13-00325.1, 2014. a, b, c
Fox, S., Crawford, A., McCrystall, M., Stroeve, J., Lukovich, J., Loeb, N., Natanine, J., and Serreze, M.: Extreme Arctic Weather and Community Impacts in Nunavut: A Case Study of One Winter’s Storms and Lessons for Local Climate Change Preparedness, Weather Clim. Soc., 15, 881–892, https://doi.org/10.1175/WCAS-D-23-0006.1, 2023. a
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
Hellmuth, F.: franzihe/CloudSat_ERA5_CMIP6_analysis: Code for “Evaluation of Biases in mid-to-high latitudes Surface Snowfall and Cloud Phase in ERA5 and CMIP6 using Satellite Observations”, (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.14179823, 2024. a
Henderson, D. S., L’Ecuyer, T., Stephens, G., Partain, P., and Sekiguchi, M.: A Multisensor Perspective on the Radiative Impacts of Clouds and Aerosols, J. Appl. Meteorol. Clim., 52, 853–871, https://doi.org/10.1175/JAMC-D-12-025.1, 2013. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Heymsfield, A. J., Schmitt, C., Chen, C.-C.-J., Bansemer, A., Gettelman, A., Field, P. R., and Liu, C.: Contributions of the Liquid and Ice Phases to Global Surface Precipitation: Observations and Global Climate Modeling, J. Atmos. Sci., 77, 2629–2648, https://doi.org/10.1175/JAS-D-19-0352.1, 2020. a, b
Hiley, M. J., Kulie, M. S., and Bennartz, R.: Uncertainty Analysis for CloudSat Snowfall Retrievals, J. Appl. Meteorol. Clim., 50, 399–418, https://doi.org/10.1175/2010JAMC2505.1, 2011. a, b, c
Hofer, S., Tedstone, A. J., Fettweis, X., and Bamber, J. L.: Cloud microphysics and circulation anomalies control differences in future Greenland melt, Nat. Clim. Chang., 9, 523–528, https://doi.org/10.1038/s41558-019-0507-8, 2019. a
Imura, Y. and Michibata, T.: Too Frequent and Too Light Arctic Snowfall With Incorrect Precipitation Phase Partitioning in the MIROC6 GCM, J. Adv. Model. Earth Sy., 14, e2022MS003 046, https://doi.org/10.1029/2022MS003046, 2022. a, b
Jiusto, J. E. and Weickmann, H. K.: Types of snowfall, B. Am. Meteorol. Soc., 54, 1148–1162, https://doi.org/10.1175/1520-0477(1973)054<1148:TOS>2.0.CO;2, 1973. a, b
Kay, J. E., Bourdages, L., Miller, N. B., Morrison, A., Yettella, V., Chepfer, H., and Eaton, B.: Evaluating and improving cloud phase in the Community Atmosphere Model version 5 using spaceborne lidar observations, J. Geophys. Res.-Atmos., 121, 4162–4176, https://doi.org/10.1002/2015JD024699, 2016. a, b, c
Keys, D. J.: Wilkinson Microwave Anisotropy Probe (WMAP) Battery Operations Problem Resolution Team (PRT), Tech. Rep. NF1676L-11245, NASA Goddard Space Flight Center, https://ntrs.nasa.gov/citations/20100030608 (last access: 7 December 2023), 2010. a
Kiszler, T., Ori, D., and Schemann, V.: Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds, Atmos. Chem. Phys., 24, 10039–10053, https://doi.org/10.5194/acp-24-10039-2024, 2024. a
Korolev, A.: Limitations of the Wegener–Bergeron–Findeisen Mechanism in the Evolution of Mixed-Phase Clouds, J. Atmos. Sci., 64, 3372–3375, https://doi.org/10.1175/JAS4035.1, 2007. a
Korolev, A. and Milbrandt, J.: How Are Mixed-Phase Clouds Mixed?, Geophys. Res. Lett., 49, e2022GL099578, https://doi.org/10.1029/2022GL099578, 2022. a, b
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-Phase Clouds: Progress and Challenges, Meteor. Mon., 58, 5.1–5.50, https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017. a, b, c
Korolev, A. V. and Mazin, I. P.: Supersaturation of Water Vapor in Clouds, J. Atmos. Sci., 60, 2957–2974, https://doi.org/10.1175/1520-0469(2003)060<2957:SOWVIC>2.0.CO;2, 2003. a
Kotarba, A. Z.: Errors in global cloud climatology due to transect sampling with the CALIPSO satellite lidar mission, Atmos. Res., 279, 106379, https://doi.org/10.1016/j.atmosres.2022.106379, 2022. a, b, c
Kulie, M. S. and Bennartz, R.: Utilizing Spaceborne Radars to Retrieve Dry Snowfall, J. Appl. Meteorol. Clim., 48, 2564–2580, https://doi.org/10.1175/2009JAMC2193.1, 2009. a
Lavers, D. A., Harrigan, S., and Prudhomme, C.: Precipitation Biases in the ECMWF Integrated Forecasting System, J. Hydrometeorol., 22, 1187–1198, https://doi.org/10.1175/JHM-D-20-0308.1, 2021. a, b, c
Lenaerts, J. T. M., Van Tricht, K., Lhermitte, S., and L'Ecuyer, T. S.: Polar clouds and radiation in satellite observations, reanalyses, and climate models, Geophys. Res. Lett., 44, 3355–3364, https://doi.org/10.1002/2016GL072242, 2017. a
Lenaerts, J. T. M., Gettelman, A., Van Tricht, K., van Kampenhout, L., and Miller, N. B.: Impact of Cloud Physics on the Greenland Ice Sheet Near-Surface Climate: A Study With the Community Atmosphere Model, J. Geophys. Res.-Atmos., 125, e2019JD031470, https://doi.org/10.1029/2019JD031470, 2020. a, b
Liu, G.: Deriving snow cloud characteristics from CloudSat observations, J. Geophys. Res.-Atmos., 113, D00A09, https://doi.org/10.1029/2007JD009766, 2008. a
Liu, Y. and Key, J. R.: Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data, J. Climate, 29, 6065–6083, https://doi.org/10.1175/JCLI-D-15-0861.1, 2016. a
Lohmann, U. and Diehl, K.: Sensitivity Studies of the Importance of Dust Ice Nuclei for the Indirect Aerosol Effect on Stratiform Mixed-Phase Clouds, J. Atmos. Sci., 63, 968–982, https://doi.org/10.1175/JAS3662.1, 2006. a
Mateling, M. E., Pettersen, C., Kulie, M. S., and L’Ecuyer, T. S.: Marine Cold-Air Outbreak Snowfall in the North Atlantic: A CloudSat Perspective, J. Geophys. Res.-Atmos., 128, e2022JD038053, https://doi.org/10.1029/2022JD038053, 2023. a
Matus, A. V. and L'Ecuyer, T. S.: The role of cloud phase in Earth's radiation budget, J. Geophys. Res.-Atmos., 122, 2559–2578, https://doi.org/10.1002/2016JD025951, 2017. a, b, c, d
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019. a
Melsheimer, C. and Spreen, G.: AMSR2 ASI sea ice concentration data, Antarctic, version 5.4 (NetCDF) (July 2012–December 2019), https://doi.org/10.1594/PANGAEA.898400, 2019a. a
Melsheimer, C. and Spreen, G.: AMSR2 ASI sea ice concentration data, Arctic, version 5.4 (NetCDF) (July 2012–December 2019), https://doi.org/10.1594/PANGAEA.898399, 2019b. a
Melsheimer, C. and Spreen, G.: AMSR-E ASI sea ice concentration data, Antarctic, version 5.4 (NetCDF) (June 2002–September 2011), https://doi.org/10.1594/PANGAEA.919778, 2020a. a
Melsheimer, C. and Spreen, G.: AMSR-E ASI sea ice concentration data, Arctic, version 5.4 (NetCDF) (June 2002–September 2011), https://doi.org/10.1594/PANGAEA.919777, 2020b. a
Milani, L., Kulie, M. S., Casella, D., Dietrich, S., L’Ecuyer, T. S., Panegrossi, G., Porcù, F., Sanò, P., and Wood, N. B.: CloudSat snowfall estimates over Antarctica and the Southern Ocean: An assessment of independent retrieval methodologies and multi-year snowfall analysis, Atmos. Res., 213, 121–135, https://doi.org/10.1016/j.atmosres.2018.05.015, 2018. a, b, c, d, e
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
Mülmenstädt, J., Nam, C., Salzmann, M., Kretzschmar, J., L’Ecuyer, T. S., Lohmann, U., Ma, P.-L., Myhre, G., Neubauer, D., Stier, P., Suzuki, K., Wang, M., and Quaas, J.: Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes, Sci. Adv., 6, eaaz6433, https://doi.org/10.1126/sciadv.aaz6433, 2020. a, b, c
Mülmenstädt, J., Salzmann, M., Kay, J. E., Zelinka, M. D., Ma, P.-L., Nam, C., Kretzschmar, J., Hörnig, S., and Quaas, J.: An underestimated negative cloud feedback from cloud lifetime changes, Nat. Clim. Chang., 11, 508–513, https://doi.org/10.1038/s41558-021-01038-1, 2021. a, b
Notz, D. and Stroeve, J.: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission, Science, 354, 747–750, https://doi.org/10.1126/science.aag2345, 2016. a
Papritz, L. and Sodemann, H.: Characterizing the Local and Intense Water Cycle during a Cold Air Outbreak in the Nordic Seas, Mon. Weather Rev., 146, 3567–3588, https://doi.org/10.1175/MWR-D-18-0172.1, 2018. a
Picard, G., Domine, F., Krinner, G., Arnaud, L., and Lefebvre, E.: Inhibition of the positive snow-albedo feedback by precipitation in interior Antarctica, Nat. Clim. Change, 2, 795–798, https://doi.org/10.1038/nclimate1590, 2012. a
Proske, U., Bessenbacher, V., Dedekind, Z., Lohmann, U., and Neubauer, D.: How frequent is natural cloud seeding from ice cloud layers (< −35 °C) over Switzerland?, Atmos. Chem. Phys., 21, 5195–5216, https://doi.org/10.5194/acp-21-5195-2021, 2021. a
Quante, L., Willner, S. N., Middelanis, R., and Levermann, A.: Regions of intensification of extreme snowfall under future warming, Sci. Rep., 11, 16 621, https://doi.org/10.1038/s41598-021-95979-4, 2021. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6109, variable versions: clw (20210313), tas (20210312), prsn (20210312), phalf (20210313), 2019. a
Roberts, M. J., Baker, A., Blockley, E. W., Calvert, D., Coward, A., Hewitt, H. T., Jackson, L. C., Kuhlbrodt, T., Mathiot, P., Roberts, C. D., Schiemann, R., Seddon, J., Vannière, B., and Vidale, P. L.: Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments, Geosci. Model Dev., 12, 4999–5028, https://doi.org/10.5194/gmd-12-4999-2019, 2019. a
Rosenfeld, D., Yu, X., Liu, G., Xu, X., Zhu, Y., Yue, Z., Dai, J., Dong, Z., Dong, Y., and Peng, Y.: Glaciation temperatures of convective clouds ingesting desert dust, air pollution and smoke from forest fires, Geophys. Res. Lett., 38, L21804, https://doi.org/10.1029/2011GL049423, 2011. a
Roussel, M.-L., Lemonnier, F., Genthon, C., and Krinner, G.: Brief communication: Evaluating Antarctic precipitation in ERA5 and CMIP6 against CloudSat observations, The Cryosphere, 14, 2715–2727, https://doi.org/10.5194/tc-14-2715-2020, 2020. a, b, c, d
Sassen, K., Wang, Z., and Liu, D.: Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2008JD009972, 2008. a
Schirmacher, I., Kollias, P., Lamer, K., Mech, M., Pfitzenmaier, L., Wendisch, M., and Crewell, S.: Assessing Arctic low-level clouds and precipitation from above – a radar perspective, Atmos. Meas. Tech., 16, 4081–4100, https://doi.org/10.5194/amt-16-4081-2023, 2023. a
Scott, D., Dawson, J., and Jones, B.: Climate change vulnerability of the US Northeast winter recreation– tourism sector, Mitig. Adapt. Strateg. Glob. Change, 13, 577–596, https://doi.org/10.1007/s11027-007-9136-z, 2008. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4068, variable versions: clw (20180805), tas (20180610), prsn (20180714), areacella (20180903), 2018. a
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K. Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019. a
Sepulchre, P., Caubel, A., Ladant, J.-B., Bopp, L., Boucher, O., Braconnot, P., Brockmann, P., Cozic, A., Donnadieu, Y., Dufresne, J.-L., Estella-Perez, V., Ethé, C., Fluteau, F., Foujols, M.-A., Gastineau, G., Ghattas, J., Hauglustaine, D., Hourdin, F., Kageyama, M., Khodri, M., Marti, O., Meurdesoif, Y., Mignot, J., Sarr, A.-C., Servonnat, J., Swingedouw, D., Szopa, S., and Tardif, D.: IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations, Geosci. Model Dev., 13, 3011–3053, https://doi.org/10.5194/gmd-13-3011-2020, 2020. 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
Shupe, M. D. and Intrieri, J. M.: Cloud Radiative Forcing of the Arctic Surface: The Influence of Cloud Properties, Surface Albedo, and Solar Zenith Angle, J. Climate, 17, 616–628, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2, 2004. a
Shupe, M. D., Turner, D. D., Walden, V. P., Bennartz, R., Cadeddu, M. P., Castellani, B. B., Cox, C. J., Hudak, D. R., Kulie, M. S., Miller, N. B., Neely, R. R., Neff, W. D., and Rowe, P. M.: High and Dry: New Observations of Tropospheric and Cloud Properties above the Greenland Ice Sheet, B. Am. Meteorol. Soc., 94, 169–186, https://doi.org/10.1175/BAMS-D-11-00249.1, 2013. a
Simmons, A., Uppala, S., Dee, D., and Kobayashi, S.: ERA-Interim: New ECMWF reanalysis products from 1989 onwards, https://doi.org/10.21957/pocnex23c6, 2007. a
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, C02S03, https://doi.org/10.1029/2005JC003384, 2008. a, b
Stanford, M. W., Fridlind, A. M., Silber, I., Ackerman, A. S., Cesana, G., Mülmenstädt, J., Protat, A., Alexander, S., and McDonald, A.: Earth-system-model evaluation of cloud and precipitation occurrence for supercooled and warm clouds over the Southern Ocean's Macquarie Island, Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023, 2023. a
Stephens, G. L. and Kummerow, C. D.: The Remote Sensing of Clouds and Precipitation from Space: A Review, J. Atmos. Sci., 64, 3742–3765, https://doi.org/10.1175/2006JAS2375.1, 2007. a, b, c
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., and Mitrescu, C.: The cloudsat mission and the a-train, Bull. Amer. Meteor. Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002. a, b
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, b
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019a. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3610, variable versions: clw (20190429), tas (20190429), prsn (20190429), areacella (20190429), 2019b. a
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A., Rocher, M., Roehrig, R., Salas-y Mélia, D., Sanchez, E., Terray, L., Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in Present-Day and Future Climate, J. Adv. Model. Earth Sy., 11, 4182–4227, https://doi.org/10.1029/2019MS001791, 2019. a
Tan, I. and Storelvmo, T.: Sensitivity Study on the Influence of Cloud Microphysical Parameters on Mixed-Phase Cloud Thermodynamic Phase Partitioning in CAM5, J. Atmos. Sci., 73, 709–728, https://doi.org/10.1175/JAS-D-15-0152.1, 2016. a, b, c
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6113, variable versions: clw (20191114), tas (20191105), prsn (20191105), phalf (20191114), 2019. a
Tatebe, H. and Watanabe, M.: MIROC MIROC6 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5603, variable versions: clw (20191127), tas (20190830), prsn (20190830), areacella (20190218), 2018. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Vergara-Temprado, J., Miltenberger, A. K., Furtado, K., Grosvenor, D. P., Shipway, B. J., Hill, A. A., Wilkinson, J. M., Field, P. R., Murray, B. J., and Carslaw, K. S.: Strong control of Southern Ocean cloud reflectivity by ice-nucleating particles, P. Natl. Acad. Sci. USA, 115, 2687–2692, https://doi.org/10.1073/pnas.1721627115, 2018. a, b
Voldoire, A.: CMIP6 simulations of the CNRM-CERFACS based on CNRM-CM6-1 model for CMIP experiment historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4066, variable versions: clw (20180724), tas (20180620), prsn (20180713), areacella (20180701), 2018. a
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK Experiments With CNRM-CM6-1, J. Adv. Model. Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019. a
von Lerber, A., Mech, M., Rinke, A., Zhang, D., Lauer, M., Radovan, A., Gorodetskaya, I., and Crewell, S.: Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic, Atmos. Chem. Phys., 22, 7287–7317, https://doi.org/10.5194/acp-22-7287-2022, 2022. a, b, c
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019. a, b
Wang, Z. and Sassen, K.: Level 2 Cloud Scenario Classification Product Process Description and Interface Control Document, Tech. rep., A NASA Earth System Science Pathfinder Mission, 1, 2019. a
Warren, S. G., Hahn, C. J., London, J., Chervin, R. M., Jenne, R. L.: Global distribution of total cloud cover and cloud type amounts over the ocean, Tech. Rep., DOE/ER-0406, NCAR/TN-317-STR, USDOE Office of Energy Research, Washington, DC (USA), Carbon Dioxide Research Div., National Center for Atmospheric Research, Boulder, CO, USA, https://doi.org/10.2172/5415329, 1988. a, b
Wegener, A.: Thermodynamik der Atmosphäre, Nature, 90, 31–31, https://doi.org/10.1038/090031a0, 1912. a
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6595, variable versions: clw (20210923), tas (20210923), prsn (20210923), areacella (20210923), 2019. a
Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance assessment of CALIOP, Geophys. Res. Lett., 34, L19803, https://doi.org/10.1029/2007GL030135, 2007. a
Winker, D. M., Pelon, J., Coakley, J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L., Treut, H. L., Mccormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO Mission: A Global 3D View of Aerosols and Clouds, B. Am. Meteorol. Soc., 91, 1211–1230, https://doi.org/10.1175/2010BAMS3009.1, 2010. a, b
Wood, N. B., L'Ecuyer, T. S., Heymsfield, A. J., Stephens, G. L., Hudak, D. R., and Rodriguez, P.: Estimating snow microphysical properties using collocated multisensor observations, J. Geophys. Res.-Atmos., 119, 8941–8961, https://doi.org/10.1002/2013JD021303, 2014. a, b
Young, G., Connolly, P. J., Jones, H. M., and Choularton, T. W.: Microphysical sensitivity of coupled springtime Arctic stratocumulus to modelled primary ice over the ice pack, marginal ice, and ocean, Atmos. Chem. Phys., 17, 4209–4227, https://doi.org/10.5194/acp-17-4209-2017, 2017. 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
Zhang, D., Wang, Z., and Liu, D.: A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements, J. Geophys. Res.-Atmos., 115, D00H13, https://doi.org/10.1029/2009JD012143, 2010. a
Zhang, T.: Influence of the seasonal snow cover on the ground thermal regime: An overview, Rev. Geophys., 43, RG4002, https://doi.org/10.1029/2004RG000157, 2005. a
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.
This article compares the occurrence of supercooled liquid-containing clouds (sLCCs) and their...
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