Articles | Volume 26, issue 6
https://doi.org/10.5194/acp-26-4189-2026
© Author(s) 2026. 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-26-4189-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Understanding the spring cloud onset over the Arctic sea-ice
LMD/IPSL, Sorbonne Université, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Université, CNRS, Palaiseau, France
Hélène Chepfer
LMD/IPSL, Sorbonne Université, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Université, CNRS, Palaiseau, France
Matthew D. Shupe
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA
National Snow and Ice Data Center, University of Colorado, Boulder, Colorado, USA
National Oceanic and Atmospheric Administration, Physical Sciences Laboratory, Boulder, Colorado, USA
Hannes Griesche
Leibniz Institute for Tropospheric Research, Remote Sensing of Atmospheric Processes, Leipzig, 04318, Germany
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Pradeep K. Aggarwal, Courtney Schumacher, Frederick J. Longstaffe, Aaron Funk, and Matthew D. Shupe
EGUsphere, https://doi.org/10.5194/egusphere-2026-687, https://doi.org/10.5194/egusphere-2026-687, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Riming, where supercooled liquid freezes directly on ice particles in mixed-phase clouds, is an efficient process of precipitation formation. We show that the relative composition of oxygen and hydrogen isotopes or d-excess is lower in rimed ice than the liquid, contrary to the existing view. This result will help better understand past climates from ice cores and help improve climate models by providing spatial and temporal constraints on the fraction of precipitation that formed by riming.
Kevin Ohneiser, Markus Hartmann, Heike Wex, Patric Seifert, Anja Hardt, Anna Miller, Katharina Baudrexl, Werner Thomas, Veronika Ettrichrätz, Maximilian Maahn, Tom Gaudek, Willi Schimmel, Fabian Senf, Hannes Griesche, Martin Radenz, and Jan Henneberger
Atmos. Chem. Phys., 26, 3223–3236, https://doi.org/10.5194/acp-26-3223-2026, https://doi.org/10.5194/acp-26-3223-2026, 2026
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This study highlights the efficiency of supercooled liquid-dominated stratus clouds to remove ice-nucleating particles (INPs). When within the planetary boundary layer (PBL), lower concentrations of INPs were measured under supercooled stratus conditions, compared to when the stratus cloud layers occurred at temperatures above freezing. Under supercooling conditions, the free-troposphere above the supercooled stratus covered PBL was found to be an additional potential source of INP.
Peggy Achtert, Torsten Seelig, Gabriella Wallentin, Luisa Ickes, Matthew D. Shupe, Corinna Hoose, and Matthias Tesche
Atmos. Chem. Phys., 26, 3049–3068, https://doi.org/10.5194/acp-26-3049-2026, https://doi.org/10.5194/acp-26-3049-2026, 2026
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We quantify the occurrence of single- and multi-layer clouds in the Arctic based on combining soundings with cloud-radar observations. We also assess the rate of ice-crystal seeding in multi-layer cloud systems as this is an important initiator of glaciation in super-cooled liquid cloud layers. We find an abundance of multi-layer clouds in the Arctic with seeding in about half to two thirds of cases in which the gap between upper and lower layers ranges between 100 and 1000 m.
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem G. Feofilov, Marie-Laure Roussel, Benjamin Witschas, and Sophie Bastin
Atmos. Chem. Phys., 26, 443–475, https://doi.org/10.5194/acp-26-443-2026, https://doi.org/10.5194/acp-26-443-2026, 2026
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Aeolus spaceborne Doppler Wind Lidar observes perfectly co-located vertical profiles of clouds and vertical profiles of horizontal wind that can be used to study cloud-wind interactions. At regional scale, we show that over the Indian Ocean, high cloud fractions increase when the Tropical Easterly Jet is active. At a smaller scale, we observe for the first time from space differences in the wind profiles within the cloud and its surrounding clear sky, that can be imputed to convective motions.
Marie-Laure Roussel, Hélène Chepfer, Zacharie Titus, and Marine Bonazzola
Atmos. Chem. Phys., 26, 117–134, https://doi.org/10.5194/acp-26-117-2026, https://doi.org/10.5194/acp-26-117-2026, 2026
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Clouds are crucial for understanding and predicting climate, yet their processes remain uncertain in global models. We reproduced detailed observations from a lidar and a wind-lidar using an advanced instrument simulator to evaluate how well the model represents clouds. A dedicated module was developed for the wind-lidar. Both instruments reveal comparable cloud biases, confirming its value for improving model evaluation and enabling consistent multi-decade assessments with more recent sensors.
Hannes Jascha Griesche, Ronny Engelmann, Martin Radenz, Julian Hofer, Dietrich Althausen, Albert Ansmann, Kevin Barry, Jessie Creamean, Cristofer Jimenez, and Patric Seifert
EGUsphere, https://doi.org/10.5194/egusphere-2025-5708, https://doi.org/10.5194/egusphere-2025-5708, 2025
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A full annual cycle of mixed-phase ice-formation temperatures in the high Arctic is presented. Ship-based remote sensing with lidar and cloud radar from the Arctic expedition MOSAiC was used to investigate the impact of surface processes on mixed-phase cloud properties. Surface mixed-layer cloud coupling was derived base on radiosonde profiles. Combined with INP filter samples, sea ice concentration and back-trajectory analysis an influence of surface processes on the cloud properties was found.
Kevin Ohneiser, Patric Seifert, Willi Schimmel, Fabian Senf, Tom Gaudek, Martin Radenz, Audrey Teisseire, Veronika Ettrichrätz, Teresa Vogl, Nina Maherndl, Nils Pfeifer, Jan Henneberger, Anna J. Miller, Nadja Omanovic, Christopher Fuchs, Huiying Zhang, Fabiola Ramelli, Robert Spirig, Anton Kötsche, Heike Kalesse-Los, Maximilian Maahn, Heather Corden, Alexis Berne, Majid Hajipour, Hannes Griesche, Julian Hofer, Ronny Engelmann, Annett Skupin, Albert Ansmann, and Holger Baars
Atmos. Chem. Phys., 25, 17363–17386, https://doi.org/10.5194/acp-25-17363-2025, https://doi.org/10.5194/acp-25-17363-2025, 2025
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This study focuses on a seeder-feeder cloud system on 8 Jan 2024 in Eriswil, Switzerland. It is shown how the interaction of these cloud systems changes the cloud microphysical properties and the precipitation patterns. A big set of advanced remote-sensing techniques and retrieval algorithms are applied, so that a detailed view on the seeder-feeder cloud system is available. The gained knowledge can be used to improve weather models and weather forecasts.
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem Feofilov, and Marie-Laure Roussel
EGUsphere, https://doi.org/10.5194/egusphere-2025-5335, https://doi.org/10.5194/egusphere-2025-5335, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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In this paper, we use satellite observations to study the relationships between profiles of clouds and profiles of wind from June to October around India. We found that thin clouds between 14 and 17 km are transported over the Arabian Sea by fast westward winds. We also show that the wind can be modified by 3 m/s because of the presence of deep convective clouds that spread out between 14 and 17 km of altitude. We discuss the implications of these interactions in a warming climate.
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
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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.
Cristofer Jimenez, Albert Ansmann, Kevin Ohneiser, Hannes Griesche, Ronny Engelmann, Martin Radenz, Julian Hofer, Dietrich Althausen, Daniel A. Knopf, Sandro Dahlke, Johannes Bühl, Holger Baars, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 25, 12955–12981, https://doi.org/10.5194/acp-25-12955-2025, https://doi.org/10.5194/acp-25-12955-2025, 2025
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We studied the water and ice phases of Arctic mixed-phase clouds (MPCs) using dual FOV polarization lidar and Doppler radar on board Polarstern during the MOSAiC expedition. Two long-lasting Arctic MPCs and year-round statistics show persistent droplet activation and dominant immersion freezing, indicating well-filled cloud condensation nuclei and ice-nucleating particle reservoirs. These findings help explain MPC longevity and may improve cloud life cycle representation in weather and climate models.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
Atmos. Meas. Tech., 18, 5393–5414, https://doi.org/10.5194/amt-18-5393-2025, https://doi.org/10.5194/amt-18-5393-2025, 2025
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This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, and application of the model to another observatory and quantify how the models respond to instrument outages.
Majid Hajipour, Patric Seifert, Hannes Griesche, Kevin Ohneiser, and Martin Radenz
Atmos. Meas. Tech., 18, 5199–5222, https://doi.org/10.5194/amt-18-5199-2025, https://doi.org/10.5194/amt-18-5199-2025, 2025
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This study presents an approach that enables the detection of the shape and orientation of multiple types of co-located hydrometeors in mixed-phase cloud systems. This information is key for improving the understanding of these clouds, as they do contain ice and liquid water simultaneously, making them relevant for the precipitation budget and radiative balance of the Earth's atmosphere. The retrieval is based on elevation scans of polarimetric cloud radars and can therefore be flexibly applied.
Yugo Kanaya, Roberto Sommariva, Alfonso Saiz-Lopez, Andrea Mazzeo, Theodore K. Koenig, Kaori Kawana, James E. Johnson, Aurélie Colomb, Pierre Tulet, Suzie Molloy, Ian E. Galbally, Rainer Volkamer, Anoop Mahajan, John W. Halfacre, Paul B. Shepson, Julia Schmale, Hélène Angot, Byron Blomquist, Matthew D. Shupe, Detlev Helmig, Junsu Gil, Meehye Lee, Sean C. Coburn, Ivan Ortega, Gao Chen, James Lee, Kenneth C. Aikin, David D. Parrish, John S. Holloway, Thomas B. Ryerson, Ilana B. Pollack, Eric J. Williams, Brian M. Lerner, Andrew J. Weinheimer, Teresa Campos, Frank M. Flocke, J. Ryan Spackman, Ilann Bourgeois, Jeff Peischl, Chelsea R. Thompson, Ralf M. Staebler, Amir A. Aliabadi, Wanmin Gong, Roeland Van Malderen, Anne M. Thompson, Ryan M. Stauffer, Debra E. Kollonige, Juan Carlos Gómez Martin, Masatomo Fujiwara, Katie Read, Matthew Rowlinson, Keiichi Sato, Junichi Kurokawa, Yoko Iwamoto, Fumikazu Taketani, Hisahiro Takashima, Mónica Navarro-Comas, Marios Panagi, and Martin G. Schultz
Earth Syst. Sci. Data, 17, 4901–4932, https://doi.org/10.5194/essd-17-4901-2025, https://doi.org/10.5194/essd-17-4901-2025, 2025
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The first comprehensive dataset of tropospheric ozone over oceans/polar regions is presented, including 77 ship/buoy and 48 aircraft campaign observations (1977–2022, 0–5000 m altitude), supplemented by ozonesonde and surface data. Air masses isolated from land for 72+ hours are systematically selected as essentially oceanic. Among the 11 global regions, they show daytime decreases of 11–16 % in the tropics, while near-zero depletions are rare, unlike in the Arctic, implying different mechanisms.
Albert Ansmann, Cristofer Jimenez, Johanna Roschke, Johannes Bühl, Kevin Ohneiser, Ronny Engelmann, Martin Radenz, Hannes Griesche, Julian Hofer, Dietrich Althausen, Daniel A. Knopf, Sandro Dahlke, Tom Gaudek, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 25, 4847–4866, https://doi.org/10.5194/acp-25-4847-2025, https://doi.org/10.5194/acp-25-4847-2025, 2025
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In this study, we focus on the potential impact of wildfire smoke on cirrus formation. For the first time, state-of-the-art aerosol and cirrus observations with lidar and radar, presented in this paper (Part 1 of a series of two articles), are closely linked to the comprehensive modeling of gravity-wave-induced ice nucleation in cirrus evolution processes, presented in a companion paper (Part 2). We found a clear impact of wildfire smoke on cirrus evolution.
Christopher J. Cox, Janet M. Intrieri, Brian J. Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, and James M. Wilczak
Earth Syst. Sci. Data, 17, 1481–1499, https://doi.org/10.5194/essd-17-1481-2025, https://doi.org/10.5194/essd-17-1481-2025, 2025
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Snow is an essential water resource in the intermountain western United States, and predictions are made using models. We made observations to validate, constrain, and develop the models. The data are from the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign in Colorado's East River valley, 2021–2023. The measurements include meteorology and variables that quantify energy transfer between the atmosphere and surface. The data are available publicly.
Benedikt Gast, Cristofer Jimenez, Albert Ansmann, Moritz Haarig, Ronny Engelmann, Felix Fritzsch, Athena A. Floutsi, Hannes Griesche, Kevin Ohneiser, Julian Hofer, Martin Radenz, Holger Baars, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 25, 3995–4011, https://doi.org/10.5194/acp-25-3995-2025, https://doi.org/10.5194/acp-25-3995-2025, 2025
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In this study, we discuss the enhanced detection capabilities of a fluorescence lidar in the case of optically thin aerosol layers in the upper troposphere and lower stratosphere (UTLS) region. Our results suggest that such thin aerosol layers are not so rare in the UTLS and can potentially trigger and impact cirrus cloud formation through heterogeneous ice nucleation. By altering the microphysical cloud properties, this could affect clouds' evolution and lifetime and thus their climate effect.
Carola Barrientos-Velasco, Christopher J. Cox, Hartwig Deneke, J. Brant Dodson, Anja Hünerbein, Matthew D. Shupe, Patrick C. Taylor, and Andreas Macke
Atmos. Chem. Phys., 25, 3929–3960, https://doi.org/10.5194/acp-25-3929-2025, https://doi.org/10.5194/acp-25-3929-2025, 2025
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Understanding how clouds affect the climate, especially in the Arctic, is crucial. This study used data from the largest polar expedition in history, MOSAiC, and the CERES satellite to analyse the impact of clouds on radiation. Simulations showed accurate results, aligning with observations. Over the year, clouds caused the atmospheric surface system to lose 5.2 W m−² of radiative energy to space, while the surface gained 25 W m−² and the atmosphere cooled by 30.2 W m−².
Benjamin Heutte, Nora Bergner, Hélène Angot, Jakob B. Pernov, Lubna Dada, Jessica A. Mirrielees, Ivo Beck, Andrea Baccarini, Matthew Boyer, Jessie M. Creamean, Kaspar R. Daellenbach, Imad El Haddad, Markus M. Frey, Silvia Henning, Tiia Laurila, Vaios Moschos, Tuukka Petäjä, Kerri A. Pratt, Lauriane L. J. Quéléver, Matthew D. Shupe, Paul Zieger, Tuija Jokinen, and Julia Schmale
Atmos. Chem. Phys., 25, 2207–2241, https://doi.org/10.5194/acp-25-2207-2025, https://doi.org/10.5194/acp-25-2207-2025, 2025
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Limited aerosol measurements in the central Arctic hinder our understanding of aerosol–climate interactions in the region. Our year-long observations of aerosol physicochemical properties during the MOSAiC expedition reveal strong seasonal variations in aerosol chemical composition, where the short-term variability is heavily affected by storms in the Arctic. Local wind-generated particles are shown to be an important source of cloud seeds, especially in autumn.
Madison M. Smith, Niels Fuchs, Evgenii Salganik, Donald K. Perovich, Ian Raphael, Mats A. Granskog, Kirstin Schulz, Matthew D. Shupe, and Melinda Webster
The Cryosphere, 19, 619–644, https://doi.org/10.5194/tc-19-619-2025, https://doi.org/10.5194/tc-19-619-2025, 2025
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The fate of freshwater from Arctic sea ice and snowmelt impacts interactions of the atmosphere, sea ice, and ocean. We complete a comprehensive analysis of datasets from a 2020 central Arctic field campaign to understand the drivers of the sea ice freshwater budget and the fate of this water. Over half of the freshwater comes from surface melt, and a majority fraction is incorporated into the ocean. Results suggest that the representation of melt ponds is a key area for future development.
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024, https://doi.org/10.5194/amt-17-6223-2024, 2024
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We developed retrievals of integrated water vapour (IWV), temperature profiles, and humidity profiles from ground-based passive microwave remote sensing measurements gathered during the MOSAiC expedition. We demonstrate and quantify the benefit of combining low- and high-frequency microwave radiometers to improve humidity profiling and IWV estimates by comparing the retrieved quantities to single-instrument retrievals and reference datasets (radiosondes).
Johanna Tjernström, Michael Gallagher, Jareth Holt, Gunilla Svensson, Matthew D. Shupe, Jonathan J. Day, Lara Ferrighi, Siri Jodha Khalsa, Leslie M. Hartten, Ewan O'Connor, Zen Mariani, and Øystein Godøy
EGUsphere, https://doi.org/10.5194/egusphere-2024-2088, https://doi.org/10.5194/egusphere-2024-2088, 2024
Preprint archived
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The value of numerical weather predictions can be enhanced in several ways, one is to improve the representations of small-scale processes in models. To understand what needs to be improved, the model results need to be evaluated. Following standardized principles, a file format has been defined to be as similar as possible for both observational and model data. Python packages and toolkits are presented as a community resource in the production of the files and evaluation analysis.
Michael Lonardi, Elisa F. Akansu, André Ehrlich, Mauro Mazzola, Christian Pilz, Matthew D. Shupe, Holger Siebert, and Manfred Wendisch
Atmos. Chem. Phys., 24, 1961–1978, https://doi.org/10.5194/acp-24-1961-2024, https://doi.org/10.5194/acp-24-1961-2024, 2024
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Profiles of thermal-infrared irradiance were measured at two Arctic sites. The presence or lack of clouds influences the vertical structure of these observations. In particular, the cloud top region is a source of radiative energy that can promote cooling and mixing in the cloud layer. Simulations are used to further characterize how the amount of water in the cloud modifies this forcing. A case study additionally showcases the evolution of the radiation profiles in a dynamic atmosphere.
Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe
Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, https://doi.org/10.5194/amt-17-899-2024, 2024
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The open-source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for characterizing particle shape, size, and sedimentation velocity in snowfall. It combines a large observation volume with relatively high resolution and a design that limits wind perturbations. The open-source nature of the VISSS hardware and software invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
Hannes Jascha Griesche, Carola Barrientos-Velasco, Hartwig Deneke, Anja Hünerbein, Patric Seifert, and Andreas Macke
Atmos. Chem. Phys., 24, 597–612, https://doi.org/10.5194/acp-24-597-2024, https://doi.org/10.5194/acp-24-597-2024, 2024
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The Arctic is strongly affected by climate change and the role of clouds therein is not yet completely understood. Measurements from the Arctic expedition PS106 were used to simulate radiative fluxes with and without clouds at very low altitudes (below 165 m), and their radiative effect was calculated to be 54 Wm-2. The low heights of these clouds make them hard to observe. This study shows the importance of accurate measurements and simulations of clouds and gives suggestions for improvements.
Pablo Saavedra Garfias, Heike Kalesse-Los, Luisa von Albedyll, Hannes Griesche, and Gunnar Spreen
Atmos. Chem. Phys., 23, 14521–14546, https://doi.org/10.5194/acp-23-14521-2023, https://doi.org/10.5194/acp-23-14521-2023, 2023
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An important Arctic climate process is the release of heat fluxes from sea ice openings to the atmosphere that influence the clouds. The characterization of this process is the objective of this study. Using synergistic observations from the MOSAiC expedition, we found that single-layer cloud properties show significant differences when clouds are coupled or decoupled to the water vapour transport which is used as physical link between the upwind sea ice openings and the cloud under observation.
Albert Ansmann, Kevin Ohneiser, Ronny Engelmann, Martin Radenz, Hannes Griesche, Julian Hofer, Dietrich Althausen, Jessie M. Creamean, Matthew C. Boyer, Daniel A. Knopf, Sandro Dahlke, Marion Maturilli, Henriette Gebauer, Johannes Bühl, Cristofer Jimenez, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 23, 12821–12849, https://doi.org/10.5194/acp-23-12821-2023, https://doi.org/10.5194/acp-23-12821-2023, 2023
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The 1-year MOSAiC (2019–2020) expedition with the German ice breaker Polarstern was the largest polar field campaign ever conducted. The Polarstern, with our lidar aboard, drifted with the pack ice north of 85° N for more than 7 months (October 2019 to mid-May 2020). We measured the full annual cycle of aerosol conditions in terms of aerosol optical and cloud-process-relevant properties. We observed a strong contrast between polluted winter and clean summer aerosol conditions.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
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Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Manfred Wendisch, Johannes Stapf, Sebastian Becker, André Ehrlich, Evelyn Jäkel, Marcus Klingebiel, Christof Lüpkes, Michael Schäfer, and Matthew D. Shupe
Atmos. Chem. Phys., 23, 9647–9667, https://doi.org/10.5194/acp-23-9647-2023, https://doi.org/10.5194/acp-23-9647-2023, 2023
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Atmospheric radiation measurements have been conducted during two field campaigns using research aircraft. The data are analyzed to see if the near-surface air in the Arctic is warmed or cooled if warm–humid air masses from the south enter the Arctic or cold–dry air moves from the north from the Arctic to mid-latitude areas. It is important to study these processes and to check if climate models represent them well. Otherwise it is not possible to reliably forecast the future Arctic climate.
Shijie Peng, Qinghua Yang, Matthew D. Shupe, Xingya Xi, Bo Han, Dake Chen, Sandro Dahlke, and Changwei Liu
Atmos. Chem. Phys., 23, 8683–8703, https://doi.org/10.5194/acp-23-8683-2023, https://doi.org/10.5194/acp-23-8683-2023, 2023
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Due to a lack of observations, the structure of the Arctic atmospheric boundary layer (ABL) remains to be further explored. By analyzing a year-round radiosonde dataset collected over the Arctic sea-ice surface, we found the annual cycle of the ABL height (ABLH) is primarily controlled by the evolution of ABL thermal structure, and the surface conditions also show a high correlation with ABLH variation. In addition, the Arctic ABLH is found to be decreased in summer compared with 20 years ago.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
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Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Ulrike Egerer, John J. Cassano, Matthew D. Shupe, Gijs de Boer, Dale Lawrence, Abhiram Doddi, Holger Siebert, Gina Jozef, Radiance Calmer, Jonathan Hamilton, Christian Pilz, and Michael Lonardi
Atmos. Meas. Tech., 16, 2297–2317, https://doi.org/10.5194/amt-16-2297-2023, https://doi.org/10.5194/amt-16-2297-2023, 2023
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This paper describes how measurements from a small uncrewed aircraft system can be used to estimate the vertical turbulent heat energy exchange between different layers in the atmosphere. This is particularly important for the atmosphere in the Arctic, as turbulent exchange in this region is often suppressed but is still important to understand how the atmosphere interacts with sea ice. We present three case studies from the MOSAiC field campaign in Arctic sea ice in 2020.
Felix Pithan, Marylou Athanase, Sandro Dahlke, Antonio Sánchez-Benítez, Matthew D. Shupe, Anne Sledd, Jan Streffing, Gunilla Svensson, and Thomas Jung
Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, https://doi.org/10.5194/gmd-16-1857-2023, 2023
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Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Albert Ansmann, Kevin Ohneiser, Alexandra Chudnovsky, Daniel A. Knopf, Edwin W. Eloranta, Diego Villanueva, Patric Seifert, Martin Radenz, Boris Barja, Félix Zamorano, Cristofer Jimenez, Ronny Engelmann, Holger Baars, Hannes Griesche, Julian Hofer, Dietrich Althausen, and Ulla Wandinger
Atmos. Chem. Phys., 22, 11701–11726, https://doi.org/10.5194/acp-22-11701-2022, https://doi.org/10.5194/acp-22-11701-2022, 2022
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For the first time we present a systematic study on the impact of wildfire smoke on ozone depletion in the Arctic (2020) and Antarctic stratosphere (2020, 2021). Two major fire events in Siberia and Australia were responsible for the observed record-breaking stratospheric smoke pollution. Our analyses were based on lidar observations of smoke parameters (Polarstern, Punta Arenas) and NDACC Arctic and Antarctic ozone profiles as well as on Antarctic OMI satellite observations of column ozone.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Carola Barrientos-Velasco, Hartwig Deneke, Anja Hünerbein, Hannes J. Griesche, Patric Seifert, and Andreas Macke
Atmos. Chem. Phys., 22, 9313–9348, https://doi.org/10.5194/acp-22-9313-2022, https://doi.org/10.5194/acp-22-9313-2022, 2022
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This article describes an intercomparison of radiative fluxes and cloud properties from satellite, shipborne observations, and 1D radiative transfer simulations. The analysis focuses on research for PS106 expedition aboard the German research vessel, Polarstern. The results are presented in detailed case studies, time series for the PS106 cruise and extended to the central Arctic region. The findings illustrate the main periods of agreement and discrepancies of both points of view.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
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We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Philipp Richter, Mathias Palm, Christine Weinzierl, Hannes Griesche, Penny M. Rowe, and Justus Notholt
Earth Syst. Sci. Data, 14, 2767–2784, https://doi.org/10.5194/essd-14-2767-2022, https://doi.org/10.5194/essd-14-2767-2022, 2022
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We present a dataset of cloud optical depths, effective radii and water paths from optically thin clouds observed in the Arctic around Svalbard. The data have been retrieved from infrared spectral radiance measured using a Fourier-transform infrared (FTIR) spectrometer. Besides a description of the measurements and retrieval technique, the data are put into context with results of corresponding measurements from microwave radiometer, lidar and cloud radar.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
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Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Michael R. Gallagher, Matthew D. Shupe, Hélène Chepfer, and Tristan L'Ecuyer
The Cryosphere, 16, 435–450, https://doi.org/10.5194/tc-16-435-2022, https://doi.org/10.5194/tc-16-435-2022, 2022
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By using direct observations of snowfall and mass changes, the variability of daily snowfall mass input to the Greenland ice sheet is quantified for the first time. With new methods we conclude that cyclones west of Greenland in summer contribute the most snowfall, with 1.66 Gt per occurrence. These cyclones are contextualized in the broader Greenland climate, and snowfall is validated against mass changes to verify the results. Snowfall and mass change observations are shown to agree well.
Kevin Ohneiser, Albert Ansmann, Alexandra Chudnovsky, Ronny Engelmann, Christoph Ritter, Igor Veselovskii, Holger Baars, Henriette Gebauer, Hannes Griesche, Martin Radenz, Julian Hofer, Dietrich Althausen, Sandro Dahlke, and Marion Maturilli
Atmos. Chem. Phys., 21, 15783–15808, https://doi.org/10.5194/acp-21-15783-2021, https://doi.org/10.5194/acp-21-15783-2021, 2021
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The highlight of the lidar measurements during the 1-year MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition of the German icebreaker Polarstern (October 2019–October 2020) was the detection of a persistent, 10 km deep Siberian wildfire smoke layer in the upper troposphere and lower stratosphere (UTLS) from about 7–8 km to 17–18 km height that could potentially have impacted the record-breaking ozone depletion over the Arctic in the spring of 2020.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
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We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Ronny Engelmann, Albert Ansmann, Kevin Ohneiser, Hannes Griesche, Martin Radenz, Julian Hofer, Dietrich Althausen, Sandro Dahlke, Marion Maturilli, Igor Veselovskii, Cristofer Jimenez, Robert Wiesen, Holger Baars, Johannes Bühl, Henriette Gebauer, Moritz Haarig, Patric Seifert, Ulla Wandinger, and Andreas Macke
Atmos. Chem. Phys., 21, 13397–13423, https://doi.org/10.5194/acp-21-13397-2021, https://doi.org/10.5194/acp-21-13397-2021, 2021
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A Raman lidar was operated aboard the icebreaker Polarstern during MOSAiC and monitored aerosol and cloud layers in the central Arctic up to 30 km height. The article provides an overview of the spectrum of aerosol profiling observations and shows aerosol–cloud interaction studies for liquid-water and ice clouds. A highlight was the detection of a 10 km deep wildfire smoke layer over the North Pole up to 17 km height from the fire season of 2019, which persisted over the whole winter period.
Hannes J. Griesche, Kevin Ohneiser, Patric Seifert, Martin Radenz, Ronny Engelmann, and Albert Ansmann
Atmos. Chem. Phys., 21, 10357–10374, https://doi.org/10.5194/acp-21-10357-2021, https://doi.org/10.5194/acp-21-10357-2021, 2021
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Heterogeneous ice formation in Arctic mixed-phase clouds under consideration of their surface-coupling state is investigated. Cloud phase and macrophysical properties were determined by means of lidar and cloud radar measurements, the coupling state, and cloud minimum temperature by radiosonde profiles. Above −15 °C cloud minimum temperature, surface-coupled clouds are more likely to contain ice by a factor of 2–6. By means of a literature survey, causes of the observed effects are discussed.
Ulrike Egerer, André Ehrlich, Matthias Gottschalk, Hannes Griesche, Roel A. J. Neggers, Holger Siebert, and Manfred Wendisch
Atmos. Chem. Phys., 21, 6347–6364, https://doi.org/10.5194/acp-21-6347-2021, https://doi.org/10.5194/acp-21-6347-2021, 2021
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This paper describes a case study of a three-day period with a persistent humidity inversion above a mixed-phase cloud layer in the Arctic. It is based on measurements with a tethered balloon, complemented with results from a dedicated high-resolution large-eddy simulation. Both methods show that the humidity layer acts to provide moisture to the cloud layer through downward turbulent transport. This supply of additional moisture can contribute to the persistence of Arctic clouds.
Cited articles
Andreas, E. L., Guest, P. S., Persson, P. O. G., Fairall, C. W., Horst, T. W., Moritz, R. E., and Semmer, S. R.: Near-surface water vapor over polar sea ice is always near ice saturation, J. Geophys. Res.-Oceans, 107, SHE-8, https://doi.org/10.1029/2000JC000411, 2002. a
Ansmann, A., Ohneiser, K., Engelmann, R., Radenz, M., Griesche, H., Hofer, J., Althausen, D., Creamean, J. M., Boyer, M. C., Knopf, D. A., Dahlke, S., Maturilli, M., Gebauer, H., Bühl, J., Jimenez, C., Seifert, P., and Wandinger, U.: Annual cycle of aerosol properties over the central Arctic during MOSAiC 2019–2020 – light-extinction, CCN, and INP levels from the boundary layer to the tropopause, Atmos. Chem. Phys., 23, 12821–12849, https://doi.org/10.5194/acp-23-12821-2023, 2023. a
Arouf, A., Chepfer, H., Vaillant de Guélis, T., Chiriaco, M., Shupe, M. D., Guzman, R., Feofilov, A., Raberanto, P., L'Ecuyer, T. S., Kato, S., and Gallagher, M. R.: The surface longwave cloud radiative effect derived from space lidar observations, Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, 2022. a, b
Arouf, A., Chepfer, H., Kay, J. E., L'Ecuyer, T. S., and Lac, J.: Surface cloud warming increases as late fall Arctic sea ice cover decreases, Geophys. Res. Lett., 51, e2023GL105805, https://doi.org/10.1029/2023GL105805, 2024. a
Barrientos-Velasco, C., Cox, C. J., Deneke, H., Dodson, J. B., Hünerbein, A., Shupe, M. D., Taylor, P. C., and Macke, A.: Estimation of the radiation budget during MOSAiC based on ground-based and satellite remote sensing observations, Atmos. Chem. Phys., 25, 3929–3960, https://doi.org/10.5194/acp-25-3929-2025, 2025. a
Barton, N. P. and Veron, D. E.: Response of clouds and surface energy fluxes to changes in sea-ice cover over the Laptev Sea (Arctic Ocean), Clim. Res., 54, 69–84, 2012. a
Bergeron, T.: On the physics of clouds and precipitation, in: Proc. 5th Assembly UGGI, Lisbon, Portugal, 1935, 156–180, 1935. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, 2001. a
Blanchard, Y., Pelon, J., Eloranta, E. W., Moran, K. P., Delanoë, J., and Sèze, G.: A synergistic analysis of cloud cover and vertical distribution from A-Train and ground-based sensors over the high Arctic station EUREKA from 2006 to 2010, J. Appl. Meteorol. Clim., 53, 2553–2570, 2014. a
Boudala, F. S., Isaac, G. A., Cober, S. G., and Fu, Q.: Liquid fraction in stratiform mixed-phase clouds from in situ observations, Q. J. Roy. Meteor. Soc., 130, 2919–2931, 2004. a
Brooks, I. M., Tjernström, M., Persson, P. O. G., Shupe, M. D., Atkinson, R. A., Canut, G., Birch, C. E., Mauritsen, T., Sedlar, J., and Brooks, B. J.: The turbulent structure of the Arctic summer boundary layer during the Arctic summer cloud–ocean study, J. Geophys. Res.-Atmos., 122, 9685–9704, 2017. a
Cesana, G., Kay, J., Chepfer, H., English, J., and De Boer, G.: Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL053385, 2012. a, b, c
Cesana, G., Chepfer, H., Winker, D., Getzewich, B., Cai, X., Jourdan, O., Mioche, G., Okamoto, H., Hagihara, Y., Noël, V., and Reverdy, M.: Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO, J. Geophys. Res.-Atmos., 121, 5788–5808, https://doi.org/10.1002/2015JD024334, 2016. a, b, c, d
Cesana, G. V., Khadir, T., Chepfer, H., and Chiriaco, M.: Southern Ocean solar reflection biases in CMIP6 models linked to cloud phase and vertical structure representations, Geophys. Res. Lett., 49, e2022GL099777, https://doi.org/10.1029/2022GL099777, 2022. a, b, c
Chan, M. A. and Comiso, J. C.: Arctic cloud characteristics as derived from MODIS, CALIPSO, and CloudSat, J. Climate, 26, 3285–3306, 2013. a
Collis, R. T.: Laser monitoring of the atmosphere, Top. Appl. Phys., 14, 71–151, 1976. a
Cox, C. J., Uttal, T., Long, C. N., Shupe, M. D., Stone, R. S., and Starkweather, S.: The role of springtime Arctic clouds in determining autumn sea ice extent, J. Climate, 29, 6581–6596, 2016. a
Creamean, J. M., Barry, K., Hill, T. C., 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
Di Pierro, M., Jaeglé, L., Eloranta, E. W., and Sharma, S.: Spatial and seasonal distribution of Arctic aerosols observed by the CALIOP satellite instrument (2006–2012), Atmos. Chem. Phys., 13, 7075–7095, https://doi.org/10.5194/acp-13-7075-2013, 2013. a
Dong, X., Xi, B., Crosby, K., Long, C. N., Stone, R. S., and Shupe, M. D.: A 10 year climatology of Arctic cloud fraction and radiative forcing at Barrow, Alaska, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2009JD013489, 2010. a
Eirund, G. K., Possner, A., and Lohmann, U.: Response of Arctic mixed-phase clouds to aerosol perturbations under different surface forcings, Atmos. Chem. Phys., 19, 9847–9864, https://doi.org/10.5194/acp-19-9847-2019, 2019. a
Engelmann, R., Kanitz, T., Baars, H., Heese, B., Althausen, D., Skupin, A., Wandinger, U., Komppula, M., Stachlewska, I. S., Amiridis, V., Marinou, E., Mattis, I., Linné, H., and Ansmann, A.: The automated multiwavelength Raman polarization and water-vapor lidar PollyXT: the neXT generation, Atmos. Meas. Tech., 9, 1767–1784, https://doi.org/10.5194/amt-9-1767-2016, 2016. a
Engelmann, R., Althausen, D., Baars, H., Griesche, H., Hofer, J., Radenz, M., and Seifert, P.: Custom collection of categorize, classification, ice water content, lidar, and liquid water content data from RV Polarstern between 1 Oct 2019 and 1 Oct 2020, ACTRIS Cloud remote sensing data centre unit (CLU) [data set], https://doi.org/10.60656/59216bca3a304156, 2023. a, b
Filioglou, M., Mielonen, T., Balis, D., Giannakaki, E., Arola, A., Kokkola, H., Komppula, M., and Romakkaniemi, S.: Aerosol effect on the cloud phase of low-level clouds over the Arctic, J. Geophys. Res.-Atmos., 124, 7886–7899, 2019. a
Findeisen, W.: Kolloid-meteorologische Vorgänge bei Niederschlagsbildung, Meteorol. Z., 55, 121–133, 1938. a
Francis, J. A. and Wu, B.: Why has no new record-minimum Arctic sea-ice extent occurred since September 2012?, Environ. Res. Lett., 15, 114034, https://doi.org/10.1088/1748-9326/abc047, 2020. a
Griesche, H. J. and Seifert, P.: MOSAiC Cloud-net issue data set (1.1), Zenodo [data set], https://doi.org/10.5281/zenodo.15675285, 2025. a, b
Griesche, H. J., Ohneiser, K., Seifert, P., Radenz, M., Engelmann, R., and Ansmann, A.: Contrasting ice formation in Arctic clouds: surface-coupled vs. surface-decoupled clouds, Atmos. Chem. Phys., 21, 10357–10374, https://doi.org/10.5194/acp-21-10357-2021, 2021. a
Griesche, H. J., Barrientos-Velasco, C., Deneke, H., Hünerbein, A., Seifert, P., and Macke, A.: Low-level Arctic clouds: a blind zone in our knowledge of the radiation budget, Atmos. Chem. Phys., 24, 597–612, https://doi.org/10.5194/acp-24-597-2024, 2024a. a
Griesche, H. J., Seifert, P., Engelmann, R., Radenz, M., Hofer, J., Althausen, D., Walbröl, A., Barrientos-Velasco, C., Baars, H., Dahlke, S., Maturilli, M., Wendisch, M., Ehrlich, A., Crewell, S., Deneke, H., and Macke, A.: Cloud micro-and macrophysical properties from ground-based remote sensing during the MOSAiC drift experiment, Scientific Data, 11, 505, https://doi.org/10.1038/s41597-024-03325-w 2024b. a
Groves, D. G. and Francis, J. A.: Moisture budget of the Arctic atmosphere from TOVS satellite data, J. Geophys. Res.-Atmos., 107, ACL-11, https://doi.org/10.1029/2001JD001191, 2002. a
Guzman, R., Chepfer, H., Noël, V., Vaillant de Guélis, T., Kay, J., Raberanto, P., Cesana, G., Vaughan, M., and Winker, D.: Direct atmosphere opacity observations from CALIPSO provide new constraints on cloud-radiation interactions, J. Geophys. Res.-Atmos., 122, 1066–1085, 2017. a
Herrmannsdörfer, L., Müller, M., Shupe, M. D., and Rostosky, P.: Surface temperature comparison of the Arctic winter MOSAiC observations, ERA5 reanalysis, and MODIS satellite retrieval, Elem. Sci. Anth., 11, 00085, https://doi.org/10.1525/elementa.2022.00085, 2023. a
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
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
Heutte, B., Bergner, N., Beck, I., Angot, H., Dada, L., Quéléver, L. L., Laurila, T., Boyer, M., Brasseur, Z., Daellenbach, K. R., Henning, S., Kuang, C., Kulmala, M., Lampilahti, J., Lampimäki, M., Petäjä, T., Shupe, M. D., Sipilä, M., Uin, J., Jokinen, T., and Schmale, J.: Measurements of aerosol microphysical and chemical properties in the central Arctic atmosphere during MOSAiC, Scientific Data, 10, 690, https://doi.org/10.1038/s41597-023-02586-1, 2023. a
Hu, Y., Rodier, S., Xu, K.-m., Sun, W., Huang, J., Lin, B., Zhai, P., and Josset, D.: Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2009JD012384, 2010. a
Huang, Y., Dong, X., Bailey, D. A., Holland, M. M., Xi, B., DuVivier, A. K., Kay, J. E., Landrum, L. L., and Deng, Y.: Thicker clouds and accelerated Arctic sea ice decline: The atmosphere-sea ice interactions in spring, Geophys. Res. Lett., 46, 6980–6989, 2019. a
Huschke, R. E.: Arctic cloud statistics from “air-calibrated” surface weather observations, RAND Corporation Memorandum PM-6173, 79, 1969. a
Intrieri, J. M., Fairall, C., Shupe, M., Persson, P., Andreas, E., Guest, P., and Moritz, R.: An annual cycle of Arctic surface cloud forcing at SHEBA, J. Geophys. Res.-Oceans, 107, SHE-13, https://doi.org/10.1029/2000JC000439, 2002. a
Jiang, Z., Ding, M., Zhong, L., Li, Y., and Hu, X.: Seasonal variations of Arctic cloud in recent 14 years using CALIPSO-GOCCP, Atmos. Res., 309, 107598, https://doi.org/10.1016/j.atmosres.2024.107598, 2024. a
Kay, J. E. and Gettelman, A.: Cloud influence on and response to seasonal Arctic sea ice loss, J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2009JD011773, 2009. a
Kay, J. E. and L'Ecuyer, T.: Observational constraints on Arctic Ocean clouds and radiative fluxes during the early 21st century, J. Geophys. Res.-Atmos., 118, 7219–7236, 2013. a
Kirbus, B., Tiedeck, S., Camplani, A., Chylik, J., Crewell, S., Dahlke, S., Ebell, K., Gorodetskaya, I., Griesche, H., Handorf, D., Höschel, I., Lauer, M., Neggers, R., Rückert, J., Shupe, M. D., Spreen, G., Walbröl, A., Wendisch, M., and Rinke, A.: Surface impacts and associated mechanisms of a moisture intrusion into the Arctic observed in mid-April 2020 during MOSAiC, Front. Earth Sci., 11, 1147848, https://doi.org/10.3389/feart.2023.1147848, 2023. a
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
Korolev, A. V., Isaac, G. A., Cober, S. G., Strapp, J. W., and Hallett, J.: Microphysical characterization of mixed-phase clouds, Q. J. Roy. Meteor. Soc., 129, 39–65, 2003. a
Lac, J., Chepfer, H., Arouf, A., Shupe, M. D., and Gallagher, M. R.: Polar low circulation enhances Greenland's west coast cloud surface warming, J. Geophys. Res.-Atmos., 129, e2023JD040450, https://doi.org/10.1029/2023JD040450, 2024. a
Lance, S., Shupe, M. D., Feingold, G., Brock, C. A., Cozic, J., Holloway, J. S., Moore, R. H., Nenes, A., Schwarz, J. P., Spackman, J. R., Froyd, K. D., Murphy, D. M., Brioude, J., Cooper, O. R., Stohl, A., and Burkhart, J. F.: Cloud condensation nuclei as a modulator of ice processes in Arctic mixed-phase clouds, Atmos. Chem. Phys., 11, 8003–8015, https://doi.org/10.5194/acp-11-8003-2011, 2011. a
Lacour, A., Chepfer, H., Shupe, M. D., Miller, N. B., Noel, V., Kay, J., Turner, D. D., and Guzman, R.: Greenland clouds observed in CALIPSO-GOCCP: Comparison with ground-based summit observations, J. Climate, 30, 6065–6083, https://doi.org/10.1175/JCLI-D-16-0552.1, 2017. a, b
L'Ecuyer, T. S., Hang, Y., Matus, A. V., and Wang, Z.: Reassessing the effect of cloud type on Earth’s energy balance in the age of active spaceborne observations. Part I: Top of atmosphere and surface, J. Climate, 32, 6197–6217, 2019. a
Li, X., Tan, Z., Zheng, Y., Bushuk, M., and Donner, L. J.: Open Water in Sea Ice Causes High Bias in Polar Low-Level Clouds in GFDL CM4, Geophys. Res. Lett., 50, e2023GL106322, https://doi.org/10.1029/2023GL106322, 2023. a, b, c
Markus, T., Stroeve, J. C., and Miller, J.: Recent changes in Arctic sea ice melt onset, freezeup, and melt season length, J. Geophys. Res.-Oceans, 114, https://doi.org/10.1029/2009JC005436, 2009. a
Maturilli, M., Sommer, M., Holdridge, D. J., Dahlke, S., Graeser, J., and Jaiser, R.: Radiosonde measurements in 2020-04 during MOSAiC Leg PS122/3 (level 3 data), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.944529, 2022. a
Mayer, M., Tietsche, S., Haimberger, L., Tsubouchi, T., Mayer, J., and Zuo, H.: An improved estimate of the coupled Arctic energy budget, J. Climate, 32, 7915–7934, 2019. a
McFarquhar, G. M., Zhang, G., Poellot, M. R., Kok, G. L., McCoy, R., Tooman, T., Fridlind, A., and Heymsfield, A. J.: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 1. Observations, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2007JD008633, 2007. a
Morrison, A., Kay, J., Chepfer, H., Guzman, R., and Yettella, V.: Isolating the liquid cloud response to recent Arctic sea ice variability using spaceborne lidar observations, J. Geophys. Res.-Atmos., 123, 473–490, 2018. a
Ohneiser, K., Ansmann, A., Chudnovsky, A., Engelmann, R., Ritter, C., Veselovskii, I., Baars, H., Gebauer, H., Griesche, H., Radenz, M., Hofer, J., Althausen, D., Dahlke, S., and Maturilli, M.: The unexpected smoke layer in the High Arctic winter stratosphere during MOSAiC 2019–2020, Atmos. Chem. Phys., 21, 15783–15808, https://doi.org/10.5194/acp-21-15783-2021, 2021. a
Olason, E. and Notz, D.: Drivers of variability in A rctic sea-ice drift speed, J. Geophys. Res.-Oceans, 119, 5755–5775, 2014. a
Overland, J. E., Wang, M., Walsh, J. E., and Stroeve, J. C.: Future Arctic climate changes: Adaptation and mitigation time scales, Earths Future, 2, 68–74, 2014. a
Peng, G., Meier, W. N., Scott, D. J., and Savoie, M. H.: A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring, Earth Syst. Sci. Data, 5, 311–318, https://doi.org/10.5194/essd-5-311-2013, 2013. a
Persson, P. O. G.: Onset and end of the summer melt season over sea ice: Thermal structure and surface energy perspective from SHEBA, Clim. Dynam., 39, 1349–1371, 2012. a
Pithan, F., Svensson, G., Caballero, R., Chechin, D., Cronin, T. W., Ekman, A. M., Neggers, R., Shupe, M. D., Solomon, A., Tjernström, M., and Wendisch, M.: Role of air-mass transformations in exchange between the Arctic and mid-latitudes, Nat. Geosci., 11, 805–812, 2018. a
Previdi, M., Smith, K. L., and Polvani, L. M.: Arctic amplification of climate change: a review of underlying mechanisms, Environ. Res. Lett., 16, 093003, https://doi.org/10.1088/1748-9326/ac1c29, 2021. a
Raillard, L., Vignon, É., Rivière, G., Madeleine, J.-B., Meurdesoif, Y., Delanoë, J., Caubel, A., Jourdan, O., Baudoux, A., Fromang, S., and Conesa, P.: Leveraging RALI-THINICE observations to assess how the ICOLMDZ model simulates clouds embedded in Arctic cyclones, J. Geophys. Res.-Atmos., 129, e2024JD040973, https://doi.org/10.1029/2024JD040973, 2024. a
Sedlar, J., Tjernström, M., Mauritsen, T., Shupe, M. D., Brooks, I. M., Persson, P. O. G., Birch, C. E., Leck, C., Sirevaag, A., and Nicolaus, M.: A transitioning Arctic surface energy budget: the impacts of solar zenith angle, surface albedo and cloud radiative forcing, Clim. Dynam., 37, 1643–1660, 2011. a
Seidel, C., Althausen, D., Ansmann, A., Wendisch, M., Griesche, H., Radenz, M., Hofer, J., Dahlke, S., Maturilli, M., Walbröl, A., Baars, H., and Engelmann, R.: Close correlation between vertically integrated tropospheric water vapor and the downward, broadband thermal-infrared irradiance at the ground: Observations in the Central Arctic during MOSAiC, J. Geophys. Res.-Atmos., 130, e2024JD042378, https://doi.org/10.1029/2024JD042378, 2025. a
Serreze, M. C. and Barry, R. G.: The Arctic climate system, Cambridge University Press, https://doi.org/10.1017/CBO9781139583817, 2014. a
Serreze, M. C., Barrett, A. P., Slater, A. G., Steele, M., Zhang, J., and Trenberth, K. E.: The large-scale energy budget of the Arctic, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2006JD008230, 2007. a, b
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
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, 2004. a
Shupe, M. D., Matrosov, S. Y., and Uttal, T.: Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA, J. Atmos. Sci., 63, 697–711, 2006. a
Shupe, M. D., Kollias, P., Persson, P. O. G., and McFarquhar, G. M.: Vertical motions in Arctic mixed-phase stratiform clouds, J. Atmos. Sci., 65, 1304–1322, 2008. a
Shupe, M. D., Persson, P. O. G., Brooks, I. M., Tjernström, M., Sedlar, J., Mauritsen, T., Sjogren, S., and Leck, C.: Cloud and boundary layer interactions over the Arctic sea ice in late summer, Atmos. Chem. Phys., 13, 9379–9399, https://doi.org/10.5194/acp-13-9379-2013, 2013. a
Shupe, M. D., Rex, M., Blomquist, B., et al.: Overview of the MOSAiC expedition: Atmosphere, Elem. Sci. Anth., 10, 00060, https://doi.org/10.1525/elementa.2021.00060, 2022. a, b
Silber, I. and Shupe, M. D.: Insights on sources and formation mechanisms of liquid-bearing clouds over MOSAiC examined from a Lagrangian framework, Elem. Sci. Anth., 10, 000071, https://doi.org/10.1525/elementa.2021.000071, 2022. a
Smith, A. and Jahn, A.: Definition differences and internal variability affect the simulated Arctic sea ice melt season, The Cryosphere, 13, 1–20, https://doi.org/10.5194/tc-13-1-2019, 2019. a, b
Solomon, A., de Boer, G., Creamean, J. M., McComiskey, A., Shupe, M. D., Maahn, M., and Cox, C.: The relative impact of cloud condensation nuclei and ice nucleating particle concentrations on phase partitioning in Arctic mixed-phase stratocumulus clouds, Atmos. Chem. Phys., 18, 17047–17059, https://doi.org/10.5194/acp-18-17047-2018, 2018. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all seasons, Environ. Res. Lett., 13, 103001, https://doi.org/10.1088/1748-9326/aade56, 2018. a, b
Sverdrup, H. U.: The Norwegian North Polar Expedition with the “Maud” 1918–1925, Meteorology, 3, John Griegs Boktr, 1930. a
Tan, I., Sotiropoulou, G., Taylor, P. C., Zamora, L., and Wendisch, M.: A review of the factors influencing Arctic mixed-phase clouds: Progress and outlook, in: Clouds and their climatic impacts: Radiation, circulation, and precipitation, Wiley Online Library, 103–132, https://doi.org/10.1002/9781119700357.ch5, 2023. a
Taylor, P. C. and Monroe, E.: Isolating the surface type influence on Arctic low-clouds, J. Geophys. Res.-Atmos., 128, e2022JD038098, https://doi.org/10.1029/2022JD038098, 2023. a
Taylor, P. C., Boeke, R. C., Li, Y., and Thompson, D. W. J.: Arctic cloud annual cycle biases in climate models, Atmos. Chem. Phys., 19, 8759–8782, https://doi.org/10.5194/acp-19-8759-2019, 2019. a
Tjernström, M., Zieger, P., and Murto, S. and the ARTofMELT Science Team: Arctic spring and the onset of sea-ice melt: Early impressions from the ARTofMELT expedition, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15627, https://doi.org/10.5194/egusphere-egu24-15627, 2024. a, b
Uttal, T., Curry, J. A., McPhee, M. G., Perovich, D. G., Moritz, R. E., Maslanik, J. A., Guest, P. S., Stern, H. L., Moore, J. A., Turenne, R., Heiberg, A., Serreze, M. C., Wylie, D. P., Persson, O. G., Paulson, C. A., Halle, C., Morison, J. H., Wheeler, P. A., Makshtas, A., Welch, H., Shupe, M. D., Intrieri, J. M., Stamnes, K., Lindsey, R. W., Pinkel, R., Pegau, W. S., Stanton, T. P., and Grenfeld, T. C.: Surface Heat Budget of the Arctic Ocean, Bulletin of the American Meteorological Society, 18, 255–276, 2003. a
Walter, B. A., Overland, J. E., and Turet, P.: A comparison of satellite-derived and aircraft-measured regional surface sensible heat fluxes over the Beaufort Sea, J. Geophys. Res.-Oceans, https://doi.org/10.1029/94JC02653, 1995. a
Wegener, A.: Thermodynamik der Atmosphäre, in: Anwendung der Thermodynamik, Springer, 156–189, https://doi.org/10.1007/978-3-642-90779-1_3, 1926. a
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, 2009. a
Short summary
Satellite observations show that Arctic spring experiences a rapid increase in liquid-containing clouds over sea ice. Our study shows that this transition is mostly driven by warmer temperatures in early spring than in late spring, favoring more liquid clouds formation, rather than a limited moisture source in early spring. It suggests that, in the future, this transition is likely to occur earlier in spring considering the rapid Arctic warming.
Satellite observations show that Arctic spring experiences a rapid increase in liquid-containing...
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