Articles | Volume 25, issue 7
https://doi.org/10.5194/acp-25-3929-2025
© 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-25-3929-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Apr 2025
Research article |
| 08 Apr 2025
| 08 Apr 2025
Estimation of the radiation budget during MOSAiC based on ground-based and satellite remote sensing observations
Carola Barrientos-Velasco
CORRESPONDING AUTHOR
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, QC, Canada
Christopher J. Cox
NOAA Physical Sciences Laboratory, Boulder, CO, USA
Hartwig Deneke
Leibniz Institute for Tropospheric Research, Leipzig, Germany
J. Brant Dodson
Science Systems and Applications, Inc., Hampton, VA 23666, USA
Anja Hünerbein
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Matthew D. Shupe
NOAA Physical Sciences Laboratory, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
Patrick C. Taylor
NASA Langley Research Center, Hampton, VA, USA
Andreas Macke
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Related authors
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Hartwig Deneke, Carola Barrientos-Velasco, Sebastian Bley, Anja Hünerbein, Stephan Lenk, Andreas Macke, Jan Fokke Meirink, Marion Schroedter-Homscheidt, Fabian Senf, Ping Wang, Frank Werner, and Jonas Witthuhn
Atmos. Meas. Tech., 14, 5107–5126, https://doi.org/10.5194/amt-14-5107-2021, https://doi.org/10.5194/amt-14-5107-2021, 2021
Short summary
Short summary
The SEVIRI instrument flown on the European geostationary Meteosat satellites acquires multi-spectral images at a relatively coarse pixel resolution of 3 × 3 km2, but it also has a broadband high-resolution visible channel with 1 × 1 km2 spatial resolution. In this study, the modification of an existing cloud property and solar irradiance retrieval to use this channel to improve the spatial resolution of its output products as well as the resulting benefits for applications are described.
Hannes J. Griesche, Patric Seifert, Albert Ansmann, Holger Baars, Carola Barrientos Velasco, Johannes Bühl, Ronny Engelmann, Martin Radenz, Yin Zhenping, and Andreas Macke
Atmos. Meas. Tech., 13, 5335–5358, https://doi.org/10.5194/amt-13-5335-2020, https://doi.org/10.5194/amt-13-5335-2020, 2020
Short summary
Short summary
In summer 2017, the research vessel Polarstern performed cruise PS106 to the Arctic north of Svalbard. In the frame of the cruise, remote-sensing observations of the atmosphere were performed on Polarstern to continuously monitor aerosol and clouds above the vessel. In our study, we present the deployed instrumentation and applied data analysis methods and provide case studies of the aerosol and cloud observations made during the cruise. Statistics of low-cloud occurrence are presented as well.
Job I. Wiltink, Hartwig Deneke, Chiel C. van Heerwaarden, and Jan Fokke Meirink
Atmos. Meas. Tech., 18, 3917–3936, https://doi.org/10.5194/amt-18-3917-2025, https://doi.org/10.5194/amt-18-3917-2025, 2025
Short summary
Short summary
Global horizontal irradiance retrievals from satellite observations are affected by spatial displacements due to parallax and cloud shadows. We assess different approaches to correct for these displacements and quantify their added value by comparison with a network of ground-based pyranometer observations. The corrections are found to become increasingly important at higher spatial resolutions and are most relevant for variable cloud types.
Peggy Achtert, Torsten Seelig, Gabriella Wallentin, Luisa Ickes, Matthew D. Shupe, Corinna Hoose, and Matthias Tesche
EGUsphere, https://doi.org/10.5194/egusphere-2025-3529, https://doi.org/10.5194/egusphere-2025-3529, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
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.
Jean Lac, Hélène Chepfer, Matthew D. Shupe, and Hannes Griesche
EGUsphere, https://doi.org/10.5194/egusphere-2025-3549, https://doi.org/10.5194/egusphere-2025-3549, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
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.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O’Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, Vanessa Hernaman, Céline Heuzé, Doroteaciro Iovino, Gaurav Madan, André L. Marquez, François Massonnet, Jenny Mecking, Dhrubajyoti Samanta, Patrick C. Taylor, Wan-Ling Tseng, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-3083, https://doi.org/10.5194/egusphere-2025-3083, 2025
Short summary
Short summary
The earth system model variables needed for studies of the ocean and sea ice are prioritized and requested.
Aidan D. Pantoya, Stephanie R. Simonsen, Elisabeth Andrews, Ross Burgener, Christopher J. Cox, Gijs de Boer, Bryan D. Thomas, and Naruki Hiranuma
Aerosol Research, 3, 253–270, https://doi.org/10.5194/ar-3-253-2025, https://doi.org/10.5194/ar-3-253-2025, 2025
Short summary
Short summary
We present continuous ice-nucleating particle data that were measured in the Alaskan Arctic from October 2021 to December 2023. We found a greater efficiency in the arctic immersion freezing during fall compared to those found previously at two mid-latitude sites, together with profound freezing efficiencies in spring, presumably due to arctic haze events. Our study will be useful for improving atmospheric models to simulate cloud feedback and determine their impact on the global radiative energy budget.
Manfred Wendisch, Benjamin Kirbus, Davide Ori, Matthew D. Shupe, Susanne Crewell, Harald Sodemann, and Vera Schemann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2062, https://doi.org/10.5194/egusphere-2025-2062, 2025
Short summary
Short summary
Aircraft observations of air parcels moving into and out of the Arctic are reported. From the data, heating and cooling as well as drying and moistening of the air masses along their way into and out of the Arctic could be measured for the first time. These data enable to evaluate if numerical weather prediction models are able to accurately represent these air mass transformations. This work helps to model the future climate changes in the Arctic, which are important for mid-latitude weather.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1501, https://doi.org/10.5194/egusphere-2025-1501, 2025
Short summary
Short summary
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, application of the model to another observatory, and quantify how the models respond to instrument outages.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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, Monica Navarro Comas, Marios Panagi, and Martin G. Schultz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-566, https://doi.org/10.5194/essd-2024-566, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
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 10–16% in the tropics, while near-zero depletions are rare, unlike in the Arctic, implying different mechanisms.
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
Short summary
Short summary
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.
Marcus Klingebiel, André Ehrlich, Micha Gryschka, Nils Risse, Nina Maherndl, Imke Schirmacher, Sophie Rosenburg, Sabine Hörnig, Manuel Moser, Evelyn Jäkel, Michael Schäfer, Hartwig Deneke, Mario Mech, Christiane Voigt, and Manfred Wendisch
EGUsphere, https://doi.org/10.5194/egusphere-2025-201, https://doi.org/10.5194/egusphere-2025-201, 2025
Short summary
Short summary
Our study is using aircraft measurements from the HALO-(𝒜𝒞)³ campaign to investigate the transition from organized Arctic cloud street structures to more scattered cloud shapes. We show that lower wind speeds cause this transition. In addition we look at the changes of the cloud coverage, the height of the clouds, the cloud particles and the radiative properties.
Job I. Wiltink, Hartwig Deneke, Yves-Marie Saint-Drenan, Chiel C. van Heerwaarden, and Jan Fokke Meirink
Atmos. Meas. Tech., 17, 6003–6024, https://doi.org/10.5194/amt-17-6003-2024, https://doi.org/10.5194/amt-17-6003-2024, 2024
Short summary
Short summary
Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) global horizontal irradiance (GHI) retrievals are validated at standard and increased spatial resolution against a network of 99 pyranometers. GHI accuracy is strongly dependent on the cloud regime. Days with variable cloud conditions show significant accuracy improvements when retrieved at higher resolution. We highlight the benefits of dense network observations and a cloud-regime-resolved approach in validating GHI retrievals.
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
Short summary
Short summary
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.
Zili He, Quentin Libois, Najda Villefranque, Hartwig Deneke, Jonas Witthuhn, and Fleur Couvreux
Atmos. Chem. Phys., 24, 11391–11408, https://doi.org/10.5194/acp-24-11391-2024, https://doi.org/10.5194/acp-24-11391-2024, 2024
Short summary
Short summary
This study uses observations and simulations to analyze how cumulus clouds affect spacial solar radiation variability on the ground. Results show that the simulations reproduce the observations well and improve understanding of cloud impacts on radiation. The research also indicates that a few strategically placed sensors, capitalizing on measurement timing, can effectively measure these variations, aiding in the development of detailed weather prediction models.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
Short summary
Short summary
A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Zen Mariani, Sara M. Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie M. Hartten, Jareth Holt, Christopher J. Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data, 16, 3083–3124, https://doi.org/10.5194/essd-16-3083-2024, https://doi.org/10.5194/essd-16-3083-2024, 2024
Short summary
Short summary
During the Year of Polar Prediction (YOPP), we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of datasets that have been collected to facilitate user uptake and model–observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODFs), including a description of the sites, data format, and instruments.
Nicole Docter, Anja Hünerbein, David P. Donovan, Rene Preusker, Jürgen Fischer, Jan Fokke Meirink, Piet Stammes, and Michael Eisinger
Atmos. Meas. Tech., 17, 2507–2519, https://doi.org/10.5194/amt-17-2507-2024, https://doi.org/10.5194/amt-17-2507-2024, 2024
Short summary
Short summary
MSI is the imaging spectrometer on board EarthCARE and will provide across-track information on clouds and aerosol properties. The MSI solar channels exhibit a spectral misalignment effect (SMILE) in the measurements. This paper describes and evaluates how the SMILE will affect the cloud and aerosol retrievals that do not account for it.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
Short summary
Short summary
When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer
Atmos. Chem. Phys., 24, 1429–1450, https://doi.org/10.5194/acp-24-1429-2024, https://doi.org/10.5194/acp-24-1429-2024, 2024
Short summary
Short summary
Observations collected during MOSAiC were used to identify the range in vertical structure and stability of the central Arctic lower atmosphere through a self-organizing map analysis. Characteristics of wind features (such as low-level jets) and atmospheric moisture features (such as clouds) were analyzed in the context of the varying vertical structure and stability. Thus, the results of this paper give an overview of the thermodynamic and kinematic features of the central Arctic atmosphere.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
Short summary
Short summary
The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
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
Short summary
Short summary
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.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
Short summary
Short summary
The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
Short summary
Short summary
Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
James Barry, Stefanie Meilinger, Klaus Pfeilsticker, Anna Herman-Czezuch, Nicola Kimiaie, Christopher Schirrmeister, Rone Yousif, Tina Buchmann, Johannes Grabenstein, Hartwig Deneke, Jonas Witthuhn, Claudia Emde, Felix Gödde, Bernhard Mayer, Leonhard Scheck, Marion Schroedter-Homscheidt, Philipp Hofbauer, and Matthias Struck
Atmos. Meas. Tech., 16, 4975–5007, https://doi.org/10.5194/amt-16-4975-2023, https://doi.org/10.5194/amt-16-4975-2023, 2023
Short summary
Short summary
Measured power data from solar photovoltaic (PV) systems contain information about the state of the atmosphere. In this work, power data from PV systems in the Allgäu region in Germany were used to determine the solar irradiance at each location, using state-of-the-art simulation and modelling. The results were validated using concurrent measurements of the incoming solar radiation in each case. If applied on a wider scale, this algorithm could help improve weather and climate models.
Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer
Atmos. Chem. Phys., 23, 13087–13106, https://doi.org/10.5194/acp-23-13087-2023, https://doi.org/10.5194/acp-23-13087-2023, 2023
Short summary
Short summary
Observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) were used to determine the frequency of occurrence of various central Arctic lower atmospheric stability regimes and how the stability regimes transition between each other. Wind and radiation observations were analyzed in the context of stability regime and season to reveal the relationships between Arctic atmospheric stability and mechanically and radiatively driven turbulent forcings.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Anja Hünerbein, Sebastian Bley, Stefan Horn, Hartwig Deneke, and Andi Walther
Atmos. Meas. Tech., 16, 2821–2836, https://doi.org/10.5194/amt-16-2821-2023, https://doi.org/10.5194/amt-16-2821-2023, 2023
Short summary
Short summary
The Multi-Spectral Imager (MSI) on board the EarthCARE satellite will provide the information needed for describing the cloud and aerosol properties in the cross-track direction, complementing the measurements from the Cloud Profiling Radar, Atmospheric Lidar and Broad-Band Radiometer. The accurate discrimination between clear and cloudy pixels is an essential first step. Therefore, the cloud mask algorithm provides a cloud flag, cloud phase and cloud type product for the MSI observations.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Kevin Ohneiser, Albert Ansmann, Jonas Witthuhn, Hartwig Deneke, Alexandra Chudnovsky, Gregor Walter, and Fabian Senf
Atmos. Chem. Phys., 23, 2901–2925, https://doi.org/10.5194/acp-23-2901-2023, https://doi.org/10.5194/acp-23-2901-2023, 2023
Short summary
Short summary
This study shows that smoke layers can reach the tropopause via the self-lofting effect within 3–7 d in the absence of pyrocumulonimbus convection if the
aerosol optical thickness is larger than approximately 2 for a longer time period. When reaching the stratosphere, wildfire smoke can sensitively influence the stratospheric composition on a hemispheric scale and thus can affect the Earth’s climate and the ozone layer.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Gijs de Boer, Steven Borenstein, Radiance Calmer, Christopher Cox, Michael Rhodes, Christopher Choate, Jonathan Hamilton, Jackson Osborn, Dale Lawrence, Brian Argrow, and Janet Intrieri
Earth Syst. Sci. Data, 14, 19–31, https://doi.org/10.5194/essd-14-19-2022, https://doi.org/10.5194/essd-14-19-2022, 2022
Short summary
Short summary
This article provides a summary of the collection of atmospheric data over the near-coastal zone upwind of Barbados during the ATOMIC and EUREC4A field campaigns. These data were collected to improve our understanding of the structure and dynamics of the lower atmosphere in the tropical trade-wind regime over the Atlantic Ocean and the influence of that portion of the atmosphere on the development and maintenance of clouds.
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
Short summary
Short summary
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.
Jonas Witthuhn, Anja Hünerbein, Florian Filipitsch, Stefan Wacker, Stefanie Meilinger, and Hartwig Deneke
Atmos. Chem. Phys., 21, 14591–14630, https://doi.org/10.5194/acp-21-14591-2021, https://doi.org/10.5194/acp-21-14591-2021, 2021
Short summary
Short summary
Knowledge of aerosol–radiation interactions is important for understanding the climate system and for the renewable energy sector. Here, two complementary approaches are used to assess the consistency of the underlying aerosol properties and the resulting radiative effect in clear-sky conditions over Germany in 2015. An approach based on clear-sky models and broadband irradiance observations is contrasted to the use of explicit radiative transfer simulations using CAMS reanalysis data.
Sean Horvath, Linette Boisvert, Chelsea Parker, Melinda Webster, Patrick Taylor, and Robyn Boeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-297, https://doi.org/10.5194/tc-2021-297, 2021
Preprint withdrawn
Short summary
Short summary
Arctic sea ice has been experiencing a dramatic decline since the late 1970s. A database is presented that combines satellite observations with daily sea ice parcel drift tracks. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. This has multiple applications for the scientific community that can shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.
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
Short summary
Short summary
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.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
Short summary
Short summary
The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
J. Brant Dodson, Patrick C. Taylor, Richard H. Moore, David H. Bromwich, Keith M. Hines, Kenneth L. Thornhill, Chelsea A. Corr, Bruce E. Anderson, Edward L. Winstead, and Joseph R. Bennett
Atmos. Chem. Phys., 21, 11563–11580, https://doi.org/10.5194/acp-21-11563-2021, https://doi.org/10.5194/acp-21-11563-2021, 2021
Short summary
Short summary
Aircraft in situ observations of low-level Beaufort Sea cloud properties and thermodynamics from the ARISE campaign are compared with the Arctic System Reanalysis (ASR) to better understand deficiencies in simulated clouds. ASR produces too little cloud water, which coincides with being too warm and dry. In addition, ASR struggles to produce cloud water even in favorable thermodynamic conditions. A random sampling experiment also shows the effects of the limited aircraft sampling on the results.
Hartwig Deneke, Carola Barrientos-Velasco, Sebastian Bley, Anja Hünerbein, Stephan Lenk, Andreas Macke, Jan Fokke Meirink, Marion Schroedter-Homscheidt, Fabian Senf, Ping Wang, Frank Werner, and Jonas Witthuhn
Atmos. Meas. Tech., 14, 5107–5126, https://doi.org/10.5194/amt-14-5107-2021, https://doi.org/10.5194/amt-14-5107-2021, 2021
Short summary
Short summary
The SEVIRI instrument flown on the European geostationary Meteosat satellites acquires multi-spectral images at a relatively coarse pixel resolution of 3 × 3 km2, but it also has a broadband high-resolution visible channel with 1 × 1 km2 spatial resolution. In this study, the modification of an existing cloud property and solar irradiance retrieval to use this channel to improve the spatial resolution of its output products as well as the resulting benefits for applications are described.
Hong Chen, Sebastian Schmidt, Michael D. King, Galina Wind, Anthony Bucholtz, Elizabeth A. Reid, Michal Segal-Rozenhaimer, William L. Smith, Patrick C. Taylor, Seiji Kato, and Peter Pilewskie
Atmos. Meas. Tech., 14, 2673–2697, https://doi.org/10.5194/amt-14-2673-2021, https://doi.org/10.5194/amt-14-2673-2021, 2021
Short summary
Short summary
In this paper, we accessed the shortwave irradiance derived from MODIS cloud optical properties by using aircraft measurements. We developed a data aggregation technique to parameterize spectral surface albedo by snow fraction in the Arctic. We found that undetected clouds have the most significant impact on the imagery-derived irradiance. This study suggests that passive imagery cloud detection could be improved through a multi-pixel approach that would make it more dependable in the Arctic.
Christopher J. Cox, Sara M. Morris, Taneil Uttal, Ross Burgener, Emiel Hall, Mark Kutchenreiter, Allison McComiskey, Charles N. Long, Bryan D. Thomas, and James Wendell
Atmos. Meas. Tech., 14, 1205–1224, https://doi.org/10.5194/amt-14-1205-2021, https://doi.org/10.5194/amt-14-1205-2021, 2021
Short summary
Short summary
Solar and infrared radiation are measured regularly for research, industry, and climate monitoring. In cold climates, icing of sensors is a poorly constrained source of uncertainty. D-ICE was carried out in Alaska to document the effectiveness of ice-mitigation technology and quantify errors associated with ice. Technology was more effective than anticipated, and while instantaneous errors were large, mean biases were small. Attributes of effective ice mitigation design were identified.
Jessie M. Creamean, Gijs de Boer, Hagen Telg, Fan Mei, Darielle Dexheimer, Matthew D. Shupe, Amy Solomon, and Allison McComiskey
Atmos. Chem. Phys., 21, 1737–1757, https://doi.org/10.5194/acp-21-1737-2021, https://doi.org/10.5194/acp-21-1737-2021, 2021
Short summary
Short summary
Arctic clouds play a role in modulating sea ice extent. Importantly, aerosols facilitate cloud formation, and thus it is crucial to understand the interactions between aerosols and clouds. Vertical measurements of aerosols and clouds are needed to tackle this issue. We present results from balloon-borne measurements of aerosols and clouds over the course of 2 years in northern Alaska. These data shed light onto the vertical distributions of aerosols relative to clouds spanning multiple seasons.
Peggy Achtert, Ewan J. O'Connor, Ian M. Brooks, Georgia Sotiropoulou, Matthew D. Shupe, Bernhard Pospichal, Barbara J. Brooks, and Michael Tjernström
Atmos. Chem. Phys., 20, 14983–15002, https://doi.org/10.5194/acp-20-14983-2020, https://doi.org/10.5194/acp-20-14983-2020, 2020
Short summary
Short summary
We present observations of precipitating and non-precipitating Arctic liquid and mixed-phase clouds during a research cruise along the Russian shelf in summer and autumn of 2014. Active remote-sensing observations, radiosondes, and auxiliary measurements are combined in the synergistic Cloudnet retrieval. Cloud properties are analysed with respect to cloud-top temperature and boundary layer structure. About 8 % of all liquid clouds show a liquid water path below the infrared black body limit.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
Short summary
Short summary
Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Hannes J. Griesche, Patric Seifert, Albert Ansmann, Holger Baars, Carola Barrientos Velasco, Johannes Bühl, Ronny Engelmann, Martin Radenz, Yin Zhenping, and Andreas Macke
Atmos. Meas. Tech., 13, 5335–5358, https://doi.org/10.5194/amt-13-5335-2020, https://doi.org/10.5194/amt-13-5335-2020, 2020
Short summary
Short summary
In summer 2017, the research vessel Polarstern performed cruise PS106 to the Arctic north of Svalbard. In the frame of the cruise, remote-sensing observations of the atmosphere were performed on Polarstern to continuously monitor aerosol and clouds above the vessel. In our study, we present the deployed instrumentation and applied data analysis methods and provide case studies of the aerosol and cloud observations made during the cruise. Statistics of low-cloud occurrence are presented as well.
Cited articles
Barker, H. W., Stephens, G. L., Partain, P. T., Bergman, J. W., Bonnel, B., Campana, K., Clothiaux, E. E., Clough, S., Cusack, S., Delamere, J., Edwards, J., Evans, K. F., Fouquart, Y., Freidenreich, S., Galin, V., Hou, Y., Kato, S., Li, J., Mlawer, E., Morcrette, J.-J., O'Hirok, W., Räisänen, P., Ramaswamy, V., Ritter, B., Rozanov, E., Schlesinger, M., Shibata, K., Sporyshev, P., Sun, Z., Wendisch, M., Wood, N., and Yang, F.: Assessing 1D Atmospheric Solar Radiative Transfer Models: Interpretation and Handling of Unresolved Clouds, J. Climate, 16, 2676–2699, https://doi.org/10.1175/1520-0442(2003)016<2676:ADASRT>2.0.CO;2, 2003. a
Barlakas, V., Deneke, H., and Macke, A.: The sub-adiabatic model as a concept for evaluating the representation and radiative effects of low-level clouds in a high-resolution atmospheric model, Atmos. Chem. Phys., 20, 303–322, https://doi.org/10.5194/acp-20-303-2020, 2020. a
Barrientos-Velasco, C.: 1D broadband radiative transfer model simulations for the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, Zenodo [data set], https://doi.org/10.5281/zenodo.12514679, 2024. a, b, c
Barrientos Velasco, C., Deneke, H., Griesche, H., Seifert, P., Engelmann, R., and Macke, A.: Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice, Atmos. Meas. Tech., 13, 1757–1775, https://doi.org/10.5194/amt-13-1757-2020, 2020. a
Barrientos-Velasco, C., Deneke, H., Hünerbein, A., Griesche, H. J., Seifert, P., and Macke, A.: Radiative closure and cloud effects on the radiation budget based on satellite and shipborne observations during the Arctic summer research cruise, PS106, Atmos. Chem. Phys., 22, 9313–9348, https://doi.org/10.5194/acp-22-9313-2022, 2022. a, b, c, d, e, f, g
Boyer, M., Aliaga, D., Pernov, J. B., Angot, H., Quéléver, L. L. J., Dada, L., Heutte, B., Dall'Osto, M., Beddows, D. C. S., Brasseur, Z., Beck, I., Bucci, S., Duetsch, M., Stohl, A., Laurila, T., Asmi, E., Massling, A., Thomas, D. C., Nøjgaard, J. K., Chan, T., Sharma, S., Tunved, P., Krejci, R., Hansson, H. C., Bianchi, F., Lehtipalo, K., Wiedensohler, A., Weinhold, K., Kulmala, M., Petäjä, T., Sipilä, M., Schmale, J., and Jokinen, T.: A full year of aerosol size distribution data from the central Arctic under an extreme positive Arctic Oscillation: insights from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, Atmos. Chem. Phys., 23, 389–415, https://doi.org/10.5194/acp-23-389-2023, 2023. a
Cadeddu, M. P., Marchand, R., Orlandi, E., Turner, D. D., and Mech, M.: Microwave Passive Ground-Based Retrievals of Cloud and Rain Liquid Water Path in Drizzling Clouds: Challenges and Possibilities, IEEE T. Geosci. Remote, 55, 6468–6481, https://doi.org/10.1109/TGRS.2017.2728699, 2017. a
Cesana, G. V., Pierpaoli, O., Ottaviani, M., Vu, L., Jin, Z., and Silber, I.: The correlation between Arctic sea ice, cloud phase and radiation using A-Train satellites, Atmos. Chem. Phys., 24, 7899–7909, https://doi.org/10.5194/acp-24-7899-2024, 2024. a
Christensen, M. W., Behrangi, A., L’ecuyer, T. S., Wood, N. B., Lebsock, M. D., and Stephens, G. L.: Arctic Observation and Reanalysis Integrated System: A New Data Product for Validation and Climate Study, B. Am. Meteorol. Soc., 97, 907–916, https://doi.org/10.1175/BAMS-D-14-00273.1, 2016. a
Clough, S., Shephard, M., Mlawer, E., Delamere, J., Iacono, M., Cady-Pereira, K., Boukabara, S., and Brown, P.: Atmospheric radiative transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005. a
Collins, W. D., Rasch, P. J., Eaton, B. E., Khattatov, B. V., Lamarque, J.-F., and Zender, C. S.: Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: Methodology for INDOEX, J. Geophys. Res.-Atmos., 106, 7313–7336, https://doi.org/10.1029/2000JD900507, 2001. a
Cox, C., Gallagher, M., Shupe, M., Persson, O., Blomquist, B., Grachev, A., Riihimaki, L., Kutchenreiter, M., Morris, V., Solomon, A., Brooks, I., Costa, D., Gottas, D., Hutchings, J., Osborn, J., Morris, S., Preusser, A., and Uttal, T.: Met City meteorological and surface flux measurements (Level 3 Final), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 - September 2020, Arctic Data Center [data set], https://doi.org/10.18739/A2PV6B83F, 2023a. a, b, c
Cox, C., Gallagher, M., Shupe, M., Persson, O., Grachev, A., Solomon, A., Ayers, T., Costa, D., Hutchings, J., Leach, J., Morris, S., Osborn, J., Pezoa, S., and Uttal, T.: Atmospheric Surface Flux Station 30 measurements (Level 3 Final), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 - September 2020, Arctic Data Center [data set], https://doi.org/10.18739/A2FF3M18K, 2023b. a, b, c, d
Cox, C., Gallagher, M., Shupe, M., Persson, O., Grachev, A., Solomon, A., Ayers, T., Costa, D., Hutchings, J., Leach, J., Morris, S., Osborn, J., Pezoa, S., and Uttal, T.: Atmospheric Surface Flux Station 40 measurements (Level 3 Final), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 - September 2020, Arctic Data Center [data set], https://doi.org/10.18739/A25X25F0P, 2023c. a, b
Cox, C., Gallagher, M., Shupe, M., Persson, O., Grachev, A., Solomon, A., Ayers, T., Costa, D., Hutchings, J., Leach, J., Morris, S., Osborn, J., Pezoa, S., and Uttal, T.: Atmospheric Surface Flux Station 50 measurements (Level 3 Final), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 - September 2020, Arctic Data Center [data set], https://doi.org/10.18739/A2XD0R00S, 2023d. a, b, c
Cox, C. J., Gallagher, M. R., Shupe, M. D., Persson, P. O. G., Solomon, A., Fairall, C. W., Ayers, T., Blomquist, B., Brooks, I. M., Costa, D., Grachev, A., Gottas, D., Hutchings, J. K., Kutchenreiter, M., Leach, J., Morris, S. M., Morris, V., Osborn, J., Pezoa, S., Preußer, A., Riihimaki, L. D., and Uttal, T.: Continuous observations of the surface energy budget and meteorology over the Arctic sea ice during MOSAiC, Scientific Data, 10, 519, https://doi.org/10.1038/s41597-023-02415-5, 2023e. a, b, c, d, e, f, g, h, i, j
Dada, L., Angot, H., Beck, I., Baccarini, A., Quéléver, L. L. J., Boyer, M., Laurila, T., Brasseur, Z., Jozef, G., de Boer, G., Shupe, M. D., Henning, S., Bucci, S., Dütsch, M., Stohl, A., Petäjä, T., Daellenbach, K. R., Jokinen, T., and Schmale, J.: A central arctic extreme aerosol event triggered by a warm air-mass intrusion, Nat. Commun., 13, 5290, https://doi.org/10.1038/s41467-022-32872-2, 2022. a
Dahlke, S., Shupe, M. D., Cox, C. J., Brooks, I. M., Blomquist, B., and Persson, P. O. G.: Extended radiosonde profiles 2019/09-2020/10 during MOSAiC Legs PS122/1 - PS122/5, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.961881, 2023. a, b, c
Deneke, H.: hdeneke/pyRRTMG: Release with correct versioning scheme, Version 0.9.1, Zenodo [code], https://doi.org/10.5281/zenodo.11147087, 2024. a, b
Dong, X., Xi, B., Qiu, S., Minnis, P., Sun-Mack, S., and Rose, F.: A radiation closure study of Arctic stratus cloud microphysical properties using the collocated satellite-surface data and Fu-Liou radiative transfer model, J. Geophys. Res.-Atmos., 121, 10175–10198, https://doi.org/10.1002/2016JD025255, 2016. a, b
Duncan, B. N., Ott, L. E., Abshire, J. B., Brucker, L., Carroll, M. L., Carton, J., Comiso, J. C., Dinnat, E. P., Forbes, B. C., Gonsamo, A., Gregg, W. W., Hall, D. K., Ialongo, I., Jandt, R., Kahn, R. A., Karpechko, A., Kawa, S. R., Kato, S., Kumpula, T., Kyrölä, E., Loboda, T. V., McDonald, K. C., Montesano, P. M., Nassar, R., Neigh, C. S., Parkinson, C. L., Poulter, B., Pulliainen, J., Rautiainen, K., Rogers, B. M., Rousseaux, C. S., Soja, A. J., Steiner, N., Tamminen, J., Taylor, P. C., Tzortziou, M. A., Virta, H., Wang, J. S., Watts, J. D., Winker, D. M., and Wu, D. L.: Space-Based Observations for Understanding Changes in the Arctic-Boreal Zone, Rev. Geophys., 58, e2019RG000652, https://doi.org/10.1029/2019RG000652, 2020. a
Dunn, M., Johnson, K., and Jensen, M.: The Microbase Value-Added Product: A Baseline Retrieval of Cloud Microphysical Properties, Atmospheric Radiation Measurement (ARM) Climate Research Facility, DOE/SC-ARM/TR-095, https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-095.pdf (last access: 26 March 2025), 2011. a
Dutton, G., Hall, B., Dlugokencky, E., Lan, X., Nance, J., and Madronich, M.: Combined Atmospheric Nitrous Oxide Dry Air Mole Fractions from the NOAA GML Halocarbons Sampling Network, 1977-2023, version: 2023-04-13, NOAA GML [data set], https://doi.org/10.15138/GMZ7-2Q16, 2023a. a
Dutton, G., Hall, B., Montzka, S., and Nance, J.: Combined Atmospheric Carbon Tetrachloride Dry Air Mole Fractions from the NOAA GML Halocarbons Sampling Network, 1995-2023, version: 2023-04-13, NOAA GML [data set], https://doi.org/10.15138/CV0A-J604, 2023b. a
Dutton, G., Hall, S., Montzka, J., and Nance, J.: Combined Atmospheric Chlorofluorocarbon-12 Dry Air Mole Fractions from the NOAA GML Halocarbon Sampling Network, 1977-2023, version: 2023-04-13, NOAA GML [data set], https://doi.org/10.15138/PJ63-H440, 2023c. a
Dutton, G., Hall, S., Montzka, J. D., and Nance, J.: Combined Atmospheric Chlorofluorocarbon-11 Dry Air Mole Fractions from the NOAA GML Halocarbons Sampling Network, 1977-2023, version: 2023-04-13, NOAA GML [data set], https://doi.org/10.15138/BVQ6-2S69, 2023d. a
Eastman, R. and Warren, S. G.: Arctic Cloud Changes from Surface and Satellite Observations, J. Climate, 23, 4233–4242, https://doi.org/10.1175/2010JCLI3544.1, 2010. a
Ebell, K., Nomokonova, T., Maturilli, M., and Ritter, C.: Radiative Effect of Clouds at Ny-Ålesund, Svalbard, as Inferred from Ground-Based Remote Sensing Observations, J. Appl. Meteorol. Clim., 59, 3–22, https://doi.org/10.1175/JAMC-D-19-0080.1, 2020. a, b, c, d
Ebell, K., Walbröl, A., Engelmann, R., Griesche, H., Radenz, M., Hofer, J., and Althausen, D.: Temperature and humidity profiles, integrated water vapour and liquid water path derived from the HATPRO microwave radiometer onboard the Polarstern during the MOSAiC expedition, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.941389, 2022. a
Fu, Q. and Liou, K. N.: On the Correlated k-Distribution Method for Radiative Transfer in Nonhomogeneous Atmospheres, J. Atmos. Sci., 49, 2139–2156, https://doi.org/10.1175/1520-0469(1992)049<2139:OTCDMF>2.0.CO;2, 1992. 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, b
Griesche, H. J., Seifert, P., Engelmann, R., Radenz, M., Hofer, J., Althausen, D., Walbröl, A., Barrientos-Velasco, C., Baars, H., Dahlke, S., Tukiainen, S., 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
Gu, B.: Evaluations and Improvements of the RRTMG and Fu-Liou Radiative Transfer Model Simulations of Clouds, Doctoral dissertation, Texas A&M University, https://hdl.handle.net/1969.1/189047 (last access: 15 July 2023), 2019. a
Gupta, S. K., Kratz, D. P., Stackhouse, Paul W., J., Wilber, A. C., Zhang, T., and Sothcott, V. E.: Improvement of Surface Longwave Flux Algorithms Used in CERES Processing, J. Appl. Meteorol. Clim., 49, 1579–1589, https://doi.org/10.1175/2010JAMC2463.1, 2010. a
Hartmann, D. L. and Ceppi, P.: Trends in the CERES Dataset, 2000–13: The Effects of Sea Ice and Jet Shifts and Comparison to Climate Models, J. Climate, 27, 2444–2456, https://doi.org/10.1175/JCLI-D-13-00411.1, 2014. a
Heinemann, G., Schefczyk, L., Zentek, R., Brooks, I. M., Dahlke, S., and Walbröl, A.: Evaluation of Vertical Profiles and Atmospheric Boundary Layer Structure Using the Regional Climate Model CCLM during MOSAiC, Meteorology, 2, 257–275, https://doi.org/10.3390/meteorology2020016, 2023. 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, Elementa: Science of the Anthropocene, 11, 00085, https://doi.org/10.1525/elementa.2022.00085, 2023. a, b, c
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 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018a. 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 single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018b. 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, b, c
Hu, Y. X. and Stamnes, K.: An Accurate Parameterization of the Radiative Properties of Water Clouds Suitable for Use in Climate Models, J. Climate, 6, 728–742, https://doi.org/10.1175/1520-0442(1993)006<0728:AAPOTR>2.0.CO;2, 1993. a
Huang, Y., Dong, X., Xi, B., Dolinar, E. K., Stanfield, R. E., and Qiu, S.: Quantifying the Uncertainties of Reanalyzed Arctic Cloud and Radiation Properties Using Satellite Surface Observations, J. Climate, 30, 8007–8029, https://doi.org/10.1175/JCLI-D-16-0722.1, 2017. a, b
Huang, Y., Taylor, P. C., Rose, F. G., Rutan, D. A., Shupe, M. D., Webster, M. A., and Smith, M. M.: Toward a more realistic representation of surface albedo in NASA CERES-derived surface radiative fluxes: A comparison with the MOSAiC field campaign: Comparison of CERES and MOSAiC surface radiation fluxes, Elementa: Science of the Anthropocene, 10, 00013, https://doi.org/10.1525/elementa.2022.00013, 2022. a, b, c, d, e, f, g, h
Intrieri, J. M., Fairall, C. W., Shupe, M. D., Persson, P. O. G., Andreas, E. L., Guest, P. S., and Moritz, R. E.: An annual cycle of Arctic surface cloud forcing at SHEBA, J. Geophys. Res.-Oceans, 107, SHE 13-1–SHE 13-14, https://doi.org/10.1029/2000JC000439, 2002. a, b, c
Jäkel, E., Becker, S., Sperzel, T. R., Niehaus, H., Spreen, G., Tao, R., Nicolaus, M., Dorn, W., Rinke, A., Brauchle, J., and Wendisch, M.: Observations and modeling of areal surface albedo and surface types in the Arctic, The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, 2024. a, b
Jensen, M., Giangrande, S., Fairless, T., and Zhou, A.: interpolatedsonde, Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1095316, 1998. a
Jin, Z., Charlock, T. P., Smith Jr., W. L., and Rutledge, K.: A parameterization of ocean surface albedo, Geophys. Res. Lett., 31, L22301, https://doi.org/10.1029/2004GL021180, 2004. a
Johnson, K., Giangrande, S., and Toto, T.: KAZRARSCL-c0-All-inclusive data stream, Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1393437, 2014. a, b
Jozef, G. C., Cassano, J. J., Dahlke, S., Dice, M., Cox, C. J., and de Boer, G.: Thermodynamic and kinematic drivers of atmospheric boundary layer stability in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), Atmos. Chem. Phys., 23, 13087–13106, https://doi.org/10.5194/acp-23-13087-2023, 2023. a
Kato, S., Rose, F. G., and Charlock, T. P.: Computation of Domain-Averaged Irradiance Using Satellite-Derived Cloud Properties, J. Atmos. Ocean. Tech., 22, 146–164, https://doi.org/10.1175/JTECH-1694.1, 2005. a
Kato, S., Loeb, N. G., Rutan, D. A., Rose, F. G., Sun-Mack, S., Miller, W. F., and Chen, Y.: Uncertainty Estimate of Surface Irradiances Computed with MODIS-, CALIPSO-, and CloudSat-Derived Cloud and Aerosol Properties, Surv. Geophys., 33, 395–412, https://doi.org/10.1007/s10712-012-9179-x, 2012. a
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling, D. R., Huang, X., Smith, W. L., Su, W., and Ham, S.-H.: Surface Irradiances of Edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product, J. Climate, 31, 4501–4527, https://doi.org/10.1175/JCLI-D-17-0523.1, 2018. a
Kay, J. E., L'Ecuyer, T., Chepfer, H., Loeb, N., Morrison, A., and Cesana, G.: Recent Advances in Arctic Cloud and Climate Research, Current Climate Change Reports, 2, 159–169, https://doi.org/10.1007/s40641-016-0051-9, 2016. a
Key, J. R.: Streamer User's Guide, NOAA/NESDIS, Madison, Wisconsin, https://geocryos.ssec.wisc.edu/streamer/userman.pdf (last access: 27 March 2025), 1996. 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, Frontiers in Earth Science, 11, 1147848, https://doi.org/10.3389/feart.2023.1147848, 2023. a, b
Krumpen, T., von Albedyll, L., Goessling, H. F., Hendricks, S., Juhls, B., Spreen, G., Willmes, S., Belter, H. J., Dethloff, K., Haas, C., Kaleschke, L., Katlein, C., Tian-Kunze, X., Ricker, R., Rostosky, P., Rückert, J., Singha, S., and Sokolova, J.: MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years, The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, 2021. a, b
Lan, X., Dlugokencky, E., Mund, J., Crotwell, A., Crotwell, M., Moglia, E., Madronich, M., Neff, D., and Thoning, K.: Atmospheric Carbon Dioxide Dry Air Mole Fractions from the NOAA GML Carbon Cycle Cooperative Global Air Sampling Network, 1968-2021, version: 2022-11-21, NOAA GML [data set], https://doi.org/10.15138/wkgj-f215, 2022a. a
Lan, X., Dlugokencky, E., Mund, J., Crotwell, A., Crotwell, M., Moglia, E., Madronich, M., Neff, D., and Thoning, K.: Atmospheric Methane Dry Air Mole Fractions from the NOAA GML Carbon Cycle Cooperative Global Air Sampling Network, 1983-2021, version: 2022-11-21, NOAA GML [data set], https://doi.org/10.15138/VNCZ-M766, 2022b. a
Lanconelli, C., Busetto, M., Dutton, E. G., König-Langlo, G., Maturilli, M., Sieger, R., Vitale, V., and Yamanouchi, T.: Polar baseline surface radiation measurements during the International Polar Year 2007–2009, Earth Syst. Sci. Data, 3, 1–8, https://doi.org/10.5194/essd-3-1-2011, 2011. a
Lawrence, Z. D., Perlwitz, J., Butler, A. H., Manney, G. L., Newman, P. A., Lee, S. H., and Nash, E. R.: The Remarkably Strong Arctic Stratospheric Polar Vortex of Winter 2020: Links to Record-Breaking Arctic Oscillation and Ozone Loss, J. Geophys. Res.-Atmos., 125, e2020JD033271, https://doi.org/10.1029/2020JD033271, 2020. a
Lelli, L., Vountas, M., Khosravi, N., and Burrows, J. P.: Satellite remote sensing of regional and seasonal Arctic cooling showing a multi-decadal trend towards brighter and more liquid clouds, Atmos. Chem. Phys., 23, 2579–2611, https://doi.org/10.5194/acp-23-2579-2023, 2023. a
Light, B., Smith, M., Perovich, D., Webster, M., Holland, M., Linhardt, F., Raphael, I., Clemens-Sewall, D., Macfarlane, A., Anhaus, P., and Bailey, D.: Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift, Elementa: Science of the Anthropocene, 10, 000103, https://doi.org/10.1525/elementa.2021.000103, 2022. a, b
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018. a
López-García, V., Neely, Ryan R., I., Dahlke, S., and Brooks, I. M.: Low-level jets over the Arctic Ocean during MOSAiC, Elementa: Science of the Anthropocene, 10, 00063, https://doi.org/10.1525/elementa.2022.00063, 2022. a
Mace, G. G., Benson, S., and Kato, S.: Cloud radiative forcing at the Atmospheric Radiation Measurement Program Climate Research Facility: 2. Vertical redistribution of radiant energy by clouds, J. Geophys. Res.-Atmos., 111, D11S91, https://doi.org/10.1029/2005JD005922, 2006. a
Macke, A. and Flores, H.: The Expeditions PS106/1 and 2 of the Research Vessel POLARSTERN to the Arctic Ocean in 2017, Berichte zur Polar- und Meeresforschung, 719, 1–171, https://doi.org/10.2312/BzPM_0719_2018, 2018. a
Maturilli, M., Holdridge, D. J., Dahlke, S., Graeser, J., Sommerfeld, A., Jaiser, R., Deckelmann, H., and Schulz, A.: Initial radiosonde data from 2019-10 to 2020-09 during project MOSAiC, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.928656, 2021. a
Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M., Ottersen, G., Pritchard, H., and Schuur, E.: Polar Regions, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 203–320, https://doi.org/10.1017/9781009157964.005, 2019. a
Miller, M. A., Nitschke, K., Ackerman, T. P., Ferrell, W. R., Hickmon, N., and Ivey, M.: The ARM Mobile Facilities, Meteor. Mon., 57, 9.1–9.15, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0051.1, 2016. a
Miller, N. B., Shupe, M. D., Cox, C. J., Walden, V. P., Turner, D. D., and Steffen, K.: Cloud Radiative Forcing at Summit, Greenland, J. Climate, 28, 6267–6280, https://doi.org/10.1175/JCLI-D-15-0076.1, 2015. a, b, c
Minnis, P., Sun-Mack, S., Chen, Y., Chang, F., Yost, C. R., Smith, W. L., Heck, P. W., Arduini, R. F., Bedka, S. T., Yi, Y., Hong, G., Jin, Z., Painemal, D., Palikonda, R., Scarino, B. R., Spangenberg, D. A., Smith, R. A., Trepte, Q. Z., Yang, P., and Xie, Y.: CERES MODIS Cloud Product Retrievals for Edition 4–Part I: Algorithm Changes, IEEE T. Geosci. Remote, 59, 2744–2780, https://doi.org/10.1109/TGRS.2020.3008866, 2021. a, b, c
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Montzka, S.: HCFCs, Halons, Methyl Chloroform, HFC-134a and HFC-152a, NOAA GML, https://gml.noaa.gov/hats/data.html (last access: 1 March 2023), 2024. a
Morris, V., Zhang, D., and Ermold, B.: ceil, Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1181954, 1996. a, b
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012. a
Murray-Watson, R. J., Gryspeerdt, E., and Goren, T.: Investigating the development of clouds within marine cold-air outbreaks, Atmos. Chem. Phys., 23, 9365–9383, https://doi.org/10.5194/acp-23-9365-2023, 2023. a
NASA/LARC/SD/ASDC: CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols 1-Hourly Terra-Aqua Edition4A, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/TERRA+AQUA/CERES/SYN1DEG-1HOUR_L3.004A, 2017. a
Nicolaus, M., Perovich, D. K., Spreen, G., Granskog, M. A., von Albedyll, L., Angelopoulos, M., Anhaus, P., Arndt, S., Belter, H. J., Bessonov, V., Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B., Clemens-Sewall, D., Dadic, R., Damm, E., de Boer, G., Demir, O., Dethloff, K., Divine, D. V., Fong, A. A., Fons, S., Frey, M. M., Fuchs, N., Gabarró, C., Gerland, S., Goessling, H. F., Gradinger, R., Haapala, J., Haas, C., Hamilton, J., Hannula, H.-R., Hendricks, S., Herber, A., Heuzé, C., Hoppmann, M., Høyland, K. V., Huntemann, M., Hutchings, J. K., Hwang, B., Itkin, P., Jacobi, H.-W., Jaggi, M., Jutila, A., Kaleschke, L., Katlein, C., Kolabutin, N., Krampe, D., Kristensen, S. S., Krumpen, T., Kurtz, N., Lampert, A., Lange, B. A., Lei, R., Light, B., Linhardt, F., Liston, G. E., Loose, B., Macfarlane, A. R., Mahmud, M., Matero, I. O., Maus, S., Morgenstern, A., Naderpour, R., Nandan, V., Niubom, A., Oggier, M., Oppelt, N., Pätzold, F., Perron, C., Petrovsky, T., Pirazzini, R., Polashenski, C., Rabe, B., Raphael, I. A., Regnery, J., Rex, M., Ricker, R., Riemann-Campe, K., Rinke, A., Rohde, J., Salganik, E., Scharien, R. K., Schiller, M., Schneebeli, M., Semmling, M., Shimanchuk, E., Shupe, M. D., Smith, M. M., Smolyanitsky, V., Sokolov, V., Stanton, T., Stroeve, J., Thielke, L., Timofeeva, A., Tonboe, R. T., Tavri, A., Tsamados, M., Wagner, D. N., Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC expedition: Snow and sea ice, Elementa: Science of the Anthropocene, 10, 000046, https://doi.org/10.1525/elementa.2021.000046, 2022. a, b
NOAA: Global Ocean Monitoring and Observing Program (GOMO), https://doi.org/10.13039/100018302, 2021 a
NOAA-AGGI: The NOAA Annual Greenhouse Gas Index, Global Monitoring Laboratory, National Oceanic and Atmospheric Administration (NOAA), https://gml.noaa.gov/aggi/aggi.html (last access: 1 March 2024), 2024. a
Pithan, F., Athanase, M., Dahlke, S., Sánchez-Benítez, A., Shupe, M. D., Sledd, A., Streffing, J., Svensson, G., and Jung, T.: Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition, Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, 2023. a
Rabe, B., Heuzé, C., Regnery, J., Aksenov, Y., Allerholt, J., Athanase, M., Bai, Y., Basque, C., Bauch, D., Baumann, T. M., Chen, D., Cole, S. T., Craw, L., Davies, A., Damm, E., Dethloff, K., Divine, D. V., Doglioni, F., Ebert, F., Fang, Y.-C., Fer, I., Fong, A. A., Gradinger, R., Granskog, M. A., Graupner, R., Haas, C., He, H., He, Y., Hoppmann, M., Janout, M., Kadko, D., Kanzow, T., Karam, S., Kawaguchi, Y., Koenig, Z., Kong, B., Krishfield, R. A., Krumpen, T., Kuhlmey, D., Kuznetsov, I., Lan, M., Laukert, G., Lei, R., Li, T., Torres-Valdés, S., Lin, L., Lin, L., Liu, H., Liu, N., Loose, B., Ma, X., McKay, R., Mallet, M., Mallett, R. D. C., Maslowski, W., Mertens, C., Mohrholz, V., Muilwijk, M., Nicolaus, M., O’Brien, J. K., Perovich, D., Ren, J., Rex, M., Ribeiro, N., Rinke, A., Schaffer, J., Schuffenhauer, I., Schulz, K., Shupe, M. D., Shaw, W., Sokolov, V., Sommerfeld, A., Spreen, G., Stanton, T., Stephens, M., Su, J., Sukhikh, N., Sundfjord, A., Thomisch, K., Tippenhauer, S., Toole, J. M., Vredenborg, M., Walter, M., Wang, H., Wang, L., Wang, Y., Wendisch, M., Zhao, J., Zhou, M., and Zhu, J.: Overview of the MOSAiC expedition: Physical oceanography, Elementa: Science of the Anthropocene, 10, 00062, https://doi.org/10.1525/elementa.2021.00062, 2022. a
Rabe, B., Cox, C. J., Fang, Y.-C., Goessling, H., Granskog, M. A., Hoppmann, M., Hutchings, J. K., Krumpen, T., Kuznetsov, I., Lei, R., Li, T., Maslowski, W., Nicolaus, M., Perovich, D., Persson, O., Regnery, J., Rigor, I., Shupe, M. D., Sokolov, V., Spreen, G., Stanton, T., Watkins, D. M., Blockley, E., Buenger, H. J., Cole, S., Fong, A., Haapala, J., Heuzé, C., Hoppe, C. J. M., Janout, M., Jutila, A., Katlein, C., Krishfield, R., Lin, L., Ludwig, V., Morgenstern, A., O’Brien, J., Zurita, A. Q., Rackow, T., Riemann-Campe, K., Rohde, J., Shaw, W., Smolyanitsky, V., Solomon, A., Sperling, A., Tao, R., Toole, J., Tsamados, M., Zhu, J., and Zuo, G.: The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms, Elementa: Science of the Anthropocene, 12, 00103, https://doi.org/10.1525/elementa.2023.00103, 2024. a, b, c, d
Rienecker, M. M. . C.: The GOES-5 data assimilation system-documentation of Versions 5.0.1, 5.1.0, and 5.2.0, NASA Tech. Rep., Series on Global Modeling and Data Assimilation, NASA/TM-2008-104606, https://ntrs.nasa.gov/citations/20120011955 (last access: 1 March 2024), 2008. a
Riihelä, A., Key, J. R., Meirink, J. F., Kuipers Munneke, P., Palo, T., and Karlsson, K.-G.: An intercomparison and validation of satellite-based surface radiative energy flux estimates over the Arctic, J. Geophys. Res.-Atmos., 122, 4829–4848, https://doi.org/10.1002/2016JD026443, 2017. a
Riihimaki, L. D., Cronin, M. F., Acharya, R., Anderson, N., Augustine, J., Balmes, K. A., Berk, P., Bozzano, R., Bucholtz, A., Connell, K. J., Cox, C., Di Sarra, A. G., James, E. B., Fairall, C., Farrar, J. T., Grissom, K., Guerra, M. T., Hormann, V., Joseph, K. J., Lanconelli, C., Melin, F., Meloni, D., Ottaviani, M., Pensieri, S., Ramesh, K., Rutan, D., Samarinas, N., Smith, S. R., Swart, S., Tandon, A., Venkatesan, R., Thompson, E., Verma, R. K., Watkins-Brandt, K. S., Weller, R. A., Zappa, C. J., and Zhang, D.: Ocean Surface Radiation Measurement Best Practices, Frontiers, 11, 1359149, https://doi.org/10.3389/fmars.2024.1359149, 2024. a
Rose, F. G., Rutan, D. A., Charlock, T., Smith, G. L., and Kato, S.: An Algorithm for the Constraining of Radiative Transfer Calculations to CERES-Observed Broadband Top-of-Atmosphere Irradiance, J. Atmos. Ocean. Tech., 30, 1091–1106, https://doi.org/10.1175/JTECH-D-12-00058.1, 2013. a
Rossow, W. B. and Zhang, Y.-C.: Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets: 2. Validation and first results, J. Geophys. Res.-Atmos., 100, 1167–1197, https://doi.org/10.1029/94JD02746, 1995. a
Rutan, D. A., Kato, S., Doelling, D. R., Rose, F. G., Nguyen, L. T., Caldwell, T. E., and Loeb, N. G.: CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux, J. Atmos. Ocean. Tech., 32, 1121–1143, https://doi.org/10.1175/JTECH-D-14-00165.1, 2015. a, b, c, d
Rückert, J. E., Rostosky, P., Huntemann, M., Clemens-Sewall, D., Ebell, K., Kaleschke, L., Lemmetyinen, J., Macfarlane, A. R., Naderpour, R., Stroeve, J., Walbröl, A., and Spreen, G.: Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition, Elementa: Science of the Anthropocene, 11, 00039, https://doi.org/10.1525/elementa.2023.00039, 2023. a
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification: A research synthesis, Global Planet. Change, 77, 85–96, https://doi.org/10.1016/j.gloplacha.2011.03.004, 2011. a
Shupe, M. D.: A ground-based multisensor cloud phase classifier, Geophys. Res. Lett., 34, L22809, https://doi.org/10.1029/2007GL031008, 2007. a
Shupe, M. D.: ShupeTurner cloud microphysics product, ARM Mobile Facility (MOS) MOSAiC (Drifting Obs—Study of Arctic Climate) [data set], https://doi.org/10.5439/1871015, 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, b
Shupe, M. D. and Rex, M.: A Year in the Changing Arctic Sea Ice, Oceanography, 35, 224–225, https://doi.org/10.5670/oceanog.2022.126, 2022. a, b, c
Shupe, M. D., Uttal, T., and Matrosov, S. Y.: Arctic Cloud Microphysics Retrievals from Surface-Based Remote Sensors at SHEBA, J. Appl. Meteorol. Clim., 44, 1544–1562, https://doi.org/10.1175/JAM2297.1, 2005. a, b
Shupe, M. D., Walden, V. P., Eloranta, E., Uttal, T., Campbell, J. R., Starkweather, S. M., and Shiobara, M.: Clouds at Arctic Atmospheric Observatories. Part I: Occurrence and Macrophysical Properties, J. Appl. Meteorol. Clim., 50, 626–644, https://doi.org/10.1175/2010JAMC2467.1, 2011. a
Shupe, M. D., Turner, D. D., Zwink, A., Thieman, M. M., Mlawer, E. J., and Shippert, T.: Deriving Arctic Cloud Microphysics at Barrow, Alaska: Algorithms, Results, and Radiative Closure, J. Appl. Meteorol. Clim., 54, 1675–1689, https://doi.org/10.1175/JAMC-D-15-0054.1, 2015. a, b
Shupe, M. D., Rex, M., Dethloff, K., Damm, E., Fong, A. A., Gradinger, R., Heuzé, C., Loose, B., Makarov, A., Maslowski, W., Nicolaus, M., Perovich, D., Rabe, B., Rinke, A., Sokolov, V., and Sommerfeld, A.: Arctic Report Card 2020: The MOSAiC Expedition: A Year Drifting with the Arctic Sea Ice, NOAA, https://doi.org/10.25923/9g3v-xh92, 2020. a
Shupe, M. D., Rex, M., Blomquist, B., Persson, P. O. G., Schmale, J., Uttal, T., Althausen, D., Angot, H., Archer, S., Bariteau, L., Beck, I., Bilberry, J., Bucci, S., Buck, C., Boyer, M., Brasseur, Z., Brooks, I. M., Calmer, R., Cassano, J., Castro, V., Chu, D., Costa, D., Cox, C. J., Creamean, J., Crewell, S., Dahlke, S., Damm, E., de Boer, G., Deckelmann, H., Dethloff, K., Dütsch, M., Ebell, K., Ehrlich, A., Ellis, J., Engelmann, R., Fong, A. A., Frey, M. M., Gallagher, M. R., Ganzeveld, L., Gradinger, R., Graeser, J., Greenamyer, V., Griesche, H., Griffiths, S., Hamilton, J., Heinemann, G., Helmig, D., Herber, A., Heuzé, C., Hofer, J., Houchens, T., Howard, D., Inoue, J., Jacobi, H.-W., Jaiser, R., Jokinen, T., Jourdan, O., Jozef, G., King, W., Kirchgaessner, A., Klingebiel, M., Krassovski, M., Krumpen, T., Lampert, A., Landing, W., Laurila, T., Lawrence, D., Lonardi, M., Loose, B., Lüpkes, C., Maahn, M., Macke, A., Maslowski, W., Marsay, C., Maturilli, M., Mech, M., Morris, S., Moser, M., Nicolaus, M., Ortega, P., Osborn, J., Pätzold, F., Perovich, D. K., Petäjä, T., Pilz, C., Pirazzini, R., Posman, K., Powers, H., Pratt, K. A., Preußer, A., Quéléver, L., Radenz, M., Rabe, B., Rinke, A., Sachs, T., Schulz, A., Siebert, H., Silva, T., Solomon, A., Sommerfeld, A., Spreen, G., Stephens, M., Stohl, A., Svensson, G., Uin, J., Viegas, J., Voigt, C., von der Gathen, P., Wehner, B., Welker, J. M., Wendisch, M., Werner, M., Xie, Z., and Yue, F.: Overview of the MOSAiC expedition: Atmosphere, Elementa: Science of the Anthropocene, 10, 00060, https://doi.org/10.1525/elementa.2021.00060, 2022. a, b, c, d, e
Sivaraman, C., Flynn, D., Riihimaki, L., Comstock, J., and Zhang, D.: Cloud Mask from Micropulse Lidar, Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1508389, 2019. a, b
Sledd, A. and L'Ecuyer, T.: How Much Do Clouds Mask the Impacts of Arctic Sea Ice and Snow Cover Variations? Different Perspectives from Observations and Reanalyses, Atmosphere, 10, 12, https://doi.org/10.3390/atmos10010012, 2019. a
Stapf, J., Ehrlich, A., Jäkel, E., Lüpkes, C., and Wendisch, M.: Reassessment of shortwave surface cloud radiative forcing in the Arctic: consideration of surface-albedo–cloud interactions, Atmos. Chem. Phys., 20, 9895–9914, https://doi.org/10.5194/acp-20-9895-2020, 2020. a
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G., Chepfer, H., Girolamo, L. D., Getzewich, B., Guignard, A., Heidinger, A., Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen, C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.: Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel, B. Am. Meteorol. Soc., 94, 1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1, 2013. a
Svensson, G., Murto, S., Shupe, M. D., Pithan, F., Magnusson, L., Day, J. J., Doyle, J. D., Renfrew, I. A., Spengler, T., and Vihma, T.: Warm air intrusions reaching the MOSAiC expedition in April 2020 – The YOPP targeted observing period (TOP), Elementa: Science of the Anthropocene, 11, 00016, https://doi.org/10.1525/elementa.2023.00016, 2023. a
Tan, I. and Storelvmo, T.: Evidence of Strong Contributions From Mixed-Phase Clouds to Arctic Climate Change, Geophys. Res. Lett., 46, 2894–2902, https://doi.org/10.1029/2018GL081871, 2019. a
Taylor, P. C., Boeke, R. C., Boisvert, L. N., Feldl, N., Henry, M., Huang, Y., Langen, P. L., Liu, W., Pithan, F., Sejas, S. A., and Tan, I.: Process Drivers, Inter-Model Spread, and the Path Forward: A Review of Amplified Arctic Warming, Frontiers in Earth Science, 9, 758361, https://doi.org/10.3389/feart.2021.758361, 2022. a
Taylor, P. C., Langen, P. L., and Tan, I.: Editorial: Arctic amplification: Feedback process interactions and contributions, Frontiers in Earth Science, 11, 1140871, https://doi.org/10.3389/feart.2023.1140871, 2023. a
Uttal, T., Starkweather, S., Drummond, J. R., Vihma, T., Makshtas, A. P., Darby, L. S., Burkhart, J. F., Cox, C. J., Schmeisser, L. N., Haiden, T., Maturilli, M., Shupe, M. D., Boer, G. D., Saha, A., Grachev, A. A., Crepinsek, S. M., Bruhwiler, L., Goodison, B., McArthur, B., Walden, V. P., Dlugokencky, E. J., Persson, P. O. G., Lesins, G., Laurila, T., Ogren, J. A., Stone, R., Long, C. N., Sharma, S., Massling, A., Turner, D. D., Stanitski, D. M., Asmi, E., Aurela, M., Skov, H., Eleftheriadis, K., Virkkula, A., Platt, A., Førland, E. J., Iijima, Y., Nielsen, I. E., Bergin, M. H., Candlish, L., Zimov, N. S., Zimov, S. A., O’Neill, N. T., Fogal, P. F., Kivi, R., Konopleva-Akish, E. A., Verlinde, J., Kustov, V. Y., Vasel, B., Ivakhov, V. M., Viisanen, Y., and Intrieri, J. M.: International Arctic Systems for Observing the Atmosphere: An International Polar Year Legacy Consortium, B. Am. Meteorol. Soc., 97, 1033–1056, https://doi.org/10.1175/BAMS-D-14-00145.1, 2016. a, b
Walbröl, A., Crewell, S., Engelmann, R., Orlandi, E., Griesche, H., Radenz, M., Hofer, J., Althausen, D., Maturilli, M., and Ebell, K.: Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC, Scientific Data, 9, 534, https://doi.org/10.1038/s41597-022-01504-1, 2022. a, b
Wang, M., Johnson, K., and Giangrande, S.: arsclwacr1kolliasshp.c0, Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1823070, 2022. a, b
Webster, M. A., Holland, M., Wright, N. C., Hendricks, S., Hutter, N., Itkin, P., Light, B., Linhardt, F., Perovich, D. K., Raphael, I. A., Smith, M. M., von Albedyll, L., and Zhang, J.: Spatiotemporal evolution of melt ponds on Arctic sea ice: MOSAiC observations and model results, Elementa: Science of the Anthropocene, 10, 000072, https://doi.org/10.1525/elementa.2021.000072, 2022. a, b, c
Wendisch, M., Macke, A., Ehrlich, A., Lüpkes, C., Mech, M., Chechin, D., Dethloff, K., Velasco, C. B., Bozem, H., Brückner, M., Clemen, H.-C., Crewell, S., Donth, T., Dupuy, R., Ebell, K., Egerer, U., Engelmann, R., Engler, C., Eppers, O., Gehrmann, M., Gong, X., Gottschalk, M., Gourbeyre, C., Griesche, H., Hartmann, J., Hartmann, M., Heinold, B., Herber, A., Herrmann, H., Heygster, G., Hoor, P., Jafariserajehlou, S., Jäkel, E., Järvinen, E., Jourdan, O., Kästner, U., Kecorius, S., Knudsen, E. M., Köllner, F., Kretzschmar, J., Lelli, L., Leroy, D., Maturilli, M., Mei, L., Mertes, S., Mioche, G., Neuber, R., Nicolaus, M., Nomokonova, T., Notholt, J., Palm, M., van Pinxteren, M., Quaas, J., Richter, P., Ruiz-Donoso, E., Schäfer, M., Schmieder, K., Schnaiter, M., Schneider, J., Schwarzenböck, A., Seifert, P., Shupe, M. D., Siebert, H., Spreen, G., Stapf, J., Stratmann, F., Vogl, T., Welti, A., Wex, H., Wiedensohler, A., Zanatta, M., and Zeppenfeld, S.: The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multiplatform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification, B. Am. Meteorol. Soc., 100, 841–871, https://doi.org/10.1175/BAMS-D-18-0072.1, 2019. a, b
Wendisch, M., Brückner, M., Crewell, S., Ehrlich, A., Notholt, J., Lüpkes, C., Macke, A., Burrows, J. P., Rinke, A., Quaas, J., Maturilli, M., Schemann, V., Shupe, M. D., Akansu, E. F., Barrientos-Velasco, C., Bärfuss, K., Blechschmidt, A.-M., Block, K., Bougoudis, I., Bozem, H., Böckmann, C., Bracher, A., Bresson, H., Bretschneider, L., Buschmann, M., Chechin, D. G., Chylik, J., Dahlke, S., Deneke, H., Dethloff, K., Donth, T., Dorn, W., Dupuy, R., Ebell, K., Egerer, U., Engelmann, R., Eppers, O., Gerdes, R., Gierens, R., Gorodetskaya, I. V., Gottschalk, M., Griesche, H., Gryanik, V. M., Handorf, D., Harm-Altstädter, B., Hartmann, J., Hartmann, M., Heinold, B., Herber, A., Herrmann, H., Heygster, G., Höschel, I., Hofmann, Z., Hölemann, J., Hünerbein, A., Jafariserajehlou, S., Jäkel, E., Jacobi, C., Janout, M., Jansen, F., Jourdan, O., Jurányi, Z., Kalesse-Los, H., Kanzow, T., Käthner, R., Kliesch, L. L., Klingebiel, M., Knudsen, E. M., Kovács, T., Körtke, W., Krampe, D., Kretzschmar, J., Kreyling, D., Kulla, B., Kunkel, D., Lampert, A., Lauer, M., Lelli, L., von Lerber, A., Linke, O., Löhnert, U., Lonardi, M., Losa, S. N., Losch, M., Maahn, M., Mech, M., Mei, L., Mertes, S., Metzner, E., Mewes, D., Michaelis, J., Mioche, G., Moser, M., Nakoudi, K., Neggers, R., Neuber, R., Nomokonova, T., Oelker, J., Papakonstantinou-Presvelou, I., Pätzold, F., Pefanis, V., Pohl, C., van Pinxteren, M., Radovan, A., Rhein, M., Rex, M., Richter, A., Risse, N., Ritter, C., Rostosky, P., Rozanov, V. V., Donoso, E. R., Garfias, P. S., Salzmann, M., Schacht, J., Schäfer, M., Schneider, J., Schnierstein, N., Seifert, P., Seo, S., Siebert, H., Soppa, M. A., Spreen, G., Stachlewska, I. S., Stapf, J., Stratmann, F., Tegen, I., Viceto, C., Voigt, C., Vountas, M., Walbröl, A., Walter, M., Wehner, B., Wex, H., Willmes, S., Zanatta, M., and Zeppenfeld, S.: Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC)3 Project, B. Am. Meteorol. Soc., 104, E208–E242, https://doi.org/10.1175/BAMS-D-21-0218.1, 2023. a
Wilber, A., Kratz, D., and Gupta, S.: Surface Emissivity Maps for Use in Retrievals of Longwave Radiation Satellite, NASA/TP-1999-209362, https://www-calipso.larc.nasa.gov/resources/calipso_users_guide/data_summaries/pdfs/Wilber.NASATchNote99.pdf (last access: 1 November 2024), 1999. a
Witthuhn, J., Hünerbein, A., Filipitsch, F., Wacker, S., Meilinger, S., and Deneke, H.: Aerosol properties and aerosol–radiation interactions in clear-sky conditions over Germany, Atmos. Chem. Phys., 21, 14591–14630, https://doi.org/10.5194/acp-21-14591-2021, 2021. a
Zhang, D.: mwrret1liljclou, Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1027369, 1996. a, b
Zib, B. J., Dong, X., Xi, B., and Kennedy, A.: Evaluation and Intercomparison of Cloud Fraction and Radiative Fluxes in Recent Reanalyses over the Arctic Using BSRN Surface Observations, J. Climate, 25, 2291–2305, https://doi.org/10.1175/JCLI-D-11-00147.1, 2012. a
Short summary
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.
Understanding how clouds affect the climate, especially in the Arctic, is crucial. This study...
Altmetrics
Final-revised paper
Preprint