Articles | Volume 15, issue 12
https://doi.org/10.5194/amt-15-3893-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-3893-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jul 2022
Research article |
| 01 Jul 2022
| 01 Jul 2022
The surface longwave cloud radiative effect derived from space lidar observations
LMD/IPSL, Sorbonne Université, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Université, CNRS, Palaiseau, France
Hélène Chepfer
LMD/IPSL, Sorbonne Université, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Université, CNRS, Palaiseau, France
Thibault Vaillant de Guélis
Science Systems and Applications, Inc., Hampton, Virginia, USA
NASA Langley Research Center, Hampton, Virginia, USA
Marjolaine Chiriaco
LATMOS/IPSL, UVSQ, Université Paris-Saclay, Sorbonne
Université, CNRS, 78280, Guyancourt, France
Matthew D. Shupe
Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, Colorado, USA
NOAA Physical Sciences Laboratory, Boulder, Colorado, USA
Rodrigo Guzman
LMD/IPSL, Sorbonne Université, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Université, CNRS, Palaiseau, France
Artem Feofilov
LMD/IPSL, Sorbonne Université, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Université, CNRS, Palaiseau, France
Patrick Raberanto
LMD/IPSL, CNRS, Sorbonne Université, École Polytechnique,
Institut Polytechnique de Paris, ENS, PSL Université, Palaiseau, France
Tristan S. L'Ecuyer
Department of Atmospheric and Oceanic Sciences, University of
Wisconsin-Madison, Madison, USA
Seiji Kato
NASA Langley Research Center, Hampton, Virginia, USA
Michael R. Gallagher
Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, Colorado, USA
NOAA Physical Sciences Laboratory, Boulder, Colorado, USA
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EGUsphere, https://doi.org/10.5194/egusphere-2026-2443, https://doi.org/10.5194/egusphere-2026-2443, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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The Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) satellite mission measures the Earth in a rarely measured part of the electromagnetic spectrum. To build confidence in these new measurements, we test them with an experiment observing the same atmosphere as PREFIRE but from the ground. We find that the PREFIRE measurements agree with what we expect from our ground-based observations, with some unexplained differences needing further study.
Joseph Hung, Penny M. Rowe, Christopher J. Cox, Emily M. McCullough, Liam Kroll, Raia Ottenheimer, Matthew D. Shupe, Von P. Walden, and Kimberly Strong
EGUsphere, https://doi.org/10.5194/egusphere-2026-1906, https://doi.org/10.5194/egusphere-2026-1906, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Climate models struggle to accurately represent polar regions largely due to challenges in modelling clouds. In this article, we sampled clouds at Arctic and Antarctic field sites, and found that Antarctic clouds generally featured lower temperatures, smaller liquid droplets, and are thinner because they have a higher proportion of ice than Arctic clouds. This knowledge improves our ability to monitor remote regions, and serves to improve our understanding of cloud processes.
Kara Hartig, John J. Cassano, Matthew D. Shupe, and Amy Solomon
EGUsphere, https://doi.org/10.5194/egusphere-2026-2426, https://doi.org/10.5194/egusphere-2026-2426, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Liquid-containing Arctic clouds have a strong influence on surface temperature and sea ice extent in winter, but how weather conditions impact cloud cover remains a mystery. This study uses observations from northern Alaska to investigate this relationship. We find that, contrary to expectations, the amount of cloud liquid is remarkably insensitive to temperature, moisture, wind direction, and the large-scale atmospheric flow.
Anne Sledd, Michael R. Gallagher, Matthew D. Shupe, Christopher J. Cox, Robert Hawley, Michael S. Town, Heather Guy, Hans-Peter Marshall, Ryan R. Neely III, Claire Pettersen, Von P. Walden, Catherine Hebson, Andrew Martin, Erik Olson, and Derek Pickell
EGUsphere, https://doi.org/10.5194/egusphere-2026-1842, https://doi.org/10.5194/egusphere-2026-1842, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Understanding energetic processes at the surface is important for understanding how the Greenland ice sheet melts. However, most temperature observations of firn are insufficient for studying energetic processes near the surface where melt happens. In this work we present new ground observations of high-resolution temperature profiles in the top meter of firn near the southwest coast of Greenland. Using these observations we characterize how temperature and energy vary during the melt season.
Jean Lac, Hélène Chepfer, Matthew D. Shupe, and Hannes Griesche
Atmos. Chem. Phys., 26, 4189–4213, https://doi.org/10.5194/acp-26-4189-2026, https://doi.org/10.5194/acp-26-4189-2026, 2026
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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.
Cameron Bertossa, Tristan L'Ecuyer, and David Henderson
Atmos. Chem. Phys., 26, 3653–3667, https://doi.org/10.5194/acp-26-3653-2026, https://doi.org/10.5194/acp-26-3653-2026, 2026
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This study evaluates how well an Arctic-specific model can capture two key cloud states that control how the region traps surface radiation. The model reproduces these states better than others but still produces too many thick, low clouds. With further improvements, it could offer valuable insight into how Arctic cloud behavior and surface heat balance may evolve under future climate change.
Pradeep K. Aggarwal, Courtney Schumacher, Frederick J. Longstaffe, Aaron Funk, and Matthew D. Shupe
EGUsphere, https://doi.org/10.5194/egusphere-2026-687, https://doi.org/10.5194/egusphere-2026-687, 2026
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Riming, where supercooled liquid freezes directly on ice particles in mixed-phase clouds, is an efficient process of precipitation formation. We show that the relative composition of oxygen and hydrogen isotopes or d-excess is lower in rimed ice than the liquid, contrary to the existing view. This result will help better understand past climates from ice cores and help improve climate models by providing spatial and temporal constraints on the fraction of precipitation that formed by riming.
Peggy Achtert, Torsten Seelig, Gabriella Wallentin, Luisa Ickes, Matthew D. Shupe, Corinna Hoose, and Matthias Tesche
Atmos. Chem. Phys., 26, 3049–3068, https://doi.org/10.5194/acp-26-3049-2026, https://doi.org/10.5194/acp-26-3049-2026, 2026
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We quantify the occurrence of single- and multi-layer clouds in the Arctic based on combining soundings with cloud-radar observations. We also assess the rate of ice-crystal seeding in multi-layer cloud systems as this is an important initiator of glaciation in super-cooled liquid cloud layers. We find an abundance of multi-layer clouds in the Arctic with seeding in about half to two thirds of cases in which the gap between upper and lower layers ranges between 100 and 1000 m.
Sergey Khaykin, Michaël Sicard, Thierry Leblanc, Tetsu Sakai, Nickolay Balugin, Gwenaël Berthet, Stëphane Chevrier, Fernando Chouza, Artem Feofilov, Dominique Gantois, Sophie Godin-Beekmann, Arezki Haddouche, Yoshitaka Jin, Isamu Morino, Nicolas Kadygrov, Thomas Lecas, Ben Liley, Richard Querel, Ghasssan Taha, and Vladimir Yushkov
Atmos. Chem. Phys., 26, 607–622, https://doi.org/10.5194/acp-26-607-2026, https://doi.org/10.5194/acp-26-607-2026, 2026
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In April 2024, the Ruang volcano in Indonesia sent large amounts of gas and particles high into the atmosphere, which then spread worldwide. Using the new European EarthCARE satellite and its advanced laser instrument ATLID (Atmospheric LIDar), together with ground and balloon observations, we tracked how these particles doubled levels in the tropics and spread into both hemispheres. The study shows ATLID’s power to reveal how eruptions can affect climate, clouds, and ozone for more than a year.
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem G. Feofilov, Marie-Laure Roussel, Benjamin Witschas, and Sophie Bastin
Atmos. Chem. Phys., 26, 443–475, https://doi.org/10.5194/acp-26-443-2026, https://doi.org/10.5194/acp-26-443-2026, 2026
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Aeolus spaceborne Doppler Wind Lidar observes perfectly co-located vertical profiles of clouds and vertical profiles of horizontal wind that can be used to study cloud-wind interactions. At regional scale, we show that over the Indian Ocean, high cloud fractions increase when the Tropical Easterly Jet is active. At a smaller scale, we observe for the first time from space differences in the wind profiles within the cloud and its surrounding clear sky, that can be imputed to convective motions.
Marie-Laure Roussel, Hélène Chepfer, Zacharie Titus, and Marine Bonazzola
Atmos. Chem. Phys., 26, 117–134, https://doi.org/10.5194/acp-26-117-2026, https://doi.org/10.5194/acp-26-117-2026, 2026
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Clouds are crucial for understanding and predicting climate, yet their processes remain uncertain in global models. We reproduced detailed observations from a lidar and a wind-lidar using an advanced instrument simulator to evaluate how well the model represents clouds. A dedicated module was developed for the wind-lidar. Both instruments reveal comparable cloud biases, confirming its value for improving model evaluation and enabling consistent multi-decade assessments with more recent sensors.
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem Feofilov, and Marie-Laure Roussel
EGUsphere, https://doi.org/10.5194/egusphere-2025-5335, https://doi.org/10.5194/egusphere-2025-5335, 2025
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In this paper, we use satellite observations to study the relationships between profiles of clouds and profiles of wind from June to October around India. We found that thin clouds between 14 and 17 km are transported over the Arabian Sea by fast westward winds. We also show that the wind can be modified by 3 m/s because of the presence of deep convective clouds that spread out between 14 and 17 km of altitude. We discuss the implications of these interactions in a warming climate.
Manfred Wendisch, Benjamin Kirbus, Davide Ori, Matthew D. Shupe, Susanne Crewell, Harald Sodemann, and Vera Schemann
Atmos. Chem. Phys., 25, 15047–15076, https://doi.org/10.5194/acp-25-15047-2025, https://doi.org/10.5194/acp-25-15047-2025, 2025
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Aircraft observations of air parcels moving into and out of the Arctic are reported. From the data, heating and cooling as well as drying and moistening of the air masses along their way into and out of the Arctic could be measured for the first time. These data are used to evaluate if weather prediction models are able to accurately represent these air mass transformations. This work helps to model the future Arctic climate changes, which may have an impact for mid-latitude weather as well.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
Atmos. Meas. Tech., 18, 5393–5414, https://doi.org/10.5194/amt-18-5393-2025, https://doi.org/10.5194/amt-18-5393-2025, 2025
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This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, and application of the model to another observatory and quantify how the models respond to instrument outages.
Yugo Kanaya, Roberto Sommariva, Alfonso Saiz-Lopez, Andrea Mazzeo, Theodore K. Koenig, Kaori Kawana, James E. Johnson, Aurélie Colomb, Pierre Tulet, Suzie Molloy, Ian E. Galbally, Rainer Volkamer, Anoop Mahajan, John W. Halfacre, Paul B. Shepson, Julia Schmale, Hélène Angot, Byron Blomquist, Matthew D. Shupe, Detlev Helmig, Junsu Gil, Meehye Lee, Sean C. Coburn, Ivan Ortega, Gao Chen, James Lee, Kenneth C. Aikin, David D. Parrish, John S. Holloway, Thomas B. Ryerson, Ilana B. Pollack, Eric J. Williams, Brian M. Lerner, Andrew J. Weinheimer, Teresa Campos, Frank M. Flocke, J. Ryan Spackman, Ilann Bourgeois, Jeff Peischl, Chelsea R. Thompson, Ralf M. Staebler, Amir A. Aliabadi, Wanmin Gong, Roeland Van Malderen, Anne M. Thompson, Ryan M. Stauffer, Debra E. Kollonige, Juan Carlos Gómez Martin, Masatomo Fujiwara, Katie Read, Matthew Rowlinson, Keiichi Sato, Junichi Kurokawa, Yoko Iwamoto, Fumikazu Taketani, Hisahiro Takashima, Mónica Navarro-Comas, Marios Panagi, and Martin G. Schultz
Earth Syst. Sci. Data, 17, 4901–4932, https://doi.org/10.5194/essd-17-4901-2025, https://doi.org/10.5194/essd-17-4901-2025, 2025
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The first comprehensive dataset of tropospheric ozone over oceans/polar regions is presented, including 77 ship/buoy and 48 aircraft campaign observations (1977–2022, 0–5000 m altitude), supplemented by ozonesonde and surface data. Air masses isolated from land for 72+ hours are systematically selected as essentially oceanic. Among the 11 global regions, they show daytime decreases of 11–16 % in the tropics, while near-zero depletions are rare, unlike in the Arctic, implying different mechanisms.
Chanyoung Park, Brian J. Soden, Ryan J. Kramer, Tristan S. L'Ecuyer, and Haozhe He
Atmos. Chem. Phys., 25, 7299–7313, https://doi.org/10.5194/acp-25-7299-2025, https://doi.org/10.5194/acp-25-7299-2025, 2025
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This study addresses the long-standing challenge of quantifying the impact of aerosol–cloud interactions. Using satellite observations, reanalysis data, and a "perfect-model" cross-validation, we show that explicitly accounting for aerosol–cloud droplet activation rates is key to accurately estimating ERFaci (effective radiative forcing due to aerosol–cloud interactions). Our results indicate a smaller and less uncertain ERFaci than previously assessed, implying the reduced role of aerosol–cloud interactions in shaping climate sensitivity.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Christopher J. Cox, Janet M. Intrieri, Brian J. Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, and James M. Wilczak
Earth Syst. Sci. Data, 17, 1481–1499, https://doi.org/10.5194/essd-17-1481-2025, https://doi.org/10.5194/essd-17-1481-2025, 2025
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Snow is an essential water resource in the intermountain western United States, and predictions are made using models. We made observations to validate, constrain, and develop the models. The data are from the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign in Colorado's East River valley, 2021–2023. The measurements include meteorology and variables that quantify energy transfer between the atmosphere and surface. The data are available publicly.
Carola Barrientos-Velasco, Christopher J. Cox, Hartwig Deneke, J. Brant Dodson, Anja Hünerbein, Matthew D. Shupe, Patrick C. Taylor, and Andreas Macke
Atmos. Chem. Phys., 25, 3929–3960, https://doi.org/10.5194/acp-25-3929-2025, https://doi.org/10.5194/acp-25-3929-2025, 2025
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Understanding how clouds affect the climate, especially in the Arctic, is crucial. This study used data from the largest polar expedition in history, MOSAiC, and the CERES satellite to analyse the impact of clouds on radiation. Simulations showed accurate results, aligning with observations. Over the year, clouds caused the atmospheric surface system to lose 5.2 W m−² of radiative energy to space, while the surface gained 25 W m−² and the atmosphere cooled by 30.2 W m−².
Benjamin Heutte, Nora Bergner, Hélène Angot, Jakob B. Pernov, Lubna Dada, Jessica A. Mirrielees, Ivo Beck, Andrea Baccarini, Matthew Boyer, Jessie M. Creamean, Kaspar R. Daellenbach, Imad El Haddad, Markus M. Frey, Silvia Henning, Tiia Laurila, Vaios Moschos, Tuukka Petäjä, Kerri A. Pratt, Lauriane L. J. Quéléver, Matthew D. Shupe, Paul Zieger, Tuija Jokinen, and Julia Schmale
Atmos. Chem. Phys., 25, 2207–2241, https://doi.org/10.5194/acp-25-2207-2025, https://doi.org/10.5194/acp-25-2207-2025, 2025
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Limited aerosol measurements in the central Arctic hinder our understanding of aerosol–climate interactions in the region. Our year-long observations of aerosol physicochemical properties during the MOSAiC expedition reveal strong seasonal variations in aerosol chemical composition, where the short-term variability is heavily affected by storms in the Arctic. Local wind-generated particles are shown to be an important source of cloud seeds, especially in autumn.
Madison M. Smith, Niels Fuchs, Evgenii Salganik, Donald K. Perovich, Ian Raphael, Mats A. Granskog, Kirstin Schulz, Matthew D. Shupe, and Melinda Webster
The Cryosphere, 19, 619–644, https://doi.org/10.5194/tc-19-619-2025, https://doi.org/10.5194/tc-19-619-2025, 2025
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The fate of freshwater from Arctic sea ice and snowmelt impacts interactions of the atmosphere, sea ice, and ocean. We complete a comprehensive analysis of datasets from a 2020 central Arctic field campaign to understand the drivers of the sea ice freshwater budget and the fate of this water. Over half of the freshwater comes from surface melt, and a majority fraction is incorporated into the ocean. Results suggest that the representation of melt ponds is a key area for future development.
Johanna Tjernström, Michael Gallagher, Jareth Holt, Gunilla Svensson, Matthew D. Shupe, Jonathan J. Day, Lara Ferrighi, Siri Jodha Khalsa, Leslie M. Hartten, Ewan O'Connor, Zen Mariani, and Øystein Godøy
EGUsphere, https://doi.org/10.5194/egusphere-2024-2088, https://doi.org/10.5194/egusphere-2024-2088, 2024
Preprint archived
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The value of numerical weather predictions can be enhanced in several ways, one is to improve the representations of small-scale processes in models. To understand what needs to be improved, the model results need to be evaluated. Following standardized principles, a file format has been defined to be as similar as possible for both observational and model data. Python packages and toolkits are presented as a community resource in the production of the files and evaluation analysis.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Natasha Vos, Tristan S. L'Ecuyer, and Tim Michaels
EGUsphere, https://doi.org/10.5194/egusphere-2024-2040, https://doi.org/10.5194/egusphere-2024-2040, 2024
Preprint withdrawn
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PREFIRE uses two CubeSats to make novel measurements of outgoing energy. The CubeSats will frequently resample regions, forming orbit “intersections” that reveal how polar processes impact thermal emissions. This study develops new methods to characterize orbit intersections and applies them to simulated PREFIRE orbits to assess the hypothetical resampling distribution. Generalizing our results informs future missions that two CubeSats at different altitudes greatly enhance resampling coverage.
Alexander Kutepov and Artem Feofilov
Geosci. Model Dev., 17, 5331–5347, https://doi.org/10.5194/gmd-17-5331-2024, https://doi.org/10.5194/gmd-17-5331-2024, 2024
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Infrared CO2 cooling of the middle and upper atmosphere is increasing. We developed a new routine for very fast and accurate calculations of this cooling in general circulation models. The new algorithm accounts for non-local thermodynamic equilibrium and is about 1000 times faster than the standard matrix algorithms. It is based on advanced techniques for non-equilibrium emission calculations in stellar atmospheres, which so far have not been used in Earth’s and planetary atmospheres.
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
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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.
Michael Lonardi, Elisa F. Akansu, André Ehrlich, Mauro Mazzola, Christian Pilz, Matthew D. Shupe, Holger Siebert, and Manfred Wendisch
Atmos. Chem. Phys., 24, 1961–1978, https://doi.org/10.5194/acp-24-1961-2024, https://doi.org/10.5194/acp-24-1961-2024, 2024
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Profiles of thermal-infrared irradiance were measured at two Arctic sites. The presence or lack of clouds influences the vertical structure of these observations. In particular, the cloud top region is a source of radiative energy that can promote cooling and mixing in the cloud layer. Simulations are used to further characterize how the amount of water in the cloud modifies this forcing. A case study additionally showcases the evolution of the radiation profiles in a dynamic atmosphere.
Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe
Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, https://doi.org/10.5194/amt-17-899-2024, 2024
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The open-source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for characterizing particle shape, size, and sedimentation velocity in snowfall. It combines a large observation volume with relatively high resolution and a design that limits wind perturbations. The open-source nature of the VISSS hardware and software invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
Brian Kahn, Cameron Bertossa, Xiuhong Chen, Brian Drouin, Erin Hokanson, Xianglei Huang, Tristan L'Ecuyer, Kyle Mattingly, Aronne Merrelli, Tim Michaels, Nate Miller, Federico Donat, Tiziano Maestri, and Michele Martinazzo
EGUsphere, https://doi.org/10.5194/egusphere-2023-2463, https://doi.org/10.5194/egusphere-2023-2463, 2023
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A cloud detection mask algorithm is developed for the upcoming Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) satellite mission to be launched by NASA in May 2024. The cloud mask is compared to "truth" and is capable of detecting over 90 % of all clouds globally tested with simulated data, and about 87 % of all clouds in the Arctic region.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
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Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Manfred Wendisch, Johannes Stapf, Sebastian Becker, André Ehrlich, Evelyn Jäkel, Marcus Klingebiel, Christof Lüpkes, Michael Schäfer, and Matthew D. Shupe
Atmos. Chem. Phys., 23, 9647–9667, https://doi.org/10.5194/acp-23-9647-2023, https://doi.org/10.5194/acp-23-9647-2023, 2023
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Atmospheric radiation measurements have been conducted during two field campaigns using research aircraft. The data are analyzed to see if the near-surface air in the Arctic is warmed or cooled if warm–humid air masses from the south enter the Arctic or cold–dry air moves from the north from the Arctic to mid-latitude areas. It is important to study these processes and to check if climate models represent them well. Otherwise it is not possible to reliably forecast the future Arctic climate.
Shijie Peng, Qinghua Yang, Matthew D. Shupe, Xingya Xi, Bo Han, Dake Chen, Sandro Dahlke, and Changwei Liu
Atmos. Chem. Phys., 23, 8683–8703, https://doi.org/10.5194/acp-23-8683-2023, https://doi.org/10.5194/acp-23-8683-2023, 2023
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Due to a lack of observations, the structure of the Arctic atmospheric boundary layer (ABL) remains to be further explored. By analyzing a year-round radiosonde dataset collected over the Arctic sea-ice surface, we found the annual cycle of the ABL height (ABLH) is primarily controlled by the evolution of ABL thermal structure, and the surface conditions also show a high correlation with ABLH variation. In addition, the Arctic ABLH is found to be decreased in summer compared with 20 years ago.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
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Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Ulrike Egerer, John J. Cassano, Matthew D. Shupe, Gijs de Boer, Dale Lawrence, Abhiram Doddi, Holger Siebert, Gina Jozef, Radiance Calmer, Jonathan Hamilton, Christian Pilz, and Michael Lonardi
Atmos. Meas. Tech., 16, 2297–2317, https://doi.org/10.5194/amt-16-2297-2023, https://doi.org/10.5194/amt-16-2297-2023, 2023
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This paper describes how measurements from a small uncrewed aircraft system can be used to estimate the vertical turbulent heat energy exchange between different layers in the atmosphere. This is particularly important for the atmosphere in the Arctic, as turbulent exchange in this region is often suppressed but is still important to understand how the atmosphere interacts with sea ice. We present three case studies from the MOSAiC field campaign in Arctic sea ice in 2020.
Felix Pithan, Marylou Athanase, Sandro Dahlke, Antonio Sánchez-Benítez, Matthew D. Shupe, Anne Sledd, Jan Streffing, Gunilla Svensson, and Thomas Jung
Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, https://doi.org/10.5194/gmd-16-1857-2023, 2023
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Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Alyson Rose Douglas and Tristan L'Ecuyer
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-688, https://doi.org/10.5194/acp-2022-688, 2022
Revised manuscript not accepted
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Aerosol, or small particles released by human activities, enter the atmosphere and eventually interact with clouds in what we term aerosol-cloud interactions. As more aerosol enter a cloud, they act as cloud droplet nuclei, increasing the number of cloud droplets in a cloud and delaying rain formation, leading to a larger cloud. We use machine learning and found that these interactions lead to 1.27 % more cloudiness on Earth and offset ~1/4 of the warming due to CO2.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
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This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
David W. Fillmore, David A. Rutan, Seiji Kato, Fred G. Rose, and Thomas E. Caldwell
Atmos. Chem. Phys., 22, 10115–10137, https://doi.org/10.5194/acp-22-10115-2022, https://doi.org/10.5194/acp-22-10115-2022, 2022
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This paper presents an evaluation of the aerosol analysis incorporated into the Clouds and the Earth's Radiant Energy System (CERES) data products as well as the aerosols' impact on solar radiation reaching the surface. CERES is a NASA Earth observation mission with instruments flying on various polar-orbiting satellites. Its primary objective is the study of the radiative energy balance of the climate system as well as examination of the influence of clouds and aerosols on this balance.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Thibault Vaillant de Guélis, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker
Atmos. Meas. Tech., 15, 1931–1956, https://doi.org/10.5194/amt-15-1931-2022, https://doi.org/10.5194/amt-15-1931-2022, 2022
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A new IIR-based cloud and aerosol discrimination (CAD) algorithm is developed using the IIR brightness temperature differences for cloud and aerosol features confidently identified by the CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduce the ambiguity in a notable fraction of
not confidentV4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
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Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Michael R. Gallagher, Matthew D. Shupe, Hélène Chepfer, and Tristan L'Ecuyer
The Cryosphere, 16, 435–450, https://doi.org/10.5194/tc-16-435-2022, https://doi.org/10.5194/tc-16-435-2022, 2022
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By using direct observations of snowfall and mass changes, the variability of daily snowfall mass input to the Greenland ice sheet is quantified for the first time. With new methods we conclude that cyclones west of Greenland in summer contribute the most snowfall, with 1.66 Gt per occurrence. These cyclones are contextualized in the broader Greenland climate, and snowfall is validated against mass changes to verify the results. Snowfall and mass change observations are shown to agree well.
Oscar Javier Rojas Muñoz, Marjolaine Chiriaco, Sophie Bastin, and Justine Ringard
Atmos. Chem. Phys., 21, 15699–15723, https://doi.org/10.5194/acp-21-15699-2021, https://doi.org/10.5194/acp-21-15699-2021, 2021
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A method is developed and evaluated to quantify each process that affects hourly 2 m temperature variations on a local scale, based almost exclusively on observations retrieved from an observatory near the Paris region. Each term involved in surface temperature variations is estimated, and its contribution and importance are also assessed. It is found that clouds are the main modulator on hourly temperature variations for most hours of the day, and thus their characterization is addressed.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
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We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Alyson Douglas and Tristan L'Ecuyer
Atmos. Chem. Phys., 21, 15103–15114, https://doi.org/10.5194/acp-21-15103-2021, https://doi.org/10.5194/acp-21-15103-2021, 2021
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When aerosols enter the atmosphere, they interact with the clouds above in what we term aerosol–cloud interactions and lead to a series of reactions which delay the onset of rain. This delay may lead to increased rain rates, or invigoration, when the cloud eventually rains. We show that aerosol leads to invigoration in certain environments. The strength of the invigoration depends on how large the cloud is, which suggests that it is highly tied to the organization of the cloud system.
Erik Johansson, Abhay Devasthale, Michael Tjernström, Annica M. L. Ekman, Klaus Wyser, and Tristan L'Ecuyer
Geosci. Model Dev., 14, 4087–4101, https://doi.org/10.5194/gmd-14-4087-2021, https://doi.org/10.5194/gmd-14-4087-2021, 2021
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Understanding the coupling of clouds to large-scale circulation is a grand challenge for the climate community. Cloud radiative heating (CRH) is a key parameter in this coupling and is therefore essential to model realistically. We, therefore, evaluate a climate model against satellite observations. Our findings indicate good agreement in the seasonal pattern of CRH even if the magnitude differs. We also find that increasing the horizontal resolution in the model has little effect on the CRH.
Cited articles
Acquaotta, F. and Fratianni, S.: The Importance Of The Quality And Reliability Of The Historical Time Series For The Study Of Climate Change, ABClima, 14, 20–38, https://doi.org/10.5380/abclima.v14i1.38168, 2014.
Allan, R. P.: Combining satellite data and models to estimate cloud
radiative effect at the surface and in the atmosphere: Cloud radiative
effect at the surface and in the atmosphere, Met. Apps, 18, 324–333,
https://doi.org/10.1002/met.285, 2011.
Arouf, A., Chepfer, H., Vaillant de Guélis, T., Guzman, R., Feofilov, A., and Raberanto, P.: Longwave Cloud Radiative Effect derived from Space Lidar Observations at the Surface and TOA – Edition 1: Monthly Gridded Product, IPSL [data set], https://doi.org/10.14768/70d5f4b5-e740-4d4c-b1ec-f6459f7e5563, 2022.
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature, J. Geophys. Res., 114, D00A23, https://doi.org/10.1029/2008JD010049, 2009.
Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and Boer, G.:
Ubiquitous low-level liquid-containing Arctic clouds: New observations and
climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39,
2012GL053385, https://doi.org/10.1029/2012GL053385, 2012.
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P.,
Stubenrauch, C. J., and Zeng, S.: The GCM-Oriented CALIPSO Cloud Product
(CALIPSO–GOCCP), J. Geophys. Res., 115, D00H16, https://doi.org/10.1029/2009JD012251, 2010.
Chepfer, H., Brogniez, H., and Noel, V.: Diurnal variations of cloud and
relative humidity profiles across the tropics, Sci. Rep., 9, 16045, https://doi.org/10.1038/s41598-019-52437-6, 2019.
Chiriaco, M., Dupont, J.-C., Bastin, S., Badosa, J., Lopez, J., Haeffelin, M., Chepfer, H., and Guzman, R.: ReOBS: a new approach to synthesize long-term multi-variable dataset and application to the SIRTA supersite, Earth Syst. Sci. Data, 10, 919–940, https://doi.org/10.5194/essd-10-919-2018, 2018.
Chylek, P., Lohmann, U., Dubey, M., Mishchenko, M., Kahn, R., and Ohmura,
A.: Limits on climate sensitivity derived from recent satellite and surface
observations, J. Geophys. Res., 112, D24S04, https://doi.org/10.1029/2007JD008740, 2007.
Corti, T. and Peter, T.: A simple model for cloud radiative forcing, Atmos. Chem. Phys., 9, 5751–5758, https://doi.org/10.5194/acp-9-5751-2009, 2009.
Curry, J. A., Schramm, J. L., Rossow, W. B., and Randall, D.: Overview of
Arctic cloud and radiation characteristics, J. Climate, 9, 1731–1764, https://doi.org/10.1175/1520-0442(1996)009<1731:OOACAR>2.0.CO;2, 1996.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy.
Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828,
2011.
Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G.: Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017), Earth Syst. Sci. Data, 10, 1491–1501, https://doi.org/10.5194/essd-10-1491-2018, 2018.
Dubuisson, P., Dessailly, D., Vesperini, M., and Frouin, R.: Water vapor retrieval over ocean using near-infrared radiometry, J. Geophys. Res., 109, D19106, https://doi.org/10.1029/2004JD004516, 2004.
Dupont, J.-C. and Haeffelin, M.: Observed instantaneous cirrus radiative
effect on surface-level shortwave and longwave irradiances, J. Geophys.
Res., 113, D21202, https://doi.org/10.1029/2008JD009838, 2008.
Gallagher, M. R., Shupe, M. D., and Miller, N. B.: Impact of Atmospheric
Circulation on Temperature, Clouds, and Radiation at Summit Station,
Greenland, with Self-Organizing Maps, J. Climate, 31, 8895–8915, https://doi.org/10.1175/JCLI-D-17-0893.1, 2018.
Garnier, A., Pelon, J., Vaughan, M. A., Winker, D. M., Trepte, C. R., and Dubuisson, P.: Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans, Atmos. Meas. Tech., 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, 2015.
Guzman, R., Chepfer, H., Noel, V., Vaillant de Guélis, T., Kay, J. E.,
Raberanto, P., Cesana, G., Vaughan, M. A., and Winker, D. M.: Direct
atmosphere opacity observations from CALIPSO provide new constraints on
cloud-radiation interactions: GOCCP v3.0 OPAQ Algorithm, J. Geophys. Res.-Atmos., 122, 1066–1085, https://doi.org/10.1002/2016JD025946,
2017.
Haeffelin, M., Barthès, L., Bock, O., Boitel, C., Bony, S., Bouniol, D., Chepfer, H., Chiriaco, M., Cuesta, J., Delanoë, J., Drobinski, P., Dufresne, J.-L., Flamant, C., Grall, M., Hodzic, A., Hourdin, F., Lapouge, F., Lemaître, Y., Mathieu, A., Morille, Y., Naud, C., Noël, V., O'Hirok, W., Pelon, J., Pietras, C., Protat, A., Romand, B., Scialom, G., and Vautard, R.: SIRTA, a ground-based atmospheric observatory for cloud and aerosol research, Ann. Geophys., 23, 253–275, https://doi.org/10.5194/angeo-23-253-2005, 2005.
Ham, S.-H., Kato, S., Rose, F. G., Winker, D., L'Ecuyer, T., Mace, G. G.,
Painemal, D., Sun-Mack, S., Chen, Y., and Miller, W. F.: Cloud occurrences
and cloud radiative effects (CREs) from CERES-CALIPSO-CloudSat-MODIS (CCCM)
and CloudSat radar-lidar (RL) products: CCCM Versus CloudSat RL Products, J.
Geophys. Res.-Atmos., 122, 8852–8884, https://doi.org/10.1002/2017JD026725, 2017.
Hang, Y., L'Ecuyer, T. S., Henderson, D. S., Matus, A. V., and Wang, Z.:
Reassessing the Effect of Cloud Type on Earth's Energy Balance in the Age of
Active Spaceborne Observations. Part II: Atmospheric Heating, J. Climate, 32,
6219–6236, https://doi.org/10.1175/JCLI-D-18-0754.1, 2019.
He, Y., Risi, C., Gao, J., Masson-Delmotte, V., Yao, T., Lai, C.-T., Ding,
Y., Worden, J., Frankenberg, C., Chepfer, H., and Cesana, G.: Impact of
atmospheric convection on south Tibet summer precipitation isotopologue
composition using a combination of in situ measurements, satellite data, and
atmospheric general circulation modeling: IMPACT OF CONVECTION ON TP
ISOTOPIC, J. Geophys. Res.-Atmos., 120, 3852–3871, https://doi.org/10.1002/2014JD022180, 2015.
Henderson, D. S., L'Ecuyer, T., Stephens, G., Partain, P., and Sekiguchi,
M.: A Multisensor Perspective on the Radiative Impacts of Clouds and
Aerosols, 52, 853–871, https://doi.org/10.1175/JAMC-D-12-025.1, 2013.
Hofer, S., Tedstone, A. J., Fettweis, X., and Bamber, J. L.: Decreasing
cloud cover drives the recent mass loss on the Greenland Ice Sheet, Sci. Adv., 3, e1700584, https://doi.org/10.1126/sciadv.1700584, 2017.
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H.,
Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P.,
Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota,
T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H.,
Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez,
A., Wandinger, U., Wehr, T., and Zadelhoff, G.-J. van: The EarthCARE
Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols,
Precipitation, and Radiation, B. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015.
Intrieri, J. M., Fairall, C. F., Shupe, M. D., Persson, P. O. G., Andreas,
E. L., Guest, P., and Moritz, R. M.: An annual cycle of Arctic surface cloud
forcing at SHEBA, J. Geophys. Res., 107, 8039, https://doi.org/10.1029/2000JC000439, 2002.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution
of Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., L, Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M., I, Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., B, R,
Maycock, T., K, Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B.,
Cambridge University Press, in press, 2022.
Kato, S., Sun-Mack, S., Miller, W. F., Rose, F. G., Chen, Y., Minnis, P.,
and Wielicki, B. A.: Relationships among cloud occurrence frequency,
overlap, and effective thickness derived from CALIPSO and CloudSat merged
cloud vertical profiles, J. Geophys. Res., 115, D00H28, https://doi.org/10.1029/2009JD012277, 2010.
Kato, S., Rose, F. G., Sun-Mack, S., Miller, W. F., Chen, Y., Rutan, D. A., Stephens, G. L., Loeb, N. G., Minnis, P., Wielicki, B. A., Winker, D. M., Charlock, T. P., Stackhouse, P. W., Xu, K.-M., and Collins, W. D.: Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties, J. Geophys. Res., 116, D19209, https://doi.org/10.1029/2011JD016050, 2011.
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.
Kato, S., Rose, F. G., Ham, S. H., Rutan, D. A., Radkevich, A., Caldwell, T.
E., Sun-Mack, S., Miller, W. F., and Chen, Y.: Radiative Heating Rates
Computed With Clouds Derived From Satellite-Based Passive and Active Sensors
and their Effects on Generation of Available Potential Energy, J. Geophys.
Res.-Atmos., 124, 1720–1740, https://doi.org/10.1029/2018JD028878, 2019.
Kay, J. E., Hillman, B. R., Klein, S. A., Zhang, Y., Medeiros, B., Pincus,
R., Gettelman, A., Eaton, B., Boyle, J., Marchand, R., and Ackerman, T. P.:
Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using
Satellite Observations and Their Corresponding Instrument Simulators, J. Climate, 25, 5190–5207, https://doi.org/10.1175/JCLI-D-11-00469.1, 2012.
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) Large Ensemble Project: A Community
Resource for Studying Climate Change in the Presence of Internal Climate
Variability, B. Am. Meteorol. Soc., 96, 1333–1349, https://doi.org/10.1175/BAMS-D-13-00255.1, 2015.
King, J. C., Gadian, A., Kirchgaessner, A., Kuipers Munneke, P.,
Lachlan-Cope, T. A., Orr, A., Reijmer, C., van den Broeke, M. R., van
Wessem, J. M., and Weeks, M.: Validation of the summertime surface energy
budget of Larsen C Ice Shelf (Antarctica) as represented in three
high-resolution atmospheric models: Surface energy budget of Larsen C, J.
Geophys. Res.-Atmos., 120, 1335–1347, https://doi.org/10.1002/2014JD022604, 2015.
Kopp, R. E., Kemp, A. C., Bittermann, K., Horton, B. P., Donnelly, J. P.,
Gehrels, W. R., Hay, C. C., Mitrovica, J. X., Morrow, E. D., and Rahmstorf,
S.: Temperature-driven global sea-level variability in the Common Era, P.
Natl. Acad. Sci. USA, 113, E1434–E1441, https://doi.org/10.1073/pnas.1517056113, 2016.
Kwok, R. and Untersteiner, N.: The thinning of Arctic sea ice, Physics
Today, 64, 36–41, https://doi.org/10.1063/1.3580491, 2011.
Lacour, A., Chepfer, H., Shupe, M. D., Miller, N. B., Noel, V., Kay, J.,
Turner, D. D., and Guzman, R.: Greenland Clouds Observed in CALIPSO -GOCCP:
Comparison with Ground-Based Summit Observations, J. Climate, 30,
6065–6083, https://doi.org/10.1175/JCLI-D-16-0552.1, 2017.
Lacour, A., Chepfer, H., Miller, N. B., Shupe, M. D., Noel, V., Fettweis,
X., Gallee, H., Kay, J. E., Guzman, R., and Cole, J.: How Well Are Clouds
Simulated over Greenland in Climate Models? Consequences for the Surface
Cloud Radiative Effect over the Ice Sheet, J. Climate, 31, 9293–9312,
https://doi.org/10.1175/JCLI-D-18-0023.1, 2018.
L'Ecuyer, T. S., Wood, N. B., Haladay, T., Stephens, G. L., and Stackhouse,
P. W.: Impact of clouds on atmospheric heating based on the R04 CloudSat
fluxes and heating rates data set, J. Geophys. Res., 113, D00A15, https://doi.org/10.1029/2008JD009951, 2008.
L'Ecuyer, T. S., Hang, Y., Matus, A. V., and Wang, Z.: Reassessing the
Effect of Cloud Type on Earth's Energy Balance in the Age of Active
Spaceborne Observations. Part I: Top of Atmosphere and Surface, J. Climate, 32, 6197–6217, https://doi.org/10.1175/JCLI-D-18-0753.1, 2019.
Lindzen, R. S. and Choi, Y.-S.: The Iris Effect: A Review, Asia-Pacific J.
Atmos. Sci., 58, 159–168, https://doi.org/10.1007/s13143-021-00238-1, 2021.
Loeb, N. G., Kato, S., Loukachine, K., and Manalo-Smith, N.: Angular
Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the
Clouds and the Earth's Radiant Energy System Instrument on the Terra
Satellite. Part I: Methodology, J. Atmos. Ocean. Tech., 22, 338–351, https://doi.org/10.1175/JTECH1712.1, 2005.
Loeb, N. G., Kato, S., Loukachine, K., Manalo-Smith, N., and Doelling, D.
R.: Angular Distribution Models for Top-of-Atmosphere Radiative Flux
Estimation from the Clouds and the Earth's Radiant Energy System Instrument
on the Terra Satellite. Part II: Validation, J. Atmos. Ocean. Tech., 24, 564–584, https://doi.org/10.1175/JTECH1983.1, 2007.
Loeb, N. G., Wang, H., Cheng, A., Kato, S., Fasullo, J. T., Xu, K.-M., and
Allan, R. P.: Observational constraints on atmospheric and oceanic
cross-equatorial heat transports: revisiting the precipitation asymmetry
problem in climate models, Clim. Dynam., 46, 3239–3257, https://doi.org/10.1007/s00382-015-2766-z, 2016.
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor Detection Using Cloudsat – An Earth-Orbiting 94-GHz Cloud Radar, J. Atmos. Ocean. Tech., 25, 519–533, https://doi.org/10.1175/2007JTECHA1006.1, 2008.
Matus, A. V. and L'Ecuyer, T. S.: The role of cloud phase in Earth's
radiation budget, J. Geophys. Res.-Atmos., 122, 2559–2578, https://doi.org/10.1002/2016JD025951, 2017.
Minnis, P., Sun-Mack, S., Trepte, Q. Z., Chang, F.-L., Heck, P. W., Chen, Y., Yi, Y., Arduini, R. F., Ayers, K., Bedka, K., Bedka, S., Brown, R., Gibson, S., Heckert, E., Hong, G., Jin, Z., Palikonda, R., Smith, R., Smith, W. L., Spangenberg, D. A., Yang, P., Yost, C. R., and Xie, Y.:
CERES Edition 3 Cloud Retrievals, 13th Conf. on Atmospheric Radiation, 28 June–2 July 2010, Portland, OR, Am. Meteorol. Soc., 5.4, https://ams.confex.com/ams/pdfpapers/171366.pdf (last access: 23 June 2022), 2010.
Mülmenstädt, J., Sourdeval, O., Henderson, D. S., L'Ecuyer, T. S., Unglaub, C., Jungandreas, L., Böhm, C., Russell, L. M., and Quaas, J.: Using CALIOP to estimate cloud-field base height and its uncertainty: the Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset, Earth Syst. Sci. Data, 10, 2279–2293, https://doi.org/10.5194/essd-10-2279-2018, 2018.
Noel, V., Chepfer, H., Chiriaco, M., and Yorks, J.: The diurnal cycle of cloud profiles over land and ocean between 51∘ S and 51∘ N, seen by the CATS spaceborne lidar from the International Space Station, Atmos. Chem. Phys., 18, 9457–9473, https://doi.org/10.5194/acp-18-9457-2018, 2018.
Norris, J. R., Allen, R. J., Evan, A. T., Zelinka, M. D., O'Dell, C. W., and
Klein, S. A.: Evidence for climate change in the satellite cloud record,
Nature, 536, 72–75, https://doi.org/10.1038/nature18273, 2016.
Ohmura, A., Dutton, E. G., Forgan, B., Fröhlich, C., Gilgen, H., Hegner,
H., Heimo, A., König-Langlo, G., McArthur, B., and Müller, G.:
Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for
climate research, B. Am. Meteorol. Soc., 79, 2115–2136, https://doi.org/10.1175/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2, 1998.
Prata, A. J.: A new long-wave formula for estimating downward clear-sky
radiation at the surface, Q. J. Roy. Meteorol. Soc., 122, 1127–1151, https://doi.org/10.1002/qj.49712253306, 1996.
Ramanathan, V.: Interactions between ice-albedo, lapse-rate and cloud-top
feedbacks: An analysis of the nonlinear response of a GCM climate model, J. Atmos. Sci., 34, 1885–1897, https://doi.org/10.1175/1520-0469(1977)034<1885:IBIALR>2.0.CO;2, 1977.
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., and Barkstrom, B.
R.: Cloud-Radiative Forcing and Climate: Results from te Earth Radiation
Budget Experiment, Science, 243, 57–63, https://doi.org/10.1126/science.243.4887.57, 1989.
Roesch, A., Wild, M., Ohmura, A., Dutton, E. G., Long, C. N., and Zhang, T.: Assessment of BSRN radiation records for the computation of monthly means, Atmos. Meas. Tech., 4, 339–354, https://doi.org/10.5194/amt-4-339-2011, 2011.
Rienecker, M. M., Suarez, M. J., Todling, R., Bacmeister, J., Takacs, L., Liu, H. C., Gu, W., Sienkiewicz, M., Koster, R. D., Gelaro, R., Stajner, I., and Nielsen, J. E.: The GEOS-5 data assimilation system: Documentation of
versions 5.0.1, 5.1.0, and 5.2.0., NASATechnical Report Series
on Global Modeling and Data Assimilation, Vol. 27, NASA/TM-2008-105606, 97 pp., https://ntrs.nasa.gov/api/citations/20120011955/downloads/20120011955.pdf (last access: 23 June 2022), 2008.
Rojas Muñoz, O. J., Chiriaco, M., Bastin, S., and Ringard, J.: Estimation of the terms acting on local 1 h surface temperature variations in Paris region: the specific contribution of clouds, Atmos. Chem. Phys., 21, 15699–15723, https://doi.org/10.5194/acp-21-15699-2021, 2021.
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.
Sassen, K. and Wang, Z.: Classifying clouds around the globe with the
CloudSat radar: 1-year of results, Geophys. Res. Lett., 35, L04805,
https://doi.org/10.1029/2007GL032591, 2008.
Scott, R. C., Lubin, D., Vogelmann, A. M., and Kato, S.: West Antarctic Ice
Sheet Cloud Cover and Surface Radiation Budget from NASA A-Train Satellites,
J. Climate, 30, 6151–6170, https://doi.org/10.1175/JCLI-D-16-0644.1, 2017.
Shepherd, A., Ivins, E. R., A, G., Barletta, V. R., Bentley, M. J., Bettadpur, S., Briggs, K. H., Bromwich, D. H., Forsberg, R., Galin, N., Horwath, M., Jacobs, S., Joughin, I., King, M. A., Lenaerts, J. T. M., Li, J., Ligtenberg, S. R. M., Luckman, A., Luthcke, S. B., McMillan, M., Meister, R., Milne, G., Mouginot, J., Muir, A., Nicolas, J. P., Paden, J., Payne, A. J., Pritchard, H., Rignot, E., Rott, H., Sørensen, L. S., Scambos, T. A., Scheuchl, B., Schrama, E. J. O., Smith, B., Sundal, A. V., van Angelen, J. H., van de Berg, W. J., van den Broeke, M. R., Vaughan, D. G., Velicogna, I., Wahr, J., Whitehouse, P. L., Wingham, D. J., Yi, D., Young, D., and Zwally, H. J.: A Reconciled Estimate of Ice-Sheet Mass Balance, Science, 338, 1183–1189, https://doi.org/10.1126/science.1228102, 2012.
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, 13, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2, 2004.
Shupe, M. D., Turner, D. D., Walden, V. P., Bennartz, R., Cadeddu, M. P.,
Castellani, B. B., Cox, C. J., Hudak, D. R., Kulie, M. S., Miller, N. B.,
Neely, R. R., Neff, W. D., and Rowe, P. M.: High and Dry: New Observations
of Tropospheric and Cloud Properties above the Greenland Ice Sheet, B. Am. Meteorol. Soc., 94,
169–186, https://doi.org/10.1175/BAMS-D-11-00249.1, 2013.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang,
Z., Illingworth, A. J., O'connor, E. J., Rossow, W. B., Durden, S. L.,
Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat
Science Team: THE CLOUDSAT MISSION AND THE A-TRAIN: A New Dimension of
Space-Based Observations of Clouds and Precipitation, B. Am. Meteorol.
Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771,
2002.
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M.,
Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J.,
Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang,
Z., and Marchand, R.: CloudSat mission: Performance and early science after
the first year of operation, J. Geophys. Res.-Atmos., 113, D00A18, https://doi.org/10.1029/2008JD009982, 2008.
Stroeve, J. C., Serreze, M. C., Holland, M. M., Kay, J. E., Maslanik, J.,
and Barrett, A. P.: The Arctic's rapidly shrinking sea ice cover: a research
synthesis, Clim. Change, 110, 1005–1027, https://doi.org/10.1007/s10584-011-0101-1, 2012.
Taylor, K. E., Crucifix, M., Braconnot, P., Hewitt, C. D., Doutriaux, C.,
Broccoli, A. J., Mitchell, J. F. B., and Webb, M. J.: Estimating Shortwave
Radiative Forcing and Response in Climate Models, J. Climate, 20, 2530–2543, https://doi.org/10.1175/JCLI4143.1, 2007.
Vaillant de Guélis, T., Chepfer, H., Noel, V., Guzman, R., Dubuisson, P., Winker, D. M., and Kato, S.: The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated, Atmos. Meas. Tech., 10, 4659–4685, https://doi.org/10.5194/amt-10-4659-2017, 2017a.
Vaillant de Guélis, T., Chepfer, H., Noel, V., Guzman, R., Winker, D.
M., and Plougonven, R.: Using Space Lidar Observations to Decompose Longwave
Cloud Radiative Effect Variations Over the Last Decade: Space lidar
decomposes LWCRE variations, Geophys. Res. Lett., 44, 11994–12003,
https://doi.org/10.1002/2017GL074628, 2017b.
Vaillant de Guélis, T., Chepfer, H., Guzman, R., Bonazzola, M., Winker,
D. M., and Noel, V.: Space lidar observations constrain longwave cloud
feedback, Sci. Rep., 8, 16570, https://doi.org/10.1038/s41598-018-34943-1, 2018.
van den Broeke, M., Bamber, J., Ettema, J., Rignot, E., Schrama, E., van de
Berg, W. J., van Meijgaard, E., Velicogna, I., and Wouters, B.: Partitioning
Recent Greenland Mass Loss, Science, 326, 984–986, https://doi.org/10.1126/science.1178176, 2009.
Van Tricht, K., Lhermitte, S., Lenaerts, J. T. M., Gorodetskaya, I. V.,
L'Ecuyer, T. S., Noël, B., van den Broeke, M. R., Turner, D. D., and van
Lipzig, N. P. M.: Clouds enhance Greenland ice sheet meltwater runoff, Nat.
Commun., 7, 10266, https://doi.org/10.1038/ncomms10266, 2016.
Vaughan, M., Pitts, M., Trepte, C., Winker, D., Detweiler, P., Garnier, A.,
Getzewich, B., Hunt, W., Lambeth, J., Lee, K.-P., Lucker, P., Murray, T.,
Rodier, S., Tremas, T., Bazureau, A., and Pelon, J.: Cloud-Aerosol LIDAR
Infrared Pathfinder Satellite Observations (CALIPSO) data management system
data products catalog, Release 4.30, NASA Langley Research Center Document
PC-SCI-503, https://www-calipso.larc.nasa.gov/products/CALIPSO_DPC_Rev4x30.pdf (last access: 23 June 2022), 2018.
Wang, W., Zender, C. S., As, D., and Miller, N. B.: Spatial Distribution of Melt Season Cloud Radiative Effects Over Greenland: Evaluating Satellite Observations, Reanalyses, and Model Simulations Against In Situ Measurements, J. Geophys. Res.-Atmos., 124, 57–71, https://doi.org/10.1029/2018JD028919, 2019.
Winker, D. M., Pelon, J., Jr, J. A. C., Ackerman, S. A., Charlson, R. J.,
Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L.,
Treut, H. L., Mccormick, M. P., Mégie, G., Poole, L., Powell, K.,
Trepte, C., Vaughan, M. A., and Wielicki, B. A.: A Global 3D View of
Aerosols and Clouds, B. Am. Meteorol. Soc., 91, 1211–1230, https://doi.org/10.1175/2010BAMS3009.1, 2010.
Zelinka, M. D., Klein, S. A., and Hartmann, D. L.: Computing and
Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud
Radiative Kernels, J. Climate, 25, 3715–3735, https://doi.org/10.1175/JCLI-D-11-00248.1, 2012a.
Zelinka, M. D., Klein, S. A., and Hartmann, D. L.: Computing and
Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II:
Attribution to Changes in Cloud Amount, Altitude, and Optical Depth, J.
Climate, 25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1, 2012b.
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.
We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from...
