Articles | Volume 21, issue 5
Atmos. Chem. Phys., 21, 4079–4101, 2021
https://doi.org/10.5194/acp-21-4079-2021
Atmos. Chem. Phys., 21, 4079–4101, 2021
https://doi.org/10.5194/acp-21-4079-2021

Research article 18 Mar 2021

Research article | 18 Mar 2021

Characterisation and surface radiative impact of Arctic low clouds from the IAOOS field experiment

Julia Maillard et al.

Related authors

The impact of deep convection representation in a global atmospheric model on the warm conveyor belt and jet stream during NAWDEX IOP6
Gwendal Rivière, Meryl Wimmer, Philippe Arbogast, Jean-Marcel Piriou, Julien Delanoë, Carole Labadie, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2021-38,https://doi.org/10.5194/wcd-2021-38, 2021
Revised manuscript under review for WCD
Short summary
A new lidar design for operational atmospheric wind and cloud/aerosol survey from space
Didier Bruneau and Jacques Pelon
Atmos. Meas. Tech., 14, 4375–4402, https://doi.org/10.5194/amt-14-4375-2021,https://doi.org/10.5194/amt-14-4375-2021, 2021
Short summary
Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3277–3299, https://doi.org/10.5194/amt-14-3277-2021,https://doi.org/10.5194/amt-14-3277-2021, 2021
Short summary
Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithms
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3253–3276, https://doi.org/10.5194/amt-14-3253-2021,https://doi.org/10.5194/amt-14-3253-2021, 2021
Short summary
Representation by two climate models of the dynamical and diabatic processes involved in the development of an explosively deepening cyclone during NAWDEX
David L. A. Flack, Gwendal Rivière, Ionela Musat, Romain Roehrig, Sandrine Bony, Julien Delanoë, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 2, 233–253, https://doi.org/10.5194/wcd-2-233-2021,https://doi.org/10.5194/wcd-2-233-2021, 2021
Short summary

Related subject area

Subject: Clouds and Precipitation | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
A new conceptual model for adiabatic fog
Felipe Toledo, Martial Haeffelin, Eivind Wærsted, and Jean-Charles Dupont
Atmos. Chem. Phys., 21, 13099–13117, https://doi.org/10.5194/acp-21-13099-2021,https://doi.org/10.5194/acp-21-13099-2021, 2021
Short summary
Deciphering organization of GOES-16 green cumulus through the empirical orthogonal function (EOF) lens
Tom Dror, Mickaël D. Chekroun, Orit Altaratz, and Ilan Koren
Atmos. Chem. Phys., 21, 12261–12272, https://doi.org/10.5194/acp-21-12261-2021,https://doi.org/10.5194/acp-21-12261-2021, 2021
Short summary
Satellite retrieval of cloud base height and geometric thickness of low-level cloud based on CALIPSO
Xin Lu, Feiyue Mao, Daniel Rosenfeld, Yannian Zhu, Zengxin Pan, and Wei Gong
Atmos. Chem. Phys., 21, 11979–12003, https://doi.org/10.5194/acp-21-11979-2021,https://doi.org/10.5194/acp-21-11979-2021, 2021
Short summary
Lightning occurrences and intensity over the Indian region: long-term trends and future projections
Rohit Chakraborty, Arindam Chakraborty, Ghouse Basha, and Madineni Venkat Ratnam
Atmos. Chem. Phys., 21, 11161–11177, https://doi.org/10.5194/acp-21-11161-2021,https://doi.org/10.5194/acp-21-11161-2021, 2021
Short summary
Contrasting ice formation in Arctic clouds: surface-coupled vs. surface-decoupled clouds
Hannes J. Griesche, Kevin Ohneiser, Patric Seifert, Martin Radenz, Ronny Engelmann, and Albert Ansmann
Atmos. Chem. Phys., 21, 10357–10374, https://doi.org/10.5194/acp-21-10357-2021,https://doi.org/10.5194/acp-21-10357-2021, 2021
Short summary

Cited articles

Blanchard, Y., Pelon, J., Eloranta, E. W., Moran, K. P., Delanoë, J., and Sèze, G.: A Synergistic Analysis of Cloud Cover and Vertical Distribution from A-Train and Ground-Based Sensors over the High Arctic Station Eureka from 2006 to 2010, J. Appl. Meteorol. Clim., 53, 2553–2570, https://doi.org/10.1175/JAMC-D-14-0021.1, 2014. a, b
Bucholtz, A.: Rayleigh-scattering calculations for the terrestrial atmosphere, OSA Proc., 34, 2765–2773, https://doi.org/10.1364/ao.34.002765, 1995. a
Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and de Boer, G.: Ubiquitous low-level liquid-containing Arctic clouds: new observations and climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39, L20804, https://doi.org/10.1029/2012GL053385, 2012. a, b
Chan, M. A. and Comiso, J. C.: Arctic Cloud Characteristics as Derived from MODIS, CALIPSO, and CloudSat, J. Climate, 26, 3285–3306, https://doi.org/10.1175/JCLI-D-12-00204.1, 2013. a, b
Cohen, L., Hudson, S. R., Walden, V. P., Graham, R. M., and Granskog, M. A.: Meteorological conditions in a thinner Arctic sea ice regime from winter to summer during the Norwegian Young Sea Ice expedition (N-ICE2015), J. Geophys. Res.-Atmos., 122, 7235–7259, https://doi.org/10.1002/2016jd026034, 2017. a, b
Download
Short summary
Clouds remain a major source of uncertainty in understanding the Arctic climate, due in part to the lack of measurements over the sea ice. In this paper, we exploit a series of lidar profiles acquired from autonomous drifting buoys deployed in the Arctic Ocean and derive a statistic of low cloud frequency and macrophysical properties. We also show that clouds contribute to warm the surface in the shoulder seasons but not significantly from May to September.
Altmetrics
Final-revised paper
Preprint