Articles | Volume 22, issue 11
https://doi.org/10.5194/acp-22-7287-2022
© 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/acp-22-7287-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2022
Research article |
| 07 Jun 2022
| 07 Jun 2022
Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic
Annakaisa von Lerber
CORRESPONDING AUTHOR
Department of Geoscience, Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Measurement Systems, Observation Services, Finnish Meteorological Institute, Helsinki, Finland
Mario Mech
Department of Geoscience, Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Annette Rinke
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
Damao Zhang
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Washington, USA
Melanie Lauer
Department of Geoscience, Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Ana Radovan
Department of Geoscience, Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Irina Gorodetskaya
CESAM – Centre for Environmental and Marine Studies, Department of Physics, University of Aveiro, Aveiro, Portugal
Susanne Crewell
CORRESPONDING AUTHOR
Department of Geoscience, Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Related authors
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025, https://doi.org/10.5194/amt-18-793-2025, 2025
Short summary
Short summary
Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
Michael Fehlmann, Mario Rohrer, Annakaisa von Lerber, and Markus Stoffel
Atmos. Meas. Tech., 13, 4683–4698, https://doi.org/10.5194/amt-13-4683-2020, https://doi.org/10.5194/amt-13-4683-2020, 2020
Short summary
Short summary
The Thies disdrometer is used to monitor precipitation intensity and its phase and thus may provide valuable information for the management of meteorological and hydrological risks. In this study, we characterize biases of this instrument using common reference instruments at a pre-alpine study site in Switzerland. We find a systematic underestimation of liquid precipitation amounts and suggest possible reasons for and corrections to this bias and relate these findings to other study sites.
Amanda Sellmaier, Ellen Damm, Torsten Sachs, Benjamin Kirbus, Inge Wiekenkamp, Annette Rinke, Falk Pätzold, Daiki Nomura, Astrid Lampert, and Markus Rex
EGUsphere, https://doi.org/10.5194/egusphere-2025-3778, https://doi.org/10.5194/egusphere-2025-3778, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study presents continuous ship-borne measurements of methane (CH4) concentration and its isotopic composition monitored during the ice drift MOSAiC expedition in 2020. By applying trajectory analysis, we linked atmospheric CH4 variabilities to air mass pathways transported either over open water or sea ice. This study will contribute to reveal the potential role of ship-borne measurements for filing significant observational gaps in the high Arctic.
Manuel Moser, Christiane Voigt, Oliver Eppers, Johannes Lucke, Elena De La Torre Castro, Johanna Mayer, Regis Dupuy, Guillaume Mioche, Olivier Jourdan, Hans-Christian Clemen, Johannes Schneider, Philipp Joppe, Stephan Mertes, Bruno Wetzel, Stephan Borrmann, Marcus Klingebiel, Mario Mech, Christof Lüpkes, Susanne Crewell, André Ehrlich, Andreas Herber, and Manfred Wendisch
EGUsphere, https://doi.org/10.5194/egusphere-2025-3876, https://doi.org/10.5194/egusphere-2025-3876, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
In this study we analyzed Arctic mixed-phase clouds using airborne in-situ measurements in spring 2022. Based on microphysical properties, we show that within these clouds a distinction must be made between classic mixed-phase clouds and a mixed-phase haze regime. Instead of supercooled droplets, the haze regime contains large wet sea salt aerosols. These findings improve our understanding of Arctic low-level cloud processes.
Henning Dorff, Florian Ewald, Heike Konow, Mario Mech, Davide Ori, Vera Schemann, Andreas Walbröl, Manfred Wendisch, and Felix Ament
Atmos. Chem. Phys., 25, 8329–8354, https://doi.org/10.5194/acp-25-8329-2025, https://doi.org/10.5194/acp-25-8329-2025, 2025
Short summary
Short summary
Using observations of an Arctic atmospheric river (AR) from a long-range research aircraft, we analyse how moisture transported into the Arctic by the AR is transformed and how it interacts with the Arctic environment. The moisture transport divergence is the main driver of local moisture change over time. Surface precipitation and evaporation are rather weak when averaged over extended AR sectors, although considerable heterogeneity of precipitation within the AR is observed.
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
Atmos. Meas. Tech., 18, 3453–3475, https://doi.org/10.5194/amt-18-3453-2025, https://doi.org/10.5194/amt-18-3453-2025, 2025
Short summary
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Kerstin Ebell, Christian Buhren, Rosa Gierens, Giovanni Chellini, Melanie Lauer, Andreas Walbröl, Sandro Dahlke, Pavel Krobot, and Mario Mech
Atmos. Chem. Phys., 25, 7315–7342, https://doi.org/10.5194/acp-25-7315-2025, https://doi.org/10.5194/acp-25-7315-2025, 2025
Short summary
Short summary
Ground-based observations of precipitation are rare in the Arctic. Since 2017, additional temporally highly resolved precipitation measurements have been carried out by a precipitation gauge and an optical precipitation sensor at Ny-Ålesund, Svalbard. These new data facilitate the distinction between liquid and solid precipitation. Using reanalysis data, we also find that water vapor transport contributes strongly to precipitation and especially to extreme precipitation events.
Dan Lubin, Xun Zou, Johannes Mülmenstädt, Andrew Vogelmann, Maria Cadeddu, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2768, https://doi.org/10.5194/egusphere-2025-2768, 2025
Short summary
Short summary
The US Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska Facility has measured solar and atmospheric infrared radiation, and cloud properties, for the past 25 years. Statistically significant trends are emerging, including increasing infrared radiation due to a warming atmosphere, and decreasing solar radiation due to increasing liquid water content in clouds. These trends are influenced by large-scale atmospheric circulation patterns and by atmospheric rivers.
Andrew M. Sayer, Brian Cairns, Kirk D. Knobelspiesse, Luca Lelli, Chamara Rajapakshe, Scott E. Giangrande, Gareth E. Thomas, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2005, https://doi.org/10.5194/egusphere-2025-2005, 2025
Short summary
Short summary
Satellites can estimate cloud height in several ways: two include a thermal technique (colder clouds being higher up), and another looking at colours of light that oxygen in the atmosphere absorbs (darker clouds being lower down). It can also be measured (from ground or space) by radar and lidar. We compare satellite data we developed using the oxygen method with other estimates to help us refine our technique.
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.
Yubing Cheng, Bin Cheng, Roberta Pirazzini, Amy R. Macfarlane, Timo Vihma, Wolfgang Dorn, Ruzica Dadic, Martin Schneebeli, Stefanie Arndt, and Annette Rinke
EGUsphere, https://doi.org/10.5194/egusphere-2025-1164, https://doi.org/10.5194/egusphere-2025-1164, 2025
Short summary
Short summary
We study snow density from the MOSAiC expedition. Several snow density schemes were tested and compared with observation. A thermodynamic ice model was employed to assess the impact of snow density and precipitation on the thermal regime of sea ice. The parameterized mean snow densities are consistent with observations. Increased snow density reduces snow and ice temperatures, promoting ice growth, while increased precipitation leads to warmer snow and ice temperatures and reduced ice thickness.
André Ehrlich, Susanne Crewell, Andreas Herber, Marcus Klingebiel, Christof Lüpkes, Mario Mech, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Matthias Buschmann, Hans-Christian Clemen, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Andreas Giez, Sarah Grawe, Christophe Gourbeyre, Jörg Hartmann, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsófia Jurányi, Benjamin Kirbus, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Christian Mallaun, Johanna Mayer, Stephan Mertes, Guillaume Mioche, Manuel Moser, Hanno Müller, Veronika Pörtge, Nils Risse, Greg Roberts, Sophie Rosenburg, Johannes Röttenbacher, Michael Schäfer, Jonas Schaefer, Andreas Schäfler, Imke Schirmacher, Johannes Schneider, Sabrina Schnitt, Frank Stratmann, Christian Tatzelt, Christiane Voigt, Andreas Walbröl, Anna Weber, Bruno Wetzel, Martin Wirth, and Manfred Wendisch
Earth Syst. Sci. Data, 17, 1295–1328, https://doi.org/10.5194/essd-17-1295-2025, https://doi.org/10.5194/essd-17-1295-2025, 2025
Short summary
Short summary
This paper provides an overview of the HALO–(AC)3 aircraft campaign data sets, the campaign-specific instrument operation, data processing, and data quality. The data set comprises in situ and remote sensing observations from three research aircraft: HALO, Polar 5, and Polar 6. All data are published in the PANGAEA database by instrument-separated data subsets. It is highlighted how the scientific analysis of the HALO–(AC)3 data benefits from the coordinated operation of three aircraft.
Fan Mei, Qi Zhang, Damao Zhang, Jerome D. Fast, Gourihar Kulkarni, Mikhail S. Pekour, Christopher R. Niedek, Susanne Glienke, Israel Silber, Beat Schmid, Jason M. Tomlinson, Hardeep S. Mehta, Xena Mansoura, Zezhen Cheng, Gregory W. Vandergrift, Nurun Nahar Lata, Swarup China, and Zihua Zhu
Atmos. Chem. Phys., 25, 3425–3444, https://doi.org/10.5194/acp-25-3425-2025, https://doi.org/10.5194/acp-25-3425-2025, 2025
Short summary
Short summary
This study highlights the unique capability of the ArcticShark, an uncrewed aerial system, in measuring vertically resolved atmospheric properties. Data from 32 research flights in 2023 reveal seasonal patterns and correlations with conventional measurements. The consistency and complementarity of in situ and remote sensing methods are highlighted. The study demonstrates the ArcticShark’s versatility in bridging data gaps and improving the understanding of vertical atmospheric structures.
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025, https://doi.org/10.5194/amt-18-793-2025, 2025
Short summary
Short summary
Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
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.
Diana Francis, Ricardo Fonseca, Narendra Nelli, Petra Heil, Jonathan Wille, Irina Gorodetskaya, and Robert Massom
EGUsphere, https://doi.org/10.5194/egusphere-2024-3535, https://doi.org/10.5194/egusphere-2024-3535, 2025
Short summary
Short summary
This study investigates the impact of atmospheric rivers and associated atmospheric dynamics on sea-ice thickness and snow depth at a coastal site in East Antarctica during July–November 2022 using in-situ measurements and numerical modelling. The passage of an atmospheric river induced a reduction of up to 0.06 m in both fields. Precipitation occurred from the convergence of katabatic winds with advected low-latitude moist air.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
Short summary
Short summary
Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Zhenhai Zhang, F. Martin Ralph, Xun Zou, Brian Kawzenuk, Minghua Zheng, Irina V. Gorodetskaya, Penny M. Rowe, and David H. Bromwich
The Cryosphere, 18, 5239–5258, https://doi.org/10.5194/tc-18-5239-2024, https://doi.org/10.5194/tc-18-5239-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are long, narrow corridors of strong water vapor transport in the atmosphere. ARs play an important role in extreme weather in polar regions, including heavy rain and/or snow, heat waves, and surface melt. The standard AR scale is developed based on the midlatitude climate and is insufficient for polar regions. This paper introduces an extended version of the AR scale tuned to polar regions, aiming to quantify polar ARs objectively based on their strength and impact.
Imke Schirmacher, Sabrina Schnitt, Marcus Klingebiel, Nina Maherndl, Benjamin Kirbus, André Ehrlich, Mario Mech, and Susanne Crewell
Atmos. Chem. Phys., 24, 12823–12842, https://doi.org/10.5194/acp-24-12823-2024, https://doi.org/10.5194/acp-24-12823-2024, 2024
Short summary
Short summary
During Arctic marine cold-air outbreaks, cold air flows from sea ice over open water. Roll circulations evolve, forming cloud streets. We investigate the initial circulation and cloud development using high-resolution airborne measurements. We compute the distance an air mass traveled over water (fetch) from back trajectories. Cloud streets form at 15 km fetch, cloud cover strongly increases at around 20 km, and precipitation forms at around 30 km.
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024, https://doi.org/10.5194/amt-17-6223-2024, 2024
Short summary
Short summary
We developed retrievals of integrated water vapour (IWV), temperature profiles, and humidity profiles from ground-based passive microwave remote sensing measurements gathered during the MOSAiC expedition. We demonstrate and quantify the benefit of combining low- and high-frequency microwave radiometers to improve humidity profiling and IWV estimates by comparing the retrieved quantities to single-instrument retrievals and reference datasets (radiosondes).
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
Short summary
Short summary
Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Lara Foth, Wolfgang Dorn, Annette Rinke, Evelyn Jäkel, and Hannah Niehaus
The Cryosphere, 18, 4053–4064, https://doi.org/10.5194/tc-18-4053-2024, https://doi.org/10.5194/tc-18-4053-2024, 2024
Short summary
Short summary
It is demonstrated that the explicit consideration of the cloud dependence of the snow surface albedo in a climate model results in a more realistic simulation of the surface albedo during the snowmelt period in late May and June. Although this improvement appears to be relatively insubstantial, it has significant impact on the simulated sea-ice volume and extent in the model due to an amplification of the snow/sea-ice albedo feedback, one of the main contributors to Arctic amplification.
Falco Monsees, Alexei Rozanov, John P. Burrows, Mark Weber, Annette Rinke, Ralf Jaiser, and Peter von der Gathen
Atmos. Chem. Phys., 24, 9085–9099, https://doi.org/10.5194/acp-24-9085-2024, https://doi.org/10.5194/acp-24-9085-2024, 2024
Short summary
Short summary
Cyclones strongly influence weather predictability but still cannot be fully characterised in the Arctic because of the sparse coverage of meteorological measurements. A potential approach to compensate for this is the use of satellite measurements of ozone, because cyclones impact the tropopause and therefore also ozone. In this study we used this connection to investigate the correlation between ozone and the tropopause in the Arctic and to identify cyclones with satellite ozone observations.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Andreas Walbröl, Janosch Michaelis, Sebastian Becker, Henning Dorff, Kerstin Ebell, Irina Gorodetskaya, Bernd Heinold, Benjamin Kirbus, Melanie Lauer, Nina Maherndl, Marion Maturilli, Johanna Mayer, Hanno Müller, Roel A. J. Neggers, Fiona M. Paulus, Johannes Röttenbacher, Janna E. Rückert, Imke Schirmacher, Nils Slättberg, André Ehrlich, Manfred Wendisch, and Susanne Crewell
Atmos. Chem. Phys., 24, 8007–8029, https://doi.org/10.5194/acp-24-8007-2024, https://doi.org/10.5194/acp-24-8007-2024, 2024
Short summary
Short summary
To support the interpretation of the data collected during the HALO-(AC)3 campaign, which took place in the North Atlantic sector of the Arctic from 7 March to 12 April 2022, we analyze how unusual the weather and sea ice conditions were with respect to the long-term climatology. From observations and ERA5 reanalysis, we found record-breaking warm air intrusions and a large variety of marine cold air outbreaks. Sea ice concentration was mostly within the climatological interquartile range.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Evelyn Jäkel, Sebastian Becker, Tim R. Sperzel, Hannah Niehaus, Gunnar Spreen, Ran Tao, Marcel Nicolaus, Wolfgang Dorn, Annette Rinke, Jörg Brauchle, and Manfred Wendisch
The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, https://doi.org/10.5194/tc-18-1185-2024, 2024
Short summary
Short summary
The results of the surface albedo scheme of a coupled regional climate model were evaluated against airborne and ground-based measurements conducted in the European Arctic in different seasons between 2017 and 2022. We found a seasonally dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. The strongest effects of the albedo model bias on the net irradiance were most apparent in the presence of optically thin clouds.
Nina Maherndl, Manuel Moser, Johannes Lucke, Mario Mech, Nils Risse, Imke Schirmacher, and Maximilian Maahn
Atmos. Meas. Tech., 17, 1475–1495, https://doi.org/10.5194/amt-17-1475-2024, https://doi.org/10.5194/amt-17-1475-2024, 2024
Short summary
Short summary
In some clouds, liquid water droplets can freeze onto ice crystals (riming). Riming leads to the formation of snowflakes. We show two ways to quantify riming using aircraft data collected in the Arctic. One aircraft had a cloud radar, while the other aircraft was measuring directly in cloud. The first method compares radar and direct observations. The second looks at snowflake shape. Both methods agree, except when there were gaps in the cloud. This improves our ability to understand riming.
Sabrina Schnitt, Andreas Foth, Heike Kalesse-Los, Mario Mech, Claudia Acquistapace, Friedhelm Jansen, Ulrich Löhnert, Bernhard Pospichal, Johannes Röttenbacher, Susanne Crewell, and Bjorn Stevens
Earth Syst. Sci. Data, 16, 681–700, https://doi.org/10.5194/essd-16-681-2024, https://doi.org/10.5194/essd-16-681-2024, 2024
Short summary
Short summary
This publication describes the microwave radiometric measurements performed during the EUREC4A campaign at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January 2020 and 14 February 2020.
Marcus Klingebiel, André Ehrlich, Elena Ruiz-Donoso, Nils Risse, Imke Schirmacher, Evelyn Jäkel, Michael Schäfer, Kevin Wolf, Mario Mech, Manuel Moser, Christiane Voigt, and Manfred Wendisch
Atmos. Chem. Phys., 23, 15289–15304, https://doi.org/10.5194/acp-23-15289-2023, https://doi.org/10.5194/acp-23-15289-2023, 2023
Short summary
Short summary
In this study we explain how we use aircraft measurements from two Arctic research campaigns to identify cloud properties (like droplet size) over sea-ice and ice-free ocean. To make sure that our measurements make sense, we compare them with other observations. Our results show, e.g., larger cloud droplets in early summer than in spring. Moreover, the cloud droplets are also larger over ice-free ocean than compared to sea ice. In the future, our data can be used to improve climate models.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
Short summary
Short summary
Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Imke Schirmacher, Pavlos Kollias, Katia Lamer, Mario Mech, Lukas Pfitzenmaier, Manfred Wendisch, and Susanne Crewell
Atmos. Meas. Tech., 16, 4081–4100, https://doi.org/10.5194/amt-16-4081-2023, https://doi.org/10.5194/amt-16-4081-2023, 2023
Short summary
Short summary
CloudSat’s relatively coarse spatial resolution, low sensitivity, and blind zone limit its assessment of Arctic low-level clouds, which affect the surface energy balance. We compare cloud fractions from CloudSat and finely resolved airborne radar observations to determine CloudSat’s limitations. Cloudsat overestimates cloud fractions above its blind zone, especially during cold-air outbreaks over open water, and misses a cloud fraction of 32 % and half of the precipitation inside its blind zone.
Melanie Lauer, Annette Rinke, Irina Gorodetskaya, Michael Sprenger, Mario Mech, and Susanne Crewell
Atmos. Chem. Phys., 23, 8705–8726, https://doi.org/10.5194/acp-23-8705-2023, https://doi.org/10.5194/acp-23-8705-2023, 2023
Short summary
Short summary
We present a new method to analyse the influence of atmospheric rivers (ARs), cyclones, and fronts on the precipitation in the Arctic, based on two campaigns: ACLOUD (early summer 2017) and AFLUX (early spring 2019). There are differences between both campaign periods: in early summer, the precipitation is mostly related to ARs and fronts, especially when they are co-located, while in early spring, cyclones isolated from ARs and fronts contributed most to the precipitation.
Manuel Moser, Christiane Voigt, Tina Jurkat-Witschas, Valerian Hahn, Guillaume Mioche, Olivier Jourdan, Régis Dupuy, Christophe Gourbeyre, Alfons Schwarzenboeck, Johannes Lucke, Yvonne Boose, Mario Mech, Stephan Borrmann, André Ehrlich, Andreas Herber, Christof Lüpkes, and Manfred Wendisch
Atmos. Chem. Phys., 23, 7257–7280, https://doi.org/10.5194/acp-23-7257-2023, https://doi.org/10.5194/acp-23-7257-2023, 2023
Short summary
Short summary
This study provides a comprehensive microphysical and thermodynamic phase analysis of low-level clouds in the northern Fram Strait, above the sea ice and the open ocean, during spring and summer. Using airborne in situ cloud data, we show that the properties of Arctic low-level clouds vary significantly with seasonal meteorological situations and surface conditions. The observations presented in this study can help one to assess the role of clouds in the Arctic climate system.
Jan Chylik, Dmitry Chechin, Regis Dupuy, Birte S. Kulla, Christof Lüpkes, Stephan Mertes, Mario Mech, and Roel A. J. Neggers
Atmos. Chem. Phys., 23, 4903–4929, https://doi.org/10.5194/acp-23-4903-2023, https://doi.org/10.5194/acp-23-4903-2023, 2023
Short summary
Short summary
Arctic low-level clouds play an important role in the ongoing warming of the Arctic. Unfortunately, these clouds are not properly represented in weather forecast and climate models. This study tries to cover this gap by focusing on clouds over open water during the spring, observed by research aircraft near Svalbard. The study combines the high-resolution model with sets of observational data. The results show the importance of processes that involve both ice and the liquid water in the clouds.
Damao Zhang, Jennifer Comstock, and Victor Morris
Atmos. Meas. Tech., 15, 4735–4749, https://doi.org/10.5194/amt-15-4735-2022, https://doi.org/10.5194/amt-15-4735-2022, 2022
Short summary
Short summary
The planetary boundary layer is the lowest part of the atmosphere. Its structure and depth (PBLHT) significantly impact air quality, global climate, land–atmosphere interactions, and a wide range of atmospheric processes. To test the robustness of the ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. DOE ARM measurements at various ARM observatories located around the world.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
Short summary
Short summary
Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Carolina Viceto, Irina V. Gorodetskaya, Annette Rinke, Marion Maturilli, Alfredo Rocha, and Susanne Crewell
Atmos. Chem. Phys., 22, 441–463, https://doi.org/10.5194/acp-22-441-2022, https://doi.org/10.5194/acp-22-441-2022, 2022
Short summary
Short summary
We focus on anomalous moisture transport events known as atmospheric rivers (ARs). During ACLOUD and PASCAL, three AR events were identified: 30 May, 6 June, and 9 June 2017. We explore their spatio-temporal evolution and precipitation patterns using measurements, reanalyses, and a model. We show the importance of the following: Atlantic and Siberian pathways during spring–summer in the Arctic, AR-associated heat/moisture increase, precipitation phase transition, and high-resolution datasets.
Claudia Acquistapace, Richard Coulter, Susanne Crewell, Albert Garcia-Benadi, Rosa Gierens, Giacomo Labbri, Alexander Myagkov, Nils Risse, and Jan H. Schween
Earth Syst. Sci. Data, 14, 33–55, https://doi.org/10.5194/essd-14-33-2022, https://doi.org/10.5194/essd-14-33-2022, 2022
Short summary
Short summary
This publication describes the unprecedented high-resolution cloud and precipitation dataset collected by two radars deployed on the Maria S. Merian research vessel. The ship operated in the west Atlantic Ocean during the measurement campaign called EUREC4A, between 19 January and 19 February 2020. The data collected are crucial to investigate clouds and precipitation and understand how they form and change over the ocean, where it is so difficult to measure them.
Hélène Bresson, Annette Rinke, Mario Mech, Daniel Reinert, Vera Schemann, Kerstin Ebell, Marion Maturilli, Carolina Viceto, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 173–196, https://doi.org/10.5194/acp-22-173-2022, https://doi.org/10.5194/acp-22-173-2022, 2022
Short summary
Short summary
Arctic warming is pronounced, and one factor in this is the poleward atmospheric transport of heat and moisture. This study assesses the 4D structure of an Arctic moisture intrusion event which occurred in June 2017. For the first time, high-resolution pan-Arctic ICON simulations are performed and compared with global models, reanalysis, and observations. Results show the added value of high resolution in the event representation and the impact of the intrusion on the surface energy fluxes.
Heike Konow, Florian Ewald, Geet George, Marek Jacob, Marcus Klingebiel, Tobias Kölling, Anna E. Luebke, Theresa Mieslinger, Veronika Pörtge, Jule Radtke, Michael Schäfer, Hauke Schulz, Raphaela Vogel, Martin Wirth, Sandrine Bony, Susanne Crewell, André Ehrlich, Linda Forster, Andreas Giez, Felix Gödde, Silke Groß, Manuel Gutleben, Martin Hagen, Lutz Hirsch, Friedhelm Jansen, Theresa Lang, Bernhard Mayer, Mario Mech, Marc Prange, Sabrina Schnitt, Jessica Vial, Andreas Walbröl, Manfred Wendisch, Kevin Wolf, Tobias Zinner, Martin Zöger, Felix Ament, and Bjorn Stevens
Earth Syst. Sci. Data, 13, 5545–5563, https://doi.org/10.5194/essd-13-5545-2021, https://doi.org/10.5194/essd-13-5545-2021, 2021
Short summary
Short summary
The German research aircraft HALO took part in the research campaign EUREC4A in January and February 2020. The focus area was the tropical Atlantic east of the island of Barbados. We describe the characteristics of the 15 research flights, provide auxiliary information, derive combined cloud mask products from all instruments that observe clouds on board the aircraft, and provide code examples that help new users of the data to get started.
Sebastian Landwehr, Michele Volpi, F. Alexander Haumann, Charlotte M. Robinson, Iris Thurnherr, Valerio Ferracci, Andrea Baccarini, Jenny Thomas, Irina Gorodetskaya, Christian Tatzelt, Silvia Henning, Rob L. Modini, Heather J. Forrer, Yajuan Lin, Nicolas Cassar, Rafel Simó, Christel Hassler, Alireza Moallemi, Sarah E. Fawcett, Neil Harris, Ruth Airs, Marzieh H. Derkani, Alberto Alberello, Alessandro Toffoli, Gang Chen, Pablo Rodríguez-Ros, Marina Zamanillo, Pau Cortés-Greus, Lei Xue, Conor G. Bolas, Katherine C. Leonard, Fernando Perez-Cruz, David Walton, and Julia Schmale
Earth Syst. Dynam., 12, 1295–1369, https://doi.org/10.5194/esd-12-1295-2021, https://doi.org/10.5194/esd-12-1295-2021, 2021
Short summary
Short summary
The Antarctic Circumnavigation Expedition surveyed a large number of variables describing the dynamic state of ocean and atmosphere, freshwater cycle, atmospheric chemistry, ocean biogeochemistry, and microbiology in the Southern Ocean. To reduce the dimensionality of the dataset, we apply a sparse principal component analysis and identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and
hotspotsof interaction. Code and data are open access.
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.
Susanne Crewell, Kerstin Ebell, Patrick Konjari, Mario Mech, Tatiana Nomokonova, Ana Radovan, David Strack, Arantxa M. Triana-Gómez, Stefan Noël, Raul Scarlat, Gunnar Spreen, Marion Maturilli, Annette Rinke, Irina Gorodetskaya, Carolina Viceto, Thomas August, and Marc Schröder
Atmos. Meas. Tech., 14, 4829–4856, https://doi.org/10.5194/amt-14-4829-2021, https://doi.org/10.5194/amt-14-4829-2021, 2021
Short summary
Short summary
Water vapor (WV) is an important variable in the climate system. Satellite measurements are thus crucial to characterize the spatial and temporal variability in WV and how it changed over time. In particular with respect to the observed strong Arctic warming, the role of WV still needs to be better understood. However, as shown in this paper, a detailed understanding is still hampered by large uncertainties in the various satellite WV products, showing the need for improved methods to derive WV.
Iris Thurnherr, Katharina Hartmuth, Lukas Jansing, Josué Gehring, Maxi Boettcher, Irina Gorodetskaya, Martin Werner, Heini Wernli, and Franziska Aemisegger
Weather Clim. Dynam., 2, 331–357, https://doi.org/10.5194/wcd-2-331-2021, https://doi.org/10.5194/wcd-2-331-2021, 2021
Short summary
Short summary
Extratropical cyclones are important for the transport of moisture from low to high latitudes. In this study, we investigate how the isotopic composition of water vapour is affected by horizontal temperature advection associated with extratropical cyclones using measurements and modelling. It is shown that air–sea moisture fluxes induced by this horizontal temperature advection lead to the strong variability observed in the isotopic composition of water vapour in the marine boundary layer.
Marek Jacob, Pavlos Kollias, Felix Ament, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 5757–5777, https://doi.org/10.5194/gmd-13-5757-2020, https://doi.org/10.5194/gmd-13-5757-2020, 2020
Short summary
Short summary
We compare clouds in different cloud-resolving atmosphere simulations with airborne remote sensing observations. The focus is on warm shallow clouds in the Atlantic trade wind region. Those clouds are climatologically important but challenging for climate models. We use forward operators to apply instrument-specific thresholds for cloud detection to model outputs. In this comparison, the higher-resolution model better reproduces the layered cloud structure.
Maialen Iturbide, José M. Gutiérrez, Lincoln M. Alves, Joaquín Bedia, Ruth Cerezo-Mota, Ezequiel Cimadevilla, Antonio S. Cofiño, Alejandro Di Luca, Sergio Henrique Faria, Irina V. Gorodetskaya, Mathias Hauser, Sixto Herrera, Kevin Hennessy, Helene T. Hewitt, Richard G. Jones, Svitlana Krakovska, Rodrigo Manzanas, Daniel Martínez-Castro, Gemma T. Narisma, Intan S. Nurhati, Izidine Pinto, Sonia I. Seneviratne, Bart van den Hurk, and Carolina S. Vera
Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, https://doi.org/10.5194/essd-12-2959-2020, 2020
Short summary
Short summary
We present an update of the IPCC WGI reference regions used in AR5 for the synthesis of climate change information. This revision was guided by the basic principles of climatic consistency and model representativeness (in particular for the new CMIP6 simulations). We also present a new dataset of monthly CMIP5 and CMIP6 spatially aggregated information using the new reference regions and describe a worked example of how to use this dataset to inform regional climate change studies.
Ilias Bougoudis, Anne-Marlene Blechschmidt, Andreas Richter, Sora Seo, John Philip Burrows, Nicolas Theys, and Annette Rinke
Atmos. Chem. Phys., 20, 11869–11892, https://doi.org/10.5194/acp-20-11869-2020, https://doi.org/10.5194/acp-20-11869-2020, 2020
Short summary
Short summary
A 22-year (1996 to 2017) consistent Arctic tropospheric BrO dataset derived from four satellite remote sensing instruments is presented. An increase in tropospheric BrO VCDs over this period, and especially during polar springs, can be seen. Comparisons of tropospheric BrO VCDs with first-year sea ice reveal a moderate spatial and temporal correlation between the two, suggesting that the increase in first-year sea ice in the Arctic has an impact on tropospheric BrO abundancies.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
Short summary
Short summary
The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Michael Fehlmann, Mario Rohrer, Annakaisa von Lerber, and Markus Stoffel
Atmos. Meas. Tech., 13, 4683–4698, https://doi.org/10.5194/amt-13-4683-2020, https://doi.org/10.5194/amt-13-4683-2020, 2020
Short summary
Short summary
The Thies disdrometer is used to monitor precipitation intensity and its phase and thus may provide valuable information for the management of meteorological and hydrological risks. In this study, we characterize biases of this instrument using common reference instruments at a pre-alpine study site in Switzerland. We find a systematic underestimation of liquid precipitation amounts and suggest possible reasons for and corrections to this bias and relate these findings to other study sites.
Cited articles
Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C.,
and Gumley, L. E.: Discriminating clear sky from clouds with MODIS, J. Geophys. Res.-Atmos., 103, 32141–32157,
https://doi.org/10.1029/1998JD200032, 1998. a
Adhikari, A., Liu, C., and Kulie, M. S.: Global Distribution of Snow
Precipitation Features and Their Properties from 3 Years of GPM
Observations, J. Climate, 31, 3731–3754,
https://doi.org/10.1175/JCLI-D-17-0012.1, 2018. a
Akkermans, T., Böhme, T., Demuzere, M., Crewell, S., Selbach, C., Reinhardt,
T., Seifert, A., Ament, F., and van Lipzig, N.: Regime-dependent evaluation
of accumulated precipitation in COSMO, Theor. Appl. Climatol.,
108, 39–52, https://doi.org/10.1007/s00704-011-0502-0, 2012. a, b
Akperov, M., Rinke, A., Mokhov, I. I., Matthes, H., Semenov, V. A., Adakudlu,
M., Cassano, J., Christensen, J. H., Dembitskaya, M. A., Dethloff, K.,
Fettweis, X., Glisan, J., Gutjahr, O., Heinemann, G., Koenigk, T., Koldunov,
N. V., Laprise, R., Mottram, R., Nikiéma, O., Scinocca, J. F., Sein, D.,
Sobolowski, S., Winger, K., and Zhang, W.: Cyclone Activity in the Arctic
From an Ensemble of Regional Climate Models (Arctic CORDEX), J.
Geophys. Res.-Atmos., 123, 2537–2554,
https://doi.org/10.1002/2017JD027703, 2018. a
Akperov, M., Rinke, A., Mokhov, I. I., Semenov, V. A., Parfenova, M. R.,
Matthes, H., Adakudlu, M., Boberg, F., Christensen, J. H., Dembitskaya,
M. A., Dethloff, K., Fettweis, X., Gutjahr, O., Heinemann, G., Koenigk, T.,
Koldunov, N. V., Laprise, R., Mottram, R., Nikiéma, O., Sein, D.,
Sobolowski, S., Winger, K., and Zhang, W.: Future projections of cyclone
activity in the Arctic for the 21st century from regional climate models
(Arctic-CORDEX), Global Planet. Change, 182, 103005,
https://doi.org/10.1016/j.gloplacha.2019.103005, 2019. a
Battaglia, A., Tanelli, S., Kobayashi, S., Zrnic, D., Hogan, R. J., and Simmer,
C.: Multiple-scattering in radar systems: A review, J. Quant.
Spectrosc. Ra. Transf., 111, 917–947,
https://doi.org/10.1016/j.jqsrt.2009.11.024, 2010. a, b
Behrangi, A., Christensen, M., Richardson, M., Lebsock, M., Stephens, G.,
Huffman, G. J., Bolvin, D., Adler, R. F., Gardner, A., Lambrigtsen, B., and
Fetzer, E.: Status of high-latitude precipitation estimates from observations
and reanalyses, J. Geophys. Res.-Atmos., 121,
4468–4486, https://doi.org/10.1002/2015JD024546, 2016. a
Bennartz, R., Fell, F., Pettersen, C., Shupe, M. D., and Schuettemeyer, D.: Spatial and temporal variability of snowfall over Greenland from CloudSat observations, Atmos. Chem. Phys., 19, 8101–8121, https://doi.org/10.5194/acp-19-8101-2019, 2019. a, b, c
Berrisford, P., Dee, D. P., Poli, P., Brugge, R., Fielding, M., Fuentes, M., Kållberg, P. W., Kobayashi, S., Uppala, S., and Simmons, A.: The ERA-Interim archive Version 2.0, ECMWF, Shinfield Park, Reading, UK [data set], https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim (last access: 25 May 2022), 2011. a
Boening, C., Lebsock, M., Landerer, F., and Stephens, G.: Snowfall-driven mass change on the East Antarctic ice sheet, Geophys. Res. Lett., 39, L21501,
https://doi.org/10.1029/2012GL053316, 2012. a
Boisvert, L. N., Webster, M. A., Petty, A. A., Markus, T., Bromwich, D. H., and
Cullather, R. I.: Intercomparison of Precipitation Estimates over the Arctic
Ocean and Its Peripheral Seas from Reanalyses, J. Climate, 31,
8441–8462, https://doi.org/10.1175/JCLI-D-18-0125.1, 2018. a, b, c, d
Box, J. E., Fettweis, X., Stroeve, J. C., Tedesco, M., Hall, D. K., and Steffen, K.: Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers, The Cryosphere, 6, 821–839, https://doi.org/10.5194/tc-6-821-2012, 2012. a
Cao, Q., Hong, Y., Chen, S., Gourley, J., Zhang, J., and Kirstetter, P.: Snowfall Detectability of NASA's CloudSat: the First Cross-Investigation of ITS 2c-Snow-Profile Product and National Multi-Sensor Mosaic QPE (NMQ) Snowfall Data, Prog.Electro. Res., 148, 55–61, https://doi.org/10.2528/PIER14030405, 2014. a
Castellani, B. B., Shupe, M. D., Hudak, D. R., and Sheppard, B. E.: The annual
cycle of snowfall at Summit, Greenland, J. Geophys. Res.-Atmos., 120, 6654–6668, https://doi.org/10.1002/2015JD023072, 2015. a
Chen, Q.-S., Bromwich, D. H., and Bai, L.: Precipitation over Greenland
Retrieved by a Dynamic Method and Its Relation to Cyclonic Activity, J. Climate, 10, 839–870,
https://doi.org/10.1175/1520-0442(1997)010<0839:POGRBA>2.0.CO;2, 1997. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hälm, E. V., Isaksen, L., Kållberg, P., Kähler, 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. a, b, c
Duffy, G., King, F., Bennartz, R., and Fletcher, C. G.: Seasonal Estimates and
Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals,
Atmosphere, 12, 363, https://doi.org/10.3390/atmos12030363, 2021. a
Forbes, R., Tompkins, A., and Untch, A.: A new prognostic bulk microphysics
scheme for the IFS, Technical memorandum, 649, ECMWF, p. 22, https://doi.org/10.21957/bf6vjvxk, 2011. a
Geerts, B., Giangrande, S., McFarquhar, G., Xue, L., Abel, S., Comstock, J., Crewell, S., DeMott, P., Ebell, K., Field, P., Hill, T., Hunzinger, A., Jensen, M., Johnson, K., Juliano, T., Kollias, P., Kosovic, B., Lackner, C., Luke, E., Lüpkes, C., Matthews, A., Neggers, R., Ovchinnikov, M., Powers, H., Shupe, M., Spengler, T., Swanson, B., Tjernström, M., Theisen, A., Wales, N., Wang, Y., and Wendisch, M. W. P.: The COMBLE campaign: a study of marine boundary-layer clouds in Arctic cold-air outbreaks, B. Am. Meteorol. Soc., 103, E1371–E1389, https://doi.org/10.1175/BAMS-D-21-0044.1, 2022. a
Gierens, R., Kneifel, S., Shupe, M. D., Ebell, K., Maturilli, M., and Löhnert, U.: Low-level mixed-phase clouds in a complex Arctic environment, Atmos. Chem. Phys., 20, 3459–3481, https://doi.org/10.5194/acp-20-3459-2020, 2020. a
Goodison, B., Louie, P., and Yang, D.: WMO solid precipitation measurement
intercomparison final report, Tech. Rep. WMO/TD No.872, IOM No. 67, 1998. a
Grabowski, W. W., Morrison, H., Shima, S.-I., Abade, G. C., Dziekan, P., and
Pawlowska, H.: Modeling of Cloud Microphysics: Can We Do Better?, B. Am. Meteorol. Soc., 100, 655–672,
https://doi.org/10.1175/BAMS-D-18-0005.1, 2019. a
Gunn, K. L. S. and Marshall, J. S.: The distribution with size of aggregate
snowflakes, J. Meteorol., 15, 452–461,
https://doi.org/10.1175/1520-0469(1958)015<0452:TDWSOA>2.0.CO;2, 1958. a
Haynes, J. M.: CloudSat 2C-PRECIP-COLUMN Data Product: Process Description and Interface Control Document, A NASA Earth System Science Pathfinder Mission, http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2C-PRECIP-COLUMN_PDICD.P1_R05.rev1_.pdf (last access: 27 May 2022), 2018. a
Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C.,
Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with
spaceborne W-band radar, J. Geophys. Res.-Atmos., 114, D00A22,
https://doi.org/10.1029/2008JD009973, 2009. a
Heymsfield, A. J. and Donner, L. J.: A Scheme for Parameterizing Ice-Cloud
Water Content in General Circulation Models, J. Atmos.
Sci., 47, 1865–1877,
https://doi.org/10.1175/1520-0469(1990)047<1865:ASFPIC>2.0.CO;2, 1990. a
Heymsfield, A. J., Matrosov, S. Y., and Wood, N. B.: Toward Improving Ice Water
Content and Snow-Rate Retrievals from Radars. Part I: X and W Bands,
Emphasizing CloudSat, J. Appl. Meteorol. Climatol., 55,
2063–2090, https://doi.org/10.1175/JAMC-D-15-0290.1, 2016. a
Hiley, M. J., Kulie, M. S., and Bennartz, R.: Uncertainty Analysis for CloudSat
Snowfall Retrievals, J. Appl. Meteorol. Climatol., 50,
399–418, https://doi.org/10.1175/2010JAMC2505.1, 2011. a
Hogan, R. J. and Westbrook, C. D.: Equation for the Microwave Backscatter Cross
Section of Aggregate Snowflakes Using the Self-Similar Rayleigh-Gans
Approximation, J. Atmos. Sci., 71, 3292–3301,
https://doi.org/10.1175/JAS-D-13-0347.1, 2014. a
Hogan, R. J., Honeyager, R., Tyynelä, J., and Kneifel, S.: Calculating the
millimetre-wave scattering phase function of snowflakes using the
self-similar Rayleigh-Gans Approximation, Q. J. Roy.
Meteorol. Soc., 143, 834–844, https://doi.org/10.1002/qj.2968, 2017. a
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D.,
Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation
Measurement Mission, B. Am. Meteorol. Soc., 95,
701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014. a
Inoue, J., Sato, K., Rinke, A., Cassano, J. J., Fettweis, X., Heinemann, G.,
Matthes, H., Orr, A., Phillips, T., Seefeldt, M., Solomon, A., and Webster,
S.: Clouds and Radiation Processes in Regional Climate Models Evaluated Using
Observations Over the Ice-free Arctic Ocean, J. Geophys. Res.-Atmos., 126, e2020JD033904, https://doi.org/10.1029/2020JD033904, 2021. a
Jenkinson, A. and Collison, F.: An initial climatology of gales over the North Sea, Synoptic Climatology Branch Memorandum No. 62, Meteorological Office, Bracknell, 1977. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteorol. Soc.,
77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a
Kessler, E.: On the Distribution and Continuity of Water Substance in
Atmospheric Circulations, 1–84 pp., Am. Meteorol. Soc., Boston,
MA, https://doi.org/10.1007/978-1-935704-36-2_1, 1969. a
Kidd, C. and Huffman, G.: Global precipitation measurement, Meteorol.
Appl., 18, 334–353, https://doi.org/10.1002/met.284, 2011. a
Klaus, D., Dethloff, K., Dorn, W., Rinke, A., and Wu, D. L.: New insight of
Arctic cloud parameterization from regional climate model simulations,
satellite-based, and drifting station data, Geophys. Res. Lett., 43,
5450–5459, https://doi.org/10.1002/2015GL067530, 2016. a, b, c, d
Knudsen, E. M., Heinold, B., Dahlke, S., Bozem, H., Crewell, S., Gorodetskaya, I. V., Heygster, G., Kunkel, D., Maturilli, M., Mech, M., Viceto, C., Rinke, A., Schmithüsen, H., Ehrlich, A., Macke, A., Lüpkes, C., and Wendisch, M.: Meteorological conditions during the ACLOUD/PASCAL field campaign near Svalbard in early summer 2017, Atmos. Chem. Phys., 18, 17995–18022, https://doi.org/10.5194/acp-18-17995-2018, 2018. a
Kodamana, R. and Fletcher, C. G.: Validation of CloudSat-CPR Derived
Precipitation Occurrence and Phase Estimates across Canada, Atmosphere, 12,
295, https://doi.org/10.3390/atmos12030295, 2021. a
Kulie, M. S. and Bennartz, R.: Utilizing Spaceborne Radars to Retrieve Dry
Snowfall, J. Appl. Meteorol. Climatol., 48, 2564–2580,
https://doi.org/10.1175/2009JAMC2193.1, 2009. a, b
Kulie, M. S., Milani, L., Wood, N. B., Tushaus, S. A., Bennartz, R., and
L'Ecuyer, T. S.: A Shallow Cumuliform Snowfall Census Using Spaceborne Radar,
J. Hydrometeorol., 17, 1261–1279, https://doi.org/10.1175/JHM-D-15-0123.1,
2016. a, b, c
Levkov, L., Rockel, B., Kapitza, H., and Raschke, E.: 3D mesoscale numerical
studies of cirrus and stratus clouds by their time and space evolution,
Beitr. Phys. Atmos., 65, 35–58, 1992. a
Liljegren, J. C., Boukabara, S. A., Cady-Pereira, K., and Clough, S. A.: The
Effect of the Half-Width of the 22-GHz Water Vapor Line on Retrievals of
Temperature and Water Vapor Profiles with a 12-Channel Microwave
Radiometer, IEEE Trans. Geosci. Remote Sens., 43,
1102–1108, https://doi.org/10.1109/TGRS.2004.839593, 2005. a
Lindsay, R., Wensnahan, M., Schweiger, A., and Zhang, J.: Evaluation of Seven
Different Atmospheric Reanalysis Products in the Arctic, J. Climate,
27, 2588–2606, https://doi.org/10.1175/JCLI-D-13-00014.1, 2014. a
Liu, G.: Deriving snow cloud characteristics from CloudSat observations, J. Geophys. Res.-Atmos., 113, D00A09, https://doi.org/10.1029/2007JD009766, 2008. a
Maahn, M., Burgard, C., Crewell, S., Gorodetskaya, I. V., Kneifel, S.,
Lhermitte, S., Van Tricht, K., and van Lipzig, N. P. M.: How does the
spaceborne radar blind zone affect derived surface snowfall statistics in
polar regions?, J. Geophys. Res.-Atmos., 119,
13604–13620, https://doi.org/10.1002/2014JD022079, 2014. a, b
Mace, G. G., Marchand, R., Zhang, Q., and Stephens, G.: Global hydrometeor
occurrence as observed by CloudSat: Initial observations from summer 2006,
Geophys. Res. Lett., 34, L09808,https://doi.org/10.1029/2006GL029017, 2007. a
Marchand, R. and Mace, G.: Level 2 GEOPROF Product Process Description and Interface Control Document, A NASA Earth System Science Pathfinder Mission, http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2B-GEOPROF_PDICD.P1_R05.rev0__0.pdf (last access: 27 May 2022), 2018. a
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor
Detection Using Cloudsat – An Earth-Orbiting 94-GHz Cloud Radar, J.
Atmos. Ocean. Technol., 25, 519–533,
https://doi.org/10.1175/2007JTECHA1006.1, 2008. a
Marshall, J. S. and Palmer, W. M. K.: The distribution of raindrops with size,
J. Meteorol., 5, 165–166,
https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2, 1948. a
Matrosov, S. Y.: Modeling Backscatter Properties of Snowfall at Millimeter
Wavelengths, J. Atmos. Sci., 64, 1727–1736,
https://doi.org/10.1175/JAS3904.1, 2007. a
Matrosov, S. Y. and Battaglia, A.: Influence of multiple scattering on CloudSat measurements in snow: A model study, Geophys. Res. Lett., 36, L12806, https://doi.org/10.1029/2009GL038704, 2009. a, b
Maturilli, M., Herber, A., and König-Langlo, G.: Climatology and time series of surface meteorology in Ny-Ålesund, Svalbard, Earth Syst. Sci. Data, 5, 155–163, https://doi.org/10.5194/essd-5-155-2013, 2013. a
Mätzler, C.: Thermal Microwave Radiation: Applications for Remote
Sensing, IET Electromagnetic Waves Series, London, United Kingdom, 2006. a
McIlhattan, E. A., L'Ecuyer, T. S., and Miller, N. B.: Observational Evidence
Linking Arctic Supercooled Liquid Cloud Biases in CESM to Snowfall
Processes, J. Climate, 30, 4477–4495,
https://doi.org/10.1175/JCLI-D-16-0666.1, 2017. a
Mech, M., Maahn, M., Ori, D., and Orlandi, E.: PAMTRA: Passive and Active Microwave TRAnsfer tool v1.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.3582992, 2019. a
Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020. a, b
Merkouriadi, I., Cheng, B., Graham, R. M., Rösel, A., and Granskog, M. A.:
Critical Role of Snow on Sea Ice Growth in the Atlantic Sector of the Arctic
Ocean, Geophys. Res. Lett., 44, 10479–10485,
https://doi.org/10.1002/2017GL075494, 2017. a
Milani, L., Kulie, M. S., Casella, D., Dietrich, S., L'Ecuyer, T. S.,
Panegrossi, G., Porcù, F., Sanò, P., and Wood, N. B.: CloudSat snowfall
estimates over Antarctica and the Southern Ocean: An assessment of
independent retrieval methodologies and multi-year snowfall analysis,
Atmos. Res., 213, 121–135, https://doi.org/10.1016/j.atmosres.2018.05.015,
2018. a, b, c
Miller, S. D. and Stephens, G. L.: CloudSat instrument requirements as
determined from ECMWF forecasts of global cloudiness, J. Geophys.
Res.-Atmos., 106, 17713–17733, https://doi.org/10.1029/2000JD900645,
2001. a
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M., and Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012. a
Nomokonova, T., Ebell, K., Löhnert, U., Maturilli, M., Ritter, C., and O'Connor, E.: Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund using ground-based sensor synergy, Atmos. Chem. Phys., 19, 4105–4126, https://doi.org/10.5194/acp-19-4105-2019, 2019. a
Norin, L., Devasthale, A., L'Ecuyer, T. S., Wood, N. B., and Smalley, M.: Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden, Atmos. Meas. Tech., 8, 5009–5021, https://doi.org/10.5194/amt-8-5009-2015, 2015. a
Omrani, H., Drobinski, P., and Dubos, T.: Spectral nudging in regional climate
modelling: how strongly should we nudge?, Q. J. Roy.
Meteorol. Soc., 138, 1808–1813, https://doi.org/10.1002/qj.1894, 2012. a
Palerme, C., Kay, J. E., Genthon, C., L'Ecuyer, T., Wood, N. B., and Claud, C.: How much snow falls on the Antarctic ice sheet?, The Cryosphere, 8, 1577–1587, https://doi.org/10.5194/tc-8-1577-2014, 2014. a, b, c
Palerme, C., Genthon, C., Claud, C., Kay, J. E., Wood, N. B., and L'Ecuyer, T.:
Evaluation of current and projected Antarctic precipitation in CMIP5 models,
Clim. Dynam., 48, 225–239, https://doi.org/10.1007/s00382-016-3071-1, 2017. a, b
Pettersen, C., Bennartz, R., Merrelli, A. J., Shupe, M. D., Turner, D. D., and Walden, V. P.: Precipitation regimes over central Greenland inferred from 5 years of ICECAPS observations, Atmos. Chem. Phys., 18, 4715–4735, https://doi.org/10.5194/acp-18-4715-2018, 2018. a
Philipp, A., Beck, C., Huth, R., and Jacobeit, J.: Development and comparison
of circulation type classifications using the COST 733 dataset and software,
Int. J. Climatol., 36, 2673–2691, https://doi.org/10.1002/joc.3920,
2016. a, b, c
Potter, B. E.: Improvements to a Commonly Used Cloud Microphysical Bulk
Parameterization, J. Appl. Meteorol., 30, 1040–1042,
https://doi.org/10.1175/1520-0450-30.7.1040, 1991. a, b
Prowse, T., Bring, A., Mård, J., Carmack, E., Holland, M., Instanes, A.,
Vihma, T., and Wrona, F. J.: Arctic Freshwater Synthesis: Summary of key
emerging issues, J. Geophys. Res.-Biogeosci., 120,
1887–1893, https://doi.org/10.1002/2015JG003128, 2015. a
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer,
A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C.,
Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.: How Well Are We Measuring
Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed, B.
Am. Meteorol. Soc., 93, 811–829,
https://doi.org/10.1175/BAMS-D-11-00052.1, 2012. a
Reitter, S., Fröhlich, K., Seifert, A., Crewell, S., and Mech, M.: Evaluation of ice and snow content in the global numerical weather prediction model GME with CloudSat, Geosci. Model Dev., 4, 579–589, https://doi.org/10.5194/gmd-4-579-2011, 2011. a, b
Riihelä, A., King, M. D., and Anttila, K.: The surface albedo of the Greenland Ice Sheet between 1982 and 2015 from the CLARA-A2 dataset and its relationship to the ice sheet's surface mass balance, The Cryosphere, 13, 2597–2614, https://doi.org/10.5194/tc-13-2597-2019, 2019. a
Rinke, A., Maturilli, M., Graham, R., Matthes, H., Handorf, D., Cohen, L.,
Hudson, S., and Moore, J.: Extreme cyclone events in the Arctic: Wintertime
variability and trends, Environ. Res. Lett., 12, 094006,
https://doi.org/10.1088/1748-9326/aa7def, 2017. a
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding, World Scientific,
https://doi.org/10.1142/3171, 2000. a
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta,
M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E., Rhodin, A.,
Schlese, U., Schulzweida, U., and Tompkins, A.: The atmospheric general
circulation model ECHAM5: Part I, Tech. Rep. 349, Max-Planck-Institut
für Meteorologie, 2003. a, b, c, d
Rogers, J. C., Bathke, D. J., Mosley-Thompson, E., and Wang, S.-H.: Atmospheric
circulation and cyclone frequency variations linked to the primary modes of
Greenland snow accumulation, Geophys. Res. Lett., 31, L23208,
https://doi.org/10.1029/2004GL021048, 2004. a
Rosenkranz, P. W.: A Model for the Complex Dielectric Constant of Supercooled
Liquid Water at Microwave Frequencies, IEEE Trans. Geosci.
Remote Sens., 53, 1387–1393, https://doi.org/10.1109/TGRS.2014.2339015, 2015. a
Sato, K. and Inoue, J.: Comparison of Arctic sea ice thickness and snow depth
estimates from CFSR with in situ observations, Climatol.
Dynam., 50, 289–301, https://doi.org/10.1007/s00382-017-3607-z, 2018. a
Schoger, S. Y., Moisseev, D., von Lerber, A., Crewell, S., and Ebell, K.:
Snowfall-Rate Retrieval for K- and W-Band Radar Measurements Designed in
Hyytiälä, Finland, and Tested at Ny-Ålesund, Svalbard, Norway, J.
Appl. Meteorol. Climatol., 60, 273–289, https://doi.org/10.1175/JAMC-D-20-0095.1,
2021. a
Screen, J. and Simmonds, I.: Declining summer snowfall in the Arctic: causes,
impacts and feedbacks, Climatol. Dynam., 38, 2243–2256,
https://doi.org/10.1007/s00382-011-1105-2, 2012. a
Sedlar, J., Tjernström, M., Rinke, A., Orr, A., Cassano, J., Fettweis, X.,
Heinemann, G., Seefeldt, M., Solomon, A., Matthes, H., Phillips, T., and
Webster, S.: Confronting Arctic Troposphere, Clouds, and Surface Energy
Budget Representations in Regional Climate Models With Observations, J. Geophys. Res.-Atmos., 125, e2019JD031783,
https://doi.org/10.1029/2019JD031783, 2020. a, b
Silverman, N. L., Maneta, M. P., Chen, S.-H., and Harper, J. T.: Dynamically
downscaled winter precipitation over complex terrain of the Central Rockies
of Western Montana, USA, Water Resour. Res., 49, 458–470,
https://doi.org/10.1029/2012WR012874, 2013. a
Souverijns, N., Gossart, A., Lhermitte, S., Gorodetskaya, I. V., Grazioli, J., Berne, A., Duran-Alarcon, C., Boudevillain, B., Genthon, C., Scarchilli, C., and van Lipzig, N. P. M.: Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars, The Cryosphere, 12, 3775–3789, https://doi.org/10.5194/tc-12-3775-2018, 2018. a, b, c, d
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, C02S03,
https://doi.org/10.1029/2005JC003384, 2008. a, b
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z.,
Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller,
S. D., Austin, R. T., Benedetti, A., and Mitrescu, C.: The Cloudsat Mission
and the A-Train: A New Dimension of Space-Based Observations of
Clouds and Precipitation, B. Am. Meteorol. Soc.,
83, 1771–1790+1742, 2002. a
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. a, b, c
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S.,
Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I.,
Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and
Roeckner, E.: Atmospheric component of the MPI-M Earth System Model: ECHAM6,
J. Adv. Model. Earth Syst., 5, 146–172,
https://doi.org/10.1002/jame.20015, 2013. a
Sundqvist, H., Berge, E., and Kristjánsson, J. E.: Condensation and Cloud
Parameterization Studies with a Mesoscale Numerical Weather Prediction Model,
Mon. Weather Rev., 117, 1641–1657,
https://doi.org/10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2, 1989. a
Tanelli, S., Durden, S. L., Im, E., Pak, K. S., Reinke, D. G.,
Partain, P., Haynes, J. M., and Marchand, R. T.: CloudSat's Cloud
Profiling Radar After Two Years in Orbit: Performance, Calibration, and
Processing, IEEE Trans. Geosci. Remote Sens., 46,
3560–3573, https://doi.org/10.1109/TGRS.2008.2002030, 2008. a, b, c
Tapiador, F., Navarro, A., Levizzani, V., García-Ortega, E., Huffman, G.,
Kidd, C., Kucera, P., Kummerow, C., Masunaga, H., Petersen, W., Roca, R.,
Sánchez, J.-L., Tao, W.-K., and Turk, F.: Global precipitation measurements
for validating climate models, Atmos. Res., 197, 1–20,
https://doi.org/10.1016/j.atmosres.2017.06.021, 2017. a
Thomas, M. A., Devasthale, A., L'Ecuyer, T., Wang, S., Koenigk, T., and Wyser, K.: Snowfall distribution and its response to the Arctic Oscillation: an evaluation of HighResMIP models in the Arctic using CPR/CloudSat observations, Geosci. Model Dev., 12, 3759–3772, https://doi.org/10.5194/gmd-12-3759-2019, 2019. a
Tiedtke, M.: Representation of clouds in large-scale models, Mon. Weather
Rev., 121, 3040–3061, 1993. a
Tompkins, A.: A prognostic parameterization for the subgrid-scale variability
of water vapor and clouds in large-scale models and its use to diagnose cloud
cover, J. Atmos. Sci., 59, 1917–1942,
https://doi.org/10.1175/1520-0469(2002)059<1917:APPFTS>2.0.CO;2, 2002. a
Turner, D. D., Cadeddu, M. P., Lohnert, U., Crewell, S., and Vogelmann, A. M.:
Modifications to the Water Vapor Continuum in the Microwave Suggested by
Ground-Based 150-GHz Observations, IEEE Trans. Geosci.
Remote Sens., 47, 3326–3337, https://doi.org/10.1109/TGRS.2009.2022262, 2009. a
Turner, D. D., Kneifel, S., and Cadeddu, M. P.: An Improved Liquid Water
Absorption Model at Microwave Frequencies for Supercooled Liquid Water
Clouds, J. Atmos. Ocean. Technol., 33, 33–44,
https://doi.org/10.1175/JTECH-D-15-0074.1, 2016. a
Tyynelä, J., Leinonen, J., Moisseev, D., and Nousiainen, T.: Radar
Backscattering from Snowflakes: Comparison of Fractal, Aggregate, and Soft
Spheroid Models, J. Atmos. Ocean. Technol., 28,
1365–1372, https://doi.org/10.1175/JTECH-D-11-00004.1, 2011. a
Undén, P., Rontu, L., Järvinen, H., Lynch, P., Calvo, J., Cats, G., Cuxart, J., Eerola, K., Fortelius, C., Garcia-Moya, J. A., Jones, C., Lenderlink, G., McDonald, A., McGrath, R., Navascues, B., Nielsen, N. W., Ødegaard, V., Rodriguez, E., Rummukainen, M., Rõõm, R., Sattler, K., Sass, B. H., Savijärvi, H., Schreur, B. W., Sigg, R., The, H., and Tijm, A.: HIRLAM-5 Scientic Documentation, Swedish Meteorological and Hydrological Institute, 144 pp., http://hirlam.org (last access: 27 May 2022), 2002. a
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. a
Verlinde, J., Zak, B. D., Shupe, M. D., Ivey, M. D., and Stamnes, K.: The ARM
North Slope of Alaska (NSA) Sites, Meteorol. Monogr., 57, 8.1–8.13,
https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0023.1, 2016.
a
Vihma, T., Screen, J., Tjernstróm, M., Newton, B., Zhang, X., Popova, V.,
Deser, C., Holland, M., and Prowse, T.: The atmospheric role in the Arctic
water cycle: A review on processes, past and future changes, and their
impacts, J. Geophys. Res.-Biogeosci., 121, 586–620,
https://doi.org/10.1002/2015JG003132, 2016. a
Webster, M., Gerland, S., Holland, M., Hunke, E., Kwok, R., Lecomte, O.,
Massom, R., Perovich, D., and Sturm, M.: Snow in the changing sea-ice
systems, Nat. Clim. Change, 8, 946–953,
https://doi.org/10.1038/s41558-018-0286-7, 2018. a
Webster, M., Parker, C., Boisvert, L., and Kwok, R.: The role of cyclone
activity in snow accumulation on Arctic sea ice, Nat. Commun., 10,
5285, https://doi.org/10.1038/s41467-019-13299-8, 2019. a
Wolff, M., Isaksen, K., Brækkan, R., Alfnes, E., Petersen-Øverleir, A.,
and Ruud, E.: Measurements of wind-induced loss of solid precipitation:
description of a Norwegian field study, Hydrol. Res., 44, 35–43,
https://doi.org/10.2166/nh.2012.166, 2012. a
Wood, N. B. and L'Ecuyer, T. S.: Level 2C Snow Profile Process Description and Interface Control Document, Product Version P1R05, NASA JPL CloudSat project document revision 0, 26 pp., https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2c-snow-profile/2C-SNOW-PROFILE_PDICD.P1_R05.rev0_.pdf, (last access: 27 May 2022), 2018. a, b, c, d, e, f, g
Short summary
Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. Excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.
Snowfall is an important climate indicator. However, microphysical snowfall processes are...
Altmetrics
Final-revised paper
Preprint