Articles | Volume 17, issue 13
https://doi.org/10.5194/gmd-17-5225-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-5225-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
08 Jul 2024
Methods for assessment of models |
| 08 Jul 2024
| 08 Jul 2024
Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics
Physical Science Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
retired
Leslie M. Hartten
CORRESPONDING AUTHOR
Cooperative Institute for Research in the Environmental Sciences, Boulder, CO 80309, USA
Physical Science Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
retired
Siri Jodha Khalsa
Cooperative Institute for Research in the Environmental Sciences, Boulder, CO 80309, USA
National Snow Ice and Data Center, Boulder, CO 80309, USA
Barbara Casati
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC H9P-1J3, Canada
Gunilla Svensson
Department of Meteorology and Bolin Centre for Climate Change, Stockholm University, 10691 Stockholm, Sweden
Jonathan Day
European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, United Kingdom
Jareth Holt
Department of Meteorology and Bolin Centre for Climate Change, Stockholm University, 10691 Stockholm, Sweden
Elena Akish
Physical Science Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
Cooperative Institute for Research in the Environmental Sciences, Boulder, CO 80309, USA
Sara Morris
Physical Science Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
Global Monitoring Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
Ewan O'Connor
Finnish Meteorological Institute, 00101 Helsinki, Finland
Roberta Pirazzini
Finnish Meteorological Institute, 00101 Helsinki, Finland
Laura X. Huang
Meteorological Research Division, Environment and Climate Change Canada, Toronto, ON M3H-5T4, Canada
Robert Crawford
Meteorological Research Division, Environment and Climate Change Canada, Toronto, ON M3H-5T4, Canada
Zen Mariani
Meteorological Research Division, Environment and Climate Change Canada, Toronto, ON M3H-5T4, Canada
Øystein Godøy
Norwegian Meteorological Institute, 0313 Olso, Norway
Johanna A. K. Tjernström
Swedish Meteorological and Hydrological Institute, 60176 Norrköping, Sweden
Norwegian Meteorological Institute, 0313 Olso, Norway
Giri Prakash
Department of Energy, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Nicki Hickmon
Department of Energy, Argonne National Laboratory, Lemont, IL 60439, USA
Marion Maturilli
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, 14473 Potsdam, Germany
Christopher J. Cox
Physical Science Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
Related authors
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
Short summary
Short summary
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.
Zen Mariani, Sara M. Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie M. Hartten, Jareth Holt, Christopher J. Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data, 16, 3083–3124, https://doi.org/10.5194/essd-16-3083-2024, https://doi.org/10.5194/essd-16-3083-2024, 2024
Short summary
Short summary
During the Year of Polar Prediction (YOPP), we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of datasets that have been collected to facilitate user uptake and model–observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODFs), including a description of the sites, data format, and instruments.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
Short summary
Short summary
Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Christopher J. Cox, Sara M. Morris, Taneil Uttal, Ross Burgener, Emiel Hall, Mark Kutchenreiter, Allison McComiskey, Charles N. Long, Bryan D. Thomas, and James Wendell
Atmos. Meas. Tech., 14, 1205–1224, https://doi.org/10.5194/amt-14-1205-2021, https://doi.org/10.5194/amt-14-1205-2021, 2021
Short summary
Short summary
Solar and infrared radiation are measured regularly for research, industry, and climate monitoring. In cold climates, icing of sensors is a poorly constrained source of uncertainty. D-ICE was carried out in Alaska to document the effectiveness of ice-mitigation technology and quantify errors associated with ice. Technology was more effective than anticipated, and while instantaneous errors were large, mean biases were small. Attributes of effective ice mitigation design were identified.
Xin Yang, Anne-M. Blechschmidt, Kristof Bognar, Audra McClure-Begley, Sara Morris, Irina Petropavlovskikh, Andreas Richter, Henrik Skov, Kimberly Strong, David W. Tarasick, Taneil Uttal, Mika Vestenius, and Xiaoyi Zhao
Atmos. Chem. Phys., 20, 15937–15967, https://doi.org/10.5194/acp-20-15937-2020, https://doi.org/10.5194/acp-20-15937-2020, 2020
Short summary
Short summary
This is a modelling-based study on Arctic surface ozone, with a particular focus on spring ozone depletion events (i.e. with concentrations < 10 ppbv). Model experiments show that model runs with blowing-snow-sourced sea salt aerosols implemented as a source of reactive bromine can reproduce well large-scale ozone depletion events observed in the Arctic. This study supplies modelling evidence of the proposed mechanism of reactive-bromine release from blowing snow on sea ice (Yang et al., 2008).
Simo Tukiainen, Tuomas Siipola, Niko Leskinen, and Ewan O'Connor
Earth Syst. Sci. Data, 17, 3797–3806, https://doi.org/10.5194/essd-17-3797-2025, https://doi.org/10.5194/essd-17-3797-2025, 2025
Short summary
Short summary
Measurement campaigns are crucial for advancing the understanding of complex cloud–aerosol interactions in the atmosphere. Ground-based remote sensing measurements were conducted in Kenttärova, Finland, during the Pallas Cloud Experiment 2022 campaign. These measurements were processed using the Cloudnet methodology, and the data are available through the ACTRIS Cloudnet data portal.
Masatomo Fujiwara, Bomin Sun, Anthony Reale, Domenico Cimini, Salvatore Larosa, Lori Borg, Christoph von Rohden, Michael Sommer, Ruud Dirksen, Marion Maturilli, Holger Vömel, Rigel Kivi, Bruce Ingleby, Ryan J. Kramer, Belay Demoz, Fabio Madonna, Fabien Carminati, Owen Lewis, Brett Candy, Christopher Thomas, David Edwards, Noersomadi, Kensaku Shimizu, and Peter Thorne
Atmos. Meas. Tech., 18, 2919–2955, https://doi.org/10.5194/amt-18-2919-2025, https://doi.org/10.5194/amt-18-2919-2025, 2025
Short summary
Short summary
We assess and illustrate the benefits of high-altitude attainment of balloon-borne radiosonde soundings up to and beyond 10 hPa level from various aspects. We show that the extra costs and technical challenges involved in consistent attainment of high ascents are more than outweighed by the benefits for a broad variety of real-time and delayed-mode applications. Consistent attainment of high ascents should therefore be pursued across the balloon observational network.
Aidan D. Pantoya, Stephanie R. Simonsen, Elisabeth Andrews, Ross Burgener, Christopher J. Cox, Gijs de Boer, Bryan D. Thomas, and Naruki Hiranuma
Aerosol Research, 3, 253–270, https://doi.org/10.5194/ar-3-253-2025, https://doi.org/10.5194/ar-3-253-2025, 2025
Short summary
Short summary
We present continuous ice-nucleating particle data that were measured in the Alaskan Arctic from October 2021 to December 2023. We found a greater efficiency in the arctic immersion freezing during fall compared to those found previously at two mid-latitude sites, together with profound freezing efficiencies in spring, presumably due to arctic haze events. Our study will be useful for improving atmospheric models to simulate cloud feedback and determine their impact on the global radiative energy budget.
Penelope Nagel, SiriJodha Khalsa, Warren Zamudio, Katsutoshi Mizuta, and Kenneth Stalker
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-7-2025, 189–193, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-189-2025, https://doi.org/10.5194/isprs-archives-XLVIII-M-7-2025-189-2025, 2025
Julia Martin, Ruzica Dadic, Brian Anderson, Roberta Pirazzini, Oliver Wigmore, and Lauren Vargo
EGUsphere, https://doi.org/10.5194/egusphere-2025-1601, https://doi.org/10.5194/egusphere-2025-1601, 2025
Short summary
Short summary
This study examines how snow distribution affects Antarctic sea ice surface temperature, a key factor in its energy balance. Using drone and ground-based data, we mapped snow depth and surface temperature on 2.4 m thick sea ice in McMurdo Sound. We corrected thermal camera inconsistencies and found that surface temperature is more influenced by topography-driven solar radiation than snow depth. Our findings highlight the need to account for small-scale processes in sea ice energy balance models.
Christopher J. Cox, Janet M. Intrieri, Brian J. Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, and James M. Wilczak
Earth Syst. Sci. Data, 17, 1481–1499, https://doi.org/10.5194/essd-17-1481-2025, https://doi.org/10.5194/essd-17-1481-2025, 2025
Short summary
Short summary
Snow is an essential water resource in the intermountain western United States, and predictions are made using models. We made observations to validate, constrain, and develop the models. The data are from the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign in Colorado's East River valley, 2021–2023. The measurements include meteorology and variables that quantify energy transfer between the atmosphere and surface. The data are available publicly.
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
Short summary
Short summary
Understanding how clouds affect the climate, especially in the Arctic, is crucial. This study used data from the largest polar expedition in history, MOSAiC, and the CERES satellite to analyse the impact of clouds on radiation. Simulations showed accurate results, aligning with observations. Over the year, clouds caused the atmospheric surface system to lose 5.2 W m−² of radiative energy to space, while the surface gained 25 W m−² and the atmosphere cooled by 30.2 W m−².
Denghui Ji, Mathias Palm, Matthias Buschmann, Kerstin Ebell, Marion Maturilli, Xiaoyu Sun, and Justus Notholt
Atmos. Chem. Phys., 25, 3889–3904, https://doi.org/10.5194/acp-25-3889-2025, https://doi.org/10.5194/acp-25-3889-2025, 2025
Short summary
Short summary
Our study explores how certain aerosols, like sea salt, affect infrared heat radiation in the Arctic, potentially speeding up warming. We used advanced technology to measure aerosol composition and found that these particles grow with humidity, significantly increasing their heat-trapping effect in the infrared region, especially in winter. Our findings suggest these aerosols could be a key factor in Arctic warming, emphasizing the importance of understanding aerosols for climate prediction.
Kristiina Verro, Cecilia Äijälä, Roberta Pirazzini, Ruzica Dadic, Damien Maure, Willem Jan van de Berg, Giacomo Traversa, Christiaan T. van Dalum, Petteri Uotila, Xavier Fettweis, Biagio Di Mauro, and Milla Johansson
EGUsphere, https://doi.org/10.5194/egusphere-2025-386, https://doi.org/10.5194/egusphere-2025-386, 2025
Short summary
Short summary
A realistic representation of Antarctic sea ice is crucial for accurate climate and ocean model predictions. We assessed how different models capture the sunlight reflectivity, snow cover, and ice thickness. Most performed well under mild weather conditions, but overestimated snow/ice reflectivity during cold, with patchy/thin snow conditions. High-resolution satellite imagery revealed spatial albedo variability that models failed to replicate.
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.
Natalie E. Theeuwes, Janet F. Barlow, Antti Mannisenaho, Denise Hertwig, Ewan O'Connor, and Alan Robins
Atmos. Meas. Tech., 18, 1355–1371, https://doi.org/10.5194/amt-18-1355-2025, https://doi.org/10.5194/amt-18-1355-2025, 2025
Short summary
Short summary
A Doppler lidar was placed in a highly built-up area in London to measure wakes from tall buildings during a period of 1 year. We were able to detect wakes and assess their dependence on wind speed, wind direction, and atmospheric stability.
Felix Pithan, Ann Kristin Naumann, and Marion Maturilli
Atmos. Chem. Phys., 25, 3269–3285, https://doi.org/10.5194/acp-25-3269-2025, https://doi.org/10.5194/acp-25-3269-2025, 2025
Short summary
Short summary
Representing the exchange of air masses between the Arctic and mid-latitudes and the associated cloud formation is difficult for climate models. We compare climate model output to temperature and humidity measurements from weather balloons to provide suggestions for model improvements. Cold biases mostly occur in air that is exported from the Arctic. Models that compute the number of ice particles in a cloud better represent humidity than models that assume a fixed number of ice particles.
Luke Harry Marsden, Øystein Godøy, Tove Margrethe Gabrielsen, Pål Gunnar Ellingsen, Marit Reigstad, Miriam Marquardt, Arnfinn Morvik, Helge Sagen, Stein Tronstad, and Lara Ferrighi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-56, https://doi.org/10.5194/essd-2025-56, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
This article presents the data management strategies of the Nansen Legacy project, developed to handle data from 300+ researchers and 20 expeditions in the northern Barents Sea. Data collection protocols were documented and followed for consistency, and a searchable data overview was available soon after each cruise. The project required early data sharing and publishing in line with FAIR principles where possible. This article details these strategies to guide future projects.
Michail Karalis, Gunilla Svensson, Manfred Wendisch, and Michael Tjernström
EGUsphere, https://doi.org/10.5194/egusphere-2024-3709, https://doi.org/10.5194/egusphere-2024-3709, 2025
Short summary
Short summary
During the spring Arctic warm-air intrusion captured by HALO-(𝒜𝒞)3, the airmass demonstrated a column-like structure. We built a Lagrangian modeling framework using a single-column model (AOSCM) to simulate the airmass transformation. Comparing to observations, reanalysis and forecast data, we found that the AOSCM can successfully reproduce the main features of the transformation. The framework can be used for future model development to improve Arctic weather and climate prediction.
Sasu Karttunen, Matthias Sühring, Ewan O'Connor, and Leena Järvi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-235, https://doi.org/10.5194/gmd-2024-235, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This paper presents PALM-SLUrb, a single-layer urban canopy model for the PALM system, designed to simulate urban-atmosphere interactions without resolving flow around individual buildings. The model is described in detail and evaluated against grid-resolved urban canopy simulations, demonstrating its ability to model urban surfaces accurately. By bridging the gap between computational efficiency and physical detail, PALM-SLUrb broadens PALM's potential for urban climate research.
Johanna Tjernström, Michael Gallagher, Jareth Holt, Gunilla Svensson, Matthew D. Shupe, Jonathan J. Day, Lara Ferrighi, Siri Jodha Khalsa, Leslie M. Hartten, Ewan O'Connor, Zen Mariani, and Øystein Godøy
EGUsphere, https://doi.org/10.5194/egusphere-2024-2088, https://doi.org/10.5194/egusphere-2024-2088, 2024
Preprint archived
Short summary
Short summary
The value of numerical weather predictions can be enhanced in several ways, one is to improve the representations of small-scale processes in models. To understand what needs to be improved, the model results need to be evaluated. Following standardized principles, a file format has been defined to be as similar as possible for both observational and model data. Python packages and toolkits are presented as a community resource in the production of the files and evaluation analysis.
Jutta Kesti, Ewan J. O'Connor, Anne Hirsikko, John Backman, Maria Filioglou, Anu-Maija Sundström, Juha Tonttila, Heikki Lihavainen, Hannele Korhonen, and Eija Asmi
Atmos. Chem. Phys., 24, 9369–9386, https://doi.org/10.5194/acp-24-9369-2024, https://doi.org/10.5194/acp-24-9369-2024, 2024
Short summary
Short summary
The study combines aerosol particle measurements at the surface and vertical profiling of the atmosphere with a scanning Doppler lidar to investigate how particle transportation together with boundary layer evolution can affect particle and SO2 concentrations at the surface in the Arabian Peninsula region. The instrumentation enabled us to see elevated nucleation mode particle and SO2 concentrations at the surface when air masses transported from polluted areas are mixed in the boundary layer.
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.
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
Short summary
Short summary
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.
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.
Zen Mariani, Sara M. Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie M. Hartten, Jareth Holt, Christopher J. Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data, 16, 3083–3124, https://doi.org/10.5194/essd-16-3083-2024, https://doi.org/10.5194/essd-16-3083-2024, 2024
Short summary
Short summary
During the Year of Polar Prediction (YOPP), we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of datasets that have been collected to facilitate user uptake and model–observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODFs), including a description of the sites, data format, and instruments.
Alexander Frank Vessey, Kevin I. Hodges, Len C. Shaffrey, and Jonathan J. Day
Nat. Hazards Earth Syst. Sci., 24, 2115–2132, https://doi.org/10.5194/nhess-24-2115-2024, https://doi.org/10.5194/nhess-24-2115-2024, 2024
Short summary
Short summary
The risk posed to ships by Arctic cyclones has seldom been quantified due to the lack of publicly available historical Arctic ship track data. This study investigates historical Arctic ship tracks, cyclone tracks, and shipping incident reports to determine the number of shipping incidents caused by the passage of Arctic cyclones. Results suggest that Arctic cyclones have not been hazardous to ships and that ships are resilient to the rough sea conditions caused by Arctic cyclones.
Xin Yang, Kimberly Strong, Alison S. Criscitiello, Marta Santos-Garcia, Kristof Bognar, Xiaoyi Zhao, Pierre Fogal, Kaley A. Walker, Sara M. Morris, and Peter Effertz
Atmos. Chem. Phys., 24, 5863–5886, https://doi.org/10.5194/acp-24-5863-2024, https://doi.org/10.5194/acp-24-5863-2024, 2024
Short summary
Short summary
This study uses snow samples collected from a Canadian high Arctic site, Eureka, to demonstrate that surface snow in early spring is a net sink of atmospheric bromine and nitrogen. Surface snow bromide and nitrate are significantly correlated, indicating the oxidation of reactive nitrogen is accelerated by reactive bromine. In addition, we show evidence that snow photochemical release of reactive bromine is very weak, and its emission flux is much smaller than the deposition flux of bromide.
Viet Le, Hannah Lobo, Ewan J. O'Connor, and Ville Vakkari
Atmos. Meas. Tech., 17, 921–941, https://doi.org/10.5194/amt-17-921-2024, https://doi.org/10.5194/amt-17-921-2024, 2024
Short summary
Short summary
This study offers a long-term overview of aerosol particle depolarization ratio at the wavelength of 1565 nm obtained from vertical profiling measurements by Halo Doppler lidars during 4 years at four different locations across Finland. Our observations support the long-term usage of Halo Doppler lidar depolarization ratio such as the detection of aerosols that may pose a safety risk for aviation. Long-range Saharan dust transport and pollen transport are also showcased here.
Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer
Atmos. Chem. Phys., 24, 1429–1450, https://doi.org/10.5194/acp-24-1429-2024, https://doi.org/10.5194/acp-24-1429-2024, 2024
Short summary
Short summary
Observations collected during MOSAiC were used to identify the range in vertical structure and stability of the central Arctic lower atmosphere through a self-organizing map analysis. Characteristics of wind features (such as low-level jets) and atmospheric moisture features (such as clouds) were analyzed in the context of the varying vertical structure and stability. Thus, the results of this paper give an overview of the thermodynamic and kinematic features of the central Arctic atmosphere.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
Short summary
Short summary
Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer
Atmos. Chem. Phys., 23, 13087–13106, https://doi.org/10.5194/acp-23-13087-2023, https://doi.org/10.5194/acp-23-13087-2023, 2023
Short summary
Short summary
Observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) were used to determine the frequency of occurrence of various central Arctic lower atmospheric stability regimes and how the stability regimes transition between each other. Wind and radiation observations were analyzed in the context of stability regime and season to reveal the relationships between Arctic atmospheric stability and mechanically and radiatively driven turbulent forcings.
Albert Ansmann, Kevin Ohneiser, Ronny Engelmann, Martin Radenz, Hannes Griesche, Julian Hofer, Dietrich Althausen, Jessie M. Creamean, Matthew C. Boyer, Daniel A. Knopf, Sandro Dahlke, Marion Maturilli, Henriette Gebauer, Johannes Bühl, Cristofer Jimenez, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 23, 12821–12849, https://doi.org/10.5194/acp-23-12821-2023, https://doi.org/10.5194/acp-23-12821-2023, 2023
Short summary
Short summary
The 1-year MOSAiC (2019–2020) expedition with the German ice breaker Polarstern was the largest polar field campaign ever conducted. The Polarstern, with our lidar aboard, drifted with the pack ice north of 85° N for more than 7 months (October 2019 to mid-May 2020). We measured the full annual cycle of aerosol conditions in terms of aerosol optical and cloud-process-relevant properties. We observed a strong contrast between polluted winter and clean summer aerosol conditions.
Maria Filioglou, Ari Leskinen, Ville Vakkari, Ewan O'Connor, Minttu Tuononen, Pekko Tuominen, Samuli Laukkanen, Linnea Toiviainen, Annika Saarto, Xiaoxia Shang, Petri Tiitta, and Mika Komppula
Atmos. Chem. Phys., 23, 9009–9021, https://doi.org/10.5194/acp-23-9009-2023, https://doi.org/10.5194/acp-23-9009-2023, 2023
Short summary
Short summary
Pollen impacts climate and public health, and it can be detected in the atmosphere by lidars which measure the linear particle depolarization ratio (PDR), a shape-relevant optical parameter. As aerosols also cause depolarization, surface aerosol and pollen observations were combined with measurements from ground-based lidars operating at different wavelengths to determine the optical properties of birch and pine pollen and quantify their relative contribution to the PDR.
Gillian Young McCusker, Jutta Vüllers, Peggy Achtert, Paul Field, Jonathan J. Day, Richard Forbes, Ruth Price, Ewan O'Connor, Michael Tjernström, John Prytherch, Ryan Neely III, and Ian M. Brooks
Atmos. Chem. Phys., 23, 4819–4847, https://doi.org/10.5194/acp-23-4819-2023, https://doi.org/10.5194/acp-23-4819-2023, 2023
Short summary
Short summary
In this study, we show that recent versions of two atmospheric models – the Unified Model and Integrated Forecasting System – overestimate Arctic cloud fraction within the lower troposphere by comparison with recent remote-sensing measurements made during the Arctic Ocean 2018 expedition. The overabundance of cloud is interlinked with the modelled thermodynamic structure, with strong negative temperature biases coincident with these overestimated cloud layers.
Pyry Pentikäinen, Ewan J. O'Connor, and Pablo Ortiz-Amezcua
Geosci. Model Dev., 16, 2077–2094, https://doi.org/10.5194/gmd-16-2077-2023, https://doi.org/10.5194/gmd-16-2077-2023, 2023
Short summary
Short summary
We used Doppler lidar to evaluate the wind profiles generated by a weather forecast model. We first compared the Doppler lidar observations with co-located radiosonde profiles, and they agree well. The model performs best over marine and coastal locations. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.
Felix Pithan, Marylou Athanase, Sandro Dahlke, Antonio Sánchez-Benítez, Matthew D. Shupe, Anne Sledd, Jan Streffing, Gunilla Svensson, and Thomas Jung
Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, https://doi.org/10.5194/gmd-16-1857-2023, 2023
Short summary
Short summary
Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Konstantinos Matthaios Doulgeris, Ville Vakkari, Ewan J. O'Connor, Veli-Matti Kerminen, Heikki Lihavainen, and David Brus
Atmos. Chem. Phys., 23, 2483–2498, https://doi.org/10.5194/acp-23-2483-2023, https://doi.org/10.5194/acp-23-2483-2023, 2023
Short summary
Short summary
We investigated how different long-range-transported air masses can affect the microphysical properties of low-level clouds in a clean subarctic environment. A connection was revealed. Higher values of cloud droplet number concentrations were related to continental air masses, whereas the lowest values of number concentrations were related to marine air masses. These were characterized by larger cloud droplets. Clouds in all regions were sensitive to increases in cloud number concentration.
Simone Kotthaus, Juan Antonio Bravo-Aranda, Martine Collaud Coen, Juan Luis Guerrero-Rascado, Maria João Costa, Domenico Cimini, Ewan J. O'Connor, Maxime Hervo, Lucas Alados-Arboledas, María Jiménez-Portaz, Lucia Mona, Dominique Ruffieux, Anthony Illingworth, and Martial Haeffelin
Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, https://doi.org/10.5194/amt-16-433-2023, 2023
Short summary
Short summary
Profile observations of the atmospheric boundary layer now allow for layer heights and characteristics to be derived at high temporal and vertical resolution. With novel high-density ground-based remote-sensing measurement networks emerging, horizontal information content is also increasing. This review summarises the capabilities and limitations of various sensors and retrieval algorithms which need to be considered during the harmonisation of data products for high-impact applications.
Julie Thérèse Pasquier, Jan Henneberger, Fabiola Ramelli, Annika Lauber, Robert Oscar David, Jörg Wieder, Tim Carlsen, Rosa Gierens, Marion Maturilli, and Ulrike Lohmann
Atmos. Chem. Phys., 22, 15579–15601, https://doi.org/10.5194/acp-22-15579-2022, https://doi.org/10.5194/acp-22-15579-2022, 2022
Short summary
Short summary
It is important to understand how ice crystals and cloud droplets form in clouds, as their concentrations and sizes determine the exact radiative properties of the clouds. Normally, ice crystals form from aerosols, but we found evidence for the formation of additional ice crystals from the original ones over a large temperature range within Arctic clouds. In particular, additional ice crystals were formed during collisions of several ice crystals or during the freezing of large cloud droplets.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
Short summary
Short summary
Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
Jenna Ritvanen, Ewan O'Connor, Dmitri Moisseev, Raisa Lehtinen, Jani Tyynelä, and Ludovic Thobois
Atmos. Meas. Tech., 15, 6507–6519, https://doi.org/10.5194/amt-15-6507-2022, https://doi.org/10.5194/amt-15-6507-2022, 2022
Short summary
Short summary
Doppler lidars and weather radars provide accurate wind measurements, with Doppler lidar usually performing better in dry weather conditions and weather radar performing better when there is precipitation. Operating both instruments together should therefore improve the overall performance. We investigate how well a co-located Doppler lidar and X-band radar perform with respect to various weather conditions, including changes in horizontal visibility, cloud altitude, and precipitation.
Xin Yang, Kimberly Strong, Alison S. Criscitiello, Marta Santos-Garcia, Kristof Bognar, Xiaoyi Zhao, Pierre Fogal, Kaley A. Walker, Sara M. Morris, and Peter Effertz
EGUsphere, https://doi.org/10.5194/egusphere-2022-696, https://doi.org/10.5194/egusphere-2022-696, 2022
Preprint archived
Short summary
Short summary
Snow pack in high Arctic plays a key role in polar atmospheric chemistry, especially in spring when photochemistry becomes active. By sampling surface snow from a Canadian high Arctic location at Eureka, Nunavut (80° N, 86° W), we demonstrate that surface snow is a net sink rather than a source of atmospheric reactive bromine and nitrate. This finding is new and opposite to previous conclusions that snowpack is a large and direct source of reactive bromine in polar spring.
Alexander F. Vessey, Kevin I. Hodges, Len C. Shaffrey, and Jonathan J. Day
Weather Clim. Dynam., 3, 1097–1112, https://doi.org/10.5194/wcd-3-1097-2022, https://doi.org/10.5194/wcd-3-1097-2022, 2022
Short summary
Short summary
Understanding the location and intensity of hazardous weather across the Arctic is important for assessing risks to infrastructure, shipping, and coastal communities. This study describes the typical lifetime and structure of intense winter and summer Arctic cyclones. Results show the composite development and structure of intense summer Arctic cyclones are different from intense winter Arctic and North Atlantic Ocean extra-tropical cyclones and from conceptual models.
Chih-Chun Chou, Paul J. Kushner, Stéphane Laroche, Zen Mariani, Peter Rodriguez, Stella Melo, and Christopher G. Fletcher
Atmos. Meas. Tech., 15, 4443–4461, https://doi.org/10.5194/amt-15-4443-2022, https://doi.org/10.5194/amt-15-4443-2022, 2022
Short summary
Short summary
Aeolus is the first satellite that provides global wind profile measurements. The mission aims to improve the weather forecasts in the tropics, but also, potentially, in the polar regions. We evaluate the performance of the instrument over the Canadian North and the Arctic by comparing its measured winds in both cloudy and non-cloudy layers to wind data from forecasts, reanalysis, and ground-based instruments. Overall, good agreement was seen, but Aeolus winds have greater dispersion.
Jonathan J. Day, Sarah Keeley, Gabriele Arduini, Linus Magnusson, Kristian Mogensen, Mark Rodwell, Irina Sandu, and Steffen Tietsche
Weather Clim. Dynam., 3, 713–731, https://doi.org/10.5194/wcd-3-713-2022, https://doi.org/10.5194/wcd-3-713-2022, 2022
Short summary
Short summary
A recent drive to develop seamless forecasting systems has culminated in the development of weather forecasting systems that include a coupled representation of the atmosphere, ocean and sea ice. Before this, sea ice and sea surface temperature anomalies were typically fixed throughout a given forecast. We show that the dynamic coupling is most beneficial during periods of rapid ice advance, where persistence is a poor forecast of the sea ice and leads to large errors in the uncoupled system.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
Short summary
Short summary
Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Sasu Karttunen, Ewan O'Connor, Olli Peltola, and Leena Järvi
Atmos. Meas. Tech., 15, 2417–2432, https://doi.org/10.5194/amt-15-2417-2022, https://doi.org/10.5194/amt-15-2417-2022, 2022
Short summary
Short summary
To study the complex structure of the lowest tens of metres of atmosphere in urban areas, measurement methods with great spatial and temporal coverage are needed. In our study, we analyse measurements with a promising and relatively new method, distributed temperature sensing, capable of providing detailed information on the near-surface atmosphere. We present multiple ways to utilise these kinds of measurements, as well as important considerations for planning new studies using the method.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Anna A. Shestakova, Dmitry G. Chechin, Christof Lüpkes, Jörg Hartmann, and Marion Maturilli
Atmos. Chem. Phys., 22, 1529–1548, https://doi.org/10.5194/acp-22-1529-2022, https://doi.org/10.5194/acp-22-1529-2022, 2022
Short summary
Short summary
This article presents a comprehensive analysis of the easterly orographic wind episode which occurred over Svalbard on 30–31 May 2017. This wind caused a significant temperature rise on the lee side of the mountains and greatly intensified the snowmelt. This episode was investigated on the basis of measurements collected during the ACLOUD/PASCAL field campaigns with the help of numerical modeling.
Jutta Kesti, John Backman, Ewan J. O'Connor, Anne Hirsikko, Eija Asmi, Minna Aurela, Ulla Makkonen, Maria Filioglou, Mika Komppula, Hannele Korhonen, and Heikki Lihavainen
Atmos. Chem. Phys., 22, 481–503, https://doi.org/10.5194/acp-22-481-2022, https://doi.org/10.5194/acp-22-481-2022, 2022
Short summary
Short summary
In this study we combined aerosol particle measurements at the surface with a scanning Doppler lidar providing vertical profiles of the atmosphere to study the effect of different boundary layer conditions on aerosol particle properties in the understudied Arabian Peninsula region. The instrumentation used in this study enabled us to identify periods when pollution from remote sources was mixed down to the surface and initiated new particle formation in the growing boundary layer.
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.
Sonja Murto, Rodrigo Caballero, Gunilla Svensson, and Lukas Papritz
Weather Clim. Dynam., 3, 21–44, https://doi.org/10.5194/wcd-3-21-2022, https://doi.org/10.5194/wcd-3-21-2022, 2022
Short summary
Short summary
This study uses reanalysis data to investigate the role of atmospheric blocking, prevailing high-pressure systems and mid-latitude cyclones in driving high-Arctic wintertime warm extreme events. These events are mainly preceded by Ural and Scandinavian blocks, which are shown to be significantly influenced and amplified by cyclones in the North Atlantic. It also highlights processes that need to be well captured in climate models for improving their representation of Arctic wintertime climate.
Gijs de Boer, Steven Borenstein, Radiance Calmer, Christopher Cox, Michael Rhodes, Christopher Choate, Jonathan Hamilton, Jackson Osborn, Dale Lawrence, Brian Argrow, and Janet Intrieri
Earth Syst. Sci. Data, 14, 19–31, https://doi.org/10.5194/essd-14-19-2022, https://doi.org/10.5194/essd-14-19-2022, 2022
Short summary
Short summary
This article provides a summary of the collection of atmospheric data over the near-coastal zone upwind of Barbados during the ATOMIC and EUREC4A field campaigns. These data were collected to improve our understanding of the structure and dynamics of the lower atmosphere in the tropical trade-wind regime over the Atlantic Ocean and the influence of that portion of the atmosphere on the development and maintenance of clouds.
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.
Anna Franck, Dmitri Moisseev, Ville Vakkari, Matti Leskinen, Janne Lampilahti, Veli-Matti Kerminen, and Ewan O'Connor
Atmos. Meas. Tech., 14, 7341–7353, https://doi.org/10.5194/amt-14-7341-2021, https://doi.org/10.5194/amt-14-7341-2021, 2021
Short summary
Short summary
We proposed a method to derive a convective boundary layer height, using insects in radar observations, and we investigated the consistency of these retrievals among different radar frequencies (5, 35 and 94 GHz). This method can be applied to radars at other measurement stations and serve as additional way to estimate the boundary layer height during summer. The entrainment zone was also observed by the 5 GHz radar above the boundary layer in the form of a Bragg scatter layer.
Kevin Ohneiser, Albert Ansmann, Alexandra Chudnovsky, Ronny Engelmann, Christoph Ritter, Igor Veselovskii, Holger Baars, Henriette Gebauer, Hannes Griesche, Martin Radenz, Julian Hofer, Dietrich Althausen, Sandro Dahlke, and Marion Maturilli
Atmos. Chem. Phys., 21, 15783–15808, https://doi.org/10.5194/acp-21-15783-2021, https://doi.org/10.5194/acp-21-15783-2021, 2021
Short summary
Short summary
The highlight of the lidar measurements during the 1-year MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition of the German icebreaker Polarstern (October 2019–October 2020) was the detection of a persistent, 10 km deep Siberian wildfire smoke layer in the upper troposphere and lower stratosphere (UTLS) from about 7–8 km to 17–18 km height that could potentially have impacted the record-breaking ozone depletion over the Arctic in the spring of 2020.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
Short summary
Short summary
We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Xiaoxia Shang, Tero Mielonen, Antti Lipponen, Elina Giannakaki, Ari Leskinen, Virginie Buchard, Anton S. Darmenov, Antti Kukkurainen, Antti Arola, Ewan O'Connor, Anne Hirsikko, and Mika Komppula
Atmos. Meas. Tech., 14, 6159–6179, https://doi.org/10.5194/amt-14-6159-2021, https://doi.org/10.5194/amt-14-6159-2021, 2021
Short summary
Short summary
The long-range-transported smoke particles from a Canadian wildfire event were observed with a multi-wavelength Raman polarization lidar and a ceilometer over Kuopio, Finland, in June 2019. The optical properties and the mass concentration estimations were reported for such aged smoke aerosols over northern Europe.
Ronny Engelmann, Albert Ansmann, Kevin Ohneiser, Hannes Griesche, Martin Radenz, Julian Hofer, Dietrich Althausen, Sandro Dahlke, Marion Maturilli, Igor Veselovskii, Cristofer Jimenez, Robert Wiesen, Holger Baars, Johannes Bühl, Henriette Gebauer, Moritz Haarig, Patric Seifert, Ulla Wandinger, and Andreas Macke
Atmos. Chem. Phys., 21, 13397–13423, https://doi.org/10.5194/acp-21-13397-2021, https://doi.org/10.5194/acp-21-13397-2021, 2021
Short summary
Short summary
A Raman lidar was operated aboard the icebreaker Polarstern during MOSAiC and monitored aerosol and cloud layers in the central Arctic up to 30 km height. The article provides an overview of the spectrum of aerosol profiling observations and shows aerosol–cloud interaction studies for liquid-water and ice clouds. A highlight was the detection of a 10 km deep wildfire smoke layer over the North Pole up to 17 km height from the fire season of 2019, which persisted over the whole winter period.
Benjamin Männel, Florian Zus, Galina Dick, Susanne Glaser, Maximilian Semmling, Kyriakos Balidakis, Jens Wickert, Marion Maturilli, Sandro Dahlke, and Harald Schuh
Atmos. Meas. Tech., 14, 5127–5138, https://doi.org/10.5194/amt-14-5127-2021, https://doi.org/10.5194/amt-14-5127-2021, 2021
Short summary
Short summary
Within the MOSAiC expedition, GNSS was used to monitor variations in atmospheric water vapor. Based on 15 months of continuously tracked data, coordinates and hourly zenith total delays (ZTDs) were determined using kinematic precise point positioning. The derived ZTD values agree within few millimeters with ERA5 and terrestrial GNSS and VLBI stations. The derived integrated water vapor corresponds to the frequently launched radiosondes (0.08 ± 0.04 kg m−2, rms of the differences of 1.47 kg m−2).
Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
Atmos. Meas. Tech., 14, 4971–4987, https://doi.org/10.5194/amt-14-4971-2021, https://doi.org/10.5194/amt-14-4971-2021, 2021
Short summary
Short summary
A cloud particle sensor (CPS) sonde is an observing system to obtain the signals of the phase, size, and the number of cloud particles. Based on the field experiments in the Arctic regions and numerical experiments, we proposed a method to correct the CPS sonde data and found that the CPS sonde system can appropriately observe the liquid cloud if our correction method is applied.
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.
Chiara Marsigli, Elizabeth Ebert, Raghavendra Ashrit, Barbara Casati, Jing Chen, Caio A. S. Coelho, Manfred Dorninger, Eric Gilleland, Thomas Haiden, Stephanie Landman, and Marion Mittermaier
Nat. Hazards Earth Syst. Sci., 21, 1297–1312, https://doi.org/10.5194/nhess-21-1297-2021, https://doi.org/10.5194/nhess-21-1297-2021, 2021
Short summary
Short summary
This paper reviews new observations for the verification of high-impact weather and provides advice for their usage in objective verification. New observations include remote sensing datasets, products developed for nowcasting, datasets derived from telecommunication systems, data collected from citizens, reports of impacts and reports from insurance companies. This work has been performed in the framework of the Joint Working Group on Forecast Verification Research (JWGFVR) of the WMO.
Ville Vakkari, Holger Baars, Stephanie Bohlmann, Johannes Bühl, Mika Komppula, Rodanthi-Elisavet Mamouri, and Ewan James O'Connor
Atmos. Chem. Phys., 21, 5807–5820, https://doi.org/10.5194/acp-21-5807-2021, https://doi.org/10.5194/acp-21-5807-2021, 2021
Short summary
Short summary
The depolarization ratio is a valuable parameter for aerosol categorization from remote sensing measurements. Here, we introduce particle depolarization ratio measurements at the 1565 nm wavelength, which is substantially longer than previously utilized wavelengths and enhances our capabilities to study the wavelength dependency of the particle depolarization ratio.
Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjæraa, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
Atmos. Meas. Tech., 14, 2451–2476, https://doi.org/10.5194/amt-14-2451-2021, https://doi.org/10.5194/amt-14-2451-2021, 2021
Short summary
Short summary
The high-resolution satellite Sentinel-5p TROPOMI observes several atmospheric gases. To account for cloud interference with the observations, S5P cloud data products (CLOUD OCRA/ROCINN_CAL, OCRA/ROCINN_CRB, and FRESCO) provide vital input: cloud fraction, cloud height, and cloud optical thickness. Here, S5P cloud parameters are validated by comparing with other satellite sensors (VIIRS, MODIS, and OMI) and with ground-based CloudNet data. The agreement depends on product type and cloud height.
Olli Peltola, Karl Lapo, Ilkka Martinkauppi, Ewan O'Connor, Christoph K. Thomas, and Timo Vesala
Atmos. Meas. Tech., 14, 2409–2427, https://doi.org/10.5194/amt-14-2409-2021, https://doi.org/10.5194/amt-14-2409-2021, 2021
Short summary
Short summary
We evaluated the suitability of fiber-optic distributed temperature sensing (DTS) for observing spatial (>25 cm) and temporal (>1 s) details of airflow within and above forests. The DTS measurements could discern up to third-order moments of the flow and observe spatial details of coherent flow motions. Similar measurements are not possible with more conventional measurement techniques. Hence, the DTS measurements will provide key insights into flows close to roughness elements, e.g. trees.
Julie M. Thériault, Stephen J. Déry, John W. Pomeroy, Hilary M. Smith, Juris Almonte, André Bertoncini, Robert W. Crawford, Aurélie Desroches-Lapointe, Mathieu Lachapelle, Zen Mariani, Selina Mitchell, Jeremy E. Morris, Charlie Hébert-Pinard, Peter Rodriguez, and Hadleigh D. Thompson
Earth Syst. Sci. Data, 13, 1233–1249, https://doi.org/10.5194/essd-13-1233-2021, https://doi.org/10.5194/essd-13-1233-2021, 2021
Short summary
Short summary
This article discusses the data that were collected during the Storms and Precipitation Across the continental Divide (SPADE) field campaign in spring 2019 in the Canadian Rockies, along the Alberta and British Columbia border. Various instruments were installed at five field sites to gather information about atmospheric conditions focussing on precipitation. Details about the field sites, the instrumentation used, the variables collected, and the collection methods and intervals are presented.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Christopher J. Cox, Sara M. Morris, Taneil Uttal, Ross Burgener, Emiel Hall, Mark Kutchenreiter, Allison McComiskey, Charles N. Long, Bryan D. Thomas, and James Wendell
Atmos. Meas. Tech., 14, 1205–1224, https://doi.org/10.5194/amt-14-1205-2021, https://doi.org/10.5194/amt-14-1205-2021, 2021
Short summary
Short summary
Solar and infrared radiation are measured regularly for research, industry, and climate monitoring. In cold climates, icing of sensors is a poorly constrained source of uncertainty. D-ICE was carried out in Alaska to document the effectiveness of ice-mitigation technology and quantify errors associated with ice. Technology was more effective than anticipated, and while instantaneous errors were large, mean biases were small. Attributes of effective ice mitigation design were identified.
Lena Frey, Frida A.-M. Bender, and Gunilla Svensson
Atmos. Chem. Phys., 21, 577–595, https://doi.org/10.5194/acp-21-577-2021, https://doi.org/10.5194/acp-21-577-2021, 2021
Short summary
Short summary
We investigate the vertical distribution of aerosol in the climate model NorESM1-M in five regions of marine stratocumulus clouds. We thereby analyze the total aerosol extinction to facilitate a comparison with satellite data. We find that the model underestimates aerosol extinction throughout the troposphere, especially elevated aerosol layers. Further, we perform sensitivity experiments to identify the processes most important for vertical aerosol distribution in our model.
Xin Yang, Anne-M. Blechschmidt, Kristof Bognar, Audra McClure-Begley, Sara Morris, Irina Petropavlovskikh, Andreas Richter, Henrik Skov, Kimberly Strong, David W. Tarasick, Taneil Uttal, Mika Vestenius, and Xiaoyi Zhao
Atmos. Chem. Phys., 20, 15937–15967, https://doi.org/10.5194/acp-20-15937-2020, https://doi.org/10.5194/acp-20-15937-2020, 2020
Short summary
Short summary
This is a modelling-based study on Arctic surface ozone, with a particular focus on spring ozone depletion events (i.e. with concentrations < 10 ppbv). Model experiments show that model runs with blowing-snow-sourced sea salt aerosols implemented as a source of reactive bromine can reproduce well large-scale ozone depletion events observed in the Arctic. This study supplies modelling evidence of the proposed mechanism of reactive-bromine release from blowing snow on sea ice (Yang et al., 2008).
Peggy Achtert, Ewan J. O'Connor, Ian M. Brooks, Georgia Sotiropoulou, Matthew D. Shupe, Bernhard Pospichal, Barbara J. Brooks, and Michael Tjernström
Atmos. Chem. Phys., 20, 14983–15002, https://doi.org/10.5194/acp-20-14983-2020, https://doi.org/10.5194/acp-20-14983-2020, 2020
Short summary
Short summary
We present observations of precipitating and non-precipitating Arctic liquid and mixed-phase clouds during a research cruise along the Russian shelf in summer and autumn of 2014. Active remote-sensing observations, radiosondes, and auxiliary measurements are combined in the synergistic Cloudnet retrieval. Cloud properties are analysed with respect to cloud-top temperature and boundary layer structure. About 8 % of all liquid clouds show a liquid water path below the infrared black body limit.
Cited articles
Aknan, A., Chen, G., Crawford, J., and Williams, E.: ICARTT File Format Standards V1.1, National Aeronautics and Space Administration (NASA), ESDS-RFC-019v1.1, 21 pp., https://espoarchive.nasa.gov/sites/default/files/archive/ESDS-RFC-019-v1.1_0.pdf (last access: 15 July 2023), 2013.
Andreas, E. L., Persson, P. O. G., Grachev, A. A., Jordan, R. E., Horst, T. W., Guest, P. S., and Fairall, C. W.: Parameterizing Turbulent Exchange over Sea Ice in Winter, J. Hydrometeorol., 11, 87–104, https://doi.org/10.1175/2009JHM1102.1, 2010.
Attribute Convention for Data Discovery 1–3: https://wiki.esipfed.org/Attribute_Convention_for_Data_Discovery_1-3, last access: 21 March 2024.
Baldocchi, D., Valentini, R., Running, S., Oechel, W., and Dahlman, R.: Strategies for measuring and modelling carbon dioxide and water vapour fluxes over terrestrial ecosystems, Global Change Biology, 2, 159–168, https://doi.org/10.1111/j.1365-2486.1996.tb00069.x, 1996.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw U, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Boden, T. A., Krassovski, M., and Yang, B.: The AmeriFlux data activity and data system: an evolving collection of data management techniques, tools, products and services, Geosci. Instrum. Method. Data Syst., 2, 165–176, https://doi.org/10.5194/gi-2-165-2013, 2013.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A. J., and Zemp, M.: The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy, B. Am. Meteorol. Soc., 95, 1431–1443, https://doi.org/10.1175/BAMS-D-13-00047.1, 2014.
Boukabara, S.-A., Krasnopolsky, V., Penny, S. G., Stewart, J. Q., McGovern, A., Hall, D., Ten Hoeve, J. E., Hickey, J., Huang, H.-L. A., Williams, J. K., Ide, K., Tissot, P., Haupt, S. E., Casey, K. S., Oza, N., Geer, A. J., Maddy, E. S., and Hoffman, R. N.: Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences, B. Am. Meteorol. Soc., 102, E1016–E1032, https://doi.org/10.1175/BAMS-D-20-0031.1, 2021.
Buck, J. J. H., Bainbridge, S. J., Burger, E. F., Kraberg, A. C., Casari, M., Casey, K. S., Darroch, L., del Rio, J., Metfies, K., Delory, E., Fischer, P. F., Gardner, T., Heffernan, R., Jirka, S., Kokkinaki, A., Loebl, M., Buttigieg, P. L., Pearlman, J. S., and Schewe, I.: Ocean Data Product Integration Through Innovation-The Next Level of Data Interoperability, Front. Marine Sci., 6, 32, https://doi.org/10.3389/fmars.2019.00032, 2019.
Buisán, S. T., Smith, C. D., Ross, A., Kochendorfer, J., Collado, J. L., Alastrué, J., Wolff, M., Roulet, Y.-A., Earle, M. E., Laine, T., Rasmussen, R., and Nitu, R.: The potential for uncertainty in Numerical Weather Prediction model verification when using solid precipitation observations, Atmos. Sci. Lett., 21, e976, https://doi.org/10.1002/asl.976, 2020.
Casati, B., Robinson, T., Lemay, F., Køltzow, M., Haiden, T., Mekis, E., Lespinas, F., Fortin, V., Gascon, G., Milbrandt, J., and Smith, G.: Performance of the Canadian Arctic Prediction System during the YOPP Special Observing Periods, Atmosphere-Ocean, 61, 1–27, https://doi.org/10.1080/07055900.2023.2191831, 2023.
CF Metadata Conventions: https://cfconventions.org, last access: 27 March 2024.
CF Standard Name Table: https://cfconventions.org/Data/cf-standard-names/current/build/cf-standard-name-table.html, last access: 18 July 2023.
Clothiaux, E. E., Miller, M. A., Perez, R. C., Turner, D. D., Moran, K. P., Martner, B. E., Ackerman, T. P., Mace, G. G., Marchand, R. T., Widener, K. B., Rodriguez, D. J., Uttal, T., Mather, J. H., Flynn, C. J., Gaustad, K. L., and Ermold, B.: The ARM Millimeter Wave Cloud Radars (MMCRs) and the Active Remote Sensing of Clouds (ARSCL) Value Added Product (VAP), ARM user facility, Pacific Northwest National Laboratory, Richland, WA, United States, 56 pp., https://doi.org/10.2172/1808567, 2001.
Cox, C. J., Morris, S. M., Uttal, T., Burgener, R., Hall, E., Kutchenreiter, M., McComiskey, A., Long, C. N., Thomas, B. D., and Wendell, J.: The De-Icing Comparison Experiment (D-ICE): a study of broadband radiometric measurements under icing conditions in the Arctic, Atmos. Meas. Tech., 14, 1205–1224, https://doi.org/10.5194/amt-14-1205-2021, 2021.
Cox, C. J., Gallagher, M., Shupe, M. D., Persson, P. O. G., Solomon, A., Fairall, C. W., Ayers, T., Blomquist, B., Brooks, I. M., Costa, D., Grachev, A., Gottas, D., Hutchings, J. K., Kutchenreiter, M., J. Leach, J., Morris, S. M., Morris, V., Osborn, J., Pezoa, S., Preusser, A., Riihimaki, L., and Uttal, T.: Continuous observations of the surface energy budget and meteorology over the Arctic sea ice during MOSAiC, Sci. Data, 10, 519, https://doi.org/10.1038/s41597-023-02415-5, 2023.
Data Citation Synthesis Group: Joint Declaration of Data Citation Principles, FORCE11, San Diego CA, https://doi.org/10.25490/a97f-egyk, 2014.
DataCite Metadata Working Group: DataCite Metadata Schema Documentation for the Publication and Citation of Research Data and Other Research Outputs. Version 4.4, DataCite e.V., 82 pp., https://doi.org/10.14454/3w3z-sa82, 2021.
Day, J., Svensson, G., Casati, B., Uttal, T., Khalsa, S.-J., Bazile, E., Akish, E., Azouz, N., Ferrighi, L., Frank, H., Gallagher, M., Godøy, Ø., Hartten, L., Huang, L. X., Holt, J., Di Stefano, M., Suomi, I., Mariani, Z., Morris, S., O'Connor, E., Pirazzini, R., Remes, T., Fadeev, R., Solomon, A., Tjernström, J., and Tolstykh, M.: The YOPP site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1951, 2023.
Eaton, B., Gregory, J., Drach, B., Taylor, K., Hankin, S., Blower, J., Caron, J., Signell, R., Bentley, P., Rappa, G., Höck, H., Pamment, A., Juckes, M., Raspaud, M., Horne, R., Whiteaker, T., Blodgett, D., Zender, C., Lee, D., Hassell, D., Snow, A. D., Kölling, T., Allured, D., Jelenak, A., Soerensen, A. M., Gaultier, L., and Herlédan, S.: NetCDF Climate and Forecast (CF) Metadata Conventions Version 1.10, https://cfconventions.org/Data/cf-conventions/cf-conventions-1.10/cf-conventions.html (last access: 29 March 2024), 2022.
Essential Climate Variables: https://public.wmo.int/en/programmes/global-climate-observing-system/essential-climate-variables, last access: 13 September 2023.
Fratini, G. and Mauder, M.: Towards a consistent eddy-covariance processing: an intercomparison of EddyPro and TK3, Atmos. Meas. Tech., 7, 2273–2281, https://doi.org/10.5194/amt-7-2273-2014, 2014.
Gettelman, A., Geer, A. J., Forbes, R. M., Carmichael, G. R., Feingold, G., Posselt, D. J., Stephens, G. L., van den Heever, S. C., Varble, A. C., and Zuidema, P.: The future of Earth system prediction: Advances in model-data fusion, Sci. Adv., 8, eabn3488, https://doi.org/10.1126/sciadv.abn3488, 2022.
Global Telecommunication System (GTS): https://community.wmo.int/en/activity-areas/global-telecommunication-system-gts, last access: 16 July 2023.
Goessling, H. F., Jung, T., Klebe, S., Baeseman, J., Bauer, P., Chen, P., Chevallier, M., Dole, R., Gordon, N., Ruti, P., Bradley, A., Bromwich, D. H., Casati, B., Chechin, D., Day, J. J., Massonnet, F., Mills, B., Renfrew, I. A., Smith, G., and Tatusko, R.: Paving the Way for the Year of Polar Prediction, B. Am. Meteorol. Soc., 97, ES85–ES88, https://doi.org/10.1175/BAMS-D-15-00270.1, 2016.
Grachev, A. A., Persson, P. O. G., Uttal, T., Akish, E. A., Cox, C. J., Morris, S. M., Fairall, C. W., Stone, R. S., Lesins, G., Makshtas, A. P., and Repina, I. A.: Seasonal and latitudinal variations of surface fluxes at two Arctic terrestrial sites, Clim. Dynam., 51, 1793–1818, https://doi.org/10.1007/s00382-017-3983-4, 2018.
Guidelines for Construction of CF Standard Names: https://cfconventions.org/Data/cf-standard-names/docs/guidelines.html, last access: 27 March 2024.
Hanisch, R., Chalk, S., Coulon, R., Cox, S., Emmerson, S., Sandoval, F. J. F., Forbes, A., Frey, J., Hall, B., Hartshorn, R., Heus, P., Hodson, S., Hosaka, K., Hutzschenreuter, D., Kang, C.-S., Picard, S., and White, R.: Stop squandering data: make units of measurement machine-readable, Nature, 605, 222–224, https://doi.org/10.1038/d41586-022-01233-w, 2022.
Hartten, L. M. and Khalsa, S. J. S.: The H-K Variable SchemaTable developed for the YOPPsiteMIP, Zenodo [code], https://doi.org/10.5281/zenodo.6255666, 2022.
Hartten, L. M., Cox, C. J., Johnston, P. E., Wolfe, D. E., Abbott, S., McColl, H. A., Quan, X.-W., and Winterkorn, M. G.: Ship- and island-based soundings from the 2016 El Niño Rapid Response (ENRR) field campaign, Earth Syst. Sci. Data, 10, 1165–1183, https://doi.org/10.5194/essd-10-1165-2018, 2018.
Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.
Hogan, R. J. and O'Connor, E. J.: Facilitating cloud radar and lidar algorithms: the Cloudnet Instrument Synergy/Target Categorization product, 14 pp., https://www.met.rdg.ac.uk/~swrhgnrj/publications/categorization.pdf (last access: 29 March 2024), 2004.
Holloway, C. E., Petch, J. C., Beare, R. J., Bechtold, P., Craig, G. C., Derbyshire, S. H., Donner, L. J., Field, P. R., Gray, S. L., Marsham, J. H., Parker, D. J., Plant, R. S., Roberts, N. M., Schultz, D. M., Stirling, A. J., and Woolnough, S. J.: Understanding and representing atmospheric convection across scales: recommendations from the meeting held at Dartington Hall, Devon, UK, 28–30 January 2013, Atmos. Sci. Lett., 15, 348–353, https://doi.org/10.1002/asl2.508, 2014.
Holtslag, A. A. M., Svensson, G., Baas, P., Basu, S., Beare, B., Beljaars, A. C. M., Bosveld, F. C., Cuxart, J., Lindvall, J., Steeneveld, G. J., Tjernström, M., and Van De Wiel, B. J. H.: Stable Atmospheric Boundary Layers and Diurnal Cycles: Challenges for Weather and Climate Models, B. Am. Meteorol. Soc., 94, 1691–1706, https://doi.org/10.1175/BAMS-D-11-00187.1, 2013.
Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Brooks, M. E., Delanoé, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Baltink, H. K., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.: Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations, B. Am. Meteorol. Soc., 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007.
Ingleby, B., Motl, M., Marlton, G., Edwards, D., Sommer, M., von Rohden, C., Vömel, H., and Jauhiainen, H.: On the quality of RS41 radiosonde descent data, Atmos. Meas. Tech., 15, 165–183, https://doi.org/10.5194/amt-15-165-2022, 2022.
Jones, M. B., Budden, A. E., Mecum, B., Clark, J., Brun, J., Lowndes, J., and McLean, E.: Data Science Training for Arctic Researchers, Arctic Data Center [data set], https://doi.org/10.18739/A24746R2N, 2020.
Jung, T., Gordon, N. D., Bauer, P., Bromwich, D. H., Chevallier, M., Day, J. J., Dawson, J., Doblas-Reyes, F. J., Fairall, C., Goessling, H. F., Holland, M., Inoue, J., Iversen, T., Klebe, S., Lemke, P., Losch, M., Makshtas, A., Mills, B., Nurmi, P., Perovich, D., Reid, P., Renfrew, I. A., Smith, G., Svensson, G., Tolstykh, M., and Yang, Q.: Advancing Polar Prediction Capabilities on Daily to Seasonal Time Scales, B. Am. Meteorol. Soc., 97, 1631–1647, https://doi.org/10.1175/BAMS-D-14-00246.1, 2016.
Jung, T., Wilson, J., Bazille, E., Bromwich, D., Casati, B., Day, J., De Coning, E., Eayrs, C., Grumbine, R., Inoue, J., Khalsa, S. J., Kristiansen, J., Lamers, M., Liggett, D., Olsen, S., Perovich, D., Renfrew, I., Sandu, I., Shupe, M., Smolyanitsky, V., Svensson, G., Sun, Q., Uttal, T., Werner, K., Yang, Q., and Heinrich, V. J.: The Year of Polar Prediction (YOPP): Achievements, impacts and lessons learnt, B. Am. Meteorol. Soc., accepted, 2024.
Kaimal, J. C. and Finnigan, J. J.: Atmospheric Boundary Layer Flows: Their Structure and Measurement, Oxford University Press, New York, ISBN 9780195062397, 1994.
Kochendorfer, J., Nitu, R., Wolff, M., Mekis, E., Rasmussen, R., Baker, B., Earle, M. E., Reverdin, A., Wong, K., Smith, C. D., Yang, D., Roulet, Y.-A., Meyers, T., Buisan, S., Isaksen, K., Brækkan, R., Landolt, S., and Jachcik, A.: Testing and development of transfer functions for weighing precipitation gauges in WMO-SPICE, Hydrol. Earth Syst. Sci., 22, 1437–1452, https://doi.org/10.5194/hess-22-1437-2018, 2018.
Kochendorfer, J., Earle, M., Rasmussen, R., Smith, C., Yang, D., Morin, S., Mekis, E., Buisan, S., Roulet, Y.-A., Landolt, S., Wolff, M., Hoover, J., Thériault, J. M., Lee, G., Baker, B., Nitu, R., Lanza, L., Colli, M., and Meyers, T.: How Well Are We Measuring Snow Post-SPICE?, B. Am. Meteorol. Soc., 103, E370–E388, https://doi.org/10.1175/BAMS-D-20-0228.1, 2022.
Køltzow, M., Casati, B., Haiden, T., and Valkonen, T.: Verification of Solid Precipitation Forecasts from Numerical Weather Prediction Models in Norway, Weather Forecast., 35, 2279–2292, https://doi.org/10.1175/WAF-D-20-0060.1, 2020.
Lavergne, T., Kern, S., Aaboe, S., Derby, L., Dybkjaer, G., Garric, G., Heil, P., Hendricks, S., Holfort, J., Howell, S., Key, J., Lieser, J. L., Maksym, T., Maslowski, W., Meier, W., Muñoz-Sabater, J., Nicolas, J., Özsoy, B., Rabe, B., Rack, W., Raphael, M., de Rosnay, P., Smolyanitsky, V., Tietsche, S., Ukita, J., Vichi, M., Wagner, P., Willmes, S., and Zhao, X.: A New Structure for the Sea Ice Essential Climate Variables of the Global Climate Observing System, B. Am. Meteorol. Soc., 103, E1502–E1521, https://doi.org/10.1175/BAMS-D-21-0227.1, 2022.
Long, C. N. and Shi, Y.: The QCRad Value Added Product: Surface Radiation Measurement Quality Control Testing, Including Climatology Configurable Limits, PNNL, Richland, Washington, United States, 70 pp., https://doi.org/10.2172/1019540, 2006.
Long, C. N. and Shi, Y.: An Automated Quality Assessment and Control Algorithm for Surface Radiation Measurements, The Open Atmospheric Science Journal , 2, 23–37, https://doi.org/10.2174/1874282300802010023, 2008.
Mahrt, L. T. and Sun, J.: The Subgrid Velocity Scale in the Bulk Aerodynamic Relationship for Spatially Averaged Scalar Fluxes, Mon. Weather Rev., 123, 3032–3041, https://doi.org/10.1175/1520-0493(1995)123<3032:TSVSIT>2.0.CO;2, 1995.
Mariani, Z., Huang, L., Crawford, R., Blanchet, J.-P., Hicks-Jalali, S., Mekis, E., Pelletier, L., Rodriguez, P., and Strawbridge, K.: Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites, Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, 2022.
Mariani, Z., Morris, S., Uttal, T., Akish, E., Crawford, R., Huang, L., Day, J., Tjernström, J., Godøy, Ø., Ferrighi, L., Hartten, L., Holt, J., Cox, C., O'Connor, E., Pirazzini, R., Maturilli, M., Prakash, G., Mather, J., Strong, K., Fogal, P., Kustov, V., Svensson, G., Gallagher, M., and Vasel, B.: Special Observing Period (SOP) Data for the Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-497, in review, 2024.
Matrosov, S. Y., Shupe, M. D., and Uttal, T.: High temporal resolution estimates of Arctic snowfall rates emphasizing gauge and radar-based retrievals from the MOSAiC expedition, Elementa: Science of the Anthropocene, 10, 00101, https://doi.org/10.1525/elementa.2021.00101, 2022.
Matsui, N., Long, C. N., Augustine, J., Halliwell, D., Uttal, T., Longenecker, D., Niebergall, O., Wendell, J., and Albee, R.: Evaluation of Arctic broadband surface radiation measurements, Atmos. Meas. Tech., 5, 429–438, https://doi.org/10.5194/amt-5-429-2012, 2012.
Mauder, M., Foken, T., Clement, R., Elbers, J. A., Eugster, W., Grünwald, T., Heusinkveld, B., and Kolle, O.: Quality control of CarboEurope flux data – Part 2: Inter-comparison of eddy-covariance software, Biogeosciences, 5, 451–462, https://doi.org/10.5194/bg-5-451-2008, 2008.
Monin, A. S. and Obukhov, A.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR, 151 163–187, 1954.
Morris, S. and Akish, E.: A-M Variable & Attribute Template Table developed for the YOPPsiteMIP, Zenodo [code], https://doi.org/10.5281/zenodo.6780400, 2022.
Morris, S. and Uttal, T.: Datagrams: Diagrammatic Metadata for Humans, B. Am. Meteorol. Soc., 103, E1343–E1350, https://doi.org/10.1175/BAMS-D-21-0219.1, 2022.
Nature Editorial : Time to recognize authorship of open data, Nature, 608, 8, https://doi.org/10.1038/d41586-022-00921-x, 2022.
Neang, A. B., Sutherland, W., Beach, M. W., and Lee, C. P.: Data Integration as Coordination: The Articulation of Data Work in an Ocean Science Collaboration, Proc. ACM Hum.-Comput. Interact., 4, 256, https://doi.org/10.1145/3432955, 2021.
Nitu, R., Roulet, Y.-A., Wolff, M., Earle, M., Reverdin, A., Smith, C., Kochendorfer, J., Morin, S., Rasmussen, R., Wong, K., Alastrué, J., Arnold, L., Baker, B., Buisán, S., Collado, J. L., Colli, M., Collins, B., Gaydos, A., Hannula, H.-R., Hoover, J., Joe, P., Kontu, A., Laine, T., Lanza, L., Lanzinger, E., Lee, G., Lejeune, Y., Leppänen, L., Mekis, E., Panel, J.-M., Poikonen, A., Ryu, S., Sabatini, F., Theriault, J., Yang, D., Genthon, C., van den Heuvel, F., Hirasawa, N., Konishi, H., Motoyoshi, H., Nakai, S., Nishimura, K., Senese, A., and Yamashita, K.: WMO Solid Precipitation Intercomparison Experiment (SPICE) (2012–2015), World Meteorological Organization (WMO), Geneva, Switzerland, IOM No. 1, 1443 pp., https://library.wmo.int/opac/ (last access: 21 August 2023), 2018.
Norwegian Meteorological Institute: MET Norway YOPP Supersite Catalog, Norwegian Meteorological Institute [data set], https://thredds.met.no/thredds/catalog/alertness/YOPP_supersite/catalog.html (last access: 4 August 2023), 2022.
Ohmura, A., Dutton, E. G., Forgan, B., Fröhlich, C., Gilgen, H., Hegner, H., Heimo, A., König-Langlo, G., McArthur, B., Müller, G., Philipona, R., Pinker, R., Whitlock, C. H., Dehne, K., and Wild, M.: 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.
Papoutsoglou, E. A., Athanasiadis, I. N., Visser, R. G. F., and Finkers, R.: The benefits and struggles of FAIR data: the case of reusing plant phenotyping data, Sci. Data, 10, 457, https://doi.org/10.1038/s41597-023-02364-z, 2023.
Pierce, H. H., Dev, A., Statham, E., and Bierer, B. E.: Credit data generators for data reuse, Nature, 570, 30–32, https://doi.org/10.1038/d41586-019-01715-4, 2019.
PPP Steering Group, Bauer, P., Bradley, A., Bromwich, D., Casati, B., Chen, P., Chevallier, M., Dawson, J., Day, J., Doblas-Reyes, F. J., Fairall, C., Goessling, H., Gordon, N., Grumbine, R., Hoke, W., Holland, M., Inoue, J., Iversen, T., Jung, T., Khalsa, S. J. S., Klebe, S., Kristiansen, J., Lamers, M., Lemke, P., Liggett, D., Ljubicic, G., Massonnet, F., Makshtas, A., Mills, B., Nurmi, P., Olsen, S., Perovich, D., Reid, P., Renfrew, I., Sandu, I., Smith, G., Stewart, E., Smolyanitsky, V., Svensson, G., Swinbank, R., Tolstykh, M., Uttal, T., Werner, K., Wilson, J., and Yang, Q.: WWRP Polar Prediction Project Implementation Plan for the Year of Polar Prediction (YOPP), 80 pp., https://www.polarprediction.net/about/implementation-and-science-plans/ (last access: 15 July 2023), 2019.
Prakash, G., Shrestha, B., Younkin, K., Jundt, R., Martin, M., and Elliott, J.: Data Always Getting Bigger – A Scalable DOI Architecture for Big and Expanding Scientific Data, Data, 1, 11, https://doi.org/10.3390/data1020011, 2016.
Sardeshmukh, P. D., Compo, G. P., and Penland, C.: Need for Caution in Interpreting Extreme Weather Statistics, J. Climate, 28, 9166–9187, https://doi.org/10.1175/JCLI-D-15-0020.1, 2015.
Sprintall, J., Coles, V. J., Reed, K. A., Butler, A. H., Foltz, G. R., Penny, S. G., and Seo, H.: Using Process Studies to Improve Climate Modeling: Strategies for Success, B. Am. Meteorol. Soc., 102, 523–526, https://doi.org/10.1175/BAMS-D-19-0263.A, 2021.
Stephan, C. C., Schnitt, S., Schulz, H., Bellenger, H., de Szoeke, S. P., Acquistapace, C., Baier, K., Dauhut, T., Laxenaire, R., Morfa-Avalos, Y., Person, R., Quiñones Meléndez, E., Bagheri, G., Böck, T., Daley, A., Güttler, J., Helfer, K. C., Los, S. A., Neuberger, A., Röttenbacher, J., Raeke, A., Ringel, M., Ritschel, M., Sadoulet, P., Schirmacher, I., Stolla, M. K., Wright, E., Charpentier, B., Doerenbecher, A., Wilson, R., Jansen, F., Kinne, S., Reverdin, G., Speich, S., Bony, S., and Stevens, B.: Ship- and island-based atmospheric soundings from the 2020 EUREC4A field campaign, Earth Syst. Sci. Data, 13, 491–514, https://doi.org/10.5194/essd-13-491-2021, 2021.
Stephens, G. L., Polcher, J., Zeng, X., van Oevelen, P., Poveda, G., Bosilovich, M., Ahn, M.-H., Balsamo, G., Duan, Q., Hegerl, G. C., Jakob, C., Lamptey, B., Leung, R., Piles, M., Su, Z., Dirmeyer, P., Findell, K. L., Verhoef, A., Ek, M., L'Ecuyer, T., Roca, R., Nazemi, A., Dominguez, F., Klocke, D., and Bony, S.: The First 30 Years of GEWEX, B. Am. Meteorol. Soc., 104, E126–E157, https://doi.org/10.1175/BAMS-D-22-0061.1, 2023.
Stokes, G. M. and Schwartz, S. E.: The Atmospheric Radiation Measurement (ARM) Program: Programmatic Background and Design of the Cloud and Radiation Test Bed, B. Am. Meteorol. Soc., 75, 1201–1222, https://doi.org/10.1175/1520-0477(1994)075<1201:TARMPP>2.0.CO;2, 1994.
Svensson, G., Casati, B., Day, J., Uttal, T., Godøy, Ø., and Hartten, L.: YOPPsiteMIP – The YOPP site Model Inter-comparison Project, Alfred-Wegener-Institut, Bremerhaven, 15 pp., https://www.polarprediction.net/fileadmin/user_upload/www.polarprediction.net/Home/Organization/Task_Teams/Atmospheric_Processes/YOPP_Supersite_common_model_output_rev8.pdf (last access: 23 April 2023), 2020.
Taylor, K. E., Durack, P. J., Elkington, M., Guilyardi, E., Hassell, D., Lautenschlager, M., and Stockhause, M.: CMIP6 Participation Guidance for Modelers, https://pcmdi.llnl.gov/CMIP6/Guide/modelers.html (last access: 15 July 2023), 2022.
Tjernström, J.: Visualizing Process-Based Model Evaluation for Numerical Weather Prediction Models, Student thesis, 16 pp., http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187157 (last access: 26 June 2024), 2022.
Unidata: UDUNITS-2, version 2.2.28 (2.2.28), UCAR/Unidata Program Center [software], https://doi.org/10.5065/D6KD1WN0, 2020.
Unidata: NetCDF User's Guide, version 1.1, NetCDF [data set], https://doi.org/10.26024/nw73-vm64, 2023.
Uttal, T., Starkweather, S., Drummond, J. R., Vihma, T., Makshtas, A. P., Darby, L. S., Burkhart, J. F., Cox, C. J., Schmeisser, L. N., Haiden, T., Maturilli, M., Shupe, M. D., De Boer, G., Saha, A., Grachev, A. A., Crepinsek, S. M., Bruhwiler, L., Goodison, B., McArthur, B., Walden, V. P., Dlugokencky, E. J., Persson, P. O. G., Lesins, G., Laurila, T., Ogren, J. A., Stone, R. S., Long, C. N., Sharma, S., Massling, A., Turner, D. D., Stanitski, D. M., Asmi, E., Aurela, M., Skov, H., Eleftheriadis, K., Virkkula, A., Platt, A., Førland, E. J., Iijima, Y., Nielsen, I. E., Bergin, M. H., Candlish, L., Zimov, N. S., Zimov, S. A., O'Neill, N. T., Fogal, F., Kivi, R., Konopleva-Akish, E. A., Verlinde, J., Kustov, V. Y., Vasel, B., Ivakhov, V. M., Viisanen, Y., and Intrieri, J. M.: International Arctic Systems for Observing the Atmosphere: An International Polar Year Legacy Consortium, B. Am. Meteorol. Soc., 97, 1033–1056, https://doi.org/10.1175/BAMS-D-14-00145.1, 2016.
Vannan, S., Downs, R. R., Meier, W., Wilson, B. E., and Gerasimov, I. V.: Data sets are foundational to research. Why don't we cite them?, Eos, 101, https://doi.org/10.1029/2020EO151665, 2020.
Vorosmarty, C., Rawlins, M., Hinzman, L., Francis, J., Serreze, M., Liljedahl, A., McDonald, K., Piasecki, M., and Rich, R.: Opportunities and Challenges in Arctic System Synthesis: A Consensus Report from the Arctic Research Community, New York, 84 pp., https://www.arcus.org/publications/28459 (last access: 15 July 2023), 2018.
Wei, Y., Shrestha, R., Pal, S., Gerken, T., Feng, S., McNelis, J., Singh, D., Thornton, M. M., Boyer, A. G., Shook, M. A., Chen, G., Baier, B. C., Barkley, Z. R., Barrick, J. D., Bennett, J. R., Browell, E. V., Campbell, J. F., Campbell, L. J., Choi, Y., Collins, J., Dobler, J., Eckl, M., Fiehn, A., Fried, A., Digangi, J. P., Barton-Grimley, R., Halliday, H., Klausner, T., Kooi, S., Kostinek, J., Lauvaux, T., Lin, B., McGill, M. J., Meadows, B., Miles, N. L., Nehrir, A. R., Nowak, J. B., Obland, M., O'Dell, C., Fao, R. M. P., Richardson, S. J., Richter, D., Roiger, A., Sweeney, C., Walega, J., Weibring, P., Williams, C. A., Yang, M. M., Zhou, Y., and Davis, K. J.: Atmospheric Carbon and Transport – America (ACT-America) Data Sets: Description, Management, and Delivery, Earth Space Sci., 8, e2020EA001634, https://doi.org/10.1029/2020EA001634, 2021.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., 't Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016.
Wilson, J., Jung, T., Bazile, E., Bromwich, D., Casati, B., Day, J., De Coning, E., Eayrs, C., Grumbine, R., Ioue, J., Khalsa, S. J. S., Kristiansen, J., Lamers, M., Liggett, D., Olsen, S. M., Perovich, D., Renfrew, I. A., Smolyanitsky, V., Svensson, G., Sun, Q., Uttal, T., and Yang, Q.: The YOPP Final Summit: Assessing Past and Forecasting Future Polar Prediction Research, B. Am. Meteorol. Soc., 104, E660–E665, https://doi.org/10.1175/BAMS-D-22-0282.1, 2023.
Wolff, M. A., Isaksen, K., Petersen-Øverleir, A., Ødemark, K., Reitan, T., and Brækkan, R.: Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study, Hydrol. Earth Syst. Sci., 19, 951–967, https://doi.org/10.5194/hess-19-951-2015, 2015.
World Meteorological Organization (WMO): Manual on the Global Telecommunication System: Annex III to the WMO Technical Regulations, 2015, WMO (Series), no. 386, Secretariat of the World Meteorological Organization, Geneva, Switzerland, 197 pp., ISBN 978-92-63-10386-4, https://library.wmo.int/idurl/4/35800 (last access: 26 June 2024), 2020.
Xie, S., McCoy, R. B., Klein, S. A., Cederwall, T., Wiscombe, W. J., Jensen, M. P., Johnson, K. L., Clothiaux, E. E., Gaustad, K. L., Long, C. N., Mather, J. H., McFarlane, S. A., Shi, Y., Golaz, J.-C., Lin, Y., Hall, S. D., McCord, R. A., Palanisamy, G., and Turner, D. D.: CLOUDS AND MORE: ARM Climate Modeling Best Estimate Data: A New Data Product for Climate Studies, B. Am. Meteorol. Soc., 91, 13–20, https://doi.org/10.1175/2009BAMS2891.1, 2010.
Zuo, G., Dou, Y., and Lei, R.: Discrimination Algorithm and Procedure of Snow Depth and Sea Ice Thickness Determination Using Measurements of the Vertical Ice Temperature Profile by the Ice-Tethered Buoys, Sensors, 18, 4162, https://doi.org/10.3390/s18124162, 2018.
Short summary
A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere,...
