Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-411-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-411-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Jan 2023
Data description paper |
| 24 Jan 2023
| 24 Jan 2023
The AntAWS dataset: a compilation of Antarctic automatic weather station observations
College of Geography and Environment, Shandong Normal University,
Jinan 250014, China
Xueying Zhang
College of Geography and Environment, Shandong Normal University,
Jinan 250014, China
Wentao Ning
College of Geography and Environment, Shandong Normal University,
Jinan 250014, China
Matthew A. Lazzara
Antarctic Meteorological Research and Data Center, Space Science and
Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
Minghu Ding
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Carleen H. Reijmer
Institute for Marine and Atmospheric Research Utrecht, Utrecht
University, Utrecht, the Netherlands
Paul C. J. P. Smeets
Institute for Marine and Atmospheric Research Utrecht, Utrecht
University, Utrecht, the Netherlands
Paolo Grigioni
Laboratory for Measurements and Observations for Environment and
Climate, ENEA, 00123 Rome, Italy
Petra Heil
Australian Antarctic Division, Kingston, Tasmania 7050, Australia
Australian Antarctic Program Partnership, University of Tasmania,
Hobart, Tasmania 7001, Australia
Elizabeth R. Thomas
British Antarctic Survey, Cambridge, UK
David Mikolajczyk
Antarctic Meteorological Research and Data Center, Space Science and
Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
Lee J. Welhouse
Antarctic Meteorological Research and Data Center, Space Science and
Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
Linda M. Keller
Antarctic Meteorological Research and Data Center, Space Science and
Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
Zhaosheng Zhai
College of Geography and Environment, Shandong Normal University,
Jinan 250014, China
Yuqi Sun
College of Geography and Environment, Shandong Normal University,
Jinan 250014, China
School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China
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The Cryosphere, 16, 1873–1887, https://doi.org/10.5194/tc-16-1873-2022, https://doi.org/10.5194/tc-16-1873-2022, 2022
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Joanne S. Johnson, Ryan A. Venturelli, Greg Balco, Claire S. Allen, Scott Braddock, Seth Campbell, Brent M. Goehring, Brenda L. Hall, Peter D. Neff, Keir A. Nichols, Dylan H. Rood, Elizabeth R. Thomas, and John Woodward
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Tian R. Tian, Alexander D. Fraser, Noriaki Kimura, Chen Zhao, and Petra Heil
The Cryosphere, 16, 1299–1314, https://doi.org/10.5194/tc-16-1299-2022, https://doi.org/10.5194/tc-16-1299-2022, 2022
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Earth Syst. Sci. Data, 14, 1215–1231, https://doi.org/10.5194/essd-14-1215-2022, https://doi.org/10.5194/essd-14-1215-2022, 2022
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The Cryosphere, 16, 779–798, https://doi.org/10.5194/tc-16-779-2022, https://doi.org/10.5194/tc-16-779-2022, 2022
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Tao Xu, Hongxi Pang, Zhaojun Zhan, Wangbin Zhang, Huiwen Guo, Shuangye Wu, and Shugui Hou
Hydrol. Earth Syst. Sci., 26, 117–127, https://doi.org/10.5194/hess-26-117-2022, https://doi.org/10.5194/hess-26-117-2022, 2022
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Joey J. Voermans, Qingxiang Liu, Aleksey Marchenko, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Takuji Waseda, Takehiko Nose, Tsubasa Kodaira, Jingkai Li, and Alexander V. Babanin
The Cryosphere, 15, 5557–5575, https://doi.org/10.5194/tc-15-5557-2021, https://doi.org/10.5194/tc-15-5557-2021, 2021
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Madison L. Ghiz, Ryan C. Scott, Andrew M. Vogelmann, Jan T. M. Lenaerts, Matthew Lazzara, and Dan Lubin
The Cryosphere, 15, 3459–3494, https://doi.org/10.5194/tc-15-3459-2021, https://doi.org/10.5194/tc-15-3459-2021, 2021
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Yetang Wang, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Shugui Hou, and Cunde Xiao
Earth Syst. Sci. Data, 13, 3057–3074, https://doi.org/10.5194/essd-13-3057-2021, https://doi.org/10.5194/essd-13-3057-2021, 2021
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Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Bert Wouters, Jakob F. Steiner, Emile J. Nieuwstraten, Walter W. Immerzeel, and Michiel R. van den Broeke
The Cryosphere, 15, 2601–2621, https://doi.org/10.5194/tc-15-2601-2021, https://doi.org/10.5194/tc-15-2601-2021, 2021
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We developed a method to estimate the aerodynamic properties of the Greenland Ice Sheet surface using either UAV or ICESat-2 elevation data. We show that this new method is able to reproduce the important spatiotemporal variability in surface aerodynamic roughness, measured by the field observations. The new maps of surface roughness can be used in atmospheric models to improve simulations of surface turbulent heat fluxes and therefore surface energy and mass balance over rough ice worldwide.
Diana Francis, Kyle S. Mattingly, Stef Lhermitte, Marouane Temimi, and Petra Heil
The Cryosphere, 15, 2147–2165, https://doi.org/10.5194/tc-15-2147-2021, https://doi.org/10.5194/tc-15-2147-2021, 2021
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The unexpected September 2019 calving event from the Amery Ice Shelf, the largest since 1963 and which occurred almost a decade earlier than expected, was triggered by atmospheric extremes. Explosive twin polar cyclones provided a deterministic role in this event by creating oceanward sea surface slope triggering the calving. The observed record-anomalous atmospheric conditions were promoted by blocking ridges and Antarctic-wide anomalous poleward transport of heat and moisture.
Shugui Hou, Wangbin Zhang, Ling Fang, Theo M. Jenk, Shuangye Wu, Hongxi Pang, and Margit Schwikowski
The Cryosphere, 15, 2109–2114, https://doi.org/10.5194/tc-15-2109-2021, https://doi.org/10.5194/tc-15-2109-2021, 2021
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We present ages for two new ice cores reaching bedrock, from the Zangser Kangri (ZK) glacier in the northwestern Tibetan Plateau and the Shulenanshan (SLNS) glacier in the western Qilian Mountains. We estimated bottom ages of 8.90±0.57/0.56 ka and 7.46±1.46/1.79 ka for the ZK and SLNS ice core respectively, constraining the time range accessible by Tibetan ice cores to the Holocene.
Ling Fang, Theo M. Jenk, Thomas Singer, Shugui Hou, and Margit Schwikowski
The Cryosphere, 15, 1537–1550, https://doi.org/10.5194/tc-15-1537-2021, https://doi.org/10.5194/tc-15-1537-2021, 2021
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The interpretation of the ice-core-preserved signal requires a precise chronology. Radiocarbon (14C) dating of the water-insoluble organic carbon (WIOC) fraction has become an important dating tool. However, this method is restricted by the low concentration in the ice. In this work, we report first 14C dating results using the dissolved organic carbon (DOC) fraction. The resulting ages are comparable in both fractions, but by using the DOC fraction the required ice mass can be reduced.
Elizabeth Ruth Thomas, Guisella Gacitúa, Joel B. Pedro, Amy Constance Faith King, Bradley Markle, Mariusz Potocki, and Dorothea Elisabeth Moser
The Cryosphere, 15, 1173–1186, https://doi.org/10.5194/tc-15-1173-2021, https://doi.org/10.5194/tc-15-1173-2021, 2021
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Here we present the first-ever radar and ice core data from the sub-Antarctic islands of Bouvet Island, Peter I Island, and Young Island. These islands have the potential to record past climate in one of the most data-sparse regions on earth. Despite their northerly location, surface melting is generally low, and the upper layer of the ice at most sites is undisturbed. We estimate that a 100 m ice core drilled on these islands could capture climate over the past 100–200 years.
Eric Keenan, Nander Wever, Marissa Dattler, Jan T. M. Lenaerts, Brooke Medley, Peter Kuipers Munneke, and Carleen Reijmer
The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, https://doi.org/10.5194/tc-15-1065-2021, 2021
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Snow density is required to convert observed changes in ice sheet volume into mass, which ultimately drives ice sheet contribution to sea level rise. However, snow properties respond dynamically to wind-driven redistribution. Here we include a new wind-driven snow density scheme into an existing snow model. Our results demonstrate an improved representation of snow density when compared to observations and can therefore be used to improve retrievals of ice sheet mass balance.
Joey J. Voermans, Jean Rabault, Kirill Filchuk, Ivan Ryzhov, Petra Heil, Aleksey Marchenko, Clarence O. Collins III, Mohammed Dabboor, Graig Sutherland, and Alexander V. Babanin
The Cryosphere, 14, 4265–4278, https://doi.org/10.5194/tc-14-4265-2020, https://doi.org/10.5194/tc-14-4265-2020, 2020
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In this work we demonstrate the existence of an observational threshold which identifies when waves are most likely to break sea ice. This threshold is based on information from two recent field campaigns, supplemented with existing observations of sea ice break-up. We show that both field and laboratory observations tend to converge to a single quantitative threshold at which the wave-induced sea ice break-up takes place, which opens a promising avenue for operational forecasting models.
Baojuan Huai, Michiel R. van den Broeke, and Carleen H. Reijmer
The Cryosphere, 14, 4181–4199, https://doi.org/10.5194/tc-14-4181-2020, https://doi.org/10.5194/tc-14-4181-2020, 2020
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This study presents the surface energy balance (SEB) of the Greenland Ice Sheet (GrIS) using a SEB model forced with observations from automatic weather stations (AWSs). We correlate ERA5 with AWSs to show a significant positive correlation of GrIS summer surface temperature and melt with the Greenland Blocking Index and weaker and opposite correlations with the North Atlantic Oscillation. This analysis may help explain melting patterns in the GrIS with respect to circulation anomalies.
Marie G. P. Cavitte, Quentin Dalaiden, Hugues Goosse, Jan T. M. Lenaerts, and Elizabeth R. Thomas
The Cryosphere, 14, 4083–4102, https://doi.org/10.5194/tc-14-4083-2020, https://doi.org/10.5194/tc-14-4083-2020, 2020
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Surface mass balance (SMB) and surface air temperature (SAT) are correlated at the regional scale for most of Antarctica, SMB and δ18O. Areas with low/no correlation are where wind processes (foehn, katabatic wind warming, and erosion) are sufficiently active to overwhelm the synoptic-scale snow accumulation. Measured in ice cores, the link between SMB, SAT, and δ18O is much weaker. Random noise can be removed by core record averaging but local processes perturb the correlation systematically.
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Short summary
Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Here we construct a new database of Antarctic automatic weather station (AWS) meteorological...
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