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  • 1
    Publication Date: 2024-04-05
    Description: The Arctic Ocean is an exceptional environment where hydrosphere, cryosphere, and atmosphere are closely interconnected. Changes in sea-ice extent and thickness affect ocean currents, as well as moisture and heat exchange with the atmosphere. Energy and water fluxes impact the formation and melting of sea ice and snow cover. Here, we present a comprehensive statistical analysis of the stable water isotopes of various hydrological components in the central Arctic obtained during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020, including the understudied Arctic winter. Our dataset comprises >2200 water, snow, and ice samples. Snow had the most depleted and variable isotopic composition, with δ18O (–16.3‰) increasing consistently from surface (–22.5‰) to bottom (–9.7‰) of the snowpack, suggesting that snow metamorphism and wind-induced transport may overprint the original precipitation isotope values. In the Arctic Ocean, isotopes also help to distinguish between different sea-ice types, and whether there is a meteoric contribution. The isotopic composition and salinity of surface seawater indicated relative contributions from different freshwater sources: lower δ18O (approximately –3.0‰) and salinities were observed near the eastern Siberian shelves and towards the center of the Transpolar Drift due to river discharge. Higher δ18O (approximately –1.5‰) and salinities were associated with an Atlantic source when the RV Polarstern crossed the Gakkel Ridge into the Nansen Basin. These changes were driven mainly by the shifts within the Transpolar Drift that carried the Polarstern across the Arctic Ocean. Our isotopic analysis highlights the importance of investigating isotope fractionation effects, for example, during sea-ice formation and melting. A systematic full-year sampling for water isotopes from different components strengthens our understanding of the Arctic water cycle and provides crucial insights into the interaction between atmosphere, sea ice, and ocean and their spatio-temporal variations during MOSAiC.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 2
    Publication Date: 2024-04-19
    Description: The stable water isotopic composition in firn and ice cores provides valuable information on past climatic conditions. Because of uneven accumulation and post‐depositional modifications on local spatial scales up to hundreds of meters, time series derived from adjacent cores differ significantly and do not directly reflect the temporal evolution of the precipitated snow isotopic signal. Hence, a characterization of how the isotopic profile in the snow develops is needed to reliably interpret the isotopic variability in firn and ice cores. By combining digital elevation models of the snow surface and repeated high‐resolution snow sampling for stable water isotope measurements of a transect at the East Greenland Ice‐core Project campsite on the Greenland Ice Sheet, we are able to visualize the buildup and post‐depositional changes of the upper snowpack across one summer season. To this end, 30 cm deep snow profiles were sampled on six dates at 20 adjacent locations along a 40 m transect. Near‐daily photogrammetry provided snow height information for the same transect. Our data shows that erosion and redeposition of the original snowfall lead to a complex stratification in the δ〈sup〉18〈/sup〉O signature. Post‐depositional processes through vapor‐snow exchange affect the near surface snow with d‐excess showing a decrease in surface and near‐surface layers. Our data suggests that the interplay of stratigraphic noise, accumulation intermittency, and local post‐depositional processes form the proxy signal in the upper snowpack.
    Description: Plain Language Summary: We study the process of the formation of the stable water isotope signal in surface snow on the Greenland Ice Sheet to better understand temperature information which is stored as a climate proxy in snow and ice. Our data consist of high‐resolution surface topography information illustrating the timing and location of snowfall, erosion, and redeposition along a transect of 40 m, as well as stable water isotope records of the upper 30 cm of the snowpack sampled biweekly on 20 positions at the same 40 m long transect. The data cover a 2‐month period during the summer of 2019. We find that the isotopic composition shows spatial variability of layers with low and high values, presumably winter and summer layers. We further observe that prevailing surface structures, such as dunes, influence the snow deposition and contribute to the found variable structure of the climatic information. Eventually, snow accumulation alone cannot explain all of the observed patterns in the isotopic data which is likely related to exchange processes between the snow and the atmosphere which modify the signal in the snow column after deposition.
    Description: Key Points: Combining digital elevation models and repeated snow sampling reveals the heterogeneous buildup of δ〈sup〉18〈/sup〉O signal in the snow column. Surface structures (stratigraphic noise) substantially contribute to internal heterogeneity in δ〈sup〉18〈/sup〉O signature in the upper snowpack. Proxy signals are formed in the surface layer by local processes, advected downwards with limited post‐depositional influences below 10 cm.
    Description: Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661
    Description: A. P. Møller Foundation, University of Copenhagen
    Description: US National Science Foundation, Office of Polar Programs
    Description: Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research
    Description: National Institute of Polar Research and Arctic Challenge for Sustainability
    Description: University of Bergen
    Description: Trond Mohn Foundation
    Description: Swiss National Science Foundation
    Description: French Polar Institute Paul‐Emile Victor, Institute for Geosciences and Environmental Research
    Description: University of Manitoba
    Description: Chinese Academy of Sciences
    Description: Beijing Normal University
    Description: https://doi.org/10.1594/PANGAEA.954944
    Description: https://doi.org/10.1594/PANGAEA.954945
    Description: https://doi.org/10.1594/PANGAEA.951583
    Description: https://doi.org/10.1594/PANGAEA.925618
    Description: https://doi.org/10.1594/PANGAEA.928827
    Description: https://www.agisoft.com/downloads/installer/
    Keywords: ddc:551 ; proxy ; Greenland ; isotopes ; structure‐from‐motion ; snow accumulation ; ice core
    Language: English
    Type: doc-type:article
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  • 3
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    In:  Supplement to: Laepple, Thomas; Hörhold, Maria; Münch, Thomas; Freitag, Johannes; Wegner, Anna; Kipfstuhl, Sepp (2016): Layering of surface snow and firn at Kohnen Station, Antarctica – noise or seasonal signal? Journal of Geophysical Research-Earth Surface, 23 pp, https://doi.org/10.1002/2016JF003919
    Publication Date: 2023-03-27
    Description: The density of firn is an important property for monitoring and modeling the ice sheet as well as to model the pore close-off and thus to interpret ice core-based greenhouse gas records. One feature, which is still in debate, is the potential existence of an annual cycle of firn density in low-accumulation regions. Several studies describe or assume seasonally successive density layers, horizontally evenly distributed, as seen in radar data. On the other hand, high-resolution density measurements on firn cores in Antarctica and Greenland showed no clear seasonal cycle in the top few meters. A major caveat of most existing snow-pit and firn-core based studies is that they represent one vertical profile from a laterally heterogeneous density field. To overcome this, we created an extensive dataset of horizontal and vertical density data at Kohnen Station, Dronning Maud Land on the East Antarctic Plateau. We drilled and analyzed three 90 m long firn cores as well as 160 one meter long vertical profiles from two elongated snow trenches to obtain a two dimensional view of the density variations. The analysis of the 45 m wide and 1 m deep density fields reveals a seasonal cycle in density. However, the seasonality is overprinted by strong stratigraphic noise, making it invisible when analyzing single firn cores. Our density dataset extends the view from the local ice-core perspective to a hundred meter scale and thus supports linking spatially integrating methods such as radar and seismic studies to ice and firn cores.
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 4
    Publication Date: 2023-03-27
    Keywords: Density, mass density; DEPTH, ice/snow; Dielectric profiling, DEP; Distance, relative, X; Dronning Maud Land, Antarctica; Kohnen_based; Kohnen_trench1; Kohnen Station; Profile; SNOWTRE; Snow trench
    Type: Dataset
    Format: text/tab-separated-values, 32511 data points
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  • 5
    Publication Date: 2023-03-27
    Keywords: Density, mass density; DEPTH, ice/snow; Dielectric profiling, DEP; Distance, relative, X; Dronning Maud Land, Antarctica; Kohnen_based; Kohnen_trench2; Kohnen Station; Profile; SNOWTRE; Snow trench
    Type: Dataset
    Format: text/tab-separated-values, 42492 data points
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  • 6
    Publication Date: 2023-01-30
    Description: This dataset contains accumulation rates as reconstructed from ice-penetrating radar. The radar data were acquired during a traverse in 2007 and are from a 250MHz ground-penetrating radar. Accumulation rates were reconstructed using an inverse method where the depth of the radar layers inform on past accumulation rates. The age of the layers were assigned from a previous study by Karlsson et al., 2016 (doi:10.3389/feart.2016.00097).
    Keywords: Accumulation; Accumulation rate in ice equivalent; Distance; GPR; Greenland; Ground-penetrating radar; LATITUDE; LONGITUDE; NorthCentral_Greenland; North Greenland
    Type: Dataset
    Format: text/tab-separated-values, 12152 data points
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  • 7
    Publication Date: 2023-01-30
    Description: This dataset contains accumulation rates as reconstructed from ice-penetrating radar. The radar data were acquired during a traverse in 2015 and are from a 250MHz ground-penetrating radar. Accumulation rates were reconstructed using an inverse method where the depth of the radar layers inform on past accumulation rates. The age of the layers were assigned from a previous study by Karlsson et al., 2016 (doi:10.3389/feart.2016.00097).
    Keywords: Accumulation; Accumulation rate in ice equivalent; Distance; GPR; Greenland; Ground-penetrating radar; LATITUDE; LONGITUDE; NorthCentral_Greenland; North Greenland
    Type: Dataset
    Format: text/tab-separated-values, 11746 data points
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  • 8
    Publication Date: 2023-01-30
    Description: Surface mass balances of polar ice sheets are essential to estimate the contribution of ice sheets to sea level rise. Uncertain snow and firn densities lead to significant uncertainties in surface mass balances, especially in the interior regions of the ice sheets, such as the East Antarctic Plateau (EAP). Robust field measurements of surface snow density are sparse and challenging due to local noise. Here, we present a snow density dataset from an overland traverse in austral summer 2016/17 on the Dronning Maud Land plateau. The sampling strategy using 1 m carbon fiber tubes covered various spatial scales, as well as a high-resolution study in a trench at 79°S, 30°E. The 1 m snow density has been derived volumetrically, and vertical snow profiles have been measured using a core-scale microfocus X-ray computer tomograph. With an error of less than 2%, our method provides higher precision than other sampling devices of smaller volume. With four spatially independent snow profiles per location, we reduce the local noise and derive a representative 1 m snow density with an error of the mean of less than 1.5%. Assessing sampling methods used in previous studies, we find the highest horizontal variability in density in the upper 0.3 m and therefore recommend the 1 m snow density as a robust measure of surface snow density in future studies. The average 1 m snow density across the EAP is 355 kg m-3, which we identify as representative surface snow density between Kohnen Station and Dome Fuji. We cannot detect a temporal trend caused by the temperature increase over the last 2 decades. A difference of more than 10% to the density of 320 kg m-3 suggested by a semiempirical firn model for the same region indicates the necessity for further calibration of surface snow density parameterizations. Our data provide a solid baseline for tuning the surface snow density parameterizations for regions with low accumulation and low temperatures like the EAP.
    Keywords: Antarctica; ANT-Land_2016_COFI; ANT-Land_2016_COFI_12; ANT-Land_2016_COFI_16; ANT-Land_2016_COFI_19; ANT-Land_2016_COFI_21; ANT-Land_2016_COFI_22; ANT-Land_2016_COFI_23; ANT-Land_2016_COFI_24; ANT-Land_2016_COFI_25; ANT-Land_2016_COFI_26; ANT-Land_2016_COFI_27; ANT-Land_2016_COFI_4; ANT-Land_2016_COFI_44; ANT-Land_2016_COFI_56; ANT-Land_2016_COFI_60; ANT-Land_2016_COFI_64; ANT-Land_2016_COFI_69; ANT-Land_2016_COFI_71; ANT-Land_2016_COFI_73; ANT-Land_2016_COFI_75; ANT-Land_2016_COFI_77; ANT-Land_2016_COFI_79; ANT-Land_2016_COFI_8; DATE/TIME; density; Density, snow, mean; Density, snow, standard deviation; DML; East Antarctica; ELEVATION; Event label; Kohnen; Kohnen Station; LATITUDE; Location; LONGITUDE; Number of profiles; snow; SNOW; Snow/ice sample; Volumetric measurement
    Type: Dataset
    Format: text/tab-separated-values, 88 data points
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  • 9
    Publication Date: 2023-05-15
    Description: This dataset presents the first fully continuous monitoring of water vapour isotopic composition at Neumayer Station III, Antarctica, for two years, from February 2017 to January 2019. In January 2017, a PICARRO L2140-i cavity ring-down spectroscopy analyser was installed in a laboratory room at Neumayer Station. The Picarro analyser runs continuously and measures the injected water vapour content and its isotopic composition approximately every two seconds. For our analyses, the data are reported as hourly mean values.
    Keywords: Antarctica; Cavity ring-down spectrometer, Picarro, L2140i; Continuous isotope monitoring; DATE/TIME; Deuterium excess; Deuterium excess, standard deviation; Dronning Maud Land, Antarctica; Georg von Neumayer; GVN; HEIGHT above ground; Monitoring station; MONS; Neumayer_based; NEUMAYER III; Number of observations; Paleoclimate; water stable isotopes; water vapour; Water vapour mixing ratio; Water vapour mixing ratio, error; δ18O, water; δ18O, water, standard deviation; δ Deuterium, water vapour; δ Deuterium, water vapour, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 164944 data points
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  • 10
    Publication Date: 2023-05-27
    Description: Stable oxygen isotopes (δ18O) were measured on discrete samples using Cavity Ring-Down spectroscopy with an L2120-i and L2130-i by Picarro Instruments. The δ18O concentration with depth is provided with a precision of better than 0.1 per mil. Using raw, un-calibrated dielectrical profiling (DEP) data from the field (NEEM Set-up), volcanic tie points were derived. Together with the smoothed density data and the isotopic composition the record was dated by layer counting. The annual mean values of δ18O were obtained based on this dating.
    Keywords: Age; AGE; AWI_Envi; AWI_Glac; AWI Arctic Land Expedition; B18_2012; Calculated, annual mean; FIRNC; firn core; Firn corer; Glaciology @ AWI; GL-Land_2012_NEEM12_NGT12; Greenland; ngt14C93.2-cont; North Greenland Traverse; Polar Terrestrial Environmental Systems @ AWI; stable water isotopes; δ18O, water
    Type: Dataset
    Format: text/tab-separated-values, 294 data points
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