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  • 1
    Publication Date: 2020-01-17
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
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  • 2
    Publication Date: 2020-01-17
    Type: Dataset
    Format: text/tab-separated-values, 2779 data points
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  • 3
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    In:  Supplement to: Picard, Ghislain; Libois, Quentin; Arnaud, Laurent; Vérin, G; Dumont, Marie (2016): Development and calibration of an automatic spectral albedometer to estimate near-surface snow SSA time series. The Cryosphere, 10(3), 1297-1316, https://doi.org/10.5194/tc-10-1297-2016
    Publication Date: 2020-01-17
    Description: Spectral albedo has been measured at Dome C since December 2012 in the visible and near infrared (400 - 1050 nm) at sub-hourly resolution using a home-made spectral radiometer. Superficial specific surface area (SSA) has been estimated by fitting the observed albedo spectra to the analytical Asymptotic Approximation Radiative Transfer theory (AART). The dataset includes fully-calibrated albedo and SSA that pass several quality checks as described in the companion article. Only data for solar zenith angles less than 75° have been included, which theoretically spans the period October-March. In addition, to correct for residual errors still affecting data after the calibration, especially at the solar zenith angles higher than 60°, we produced a higher quality albedo time-series as follows: In the SSA estimation process described in the companion paper, a scaling coefficient A between the observed albedo and the theoretical model predictions was introduced to cope with these errors. This coefficient thus provides a first order estimate of the residual error. By dividing the albedo by this coefficient, we produced the "scaled fully-calibrated albedo". We strongly recommend to use the latter for most applications because it generally remains in the physical range 0-1. The former albedo is provided for reference to the companion paper and because it does not depend on the SSA estimation process and its underlying assumptions.
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 4
    Publication Date: 2020-01-17
    Type: Dataset
    Format: text/tab-separated-values, 3550 data points
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  • 5
    Publication Date: 2017-09-25
    Description: The oldest ice core records are obtained from the East Antarctic plateau. Water stable isotopes records are key for reconstructions of past climatic conditions both over the ice sheet and at the evaporation source. The accuracy of such climate reconstructions crucially depends on the knowledge of all the processes affecting the water vapour, precipitation and snow isotopic composition. Atmospheric fractionation processes are well understood and can be integrated in Rayleigh distillation and complex isotope enabled climate models. However, a comprehensive quantitative understanding of processes potentially altering the snow isotopic composition after the deposition is still missing, especially for exchanges between vapour and snow. In low accumulation sites such as found on the East Antarctic Plateau, these poorly constrained processes are especially likely to play a significant role. This limits the interpretation of isotopic composition from ice core records, specifically at short time scales. Here, we combine observations of isotopic composition in the vapour, the precipitation, the surface snow and the buried snow from various sites of the East Antarctic Plateau. At the seasonal scale, we highlight a significant impact of metamorphism on surface snow isotopic signal compared to the initial precipitation isotopic signal. In particular, in summer, exchanges of water molecules between vapour and snow are driven by the sublimation/condensation cycles at the diurnal scale. Using highly resolved isotopic composition profiles from pits in five East Antarctic sites, we identify a common 20 cm cycle which cannot be attributed to the seasonal variability of precipitation. Altogether, the smaller range of isotopic compositions observed in the buried and in the surface snow compared to the precipitation, and also the reduced slope between surface snow isotopic composition and temperature compared to precipitation, constitute evidences of post-deposition processes affecting the variability of the isotopic composition in the snow pack. To reproduce these processes in snow-models is crucial to understand the link between snow isotopic composition and climatic conditions and to improve the interpretation of isotopic composition as a paleoclimate proxy.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 6
    Publication Date: 2018-09-11
    Description: The oldest ice core records are obtained from the East Antarctic Plateau. Water isotopes are key proxies to reconstructing past climatic conditions over the ice sheet and at the evaporation source. The accuracy of climate reconstructions depends on knowledge of all processes affecting water vapour, precipitation and snow isotopic compositions. Fractionation processes are well understood and can be integrated in trajectory-based Rayleigh distillation and isotope-enabled climate models. However, a quantitative understanding of processes potentially altering snow isotopic composition after deposition is still missing. In low-accumulation sites, such as those found in East Antarctica, these poorly constrained processes are likely to play a significant role and limit the interpretability of an ice core's isotopic composition. By combining observations of isotopic composition in vapour, precipitation, surface snow and buried snow from Dome C, a deep ice core site on the East Antarctic Plateau, we found indications of a seasonal impact of metamorphism on the surface snow isotopic signal when compared to the initial precipitation. Particularly in summer, exchanges of water molecules between vapour and snow are driven by the diurnal sublimation–condensation cycles. Overall, we observe in between precipitation events modification of the surface snow isotopic composition. Using high-resolution water isotopic composition profiles from snow pits at five Antarctic sites with different accumulation rates, we identified common patterns which cannot be attributed to the seasonal variability of precipitation. These differences in the precipitation, surface snow and buried snow isotopic composition provide evidence of post-deposition processes affecting ice core records in low-accumulation areas.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
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  • 7
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: Vegetation phenology is affected by climate change and in turn feeds back on climate by affecting the annual carbon uptake by vegetation. To quantify the impact of phenology on terrestrial carbon fluxes, we calibrate a bud-burst model and embed it in the Sheffield dynamic global vegetation model (SDGVM) in order to perform carbon budget calculations.Bud-burst dates derived from the VEGETATION sensor onboard the SPOT-4 satellite are used to calibrate a range of bud-burst models. This dataset has been recently developed using a new methodology based on the normalized difference water index, which is able to distinguish snowmelt from the onset of vegetation activity after winter. After calibration, a simple spring warming model was found to perform as well as more complex models accounting for a chilling requirement, and hence it was used for the carbon flux calculations. The root mean square difference (RMSD) between the calibrated model and the VEGETATION dataset was 6.5 days, and was 6.9 days between the calibrated model and independent ground observations of bud-burst available at nine locations over Siberia.The effects of bud-burst model uncertainties on the carbon budget were evaluated using the SDGVM. The 6.5 days RMSD in the bud-burst date (a 6% variation in the growing season length), treated as a random noise, translates into about 41 g cm−2 yr−1 in net primary production (NPP), which corresponds to 8% of the mean NPP. This is a moderate impact and suggests the calibrated model is accurate enough for carbon budget calculations.In addition to random differences between the calibrated model and VEGETATION data, systematic errors between the calibrated bud-burst model and true ground behaviour may occur, because of bias in the temperature dataset or because the bud-burst detected by VEGETATION is because of some other phenological indicator. A systematic error of 1 day in bud-burst translates into a 10 g cm−2 yr−1 error in NPP (about 2%). Based on the limited available ground data, any systematic error because of the use of VEGETATION data should not lead to significant errors in the calculated carbon flux. In contrast, widely used methods based on the normalized difference vegetation index from the advanced very high resolution radiometer satellite are likely to confuse snowmelt and vegetation greening, leading to errors of up to 15 days in bud-burst date, with consequent large errors in carbon flux calculations.
    Type of Medium: Electronic Resource
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  • 8
    Publication Date: 2019-07-13
    Description: This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T(sub B)): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated T(sub B) at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (D(sub max)) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (D(sub max)), assuming non-sticky spheres for the DMRT models. Simulated T(sub B) sensitivity analysis using the same inputs shows relatively consistent T(sub B) behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated T(sub B). Results using in-situ grain size measurements (SSA or D(sub max), depending on the model) give a mean T(sub B) RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than D(sub max) measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.
    Keywords: Meteorology and Climatology; Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN41625 , Remote Sensing of Environment (ISSN 0034-4257) (e-ISSN 1879-0704); 190; 247-259
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  • 9
    Publication Date: 2019-07-13
    Description: Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer).
    Keywords: Earth Resources and Remote Sensing
    Type: GSFC-E-DAA-TN15169 , International Geoscience and Remote Sensing Symposium (IGRRS); Jul 13, 2014 - Jul 18, 2014; Quebec; Canada
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  • 10
    Publication Date: 2019-07-13
    Description: The Antarctic Plateau is a promising site to monitor microwave radiometers' drift, and to inter-calibrate microwave radiometers, especially 1.4 GigaHertz (L-band) radiometers on board the Soil Moisture and Ocean Salinity (SMOS), and AquariusSAC-D missions. The Plateau is a thick ice cover, thermally stable in depth, with large dimensions, and relatively low heterogeneities. In addition, its high latitude location in the Southern Hemisphere enables frequent observations by polar-orbiting satellites, and no contaminations by radio frequency interference. At Dome C (75S, 123E), on the Antarctic Plateau, the substantial amount of in-situ snow measurements available allows us to interpret variations in space-borne microwave brightness temperature (TB) (e.g. Macelloni et al., 2007, 2013, Brucker et al., 2011, Champollion et al., 2013). However, to analyze the observations from the Aquarius radiometers, whose sensitivity is 0.15 K, the stability of the snow layers near the surface that are most susceptible to rapidly change needs to be precisely assessed. This study focuses on the spatial and temporal variations of the Aquarius TB over the Antarctic Plateau, and at Dome C in particular, to highlight the impact of snow surface metamorphism on the TB observations at L-band.
    Keywords: Earth Resources and Remote Sensing; Geophysics
    Type: GSFC-E-DAA-TN15158 , International Geoscience and Remote Sensing Symposium (IGARSS 2014); Jul 13, 2014 - Jul 18, 2014; Quebec, Canada; United States|Canadian Symposium on Remote Sensing (CSRS); Jul 13, 2014 - Jul 18, 2014; Quebec, Canada; United States
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