24 results on '"Dadic, R."'
Search Results
2. The Influence of Snow on Antarctic Sea Ice
- Author
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Dadic, R., Martin, J., Pirazzini, R., Schneebeli, M., Anderson, B., Cheng, B., Heil, P., Vargo, L., Jaggi, M., Leonard, G., Light, B., Rack, W., Smith, I., Wigmore, O., and Webster, M.
- Abstract
Snow cover affects the variability of the physical properties of sea ice. The snow’s unique thermal and optical properties govern the mass and energy fluxes in the sea ice system. They are important for sea ice evolution, energy exchanges between the ocean and the atmosphere, and light availability for ecosystems below the sea ice. Furthermore, snow significantly impacts remote sensing retrievals, especially for sea ice thickness. Yet, data on the physical properties of snow and its effects on sea ice are extremely limited, especially in Antarctica. This leads to large uncertainties in the coupling of climate feedback and results in significant biases in model representations of the sea ice cover. During our field campaign from October-December 2022 in McMurdo Sound, we quantitatively investigated the physical properties of snow on Antarctic sea ice, following the same protocols used during the MOSAiC expedition. The season’s unique sea ice conditions provided the ideal laboratory to study a range of snow conditions and to differentiate between sea ice and snow drivers for the atmosphere-sea ice-ocean system. Our set of snow measurements on sea ice, unprecedented in Antarctica, includes ground snow/ice measurements, automatic weather and radiation stations, and drone-based measurements. These extensive measurements made it possible to capture the physical properties of snow and their spatial variability and simultaneously measure the different components of the energy balance at varying spatial scales. We will use this dataset to improve our understanding of snow's role in the Antarctic sea ice system. , The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)
- Published
- 2023
- Full Text
- View/download PDF
3. Response of snow albedo to experimental additions of bushfire aerosols and algae
- Author
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Dadic, R., Novis, P., Hunt, J., Lauren, V., Purdie, H., Fuchs, P., Winter-Billington, A., Jolly, B., Anderson, B., Naeher, S., and Winton, H.
- Abstract
The exposure of New Zealand’s snow and ice fields to significant inputs of aerosols from Australian bushfires makes NZ an ideal site to study albedo's physical and biological controls. Under projected scenarios of increased frequency and severity of Australian droughts and bushfires, snow tainted by Australian aerosols will become increasingly common in NZ. Likewise, snow/ice algae are expected to respond to climate warming by increasing abundance, biomass, and distribution. Because the bushfire season coincides with the melt period, when snow algae undertake vegetative growth with the availability of liquid water, establishing the effect of bushfire aerosols and algae, and their interactions, on the melting of glacial systems is urgently needed. We present albedo measurements from two controlled field experiments. The experiments at Tasman Saddle used treatments applied to plots with a full factorial experimental design to determine the effects of aerosols on albedo and the resulting snow melt. The experiment involved adding dry aerosols at five treatment levels: 0, 12.5%, 25%, 50%, and 100% of the maximum deposited 2019/20 dust concentrations. At Canyon Creek, snow algae sourced from a nearby site was applied as an additional treatment at five levels. The albedo effect of the treatments was measured with a scanning spectroradiometer. We determined snow melt at each plot using small-scale DEMs before and after the dust/algae was applied and measured physical snow properties. This project will use drones and remote sensing to quantify the effects of aerosol deposition and algal growth on glacier mass balance at the catchment scale., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)
- Published
- 2023
- Full Text
- View/download PDF
4. Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
- Author
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Stroeve, J., Nandan, V., Willatt, R., Dadic, R., Rotosky, P., Gallagher, M., Mallett, R., Barrett, A., Hendricks, S., Tonboe, R., Serreze, M., Thielke, L., Spreen, G., Newman, T., Yackel, J., Ricker, R., Tsamados, M., Macfarlane, A., Hannula, H.-R., Schneebeli, M., Stroeve, J., Nandan, V., Willatt, R., Dadic, R., Rotosky, P., Gallagher, M., Mallett, R., Barrett, A., Hendricks, S., Tonboe, R., Serreze, M., Thielke, L., Spreen, G., Newman, T., Yackel, J., Ricker, R., Tsamados, M., Macfarlane, A., Hannula, H.-R., and Schneebeli, M.
- Abstract
Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.
- Published
- 2022
5. Evaluating the transferability of empirical models of debris-covered glacier melt
- Author
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Winter-Billington, A., primary, Moore, R. D., additional, and Dadic, R., additional
- Published
- 2020
- Full Text
- View/download PDF
6. Temperature‐Driven Bubble Migration as Proxy for Internal Bubble Pressures and Bubble Trapping Function in Ice Cores
- Author
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Dadic, R., primary, Schneebeli, M., additional, Wiese, M., additional, Bertler, N. A. N., additional, Salamatin, A. N., additional, Theile, T. C., additional, Alley, R. B., additional, and Lipenkov, V. Ya., additional
- Published
- 2019
- Full Text
- View/download PDF
7. The Ross Sea Dipole-temperature, snow accumulation and sea ice variability in the Ross Sea region, Antarctica, over the past 2700 years
- Author
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Bertler, N, Conway, H, Dahl-Jensen, D, Emanuelsson, D, Winstrup, M, Vallelonga, P, Lee, J, Brook, E, Severinghaus, J, Fudge, T, Keller, E, Troy Baisden, W, Hindmarsh, R, Neff, P, Blunier, T, Edwards, R, Mayewski, P, Kipfstuhl, S, Buizert, C, Canessa, S, Dadic, R, Kjær, H, Kurbatov, A, Zhang, D, Waddington, E, Baccolo, G, Beers, T, Brightley, H, Carter, L, Clemens-Sewall, D, Ciobanu, V, Delmonte, B, Eling, L, Ellis, A, Ganesh, S, Golledge, N, Haines, S, Handley, M, Hawley, R, Hogan, C, Johnson, K, Korotkikh, E, Lowry, D, Mandeno, D, Mckay, R, Menking, J, Naish, T, Noerling, C, Ollive, A, Orsi, A, Proemse, B, Pyne, A, Pyne, R, Renwick, J, Scherer, R, Semper, S, Simonsen, M, Sneed, S, Steig, E, Tuohy, A, Ulayottil Venugopal, A, Valero-Delgado, F, Venkatesh, J, Wang, F, Wang, S, Winski, D, Holly, W, Whiteford, A, Xiao, C, Yang, J, Zhang, X, Bertler, Nancy A. N., Conway, Howard, Dahl-Jensen, Dorthe, Emanuelsson, Daniel B., Winstrup, Mai, Vallelonga, Paul T., Lee, James E., Brook, Ed J., Severinghaus, Jeffrey P., Fudge, Taylor J., Keller, Elizabeth D., Troy Baisden, W., Hindmarsh, Richard C. A., Neff, Peter D., Blunier, Thomas, Edwards, Ross, Mayewski, Paul A., Kipfstuhl, Sepp, Buizert, Christo, Canessa, Silvia, Dadic, Ruzica, Kjær, Helle A., Kurbatov, Andrei, Zhang, Dongqi, Waddington, Edwin D., Baccolo, Giovanni, Beers, Thomas, Brightley, Hannah J., Carter, Lionel, Clemens-Sewall, David, Ciobanu, Viorela G., Delmonte, Barbara, Eling, Lukas, Ellis, Aja, Ganesh, Shruthi, Golledge, Nicholas R., Haines, Skylar, Handley, Michael, Hawley, Robert L., Hogan, Chad M., Johnson, Katelyn M., Korotkikh, Elena, Lowry, Daniel P., Mandeno, Darcy, McKay, Robert M., Menking, James A., Naish, Timothy R., Noerling, Caroline, Ollive, Agathe, Orsi, Anaïs, Proemse, Bernadette C., Pyne, Alexander R., Pyne, Rebecca L., Renwick, James, Scherer, Reed P., Semper, Stefanie, Simonsen, Marius, Sneed, Sharon B., Steig, Eric J., Tuohy, Andrea, Ulayottil Venugopal, Abhijith, Valero-Delgado, Fernando, Venkatesh, Janani, Wang, Feitang, Wang, Shimeng, Winski, Dominic A., Holly, Winton, Whiteford, Arran, Xiao, Cunde, Yang, Jiao, Zhang, Xin, Bertler, N, Conway, H, Dahl-Jensen, D, Emanuelsson, D, Winstrup, M, Vallelonga, P, Lee, J, Brook, E, Severinghaus, J, Fudge, T, Keller, E, Troy Baisden, W, Hindmarsh, R, Neff, P, Blunier, T, Edwards, R, Mayewski, P, Kipfstuhl, S, Buizert, C, Canessa, S, Dadic, R, Kjær, H, Kurbatov, A, Zhang, D, Waddington, E, Baccolo, G, Beers, T, Brightley, H, Carter, L, Clemens-Sewall, D, Ciobanu, V, Delmonte, B, Eling, L, Ellis, A, Ganesh, S, Golledge, N, Haines, S, Handley, M, Hawley, R, Hogan, C, Johnson, K, Korotkikh, E, Lowry, D, Mandeno, D, Mckay, R, Menking, J, Naish, T, Noerling, C, Ollive, A, Orsi, A, Proemse, B, Pyne, A, Pyne, R, Renwick, J, Scherer, R, Semper, S, Simonsen, M, Sneed, S, Steig, E, Tuohy, A, Ulayottil Venugopal, A, Valero-Delgado, F, Venkatesh, J, Wang, F, Wang, S, Winski, D, Holly, W, Whiteford, A, Xiao, C, Yang, J, Zhang, X, Bertler, Nancy A. N., Conway, Howard, Dahl-Jensen, Dorthe, Emanuelsson, Daniel B., Winstrup, Mai, Vallelonga, Paul T., Lee, James E., Brook, Ed J., Severinghaus, Jeffrey P., Fudge, Taylor J., Keller, Elizabeth D., Troy Baisden, W., Hindmarsh, Richard C. A., Neff, Peter D., Blunier, Thomas, Edwards, Ross, Mayewski, Paul A., Kipfstuhl, Sepp, Buizert, Christo, Canessa, Silvia, Dadic, Ruzica, Kjær, Helle A., Kurbatov, Andrei, Zhang, Dongqi, Waddington, Edwin D., Baccolo, Giovanni, Beers, Thomas, Brightley, Hannah J., Carter, Lionel, Clemens-Sewall, David, Ciobanu, Viorela G., Delmonte, Barbara, Eling, Lukas, Ellis, Aja, Ganesh, Shruthi, Golledge, Nicholas R., Haines, Skylar, Handley, Michael, Hawley, Robert L., Hogan, Chad M., Johnson, Katelyn M., Korotkikh, Elena, Lowry, Daniel P., Mandeno, Darcy, McKay, Robert M., Menking, James A., Naish, Timothy R., Noerling, Caroline, Ollive, Agathe, Orsi, Anaïs, Proemse, Bernadette C., Pyne, Alexander R., Pyne, Rebecca L., Renwick, James, Scherer, Reed P., Semper, Stefanie, Simonsen, Marius, Sneed, Sharon B., Steig, Eric J., Tuohy, Andrea, Ulayottil Venugopal, Abhijith, Valero-Delgado, Fernando, Venkatesh, Janani, Wang, Feitang, Wang, Shimeng, Winski, Dominic A., Holly, Winton, Whiteford, Arran, Xiao, Cunde, Yang, Jiao, and Zhang, Xin
- Abstract
High-resolution, well-dated climate archives provide an opportunity to investigate the dynamic interactions of climate patterns relevant for future projections. Here, we present data from a new, annually dated ice core record from the eastern Ross Sea, named the Roosevelt Island Climate Evolution (RICE) ice core. Comparison of this record with climate reanalysis data for the 1979-2012 interval shows that RICE reliably captures temperature and snow precipitation variability in the region. Trends over the past 2700 years in RICE are shown to be distinct from those in West Antarctica and the western Ross Sea captured by other ice cores. For most of this interval, the eastern Ross Sea was warming (or showing isotopic enrichment for other reasons), with increased snow accumulation and perhaps decreased sea ice concentration. However, West Antarctica cooled and the western Ross Sea showed no significant isotope temperature trend. This pattern here is referred to as the Ross Sea Dipole. Notably, during the Little Ice Age, West Antarctica and the western Ross Sea experienced colder than average temperatures, while the eastern Ross Sea underwent a period of warming or increased isotopic enrichment. From the 17th century onwards, this dipole relationship changed. All three regions show current warming, with snow accumulation declining in West Antarctica and the eastern Ross Sea but increasing in the western Ross Sea. We interpret this pattern as reflecting an increase in sea ice in the eastern Ross Sea with perhaps the establishment of a modern Roosevelt Island polynya as a local moisture source for RICE
- Published
- 2018
8. The Ross Sea Dipole-temperature, snow accumulation and sea ice variability in the Ross Sea region, Antarctica, over the past 2700 years
- Author
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Bertler, N., Conway, H., Dahl-Jensen, D., Emanuelsson, D., Winstrup, M., Vallelonga, P., Lee, J., Brook, E., Severinghaus, J., Fudge, T., Keller, E., Troy Baisden, W., Hindmarsh, R., Neff, P., Blunier, T., Edwards, Peter, Mayewski, P., Kipfstuhl, S., Buizert, C., Canessa, S., Dadic, R., Kjær, H., Kurbatov, A., Zhang, D., Waddington, E., Baccolo, G., Beers, T., Brightley, H., Carter, L., Clemens-Sewall, D., Ciobanu, V., Delmonte, B., Eling, L., Ellis, A., Ganesh, S., Golledge, N., Haines, S., Handley, M., Hawley, R., Hogan, C., Johnson, K., Korotkikh, E., Lowry, D., Mandeno, D., McKay, R., Menking, J., Naish, T., Noerling, C., Ollive, A., Orsi, A., Proemse, B., Pyne, A., Pyne, R., Renwick, J., Scherer, R., Semper, S., Simonsen, M., Sneed, S., Steig, E., Tuohy, A., Ulayottil Venugopal, A., Valero-Delgado, F., Venkatesh, J., Wang, F., Wang, S., Winski, D., Holly, W., Whiteford, A., Xiao, C., Yang, J., Zhang, X., Bertler, N., Conway, H., Dahl-Jensen, D., Emanuelsson, D., Winstrup, M., Vallelonga, P., Lee, J., Brook, E., Severinghaus, J., Fudge, T., Keller, E., Troy Baisden, W., Hindmarsh, R., Neff, P., Blunier, T., Edwards, Peter, Mayewski, P., Kipfstuhl, S., Buizert, C., Canessa, S., Dadic, R., Kjær, H., Kurbatov, A., Zhang, D., Waddington, E., Baccolo, G., Beers, T., Brightley, H., Carter, L., Clemens-Sewall, D., Ciobanu, V., Delmonte, B., Eling, L., Ellis, A., Ganesh, S., Golledge, N., Haines, S., Handley, M., Hawley, R., Hogan, C., Johnson, K., Korotkikh, E., Lowry, D., Mandeno, D., McKay, R., Menking, J., Naish, T., Noerling, C., Ollive, A., Orsi, A., Proemse, B., Pyne, A., Pyne, R., Renwick, J., Scherer, R., Semper, S., Simonsen, M., Sneed, S., Steig, E., Tuohy, A., Ulayottil Venugopal, A., Valero-Delgado, F., Venkatesh, J., Wang, F., Wang, S., Winski, D., Holly, W., Whiteford, A., Xiao, C., Yang, J., and Zhang, X.
- Abstract
High-resolution, well-dated climate archives provide an opportunity to investigate the dynamic interactions of climate patterns relevant for future projections. Here, we present data from a new, annually dated ice core record from the eastern Ross Sea, named the Roosevelt Island Climate Evolution (RICE) ice core. Comparison of this record with climate reanalysis data for the 1979-2012 interval shows that RICE reliably captures temperature and snow precipitation variability in the region. Trends over the past 2700 years in RICE are shown to be distinct from those in West Antarctica and the western Ross Sea captured by other ice cores. For most of this interval, the eastern Ross Sea was warming (or showing isotopic enrichment for other reasons), with increased snow accumulation and perhaps decreased sea ice concentration. However, West Antarctica cooled and the western Ross Sea showed no significant isotope temperature trend. This pattern here is referred to as the Ross Sea Dipole. Notably, during the Little Ice Age, West Antarctica and the western Ross Sea experienced colder than average temperatures, while the eastern Ross Sea underwent a period of warming or increased isotopic enrichment. From the 17th century onwards, this dipole relationship changed. All three regions show current warming, with snow accumulation declining in West Antarctica and the eastern Ross Sea but increasing in the western Ross Sea. We interpret this pattern as reflecting an increase in sea ice in the eastern Ross Sea with perhaps the establishment of a modern Roosevelt Island polynya as a local moisture source for RICE.
- Published
- 2018
9. Statistical modelling of the snow depth distribution in open alpine terrain
- Author
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Grunewald, T., Stotter, J., Pomeroy, J.W., Dadic, R., Banos, I.M., Marturia, J., Spross, M., Hopkinson, Christopher, Burlando, P., Lehnig, M., Grunewald, T., Stotter, J., Pomeroy, J.W., Dadic, R., Banos, I.M., Marturia, J., Spross, M., Hopkinson, Christopher, Burlando, P., and Lehnig, M.
- Abstract
The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions.We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A “global” model, combining all the data from all areas investigated, could only explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.
- Published
- 2013
10. Impact of the microstructure of snow on its temperature: A model validation with measurements from Summit, Greenland
- Author
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Dadic, R., Schneebeli, M., Lehning, M., Hutterli, M.A., Ohmura, A., Dadic, R., Schneebeli, M., Lehning, M., Hutterli, M.A., and Ohmura, A.
- Abstract
The influence of snow microstructure on thermal and radiative transfer in snow has not been thoroughly investigated as the tools necessary to efficiently measure microstructural geometry at millimeter resolution have not yet been available. Here we investigate the impact of snow microstructure on the thermal and radiative properties of snow and specifically determine the depth resolution of snow stratigraphy measurements needed to adequately model snow temperatures. To address this subject, detailed information on physical properties of the snow cover was collected at Summit, Greenland, in summer 2003. We present a new set of snow microstructure data, measured with a high-resolution penetrometer (SnowMicroPen (SMP)). The penetration resistance can be used to estimate the thickness of individual layers but also reflects the thickness and the number of bonds in the snowpack. SMP is a motor-driven, constant speed micropenetrometer acquiring 256 hardness measurements per millimeter. The data were used to describe the physical properties of snow and to compute snow temperatures in the topmost meter of the snowpack using the snow cover model SNOWPACK. The spatial resolution of the snow microstructure and the subsequent parametrization of SMP-estimated density strongly affected the modeled temperature and temperature gradients. The SMP-estimated density was used as an input to SNOWPACK and affected the thermal conductivity and radiative transfer in the snowpack. Our results thus show that highly resolved stratigraphic input data on the order of at least 1 cm are necessary to adequately simulate snow temperatures and snow metamorphism, including the observed formation of subsurface hoar in the high Arctic snow cover.
- Published
- 2008
11. Statistical modelling of the snow depth distribution in open alpine terrain
- Author
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Grünewald, T., primary, Stötter, J., additional, Pomeroy, J. W., additional, Dadic, R., additional, Moreno Baños, I., additional, Marturià, J., additional, Spross, M., additional, Hopkinson, C., additional, Burlando, P., additional, and Lehning, M., additional
- Published
- 2013
- Full Text
- View/download PDF
12. Statistical modelling of the snow depth distribution on the catchment scale
- Author
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Grünewald, T., primary, Stötter, J., additional, Pomeroy, J. W., additional, Dadic, R., additional, Moreno Baños, I., additional, Marturià, J., additional, Spross, M., additional, Hopkinson, C., additional, Burlando, P., additional, and Lehning, M., additional
- Published
- 2013
- Full Text
- View/download PDF
13. Parameterization for wind-induced preferential deposition of snow
- Author
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Dadic, R., primary, Mott, R., additional, Lehning, M., additional, and Burlando, P., additional
- Published
- 2010
- Full Text
- View/download PDF
14. Wind influence on snow depth distribution and accumulation over glaciers
- Author
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Dadic, R., primary, Mott, R., additional, Lehning, M., additional, and Burlando, P., additional
- Published
- 2010
- Full Text
- View/download PDF
15. Impact of the microstructure of snow on its temperature: A model validation with measurements from Summit, Greenland
- Author
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Dadic, R., primary, Schneebeli, M., additional, Lehning, M., additional, Hutterli, M. A., additional, and Ohmura, A., additional
- Published
- 2008
- Full Text
- View/download PDF
16. Statistical modelling of the snow depth distribution on the catchment scale.
- Author
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Grünewald, T., Stötter, J., Pomeroy, J. W., Dadic, R., Moreno Baños, I., Marturià, J., Spross, M., Hopkinson, C., Burlando, P., and Lehning, M.
- Abstract
The spatial distribution of alpine snow covers is characterized by a large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale, that 30% to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could still explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, there seem to be some temporal transferability, in which models developed on one peak snow season were good predictors for other peak snow seasons. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
17. Wind influence on snow depth distribution and accumulation over glaciers
- Author
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Dadic, R., Mott, R., Lehning, M., and Burlando, P.
18. Parameterization for wind-induced preferential deposition of snow
- Author
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Dadic, R., Mott, R., Lehning, M., and Burlando, P.
19. Temperature-Driven Bubble Migration as Proxy for Internal Bubble Pressures and Bubble Trapping Function in Ice Cores
- Author
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Dadic R., Schneebeli M., Wiese M., Bertler N., Salamatin A., Theile T., Alley R., Lipenkov V., Dadic R., Schneebeli M., Wiese M., Bertler N., Salamatin A., Theile T., Alley R., and Lipenkov V.
- Abstract
©2019. American Geophysical Union. All Rights Reserved. Ice core data record significant and abrupt past climate changes that are associated with large and rapid changes in atmospheric greenhouse gases, such as methane. Due to the gradual close-off of gas bubbles and the relatively fast diffusion of gases within the firn column, even a discrete or quick step increase in air composition may be smoothed or integrated in the data; current laboratory analyses of gases consider the mean gas content value across all bubbles in a sample, rather than the content of individual bubbles. The convolution of the distribution of trapping ages with the history of atmospheric composition thus smears the measured gas record in each sample. We developed a nondestructive method to determine pressure distribution in all bubbles in a sample and estimate the shape of the trapping function derived from that bubble pressure distribution and site characteristics. Our method works not only for present conditions but also through varying paleo-atmospheric conditions, while providing accurate measurements of morphological bubble properties. The method is based on using temperature-driven air bubble migration as a proxy for the pressure of individual bubbles, which we combine with a model for bubbly ice densification to obtain the gas trapping functions and constrain the age distribution of air bubbles for past conditions, which are preserved at different depths. The trapping functions will help us to obtain a more accurate gas signal in the future that is less attenuated through the age distribution of the gas during the close-off process.
20. The Ross Sea Dipole – temperature, snow accumulation and sea ice variability in the Ross Sea region, Antarctica, over the past 2700 years
- Author
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N. A. N. Bertler, H. Conway, D. Dahl-Jensen, D. B. Emanuelsson, M. Winstrup, P. T. Vallelonga, J. E. Lee, E. J. Brook, J. P. Severinghaus, T. J. Fudge, E. D. Keller, W. T. Baisden, R. C. A. Hindmarsh, P. D. Neff, T. Blunier, R. Edwards, P. A. Mayewski, S. Kipfstuhl, C. Buizert, S. Canessa, R. Dadic, H. A. Kjær, A. Kurbatov, D. Zhang, E. D. Waddington, G. Baccolo, T. Beers, H. J. Brightley, L. Carter, D. Clemens-Sewall, V. G. Ciobanu, B. Delmonte, L. Eling, A. Ellis, S. Ganesh, N. R. Golledge, S. Haines, M. Handley, R. L. Hawley, C. M. Hogan, K. M. Johnson, E. Korotkikh, D. P. Lowry, D. Mandeno, R. M. McKay, J. A. Menking, T. R. Naish, C. Noerling, A. Ollive, A. Orsi, B. C. Proemse, A. R. Pyne, R. L. Pyne, J. Renwick, R. P. Scherer, S. Semper, M. Simonsen, S. B. Sneed, E. J. Steig, A. Tuohy, A. U. Venugopal, F. Valero-Delgado, J. Venkatesh, F. Wang, S. Wang, D. A. Winski, V. H. L. Winton, A. Whiteford, C. Xiao, J. Yang, X. Zhang, Victoria University of Wellington, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Glaces et Continents, Climats et Isotopes Stables (GLACCIOS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Bertler, N, Conway, H, Dahl-Jensen, D, Emanuelsson, D, Winstrup, M, Vallelonga, P, Lee, J, Brook, E, Severinghaus, J, Fudge, T, Keller, E, Troy Baisden, W, Hindmarsh, R, Neff, P, Blunier, T, Edwards, R, Mayewski, P, Kipfstuhl, S, Buizert, C, Canessa, S, Dadic, R, Kjær, H, Kurbatov, A, Zhang, D, Waddington, E, Baccolo, G, Beers, T, Brightley, H, Carter, L, Clemens-Sewall, D, Ciobanu, V, Delmonte, B, Eling, L, Ellis, A, Ganesh, S, Golledge, N, Haines, S, Handley, M, Hawley, R, Hogan, C, Johnson, K, Korotkikh, E, Lowry, D, Mandeno, D, Mckay, R, Menking, J, Naish, T, Noerling, C, Ollive, A, Orsi, A, Proemse, B, Pyne, A, Pyne, R, Renwick, J, Scherer, R, Semper, S, Simonsen, M, Sneed, S, Steig, E, Tuohy, A, Ulayottil Venugopal, A, Valero-Delgado, F, Venkatesh, J, Wang, F, Wang, S, Winski, D, Holly, W, Whiteford, A, Xiao, C, Yang, J, and Zhang, X
- Subjects
Arctic sea ice decline ,010504 meteorology & atmospheric sciences ,lcsh:Environmental protection ,Stratigraphy ,Antarctic ice sheet ,Antarctic sea ice ,010502 geochemistry & geophysics ,01 natural sciences ,Physical Geography and Environmental Geoscience ,lcsh:Environmental pollution ,Sea ice ,Cryosphere ,lcsh:TD169-171.8 ,[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment ,lcsh:Environmental sciences ,0105 earth and related environmental sciences ,lcsh:GE1-350 ,[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere ,Global and Planetary Change ,geography ,geography.geographical_feature_category ,Paleontology ,Future sea level ,15. Life on land ,Arctic ice pack ,Climate Action ,Oceanography ,13. Climate action ,lcsh:TD172-193.5 ,Ice sheet ,Geology - Abstract
High-resolution, well-dated climate archives provide an opportunity to investigate the dynamic interactions of climate patterns relevant for future projections. Here, we present data from a new, annually dated ice core record from the eastern Ross Sea, named the Roosevelt Island Climate Evolution (RICE) ice core. Comparison of this record with climate reanalysis data for the 1979–2012 interval shows that RICE reliably captures temperature and snow precipitation variability in the region. Trends over the past 2700 years in RICE are shown to be distinct from those in West Antarctica and the western Ross Sea captured by other ice cores. For most of this interval, the eastern Ross Sea was warming (or showing isotopic enrichment for other reasons), with increased snow accumulation and perhaps decreased sea ice concentration. However, West Antarctica cooled and the western Ross Sea showed no significant isotope temperature trend. This pattern here is referred to as the Ross Sea Dipole. Notably, during the Little Ice Age, West Antarctica and the western Ross Sea experienced colder than average temperatures, while the eastern Ross Sea underwent a period of warming or increased isotopic enrichment. From the 17th century onwards, this dipole relationship changed. All three regions show current warming, with snow accumulation declining in West Antarctica and the eastern Ross Sea but increasing in the western Ross Sea. We interpret this pattern as reflecting an increase in sea ice in the eastern Ross Sea with perhaps the establishment of a modern Roosevelt Island polynya as a local moisture source for RICE.
- Published
- 2019
21. Southward migration of the zero-degree isotherm latitude over the Southern Ocean and the Antarctic Peninsula: Cryospheric, biotic and societal implications.
- Author
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González-Herrero S, Navarro F, Pertierra LR, Oliva M, Dadic R, Peck L, and Lehning M
- Abstract
The seasonal movement of the zero-degree isotherm across the Southern Ocean and Antarctic Peninsula drives major changes in the physical and biological processes around maritime Antarctica. These include spatial and temporal shifts in precipitation phase, snow accumulation and melt, thawing and freezing of the active layer of the permafrost, glacier mass balance variations, sea ice mass balance and changes in physiological processes of biodiversity. Here, we characterize the historical seasonal southward movement of the monthly near-surface zero-degree isotherm latitude (ZIL), and quantify the velocity of migration in the context of climate change using climate reanalyses and projections. From 1957 to 2020, the ZIL exhibited a significant southward shift of 16.8 km decade
-1 around Antarctica and of 23.8 km decade-1 in the Antarctic Peninsula, substantially faster than the global mean velocity of temperature change of 4.2 km decade-1 , with only a small fraction being attributed to the Southern Annular Mode (SAM). CMIP6 models reproduce the trends observed from 1957 to 2014 and predict a further southward migration around Antarctica of 24 ± 12 km decade-1 and 50 ± 19 km decade-1 under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. The southward migration of the ZIL is expected to have major impacts on the cryosphere, especially on the precipitation phase, snow accumulation and in peripheral glaciers of the Antarctic Peninsula, with more uncertain changes on permafrost, ice sheets and shelves, and sea ice. Longer periods of temperatures above 0 °C threshold will extend active biological periods in terrestrial ecosystems and will reduce the extent of oceanic ice cover, changing phenologies as well as areas of productivity in marine ecosystems, especially those located on the sea ice edge., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)- Published
- 2024
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22. Author Correction: A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition.
- Author
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Macfarlane AR, Schneebeli M, Dadic R, Tavri A, Immerz A, Polashenski C, Krampe D, Clemens-Sewall D, Wagner DN, Perovich DK, Henna-Reetta H, Raphael I, Matero I, Regnery J, Smith MM, Nicolaus M, Jaggi M, Oggier M, Webster MA, Lehning M, Kolabutin N, Itkin P, Naderpour R, Pirazzini R, Hämmerle S, Arndt S, and Fons S
- Published
- 2023
- Full Text
- View/download PDF
23. A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition.
- Author
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Macfarlane AR, Schneebeli M, Dadic R, Tavri A, Immerz A, Polashenski C, Krampe D, Clemens-Sewall D, Wagner DN, Perovich DK, Henna-Reetta H, Raphael I, Matero I, Regnery J, Smith MM, Nicolaus M, Jaggi M, Oggier M, Webster MA, Lehning M, Kolabutin N, Itkin P, Naderpour R, Pirazzini R, Hämmerle S, Arndt S, and Fons S
- Abstract
Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system's energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition., (© 2023. The Author(s).)
- Published
- 2023
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24. Environmental iodine speciation quantification in seawater and snow using ion exchange chromatography and UV spectrophotometric detection.
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Jones MR, Chance R, Dadic R, Hannula HR, May R, Ward M, and Carpenter LJ
- Subjects
- Animals, Humans, Iodides analysis, Iodates analysis, Snow, Reproducibility of Results, Seawater chemistry, Spectrophotometry, Chromatography, Ion Exchange, Iodine analysis
- Abstract
The behaviour and distribution of iodine in the environment are of significant interest in a range of scientific disciplines, from health, as iodine is an essential element for humans and animals, to climate and air quality, to geochemistry. Aquatic environments are the reservoir for iodine, where it exists in low concentrations as iodide, iodate and dissolved organic iodine and in which it undergoes redox reactions. The current measurement techniques for iodine species are typically time-consuming, subject to relatively poor precision and require specialist instrumentation including those that require mercury as an electrode. We present a new method for measuring iodine species, that is tailored towards lower dissolved organic carbon waters, such as seawater, rainwater and snow, using ion exchange chromatography (IC) with direct ultra-violet spectrophotometric detection of iodide and without the need for sample pre-concentration. Simple chemical amendments to the sample allow for the quantification of both iodate and dissolved organic iodine in addition to iodide. The developed IC method, which takes 16 min, was applied to contrasting samples that encompass a wide range of aqueous environments, from Arctic sea-ice snow (low concentrations) to coastal seawater (complex sample matrix). Linear calibrations are demonstrated for all matrices, using gravimetrically prepared potassium iodide standards. The detection limit for the iodide ion is 0.12 nM based on the standard deviation of the blank, while sample reproducibility is typically <2% at >8 nM and ∼4% at <8 nM. Since there is no environmental certified reference material for iodine species, the measurements made on seawater samples using this IC method were compared to those obtained using established analytical techniques; iodide voltammetry and iodate spectrophotometry. We calculated recoveries of 102 ± 16% (n = 107) for iodide and 116 ± 9% (n = 103) for iodate, the latter difference may be due to an underestimation of iodate by the spectrophotometric method. We further compared a chemical oxidation and reduction of the sample to an ultra-violet digestion to establish the total dissolved iodine content, the average recovery following chemical amendments was 98 ± 4% (n = 92). The new method represents a simple, efficient, green, precise and sensitive method for measuring dissolved speciated iodine in complex matrices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)
- Published
- 2023
- Full Text
- View/download PDF
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