36 results on '"Birnbaum, Gerit"'
Search Results
2. Surface albedo measurements and surface type classification from helicopter-based observations during MOSAiC
- Author
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Sperzel, Tim R., Jäkel, Evelyn, Pätzold, Falk, Lampert, Astrid, Niehaus, Hannah, Spreen, Gunnar, Rosenburg, Sophie, Birnbaum, Gerit, Neckel, Niklas, and Wendisch, Manfred
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- 2023
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3. Helicopter-borne RGB orthomosaics and photogrammetric digital elevation models from the MOSAiC Expedition
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Neckel, Niklas, Fuchs, Niels, Birnbaum, Gerit, Hutter, Nils, Jutila, Arttu, Buth, Lena, von Albedyll, Luisa, Ricker, Robert, and Haas, Christian
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- 2023
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4. Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data.
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Fuchs, Niels, von Albedyll, Luisa, Birnbaum, Gerit, Linhardt, Felix, Oppelt, Natascha, and Haas, Christian
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ARCTIC climate ,AERIAL photographs ,SURFACE topography ,WATER levels ,SOLAR energy ,SEA ice - Abstract
Melt ponds are a core component of the summer sea ice system in the Arctic, increasing the uptake of solar energy and impacting the ice-associated ecosystem. They were thus one of the key topics during the 1-year drift campaign Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in describing the surface meltwater volume; it is necessary to estimate budgets and used in model parameterization to simulate pond coverage evolution. However, observational data on pond depth are spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains unexplored. Here, we present a newly developed method to derive pond bathymetry from aerial images. We determine it from a photogrammetric multi-view reconstruction of the summer ice surface topography. Based on images recorded on dedicated grid flights and facilitated assumptions, we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric airborne retrieval methods. It can furthermore be implemented in any typical photogrammetry workflow. We present the retrieval algorithm, including requirements for the data recording and survey planning, and a correction method for refraction at the air–pond interface. In addition, we show how the retrieved surface topography model synergizes with the initial image data to retrieve the water level of individual ponds from the visually determined pond margins. We use the method to give a profound overview of the pond coverage on the MOSAiC floe, on which we found unexpected steady pond coverage and volume. We were able to derive individual pond properties of more than 1600 ponds on the floe, including their size, bathymetry, volume, surface elevation above sea level, and temporal evolution. We present a scaling factor for single in situ depth measurements, discuss the representativeness of in situ pond measurements and the importance of such high-resolution data for new satellite retrievals, and show indications for non-rigid pond bottoms. The study points out the great potential to derive geometric properties of the summer sea ice surface emerging from the increasingly available visual image data recorded from uncrewed aerial vehicles (UAVs) or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Sea Ice Melt Pond Fraction Derived From Sentinel‐2 Data: Along the MOSAiC Drift and Arctic‐Wide
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Niehaus, Hannah, primary, Spreen, Gunnar, additional, Birnbaum, Gerit, additional, Istomina, Larysa, additional, Jäkel, Evelyn, additional, Linhardt, Felix, additional, Neckel, Niklas, additional, Fuchs, Niels, additional, Nicolaus, Marcel, additional, Sperzel, Tim, additional, Tao, Ran, additional, Webster, Melinda, additional, and Wright, Nicholas, additional
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- 2023
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6. Preconditioning of Summer Melt Ponds From Winter Sea Ice Surface Temperature
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Thielke, Linda, primary, Fuchs, Niels, additional, Spreen, Gunnar, additional, Tremblay, Bruno, additional, Birnbaum, Gerit, additional, Huntemann, Marcus, additional, Hutter, Nils, additional, Itkin, Polona, additional, Jutila, Arttu, additional, and Webster, Melinda A., additional
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- 2023
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7. MOSAiC airborne laser scanning of the sea-ice surface: data product overview and insights to seasonal roughness evolution
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Jutila, Arttu Juhani, Hutter, Nils, Hendricks, Stefan, Ricker, Robert, Albedyll, Luisa, Birnbaum, Gerit, and Haas, Christian
- Abstract
Oral presentation at the 2nd MOSAiC science conference showing the MOSAiC airborne laser scanner product overview and first results of seasonal surface roughness
- Published
- 2023
8. Multi-sensor airborne observations of freeboard, snow depth, and sea-ice thickness in the Arctic
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Jutila, Arttu, Hendricks, Stefan, Ricker, Robert, von Albedyll, Luisa, Krumpen, Thomas, Hutter, Nils, Birnbaum, Gerit, and Haas, Christian
- Abstract
Sea-ice thickness is a key factor and indicator in understanding the impact of the global climate change. Deriving basin-wide sea-ice thickness estimates from satellite laser and radar altimetry relies on freeboard measurements. The freeboard-to-thickness conversion in turn requires information of snow mass and the density of the sea-ice layer that have unknown spatio-temporal variabilities and trends directly translating into the uncertainty of decadal sea-ice thickness data records. In addition, inter-mission biases arise from, e.g., different sensor types and frequencies as well as varying footprint sizes affected by surface roughness across regions and seasons. Therefore, carrying out validation and inter-calibration studies is crucial for reliable and continuous observation of the Earth’s cryosphere. To achieve this, it is beneficial to have simultaneous measurements of freeboard, snow depth, and sea-ice thickness, which provide reference data for both direct satellite observations and geophysical target parameters. Here, we present Alfred Wegener Institute’s (AWI) IceBird program, which is a series of fixed-wing aircraft campaigns to measure Arctic sea ice and to monitor its change. During two late-winter campaigns in the western Arctic Ocean in 2017 and 2019, we have carried out surveys with the unique scientific instrument configuration including an airborne laser scanner (ALS) for surface topography and freeboard measurements, a tethered electromagnetic induction sounding instrument (EM-Bird) for total (snow+ice) thickness measurements, and an ultrawideband frequency-modulated continuous-wave microwave radar to measure snow thickness. Therefore, we are able to observe all three bounding interfaces in the sea-ice–snow system in high resolution along survey tracks on regional scales. During the ship-based drift expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) between October 2019 and September 2020, helicopter surveys were carried out in high spatio-temporal resolution throughout the year, including the polar night, to measure freeboard and roughness with the ALS both in local grid pattern and in larger scale. Coincident EM-Bird ice thickness data and information from snow measurements on the ground will help linking these parameters and monitor them and their effect on satellite retrievals for a full seasonal cycle. The individual parameters are important for describing and monitoring the state of the Arctic sea ice and validating retrievals from satellite data, but combined they offer further possibilities to characterise sea ice. By assuming isostatic equilibrium, we are able to estimate up-to-date bulk density values for different sea-ice types from the IceBird data and to derive a parametrisation of sea-ice bulk density based on sea-ice freeboard. These data allow us to explore spatio-temporal variations in sea-ice parameters observable from space and to evaluate the validity of the freeboard-to-thickness conversion in satellite altimetry through comparison against dedicated satellite overpasses and orbit collections.
- Published
- 2022
9. Sea Ice Type Retrieval Algorithms from Fused TerraSAR-X and ALS Data
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Kortum, Karl, Singha, Suman, Spreen, Gunnar, Hendricks, Stefan, Hutter, Nils, Birnbaum, Gerit, Jutila, Arttu, Ricker, Robert, and von Albedyll, Luisa
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Data Fusion TerraSAR-X ,Sea Ice Type ,Sea Ice ,ALS ,Oceanography ,SAR - Published
- 2022
10. Observations of marine cold-air outbreaks: a comprehensive data set of airborne and dropsonde measurements from the Springtime Atmospheric Boundary Layer Experiment (STABLE)
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Michaelis, Janosch, primary, Schmitt, Amelie U., additional, Lüpkes, Christof, additional, Hartmann, Jörg, additional, Birnbaum, Gerit, additional, and Vihma, Timo, additional
- Published
- 2022
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11. Kilometer-scale digital elevation models of the sea ice surface with airborne laser scanning during MOSAiC
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Jutila, Arttu, Hutter, Nils, Hendricks, Stefan, Ricker, Robert, von Albedyll, Luisa, Birnbaum, Gerit, and Haas, Christian
- Abstract
An integrated sensor platform including an inertial navigation system (INS) and a commercial airborne laser scanner (ALS) among other sensor was mounted in the cargo compartment in one of the Polarstern helicopters during MOSAiC. ALS data was acquired from more than 60 flights between October 2019 and September 2020 with a range of survey types intended to map changes of the sea ice surface during the full annual cycle at high spatial resolution and coverage. Here, we provide an overview of the collected data, the challenge of achieving centimeter elevation accuracy with a helicopter platform at high polar latitudes as well as the content and specifications of ALS data products. The high spatial resolution and repeated coverage of the larger area around Polarstern allow studying various surface features (e.g. pressure ridges, floes, melt ponds, snow drifts, etc.), their seasonal evolution, and their impact on atmosphere and ocean. Finally, we outline methods for planned applications, such as identifying individual floes and surface types using both measured freeboard and surface reflectance. Collocated helicopter-based optical and infrared imagery allow analyzing sea ice properties in further applications and to upscale comparable in-situ observations.
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- 2022
12. Overview of the MOSAiC expedition
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Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Brauchle, Jörg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, de Boer, Gijs, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry V., Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niels, Gabarró, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Andreas, Heuzé, Céline, Hoppmann, Mario, Høyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik, Matero, Ilkka O., Maus, Sönke, Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Pätzold, Falk, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, Kathrin, Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randall K., Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison M., Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus Tage, Tavri, Aikaterini, Tsamados, Michel, Wagner, David N., Watkins, Daniel, Webster, Melinda, and Wendisch, Manfred
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Atmosphere–ice–ocean interaction ,Coupled climate system ,Arctic drift study ,interdisciplinary research ,SDG 13 - Climate Action ,Snow and sea ice ,SDG 14 - Life Below Water - Abstract
Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.
- Published
- 2022
13. Thermodynamic and dynamic contributions to seasonal Arctic sea ice thickness distributions from airborne observations
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von Albedyll, Luisa, primary, Hendricks, Stefan, additional, Grodofzig, Raphael, additional, Krumpen, Thomas, additional, Arndt, Stefanie, additional, Belter, H. Jakob, additional, Birnbaum, Gerit, additional, Cheng, Bin, additional, Hoppmann, Mario, additional, Hutchings, Jennifer, additional, Itkin, Polona, additional, Lei, Ruibo, additional, Nicolaus, Marcel, additional, Ricker, Robert, additional, Rohde, Jan, additional, Suhrhoff, Mira, additional, Timofeeva, Anna, additional, Watkins, Daniel, additional, Webster, Melinda, additional, and Haas, Christian, additional
- Published
- 2022
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14. Overview of the MOSAiC expedition: Snow and sea ice
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Nicolaus, Marcel, primary, Perovich, Donald K., additional, Spreen, Gunnar, additional, Granskog, Mats A., additional, von Albedyll, Luisa, additional, Angelopoulos, Michael, additional, Anhaus, Philipp, additional, Arndt, Stefanie, additional, Belter, H. Jakob, additional, Bessonov, Vladimir, additional, Birnbaum, Gerit, additional, Brauchle, Jörg, additional, Calmer, Radiance, additional, Cardellach, Estel, additional, Cheng, Bin, additional, Clemens-Sewall, David, additional, Dadic, Ruzica, additional, Damm, Ellen, additional, de Boer, Gijs, additional, Demir, Oguz, additional, Dethloff, Klaus, additional, Divine, Dmitry V., additional, Fong, Allison A., additional, Fons, Steven, additional, Frey, Markus M., additional, Fuchs, Niels, additional, Gabarró, Carolina, additional, Gerland, Sebastian, additional, Goessling, Helge F., additional, Gradinger, Rolf, additional, Haapala, Jari, additional, Haas, Christian, additional, Hamilton, Jonathan, additional, Hannula, Henna-Reetta, additional, Hendricks, Stefan, additional, Herber, Andreas, additional, Heuzé, Céline, additional, Hoppmann, Mario, additional, Høyland, Knut Vilhelm, additional, Huntemann, Marcus, additional, Hutchings, Jennifer K., additional, Hwang, Byongjun, additional, Itkin, Polona, additional, Jacobi, Hans-Werner, additional, Jaggi, Matthias, additional, Jutila, Arttu, additional, Kaleschke, Lars, additional, Katlein, Christian, additional, Kolabutin, Nikolai, additional, Krampe, Daniela, additional, Kristensen, Steen Savstrup, additional, Krumpen, Thomas, additional, Kurtz, Nathan, additional, Lampert, Astrid, additional, Lange, Benjamin Allen, additional, Lei, Ruibo, additional, Light, Bonnie, additional, Linhardt, Felix, additional, Liston, Glen E., additional, Loose, Brice, additional, Macfarlane, Amy R., additional, Mahmud, Mallik, additional, Matero, Ilkka O., additional, Maus, Sönke, additional, Morgenstern, Anne, additional, Naderpour, Reza, additional, Nandan, Vishnu, additional, Niubom, Alexey, additional, Oggier, Marc, additional, Oppelt, Natascha, additional, Pätzold, Falk, additional, Perron, Christophe, additional, Petrovsky, Tomasz, additional, Pirazzini, Roberta, additional, Polashenski, Chris, additional, Rabe, Benjamin, additional, Raphael, Ian A., additional, Regnery, Julia, additional, Rex, Markus, additional, Ricker, Robert, additional, Riemann-Campe, Kathrin, additional, Rinke, Annette, additional, Rohde, Jan, additional, Salganik, Evgenii, additional, Scharien, Randall K., additional, Schiller, Martin, additional, Schneebeli, Martin, additional, Semmling, Maximilian, additional, Shimanchuk, Egor, additional, Shupe, Matthew D., additional, Smith, Madison M., additional, Smolyanitsky, Vasily, additional, Sokolov, Vladimir, additional, Stanton, Tim, additional, Stroeve, Julienne, additional, Thielke, Linda, additional, Timofeeva, Anna, additional, Tonboe, Rasmus Tage, additional, Tavri, Aikaterini, additional, Tsamados, Michel, additional, Wagner, David N., additional, Watkins, Daniel, additional, Webster, Melinda, additional, and Wendisch, Manfred, additional
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- 2022
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15. Measurements and Modeling of Optical-Equivalent Snow Grain Sizes under Arctic Low-Sun Conditions
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Jäkel, Evelyn, primary, Carlsen, Tim, additional, Ehrlich, André, additional, Wendisch, Manfred, additional, Schäfer, Michael, additional, Rosenburg, Sophie, additional, Nakoudi, Konstantina, additional, Zanatta, Marco, additional, Birnbaum, Gerit, additional, Helm, Veit, additional, Herber, Andreas, additional, Istomina, Larysa, additional, Mei, Linlu, additional, and Rohde, Anika, additional
- Published
- 2021
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16. Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification
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Belter, H. Jakob, primary, Krumpen, Thomas, additional, von Albedyll, Luisa, additional, Alekseeva, Tatiana A., additional, Birnbaum, Gerit, additional, Frolov, Sergei V., additional, Hendricks, Stefan, additional, Herber, Andreas, additional, Polyakov, Igor, additional, Raphael, Ian, additional, Ricker, Robert, additional, Serovetnikov, Sergei S., additional, Webster, Melinda, additional, and Haas, Christian, additional
- Published
- 2021
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17. ‘Surface Drag in the Arctic Marginal Sea-ice Zone: A Comparison of Different Parameterisation Concepts’
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Lüpkes, Christof and Birnbaum, Gerit
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- 2005
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18. Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica
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Carlsen, Tim, primary, Birnbaum, Gerit, additional, Ehrlich, André, additional, Helm, Veit, additional, Jäkel, Evelyn, additional, Schäfer, Michael, additional, and Wendisch, Manfred, additional
- Published
- 2020
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19. Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery
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König, Marcel, primary, Birnbaum, Gerit, additional, and Oppelt, Natascha, additional
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- 2020
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20. Observations of marine cold-air outbreaks: A comprehensive data set of airborne and dropsonde measurements from the Springtime Atmospheric Boundary Layer Experiment (STABLE).
- Author
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Michaelis, Janosch, Schmitt, Amelie U., Lüpkes, Christof, Hartmann, Jörg, Birnbaum, Gerit, and Vihma, Timo
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ATMOSPHERIC boundary layer ,PANGAEA (Supercontinent) ,WEATHER ,AIR masses ,SERVER farms (Computer network management) ,MODEL validation - Abstract
In March 2013, the Springtime Atmospheric Boundary Layer Experiment (STABLE) was carried out in the region of Fram Strait and over Svalbard to investigate atmospheric convection and boundary layer modifications due to interactions between sea ice, atmosphere, and open water. A major goal was the observation of marine cold-air outbreaks (MCAOs), which are typically characterised by a transport of very cold air masses from the ice-covered ocean over a relatively warm water surface, and which often affect local and regional weather conditions. During STABLE, such MCAOs were observed on four days within a period of a strongly northward shifted sea ice edge north of Svalbard and thus with an unusually large Whaler's Bay Polynya. The observations mainly consisted of in situ measurements from airborne instruments and of measurements by dropsondes. Here, we present the corresponding data set from, in total, 15 aircraft vertical profiles and 22 dropsonde releases. Besides an overview on flight patterns and instrumentation, we provide a detailed presentation of the individual quality-processing mechanisms, which ensure that the data can be used, for example, for model validation. A few remarks are also given on data quality and on some characteristics of the MCAOs based on the quality-processed data. All 37 data series are published in the World Data Center PANGAEA (Lüpkes et al., 2021a, https://doi.pangaea.de/10.1594/PANGAEA.936635). [ABSTRACT FROM AUTHOR]
- Published
- 2021
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21. Modelling the climate and surface mass balance of polar ice sheets using RACMO2 - Part 2
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van Wessem, J.M., Jan Van De Berg, Willem, Noël, Brice P.Y., van Meijgaard, Erik, Amory, Charles, Birnbaum, Gerit, Jakobs, Constantijn L., Krüger, Konstantin, Lenaerts, Jan T.M., Lhermitte, Stef, Ligtenberg, Stefan R.M., Medley, Brooke, Reijmer, Carleen H., Van Tricht, Kristof, Trusel, Luke D., van Ulft, Lambertus H., Wouters, Bert, Wuite, Jan, Van Den Broeke, Michiel R., Sub Dynamics Meteorology, Marine and Atmospheric Research, Sub Dynamics Meteorology, and Marine and Atmospheric Research
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lcsh:GE1-350 ,geography ,geography.geographical_feature_category ,010504 meteorology & atmospheric sciences ,lcsh:QE1-996.5 ,Antarctic ice sheet ,010502 geochemistry & geophysics ,Snow ,Atmospheric sciences ,01 natural sciences ,Ice shelf ,Ice-sheet model ,lcsh:Geology ,Glacier mass balance ,13. Climate action ,Climatology ,Snowmelt ,Environmental science ,Climate model ,Ice sheet ,lcsh:Environmental sciences ,0105 earth and related environmental sciences ,Water Science and Technology ,Earth-Surface Processes - Abstract
We evaluate modelled Antarctic ice sheet (AIS) near-surface climate, surface mass balance (SMB) and surface energy balance (SEB) from the updated polar version of the regional atmospheric climate model, RACMO2 (1979–2016). The updated model, referred to as RACMO2.3p2, incorporates upper-air relaxation, a revised topography, tuned parameters in the cloud scheme to generate more precipitation towards the AIS interior and modified snow properties reducing drifting snow sublimation and increasing surface snowmelt. Comparisons of RACMO2 model output with several independent observational data show that the existing biases in AIS temperature, radiative fluxes and SMB components are further reduced with respect to the previous model version. The model-integrated annual average SMB for the ice sheet including ice shelves (minus the Antarctic Peninsula, AP) now amounts to 2229 Gt y−1, with an interannual variability of 109 Gt y−1. The largest improvement is found in modelled surface snowmelt, which now compares well with satellite and weather station observations. For the high-resolution (∼ 5.5 km) AP simulation, results remain comparable to earlier studies. The updated model provides a new, high-resolution data set of the contemporary near-surface climate and SMB of the AIS; this model version will be used for future climate scenario projections in a forthcoming study.
- Published
- 2018
22. Physical characteristics of melt ponds
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Oppelt, Natascha, Birnbaum, Gerit, Gege, Peter, König, Marcel, Fuchs, Nils, Macke, Andreas, and Flores, Hauke
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Arctic ,hyperspectral ,reflectance ,melt pond ,Experimentelle Verfahren ,sea ice - Abstract
Improving the predictive capabilities for the development of Arctic sea ice cover strongly depends on a better understanding of the ice-albedo feedback mechanism. Using a combination of multi- and hyperspectral airborne imagery, field spectroscopy and bio-optical modelling we aim to quantify melt pond fraction, melt pond depth, thickness of underlying ice, pond water constituents (chlorophyll, suspended organic and inorganic matter) and surface albedo in different ice regimes. The main goal is to develop a semi-automated application for melt pond analysis for airborne hyperspectral instruments. A further goal is to employ the collected data to validate and improve parameterizations of melt pond properties used in regional and global climate models. The ground based measurements are essential for the parameterization of bio-optical models as well as for the validation of results derived from airborne measurements, which is crucial for accuracy assessment.
- Published
- 2018
23. High resolution airborne melt pond data for pond characteristics retrieval
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Fuchs, Niels, Birnbaum, Gerit, and Dierking, Wolfgang
- Published
- 2017
24. Comparison of different methods to retrieve optical-equivalent snow grain size in central Antarctica
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Carlsen, Tim, primary, Birnbaum, Gerit, additional, Ehrlich, André, additional, Freitag, Johannes, additional, Heygster, Georg, additional, Istomina, Larysa, additional, Kipfstuhl, Sepp, additional, Orsi, Anaïs, additional, Schäfer, Michael, additional, and Wendisch, Manfred, additional
- Published
- 2017
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25. Validation of eight atmospheric reanalyses in the Antarctic Peninsula region
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Nygard, Tiina, Vihma, Timo, Birnbaum, Gerit, Hartmann, Jorg, King, John, Lachlan-Cope, Tom, Ladkin, Russell, Lupkes, Christof, and Weiss, Alexandra
- Abstract
Eight atmospheric reanalyses were compared against observed vertical profiles of temperature, specific humidity and wind speed collected by two research aircraft in February–March 2010 in the Antarctic Peninsula region. These data offered a rare possibility to validate reanalyses against independent in-situ data which have not been assimilated into the reanalyses. The reanalyses had generally too moist profiles with too low wind speeds, but otherwise the errors in the reanalyses had large spatial differences. On the eastern side of the peninsula, the near-surface temperatures were largely overestimated. None of the reanalysis outperformed the others in all variables, at all altitudes and on both sides of the peninsula. Generally, NCEP-CFSR and MERRA had the smallest errors in temperature profiles, JRA-55 had marginally the most accurate specific humidity profiles and NCEP-CFSR had the best wind profiles. The reanalyses were coherent, although biased, on the western side of the Antarctic Peninsula, but on the eastern side the spread was large. All the reanalyses underestimated the variability between the individual profiles of temperature and wind speed. The modern reanalyses with a sufficient spatial resolution and an adequate data assimilation method outperformed the others, especially on the eastern side
- Published
- 2016
26. The impact of specific surface area and clouds on surface albedo: Investigations made at Kohnen Station, Antarctica, during summer 2013/2014
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Belke Brea, Maria, Birnbaum, Gerit, Freitag, Johannes, Kipfstuhl, Sepp, Humbert, Angelika, Schaefer, Michael, König-Langlo, Gert, and Loose, Bernd
- Published
- 2015
27. Influences on the reflectance of Arctic sea ice and the impact of anthropogenic impurities on the surface shortwave radiation balance
- Author
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Schulz, Hannes, Herber, Andreas, Birnbaum, Gerit, and Seckmeyer, Gunther
- Abstract
In order to investigate influences on the reflectance of snow covered Arctic sea ice, a discrete ordinate method and Mie-Theory based radiative transfer model has been set up. This model, the Snow on Sea Ice Model (SoSIM), is able to investigate changes in spectral and spectrally integrated (broadband) albedo of a multi-layer snow cover on sea ice due to varying snow microphysical parameters, atmospheric composition and incoming solar radiation. For typical conditions in the Arctic sea-ice area, it was found that the size of spherical snow grains and the angle of the sun above the horizon ultimately determine both spectral and broadband albedo of a thick snow layer. At 1300 nm, doubling the snow grain size decreases the albedo by about 20%, while a lower incident angle of solar light can offset this effect. The light absorbing impurity black carbon (soot) has a distinct influence on the albedo in the ultra-violet and visible range of the solar spectrum. However, it likely only lowers the albedo by less than 2%, for present concentrations of black carbon in Arctic snow. The spectral signature of black carbon is very similar to a thinning snow cover on top of a darker surface. SoSIM was also tested against other models and parametrisations for the spectral and broadband albedo of snowpacks as well as against field measurements of the spectral albedo of snow covered sea ice. The test proved the plausibility of the model results. Further, broadband albedo data from an airborne measurement campaign has been evaluated together with accompanying data such as sea ice thickness. This data demonstrated that sea-ice dynamics cause strong local surface heterogeneities. As a consequence, a strong variation is found in the spatially averaged surface albedo. To quantify this surface heterogeneity, an algorithm has been developed that automatically classifies typical freeze-up season surface covers of the Arctic ocean from photographs. As a combination of the findings from model study and evaluation of the campaign data, a model could be developed able to re-analyse the spatial distribution of broadband surface albedo of the Arctic ocean. The model utilises spatial information based on satellite observation of sea-ice concentration and thickness as well as climatological data of snow thickness. It is an alternative approach to derive the surface albedo based on SoSIM and not relying on satellite measurements in the visible range of the spectrum. First validations with air and satellite borne measurements of albedo distributions showed that the modelled albedo is plausible, yet has no better accuracy than ±5%. The model was used to predict the surface forcing of changes in the albedo as caused by the deposition of anthropogenic light absorbing substances onto the snow cover. It was found that depositing 40 ppbw black carbon into a pure snowpack causes an extra absorption of 1.58±0.83 W/m2 on average for the sea ice covered Arctic. The high relative uncertainty is caused by the uncertainty involved in the enhancement of light absorption by BC particles due to ageing processes.
- Published
- 2014
28. Cascading decrease of the surface snow SSA at Kohnen Station, DML, Antarctica
- Author
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Klein, Katharina, Schneebeli, Martin, Birnbaum, Gerit, Reijmer, C. H., and Freitag, Johannes
- Abstract
Grain size of the surface snow is the key parameter for albedo in interior Antarctica, as impurity content is very small. The snow surface at the end of austral winter is characterized by very small grains. The small snow grains consist of broken precipitation particles and partially sublimated or mechanically fractured older ice particles. The albedo is consequently very high. The size of snow grains can be determined quite accurately by measuring its specific surface area (SSA), which is equivalent to the optical grain radius. The SSA as a material property used for albedo estimates typically shows an annual cycle. During the summer it decreases due to grain coarsening caused by snow metamorphism. However the surface layer is affected by a variety of processes including wind driven redistribution, precipitation or surface hoar formation. To discriminate the influence of the different processes on the evolution of the SSA during the summer we measured SSA along a 50m transect on a daily basis at Kohnen Station (75◦00′ S, 00◦00′ E, 2892 m a.s.l) in Dronning Maud Land (DML) indirectly by reflectance at 1310 nm. We found that the SSA was not reducing steadily but showed a cascading decrease. The peaks corresponded to precipitation events and frost formation lasting for several days and inhibiting the general expected decrease during the summer period. Our study indicates that even small amounts of precipitation during the summer period can affect the decrease of SSA, respectively the albedo, in the DML region on the East Antarctic Plateau.
- Published
- 2014
29. Isotopic exchange on the diurnal scale between near-surface snow and lower atmospheric water vapor at Kohnen station, East Antarctica
- Author
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Ritter, François, primary, Steen-Larsen, Hans Christian, additional, Werner, Martin, additional, Masson-Delmotte, Valérie, additional, Orsi, Anais, additional, Behrens, Melanie, additional, Birnbaum, Gerit, additional, Freitag, Johannes, additional, Risi, Camille, additional, and Kipfstuhl, Sepp, additional
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- 2016
- Full Text
- View/download PDF
30. A Characterization of Arctic Aerosols as Derived from Airborne Observations and their Influence on the Surface Radiation Budget
- Author
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Herber, Andreas, Stone, R. S., Liu, P., Sharma, S., Li, S.-M., Neuber, Roland, and Birnbaum, Gerit
- Abstract
The Arctic is a key player in the climate system because of the strong modification of the surface energy budget through snow and ice cover, which is tightly coupled to the global circulation of the atmosphere and the ocean. AWI (Alfred Wegener Institute) initiated therefore together with EC (Environment Canada) a special airborne program, as the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP). The past two campaigns with POLAR 5 took place during April 2009 as well as April 2011. The Instrumentation, included a tethered electromagnetic (EM) sensor for sea ice thickness measurements [Haas et al, 2010], analyzers for ozone, gaseous elementary mercury, bromine monoxide, aerosol light scattering and aerosol light absorption and refractory black carbon, aerosol number concentration and aerosol size distribution, and aerosol optical depth (AOD). In addition, aerosol and ozone LIDAR were operated, and drop sondes were launched to characterize atmospheric state variables and to use it for combined LIDAR and aerosol data analysis [Hoffmann et al., 2012]. The traverses were completed within about a month, providing 3-D snapshots of aerosol, trace gases, atmospheric condition and sea ice thickness
- Published
- 2011
31. The Spring-Time Boundary Layer in the Central Arctic Observed during PAMARCMiP 2009
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Lampert, Astrid, primary, Maturilli, Marion, additional, Ritter, Christoph, additional, Hoffmann, Anne, additional, Stock, Maria, additional, Herber, Andreas, additional, Birnbaum, Gerit, additional, Neuber, Roland, additional, Dethloff, Klaus, additional, Orgis, Thomas, additional, Stone, Robert, additional, Brauner, Ralf, additional, Kässbohrer, Johannes, additional, Haas, Christian, additional, Makshtas, Alexander, additional, Sokolov, Vladimir, additional, and Liu, Peter, additional
- Published
- 2012
- Full Text
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32. Effect of sea ice morphology during arctic summer on atmospheric drag coefficients used in climate models
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Lüpkes, Christof, primary, Gryanik, Vladimir M., additional, Rösel, Anja, additional, Birnbaum, Gerit, additional, and Kaleschke, Lars, additional
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- 2012
- Full Text
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33. Synoptic situations causing high precipitation rates on the Antarctic plateau: observations from Kohnen Station, Dronning Maud Land
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Birnbaum, Gerit, primary, Brauner, Ralf, additional, and Ries, Hinnerk, additional
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- 2006
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34. A new parameterization of surface drag in the marginal sea ice zone
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BIRNBAUM, GERIT, primary and LUPKES, CHRISTOF, additional
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- 2002
- Full Text
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35. Evaluation of atmospheric models using radio sounding observations on the Antarctic plateau.
- Author
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Birnbaum, Gerit, Krüger, Konstantin, Lüpkes, Christof, Schäfer, Michael, Van Wessem, Jan Melchior, and Loose, Bernd
- Subjects
- *
ATMOSPHERIC models , *PLATEAUS , *BOUNDARY layer (Aerodynamics) , *WIND speed , *TEMPERATURE inversions , *INVERSION (Geophysics) - Abstract
Three of the latest global reanalysis products, the regional climate model RACMO and the operational weather forecast system AMPS were evaluated against vertical profiles obtained from radio soundings on the Antarctic plateau. Measurements at Kohnen Station (75°S, 0°E, 2892 m a.s.l.), which represent independent in situ data, were performed in January 2006, December 2013 and January 2014 four times a day. JRA-55 had marginally the most accurate temperature profiles, MERRA-2 had the best relative humidity profiles and ERA-interim had the smallest errors in wind speed profiles. Biases and root mean-square errors vary considerably with altitude in the troposphere and in the tropopause region. Surface or elevated inversions in the lowermost 1000 m of the atmosphere are captured quite differently. ERA-interim always shows a surface inversion with thickness and temperature increase that hardly vary in the course of a day. Overall, the frequency of surface and elevated inversions is better captured at 00 UTC and 06 UTC compared to 12 UTC and 18 UTC in JRA-55 and MERRA-2 as well as in the two regional models, which indicates that processes in the boundary layer, which lead to an erosion of a surface inversion are still poorly represented in the models. All reanalyses and regional models are able to simulate a low-level jet. However, ERA-interim, RACMO and AMPS underestimate the frequency at all four observation times. [ABSTRACT FROM AUTHOR]
- Published
- 2019
36. Overview of the MOSAiC expedition: Snow and sea ice
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Nicolaus, Marcel, Perovich, Donald K., Spreen, Gunnar, Granskog, Mats A., von Albedyll, Luisa, Angelopoulos, Michael, Anhaus, Philipp, Arndt, Stefanie, Belter, H. Jakob, Bessonov, Vladimir, Birnbaum, Gerit, Brauchle, Joerg, Calmer, Radiance, Cardellach, Estel, Cheng, Bin, Clemens-Sewall, David, Dadic, Ruzica, Damm, Ellen, de Boer, Gijs, Demir, Oguz, Dethloff, Klaus, Divine, Dmitry, V, Fong, Allison A., Fons, Steven, Frey, Markus M., Fuchs, Niels, Gabarro, Carolina, Gerland, Sebastian, Goessling, Helge F., Gradinger, Rolf, Haapala, Jari, Haas, Christian, Hamilton, Jonathan, Hannula, Henna-Reetta, Hendricks, Stefan, Herber, Andreas, Heuze, Celine, Hoppmann, Mario, Hoyland, Knut Vilhelm, Huntemann, Marcus, Hutchings, Jennifer K., Hwang, Byongjun, Itkin, Polona, Jacobi, Hans-Werner, Jaggi, Matthias, Jutila, Arttu, Kaleschke, Lars, Katlein, Christian, Kolabutin, Nikolai, Krampe, Daniela, Kristensen, Steen Savstrup, Krumpen, Thomas, Kurtz, Nathan, Lampert, Astrid, Lange, Benjamin Allen, Lei, Ruibo, Light, Bonnie, Linhardt, Felix, Liston, Glen E., Loose, Brice, Macfarlane, Amy R., Mahmud, Mallik, Matero, Ilkka O., Morgenstern, Anne, Naderpour, Reza, Nandan, Vishnu, Niubom, Alexey, Oggier, Marc, Oppelt, Natascha, Perron, Christophe, Petrovsky, Tomasz, Pirazzini, Roberta, Polashenski, Chris, Rabe, Benjamin, Raphael, Ian A., Regnery, Julia, Rex, Markus, Ricker, Robert, Riemann-Campe, Kathrin, Rinke, Annette, Rohde, Jan, Salganik, Evgenii, Scharien, Randall K., Schiller, Martin, Schneebeli, Martin, Semmling, Maximilian, Shimanchuk, Egor, Shupe, Matthew D., Smith, Madison M., Smolyanitsky, Vasily, Sokolov, Vladimir, Stanton, Tim, Stroeve, Julienne, Thielke, Linda, Timofeeva, Anna, Tonboe, Rasmus Tage, Tavri, Aikaterini, Tsamados, Michel, Wagner, David N., Watkins, Daniel, Webster, Melinda, and Wendisch, Manfred
- Subjects
atmosphere-ice-ocean interaction ,depth ,deformation ,arctic drift study ,temperature ,snow and sea ice ,thickness ,thermodynamics ,frequency ,interdisciplinary research ,impact ,pack ice ,mass-balance ,coupled climate system ,radar - Abstract
Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice-ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.
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