5 results on '"Birnbaum, Gerit"'
Search Results
2. Preconditioning of Summer Melt Ponds From Winter Sea Ice Surface Temperature.
- Author
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Thielke, Linda, Fuchs, Niels, Spreen, Gunnar, Tremblay, Bruno, Birnbaum, Gerit, Huntemann, Marcus, Hutter, Nils, Itkin, Polona, Jutila, Arttu, and Webster, Melinda A.
- Subjects
SEA ice ,OCEAN temperature ,ICE floes ,PONDS ,ARCTIC climate ,ARCTIC exploration - Abstract
Comparing helicopter‐borne surface temperature maps in winter and optical orthomosaics in summer from the year‐long Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition, we find a strong geometric correlation between warm anomalies in winter and melt pond location the following summer. Warm anomalies are associated with thinner snow and ice, that is, surface depression and refrozen leads, that allow for water accumulation during melt. Warm surface temperature anomalies in January were 0.3–2.5 K warmer on sea ice that later formed melt ponds. A one‐dimensional steady‐state thermodynamic model shows that the observed surface temperature differences are in line with the observed ice thickness and snow depth. We demonstrate the potential of seasonal prediction of summer melt pond location and coverage from winter surface temperature observations. A threshold‐based classification achieves a correct classification for 41% of the melt ponds. Plain Language Summary: We compare winter surface temperatures from an infrared camera with summer photographs of sea ice with melt ponds. The datasets were recorded from a helicopter during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Melt ponds form on sea ice in summer when the snow melts and water accumulates in the lower locations on the ice floes. Melt ponds are very important for the Arctic energy budget because they strongly change the sea ice brightness and thus the amount of solar energy absorbed by the ice. We find surface characteristics with similar size and location between warmer areas in winter and the location of melt ponds in summer. For a better process understanding, we calculate the surface temperature with a simple model and find that the warm temperature anomalies are due to thinner ice and snow. Stronger warm temperature anomalies appear in new cracks in the ice which are covered with newly formed, thin ice. With a temperature‐based classification, we are able to estimate the summer melt pond fraction. Key Points: Winter warm surface temperature anomalies are co‐located with melt pond locations in the following summerWarm anomalies appear in refrozen leads, in refrozen melt ponds, and in troughs between ridges, due to thinner snow and iceWe show the potential for prediction of summer melt pond fraction from winter surface temperatures [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Physical characteristics of melt ponds
- Author
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Oppelt, Natascha, Birnbaum, Gerit, Gege, Peter, König, Marcel, Fuchs, Nils, Macke, Andreas, and Flores, Hauke
- Subjects
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
4. Measurements and Modeling of Optical-Equivalent Snow Grain Sizes under Arctic Low-Sun Conditions.
- Author
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Jäkel, Evelyn, Carlsen, Tim, Ehrlich, André, Wendisch, Manfred, Schäfer, Michael, Rosenburg, Sophie, Nakoudi, Konstantina, Zanatta, Marco, Birnbaum, Gerit, Helm, Veit, Herber, Andreas, Istomina, Larysa, Mei, Linlu, and Rohde, Anika
- Subjects
GRAIN size ,MODIS (Spectroradiometer) ,ALBEDO ,SOLAR radiation ,OPTICAL measurements ,RADIATION absorption - Abstract
The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size ( r opt ) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Nonhydrostatic—Aerosols and Reactive Trace gases) simulations. The retrieval methods are based on optical measurements and rely on the r opt -dependent absorption of solar radiation in snow. The measurement data were taken during a three-week campaign that was conducted in the North of Greenland in March/April 2018, such that the retrieval methods and radiation measurements are affected by enhanced uncertainties under these low-Sun conditions. An adjusted airborne retrieval method is applied which uses the albedo at 1700 nm wavelength and combines an atmospheric and snow radiative transfer model to account for the direct-to-global fraction of the solar radiation incident on the snow. From this approach, we achieved a significantly improved uncertainty (<25%) and a reduced effect of atmospheric masking compared to the previous method. Ground-based in situ measurements indicated an increase of r opt of 15 µm within a five-day period after a snowfall event which is small compared to previous observations under similar temperature regimes. ICON-ART captured the observed change of r opt during snowfall events, but systematically overestimated the subsequent snow grain growth by about 100%. Adjusting the growth rate factor to 0.012 µm 2 s − 1 minimized the difference between model and observations. Satellite-based and airborne retrieval methods showed higher r opt over sea ice (<300 µm) than over land surfaces (<100 µm) which was reduced by data filtering of surface roughness features. Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals revealed a large spread within a series of subsequent individual overpasses, indicating their limitations in observing the snow grain size evolution in early spring conditions with low Sun. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
5. Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery.
- Author
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König, Marcel, Birnbaum, Gerit, and Oppelt, Natascha
- Subjects
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SEA ice , *PONDS , *BATHYMETRY , *ATMOSPHERIC aerosols , *DRONE aircraft , *WEATHER - Abstract
Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error ( R M S E ) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra ( R M S E < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate ( R M S E = 4.04 cm), and the model explains a large portion of the variability ( R 2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
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