Towards dedicated snow modelling in the Arctic that allows quantification of the impact of light absorbing impurities in snow
Snow is an important factor in the Earth System as it influences the global energy balance due to its high albedo. Light-absorbing impurities (LAI) in snow reduce its albedo, leading to enhanced absorption of shortwave radiation that warms the snowpack and stimulates feedback mechanisms that are par...
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| Other Authors: | , , , |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
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Universität Bremen
2023
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| Subjects: | |
| Online Access: | https://media.suub.uni-bremen.de/handle/elib/7747 https://doi.org/10.26092/elib/2829 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib77476 |
| Summary: | Snow is an important factor in the Earth System as it influences the global energy balance due to its high albedo. Light-absorbing impurities (LAI) in snow reduce its albedo, leading to enhanced absorption of shortwave radiation that warms the snowpack and stimulates feedback mechanisms that are partly responsible for faster warming of the Arctic than in any other part of the world. However, to date there is a lack of sufficient measurements to quantify the concentrations of LAI in Arctic snow, their seasonal evolution and trends. Therefore, it is difficult to investigate, quantify and understand the effects of LAI in snow on the evolution of snow properties, including the snow albedo, associated feedback mechanisms and the radiative forcing, especially in remote areas. To address this gap, models simulating the evolution of snow properties taken into account the effects of LAI on the radiative energy balance can be applied. However, reliable measurements to force these simulations, i.e. reliable deposition rates, are as well limited in time and space. This dissertation has the ambitious goal to simulate reliably the impact of LAI on the radiative energy balance in snow. Several milestones had to be reached to achieve this goal. The first milestone was to find reliable forcing data for remote regions of the Arctic. The second milestone was to develop, for a snow model designed for application in the European Alps, fit-for-the-Arctic parameterisations that describe sufficiently well the evolution of snow properties. After reaching these milestones, the effects of LAI in snow, using exemplarily black carbon (BC), on the evolution of snow properties could be investigated. The analyses were performed at a site in northeast Greenland, using atmospheric in-situ and snow depth data from Villum Research Station (VRS) (2014 to 2018) together with additional snow measurements carried out during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign 2018, the modern global ... |
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