Influences on the reflectance of Arctic sea ice and the impact of anthropogenic impurities on the surface shortwave radiation balance

• Main Authors: Schulz, Hannes, Herber, Andreas, Birnbaum, Gerit, Seckmeyer, Gunther
• Format: Thesis
• Language: unknown
• Published: 2014
• Subjects: albedo; Arctic; Arctic Ocean; black carbon; Sea ice
• Online Access: https://epic.awi.de/id/eprint/38714/; https://epic.awi.de/id/eprint/38714/1/HS_Thesis_SeaIceAlbedo_rev00.pdf; https://hdl.handle.net/10013/epic.46022; https://hdl.handle.net/10013/epic.46022.d001

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 ...