Snow Covers impacts on Antarctic Sea Ice (SCASI)

过去几年南极海冰面积的增加与北极的观测结果形成对比。其原因是目前的研究课题，而数值模式无法重现南极海冰范围的增加。除大气和海洋过程外，南极海冰的非均质厚积雪是控制海冰质量平衡的主要因素，对全球气候和天气具有重要的反馈作用。这种积雪也使基于机载和卫星的遥感技术复杂化，因为它主导了许多观测并决定了方法和不确定性。这些事实表明，需要更好地了解南极海冰上的积雪，特别是为了改进遥感算法和气候模式。该项目的总体目标是量化南极海冰上积雪的数量和分布、其物理性质及其随时间的演变。该项目旨在为南极海冰开发一个新的、一致的雪数据产品原型，代表不同的长度尺度和不同的季节。这种产品将对开发和验证新的遥感算法以及改进模型模拟有很大帮助。最后，这个项目将有助于阐明南极海冰质量和能量平衡和动态。我们还将为改进对冰雪覆盖的南大洋的生物和地球化学研究作出贡献。为了实现这一目标，我们将使用高分辨率模型，以南极积雪的原位数据为指导，结合遥感数据集。建立新的德瑞合作（D-A-CH方案）将使我们能够将德国伙伴的实地观测和遥感的海冰专门知识与瑞士伙伴的观测和建模的雪专门知识结合起来。从几次成功的野外试验中可以很容易地获得全面的现场积雪数据集，并辅以漂流观测站的自主测量。来自AMSR-2、SMOS和CryoSat-2的卫星数据可在项目联盟中获得，这些数据将用于开发新的海冰雪处理算法。然后，算法和数据集将通过现场和自主观测进行验证。数值模型SNOWPACK和MEMLS将耦合模拟雪的物理性质，如雪深、温度、密度和微波发射率。这项工作的结果将支持数据分析和扩大地面观测的规模，以及对卫星数据的解释。这将最终允许生成积雪数据和地图产品。该项目将资助三位年轻科学家进行海冰研究。两位经验丰富的博士后将从他们最近的博士论文中贡献他们的知识和专业知识，使用类似的方法和数据集来实现新的目标。一个博士生将从这个项目中受益，写他的论文。

The increase of Antarctic sea-ice extent over the last years is in contrast to the observations in the Arctic. The causes are a current topic of research, and numerical models are not able to reproduce the increasing sea-ice extent in the Antarctic. Besides atmospheric and oceanic processes, the heterogeneous and thick snow cover of Antarctic sea ice is a major factor governing the sea-ice mass balance with important feedbacks on global climate and weather. This snow cover also complicates airborne- and satellite-based remote sensing techniques, because it dominates many observations and determines methodologies and uncertainties. These facts raise the need for a better understanding of snow on Antarctic sea ice, especially in order to enhance remote sensing algorithms and climate models.The overall goal of this project is to quantify the amount and distribution of snow on Antarctic sea ice, its physical properties and their evolution over time. The project is designed to develop a new and consistent snow-data product prototype for Antarctic sea ice, representing various length scales and different seasons. Such a product will be of great help in developing and validating new remote sensing algorithms and to improve model simulations. Finally, this project will help to shed light on Antarctic sea-ice mass and energy balance and dynamics. We will also contribute to improvements in biological and geochemical studies of the ice-covered Southern Ocean. To achieve this goal, we will use high-resolution modeling, guided by in-situ data on Antarctic snow cover in combination with remote sensing data sets. The establishment of a new German-Swiss cooperation (D-A-CH program) will enable us to merge sea-ice expertise from field observations and remote sensing of the German partners with snow expertise from observations and modeling of the Swiss partner. Comprehensive in-situ snow datasets are readily available from several successful field experiments, complemented by autonomous measurements from drifting observatories. Satellite data from AMSR-2, SMOS, and CryoSat-2 are available in the project consortium, which will be used to develop new processing algorithms for snow on sea ice. Algorithms and data sets will then be validated through in-situ and autonomous observations. The numerical models SNOWPACK and MEMLS will be coupled to simulate physical snow properties like snow depth, temperature, density, and microwave emissivity. The results from this work will support data analyses and up-scaling of the ground observations as well as interpretation of satellite data. This will finally allow the generation of the snow data and map product.The project will fund three young scientists for their sea-ice research. Two experienced PostDocs will contribute with their knowledge and expertise from their recent PhD theses, using similar methods and data sets for new objectives. A PhD student will benefit from the project by writing his thesis.