Snow Covers impacts on Antarctic Sea Ice (SCASI)

积雪对南极海冰的影响 (SCASI)

• 批准号: 274947488
• 负责人: Dr. Marcel Nicolaus
• 金额: --
• 依托单位: Alfred-Wegener-Institut (AWI) Helmholtz-Zentrum für Polar- und Meeresforschung
• 依托单位国家: 德国
• 项目类别: Infrastructure Priority Programmes
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/274947488?language=en
• 关键词: Snow; Covers; impacts; Antarctic; Sea

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.

过去几年来，南极海冰范围的增加与北极的观测结果形成对比。其原因是目前的研究主题，数值模型无法再现南极海冰范围的增加。除了大气和海洋过程外，南极海冰的非均匀和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。这种积雪还使机载和卫星遥感技术复杂化，因为它支配着许多观测，并决定着方法和不确定性。这些事实表明，有必要更好地了解南极海冰上的雪，特别是为了加强遥感算法和气候模型，该项目的总体目标是量化南极海冰上的雪量和分布，其物理特性及其随时间的演变。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，代表各种长度尺度和不同季节。这种产品将极大地有助于开发和验证新的遥感算法，并改进模型模拟。最后，该项目将有助于揭示南极海冰质量和能量平衡及动态。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建模，结合遥感数据集，在南极积雪的现场数据的指导下。新的德国-瑞士合作（D-A-CH计划）的建立将使我们能够将德国合作伙伴的实地观测和遥感海冰专业知识与瑞士合作伙伴的观测和建模雪专业知识相结合。从几个成功的实地实验中可以很容易地获得全面的原位雪数据集，并辅之以漂移观测站的自主测量。来自AMSR-2、SMOS和CryoSat-2的卫星数据可在项目联合体中获得，这些数据将用于开发海冰上雪的新处理算法。然后将通过现场和自主观测验证算法和数据集。数值模型SNOWPACK和MEMLS将被耦合，以模拟雪的物理特性，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地面观测的升级以及卫星数据的解释。该项目将资助三名年轻科学家进行海冰研究。两位经验丰富的博士后将贡献他们最近的博士论文中的知识和专业知识，使用类似的方法和数据集来实现新的目标。一个博士生将通过写论文从这个项目中受益。