Collaborative Research: Combining Arctic Observing Network Observations and Remote Sensing Data to Understand Sea Ice Mass Balance and Albedo Feedbacks in a Changing Arctic

合作研究：结合北极观测网络观测和遥感数据来了解不断变化的北极的海冰质量平衡和反照率反馈

• 批准号: 1417538
• 负责人: Greg DeFrancis
• 金额: $ 13.39万
• 依托单位: Montshire Museum of Science
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1417538&HistoricalAwards=false
• 关键词: Collaborative; Research; Combining; Arctic; Observing

Arctic sea ice is undergoing significant and accelerating change, which has been observed in increasing detail over the past several decades. Stakeholders in the Arctic and beyond are impacted by the cascading effects that these changes have on resource accessibility, ecosystem health, and earth?s physical climate system. Planning mitigation and adaptation strategies requires improvements in the ability to predict the arctic climate system. Confounding predictive model development are large gaps that remain in the understanding of the role albedo feedbacks are playing in sea ice loss.This project will improve the understanding the impacts of solar energy absorption and partitioning on ice mass balance by tracking Arctic Observing Network (AON) sea ice sites as they move through space and time, using the rich datasets being collected at the sites as case studies. The approach integrates AON data with measurements from prior field campaigns, atmospheric reanalysis products, and high resolution remote sensing data using a 3D resolved sea ice-ocean mixed layer coupled model. The objectives are to quantify the deposition of solar energy within a 10x10km study domain around the sites with meter-scale resolution, identify the fate of that solar energy over time (e.g. ocean storage vs. ice bottom melt vs. lateral melt), and improve the ability of our resolved scale model to represent the processes controlling solar absorption and fate. The model explicitly represents critical ice and ocean processes such as melt pond formation, brine drainage, freshwater balance, and upper ocean stratification, providing a tool for data integration that can account for all first order processes impacting radiative transfer and heat storage. Important gaps in initialization datasets will be addressed by an ensemble modeling approach. Iterative comparison of site observations with model states will inform the selection of poorly constrained initial conditions, evaluate system sensitivity, and allow testing of improved model parameterizations for processes such as pond evolution.

北极海冰正在经历显著和加速的变化，在过去几十年里，人们对这种变化的观察越来越详细。北极及其他地区的利益相关者是否受到这些变化对资源可获得性、生态系统健康和地球的级联效应的影响？的物理气候系统。规划减缓和适应战略需要提高预测北极气候系统的能力。在理解海冰损失中的太阳能反馈作用方面，预测模型开发存在很大的差距。该项目将通过跟踪北极观测网络（AON）海冰站点在空间和时间上的移动，使用在站点收集的丰富数据集作为案例研究，来提高对太阳能吸收和分配对冰质量平衡影响的理解。该方法将AON数据与先前的现场活动，大气再分析产品和高分辨率遥感数据，使用3D解决海冰-海洋混合层耦合模型的测量。其目标是以米级分辨率量化站点周围10 x10 km研究区域内的太阳能沉积，确定太阳能随时间的命运（例如海洋储存与冰底融化与横向融化），并提高我们的分辨率尺度模型的能力，以代表控制太阳能吸收和命运的过程。该模型明确表示关键的冰和海洋过程，如融化池的形成，盐水排水，淡水平衡和上层海洋分层，提供了一个工具，数据集成，可以考虑所有的一阶过程影响辐射传输和热存储。初始化数据集的重要差距将通过整体建模方法来解决。站点观测与模型状态的迭代比较将告知约束条件较差的初始条件的选择，评估系统灵敏度，并允许测试池塘演变等过程的改进模型参数化。