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

北冰洋海冰正在经历重大的加速变化，在过去几十年里，人们越来越详细地观察到这一变化。北极和其他地区的利益攸关方受到这些变化对资源可获得性、生态系统健康和地球物理气候系统的连锁效应的影响。规划缓解和适应战略需要提高预测北极气候系统的能力。令人困惑的预测模型开发是在理解反照率反馈在海冰损失中所起作用方面仍然存在的巨大差距。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中移动时的情况，以这些站点收集的丰富数据集作为案例研究，提高对太阳能吸收和分配对冰块平衡的影响的理解。该方法利用3D可分辨海冰-海洋混合层耦合模式，将AON数据与以前野外活动、大气再分析产品和高分辨率遥感数据的测量结果结合在一起。其目标是以米级分辨率量化10x10千米研究区域内太阳能的沉积，确定太阳能随时间的命运(例如，海洋储存、冰底融化和横向融化)，并提高我们分辨率模型的能力，以表示控制太阳吸收和命运的过程。该模型明确地描述了关键的冰和海洋过程，如融化池塘的形成、卤水排放、淡水平衡和上层海洋分层，提供了一个数据集成工具，可以解释影响辐射传输和热量储存的所有一级过程。初始化数据集中的重要差距将通过集合建模方法来解决。现场观测与模型状态的迭代比较将为选择约束较差的初始条件提供信息，评估系统敏感性，并允许测试用于池塘演变等过程的改进的模型参数。

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.