Collaborative Research: Decoding & Predicting Greenland's Surface Melt History & Future with Observations, Regional Atmospheric Modeling and GCMs

研究人员计划用三年的时间来提高对格陵兰冰盖表面融化的理解和预测。卫星遥感、区域气候建模和非线性分析技术将用于： 评估观测到的地表融化发生的变化；基于基准模型的熔化代理与观察到的熔化；诊断天气尺度气象/海冰对融化的控制；并根据 CMIP5（耦合模型比对项目第 5 阶段）大气环流模型 (GCM) 驱动的区域模型评估融化代理的未来变化。了解极地冰盖的表面融化非常重要，因为表面融化会影响反照率，可以产生有用的古气候记录，有助于通过径流实现质量平衡，并且是冰架崩塌的触发因素，导致冰流加速和海平面上升。更好地了解融化的天气气象原因，以及最先进的模型准确模拟融化的能力，将有助于评估未来变暖对融化、冰盖流动和海平面上升的影响。研究人员提出了三个主要研究主题来帮助解决这些问题：（1）区域大气模式技能评估和当前地表融化天气控制的诊断； (2) 将模型技能评估结果应用于基于 GCM 的气候情景，以估计未来变化； (3) 通过使用被动微波和光/热卫星数据的新型融合来检索熔体量级，扩大基于卫星的表面熔体状态表征检索。除了解决格陵兰冰盖平衡与海平面变化相关的关键问题外，该项目还主要通过网站向公众进行宣传，并产生许多教育影响。该项目将为本科生和研究生提供支持，并涉及来自拥有大量第一代大学生的本科院校和西班牙裔服务机构的研究人员。所有数据和模型结果都将存档并通过 ACADIS 数据中心提供。

The investigators plan a three-year effort to improve understanding and prediction of surface melting on the Greenland ice sheet. Satellite remote-sensing, regional climate modeling and nonlinear analysis techniques will be used to: assess variability of observed surface melt occurrence; benchmark model-based melt proxies versus observed melt; diagnose synoptic-scale meteorological/sea-ice controls on melt; and assess future change in melt proxies based on regional models driven by CMIP5 (Coupled Model Intercomparison Project Phase 5) general circulation models (GCMs). Understanding surface melt on polar ice sheets is important because surface melt affects albedo, can produce useful paleoclimatic records, contributes to mass balance through runoff, and is a trigger for ice-shelf collapse leading to ice-flow acceleration and sea-level rise. Improved understanding of the synoptic-meteorological causes of melting, and of the ability of state-of-the-art models to simulate melting accurately, would help assess the effects of future warming on melting, ice-sheet flow and sea level rise. The investigators propose three main research themes to help address these issues: (1) regional-atmospheric-model skill assessments and diagnosis of present synoptic controls on surface melt; (2) application of results from the model skill assessments to GCM-based climate scenarios for estimates of future change; and (3) expanding satellite-based retrieval of surface melt state characterization through retrieval of melt magnitude using a novel fusion of passive microwave and optical/thermal satellite data. In addition to addressing key questions relating Greenland ice sheet balance to sea-level change, the project includes outreach to the public, primarily through a web site, and numerous educational impacts. The project would support undergraduate and graduate students, and involves investigators from a primarily undergraduate institution with a significant population of first-generation college students, and from a Hispanic-serving institution. All data and model results will be archived and made available through the ACADIS data center.