Collaborative Research: The Land Unknown: Assessing Data Requirements for Modeling Change in the Antarctic Ice Sheet with an Emphasis on the Subglacial Bed

合作研究：未知的土地：评估南极冰盖变化建模的数据要求，重点关注冰下床

• 批准号: 1142165
• 负责人: Jesse Johnson
• 金额: --
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1142165&HistoricalAwards=false
• 关键词: Collaborative; Research; Land; Unknown; Assessing

Among the most dramatic changes now underway in the polar cryosphere is the several-fold speedup of Greenland and West Antarctic outlet glaciers. Computational models of ice sheets face considerable challenges in correctly simulating these events, which in turn, limits glaciologists' and climatologists' ability to predict future change. The challenge arises from limited knowledge of key variables, for example, the shape of the land surface beneath the ice. Data limitations present two problems; first, important details of ice behavior may be missed and second, models may produce results that compare well with observations but do so for the wrong reasons. The goal of this project is to use these uncertainties themselves as a guide to identifying which types of data are most important to producing useful projection of future ice sheet change. The work is focused on two regions, the Aurora basin in East Antarctica, where new high resolution data have recently become available, and the catchment surrounding South Pole station deep in the interior or East Antarctica, where ice flow is currently sluggish may have been faster in the past. Bayesian inference may be used to investigate formally the contributions of various model attributes to uncertainty in model projections by synthesizing information from different sources of data, modeling results, and corresponding error information. Here, an ice sheet model will be asked to identify the regions and processes that have the largest influence on ice flow and thickness change, according to an appropriate range of boundary conditions and initial model states. The approach will allow us to create and analyze correlation maps showing relationships between various boundary conditions and model outcome in selected study areas. Those parameters or regions that are most important should dominate the scatter in model projections of change. The associated posterior probability density will provide guidance on how to design observational strategies to meet specified scientific criteria.

在极地冰圈中，目前正在进行的最大变化是格陵兰岛和西南极西部出口冰川的几倍加速。 冰盖的计算模型在正确模拟这些事件时面临着巨大的挑战，这反过来限制了冰川学家和气候学家预测未来变化的能力。 挑战源于对关键变量的有限了解，例如，冰下的陆地表面形状。数据限制提出了两个问题；首先，可能会错过冰行为的重要细节，其次，模型可能会产生与观察结果良好相比的结果，但出于错误的原因而这样做。该项目的目的是将这些不确定性本身用作确定哪些类型数据的指南，这对于产生未来冰盖变化的有用投影最为重要。 这项工作集中在两个地区，即南极洲东部的极光盆地，最近在那里获得了新的高分辨率数据，而围绕南极站的流域深处或南极洲，目前冰流的速度可能更快。 贝叶斯推论可用于正式研究各种模型属性对模型投影的不确定性的贡献，通过从不同的数据源，建模结果和相应的错误信息中综合信息。在这里，根据适当的边界条件和初始模型状态，将要求冰盖模型确定对冰流和厚度变化最大影响的区域和过程。该方法将使我们能够创建和分析相关图，以显示所选研究领域中各种边界条件与模型结果之间的关系。那些最重要的参数或区域应主导变化模型投影中的分散。相关的后验概率密度将为如何设计观察策略以满足特定的科学标准提供指导。