Collaborative research: Developing a System Model of Arctic Glacial Lake Sedimentation for Investigating Past and Future Climate Change

合作研究：开发北极冰川湖沉积系统模型以调查过去和未来的气候变化

• 批准号: 1418000
• 负责人: Nicholas McKay
• 金额: $ 75.32万
• 依托单位: Northern Arizona University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418000&HistoricalAwards=false
• 关键词: Collaborative; research; Developing; System; Model

NontechnicalAccurate records of natural variability that cover broad temporal and spatial scales, and that capture intervals of non-linear change are needed to fully comprehend the arctic system. This project aims to develop the first system model to simulate the full chain of processes that control how weather and climate affect the processes that lead to deposition of a sediment record in lakes in glaciated watersheds. This model provides an alternative approach to previous statistically-based models traditionally used by paleo-climatologists to infer past climate variability from lake sediment records. The new process-based quantitative understanding will lay the groundwork for future studies that will be aimed at recovering records of environmental and climate change that extend back thousands of years. This project will contribute to ongoing efforts through collaborations with: utility managers of the Municipality of Anchorage who are planning for diminished glacier meltwater input to Eklutna Lake, a major source of their electricity and freshwater and with resource managers at US Fish and Wildlife Service who are developing a monitoring network for the Arctic Refuge and who are striving to foresee future changes in habitat quality associated with glacier retreat. This project will benefit climate science researchers by leading to more accurate climate reconstructions, which will be used as benchmarks for validating global climate model output. Finally, it will support four early-career scientists and will train graduate and undergraduate students in system-science research.TechnicalThe primary goal of this project is to develop a system model that encodes the major processes that govern the amount and grain size of sediment that accumulates in arctic lakes in glaciated catchments, and to acquire the field-based data for model input and testing. Sediments that accumulate at the bottom of arctic lakes contain a wealth of information about how major features of the surrounding watershed have varied on seasonal to millennial time scales, as well as how they have responded to natural and anthropogenic forcings. Lakes in glaciated watersheds record changes in the melt rate of upstream glaciers, which are among the most dynamic components of the evolving arctic system. The sediment stored in glacier-fed lakes often comprise distinct rhythmic layers that represent annual cycles. These varved sediments are among the most valuable of all natural archives on Earth because they can be placed on a precise time line, and because they accumulate at a rate that is sufficiently high to track environmental variability on annual, and often seasonal, scales. They have been used extensively to reconstruct past climate changes in the Arctic, most often relying on statistical correlations between records from long-term weather stations and varve thickness. These statistical correlations disregard the complex and time-evolving interactions within the glacier-hydrology-lake-sedimentation system that link climate to changing properties of sediment deposited at the lake bottom. A more process-based understanding of the interactions that control sedimentation within lakes of glaciated catchments is needed to provide the next generation of paleoclimate reconstructions. By incorporating a system-modeling approach, a process-based system model will be developed to capture dynamic nonlinearities in the glacier-hydrology-lake-sedimentation system. The system model will couple three existing model components: a physically based, spatially explicit hydrological model, which includes a glacier sub-model; an empirically based sediment-flux model; and a process-response, basin-filling sedimentation model. The system model will be applied to three glaciated watersheds that fall along an environmental gradient spanning from the sub-Arctic to the High Arctic, including Lake Linne (Svalbard), Lake Peters (near McCall Glacier, Arctic National Wildlife Refuge), and Eklutna Lake (near Anchorage, Alaska). This study builds on extensive previous and on-going process studies at or near each of the study sites. Existing data and proposed glacier, hydrology, limnology, and sediment process studies will provide the input data to run the system model and to validate its output.

非技术性 为了充分理解北极系统，需要准确记录涵盖广泛的时间和空间尺度的自然变化，并捕获非线性变化的间隔。该项目旨在开发第一个系统模型来模拟控制天气和气候如何影响导致冰川流域湖泊中沉积物记录沉积的过程的完整过程链。该模型为古气候学家传统上使用的基于统计的模型提供了一种替代方法，用于根据湖泊沉积物记录推断过去的气候变化。新的基于过程的定量理解将为未来的研究奠定基础，这些研究旨在恢复数千年前的环境和气候变化记录。该项目将通过与安克雷奇市的公用事业管理者合作，为持续的努力做出贡献，他们正在计划减少冰川融水输入到埃克卢特纳湖（该湖是他们的电力和淡水的主要来源），并与美国鱼类和野生动物管理局的资源管理者合作，他们为北极保护区建立一个监测网络，并努力预测未来与冰川退缩相关的栖息地质量变化。该项目将通过更准确的气候重建来使气候科学研究人员受益，这些重建将用作验证全球气候模型输出的基准。最后，它将支持四名处于职业生涯早期的科学家，并培训研究生和本科生进行系统科学研究。技术该项目的主要目标是开发一个系统模型，对控制沉积物数量和颗粒大小的主要过程进行编码积累在冰川流域的北极湖泊中，并获取用于模型输入和测试的现场数据。北极湖泊底部积累的沉积物包含了大量信息，涉及周围流域的主要特征在季节到千年时间尺度上如何变化，以及它们如何对自然和人为强迫做出反应。冰川流域的湖泊记录了上游冰川融化速度的变化，这是不断演变的北极系统中最具活力的组成部分之一。冰川湖泊中储存的沉积物通常包含代表年度循环的独特节奏层。这些变化的沉积物是地球上所有自然档案中最有价值的，因为它们可以被放置在精确的时间线上，并且因为它们的积累速度足够高，可以跟踪年度（通常是季节性）尺度上的环境变化。它们已被广泛用于重建北极过去的气候变化，最常依赖于长期气象站的记录与地层厚度之间的统计相关性。这些统计相关性忽略了冰川-水文-湖泊-沉积系统内复杂且随时间演变的相互作用，这些相互作用将气候与湖底沉积物的不断变化的特性联系起来。需要对控制冰川流域湖泊内沉积的相互作用进行更多基于过程的理解，以提供下一代古气候重建。通过结合系统建模方法，将开发基于过程的系统模型来捕获冰川-水文-湖泊-沉积系统中的动态非线性。该系统模型将耦合三个现有模型组件：基于物理的、空间明确的水文模型，其中包括冰川子模型；基于经验的沉积物通量模型；以及过程响应、盆地充填沉积模型。该系统模型将应用于沿着从亚北极到高北极的环境梯度分布的三个冰川流域，包括林纳湖（斯瓦尔巴群岛）、彼得斯湖（靠近麦考尔冰川、北极国家野生动物保护区）和埃克卢特纳湖（阿拉斯加安克雷奇附近）。这项研究建立在每个研究地点或附近之前和正在进行的广泛过程研究的基础上。现有数据和拟议的冰川、水文学、湖泊学和沉积物过程研究将为运行系统模型并验证其输出提供输入数据。