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; 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.

涵盖范围广泛的时间和空间尺度的自然变异性记录以及非线性变化的捕获间隔以充分理解北极系统。该项目旨在开发第一个系统模型，以模拟整个过程链，以控制天气和气候如何影响导致冰川流域湖中沉积物记录沉积的过程。该模型为古气候医生传统上使用的先前基于统计的模型提供了一种替代方法，以从湖泊沉积物记录中推断出过去的气候变异性。新的基于过程的定量理解将为未来的研究奠定基础，旨在恢复延长数千年的环境和气候变化记录。 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.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，这将用作验证全球气候模型输出的基准。最后，它将支持四位早期职业科学家，并将培训研究生和本科生的系统科学研究研究生。该项目的主要目标是开发一个系统模型，该系统模型编码了控制冰川湖泊中阳光量的沉积物的数量和谷物大小的主要过程，并在冰川湖中累积的集中群体，并为实地湖泊中的基于现场的数据提供模型的数据，以用于模型和测试。积聚在北极湖泊底部的沉积物包含大量信息，内容涉及周围流域的主要特征如何在季节到千禧一代的尺度各不相同，以及它们如何应对自然和人为强迫。冰川流域中的湖泊记录上游冰川熔体速率的变化，这是不断发展的北极系统中最动态的成分之一。存储在冰川喂养的湖泊中的沉积物通常包括代表年度周期的不同节奏层。这些河流沉积物是地球上所有天然档案中最有价值的沉积物之一，因为它们可以放在精确的时间线上，并且因为它们以足够高的速度积累，足以跟踪年度和季节性尺度的环境变异性。它们已被广泛用于重建北极的过去气候变化，最常依赖于长期气象站和varve厚度的记录之间的统计相关性。这些统计相关性忽略了冰川湖湖 - 湖泊沉积系统中的复杂和随时间不断发展的相互作用，该系统将气候与沉积在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中沉积的相互作用的相互作用进行更基于过程的理解，以提供下一代古气候重建。通过合并系统模型方法，将开发基于过程的系统模型，以捕获冰川 - 溶质湖 - 湖学用品系统中的动态非线性。该系统模型将对三个现有模型组件进行介绍：一个基于物理的，空间显式的水文模型，其中包括冰川子模型；基于经验的沉积物升华模型；以及一个过程反应，盆地填充沉积模型。该系统模型将应用于沿着从亚北极到高北极的环境梯度的三个冰川流域，包括林恩湖（Svalbard）（Svalbard），彼得斯湖（McCall Glacier，北极冰川，北极国家野生动物保护区）和Eklutna Lake（附近的Eklutna Lake（Alaskorage，Alaskage，Alaska）。这项研究基于在每个研究地点或附近附近或附近进行的广泛的先前和正在进行的过程研究。现有的数据和提议的冰川，水文学，羊水学和沉积物过程研究将提供输入数据以运行系统模型并验证其输出。