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

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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.