What are Digital Twins?
Originally used in product manufacturing, Digital Twinning involves creating a virtual version of a product to test and explore development options. For instance, engineers would use a digital twin to potentially see where a product might have weaknesses so they could improve the design of it before manufacturing.
This technology is now being used in a range of different disciplines but is being found to be very useful for the scientific community exploring our natural world and Earth Systems, to delve into complex processes that are difficult to observe and understand in person.
Digital twins in Environmental Science
In environmental science, Digital Twin projects involve integrating satellite data, computer models and Artificial Intelligence to create a digital replica that we can explore in detail, enhancing our understanding of poorly understood processes occurring in the cryosphere, supporting global efforts to plan for a changing climate.
ESA’s Digital Twin Earth programme aims at using the most up-to-date Earth Observation (EO) data streams alongside cutting-edge research and development to create a set of freestanding prototype Digital Twins replicating various components of the Earth system, one of which focuses on ice sheets.
These Digital Twin components will target a number of different elements of the atmosphere, hydrosphere (the Earth’s water systems), lithosphere (Earth’s geology such as land use and soil composition), biosphere (ecosystems and vegetation), anthrosphere (human activity) and the cryosphere (Earth’s ice regions), and are designed to form the building blocks for a future EO-driven Digital Twin of the Earth system.
CPOM’s Amber Leeson and Mal McMillan are part of the team developing ESA’s Digital Twin Component of the Ice Sheets which targets topics related to ice sheet hydrology, mass balance and ice shelf stability. In particular, they are interested in how a Digital Twin might provide valuable information related to hydropower, thereby empowering local communities to harness EO data in the push towards low carbon technologies. More specifically, this work aims to explore how EO data, modelling and advanced statistical techniques can be bought together within a Digital Twin to predict driven changes in the amount of freshwater flowing off the Greenland Ice Sheet.
They are using Machine Learning to map the dynamics of the surface hydrological systems that form atop the ice sheet each summer as the ice surface undergoes melt. This is important for understanding how climate warming and extreme events impact surface melting and, ultimately the ice sheet contribution to sea level rise.
What can we achieve with Digital Twinning in the Future?
Digital Twins hold significant opportunities for the future of polar science, particularly in providing a range of stakeholders with actionable information. They offer the potential to extract greater insight from the ever-increasing environmental data streams at our disposal, and to interactively explore a wide range of future climate scenarios. In turn, this supports humankind to develop mitigation strategies and enhance resilience in preparation for a potentially rapidly evolving climate.
This is particularly important for understanding the melting of the Earth’s ice sheets and glaciers, which contribute to sea level rise, a key challenge we will face in the future.