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Environmental science is primarily concerned with assessing the impacts of changing environmental conditions on the state of the natural world, whether affected by natural variability or by the impact of human activity. The Natural Environment Research Council (NERC) has recently published its digital strategy1, the first of its kind for NERC, which sets out a vision for digitally enabled environmental science for the next decade. This is echoed in the Met Office’s Research and Innovation Strategy that includes the vision of transforming the weather and climate research and services through deploying transformative technologies such as Digital Twins2. This strategy places data and digital technologies at the heart of UK environmental science. One such set of technologies are digital twins. A digital twin is a virtual representation of an object or system (for example the natural environment) updated as the system changes using observations. Observations may come from a range of sources, some traditionally used in the environmental science community such as satellite remote sensing or sensors on ships or weather stations, or through the emergence of sensors on everything from fridges to cars to large-scale built infrastructure. A digital twin then uses simulations or data-based methods such as machine learning to generate a replica (‘twin’) of the system that can be used to understand the system itself. Environmental digital twins therefore have the potential to significantly improve our understanding of the natural environment. The emergence of increasingly large, diverse, observed data sources and the development of digital twin technologies combined provides an opportunity for the environmental science community to make a step-change in our understanding of the environment. But to realise the value of environmental digital twins they need to be developed following agreed standards to make sure the information can be trusted by the user, and so that data from twins

Environmental science is primarily concerned with assessing the impacts of changing environmental conditions on the state of the natural world, whether affected by natural variability or by the impact of human activity. The Natural Environment Research Council (NERC) has recently published its digital strategy1, the first of its kind for NERC, which sets out a vision for digitally enabled environmental science for the next decade. This is echoed in the Met Office’s Research and Innovation Strategy that includes the vision of transforming the weather and climate research and services through deploying transformative technologies such as Digital Twins2. This strategy places data and digital technologies at the heart of UK environmental science. One such set of technologies are digital twins. A digital twin is a virtual representation of an object or system (for example the natural environment) updated as the system changes using observations. Observations may come from a range of sources, some traditionally used in the environmental science community such as satellite remote sensing or sensors on ships or weather stations, or through the emergence of sensors on everything from fridges to cars to large-scale built infrastructure. A digital twin then uses simulations or data-based methods such as machine learning to generate a replica (‘twin’) of the system that can be used to understand the system itself. Environmental digital twins therefore have the potential to significantly improve our understanding of the natural environment. The emergence of increasingly large, diverse, observed data sources and the development of digital twin technologies combined provides an opportunity for the environmental science community to make a step-change in our understanding of the environment. But to realise the value of environmental digital twins they need to be developed following agreed standards to make sure the information can be trusted by the user, and so that data from twins