The focus area Environment and Earth Sciences at ScaDS.AI Dresden/Leipzig uses Artificial Intelligence (AI) and big data analytics to describe the impacts of extreme weather events as well as climate change risks and biodiversity loss. AI is becoming increasingly important in this context as most parts of the Earth system are continuously monitored by sensors and AI is able to cope with both the volume of data and the heterogeneous data characteristics. For instance, satellites monitor the atmosphere, land, and ocean with unprecedented accuracy.
Air, water, soils, and biodiversity are also constantly monitored with dedicated local monitoring systems. Furthermore, citizen science projects collect data with smartphone apps. All in all, studying the Earth system and its changes has become a data-intensive research problem. At ScaDS.AI Dresden/Leipzig, we work on the methodological challenges arising in this broad context from different perspectives and consider multiple environmental facets, for instance on terrestrial ecosystems and climate dynamics.
One of the most pressing questions of our time is the prediction of the future climate and the associated risks. Various AI avenues are being explored to harness the potential of Deep Learning for better projections in this context. For instance, neural networks – the backbone of many AI methods – can now be informed by physical principles (“physics informed neuronal networks”). This leads to novel types of models that can efficiently represent uncertain processes in complex models. A prominent example is the question of how to represent clouds – still a key uncertainty in climate models. Developments of this kind will also be a key contribution to the international development of Digital Twins of the Earth (e.g. EU Destination Earth).
For different ecosystems such as forests, grasslands, and croplands, we focus on a branch of AI that incorporates explainability. This means that every prediction we make with AI shall be accompanied by some form of attribution or explanation. For instance, if we aim to predict crop failure, forest dieback, wildfires, or flood damage, we are also interested in the likely causes of these events. This is often less trivial than expected due to a combination of driving factors. Combining traditional process-based modeling approaches with innovative AI and Big Data analytics will hopefully lead to improved ecosystem management and adaptation strategies at regional to local scales.