Human-induced climate change constitutes one of the most pressing global challenges in modern times. If we fail to mitigate these changes, it will likely push the planet to a tipping point where we will experience catastrophic disruptions to ecosystems, society and our economies.
In recent decades, complex systems models have proven key for predicting climate change and for identifying anthropogenic and natural drivers of climate change, e.g. global warming levels of 2°C above preindustrial baseline or atmospheric concentrations of CO2 above 350 ppm.
As new climate change mitigation and adaptation strategies are being suggested or initiated across the globe, it is also of utmost importance to understand the relevance and consequences of such actions. However, current climate models are limited by relatively poor temporal and spatial resolution, caused by limited quality and availability of data and lack of computational power.
This is unlikely solved by a single discipline or technology but requires a united and interdisciplinary effort. Therefore, this Challenge Programme 2023 theme invites to interdisciplinary approaches to solve this major challenge.
The specific challenge of this theme is to develop data-driven, next-generation climate models with the purpose of enabling robust predictions of climate change and prediction of the effect of mitigating solutions. The aim is to significantly progress our causal understanding of the fundamental mechanisms and consequences on a regional and/or global scale.
This highly interdisciplinary Challenge bridges synergistic collaboration between physics, data science, mathematics, atmospheric chemistry, the geo-sciences, engineering, and the life sciences. The successful research plan should include theoretical modelling rooted in, e.g., complex systems models, numerical simulations, and/or AI algorithms as well as a significant experimental component, e.g., data collection from ground- or ocean-based technology platforms, laboratory or fieldwork, and/or space-based active sensors to support, confirm and advance the generated models.
Some examples of research within the scope of this Challenge theme could be: