 We are concerned with the earth system. The earth system is a very complex system where physical, biological, and chemical processes interact with each other in a complex way. It's also a very unique system. We don't have other earths where we can do experiments, so we have to rely on observations and modeling in this context. Conceptually, the earth system is divided into different spheres like the atmosphere, the biosphere, the oceans, the cryosphere, for example, and also the humans now, the anthroposphere, which interact with each other in these complex systems. This is very challenging to understand and to model. Let me just give you one example. As you know, we all emit a lot of carbon dioxide to the atmosphere causing the greenhouse effect. But luckily the CO2 concentrations don't rise as much as we emit carbon dioxide. Roughly 50% is actually absorbed by the oceans and by the ecosystem. Which help us mitigate the greenhouse effect. But we don't know if this will continue in the future. And if you look now at different model predictions where the models try to model the whole earth system from the interactions of these system components with physical and biological principles behind it, we can see that despite 20 years at least of investment in this research, there are large uncertainties. Some models say Yes, this will continue that the ecosystems will take up carbon from the atmosphere and others say no, there will be a reverse of that and there will be actually even more carbon dioxide emitted from those ecosystems in the future, which would actually exacerbate the greenhouse effect at the end. And our question is basically how can we change this? How can we improve this understanding? And we wonder how all the Earth observations and the data, the big data combined with modern artificial intelligence and machine learning can be helpful to improve this understanding of the earth system. Traditional methods of earth system modeling usually work, of course, with a lot of theory and assumptions. Usually data is not so well represented. So our idea here is to really let the data speak on itself, just extract the knowledge from the ample observations that we have actually on earth. Earth data is prototypical for big data, is large volume. But what's really important is that there is also a large variety of different data streams like remote sensing earth observation where the whole earth is covered every day, point measurements at individual stations or measurements along rivers or within oceans. There's a large variety, which is also a very big challenge to integrate these different data streams. So our question here is basically what kind of methods are the ones that are most promising to understand the earth system. And this is needed because artificial intelligence is a very broad field and there are a lot of methods out there and not necessarily all methods are very well fit to earth system questions. We have been looking at these different methods and trying to tease out and find out which are the most promising methods to gain a better understanding of the earth system purely and alone from data. What we found in our analysis is actually that recent developments in deep learning are particularly promising for understanding the earth from observations. One example is the well-known recognition of objects like cats and dogs and images where deep learning has been very, very successful in the past that translate directly to detection of weather patterns like hurricanes or droughts or heat waves and their impact on ecosystems in the earth. Another example that relates to dynamic processes and time series is what is also well known is that deep learning is used for language translation where the meaning of a word depends also a lot on what has been said before. This is again similar with dynamics on the earth where the current activities on earth also depend a lot on the history. So time series analysis and dynamic forecasting directly relates to so-called recurrent neural networks for example that are used for language translation. There are also other examples like video prediction that are also again very analog to challenges in the earth system. That said, it is not so easy to transfer these methods directly to the earth because our earth system data is a little bit different from for example photographs. For example, photograph only has three channels usually red, green and blue. While our earth system data consists of hundreds or even thousands different variables that have to be integrated in a multivariate modelling approach. Traditional methods have actually reached some limits regarding the predictive capability about climate change and the earth system. That basically means that the uncertainties of these predictions have not been reduced over the last decades. What we expect from integrating these new methods of data driven earth system science into those models is that these uncertainties can be substantially reduced in the future. Of course, these better predictions will allow us to have more focus and more targeted policy measures and economic measures and will finally benefit our society at all. We certainly believe that these data driven methods are a very valuable addition to earth system science and earth system modelling. But at the same time, we are totally aware that this is not the holy grail. And why is it not the holy grail? Because these methods as all statistical methods don't naturally obey the physical laws. They don't know about it. So they can produce physically totally implausible results and they can also produce results which are hard to explain because these methods are also usually black box methods. And so it's a big challenge to understand why deep learning methods actually do certain predictions and not others. So what we think is important to do research on combining these deep learning approaches or machine learning approaches in general with physical modelling, on the other hand, to combine the best of both worlds. We call that hybrid modelling approaches where, for example, processes that are theoretically well known are also modeled with physical equations. But other processes that are not well known and where no solid theories exist, for example, biological processes, but also often fine scale processes, are modeled from observations where we learn those processes from the data.