Yet achieving these smaller scales requires enormous computing power, the likes of which is found more and more frequently only in computers with graphics processing units (GPUs). Therefore, the model’s “inner workings”, in other words how the individual cores share and process data, have to be reprogrammed. Such powerful supercomputers make it possible to map small-scale phenomena, such as thunderclouds, or city models in new and improved ways. However, they also produce more data than can possibly be stored.
Getting a high-resolution model to run on a new computer architecture thus requires the expertise of physicists, chemists, biologists and other experts to better describe these small-scale phenomena. Computer scientists, too, are needed if we are to make efficient use of the new technologies. But at the end of the day, even the best simulation is useless if its users don’t understand it or don’t know what to use it for.
Benefits to society
For researchers, climate models are tools with which they can test their hypotheses, learn to understand processes and interpret measurement data. But they can also do more: climate models are used in forecasts to minimise the risks and vulnerabilities of society and infrastructure and to find robust means of adjustment. The dialogue with and benefit for users is key to this process. Once we climate modellers understand what information farmers or civil engineers need for what place and time frame, then we can better prepare our models for adjustment – a shining example of how technological development and inter- and transdisciplinary research work together to deliver tangible benefits to society.
