Big Data in climate research

In the study just published in the journal Nature Climate Change, the interdisciplinary team of authors around Benedikt Knüsel contrasts typical big data predictions with predictions in climate research. Using a conceptual framework, the authors show that there are several intermediate stages between classical scientific work and «pure» big data. These range from research approaches with classical climate data that are evaluated by machine learning, to studies that correlate data from social media, such as Twitter, with storm damage.

Based on this classification, the authors suggest under which conditions and for which questions in climate research big data approaches are particularly promising. The application of big-data elements in climate research requires assumptions about the sufficient constancy of the identified relations, which can only be based on background knowledge about the investigated systems. The authors therefore consider big-data elements to be particularly promising, if they are combined with classical scientific approaches. The biggest potential for big-data elements, the authors argue, lies in socioeconomic research questions, e.g., in climate impact research and climate services.

The study is the result of an interdisciplinary collaboration between researchers from the Institute for Environmental Decisions (IED) and the Institute for Atmospheric and Climate Science (IAC) at D-USYS, funded by the national research programme «Big Data» (NRP 75).