Decoding climate anomalies—From scientifically sound to societally relevant

As climate impacts worsen, decision-makers, researchers, and public sector institutions increasingly need reliable, relevant, and appropriate information about emerging weather and climate anomalies. However, with the ever-expanding choice of datasets based on ground observations, satellite data, and models, it is critical to not only use the best dataset for a specific decision-making need but to avoid misinforming and even derailing decision-making due to dataset selection. The main objective of this perspective is to illustrate the importance of understanding datasets used to translate climate data into actionable knowledge, supporting the next generation of science- and data-driven climate risk management instruments, projects, and global initiatives. Through the lens of three use cases, we illustrate what data quality means for different applications, the implications of choosing, aggregating, and combining different climate datasets, and the potential consequences of dataset and baseline choice on decision-making. Choosing different datasets often leads to trade-offs that decision-makers need to be aware of, for instance, between the spatial resolution of a dataset and length of the data record. Here, we discuss the implications of selecting the appropriate time step, resolution, and dataset and provide examples of how these choices affect the identification of societally relevant anomalies.