Advanced Data Clustering Applied to Climate Model Intercomparison

Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets in a fast and computationally-efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) may yet to be fully realised. In this poster, we explore the specific application of clustering as a mechanism for sub-sampling a climate model ensemble in order to identify outliers and achieve a deeper understanding of inter-model variability than is possible using traditional methods, which tend to be somewhat rudimentary.