Proceedings of the 2011 workshop on Climate knowledge discovery

Abstract

Numerical simulation based science follows a new paradigm: its knowledge discovery process rests upon massive amounts of data. Climate scientists generate data faster than can be interpreted and need to prepare for further exponential data increases. Current analysis approaches are primarily focused on traditional methods, best suited for large-scale phenomena and coarseresolution data sets. Tools that employ a combination of high-performance analytics, with algorithms motivated by network science, nonlinear dynamics and statistics, as well as data mining and machine learning, could provide unique insights into challenging features of the Earth system, including extreme events and chaotic regimes. The breakthroughs needed to address these challenges will come from collaborative efforts involving several disciplines, including end-user scientists, computer and computational scientists, computing engineers, and mathematicians. The SC11 Climate Knowledge Discovery workshop at Supercomputing '11 brought together experts from various domains to investigate the use and application of large-scale graph analytics, semantic technologies and knowledge discovery algorithms in climate science. The workshop was the second in a series of planned workshops, the first workshop being held in Hamburg in March 2011. The goal of the workshop was to provide more than a broad overview of the topic, but establish a platform for the ongoing discussions in this field. It was designed to be highly interactive to concentrate on the challenges facing the climate community: "What steps are required to realize the potential of data analytics, semantic technologies and knowledge discovery algorithms in climate science? Where methods and technologies already exist, how can they be leveraged? Where gaps are identified, what steps must be taken to address them?"