Numerical simulation based science follows a new paradigm: its knowledge discovery process rests upon massive amounts of data. We are entering the age of data intensive science. Data is either generated by observational equipment such as satellite sensors, microscopes, particle colliders etc., or is born digital, e.g. generated by an intensive usage of high performance computing.

One of the largest repositories of scientific data in any discipline are the model generated and observational geoscience data used in climate science. Geoscientists gather data faster than they can be interpreted. Current approaches to data volumes are primarily focused on stewardship and visualization but do not provide tools for data intensive analytics to appropriately complement climate model simulations. Such tools could provide unique insights into challenging features of the Earth system, including anomalies, nonlinear dynamics and chaos. 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.

This is the fourth in a series of planned workshops to discuss the design and development of methods and tools for knowledge discovery in climate science. The organizers invite contributions from researchers in a broad range of domains working on the development and application of large-scale graph analytics, semantic technologies, and knowledge discovery algorithms in climate science. Both research papers describing novel methods and results as well as position papers describing the current state and vision for this emerging area are welcome.