Climate change and its consequences are increasingly being recognized as among the most significant challenges of our time, yet there is considerable uncertainty regarding the social and environmental impacts because the predictive potential of numerical models of the Earth system is limited. There is a clear need to develop improved assessments of climate change, including but not limited to global and regional changes, extreme events and stresses on environment and society, and a comprehensive characterization and/or reduction of uncertainty.

Climate and earth sciences have recently experienced a rapid transformation from a data-poor to a data-rich environment. In particular, climate related observations from remote sensors on satellites and weather radars, or from in situ sensors and sensor networks, as well as outputs of climate or Earth system models from large-scale computational platforms, provide terabytes of temporal, spatial and spatio-temporal data. In addition, the rapid growth of geographical information systems leads to the availability of multi-source data. These massive and information rich datasets offer a huge potential for advancing the science of climate change and impacts. This workshop will bring together researchers who are advancing computational and data analysis methods necessary for addressing the key challenges in climate change science. A major focus of the workshop is on computational data science tools that can extract the achievable predictive insights from climate data and capture the complex dependence structures among climate variables.

The workshop will be held at the University of Minnesota in Minneapolis. The program will include invited talks by leading experts in the field, panel discussions, and poster sessions. Additional details will be posted on the workshop website as they become available.

Download the Workshop Poster (pdf)