Climate change has become the defining issue of our era. There is an increasing consensus that greenhouse gas emissions resulting from the use of fossil fuels as well as human-induced changes to the ecosystem (e.g. deforestation) are the cause of global warming, which in turn, can have dramatic consequences such as increased occurrence of extreme weather events, shocks in food and water supplies, rising sea levels. This has created an urgent need to improve our ability to answer questions such as: (i) what is the impact of global warming on the frequency, intensity and duration of extreme events such as heat waves, droughts, floods, hurricanes, large scale forest fires; (ii) what is the impact of deforestation and other land cover changes on the global carbon cycle; (iii) what is the relationship of crop prices to deforestation dynamics and greenhouse gas emissions. There is a significant interest in the ability to answer such questions from a large community that includes climate and environmental scientists, policy makers and just as importantly, the society at large. Data sets that can help us answer such questions are becoming increasingly available from remote sensors like 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. However, to be able to fully realize the potential benefits of these data sets, a number of computational challenges in spatio-temporal data mining (STDM) need to be addressed and these challenges are the focus of this project. Specifically, the focus is on the development of data mining algorithms that can help answer some of the most important questions faced by the climate and ecosystem scientists today.