Global Warming and Changes in Risk of Climate Compound Extremes
Ali Sarhadi, PhD
Department of Earth System Science, Stanford University


Anthropogenic global warming has made potentially dangerous shifts in climate and ocean
systems, leading to severe and frequent climate extremes. To reduce the damages from these
extremes in a future warming climate, we must improve our prediction capabilities with the
development of new data mining perspectives. In this talk, I discuss Deep Learning methods, by
which we can model temporal dependences in the ocean dynamic systems. By combining
Convolutional Neural Network (CNN) and LSTM models, I show how we can learn from the
memory in ocean features, and predict the destructiveness of hurricanes for a future warming
climate. In the second section, I will introduce new multidimensional dynamic risk frameworks
by developing Bayesian, dynamic copulas to model the dependence structure of climatic
compound extremes. I will present some applications of these frameworks for quantifying the
impact of anthropogenic global warming on various cases. These cases include the increased
risk of compound warm and dry conditions, weather and climate compound extremes which
lead to more intense wildfires in California, and multiple hazards from hurricanes.


Global Warming and Changes in Risk of Climate Compound Extremes
Ali Sarhadi, PhD
Department of Earth System Science, Stanford University


Anthropogenic global warming has made potentially dangerous shifts in climate and ocean
systems, leading to severe and frequent climate extremes. To reduce the damages from these
extremes in a future warming climate, we must improve our prediction capabilities with the
development of new data mining perspectives. In this talk, I discuss Deep Learning methods, by
which we can model temporal dependences in the ocean dynamic systems. By combining
Convolutional Neural Network (CNN) and LSTM models, I show how we can learn from the
memory in ocean features, and predict the destructiveness of hurricanes for a future warming
climate. In the second section, I will introduce new multidimensional dynamic risk frameworks
by developing Bayesian, dynamic copulas to model the dependence structure of climatic
compound extremes. I will present some applications of these frameworks for quantifying the
impact of anthropogenic global warming on various cases. These cases include the increased
risk of compound warm and dry conditions, weather and climate compound extremes which
lead to more intense wildfires in California, and multiple hazards from hurricanes.