Marc Alessi is a UCS Science Fellow focusing on climate attribution science with the Climate & Energy program at the Union of Concerned Scientists. In his role, he primarily uses a machine learning approach to improve climate attribution science methods to inform loss and damage funding. His background is in studying the physics of climate change through climate model data, simple approximations of the climate system, and machine learning. During his PhD, he analyzed how different patterns of ocean surface temperature affect the rate of global warming. He helped lead an intercomparison project among the world’s top climate modeling centers, and also led a project on attributing drought to changes in the evolution of soil moisture. He was previously awarded a German Academic Exchange Service (DAAD) research fellowship to conduct research at the Max Planck Institute in Hamburg, Germany, and he represented Cornell University at the UNFCCC COP 23.
Dr. Alessi earned a PhD in atmospheric science from Colorado State University, and an MS and BS in atmospheric science from Cornell University.