Sandia applied mathematician Khachik Sargsyan has brought his mind to bear on problems involving combustion, fusion and chemical kinetics — specifically from the viewpoint of probability modeling and quantifying uncertainties in physical systems.
Khachik is now turning his expertise to climate security.
“I am developing algorithms to improve model predictions in light of observational data,” he said. “These are not the weather predictions that we see on media every day, but rather climate trends on decadal scales. The exploding field of machine learning, fueled by traditional statistical methods, helps us tune climate models and provide quantifiable estimates of risks and uncertainties under various scenarios.”
And in climate change, there can be a lot of uncertainty. When considering just the nation, the many climates, carbon absorption of different species of plant life and the climate change impact of different industries are just some of the sources of data climatologists must take into account.
“Simulating complex climate models under multiple scenarios helps draw conclusions augmented with confidence estimates,” Khachik said.