Climate change is a confusing maze of inputs and outputs. Katherine Goode spends her days trying make the variable chaos behave.
Katherine is a research and development statistician at Sandia currently supporting the CLDERA Grand Challenge. She is part of a large research team working to understand climate change mitigation strategies and their potential downstream effects. Katherine lends her statistical expertise to create novel algorithms that identify patterns and relation points in inputs and variables that climate scientists believe are related.
“I’m working with observational data collected by satellites to try to understand how different climate variables are related to some sort of climate event. It’s not an easy problem, but it’s a really interesting challenge trying to understand those relationships,” Katherine said.
Machine-learning models relate inputs and outputs to predict future outcomes. Katherine applies machine learning to better understand the negative impacts of climate change, causes and potential outcomes of future mitigation techniques — such as climate geoengineering to slow the rate of change.
“We have this really complex climate system, but we try to understand if a climate change mitigation strategy was taken, how one variable may affect another variable which will affect another variable,” Katherine said. “One of the mitigation strategies we are studying is stratospheric aerosol injections, where aerosols would be injected into the stratosphere with the intention of deflecting the sun’s rays. We would like to understand how a change in the amount of aerosols relates to temperature changes.”