Rate My Professor Emily Gordon

Emily Gordon is a climate physicist whose academic journey began at the University of Otago, where she earned a BSc in Physics and Electronics in 2017, a BSc (Hons) First Class in Physics in 2018, and an MSc in Physics in 2020. Her master's thesis, titled 'Observational Evidence for the Modulation of Antarctic Springtime Ozone Depletion by Energetic Particle Precipitation,' was conducted within the Department of Physics, focusing on the impacts of energetic particle precipitation on stratospheric chemistry. During her studies at Otago, she contributed to the Space Physics Group and Atmosphere and Climate Group, participated in Antarctic expeditions to Scott Base in 2019 alongside Dr. James Brundell to maintain scientific equipment, and served as a Sir Peter Blake Ambassador in 2018, collaborating with NIWA on remote sensing and data analysis techniques at Lauder and Baring Head. In 2020, she received the Fulbright Science and Innovation Graduate Award, enabling her to pursue a PhD in Atmospheric Science at Colorado State University, from which she graduated with research on climate predictability.

Emily Gordon holds the position of Lecturer in Climate Physics at the University of Auckland. Her research specializations encompass climate variability and predictability, regional climate change and its impacts, predictability theory, and innovative applications of machine learning and neural networks to climate modeling. Notable publications include 'Oceanic harbingers of Pacific decadal oscillation predictability in CESM2 detected by neural networks' (Geophysical Research Letters, 2021), 'Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State-Dependent Predictability in CESM2' (Geophysical Research Letters, 2022), 'Evidence for energetic particle precipitation and quasi-biennial oscillation modulations of the Antarctic NO2 springtime stratospheric column from OMI observations' (Atmospheric Chemistry and Physics, 2020), 'Observational evidence of energetic particle precipitation NOx (EPP-NOx) interaction with chlorine curbing Antarctic ozone loss' (Atmospheric Chemistry and Physics, 2021), 'Separating internal and forced contributions to near term SST predictability in the CESM2-LE' (Environmental Research Letters, 2023), and 'Combining neural networks and CMIP6 simulations to learn windows of opportunity for skillful prediction of multiyear sea surface temperature variability' (Geophysical Research Letters, 2024). Her work has garnered significant citations and advances understanding of decadal climate predictions and stratospheric ozone dynamics. She has engaged in public outreach, including the 'Take 10 with Emily Gordon' discussion on machine learning approaches for climate predictability at the University of Auckland.