Rate My Professor Emily Gordon

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.