Emily Gordon

Lecturer

University of Auckland

New Zealand

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Emily Gordon is a Lecturer at the University of Auckland, New Zealand. Her research focuses on oceanography and climate variability, particularly in relation to sea surface temperature (SST) predictability utilizing deep learning and neural network frameworks. Recent publications include studies on attributing record-high SSTs, decadal variability patterns in the North Pacific, and interactions between energetic particle precipitation and Antarctic ozone loss.

Articles (8)

Year:

2025

Year:

2025

Year:

2024

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Year:

2020

Collaborators (3)

Elizabeth A. Barnes

UNITED STATES

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Annika Seppälä

University of Otago

NEW ZEALAND

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Noah Diffenbaugh

Professor

Stanford University

UNITED STATES

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Emily Gordon

Research Interests

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About

Articles (8)

Identifying a Pattern of Predictable Decadal North Pacific SST Variability in Historical Observations

Attribution of the record-high 2023 SST using a deep-learning framework

Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

Separating internal and forced contributions to near term SST predictability in the CESM2-LE

Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State-Dependent Predictability in CESM2

Observational evidence of energetic particle precipitation NO<sub><i>x</i></sub> (EPP-NO<sub><i>x</i></sub>) interaction with chlorine curbing Antarctic ozone loss

Oceanic Harbingers of Pacific Decadal Oscillation Predictability in CESM2 Detected by Neural Networks

Evidence for energetic particle precipitation and quasi-biennial oscillation modulations of the Antarctic NO<sub>2</sub> springtime stratospheric column from OMI observations

Collaborators (3)

Elizabeth A. Barnes

Annika Seppälä

Noah Diffenbaugh

Make your next move.

Emily Gordon

Research Interests

Explore related searches

Contact this professor

About

Articles (8)

Identifying a Pattern of Predictable Decadal North Pacific SST Variability in Historical Observations

Attribution of the record-high 2023 SST using a deep-learning framework

Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

Separating internal and forced contributions to near term SST predictability in the CESM2-LE

Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State-Dependent Predictability in CESM2

Observational evidence of energetic particle precipitation NO&lt;sub&gt;&lt;i&gt;x&lt;/i&gt;&lt;/sub&gt; (EPP-NO&lt;sub&gt;&lt;i&gt;x&lt;/i&gt;&lt;/sub&gt;) interaction with chlorine curbing Antarctic ozone loss

Oceanic Harbingers of Pacific Decadal Oscillation Predictability in CESM2 Detected by Neural Networks

Evidence for energetic particle precipitation and quasi-biennial oscillation modulations of the Antarctic NO&lt;sub&gt;2&lt;/sub&gt; springtime stratospheric column from OMI observations

Collaborators (3)

Elizabeth A. Barnes

Annika Seppälä

Noah Diffenbaugh

Make your next move.

Observational evidence of energetic particle precipitation NO<sub><i>x</i></sub> (EPP-NO<sub><i>x</i></sub>) interaction with chlorine curbing Antarctic ozone loss

Evidence for energetic particle precipitation and quasi-biennial oscillation modulations of the Antarctic NO<sub>2</sub> springtime stratospheric column from OMI observations