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Publication	Type	Year
Feature Pathway Detection using Random Forest Regressor Feature Importances American Geophysical Union 2022 Matthew Gregor Peterson, Irina Kalashnikova Tezaur, Jeffrey Nichol	Abstract – 2022 Abstract	2022
Global Multivariate Causal Discovery For the Analysis of Emergent Properties in Earth System Models European Seminar on Computing (ESCO 2022) Jeffrey Nichol	Conference Presentation – 2022 Conference Presentation	2022
Global Multivariate Causal Discovery for the Analysis of Emergent Properties in Earth System Models European Seminar on Computing Jeffrey Nichol	Abstract – 2022 Abstract	2022
PhD Committee Meeting 1 PhD Committee Meeting Jeffrey Nichol	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Causal Discovery for Climate Science and the Energy Exascale Earth System Model Chesapeake Large-Scale Analytics Conference Jeffrey Nichol	Conference Presentation – 2021 Conference Presentation	2021
Casual Evaluations for Identifying Differences between Observations and Earth System Models Jeffrey Nichol, Matthew Gregor Peterson, Kara J. Peterson https://www.osti.gov/search/identifier:1820528	Report – 2021 Report	2021
Causal Analysis for Climate Science Chesapeake Large-Scale Analytics Conference Jeffrey Nichol, Matthew Gregor Peterson	Abstract – 2021 Abstract	2021
Causal Inference and Causal Discovery Verification and validation of machine-learned models used in science and engineering Jeffrey Nichol	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Learning Why: Data-Driven Causal Evaluations of Climate Models ICML 2021 Workshop Tackling Climate Change with Machine Learning Jeffrey Nichol, Matthew Gregor Peterson, Kara J. Peterson	Conference Poster – 2021 Conference Poster	2021
Learning Why: Data-Driven Causal Evaluations of Climate Models ICML 2021 Workshop Tackling Climate Change with Machine Learning Jeffrey Nichol, Matthew Gregor Peterson, Matthew Fricke	Conference Proceeding – 2021 Conference Proceeding	2021
Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change Arctic Climate Machine learning Energy Exascale Earth System Model Jeffrey Nichol, Matthew Gregor Peterson, Kara J. Peterson, Matthew Fricke, Melanie Moses https://www.osti.gov/search/identifier:1784860	Journal Article – 2021 Journal Article	2021
Machine Learning Feature Analysis Illuminates Disparity Between E3SM Climate Models and Observed Climate Change Journal of Computational and Applied Mathematics Jeffrey Nichol, Matthew Gregor Peterson, Kara J. Peterson, Matthew Fricke, Melanie Moses https://www.osti.gov/search/identifier:1782577	Journal Article – 2021 Journal Article	2021
Arctic Tipping Points Triggering Global Change LDRD Final Report Kara J. Peterson, Amy Jo Powell, Irina Kalashnikova Tezaur, Erika Louise Roesler, Jeffrey Nichol, Matthew Gregor Peterson, Warren Leon Davis, John Davis Jakeman, David John Stracuzzi, Diana L Bull https://www.osti.gov/search/identifier:1669210	SAND Report – 2020 SAND Report	2020
Machine Learning to Compare Arctic Simulations With Observed Data European Seminar on Computing 2020 Jeffrey Nichol, Matthew Gregor Peterson, Kara J. Peterson, David John Stracuzzi, Matthew Fricke https://www.osti.gov/search/identifier:1807391	Conference Paper – 2020 Conference Paper	2020
Comparing Simulated and Observed Data with Random Forest Feature Importance Conference on Data Analysis Jeffrey Nichol, Matthew Gregor Peterson, Kara J. Peterson, David John Stracuzzi https://www.osti.gov/search/identifier:1766757	Conference Paper – 2020 Conference Paper	2020
Machine Learning to Compare Arctic Simulations with Observed Data European Seminar on Computing 2020 Jeffrey Nichol, Matthew Gregor Peterson, Warren Leon Davis, Kara J. Peterson, David John Stracuzzi	Abstract – 2020 Abstract	2020
Using Machine Learning to Compare Simulated and Observed Arctic Climate Data Conference on Data Analysis Jeffrey Nichol, Matthew Gregor Peterson, David John Stracuzzi, Kara J. Peterson	Abstract – 2020 Abstract	2020
Feature Comparison Of Arctic Observations And Climate Models American Geophysical Union Jeffrey Nichol, Matthew Gregor Peterson, Kara J. Peterson, David John Stracuzzi, Matthew Fricke, Melanie Moses https://www.osti.gov/search/identifier:1643593	Conference Paper – 2019 Conference Paper	2019
Predicting Minimum Arctic Sea Ice Extent Machine Learning R&D Colloquium Matthew Gregor Peterson, Jeffrey Nichol, David John Stracuzzi, Kara J. Peterson https://www.osti.gov/search/identifier:1641909	Conference Paper – 2019 Conference Paper	2019
Using Machine Learning to Compare Simulated and Observational Sea Ice Extent Data American Geophysical Union 2019 Jeffrey Nichol, Matthew Gregor Peterson, Kara J. Peterson, David John Stracuzzi	Abstract – 2019 Abstract	2019
Predicting Arctic Sea Ice Concentration with Data-Driven Models International Conference on Industrial and Applied Mathematics Matthew Gregor Peterson, Jeffrey Nichol, Kara J. Peterson, David John Stracuzzi https://www.osti.gov/search/identifier:1641185	Conference Paper – 2019 Conference Paper	2019
Document Title	Type	Year

Publications

Feature Pathway Detection using Random Forest Regressor Feature Importances

Global Multivariate Causal Discovery For the Analysis of Emergent Properties in Earth System Models

Global Multivariate Causal Discovery for the Analysis of Emergent Properties in Earth System Models

PhD Committee Meeting 1

Causal Discovery for Climate Science and the Energy Exascale Earth System Model

Casual Evaluations for Identifying Differences between Observations and Earth System Models

Causal Analysis for Climate Science

Causal Inference and Causal Discovery

Learning Why: Data-Driven Causal Evaluations of Climate Models

Learning Why: Data-Driven Causal Evaluations of Climate Models

Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change

Machine Learning Feature Analysis Illuminates Disparity Between E3SM Climate Models and Observed Climate Change

Arctic Tipping Points Triggering Global Change LDRD Final Report

Machine Learning to Compare Arctic Simulations With Observed Data

Comparing Simulated and Observed Data with Random Forest Feature Importance

Machine Learning to Compare Arctic Simulations with Observed Data

Using Machine Learning to Compare Simulated and Observed Arctic Climate Data

Feature Comparison Of Arctic Observations And Climate Models

Predicting Minimum Arctic Sea Ice Extent

Using Machine Learning to Compare Simulated and Observational Sea Ice Extent Data

Predicting Arctic Sea Ice Concentration with Data-Driven Models