PhD Committee Meeting 1
Abstract not provided.
Publications
Causal Discovery for Climate Science and the Energy Exascale Earth System Model
Abstract not provided.
Casual Evaluations for Identifying Differences between Observations and Earth System Models
We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.
Causal Inference and Causal Discovery
Abstract not provided.
Learning Why: Data-Driven Causal Evaluations of Climate Models
Abstract not provided.
Learning Why: Data-Driven Causal Evaluations of Climate Models
Abstract not provided.
Machine Learning to Compare Arctic Simulations With Observed Data
Abstract not provided.
Comparing Simulated and Observed Data with Random Forest Feature Importance
Abstract not provided.
FEATURE COMPARISON OF ARCTIC OBSERVATIONS AND CLIMATE MODELS
Abstract not provided.
Predicting Minimum Arctic Sea Ice Extent
Abstract not provided.
Predicting Arctic Sea Ice Concentration with Data-Driven Models
Abstract not provided.
11 Results