Research 14
Modeling Earth’s Future in ENVS 166
Faculty Spotlight & Course Redesign: Dr. William Rush redesigned ENVS 166 to pair climate science with Python, large-scale data analysis, and high-performance computing.
In Santa Clara University’s ENVS 166: Climate Change: Past to Future, students study climate science through both lecture and hands-on computing. The upper-division course, taught by Dr. William Rush, introduces students to climate theory, historical climate records, coding, and the large datasets used to examine possible future climate conditions.
Dr. Rush’s academic background is in paleoclimatology, or the study of past climates. His work involves using geochemical and geologic evidence, such as minerals and sediment patterns, to understand how climates changed over time. He also uses climate models to compare model results with evidence from the geologic record.
From Climate Theory to Code
ENVS 166 includes both lecture and lab components. In lecture, Dr. Rush introduces core climate science concepts, including how Earth’s temperature is affected by energy from the sun, atmospheric dynamics, climate changes throughout history, and future climate projections.
One early exercise begins with a simple climate model that Dr. Rush develops on the whiteboard. Students use basic equations to consider what Earth’s temperature might be without an atmosphere. They later translate those equations into Python during the lab.
The labs are largely built around coding and computing exercises. Students primarily use Python in JupyterHub, beginning with introductory activities such as printing “Hello World” and using arrays to organize and analyze data. As the quarter continues, students apply those foundational skills to climate data. They learn how to open climate files, organize daily data into monthly averages, and create maps, building a collection of code they can return to for later assignments.
Working with Large Climate Datasets
A major component of the course involves the CESM2 Large Ensemble, a climate-model dataset produced by the National Center for Atmospheric Research. The dataset includes climate information across the globe from 1850 through 2100 and contains 50 versions of the model. Dr. Rush explained that each version includes a small, intentional variation. Because the climate system is chaotic, those small differences can lead to different results over time, giving students a range of possible outcomes to analyze.
Dr. Rush explained that the data’s size makes it difficult to load and analyze on a typical personal computer. Students use Santa Clara’s High-Performance Computing Center (HPC Center) to work with the data, reduce it into manageable forms, and create visualizations based on their findings. Students then reduce the large dataset into forms they can visualize and use to answer questions about climate conditions in particular places and periods.
The course also introduces students to general circulation models, often called climate models. Dr. Rush described a general circulation model as similar to a multidimensional spreadsheet in which each grid cell interacts with the cells around it. The models account for processes in the atmosphere and ocean as they change over time.
Although students build simple climate models during the course, they do not run the full-scale models behind the CESM2 Large Ensemble. Instead, the HPC Center allows them to analyze the models’ output. Dr. Rush noted that even loading all of the relevant data at once can create memory challenges.
A Redesigned Lab Experience
Dr. Rush first taught the course while he was an adjunct instructor at Santa Clara. When he later returned to the University in a permanent position, he overhauled the course’s lab component.
Before the redesign, lab assignments relied more heavily on Excel and online tools. Dr. Rush wanted students to gain more experience with practical skills, including analyzing large datasets, processing data, conducting spatial analysis, and generating maps.
“Those were all things that were incorporated after I added HPC resources into the course,” he said.
Many students enter ENVS 166 with no previous experience using high-performance computing, and some have no coding experience. Dr. Rush said this can be challenging and is something he plans to keep considering as he updates the course structure.
Student Projects & Future skills
The course concludes with a group poster project. Each group chooses a region and creates at least six figures that characterize how its climate could change by the end of the century.
In Winter 2025, 33 students used HPC resources to examine climate patterns in regions around the world. Projects considered topics including changing precipitation patterns and traditional Indigenous farming practices, future sea ice conditions off the coast of Alaska, and heat stress and humidity in southern Florida. Some groups studying California also found that the model resolution could not fully capture the state’s local climate variability, which Dr. Rush described as a valid and important finding.
Dr. Rush said he has seen students leave the course with greater confidence in their coding abilities. He hopes students also come away with a stronger understanding of the climate system, the hazards associated with climate change, and the real-world meaning behind climate projections.
“It’s one thing to look at the numbers on the screen and be like, okay, this is 40 degrees. What does that actually mean?” Dr. Rush said. “I hope they’re also walking away with a valuable skill set and learning how to leverage these HPC resources.”
Based on an interview conducted by Ella Griffin, HPC Marketing Specialist (May 2026).