2026 Internship Opportunities

Anthropogenic climate change is altering global and regional temperature distributions, with direct consequences for energy demand associated with space heating and cooling. This project will analyze how these demands—quantified using Heating Degree Days (HDD) and Cooling Degree Days (CDD)—evolve under both future warming scenarios and subsequent CO₂ removal pathways. Using global climate model output, the intern will quantify changes in HDD and CDD across baseline (pre-industrial or present), peak-warming, and climate-restoration periods.

Preliminary analyses indicate substantial regional asymmetries: in many regions of the Global South, increasing cooling demand may lead to higher economic and infrastructure burdens, while parts of the Global North may experience net reductions in total demand (reduced HDD offsetting increased CDD). Moreover, initial results suggest that some of these energy-demand shifts may not fully reverse, even after atmospheric CO₂ levels are reduced, highlighting a potential long-term inequity in climate impacts. The project will evaluate these patterns systematically across models and scenarios, clarifying the robustness and uncertainty of these outcomes.

The student will learn the fundamental physical mechanisms underlying global warming and how climate models simulate the Earth system. They will become familiar with HDD and CDD metrics and will use Python to conduct statistical analyses on climate model experiments involving changes in atmospheric CO₂, focusing on outputs from GFDL and NCAR models used within the Vecchi Research Group. The student will document their methods and results, with the opportunity to develop the work into a research-style paper and, depending on progress and scope, potentially contribute to a peer-reviewed publication.

• Develop a quantitative understanding of the climate system and global warming.
• Learn how climate models represent physical processes and generate long-term projections.
• Compute HDD and CDD from climate model datasets and quantify regional changes in energy demand under warming and CO₂ removal.
• Perform statistical analysis to assess multi-model spread and uncertainty.
• Gain proficiency in Python-based climate data processing and analysis.
• Build scientific communication skills through written documentation and research-style presentation of results.

Multivariable Calculus, college-level Physics course.

Some experience with python.

• The student is encouraged to review basic Python programming and familiarize themselves with the “numpy” and “xarray” packages prior to the internship.
• Students are responsible for securing accommodations for the duration of their internship. On-campus summer housing is typically available the first week of June until the first week of August (details to become available in Spring 2026). If the internship falls outside of on-campus housing availability, students will need to secure alternate housing.