Internships

Charlotte Merchant ’24

Major

Computer Science

Project Title

Sensitivity Analysis of pCO₂ Estimations and Code Migration for Enhanced Climate Modeling

Certificate(s): Applied and Computational Mathematics, Statistics and Machine Learning

I studied the influence of sea surface temperatures on the estimation of the partial pressure of carbon dioxide (pCO₂) globally. As a fundamental indicator of the ocean’s thermodynamic interactions, mixing phenomena, and air-sea interactions, sea surface temperature remains a key predictor of pCO₂ in statistical, algorithmic and machine learning approaches. However, input sea surface temperature datasets are inconsistent across all pCO₂ estimation methods due to differences in spatial and temporal focus, leading to sources combining different instrumental records and interpolation techniques. By evaluating the sensitivity of pCO₂ predictions to different datasets, I aimed to distill the reliability of these estimations. In the program MATLAB, I used a previously described two-step neural network methodology for global pCO₂ estimation. I also worked on migrating the MATLAB code into the program Python to enable execution within a high-performance computing environment. Engaging with an early implementation of machine learning in a climate science context motivated me to explore how other computational advancements can amplify the predictive capabilities of climate models. I also enjoyed the opportunity to engage in dynamic discussions with colleagues. The intellectually stimulating environment of the institute cemented my desire to pursue further study in climate computing.