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This paper describes a method of teaching image processing in a computer science (CS) course in which students obtain and analyze polar data through a computational guided inquiry (CGI) module. In CGI, the instructor guides the students in the process of learning, through the use of a computational tool: for this course, a Jupyter Notebook is used, consisting of alternating text and blocks of Python code that the students can modify as needed and execute. The students obtain images of polar ice and use them to learn about image processing while increasing their climate literacy. Students demonstrated learning of course disciplinary objectives through assessments built into the CGI module. Pre- and post-module surveys indicate increases in student self-reporting of comfort with Python and exposure to polar data. Over half of students indicated increased interest in learning more about polar research, and students overall rated the CGI modules positively. Improvements in climate literacy were tested through asking students to ask a question about a visual representation of polar data; results of this assessment were inconclusive. Future work will focus on strengthening the connection between goals, activities, and assessment, in order to better understand whether the goal of improved climate literacy was achieved.