Visualizing Global Climate Change Trends: A Data-Driven Analysis of Temperature Anomalies and Regional Patterns
DOI:
https://doi.org/10.65469/eijournal.2026.2.15Keywords:
climate change, data visualization, temperature anomalies, global warming, Python, HadCRUTAbstract
Climate change is among the defining challenges of the twenty-first century, and global temperature anomalies are central indicators of environmental transformation. This study applies data visualization to analyse long-term global temperature trends from 1880 to 2024 using publicly available series from the Met Office Hadley Centre. We combine time series plots, heatmaps, grouped bar charts, and comparative regional views to translate dense climatological records into interpretable patterns. The visual narrative highlights sustained warming of roughly 1.1 °C since pre-industrial conditions, accelerated warming in recent decades, and pronounced regional contrasts between hemispheres and between land and ocean surfaces. Implementation relies on Python scientific stacks—Matplotlib and Seaborn—to produce reproducible, publication-quality graphics suited to researchers, educators, and policy audiences. Overall, the work underscores how rigorous visualization can bridge raw climate observations and evidence-based communication, supporting transparent interpretation of global change signals.
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