AI-Generated Narratives and Infographic Synthesis for Visualizing Climate Temperature Anomalies
DOI:
https://doi.org/10.29207/resti.v10i1.7060Keywords:
AI-generated narratives, climate data visualization, GISTEMP, sustainable communication, temperature anomaly detectionAbstract
Communicating long-term climate trends to non-specialist audiences remains a persistent challenge, despite the availability of well-validated global temperature datasets. While existing climate visualizations and reports provide accurate information, they often rely on expert-driven interpretations or static representations that limit accessibility and scalability. This study presents a proof-of-concept system that integrates analytical processing, rule-based narrative generation, and infographic synthesis to transform structured climate data into coherent public-facing communication artifacts. The proposed framework uses the NASA GISTEMP v4 dataset, covering annual global temperature anomalies from 1880 to 2024. It applies linear trend estimation and deterministic anomaly highlighting to extract salient temporal patterns. These analytical outputs are then translated into traceable natural language summaries and integrated with visual encodings within a single reproducible pipeline. The results confirm a persistent long-term warming trend, with several recent years exceeding high-anomaly thresholds, and demonstrate that analytical values, narrative descriptions, and visual emphasis can be generated consistently from a shared data source. Rather than introducing new climate indicators or predictive models, this study’s contribution lies in system-level integration: coupling data analysis, narrative synthesis, and visual composition into a unified, communication-oriented workflow. The framework is explicitly positioned as a proof of concept and does not claim causal attribution or empirical validation of user impact. Nonetheless, it demonstrates how transparent automation can reduce reliance on expert mediation while preserving scientific fidelity, supporting scalable climate communication systems.
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References
IPCC, “IPCC Sixth Assessment Report - Climate Change 2021: The Physical Science Basis,” 2021. doi: https://search.informit.org/doi/10.3316/informit.315096509383738.
R. Schuster, K. Gregory, T. Moller, and L. Koesten, “’Being Simple on Complex Issues’-Accounts on Visual Data Communication about Climate Change,” IEEE Trans. Vis. Comput. Graph., vol. 30, no. 9, pp. 6598–6611, 2024, doi: 10.1109/TVCG.2024.3352282.
C. Tong et al., “Storytelling and Visualization: An Extended Survey,” Information 2018, Vol. 9, Page 65, vol. 9, no. 3, p. 65, Mar. 2018, doi: 10.3390/INFO9030065
S. A. Habibi and L. Salim, “Static vs. dynamic methods of delivery for science communication: A critical analysis of user engagement with science on social media,” PLoS One, vol. 16, no. 3, p. e0248507, Mar. 2021, doi: 10.1371/JOURNAL.PONE.0248507
R. L. Reyes and J. A. Villanueva, “Narrative-Based Concept Representations: Fostering Visual Cognition in the Introductory Chemistry Classroom,” J. Chem. Educ., vol. 101, no. 3, pp. 1106–1119, Mar. 2024, doi: 10.1021/ACS.JCHEMED.3C01151
A. Nicolò Pratelli Tutor Nicola Tonellotto Valentina Bartalesi Lenzi Reviewer Antonella Poggi Ricardo Nuno Taborda Campos, “Formal Representation of Geospatial Knowledge in Narratives,” 2025, Accessed: Jul. 31, 2025. [Online]. Available: https://tesidottorato.depositolegale.it/handle/20.500.14242/215721.
A. Welch, “Global Portraits from a Changing Climate: The Influence of Environmental Storytelling on the Climate Narrative,” Capstone Collection, Aug. 2024, Accessed: Jul. 31, 2025. [Online]. Available: https://digitalcollections.sit.edu/capstones/3312
E. Segel and J. Heer, “Narrative visualization: Telling stories with data,” IEEE Trans. Vis. Comput. Graph., vol. 16, no. 6, pp. 1139–1148, 2010, doi: 10.1109/TVCG.2010.179.
F. C. Rodríguez Estrada and L. S. Davis, “Improving visual communication of science through the incorporation of graphic design theories and practices into science communication,” Science Communication, vol. 37, no. 1, pp. 140–148, 2015, doi: 10.1177/1075547014562914.
S. He, Y. Chen, Y. Xia, Y. Li, H. N. Liang, and L. Yu, “Visual harmony: text-visual interplay in circular infographics,” J. Vis. (Tokyo)., vol. 27, no. 2, pp. 255–271, Apr. 2024, doi: 10.1007/S12650-024-00957-3.
A. P. Selvam and S. N. S. Al-Humairi, “Environmental impact evaluation using smart real-time weather monitoring systems: a systematic review,” Innovative Infrastructure Solutions, vol. 10, no. 1, pp. 1–24, Jan. 2025, doi: 10.1007/S41062-024-01817-7.
O. Evans and M. Patel, “Natural Language Generation Systems for Automated Content Creation,” International Journal of Electrical, Electronics and Computer Systems, vol. 13, no. 1, pp. 26–32, 2024, doi: 10.5121/ijcsit.2025.17506
A. J. Cawsey, B. L. Webber, and R. B. Jones, “Natural Language Generation in Health Care,” Journal of the American Medical Informatics Association, vol. 4, no. 6, pp. 473–482, Nov. 1997, doi: 10.1136/JAMIA.1997.0040473.
A. Mishra, H. Nauhwar, and S. Jindal, “Empowering Patient Wellness: Automated Radiology Report Generation Using Deep Learning and NLG,” Lecture Notes in Computer Science, vol. 15772 LNCS, pp. 287–304, 2025, doi: 10.1007/978-3-031-93982-2_19
S. Khan, M. Serajuddin, Z. Hasan, S. A. M. Alvi, R. Ayub, and A. Sharma, “Natural Language Generation (NLG) with Reinforcement Learning (RL),” Communications in Computer and Information Science, vol. 2268 CCIS, pp. 303–318, 2025, doi: 10.1007/978-3-031-75167-7_25.
H. K. Skrodelis, A. Romanovs, N. Zenina, and H. Gorskis, “The Latest in Natural Language Generation: Trends, Tools and Applications in Industry,” 2023 IEEE 10th Jubilee Workshop on Advances in Information, Electronic and Electrical Engineering, AIEEE 2023 - Proceedings, 2023, doi: 10.1109/AIEEE58915.2023.10134841.
S. Materia et al., “Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives,” Wiley Interdiscip. Rev. Clim. Change, vol. 15, no. 6, p. e914, Nov. 2024, doi: 10.1002/WCC.914.
G. Camps-Valls et al., “Artificial intelligence for modeling and understanding extreme weather and climate events,” Nature Communications 2025 16:1, vol. 16, no. 1, pp. 1–14, Feb. 2025, doi: 10.1038/s41467-025-56573-8.
K. Hayawi, S. Shahriar, and H. Hacid, “Climate Data Imputation and Quality Improvement Using Satellite Data,” Journal of Data Science and Intelligent Systems, vol. 3, no. 2, pp. 87–97, Jun. 2025, doi: 10.47852/BONVIEWJDSIS42022857.
E. Afrifa-Yamoah, U. A. Mueller, S. M. Taylor, and A. J. Fisher, “Missing data imputation of high-resolution temporal climate time series data,” Meteorological Applications, vol. 27, no. 1, p. e1873, Jan. 2020, doi: 10.1002/MET.1873.
A. S. AlSalehy and M. Bailey, “Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset,” Smart Cities 2025, Vol. 8, Page 82, vol. 8, no. 3, p. 82, May 2025, doi: 10.3390/SMARTCITIES8030082.
R. S. V. Teegavarapu, “Temporal Interpolation Methods,” pp. 43–108, 2024, doi: 10.1007/978-3-031-60946-6_3.
T. Decorte et al., “Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring,” Sensors 2024, Vol. 24, Page 2416, vol. 24, no. 8, p. 2416, Apr. 2024, doi: 10.3390/S24082416.
I. K. Dua and P. G. Patel, “Optimizing Generative AI Workloads for Sustainability: Balancing Performance and Environmental Impact in Generative AI,” Optimizing Generative AI Workloads for Sustainability: Balancing Performance and Environmental Impact in Generative AI, pp. 1–335, Jan. 2024, doi: 10.1007/979-8-8688-0917-0.
I. K. Dua and P. G. Patel, “An Introduction to Generative AI,” Optimizing Generative AI Workloads for Sustainability, pp. 1–37, 2024, doi: 10.1007/979-8-8688-0917-0_1.
L. Nelli, “Color Quest: An interactive tool for exploring color palettes and enhancing accessibility in data visualization,” PLoS One, vol. 19, no. 3, p. e0290923, Mar. 2024, doi: 10.1371/JOURNAL.PONE.0290923.
C. Tseng, A. Z. Wang, G. J. Quadri, and D. A. Szafir, “Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes,” Apr. 2024, doi: 10.2312/evs.20241073.
L. M. Haffner, R. J. Reynolds, and S. L. Tufte, “A Map of the Ionized Component of the Intermediate-Velocity Cloud Complex K,” Astrophys. J., vol. 556, no. 1, pp. L33–L36, Jul. 2001, doi: 10.1086/322867.
G. Tumedei, C. Ceccarini, I. C. Jimenez Navarro, and C. Prandi, “From Drawings to Awareness: Exploring Narrative Visualization and AI to Teach Children About the Fragile Ecosystem of the Mar Menor Lagoon,” Proceedings of the 2025 ACM Designing Interactive Systems Conference, pp. 2684–2700, Jul. 2025, doi: 10.1145/3715336.3735722.
M. Fansher, L. Walls, C. Hao, et al., “Narrative visualizations: Depicting accumulating risks and increasing trust in data,” Cognitive Research: Principles and Implications, vol. 10, no. 7, 2025, doi: 10.1186/s41235-025-00613-w.
N. Lenssen, G. A. Schmidt, M. Hendrickson, P. Jacobs, M. J. Menne, and R. Ruedy, “A NASA GISTEMPv4 Observational Uncertainty Ensemble,” Journal of Geophysical Research: Atmospheres, vol. 129, no. 17, Sep. 2024, doi: 10.1029/2023JD040179.
S. Legg, “IPCC, 2021: Climate change 2021-the physical science basis,” Interaction, vol. 49, pp. 44–45, 2021, Accessed: Aug. 08, 2025. [Online]. Available: https://search.informit.org/doi/abs/10.3316/informit.315096509383738
B. Millet, S. J. Majumdar, A. Cairo, B. D. McNoldy, S. D. Evans, and K. Broad, “Exploring the Impact of Visualization Design on Non-Expert Interpretation of Hurricane Forecast Path,” Int. J. Hum. Comput. Interact., vol. 40, no. 2, pp. 425–440, 2024, doi: 10.1080/10447318.2022.2121036.
E. A. Jensen, K. Borkiewicz, J. Carpenter, S. Levy, and J. P. Naiman, “Transforming science narratives? The impact of explanatory labels of 3D data visualization on public understanding of space science,” Int. J. Sci. Educ. B Commun. Public Engagem., Nov. 2024, doi: 10.1080/21548455.2024.2427302.
J. García, A. Leiva-Araos, E. Diaz-Saavedra, P. Moraga, H. Pinto, and V. Yepes, “Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing,” Applied Sciences 2023, Vol. 13, Page 12497, vol. 13, no. 22, p. 12497, Nov. 2023, doi: 10.3390/APP132212497.
A. Middel, N. Nazarian, M. Demuzere, and B. Bechtel, “Urban Climate Informatics: An Emerging Research Field,” Front. Environ. Sci., vol. 10, p. 867434, May 2022, doi: 10.3389/fenvs.2022.867434
M. Terrado, L. Calvo, and I. Christel, “Towards more effective visualisations in climate services: Good practices and recommendations,” Climatic Change, vol. 172, no. 18, 2022, doi: 10.1007/s10584-022-03365-4.
L. Mia, “Evaluating the Trade-offs Between Explainability and Security in AI-Powered Cyber Defense,” Feb. 2020, doi: 10.2139/SSRN.5140427.
A. L. Priya, M. K. Gajula, S. P. Karuppiah, B. Jai Ganesh, J. Jeneetha Jebanazer, and S. R. Kumar, “Establishing a Balance between Precision and Openness in AI Systems for Essential Decision-Making: Interpretable Deep Learning Models,” ESIC 2025 - 5th International Conference on Emerging Systems and Intelligent Computing, Proceedings, pp. 658–663, 2025, doi: 10.1109/ESIC64052.2025.10962662.
A. Assis, J. Dantas, and E. Andrade, “The performance-interpretability trade-off: a comparative study of machine learning models,” J. Reliab. Intell. Environ., vol. 11, no. 1, pp. 1–15, Mar. 2025, doi: 10.1007/S40860-024-00240-0.
M. D. Gerst, M. A. Kenney, and I. Feygina, “Improving the usability of climate indicator visualizations through diagnostic design principles,” Climatic Change, vol. 166, no. 33, 2021, doi: 10.1007/s10584-021-03109-w.
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