Investigating the Relationship between Climate Variables and Solar Activity: A Regression Analysis Approach

Budiman Nasution, Goldberd Harmuda Duva Sinaga, Arip Nurahman, Ruben Cornelius Siagian


This study employs regression analysis to investigate the relationships between carbon dioxide levels, sunspot occurrences, and global temperatures, encompassing both land and sea. By uncovering these connections, the study contributes to our understanding of climate change and solar phenomena interactions. The primary objective is to reveal the intricate associations between these elements, potentially influencing climate change and solar activity. The study's outcomes have significant implications for climate change research and solar activity monitoring. The positive correlation between carbon dioxide concentration and ocean temperatures emphasizes the impact of atmospheric carbon dioxide on sea temperature fluctuations. Conversely, the inverse correlation between sunspot numbers and land/global temperatures suggests solar activity's potential role in shaping Earth's temperature oscillations. This research introduces novelty by concurrently investigating the interconnectedness of these factors. The study establishes substantial connections between carbon dioxide concentration, sunspot numbers, and global temperatures. While the models shed light on some variability, the complexity of climate change and solar activity calls for further exploration of additional factors. This underscores the need to consider multiple variables for a comprehensive understanding. Further research is recommended to enhance the precision of these models.


Regression Analysis; Carbon Dioxide; Sunspot Numbers; Global Temperatures; Climate Change Interactions


Alfaris, L., Siagian, R. C., Nasution, B., Sinaga, G. H. D., & Indah, I. (2022). Non-Linear Regresion And Bisection Method Numerical Analysis of Humidity And Temperature Relationships. Jurnal Pendidikan Fisika Dan Teknologi, 8(2), 238–244.

Ali, U., Shamsi, M. H., Hoare, C., Mangina, E., & O’Donnell, J. (2021). Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis. Energy and Buildings, 246, 111073.

Crema, E. R., & Bevan, A. (2021). Inference from large sets of radiocarbon dates: Software and methods. Radiocarbon, 63(1), 23–39.

Elbasiouny, H., El-Ramady, H., Elbehiry, F., Rajput, V. D., Minkina, T., & Mandzhieva, S. (2022). Plant nutrition under climate change and soil carbon sequestration. Sustainability, 14(2), 914.

Fu, Z., Corker, J., Papathanasiou, T., Wang, Y., Zhou, Y., Madyan, O. A., Liao, F., & Fan, M. (2022). Critical review on the thermal conductivity modelling of silica aerogel composites. Journal of Building Engineering, 57, 104814.

Furnari, S., Crilly, D., Misangyi, V. F., Greckhamer, T., Fiss, P. C., & Aguilera, R. V. (2021). Capturing causal complexity: Heuristics for configurational theorizing. Academy of Management Review, 46(4), 778–799.

Giudice, L. C., Llamas‐Clark, E. F., DeNicola, N., Pandipati, S., Zlatnik, M. G., Decena, D. C. D., Woodruff, T. J., Conry, J. A., & FIGO Committee on Climate Change and Toxic Environmental Exposures. (2021). Climate change, women’s health, and the role of obstetricians and gynecologists in leadership. International Journal of Gynecology & Obstetrics, 155(3), 345–356.

Kavitha, G., Bhuvaneswari, S., Karunakaran, V., & Piriadarshani, D. (2023). An optimal predictive technique for the swing of the stock market using machine learning based on digital platform. 2797(1).

Kaya, E., Agca, M., Adiguzel, F., & Cetin, M. (2019). Spatial data analysis with R programming for environment. Human and Ecological Risk Assessment: An International Journal, 25(6), 1521–1530.

Kim, G. G., Choi, J. H., Park, S. Y., Bhang, B. G., Nam, W. J., Cha, H. L., Park, N., & Ahn, H.-K. (2019). Prediction model for PV performance with correlation analysis of environmental variables. IEEE Journal of Photovoltaics, 9(3), 832–841.

Liu, R., Liu, S., & Zhang, X. (2021). A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing. The International Journal of Advanced Manufacturing Technology, 113(7–8), 1943–1958.

Love, J., Selker, R., Marsman, M., Jamil, T., Dropmann, D., Verhagen, J., Ly, A., Gronau, Q. F., Šmíra, M., & Epskamp, S. (2019). JASP: Graphical statistical software for common statistical designs. Journal of Statistical Software, 88, 1–17.

Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(4), 140–147.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.

Nielsen, K. S., Clayton, S., Stern, P. C., Dietz, T., Capstick, S., & Whitmarsh, L. (2021). How psychology can help limit climate change. American Psychologist, 76(1), 130.

Siagian, R. C., Alfaris, L., Ahmad, G. N., Laeiq, N., Muhammad, A. C., Nyuswantoro, U. I., & Nasution, B. (2023). Relationship between Solar Flux and Sunspot Activity Using Several Regression Models. JURNAL ILMU FISIKA| UNIVERSITAS ANDALAS, 15(2), 146–165.

Siagian, R. C., Alfaris, L., Nasution, B., & Nasution, H. A. (2023). Analysis of Solar Flux and Sunspot Correlation Case Study: A Statistical Perspective. Kappa Journal, 7(1), 114–127.

Sovacool, B. K., Griffiths, S., Kim, J., & Bazilian, M. (2021). Climate change and industrial F-gases: A critical and systematic review of developments, sociotechnical systems and policy options for reducing synthetic greenhouse gas emissions. Renewable and Sustainable Energy Reviews, 141, 110759.

Stewart, M., Carleton, W. C., & Groucutt, H. S. (2021). Climate change, not human population growth, correlates with Late Quaternary megafauna declines in North America. Nature Communications, 12(1), 965.

Walker, P. G., Whittaker, C., Watson, O. J., Baguelin, M., Winskill, P., Hamlet, A., Djafaara, B. A., Cucunubá, Z., Olivera Mesa, D., & Green, W. (2020). The impact of COVID-19 and strategies for mitigation and suppression in low-and middle-income countries. Science, 369(6502), 413–422.

Zhang, Z. (2023). Effects of global warming on plant phenology. 12611, 96–101.

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DOI: 10.30595/jrst.v7i2.16922

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ISSN: 2549-9750