Data driven pathway analysis and forecast of global warming and sea level rise

• Published in: Scientific Reports
• Main Authors: Jiecheng Song, Guanchao Tong, Jiayou Chao, Jean Chung, Minghua Zhang, Wuyin Lin, Tao Zhang, Peter M. Bentler, Wei Zhu
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Portfolio 2023
• Subjects: Medicine; R; Science; Q; Sea ice
• Online Access: https://doi.org/10.1038/s41598-023-30789-4; https://doaj.org/article/8713475028e24682b259fe98975a15c2

Abstract Climate change is a critical issue of our time, and its causes, pathways, and forecasts remain a topic of broader discussion. In this paper, we present a novel data driven pathway analysis framework to identify the key processes behind mean global temperature and sea level rise, and to forecast the magnitude of their increase from the present to 2100. Based on historical data and dynamic statistical modeling alone, we have established the causal pathways that connect increasing greenhouse gas emissions to increasing global mean temperature and sea level, with its intermediate links encompassing humidity, sea ice coverage, and glacier mass, but not for sunspot numbers. Our results indicate that if no action is taken to curb anthropogenic greenhouse gas emissions, the global average temperature would rise to an estimated 3.28 °C (2.46–4.10 °C) above its pre-industrial level while the global sea level would be an estimated 573 mm (474–671 mm) above its 2021 mean by 2100. However, if countries adhere to the greenhouse gas emission regulations outlined in the 2021 United Nations Conference on Climate Change (COP26), the rise in global temperature would lessen to an average increase of 1.88 °C (1.43–2.33 °C) above its pre-industrial level, albeit still higher than the targeted 1.5 °C, while the sea level increase would reduce to 449 mm (389–509 mm) above its 2021 mean by 2100.