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Elneel L, Zitouni MS, Mukhtar H, Al-Ahmad H. Examining sea levels forecasting using autoregressive and prophet models. Sci Rep 2024; 14:14337. [PMID: 38906913 PMCID: PMC11192949 DOI: 10.1038/s41598-024-65184-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/18/2024] [Indexed: 06/23/2024] Open
Abstract
Global climate change in recent years has resulted in significant changes in sea levels at both global and local scales. Various oceanic and climatic factors play direct and indirect roles in influencing sea level changes, such as temperature, ocean heat, and Greenhouse gases (GHG) emissions. This study examined time series analysis models, specifically Autoregressive Moving Average (ARIMA) and Facebook's prophet, in forecasting the Global Mean Sea Level (GMSL). Additionally, Vector Autoregressive (VAR) model was utilized to investigate the influence of selected oceanic and climatic factors contributing to sea level rise, including ocean heat, air temperature, and GHG emissions. Moreover, the models were applied to regional sea level data from the Arabian Gulf, which experienced higher fluctuations compared to GMSL. Results showed the capability of autoregressive models in long-term forecasting, while the Prophet model excelled in capturing trends and patterns in the time series over extended periods of time.
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Affiliation(s)
- Leena Elneel
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates.
| | - M Sami Zitouni
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Husameldin Mukhtar
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Hussain Al-Ahmad
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
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Song J, Tong G, Chao J, Chung J, Zhang M, Lin W, Zhang T, Bentler PM, Zhu W. Data driven pathway analysis and forecast of global warming and sea level rise. Sci Rep 2023; 13:5536. [PMID: 37015939 PMCID: PMC10073234 DOI: 10.1038/s41598-023-30789-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 03/01/2023] [Indexed: 04/06/2023] Open
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.
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Affiliation(s)
- Jiecheng Song
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA.
| | - Guanchao Tong
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA
| | - Jiayou Chao
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA
| | - Jean Chung
- Duke University, 2080 Duke University Road, Durham, NC, 27708, USA
| | - Minghua Zhang
- School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, NY, 11794-5000, USA
| | - Wuyin Lin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973-5000, USA
| | - Tao Zhang
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973-5000, USA
| | - Peter M Bentler
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA, 90095-1554, USA
| | - Wei Zhu
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA.
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