Predictive Study of Monthly Changes in Global Atmospheric Methane Based on Arimr Modeling

Haomiao Wang; Shixian Deng; Zixuan He

doi:10.54097/d40cxr57

Authors

Haomiao Wang
Shixian Deng
Zixuan He

DOI:

https://doi.org/10.54097/d40cxr57

Keywords:

Atmospheric Methane, Monthly Variations, ARIMR Model, Global Climate Change.

Abstract

Atmospheric methane is the second most abundant greenhouse gas in the atmosphere after carbon dioxide, and its concentration changes significantly regulate the intensity of the greenhouse effect, thereby exerting a profound influence on global warming and related climate change. Accurately understanding the changing patterns and future trends of atmospheric methane concentrations is of critical importance for elucidating the mechanisms of climate change, developing scientifically sound and effective emission reduction strategies, and advancing the realization of the global carbon neutrality objective. This study aims to construct a statistical model using the ARIMR model by integrating the characteristics of autoregression (AR), moving average (MA), and regression (R) to conduct predictive analysis of monthly changes in global atmospheric methane concentrations. The research data are sourced from monitoring stations under the World Meteorological Organization (WMO) Global Atmospheric Watch (GAW) program, covering monthly atmospheric methane concentration observations from 1994 to 2021. First, the data were subjected to stationarity tests, with the ADF test used to determine stationarity, and non-stationary data were differenced. Subsequently, the autoregressive correlation function (ACF) plot and partial autoregressive correlation function (PACF) plot were used for model order determination, ultimately selecting the ARIMR(0,1,1) model for predictive analysis. The results indicate that the ARIMR(0,1,1) model with a drift term demonstrates good fitting performance and predictive capability when forecasting changes in atmospheric methane concentrations, with small prediction errors and the ability to effectively continue the original trend of the series. Using the ARIMR model with a drift term to forecast atmospheric methane concentrations for 2025, the results show that atmospheric methane concentrations are expected to continue rising in the future.

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