Mathematical Modeling and Analysis of Atmospheric CO₂ Concentration and Global Temperature Change: A Multi-Model Approach

Hangyu Liang; Xinnuo Zhou

doi:10.54097/ehhj9991

Authors

Hangyu Liang
Xinnuo Zhou

DOI:

https://doi.org/10.54097/ehhj9991

Keywords:

CO2 concentration; global warming; time series analysis; ARIMA; autoregressive model; climate change prediction.

Abstract

This research works on the trend of atmospheric CO₂ levels and global temperature change by deployment multiple forecasting models. Historical data from 1959 to 2021 is used for analysis by fitting polynomials, exponential function, and autoregressive models in the forecasting of CO₂ concentration. ARIMA and hybrid ARMA-polynomial models are used in forecasting temperatures. The results showed that atmospheric CO₂ can achieve 512-513 ppm by 2050 and 688-835 ppm by 2100. Global land-ocean temperature will increase to 1.25°C, 1.50°C, and 2°C in the years 2030, 2038, and 2051, respectively. We have proven high correlation between CO₂ concentration and temperature change based on our model's having good reliability over approximately a seventy-year period. This research bears valuable information regarding future climate trends and underscores pressing needs for continued surveillance as well as mitigation efforts.

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