Applicability of Machine Learning Model to Simulate Atmospheric CO₂ Variability

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 6
• Main Authors: Girach, Imran A., Ponmalar, M., Murugan, S., Rahman, P. Abdul, Babu, S. Suresh, Ramachandran, Radhika
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anthropogenic factors; Artificial intelligence (AI); Atmospheric modeling; Atmospheric models; Carbon dioxide; Climate; climate change; Climate models; Computational modeling; Data centers; Data models; El Nino; El Nino phenomena; Gases; global warming; Greenhouse effect; Greenhouse gases; Human influences; Indexes; Information centres; Learning algorithms; Machine learning; Meteorological parameters; Modelling; Normalized difference vegetative index; Observational learning; Ocean temperature; Parameters; Residues; Root-mean-square errors; Satellites; Sea surface; Sea surface temperature; Simulation; Temperature anomalies; Variability; Mauna Loa
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3157774

Carbon dioxide (CO 2 ) is the most important greenhouse gas influencing the Earth's climate; therefore, accurate modeling of its variability has paramount significance. In this regard, we have performed the simulation of CO 2 residue (i.e., detrended depersonalized CO 2 ) based on input of meteorological parameters (temperature, humidity, pressure, and wind), El Niño index, sea surface temperature, and normalized difference vegetation index in a machine learning (ML) model. Long-term observations available from the World Data Centre for Greenhouse Gases (WDCGG) and the National Oceanic and Atmospheric Administration (NOAA) have been used for training and validation of ML model. The model successfully reproduced 72% of observed variability in CO 2 residue with an error of 0.45 ppmv over Mauna Loa (19.54°N; −155.58°E). The cumulative temperature anomaly is found to play a key role in the simulation of CO 2 residue over Mauna Loa. Evaluation reveals a reasonably good agreement between modeled and observed CO 2 residue (<inline-formula> <tex-math notation="LaTeX">R^{2} = 0.20 </tex-math></inline-formula>-0.55 and root-mean-square error (RMSE) = 20%-60%) over regional sites and for global mean CO 2 . However, the model shows a limitation in capturing spikes likely caused by strong local influences. Inclusion of additional input parameters, representing local anthropogenic influences, is recommended to further improve the model performance over regional sites. Our study demonstrates the potential of ML modeling for the simulations of CO 2 variability to complement the computationally expensive climate models.