Abstract
Abstract. Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2, i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.
Highlights
Climate change is one of the greatest challenges to the future of Earth, and it stems from global warming, which is accelerated by anthropogenic emissions of greenhouse gases (Lamminpää et al, 2019)
The estimation of anthropogenic CO2 emissions includes three major steps, as shown in Fig. 1: the first step includes enhancing the XCO2 concentration influenced by anthropogenic activities; the second step involves setting up the Generalized Regression Neural Network (GRNN) model using the XCO2, net primary productivity (NPP), and Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) datasets; and the final step is the validation of estimated CO2 emissions against the actual ODIAC emission dataset
The study was carried out using ODIAC CO2 emissions, Orbiting Carbon Observatory-2 (OCO-2) XCO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) NPP datasets from 2015 to 2019
Summary
Climate change is one of the greatest challenges to the future of Earth, and it stems from global warming, which is accelerated by anthropogenic emissions of greenhouse gases (Lamminpää et al, 2019). It is known that such information can be subject to errors and biases, leading to considerable discrepancies and uncertainties in emission estimates, especially in the case of rapidly growing developing economies such as China and India (Guan et al, 2012; Korsbakken et al, 2016). These discrepancies can result in ∼40 % to ∼100 % uncertainty in emission estimations at the country and the local scales, respectively (Peylin et al, 2013; Wang et al, 2013). It is becoming increasingly important to find efficient and reliable ways of monitoring CO2 reduction progress and to evaluate how well specific CO2 reduction policies are working
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