Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution

Jiancheng Shi,Tianxing Wang,Yingying Jing,Ralf Sussmann

doi:10.3390/atmos5040870

Abstract

Satellite measurements of the spatiotemporal distributions of atmospheric CO2 concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO2 column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO2 data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO2 concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO2 concentrations is presented with the goal of generating global land CO2 distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO2 distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO2 distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO2 distribution with high accuracies and high spatiotemporal resolution.

Highlights

Atmospheric carbon dioxide (CO2) is the most important anthropogenic greenhouse gas, and since the industrial revolution, the CO2 concentration in the Earth’s atmosphere has increased significantly from 280 to 379 ppm in 2005 [1]
The XCO2 data points from BESD are restricted to land regions because of the low signal-to-noise ratio over the ocean
The comparison results show that the global land spatial coverage of the fused data could reach up to 20.04% within 30 days, while the average global coverage of Atmospheric CO2 Observations from Space (ACOS) and BESD was approximately 8.86% and 14.60%, respectively

Summary

Introduction

Atmospheric carbon dioxide (CO2) is the most important anthropogenic greenhouse gas, and since the industrial revolution, the CO2 concentration in the Earth’s atmosphere has increased significantly from 280 to 379 ppm in 2005 [1]. CO2 depends on the accurate quantification of distribution and variability for CO2 sources and sinks, which have been derived from atmospheric CO2 concentration measurements by using inverse modeling [2,3,4]. For this purpose, globally distributed measurements of atmospheric CO2 concentrations with high accuracy and precision as well as high measurement density are required. Rayner and O’Brien [5] demonstrated that global column-averaged CO2 concentrations (precision ≤1%) can help to reduce the uncertainties in regional CO2 source and sink estimates

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Results

Conclusion