Spatiotemporal variation analysis of global XCO

Area of Science:

• Earth and Environmental Sciences
• Atmospheric Science
• Remote Sensing

Background:

• Accurate, high-coverage spatio-temporal data for carbon dioxide column concentration (XCO2) is vital for scientific research.
• Existing satellite data (GOSAT, OCO-2, OCO-3) requires integration for comprehensive global analysis.

Purpose of the Study:

• To generate a precise, long-term global XCO2 dataset using satellite remote sensing data.
• To assess the spatio-temporal variation and trends of global XCO2 from 2010 to 2020.

Main Methods:

• Integration of XCO2 data from GOSAT, OCO-2, and OCO-3 satellites.
• Application of the Data Interpolating Empirical Orthogonal Functions (DINEOF) and Blending-based Method (BME) framework for data fusion and interpolation.
• Validation using cross-comparison with Total Carbon Column Observing Network (TCCON) data.

Main Results:

• Generated a global XCO2 dataset with over 96% monthly coverage from 2010-2020.
• Achieved high interpolation accuracy (R²=0.920) compared to TCCON data.
• Identified a rising trend of ~23 ppm in global XCO2, with distinct seasonal patterns and hemispheric differences.

Conclusions:

• The DINEOF-BME framework provides accurate and generalizable XCO2 data integration.
• The resulting long-term XCO2 dataset and revealed spatio-temporal variations offer significant support for climate change research.
• EOF and wavelet analyses confirmed dominant variability modes and cyclical patterns in XCO2 concentration.