India is primarily concerned with comprehending regional carbon source-sink response in the context of changes in atmospheric CO concentrations or anthropogenic emissions. Recent advancements in high-resolution satellite's fine-scale XCO measurements provide an opportunity to understand unprecedented details of source-sink activity on a regional scale. In this study, we investigated the long-term variations of XCO concentration and growth rates as well as its covarying relationship with ENSO and regional climate parameters (temperature, precipitation, soil moisture, and NDVI) over India from 2010 to 2021 using GOSAT and OCO-2 retrievals. The results show since the launch of OCO-2 in 2014, the number of monthly high-quality XCO soundings over India has grown nearly 100-fold compared to GOSAT, launched in 2009. Also, the discrepancy in XCO increase of 2.54(2.43) ppm/yr was observed in GOSAT (OCO-2) retrieval during an overlapping measurement period (2015-2021). Additionally, wavelet analysis indicated that the OCO-2 retrieval is able to capture a better frequency of local-scale XCO variability compared to GOSAT, owing to its high-resolution cloud-free XCO soundings, providing more well-defined regional-scale source-sink features. Furthermore, dominant spatial pattern of XCO variability observed over south and southeast of India in both satellites, with XCO semi-annual and annual variability more distinctly present in OCO-2 compared to GOSAT. A cross-correlation analysis suggested GOSAT XCO growth rate positively correlates with ENSO in different homogeneous monsoon regions of India, with ENSO leading the GOSAT XCO growth rate in all homogeneous regions by 3-9 months. The South Peninsular region sensitive to ENSO changes, especially during 2015-2016 ENSO event, where a decrease in CO uptake was observed is closely linked with precipitation, soil moisture, and temperature anomalies. However, regional climate parameters show a low correlation with XCO growth since CO is a long-lived well-mixed gas primarily having an imprint of large-scale transport in column CO.
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http://dx.doi.org/10.1016/j.scitotenv.2023.166176 | DOI Listing |
J Geophys Res Atmos
November 2024
The threat posed by the increasing concentration of carbon dioxide (CO) in the atmosphere motivates a detailed and precise estimation of CO emissions and removals over the globe. This study refines the spatial resolution of the CAMS/LSCE inversion system, achieving a global resolution of 0.7° latitude and 1.
View Article and Find Full Text PDFSci Data
November 2024
Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, United States of America.
Monitoring China's carbon dioxide (CO) concentration is essential for formulating effective carbon cycle policies to achieve carbon peaking and neutrality. Despite insufficient satellite observation coverage, this study utilizes high-resolution spatiotemporal data from the Orbiting Carbon Observatory 2 (OCO-2), supplemented with various auxiliary datasets, to estimate full-coverage, monthly, column-averaged carbon dioxide (XCO) values across China from 2015 to 2022 at a spatial resolution of 0.05° via the deep forest model.
View Article and Find Full Text PDFAcc Chem Res
November 2024
Department of Chemistry, Rutgers University, 73 Warren Street, Newark, New Jersey 07102, United States.
Sci Total Environ
December 2024
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China; Collaborative Innovation Center of Geospatial Technology, Wuhan, China. Electronic address:
We reconstructed a global continuous 8-day XCO (column-averaged CO dry air mole fraction) product (GCXCO) at a spatial resolution of 0.05° from 2000 to 2020, combining terrestrial/marine remote sensing data and model simulations based on developed and tested stacking machine learning method. The GCXCO product has the similar spatial pattern with OCO-2 satellite observations but with global seamless coverage, showing a higher spatial resolution and accuracy than CarbonTracker and CAMS model simulation data.
View Article and Find Full Text PDFEnviron Sci Technol
October 2024
College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China.
Top-down estimates of fossil fuel CO (FFCO) emissions are crucial for tracking emissions and evaluating mitigation strategies. However, their practical application is hindered by limited data coverage and overreliance on NOx-to-CO emission ratios from emission inventories. We developed the Machine Learning-Driven Mapping Satellite-based XCO (ML-MSXE) model using the column-averaged dry-air mole fraction of CO enhancement (XCO) derived from OCO-2 and OCO-3 measurements to reconstruct the XCO distribution for monitoring FFCO emissions.
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