the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Oct 2024
A full-coverage satellite-based global atmospheric CO2 dataset at 0.05° resolution from 2015 to 2021 for exploring global carbon dynamics
Abstract. The irreversible trend for global warming underscores the necessity for accurate monitoring and analysis of atmospheric carbon dynamics on a global scale. Carbon satellites hold significant potential for atmospheric CO2 monitoring. However, existing studies on global CO2 are constrained by coarse resolution (ranging from 0.25° to 2°) and limited spatial coverage. In this study, we developed a new global dataset of column-averaged dry-air mole fraction of CO2 (XCO2) at 0.05° resolution with full coverage using carbon satellite observations, multi-source satellite products, and an improved deep learning model. We then investigated changes in global atmospheric CO2 and anomalies from 2015 to 2021. The reconstructed XCO2 products show a better agreement with Total Carbon Column Observing Network (TCCON) measurements, with R2 of 0.92 and RSME of 1.54 ppm. The products also provide more accurate information on the global and regional spatial patterns of XCO2 compared to origin carbon satellite monitoring and previous XCO2 products. The global pattern of XCO2 exhibited a distinct increasing trend with a growth rate of 2.32 ppm/year, reaching 414.00 ppm in 2021. Globally, XCO2 showed obvious spatial variability across different latitudes and continents. Higher XCO2 concentrations were primarily observed in the Northern Hemisphere, particularly in regions with intensive anthropogenic activity, such as East Asia and North America. We also validated the effectiveness of our XCO2 products in detecting intensive CO2 emission sources. The XCO2 dataset is publicly accessible on the Zenodo platform at https://doi.org/10.5281/zenodo.12706142 (Wang et al., 2024). Our findings represent a promising advancement in monitoring carbon emission across various countries and enhancing the understanding of global carbon dynamics.
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RC1: 'Comment on essd-2024-315', Anonymous Referee #1, 04 Apr 2025
This manuscript presents the development of a global dataset of column-averaged dry-air mole fraction of CO₂ (XCO₂) at high resolution (0.05°) using multi satellite products, and an improved deep learning model. They further evaluate new datasets using the measurements from the TCCON network. While the study shows promise, there are significant concerns regarding methodological transparency, and clarity of explicit demonstration of advantages over existing satellite products. Addressing these issues will greatly enhance the manuscript’s impact and originality.
- The manuscript lacks in describing the methodological details of the improved deep learning model. The authors should clearly outline the specific innovations or modifications that lead to improved accuracy. Additionally, since the formation and distribution of XCO₂ are influenced by atmospheric transport processes across multiple vertical layers (and not solely by surface fluxes), it is important that the manuscript explains how these vertical transport processes are incorporated into the model. If these processes are not accounted for, this limitation should be explicitly acknowledged.
- Although the authors present a new global XCO₂ product at 0.05° resolution, the distinct novelty and advantages over existing datasets remain unclear. The study should explicitly state how the analyzed information significantly differs from or improves upon existing satellite data. While validation against TCCON is good, the authors should explicitly compare these results with those from existing datasets to clearly demonstrate accuracy improvements.
- The study lacks specificity in demonstrating how the new dataset quantitatively improves understanding relative to existing satellite data. Providing explicit examples or quantifiable differences would enhance the significance of this study.
- The conclusion stating "promising advancement" is too broad. It should be explicitly clarified what specific policy, modeling, or scientific implications this advancement has, thus highlighting concrete applications or benefits.
Citation: https://doi.org/10.5194/essd-2024-315-RC1 -
RC2: 'Comment on essd-2024-315', Anonymous Referee #2, 24 Apr 2025
This study reconstructed a global full-coverage XCO2 product with a 0.05º spatial resolution using multi-component satellite data and an advanced deep learning method. However, the manuscript lacks innovation and sufficient detail in several aspects. My comments are listed below:
- The manuscript notes that the spatial resolution of current global full-coverage XCO2 products is relatively coarse, ranging from approximately 0.25º to 2º (Line 128). However, global XCO2 products with a 0.1º spatial resolution already exist (https://doi.org/10.1016/j.envint.2023.108057), indicating a need for more comprehensive literature review. Although this study improves the XCO2 spatial resolution to 0.05º, its innovation and advantages compared to other datasets remain unclear. It is recommended to clearly articulate the study’s novelty and specific strengths.
- The model methodology section lacks essential explanations. The study employs the Attention-based Bidirectional Long Short-Term Memory (At-BiLSTM) model for global XCO2 reconstruction, but it does not justify the choice of this model or clarify its advantages over traditional LSTM models. Additionally, the model’s interpretation remains unclear. It is recommended to provide a rationale for selecting At-BiLSTM and elucidate its specific benefits and interpretive framework.
- The discussion section requires further elaboration. It should comprehensively address the advantages of the model used and the resulting full-coverage XCO2 product compared to other models and datasets. Additionally, the global spatial distribution characteristics of XCO2 need more detailed discussion.
- In the conclusion or discussion section, please clearly specify the concrete data or scientific significance of the high-resolution XCO2 Additionally, provide an outlook for future research, outlining key issues to address in global XCO2 or CO2 concentration reconstruction studies, such as critical challenges or priorities that should be focused on.
- The construction of the OCO dataset is unclear. For instance, it is not specified how grids containing both OCO-2 and OCO-3 data within the same time period were processed.
- The study utilized various satellite-derived variables, including land flux, anthropogenic flux, and climatic impacts, for global XCO2 However, it is unclear whether these satellite data have gaps, particularly in high-latitude regions. If gaps exist, the study should specify how they were addressed.
- Line 243-244. “…spatial resolutions to 1 km resolution”. The ‘1 km resolution’ is inconsistent with the study’s focus on a 0.05º
- The manuscript contains several typo errors. For instance, in Line 303, “Figure 5. (a) Density scatterplots of sample-based…” includes an unnecessary ‘(a)’. In Line 314, the value ‘1.21’ should be corrected to ‘1.22 ppm’. These errors should be revised for accuracy and clarity.
Citation: https://doi.org/10.5194/essd-2024-315-RC2
Data sets
A monthly full-coverage satellite-based global atmospheric CO2 dataset at 0.05° resolution from 2015 to 2021 Zhige Wang, Ce Zhang, Kejian Shi, Yulin Shangguan, Bifeng Hu, Xueyao Chen, Danqing Wei, Songchao Chen, Peter M. Atkinson, and Qiang Zhang https://doi.org/10.5281/zenodo.12706142
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