The Global Spatiotemporal Distribution of the Mid-Tropospheric CO2 Concentration and Analysis of the Controlling Factors

Reconstructing annual XCO2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method

Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO2 datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO2 data (GLM-XCO2). The 1-km-spatial-resolution dataset containing XCO2 values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R2 = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO2 showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO2 concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO2 over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO2 in the scale of nation and city agglomeration. These long-term and high resolution XCO2 data help understand the spatiotemporal variations in XCO2, thereby improving policy decisions and planning about carbon reduction.

A high concentration CO2 pool over the Indo-Pacific Warm Pool

Anthropogenic emissions have produced significant amount of carbon dioxide (CO₂) in the atmosphere since the beginning of the industrial revolution. High levels of atmospheric CO₂ increases global temperature as CO₂ absorbs outgoing longwave radiation and re-emits. Though a well-mixed greenhouse gas, CO₂ concentration is not uniform in the atmosphere across different altitudes and latitudes. Here, we uncover a region of high CO₂ concentration (i.e. CO₂ pool) in the middle troposphere (500-300 hPa) over the Indo-Pacific Warm Pool (IPWP, 40° E-140° W, 25° S-25° N), in which the CO₂ concentration is higher than that of other regions in the same latitude band (20° N-20° S), by using CO₂ satellite measurements for the period 2002-2017. This CO₂ pool extends from the western Pacific to the eastern Indian Ocean. Much of the CO₂ pool is over the western Pacific Ocean (74.87%), and the remaining lies over the eastern Indian Ocean (25.13%). The rising branch of Walker circulation acts as a "CO₂ Chimney" that constantly transports CO₂ released from the natural, human-induced and ocean outgassing processes to the middle and upper troposphere. The CO₂ pool evolves throughout the year with an average annual trend of about 2.17 ppm yr^-1, as estimated for the period 2003-2016. Our analysis further reveals that La Niña (El Niño) events strengthen (weaken) the CO₂ pool in the mid-troposphere. The radiative forcing for the CO₂ pool suggests more warming in the region and is a grave concern for global warming and climate change.

Satellite observation of atmospheric CO2 and water storage change over Iran

Like many other Middle East countries, Iran has been suffering from severe water shortages over the last two decades, as evidenced by significant decline in surface water and groundwater levels. The observed changes in water storage can be attributed to the mutually reinforcing effects of human activities, climatic variability, and of course the climate change. The objective of this study is to analyze the dependency of atmospheric CO₂ increase on the water shortage of Iran, for which we investigate the spatial relationship between water storage change and CO₂ concentration using large scale satellite data. We conduct our analysis using water storage change data from GRACE satellite and atmospheric CO₂ concentration from GOSAT and SCIAMACHY satellites during 2002-2015. To analyze the long-term behavior of time series we benefit from Mann-Kendal test and for the investigation of the relationship between atmospheric CO₂ concentration and total water storage we use Canonical Correlation Analysis (CCA) and Regression model. Our Results show that the water storage change anomaly and CO₂ concentration are negatively correlated especially in northern, western, southwest (Khuzestan province), and also southeast (Kerman, Hormozgan, Sistan, and Baluchestan provinces) of Iran. CCA results reveal that in the most of northern regions, the decrease in water storage is significantly influenced by the increase of CO₂ concentration. The results further show that precipitation in the highland and peaks does not seem to be influenced by the long and short-term variation in CO₂ concentration. Besides, our results show that the CO₂ concentration is slightly correlated with a weak positive trend in evapotranspiration over agricultural areas. Thus, the indirect effect of CO₂ on increasing evapotranspiration is observed spatially in the whole of Iran. The results of the regression model between total water storage change and carbon dioxide (R² = 0.91)/water discharge/water consumption show that carbon dioxide has the highest effect on total water storage change at large scale. The results of this study will contribute to both water resource management and mitigation plans to achieve the goal of CO₂ emission reduction.

The increasing atmospheric CO2 over India: Comparison to global trends

Atmospheric CO₂ is the key Greenhouse Gas in terms of its global warming potential and anthropogenic sources. Therefore, it is important to analyze the changes in the concentration of atmospheric CO₂ to monitor regional and global climate change. Here, we use ground-based and satellite measurements for the 2002-2020 period to assess CO₂ over India. The average CO₂ trend over India is about 2.1 ppm/yr, and the highest trends are in agreement with the increase in total energy consumption during the period, and the highest trends are found in the areas of mines and refineries in the west and east India. The estimated CO₂ trends for India are comparable to that of global tropical and mid-latitude regions. The increasing CO₂ implies serious anthropogenic global warming and thus, calls for mitigation measures and continuous monitoring for timely policy interventions.

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All satellite CO₂ measurements show a bias between −0.5 and 3.0 ppm

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Coastal India shows high concentrations and the highest trend is 2.4 ppm/yr

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The global average CO₂ trends are similar to that of India, about 1.8-2.1 ppm/yr

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The Increasing CO₂ is a concern for regional warming and global climate change

Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios

Increases in atmospheric carbon dioxide (CO₂) concentrations is the main driver of global warming due to fossil fuel combustion. Satellite observations provide continuous global CO₂ retrieval products, that reveal the nonuniform distributions of atmospheric CO₂ concentrations. However, climate simulation studies are almost based on a globally uniform mean or latitudinally resolved CO₂ concentrations assumption. In this study, we reconstructed the historical global monthly distributions of atmospheric CO₂ concentrations with 1° resolution from 1850 to 2013 which are based on the historical monthly and latitudinally resolved CO₂ concentrations accounting longitudinal features retrieved from fossil-fuel CO₂ emissions from Carbon Dioxide Information Analysis Center. And the spatial distributions of nonuniform CO₂ under Shared Socio-economic Pathways and Representative Concentration Pathways scenarios were generated based on the spatial, seasonal and interannual scales of the current CO₂ concentrations from 2015 to 2150. Including the heterogenous CO₂ distributions could enhance the realism of global climate modeling, to better anticipate the potential socio-economic implications, adaptation practices, and mitigation of climate change.

Variation patterns and driving factors of regional atmospheric CO2 anomalies in China

Atmospheric CO₂ anomaly (△XCO₂) is essential in evaluating regional carbon balance. However, it is difficult to understand △XCO₂ variation characteristics due to regional differences. This paper explored the inter-annual and inter-monthly variation patterns of △XCO₂ in different regions of China based on satellite observations. The relation model between regional △XCO₂ and anthropogenic emissions, gross primary productivity (GPP), wind speed, upwind region's emission, and upwind region's CO₂ concentration was established. Results show that the annual average △XCO₂ in the northwest and southeast regions is stable at around 0 and 1-2 ppm, respectively. Some municipalities directly under the central government and the southern coastal areas showed relatively intense inter-annual fluctuations. Four inter-monthly △XCO₂ variation patterns were observed: the northern region has a stable change, the northeast region has the lowest in summer, the southwest region has the highest in summer, and the central region has no obvious change rule. Furthermore, △XCO₂ in most areas can be explained by the emission-absorption-transportation model. Significant positive △XCO₂ in the southern coastal region in summer may be related to the stable GPP seasonal variation and increased power generation. In the southwestern plateau region, it may be related to the low wind speed and increased soil emission with rising temperature. The stability of the plateau carbon sink and inter-regional cooperation cannot be ignored for improving regional atmospheric environments.

Seasonal dynamics and impact factors of atmospheric CO2 concentration over subtropical forest canopies: observation from eddy covariance tower and OCO-2 satellite in Northwest Himalaya, India

Carbon dioxide (CO₂) is the key atmospheric gas that controls the earth's greenhouse effect, and forests play a major role in abating the atmospheric CO₂ by storing carbon as biomass. Therefore, it is vital to understand the role of different forests in regulating the spatiotemporal dynamics of atmospheric CO₂ concentration. In this study, we have used eddy covariance (EC) tower-based atmospheric CO₂ concentration measurements and satellite-retrieved column average CO₂ concentration of 2018 to understand the diurnal and seasonal dynamics of atmospheric CO₂ concentration over the sub-tropical forest in the foothills of northwest Himalaya, Uttarakhand, India. EC study revealed that the CO₂ concentration over the forest canopy peaks during mid-night to early morning and drop to a minimum during the afternoon. On a monthly scale, peak atmospheric CO₂ concentration was observed during July in both the sites, which was a result of more release of CO₂ by the forest ecosystem through ecosystem respiration and microbial decomposition. Enhanced photosynthetic activities during the late monsoon and post-monsoon resulted in the decrease of atmospheric CO₂ concentration over the forest ecosystem. Among the meteorological variables, rainfall was found to have the highest control over the seasonal variability of the atmospheric CO₂ concentration. Orbiting Carbon Observatory-2 (OCO-2) satellite-retrieved column average CO₂ (XCO₂) was also examined to comprehend its reliability on an ecosystem scale. The OCO-2 retrieved XCO₂ value was higher than the EC carbon flux tower-measured atmospheric CO₂ concentration, which might be due to differences in the vertical resolution of the CO₂ column and scale difference. However, the monthly atmospheric XCO₂ retrieved from OCO-2 strongly adheres with the ground-measured monthly pattern. Our study highlights that forests with varying functional traits within the same climatic conditions show variability in the regulation of atmospheric CO₂ concentration.

A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.