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The article discusses the history and development of the Surface Ocean Carbon Dioxide (CO2) Atlas (SOCAT) project initiated by international marine carbon science community in 2007 aiming to provide data set of CO2. It states that SOCAT includes 6.3 million data points measured in all ocean areas worldwide from 1968-2007. It also explores the establishment by Taro Takahashi from the Lamont-Doherty Earth Observatory (LDEO) of an initial data on marine carbon.

The article focuses on the Surface Ocean Carbon Dioxide (CO2) Atlas (SOCAT) project. The project was pioneered by the international marine carbon science community in 2007 with the goal to give a comprehensive and updated global data set of marine surface CO2, which have been subject to quality control (QC). It discusses the gridded SOCAT, the second data product from the project, which provide robust regularly spaced fCO2 product with minimal interpolation.

Climatologies, which depict mean fields of oceanographic variables on a regular geographic grid, and atlases, which provide graphical depictions of specific areas, play pivotal roles in comprehending the societal vulnerabilities linked to ocean acidification (OA). This significance is particularly pronounced in coastal regions where most economic activities related to commercial and recreational fisheries as well as aquaculture industries occur. In this paper, we unveil a comprehensive data product featuring coastal climatologies and atlases for ten OA indicators, including fugacity of carbon dioxide, pH on the total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. These indicators are provided on 1°×1° degree spatial grids at 14 standardized depth levels, ranging from the surface to a depth of 500 meters, along the North American ocean margins – defined as the region between the coastline and a distance of 200 nautical miles (∼370 km) offshore. The climatologies and atlases were developed using the World Ocean Atlas (WOA) gridding methods of the NOAA National Centers for Environmental Information (NCEI), based on the recently released Coastal Ocean Data Analysis Product in North America (CODAP-NA), along with the 2021 update to the Global Ocean Data Analysis Project version 2 (GLODAPv2.2021) data product. The relevant variables were adjusted to the index year of 2010. The data product is available in NetCDF (DOI: 10.25921/g8pb-zy76) at the NOAA Ocean Carbon and Acidification Data System: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0270962.html. It is recommended to use the objectively analyzed mean fields (with '_an' suffix) for each variable. The atlases can be accessed at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/nacoastal.html. [ABSTRACT FROM AUTHOR]

For carbon dioxide concentration (XCO2) distribution, the improvement of spatial and temporal resolution is very important in some scientific studies (e.g., studies of the carbon cycle and assessment of carbon emissions based on top-down theory). However, carbon sniffing satellites based on passive theory (e.g., Gosat-2, OCO-2, and OCO-3) are susceptible to cloud and aerosol interference when the data are captured. Therefore, the data collected by carbon sniffing satellites have relatively low utilization, especially in some regions where data gaps exist. Here, we present the Carbon Dioxide Coverage (CDC) dataset, an innovative theory to obtain high spatial and temporal resolution maps of XCO2 distribution by combining spatial attributes and extracted temporal attributes from the GOSAT satellite series data. This theory is divided into the following three parts. Firstly, several background values in the raw GOSAT data were removed through data pre-processing, and for spatial attributes, GOSAT satellite data gap areas were filled by combining adjacent GOSAT data and empirical Bayesian kriging (EBK) theory in the study area. Secondly, for the temporal attributes, we constructed a time profile parameter library, based on the GOSAT data of the time series to extract the temporal parameters from a specific formula at each point of the study area. Finally, for the integration of temporal and spatial information, based on the GOSAT satellite data and the populated data based on spatial attributes, we assign the temporal parameter information from the time parameter library to each pixel location in the study area, combining the transfer component analysis (TCA) theory, and then combine the assigned parameters with specific formulas to complete the prediction of XCO2 distribution. For temporal resolution, both the GOSAT_FTS_L3_V2.95 and CDC datasets are monthly-averaged resolution datasets from 2010 to 2020. And for spatial resolution, the CDC dataset is 0.25° resolution with a significant improvement compared to GOSAT_FTS_L3_V2.95 which is 2.5° resolution. And the dataset contained 136 files. Besides, for the data validation part, we used OCO-2 satellite data from 2009 to 2020 and TCCON data at mid and low latitudes, respectively. This CDC dataset and the original data from the TCCON sites were compared on a monthly-averaged scale. And the results showed that R2 was 0.9686, and RMSE was 1.3811 ppm. We also derived statistical monthly averaged XCO2 from OCO-2 data and compared it with the data set from our theory. And our evaluation index R was greater than 0.7, by comparison with OCO-2 during 2014-2020. Finally, to assess the accuracy of the algorithm, we compared the predicted results with the input data for the period of 2009-2020. And the comparison results show that the mean value of R2 is 0.93 and the mean value of RMSE is 0.53 ppm during 2010-2020. Data gaps produced by sniffer satellites are disturbed by factors such as clouds and aerosols and can be filled by this mapping technique is mentioned in this paper. This technique improves the utilization of XCO2 and the accuracy and resolution of the CDC dataset is sufficient for scientific applications. And the CDC dataset is publicly available at https://doi.org/10.6084/m9.figshare.17826404.v4 (Zhang et al., 2022), which is of significance for a multitude of scientific carbon research. [ABSTRACT FROM AUTHOR]

We describe the instrumentation, calibration, and uncertainty of the network of ground-based, in situ, cavity ring down spectroscopy (CRDS) greenhouse gas (GHG) measurements deployed in the Permian basin. The primary goal of the network is to be used in conjunction with atmospheric transport modeling to determine methane emissions of the Delaware sub-basin of the Permian Basin oil and natural gas extraction area in Texas and New Mexico. Four of the measurements are based on tall communications towers, while one is on a building on a mountain ridge, with the recent addition of a small tower at that site. Although methane (CH4) is the primary specie of interest, carbon dioxide (CO2), hydrogen sulfide (H2S), and the isotopic ratio of methane (δ13CH4) are also reported for a subset of the sites. Measurements were reported following WMO X2004A scale for CH4, and the WMO X2019 scale for CO2. CRDS instruments were calibrated for CH4 and CO2 in laboratory prior deployment. For H2S, data was offset-corrected using the minimum 40-min running mean value of the day, and for δ13CH4, calibrations were based on laboratory data. We describe the characteristics of the data set with a set of illustrative analyses. Methane and carbon dioxide showed strong seasonality, with a well-defined diurnal cycle during the summer, which was opposed to the winter, when a diurnal cycle was absent. CH4 enhancements to the background, during the winter, are up to twice the summer values, which is attributed to the changes in boundary layer depth and wind speed. The largest CH4 enhancements occurred when winds blow from the center of the Delaware sub-basin, where most of the methane emissions come from. The magnitude of enhancements of CO2 did not present seasonality. H2S enhancements indicated a potential source northeast of the tower where the inlet is installed. Isotopic ratios of methane indicated that oil and natural gas extraction is the source of local methane in the region. The hourly-averaged data, starting on 1 March 2020 and described in this paper, are archived at The Pennsylvania State University Data Commons at https://doi.org/10.26208/98y5-t941 (Monteiro et al., 2021). [ABSTRACT FROM AUTHOR]

The Utah Urban CO2 Network (UUCON) is a network of near-surface atmospheric carbon dioxide (CO2) measurement sites aimed at quantifying long-term changes in urban and rural locations throughout northern Utah since 2001. We document improvements to UUCON made in 2015 that increase measurement precision, standardize sampling protocols, and expand the number of measurement locations to represent a larger region in northern Utah. In a parallel effort, near-surface CO2 and methane (CH4) measurement sites were assembled as part of the Uintah Basin Greenhouse Gas (GHG) network in a region of oil and natural gas extraction located in northeastern Utah. Additional efforts have resulted in automated quality control, calibration, and visualization of data through utilities hosted online (http://https://air.utah.edu). These improvements facilitate atmospheric modeling efforts and quantify atmospheric composition in urban and rural locations throughout northern Utah. Here we present an overview of the instrumentation design and methods within UUCON and the Uintah Basin GHG networks as well as describe and report measurement uncertainties using a broadly applicable and novel method. Historic and modern data described in this paper are archived with the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) and can be found at https://doi.org/10.7289/V50R9MN2 and https://doi.org/10.25921/8vaj-bk51 respectively [ABSTRACT FROM AUTHOR]

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (~biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (T-D, exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories, and data-driven approaches (B-U, including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by T-D inversions at 558 & Tg & CH4 & yr-1 (range [540-568]). About 60 & % of global emissions are anthropogenic (range [50-65 & %]). B-U approaches suggest larger global emissions (736 & Tg & CH4 & yr-1 [596-884]) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the T-D budget, it is likely that some of the individual emissions reported by the B-U approaches are overestimated, leading to too large global emissions. Latitudinal data from T-D emissions indicate a predominance of tropical emissions (~64 & % of the global budget, < & 30° & N) as compared to mid (~32 & %, 30° & N-60° & N) and high northern latitudes (~ & 4 & %, 60° & N-90° & N). T-D inversions consistently infer lower emissions in China (~58 & Tg & CH4 & yr-1 [51-72], -14 & %) and higher emissions in Africa (86 & Tg & CH4 & yr-1 [73-108], +19 & %) than B-U values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for T-D inversions than for B-U inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute for 30-40 & % on the estimated range for wetland emissions. Other priorities for improving the methane budget include: i) the development of process-based models for inland-water emissions, ii) the intensification of methane observations at local scale (flux measurements) to constrain B-U land surface models, and at regional scale (surface networks and satellites) to constrain T-D inversions, iii) improvements in the estimation of atmospheric loss by OH, and iv) improvements of the transport models integrated in T-D inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/Global_Methane_Budget_2016). [ABSTRACT FROM AUTHOR]

The article presents the ways and means of assembling and quality controlling the Irminger Sea and Iceland Sea time-series biogeochemical data. It notes the study of the carbon chemistry with measurements of the partial pressure of carbon dioxide in seawater and total dissolved inorganic carbon. It infers that the quality of the data can be assessed on the basis of the results obtained with the use of reference materials.