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The air–sea exchange and oceanic cycling of greenhouse gases (GHG), including carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), carbon monoxide (CO), and nitrogen oxides (NOx ¼ NO þ NO2), are fundamental in controlling the evolution of the Earth’s atmospheric chemistry and climate. Significant advances have been made over the last 10 years in understanding, instrumentation and methods, as well as deciphering the production and consumption pathways of GHG in the upper ocean (including the surface and subsurface ocean down to approximately 1000 m). The global ocean under current conditions is now well established as a major sink for CO2, a major source for N2O and a minor source for both CH4 and CO. The importance of the ocean as a sink or source of NOx is largely unknown so far. There are still considerable uncertainties about the processes and their major drivers controlling the distributions of N2O, CH4, CO, and NOx in the upper ocean. Without having a fundamental understanding of oceanic GHG production and consumption pathways, our knowledge about the effects of ongoing major oceanic changes—warming, acidification, deoxygenation, and eutrophication—on the oceanic cycling and air–sea exchange of GHG remains rudimentary at best. We suggest that only through a comprehensive, coordinated, and interdisciplinary approach that includes data collection by global observation networks as well as joint process studies can the necessary data be generated to (1) identify the relevant microbial and phytoplankton communities, (2) quantify the rates of ocean GHG production and consumption pathways, (3) comprehend their major drivers, and (4) decipher economic and cultural implications of mitigation solutions

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated, quality controlled and made publicly available. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (e. g. become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (e. g. a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching surface in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into ‘regions of interest’: spatiotemporal cylinders consisting of circles on the Earth’s surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All surface in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is applied to each of the non-in situ datasets in turn, producing a dataset of coincident matchups that are consistent in space and time. About 35 million in situ data points were matched with data

Large rivers play an important role in transferring water and all of its constituents, including carbon in its various forms, from the land to the ocean, but the seasonal and inter-annual variations in these riverine flows remain unclear. Satellite Earth observation datasets and reanalysis products can now be used to observe synoptic-scale spatial and temporal variations in the carbonate system within large river outflows. Here, we present the University of Exeter (UNEXE) Satellite Oceanographic Datasets for Acidification (OceanSODA) dataset (OceanSODA-UNEXE) time series, a dataset of the full carbonate system in the surface water outflows of the Amazon (2010–2020) and Congo (2002–2016) rivers. Optimal empirical approaches were used to generate gridded total alkalinity (TA) and dissolved inorganic carbon (DIC) fields in the outflow regions. These combinations were determined by equitably evaluating all combinations of algorithms and inputs against a reference matchup database of in situ observations. Gridded TA and DIC along with gridded temperature and salinity data enable the calculation of the full carbonate system in the surface ocean (which includes pH and the partial pressure of carbon dioxide, pCO2). The algorithm evaluation constitutes a Type-A uncertainty evaluation for TA and DIC, in which model, input and sampling uncertainties are considered. Total combined uncertainties for TA and DIC were propagated through the carbonate system calculation, allowing all variables to be provided with an associated uncertainty estimate. In the Amazon outflow, the total combined uncertainty for TA was 36 µmol kg−1 (weighted root-mean-squared difference, RMSD, of 35 µmol kg−1 and weighted bias of 8 µmol kg−1 for n = 82), whereas it was 44 µmol kg−1 for DIC (weighted RMSD of 44 µmol kg−1 and weighted bias of −6 µmol kg−1 for n = 70). The spatially averaged propagated combined uncertainties for the pCO2 and pH were 85 µatm and 0.08, respectively, where the pH uncertainty was

A complete description of the dataset is given by Jones et al. (2023). Key information is provided below. A dataset describing the global warming response to national emissions CO2, CH4 and N2O from fossil and land use sources during 1851-2021. National CO2 emissions data are collated from the Global Carbon Project (Andrew and Peters, 2022; Friedlingstein et al., 2022). National CH4 and N2O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2022). We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH4 and N2O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021). Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST). The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO2, CH4, N2O or 3-GHG total) and source (fossil emissions, land use emissions or the total)., A complete description of the dataset is given by Jones et al. (2023). Key information is provided below. Background A dataset describing the global warming response to national emissions CO2, CH4 and N2O from fossil and land use sources during 1851-2021. National CO2 emissions data are collated from the Global Carbon Project (Andrew and Peters, 2022; Friedlingstein et al., 2022). National CH4 and N2O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2022). We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH4 and N2O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021). Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST). The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO2, CH4, N2O or 3-GHG total) and source (fossil emissions, land use emissions or the total). Data records: overview The data records include three comma separated values (.csv) files as described below. All files are in ‘long’ format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns. Component specifies fossil emissions, LULUCF emissions or total emissions of the gas. Gas specifies CO2, CH4, N

The ocean plays a central role in modulating the Earth’s carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and light, The ocean plays a central role in modulating the Earth's carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and li

Supplement containing data related to the 2022 Global Carbon Budget from the Global Carbon Project. The original article is Friedlingstein et al: Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022. Further information is available on: http://www.globalcarbonproject.org/carbonbudget, Supplement containing data related to the 2022 Global Carbon Budget from the Global Carbon Project. The original article is Friedlingstein et al: Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022. Further information is available on: http://www.globalcarbonproject.org/carbonbudget. File Global_Carbon_Budget_2022v1.0.xlsx includes the following: 1. Summary 2. Global Carbon Budget 3. Fossil fuel emissions by Fuel Type 4. Land-use change emissions 5. Ocean Sink 6. Terrestrial sink 7. Historical Budget. File National_Carbon_Emissions_2022v1.0.xlsx includes the following: 1. Summary 2. Territorial emissions 3. Consumption emissions 4. Emissions transfers 5. Country definitions 6. Disaggregation 7. Aggregation

Rocket emissions and debris from spacecraft falling out of orbit are having increasingly detrimental effects on global atmospheric chemistry. Improved monitoring and regulation are urgently needed to create an environmentally sustainable space industry.