Available JWST observations of the TRAPPIST-1 system have suggested that several of the planets are likely airless, or possess a very tenuous atmosphere. However, the high atmospheric escape rates expected for these planets suggest that any tenuous atmosphere must be replenished by constant outgassing, and past studies on modeling potential atmospheres for the planets have not widely considered surface pressures << 1 bar. Here, we show that tenuous atmospheres on the TRAPPIST-1 planets are likely possible, supported by constant plausible rates of water and/or CO2 outgassing against assumed high escape rates (up to similar to 10(30) s(-1)). We use a coupled photochemical-climate model and sample from a broad phase space of outgassing, surface deposition, and top-of-atmosphere escape rates to test hundreds of atmospheres per planet. Critically, our model also allows the surface pressure to vary based on the balance of sources and sinks. We find that six different compositional archetypes are generated via H2O and/or CO2 outgassing across our phase space, and atmospheres commonly fall between 10(-4) and 1 bar. We find that potentially habitable surface environments are possible for TRAPPIST-1 d and e at pressures between 0.05-2 bar and 0.5-1 bar, respectively. Where possible, we compare our models to JWST observational data of TRAPPIST-1 b, c, d, and e; all atmospheres found in this study for these planets match the available transmission data to <3 sigma. However, the emission data are consistent with atmospheric outcomes constrained to thin O-2-dominated compositions for TRAPPIST-1 b (less than or similar to 0.01 bar) and c (less than or similar to 0.2 bar), which may or may not contain trace SO2.

Managing carbon stocks in the land, ocean, and atmosphere under changing climate requires a globally‐integrated view of carbon cycle processes at local and regional scales. The growing Earth Observation (EO) record is the backbone of this multi‐scale system, providing local information with discrete coverage from surface measurements and regional information at global scale from satellites. Carbon flux information, anchored by inverse estimates from spaceborne Greenhouse Gas (GHG) concentrations, provides an important top‐down view of carbon emissions and sinks, but currently lacks global continuity at assessment and management scales (<100 km). Partial‐column data can help separate signals in the boundary layer from the overlying atmosphere, providing an opportunity to enhance surface sensitivity and bring flux resolution down from that of column‐integrated data (100–500 km). Based on a workshop held in September 2024, the carbon cycle community envisions a carbon observation system leveraging GHG partial columns in the lower and upper troposphere to weave together information across scales from surface and satellite EO data, and integration of top‐down/bottom‐up analyses to link process understanding to global assessment.

We introduce a new method for calculating the carbon dioxide () emissions from point sources (e.g., power stations) and cities using the cross‐sectional flux method constrained by space‐based and nitrogen dioxide () observations. First, we derive a proxy estimate for enhancements from observations through linear regression near the plume cross‐section. Then, we fit a Gaussian function to the resulting ‐based enhancement data. We apply this method to data from the Orbiting Carbon Observatory‐2 (OCO‐2) and the Sentinel‐5 Precursor TROPOspheric Monitoring Instrument (S5P/TROPOMI) starting from May 2018. The method is tested on the Matimba and Medupi power stations in South Africa, as well as the cities of Madrid (Spain), Las Vegas (USA), and Baghdad (Iraq). The corresponding mean emission estimates are 49 17 Mt/yr, 22 10 Mt/yr, 30 8 Mt/yr, and 41 19 Mt/yr, respectively. The results show that the method is robust and can be applied to challenging cases where data helps constrain the fit. The proxy approach allows evaluation of 17 additional scenes (out of 53), reducing the average error of individual emission estimates from approximately 30%–40% to 22%–25% compared with the traditional method. Furthermore, we highlight the significant potential of satellite data to uncover discrepancies in reported emission estimates, for example, by identifying under‐reported or missing emission sources. The proposed approach can be extended to other case studies and applied to future satellite missions with joint observations, such as CO2M, GOSAT‐GW, TanSat‐2, and TANGO.

In this paper, we characterize major anthropogenic point sources in the South African Highveld region using Orbiting Carbon Observatory-3 (OCO-3) Snapshot Area Map (SAM) carbon dioxide (CO 2 ) and Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO 2 ) observations. Altogether we analyze six OCO-3 SAMs. We estimate the emissions of six power stations (Kendal, Kriel, Matla, Majuba, Tutuka and Grootvlei) and the largest single emitter of greenhouse gas (GHG) in the world, Secunda CTL synthetic fuel plant. We apply the cross-sectional flux method for the emission estimation and we extend the method to fit several plumes at the same time. Overall, the satellite-based emission estimates are in good agreement (within the uncertainties) as compared to emission inventories, even for the cases where several plumes are mixed. We also discuss the advantages and challenges of the current measurement systems for GHG emission monitoring and reporting, and the applicability of different emission estimation approaches to future satellite missions such as the Copernicus CO 2 Monitoring Mission (CO2M) and the Global Observing SATellite for GHGs and Water cycle (GOSAT-GW), including the joint analysis of CO 2 and NO 2 observations.

The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near-term climate change. Emission inventories generated through surface activity measurements are reported annually or bi-annually and evaluated periodically through a “Global Stocktake”. Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (smoothing error), as-well-as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state-of-the-art inventory (EDGAR v6.0) using satellite-based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite-based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, -11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25 to 1.3 Tg CH4/ yr / yr), primarily in the Indo-Pakistan region, sub-tropical Africa, and the Brazilian arc of deforestation; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.

Space-based remote sensing can make an important contribution toward moni-toring greenhouse gas emissions and removals from the agriculture, forestry, and other land use (AFOLU) sector, and to understanding and addressing hu-man-caused climate change through the UNFCCC Paris Agreement. Space agencies have begun to coordinate their efforts to identify needs, collect and harmonize available data and efforts, and plan and maintain a long-term roadmap for observations. International cooperation is crucial in developing and realizing the roadmap, and the Committee on Earth Observation Satellites (CEOS) is a key coordinating driver of this effort. Here, we first identify the data and information that will be useful to support the global stocktake (GST) of the Paris Agreement. Then, the paper explains how existing and planned space-based capabilities and products can be used and combined, particularly in the land use sector, and pro-vides a workflow for their harmonization and contribution to greenhouse gas in-ventories and assessments at the national and global level.

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO2 over small areas, as well as XCO2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

The James Webb Space Telescope (JWST) will enable the search for and characterization of terrestrial exoplanet atmospheres in the habitable zone via transmission spectroscopy. However, relatively little work has been done to use solar system data, where ground truth is known, to validate spectroscopic retrieval codes intended for exoplanet studies, particularly in the limit of high resolution and high signal-to-noise ratio (S/N). In this work, we perform such a validation by analyzing a high-S/N empirical transmission spectrum of Earth using a new terrestrial exoplanet atmospheric retrieval model with heritage in solar system remote sensing and gaseous exoplanet retrievals. We fit the Earth’s 2–14 μ m transmission spectrum in low resolution ( R = 250 at 5 μ m) and high resolution ( R = 100,000 at 5 μ m) under a variety of assumptions about the 1D vertical atmospheric structure. In the limit of noiseless transmission spectra, we find excellent agreement between model and data (deviations <10%) that enable the robust detection of H _2 O, CO _2 , O _3 , CH _4 , N _2 , N _2 O, NO _2 , HNO _3 , CFC-11, and CFC-12 thereby providing compelling support for the detection of habitability, biosignature, and technosignature gases in the atmosphere of the planet using an exoplanet-analog transmission spectrum. Our retrievals at high spectral resolution show a marked sensitivity to the thermal structure of the atmosphere, trace gas abundances, density-dependent effects, such as collision-induced absorption and refraction, and even hint at 3D spatial effects. However, we used synthetic observations of TRAPPIST-1e to verify that the use of simple 1D vertically homogeneous atmospheric models will likely suffice for JWST observations of terrestrial exoplanets transiting M dwarfs.

Despite great interest by the planetary science community in the robotic exploration of Venus, solutions for operating in this extreme environment present significant challenges to mission architects. A particular challenge in designing any in situ Venus mission of reasonable duration is the selection of a suitable power source. State-of-the-art space power technologies comprising solar arrays, batteries and radioisotope thermoelectric generators are not capable of operating on the surface of Venus, limited by the high temperatures, high pressures and corrosive environment. Despite its proximity to the Sun, the solar resource on the surface is limited by the dense cloud layers. One potential approach for circumventing this limitation includes collecting the abundant visible solar energy above the Venus atmosphere, and wirelessly transferring this power to the surface at a wavelength that can more effectively penetrate the dense cloud layer. The feasibility of such an approach and other related mission concepts are discussed herein from a perspective of atmospheric absorption and scattering of the beamed energy.

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near-term climate change. Emission inventories generated through surface activity measurements are reported annually or bi-annually and evaluated periodically through a “Global Stocktake”. Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (smoothing error), as-well-as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state-of-the-art inventory (EDGAR v6.0) using satellite-based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite-based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, -11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25 to 1.3 Tg CH4/ yr / yr), primarily in the Indo-Pakistan region, sub-tropical Africa, and the Brazilian arc of deforestation; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.

We use optimal estimation (OE) to quantify methane fluxes based on total column CH4 data from the Greenhouse Gases Observing Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We then project these fluxes to emissions by sector at 1∘ resolution and then to each country using a new Bayesian algorithm that accounts for prior and posterior uncertainties in the methane emissions. These estimates are intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are ∼ 263 ± 24 Tg CH4 yr−1, anthropogenic fossil emissions are 82 ± 12 Tg CH4 yr−1, and natural wetland/aquatic emissions are 180 ± 10 Tg CH4 yr−1. These estimates are consistent with previous inversions based on GOSAT data and the GEOS-Chem model. In addition, anthropogenic fossil estimates are consistent with those reported to the United Nations Framework Convention on Climate Change (80.4 Tg CH4 yr−1 for 2019). Alternative priors can be easily tested with our new Bayesian approach (also known as prior swapping) to determine their impact on posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on isotopic evidence) and find little impact on our posterior fluxes. This indicates that these alternative inventories are inconsistent with our remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the prior inventories. We find that total emissions for approximately 57 countries can be resolved with this observing system based on the degrees-of-freedom for signal metric (DOFS > 1.0) that can be calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of prior inventories. The top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global anthropogenic budget, similar to our choice of prior emissions but with the posterior emissions shifted towards the agricultural sector and less towards fossil emissions, consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different (although within uncertainty) than bottom-up inventories, isotopic evidence, or estimates based on sparse in situ data, indicating a need for further studies reconciling these different approaches for quantifying the methane budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and those in formulation such as the Copernicus CO2M, MethaneSat, or Carbon Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates.

The Orbiting Carbon Observatory-3 (OCO-3) measures carbon dioxide and solar-induced fluorescence from the International Space Station (ISS). It uses the flight spare spectrometers from its predecessor, OCO-2, and produces spectral images in three near-infrared channels. Its preflight radiometric calibration was performed at the National Aeronautics and Space Administration's (NASA) Jet Propulsion Laboratory and is constantly updated inflight to account for both gradual and abrupt changes in instrument response that display a wavelength dependence within the bands. Some of these are caused by the accumulation of contaminants and their subsequent removal after scheduled decontamination events, but sudden changes of the overall gain state of the two longest wavelength bands, unrelated to contamination, are also observed. They were found to be triggered by instrument resets as well as by decontamination events. OCO-3 cannot perform solar calibration due to its position on the ISS and the inflight updates to its radiometric calibration depend solely on its on-board calibrator, which consists of three lamps and a reflective diffuser. The lamps are observed with different cadences and thus degrade at different rates. Information from all three lamps is combined to provide high temporal resolution and minimize the impact of lamp aging on the gain degradation coefficients that describe the changes in the radiometric response of the instrument inflight. We use thousands of soundings from ocean scenes to assess the relative calibration within the two shortest wavelength bands. Here we present the algorithm developed for the inflight relative radiometric calibration of OCO-3 Version 10 products and discuss the state of the calibration.

We quantify CO2 emissions from Europe’s largest fossil fuel power plant, the Bełchatόw Power Station in Poland, using CO2 observations from NASA’s Orbiting Carbon Observatory (OCO) 2 and 3 missions on 10 occasions from March 2017 to June 2022. The space-based CO2 emission estimates reveal emission changes with a trend that is consistent with the independent reported hourly power generation trend that results from both permanent and temporary unit shutdowns. OCO-2 and OCO-3 emission estimates agree with the bottom-up emission estimates within their respective 1σ uncertainties for 9 of the 10 occasions. Different methods for defining background values and corresponding uncertainties are explored in order to better understand this important potential error contribution. These results demonstrate the ability of existing space-based CO2 observations to quantify emission reductions for a large facility when adequate coverage and revisits are available. The results are informative for understanding the expected capability and potential limitations of the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) and other future satellites to support monitoring and verification of CO2 emission reductions resulting from climate change mitigation efforts such as the Paris Agreement.

The Orbiting Carbon Observatory 2 (OCO-2) satellite has been providing information to estimate carbon dioxide (CO2) fluxes at global and regional scales since 2014 through the combination of CO2 retrievals with top–down atmospheric inversion methods. Column average CO2 dry-air mole fraction retrievals have been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of 10 atmospheric inversions all characterized by different transport models, data assimilation algorithms, and prior fluxes using first OCO-2 v7 in 2015–2016 and then OCO-2 version 9 land observations for the longer period 2015–2018. Inversions assimilating in situ (IS) measurements have also been used to provide a baseline against which the satellite-driven results are compared. The time series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiment using either OCO-2 or IS data. We then evaluate the inversion ensemble based on the dataset from the Total Carbon Column Observing Network (TCCON), aircraft, and in situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region, which seems to show the impact of corrections applied in retrievals. However, the lack of data in the tropics limits our conclusions, and the estimation of carbon emissions over tropical Africa require further analysis.

The recent discovery of near-infrared (near-IR) emission from the nightside of Venus provides a powerful new technique for studying its lower atmosphere and surface.

Space-based Earth observation (EO), in the form of long-term climate data records, has been crucial in the monitoring and quantification of slow changes in the climate system—from accumulating greenhouse gases (GHGs) in the atmosphere, increasing surface temperatures, and melting sea-ice, glaciers and ice sheets, to rising sea-level. In addition to documenting a changing climate, EO is needed for effective policy making, implementation and monitoring, and ultimately to measure progress and achievements towards the overarching goals of the United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement to combat climate change. The best approach for translating EO into actionable information for policymakers and other stakeholders is, however, far from clear. For example, climate change is now self-evident through increasingly intense and frequent extreme events—heatwaves, droughts, wildfires, and flooding—costing human lives and significant economic damage, even though single events do not constitute “climate”. EO can capture and visualize the impacts of such events in single images, and thus help quantify and ultimately manage them within the framework of the UNFCCC Paris Agreement, both at the national level (via the Enhanced Transparency Framework) and global level (via the Global Stocktake). We present a transdisciplinary perspective, across policy and science, and also theory and practice, that sheds light on the potential of EO to inform mitigation, including sinks and reservoirs of greenhouse gases, and adaptation, including loss and damage. Yet to be successful with this new mandate, EO science must undergo a radical overhaul: it must become more user-oriented, collaborative, and transdisciplinary; span the range from fiducial to contextual data; and embrace new technologies for data analysis (e.g., artificial intelligence). Only this will allow the creation of the knowledge base and actionable climate information needed to guide the UNFCCC Paris Agreement to a just and equitable success.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (<1 ×106 out of 24×106). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (1σ) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Global mean biases against TCCON and models are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of the order of 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the per-orbit full format (https://doi.org/10.5067/OSGTIL9OV0PN, OCO-2 Science Team et al., 2019b) and in the per-day lite format (https://doi.org/10.5067/VWSABTO7ZII4, OCO-2 Science Team et al., 2019a). In addition, a new set of monthly super-lite files, containing only the most essential variables for each satellite observation, has been generated to provide entry level users with a light-weight satellite product for initial exploration (CaltechDATA, https://doi.org/10.22002/D1.2178, Eldering, 2021). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the GOSAT data (O'Dell et al., 2020). The GOSAT v9 data set should be especially useful for studies of carbon cycle phenomena that span a full decade or more and may serve as a useful complement to the shorter OCO-2 v10 data set, which begins in September 2014.

Site-level observations have shown pervasive cold season CO2 release across Arctic and boreal ecosystems, impacting annual carbon budgets. Still, the seasonality of CO2 emissions are poorly quantified across much of the high latitudes due to the sparse coverage of site-level observations. Space-based observations provide the opportunity to fill some observational gaps for studying these high-latitude ecosystems, particularly across poorly sampled regions of Eurasia. Here, we show that data-driven net ecosystem exchange (NEE) from atmospheric CO2 observations implies strong summer uptake followed by strong autumn release of CO2 over the entire cold northeastern region of Eurasia during the 2015–2019 study period. Combining data-driven NEE with satellite-based estimates of gross primary production (GPP), we show that this seasonality implies less summer heterotrophic respiration (Rh) and greater autumn Rh than would be expected given an exponential relationship between respiration and surface temperature. Furthermore, we show that this seasonality of NEE and Rh over northeastern Eurasia is not captured by the TRENDY v8 ensemble of dynamic global vegetation models (DGVMs), which estimate that 47 %–57 % (interquartile range) of annual Rh occurs during August–April, while the data-driven estimates suggest 59 %–76 % of annual Rh occurs over this period. We explain this seasonal shift in Rh by respiration from soils at depth during the zero-curtain period, when sub-surface soils remain unfrozen up to several months after the surface has frozen. Additional impacts of physical processes related to freeze–thaw dynamics may contribute to the seasonality of Rh. This study confirms a significant and spatially extensive early cold season CO2 efflux in the permafrost-rich region of northeast Eurasia and suggests that autumn Rh from subsurface soils in the northern high latitudes is not well captured by current DGVMs.