Precision requirements are determined for space‐based column‐averaged CO₂ dry air mole fraction data. These requirements result from an assessment of spatial and temporal gradients in the relationship between precision and surface CO₂ flux uncertainties inferred from inversions of the data, and the effects of biases on the fidelity of CO₂ flux inversions. Observational system simulation experiments and synthesis inversion modeling demonstrate that the Orbiting Carbon Observatory mission design and sampling strategy provide the means to achieve these data precision requirements.

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO₂) with the accuracy, resolution, and coverage needed to quantify CO₂ fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO₂ dry air mole fraction, XCO₂, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO₂ derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO₂ enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO₂ gradient is small. Enhanced XCO₂ coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO₂ gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO₂ across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO₂. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO₂ changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO₂ and CH₄ fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO₂ proxy measurements such as radiocarbon in CO₂ and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

The global characteristics of retrievals of the column-averaged CO₂ dry air mole fraction, X_CO2, from shortwave infrared observations has been studied using the expected measurement performance of the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. This study focuses on X_CO2 retrieval precision and averaging kernels and their sensitivity to key parameters such as solar zenith angle (SZA), surface pressure, surface type and aerosol optical depth (AOD), for both nadir and sunglint observing modes. Realistic simulations have been carried out and the single sounding retrieval errors for X_CO2 have been derived from the formal retrieval error covariance matrix under the assumption that the retrieval has converged to the correct answer and that the forward model can adequately describe the measurement. Thus, the retrieval errors presented in this study represent an estimate of the retrieval precision. For nadir observations, we find single-sounding retrieval errors with values typically less than 1 part per million (ppm) over most land surfaces for SZAs less than 70° and up to 2.5 ppm for larger SZAs. Larger errors are found over snow/ice and ocean surfaces due to their low albedo in the spectral regions of the CO₂ absorption bands and, for ocean, also in the O₂ A band. For sunglint observations, errors over the ocean are significantly smaller than in nadir mode with values in the range of 0.3 to 0.6 ppm for small SZAs which can decrease to values as small as 0.15 for the largest SZAs. The vertical sensitivity of the retrieval that is represented by the column averaging kernel peaks near the surface and exhibits values near unity throughout most of the troposphere for most anticipated scenes. Nadir observations over dark ocean or snow/ice surfaces and observations with large AOD and large SZA show a decreased sensitivity to near-surface CO₂. All simulations are carried out for a mid-latitude summer atmospheric profile, a given aerosol type and vertical distribution, a constant windspeed for ocean sunglint and by excluding the presence of thin cirrus clouds. The impact of these parameters on averaging kernels and X_CO2 retrieval errors are studied with sensitivity studies. Systematic biases in retrieved X_CO2, as can be introduced by uncertainties in the spectroscopic parameters, instrument calibration or deficiencies in the retrieval algorithm itself, are not included in this study. The presented error estimates will therefore only describe the true retrieval errors once systematic biases are eliminated. It is expected that it will be possible to retrieve X_CO2 for cloud free observations and for low AOD (here less than 0.3 for the wavelength region of the O₂ A band) with sufficient accuracy for improving CO₂ surface flux estimates and we find that on average 18% to 21% of all observations are sufficiently cloud-free with only few areas suffering from the presence of persistent clouds or high AOD. This results typically in tens of useful observations per 16 day ground track repeat cycle at a 1° × 1° resolution. Averaging observations acquired along ~1° intervals for individual ground tracks will significantly reduce the random component of the errors of the X_CO2 average product for ingestion into data assimilation/inverse models. If biases in the X_CO2 retrieval of the order of a few tenth ppm can be successfully removed by validation or by bias-correction in the flux inversion, then it can be expected that OCO-2 X_CO2 data can lead to tremendous improvements in estimates of CO₂ surface-atmosphere fluxes.

INTRODUCTION: Although the carbon budget is often presented in terms of global-scale fluxes, many of the contributing processes occur through localized point sources, which have been challenging to measure from space. Persistent anthropogenic carbon dioxide (CO ₂ ) emissions have altered the natural balance of Earth’s carbon sources and sinks. These emissions are driven by a multitude of individual mobile and stationary point sources that combust fossil fuels, with urban areas accounting for more than 70% of anthropogenic emissions to the atmosphere. Natural point-source emissions are dominated by wildfires and persistent volcanic degassing. RATIONALE: Comprehensive global measurements from space could help to more completely characterize anthropogenic and natural point-source emissions. In global carbon cycle models, anthropogenic point-source information comes from bottom-up emission inventories, whereas natural point-source information comes from a sparse in situ measurement network. Whereas clusters of urban CO ₂ point-source plumes merge together, isolated point sources (e.g., remote power plants, cement production plants, and persistently degassing volcanoes) create localized plumes. Because turbulent mixing and diffusion cause rapid downwind dilution, they are challenging to detect and analyze. Point-source detection from space is complicated by signal dilution: The observed values of Δ X CO 2 (enhancement of the column-averaged dry-air CO ₂ mole fraction) correspond to in situ CO ₂ enhancements of 10-fold or higher. Space-based sensors that detect and quantify CO ₂ in plumes from individual point sources would enable validation of reported inventory fluxes for power plants. These sensors would also advance the detectability of volcanic eruption precursors and improve volcanic CO ₂ emission inventories. RESULTS: Spaceborne measurements of atmospheric CO ₂ using kilometer-scale data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) reveal distinct structures caused by known anthropogenic and natural point sources, including megacities and volcanoes. Continuous along-track sampling across Los Angeles (USA) by OCO-2 at its ~2.25-km spatial resolution exposes intra-urban spatial variability in the atmospheric X CO 2 distribution that corresponds to the structure of the urban dome, which is detectable under favorable wind conditions. Los Angeles X CO 2 peaks over the urban core and decreases through suburban areas to rural background values more than ~100 km away. Enhancements of X CO 2 in the Los Angeles urban CO ₂ dome observed by OCO-2 vary seasonally from 4.4 to 6.1 parts per million (ppm). We also detected isolated CO ₂ plumes from the persistently degassing Yasur, Ambrym, and Aoba volcanoes (Vanuatu), corroborated by near-simultaneous sulfur dioxide plume detections by NASA’s Ozone Mapping and Profiler Suite. An OCO-2 transect passing directly downwind of Yasur volcano yielded a narrow filament of enhanced X CO 2 ( Δ X CO 2 ≈ 3.4 ppm), consistent with plume modeling of a CO ₂ point source emitting 41.6 ± 19.7 kilotons per day (15.2 ± 7.2 megatons per year). These highest continuous volcanic CO ₂ emissions are collectively dwarfed by about 70 fossil fuel–burning power plants on Earth, which each emit more than 15 megatons per year of CO ₂ . CONCLUSION: OCO-2’s sampling strategy was designed to characterize CO ₂ sources and sinks on regional to continental and ocean-basin scales, but the unprecedented kilometer-scale resolution and high sensitivity enables detection of CO ₂ from natural and anthropogenic localized emission sources. OCO-2 captures seasonal, intra-urban, and isolated plume signals. Capitalizing on OCO-2’s sensitivity, a much higher temporal resolution would capture anthropogenic emission signal variations from diurnal, weekly, climatic, and economic effects, and, for volcanoes, precursory emission variability. Future sampling strategies will benefit from a continuous mapping approach with the sensitivity of OCO-2 to systematically and repeatedly capture these smaller, urban to individual plume scales of CO ₂ point sources. OCO-2 detects urban CO ₂ signals with unprecedented detail over Los Angeles.: Individual “footprints” of OCO-2 X CO 2 data from early fall 2014 and summer 2015 over the city of Los Angeles strongly contrast with values over the distant, rural Antelope Valley. X CO 2 is the averaged dry-air molar CO ₂ concentration between the spacecraft and Earth’s surface.