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Activity reductions in early 2020 due to the coronavirus disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions. Despite their record size, the resulting atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019–2020 Indian Ocean Dipole. Monitoring CO2 anomalies and distinguishing human and climatic causes thus remain a new frontier in Earth system science. We show that the impact of short-term regional changes in fossil fuel emissions on CO2 concentrations was observable from space. Starting in February and continuing through May, column CO2 over many of the world’s largest emitting regions was 0.14 to 0.62 parts per million less than expected in a pandemic-free scenario, consistent with reductions of 3 to 13% in annual global emissions. Current spaceborne technologies are therefore approaching levels of accuracy and precision needed to support climate mitigation strategies with future missions expected to meet those needs.

We use a global transport model and satellite retrievals of the carbon dioxide (CO2) column average to explore the impact of CO2 emissions reductions that occurred during the economic downturn at the start of the Covid-19 pandemic. The changes in the column averages are substantial in a few places of the model global grid, but the induced gradients are most often less than the random errors of the retrievals. The current necessity to restrict the quality-assured column retrievals to almost cloud-free areas appears to be a major obstacle in identifying changes in CO2 emissions. Indeed, large changes have occurred in the presence of clouds, and in places that were cloud free in 2020, the comparison with previous years is hampered by different cloud conditions during these years. We therefore recommend to favor all-weather CO2 monitoring systems, at least in situ, to support international efforts to reduce emissions.

Author(s): Jing Wang [sup.1] [sup.2] [sup.3] [sup.4] , Liang Feng [sup.3] [sup.4] , Paul I. Palmer [sup.3] [sup.4] , Yi Liu [sup.1] [sup.2] , Shuangxi Fang [sup.5] [sup.6] , Hartmut [...], Limiting the rise in global mean temperatures relies on reducing carbon dioxide (CO.sub.2) emissions and on the removal of CO.sub.2 by land carbon sinks. China is currently the single largest emitter of CO.sub.2, responsible for approximately 27 per cent (2.67 petagrams of carbon per year) of global fossil fuel emissions in 2017.sup.1. Understanding of Chinese land biosphere fluxes has been hampered by sparse data coverage.sup.2-4, which has resulted in a wide range of a posteriori estimates of flux. Here we present recently available data on the atmospheric mole fraction of CO.sub.2, measured from six sites across China during 2009 to 2016. Using these data, we estimate a mean Chinese land biosphere sink of -1.11 [plus or minus] 0.38 petagrams of carbon per year during 2010 to 2016, equivalent to about 45 per cent of our estimate of annual Chinese anthropogenic emissions over that period. Our estimate reflects a previously underestimated land carbon sink over southwest China (Yunnan, Guizhou and Guangxi provinces) throughout the year, and over northeast China (especially Heilongjiang and Jilin provinces) during summer months. These provinces have established a pattern of rapid afforestation of progressively larger regions.sup.5,6, with provincial forest areas increasing by between 0.04 million and 0.44 million hectares per year over the past 10 to 15 years. These large-scale changes reflect the expansion of fast-growing plantation forests that contribute to timber exports and the domestic production of paper.sup.7. Space-borne observations of vegetation greenness show a large increase with time over this study period, supporting the timing and increase in the land carbon sink over these afforestation regions. Newly available atmospheric carbon dioxide measurements from six sites across China during 2009 to 2016 indicate a larger land carbon sink than previously thought, reflecting increased afforestation.

The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb were to advance our knowledge of the carbon cycle, in particular, land–atmosphere fluxes of the greenhouse gases carbon dioxide (CO 2) and methane (CH 4) and the effects of these fluxes on the Earth's radiation budget. GeoCarb would retrieve column-integrated dry-air mole fractions of CO 2 (XCO2), CH 4 (XCH4) and CO (XCO), important for understanding tropospheric chemistry), in addition to solar-induced fluorescence (SIF), from hyperspectral resolution measurements in the O 2 A-band at 0.76 µ m, the weak CO 2 band at 1.6 µ m, the strong CO 2 band at 2.06 µ m, and a CH 4 /CO band at 2.32 µ m. Unlike its predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb would be in a geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high-spatial-resolution imaging capabilities would provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land–atmosphere CO 2 and CH 4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the L2 retrieval algorithms which will be followed by simulation experiments to determine an error budget for each target gas. Several sources of uncertainty will be explored, including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to forward model parameters including meteorology and spectroscopy, although there are some other instrument-related sources of uncertainty that are left out for this study, including that from "smile", the keystone effect, stray light, detector persistence, and scene inhomogeneity. The results indicate that the errors (1 σ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10 %), for XCO2 , XCH4 , and XCO , respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMSEs of 1.06 ppm for XCO2 , 8.2 ppb for XCH4 , and 2.5 ppb for XCO , respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO are dominated by measurement noise. It is important to note that the GeoCarb mission was canceled by NASA; however, the instrument is still in development and will be delivered to NASA, in full, with the hope that it will eventually be adopted in a future mission proposal. [ABSTRACT FROM AUTHOR]

Measurements of column-averaged dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) contain systematic errors and regional-scale biases, often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlated with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction in error variance over the operational correction by using a set of nonlinear machine learning models, one for land and one for ocean soundings. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a nonlinear bias correction, which allows for an increase in sounding throughput by 14 % while maintaining the residual error in the operational correction. The method can readily be applied to future Atmospheric CO2 Observations from Space (ACOS) algorithm updates, to OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest. [ABSTRACT FROM AUTHOR]

The International Satellite Cloud Climatology Project (ISCCP) dataset and the Extended Pathfinder Atmospheres (PATMOS-x) dataset are the two longest satellite records of cloudiness (6,7). The datasets consist of cloud retrievals [...], Clouds substantially affect Earth's energy budget by reflecting solar radiation back to space and by restricting emission of thermal radiation to space (1). They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming (2,3). This is because observational systems originally designed for monitoring weather have lacked sufficient stability to detect cloud changes reliably over decades unless they have been corrected to remove artefacts (4,5). Here we show that several independent, empirically corrected satellite records exhibit large-scale patterns of cloud change between the 1980s and the 2000s that are similar to those produced by model simulations of climate with recent historical external radiative forcing. Observed and simulated cloud change patterns are consistent with poleward retreat of mid-latitude storm tracks, expansion of subtropical dry zones, and increasing height of the highest cloud tops at all latitudes. The primary drivers of these cloud changes appear to be increasing greenhouse gas concentrations and a recovery from volcanic radiative cooling. These results indicate that the cloud changes most consistently predicted by global climate models are currently occurring in nature.

Measurements of column averaged, dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Obersvatory-2 (OCO-2) contain systematic errors and regional scale biases; often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlate with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction of error variance over the operational method using a robust data driven, non-linear method. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a non-linear bias correction, which allows for an increase of sounding throughput by 16 % while maintaining the residual error of the operational correction. The method can readily be applied to future ACOS algorithm updates, OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest. [ABSTRACT FROM AUTHOR]

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm has been applied to multi-year 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 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 ppm and -0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements show root mean squared errors (RMSE) of approximately 0.8 ppm 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 ppm and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of errors in small areas, as well as biases across land-ocean crossings, also show encouraging results, for each sensor and in their agreement. Taken together, our results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses. [ABSTRACT FROM AUTHOR]