Satellite and ground-based measurements of XCO2 in a remote semiarid region of Australia

In this study, we present ground-based measurements of column-averaged dry-air mole fractions (DMFs) of CO2 (or XCO2) taken in a semiarid region of Australia with an EM27/SUN portable spectrometer equipped with an automated clamshell cover. We compared these measurements to space-based XCO2 retrievals from the Greenhouse Gases Observing Satellite (GOSAT). Side-by-side measurements of EM27/SUN with the Total Carbon Column Observing Network (TCCON) instrument at the University of Wollongong were conducted in 2015-2016 to derive an XCO2 scaling factor of 0.9954 relative to TCCON. Although we found a slight drift of 0.13 % over 3 months in the calibration curve of the EM27/SUN vs. TCCON XCO2, the alignment of the EM27/SUN proved stable enough for a 2-week campaign, keeping the retrieved Xair values, another measure of stability, to within 0.5 % and the modulation efficiency to within 2 %. From the measurements in Alice Springs, we confirm a small bias of around 2 ppm in the GOSAT M-gain to H-gain XCO2 retrievals, as reported by the NIES GOSAT validation team. Based on the reported random errors from GOSAT, we estimate the required duration of a future campaign in order to better understand the estimated bias between the EM27/SUN and GOSAT. The dataset from the Alice Springs measurements is accessible at https://doi.org/10.4225/48/5b21f16ce69bc (Velazco et al., 2018)

A geostatistical framework for quantifying the imprint of mesoscale atmospheric transport on satellite trace gas retrievals

National Aeronautics and Space Administration's Orbiting Carbon Observatory‐2 (OCO‐2) satellite provides observations of total column‐averaged CO2 mole fractions (X_(CO₂)) at high spatial resolution that may enable novel constraints on surface‐atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO₂ observations and reliable representations of atmospheric transport. Since X_(CO₂) observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along‐track OCO‐2 soundings. We compare high‐pass‐filtered (<250 km, spatial scales that primarily isolate mesoscale or finer‐scale variations) along‐track spatial variability in X_(CO₂) and X_(H₂O) from OCO‐2 tracks to temporal synoptic and mesoscale variability from ground‐based X_(CO₂) and X_(H₂O) observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along‐track, high‐frequency variability for OCO‐2 X_(H₂O). For X_(CO₂), both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO‐2 retrieval state vector are important drivers of the along‐track variance budget

Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model

The GEOS-Chem simulation of atmospheric CH$_{4}$ was evaluated against observations from the Thermal and Near Infrared Sensor for Carbon Observations Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse Gases Observing Satellite (GOSAT), the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and the Total Carbon Column Observing Network (TCCON). We focused on the model simulations at the 4°×5° and 2°×2.5° horizontal resolutions for the period of February–May 2010. Compared to the GOSAT, TCCON, and ACE-FTS data, we found that the 2°×2.5° model produced a better simulation of CH$_{4}$, with smaller biases and a higher correlation to the independent data. We found large resolution-dependent differences such as a latitude-dependent XCH$_{4}$ bias, with higher column abundances of CH$_{4}$ at high latitudes and lower abundances at low latitudes at the 4°×5° resolution than at 2°×2.5°. We also found large differences in CH$_{4}$ column abundances between the two resolutions over major source regions such as China. These differences resulted in up to 30 % differences in inferred regional CH$_{4}$ emission estimates from the two model resolutions. We performed several experiments using 222Rn, 7Be, and CH$_{4}$ to determine the origins of the resolution-dependent errors. The results suggested that the major source of the latitude-dependent errors is excessive mixing in the upper troposphere and lower stratosphere, including mixing at the edge of the polar vortex, which is pronounced at the 4°×5° resolution. At the coarser resolution, there is weakened vertical transport in the troposphere at midlatitudes to high latitudes due to the loss of sub-grid tracer eddy mass flux in the storm track regions. The vertical air mass fluxes are calculated in the model from the degraded coarse-resolution wind fields and the model does not conserve the air mass flux between model resolutions; as a result, the low resolution does not fully capture the vertical transport. This produces significant localized discrepancies, such as much greater CH$_{4}$ abundances in the lower troposphere over China at 4°×5° than at 2°×2.5°. Although we found that the CH$_{4}$ simulation is significantly better at 2°×2.5° than at 4°×5°, biases may still be present at 2°×2.5° resolution. Their importance, particularly in regards to inverse modeling of CH$_{4}$ emissions, should be evaluated in future studies using online transport in the native general circulation model as a benchmark simulation

Trend analysis of greenhouse gases over Europe measured by a network of ground-based remote FTIR instruments

This paper describes the statistical analysis of annual trends in long term datasets of greenhouse gas measurements taken over ten or more years. The analysis technique employs a bootstrap resampling method to determine both the long-term and intra-annual variability of the datasets, together with the uncertainties on the trend values. The method has been applied to data from a European network of ground-based solar FTIR instruments to determine the trends in the tropospheric, stratospheric and total columns of ozone, nitrous oxide, carbon monoxide, methane, ethane and HCFC-22. The suitability of the method has been demonstrated through statistical validation of the technique, and comparison with ground-based in-situ measurements and 3-D atmospheric models.Peer reviewe

A Geostatistical Framework for Quantifying the Imprint of Mesoscale Atmospheric Transport on Satellite Trace Gas Retrievals

National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 (OCO-2) satellite provides observations of total column-averaged CO2 mole fractions (XCO2) at high spatial resolution that may enable novel constraints on surface-atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO2 observations and reliable representations of atmospheric transport. Since XCO2 observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along-track OCO-2 soundings. We compare high-pass-filtered (<250 km, spatial scales that primarily isolate mesoscale or finer-scale variations) along-track spatial variability in XCO2 and XH2O from OCO-2 tracks to temporal synoptic and mesoscale variability from ground-based XCO2 and XH2O observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along-track, high-frequency variability for OCO-2 XH2O. For XCO2, both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO-2 retrieval state vector are important drivers of the along-track variance budget.Plain Language SummaryNumerous efforts have been made to quantify sources and sinks of atmospheric CO2 at regional spatial scales. A common approach to infer these sources and sinks requires accurate representation of variability of CO2 observations attributed to transport by weather systems. While numerical weather prediction models have a fairly reasonable representation of larger-scale weather systems, such as frontal systems, representation of smaller-scale features (<250 km), is less reliable. In this study, we find that the variability of total column-averaged CO2 observations attributed to these fine-scale weather systems accounts for up to half of the variability attributed to local sources and sinks. Here, we provide a framework for quantifying the drivers of spatial variability of atmospheric trace gases rather than simply relying on numerical weather prediction models. We use this framework to quantify potential sources of errors in measurements of total column-averaged CO2 and water vapor from National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 satellite.Key PointsWe developed a framework to relate high-frequency spatial variations to transport-induced temporal fluctuations in atmospheric tracersWe use geostatistical analysis to quantify the variance budget for XCO2 and XH2O retrieved from NASA’s OCO-2 satelliteAccounting for random errors, systematic errors, and real geophysical coherence in remotely sensed trace gas observations may yield improved flux constraintsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151988/1/jgrd55658.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151988/2/jgrd55658_am.pd

Observed Hemispheric Asymmetry in Stratospheric Transport Trends From 1994 to 2018

©2020. American Geophysical Union. All Rights Reserved. Total columns of the trace gases nitric acid (HNO3) and hydrogen chloride (HCl) are sensitive to variations in the lower stratospheric age of air, a quantity that describes transport time scales in the stratosphere. Analyses of HNO3 and HCl columns from the Network for the Detection of Atmospheric Composition Change panning 77°S to 79°N have detected changes in the extratropical stratospheric transport circulation from 1994 to 2018. The HNO3 and HCl analyses combined with the age of air from a simulation using the MERRA2 reanalysis show that the Southern Hemisphere lower stratosphere has become 1 month/decade younger relative to the Northern Hemisphere, largely driven by the Southern Hemisphere transport circulation. The analyses reveal multiyear anomalies with a 5- to 7-year period driven by interactions between the circulation and the quasi-biennial oscillation in tropical winds. This hitherto unrecognized variability is large relative to hemispheric transport trends and may bias ozone trend regressions

Educate to prevent: science-based materials on food hygiene and safety

Uma importante estratégia para a redução do impacto das doenças de origem alimentar é a prevenção e a promoção da saúde. A população escolar foi escolhida como público-alvo para aumentar a literacia para a saúde e promover práticas saudáveis e seguras relacionadas com os alimentos, através do projeto “Educar para Prevenir”. Foram produzidos e publicados materiais educativos para o público escolar e professores. Estes materiais, que compreendem três diferentes tipos de ferramentas, foram publicados como um kit. O desenvolvimento destes materiais baseou-se na recolha de dados de surtos de doenças de origem alimentar, de 2009 a 2013, do Instituto Nacional de Saúde Doutor Ricardo Jorge (INSA). O risco de ocorrência e os fatores contributivos, bem como as boas práticas, foram identificados e usados como base para a elaboração dos materiais educativos. Adicionalmente, foram usados materiais da Organização Mundial da Saúde como o programa “Cinco Chaves para uma Alimentação Mais Segura”. Nas próximas etapas deste projeto serão produzidos novos materiais para estudantes contendo informação sobre a composição nutricional dos alimentos e a compreensão da rotulagem alimentar.An important strategy to reduce food borne diseases burden is prevention and health promotion. The student’s population was chosen as the target audience for improving health literacy and promoting healthy and safe practices relating to food trough the Project “Educar para Prevenir” (Education for Prevention). School educational materials on food safety, on teacher level, were developed and published, aiming the different school levels. These materials comprised 3 different kinds of tools were published as a kit. The development of these materials was based on data collected foodborne outbreaks from 2009 to 2013, at the National Institute of Health (INSA). The occurrence risk and contributing factors were identified as well as the good practices and were the basis for the elaboration of the educational materials. In addition, some World Health Organization materials, such as “Five Keys to Safer Food” programme, were used. On the next steps of the project include new materials for students will be produced, including information about nutritional composition of the food and understanding of the food labelling.info:eu-repo/semantics/publishedVersio

Characteristics of interannual variability in space-based XCO2_2 global observations

Atmospheric carbon dioxide (CO$_2$) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at which CO$_2$ accumulates in the atmosphere, including the interannual variations (IAVs) in this rate. IAV in the CO$_2$ growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric CO$_2$, and IAV is tied to climatic variations that may provide insights into long-term carbon–climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO$_2$ IAV since the satellite can measure over remote terrestrial regions and the open ocean, where traditional in situ CO$_2$ monitoring is difficult, providing better spatial coverage compared to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO$_2$ mole fraction (XCO$_2$) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2-detected XCO$_2$ IAV shows a clear relationship with El Niño–Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO2 IAV time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations can be used reliably to estimate IAV. Furthermore, the extensive spatial coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities for revealing small IAV signals despite sources of noise and error that are inherent to remote-sensing datasets