Modification of Fraser’s Method for the Atmospheric CO2 Mass Estimation by Using Satellite Data

One of the most critical greenhouse gases in the atmosphere is carbon dioxide (CO2) due to its long-lasting and negative impact on climate change. The global atmospheric monthly mean CO2 concentration is currently greater than 410 ppm which has changed dramatically since the industrial era. To choose suitable climate change mitigation and adaptation strategies it is necessary to define carbon dioxide mass distribution and global atmospheric carbon dioxide mass. The available method to estimate the global atmospheric CO2 mass was proposed in 1980. In this study, to increase the accuracy of the available method, various observation platforms such as ground-based stations, ground-based tall towers, aircrafts, balloons, ships, and satellites are compared to define the best available observations, considering the temporal and spatial resolution. In the method proposed in this study, satellite observations (OCO2 data), from January 2019 to December 2021, are used to estimate atmospheric CO2 mass. The global atmospheric CO2 mass is estimated around 3.24 × 1015 kg in 2021. For the sake of comparison, global atmospheric CO2 mass was estimated by Fraser’s method using NOAA data for the mentioned study period. The proposed methodology in this study estimated slightly greater amounts of CO2 in comparison to Fraser’s method. This comparison resulted in 1.23% and 0.15% maximum and average difference, respectively, between the proposed method and Fraser’s method. The proposed method can be used to estimate the required capacity of systems for carbon capturing and can be applied to smaller districts to find the most critical locations in the world to plan for climate change mitigation and adaptation.

Spatio-Temporal Validation of AIRS CO2 Observations Using GAW, HIPPO and TCCON

Carbon dioxide (CO2) is a significant atmospheric greenhouse gas and its concentrations can be observed by in situ surface stations, aircraft flights and satellite sensors. This paper investigated the ability of the CO2 satellite observations to monitor, analyze and predict the horizontal and vertical distribution of atmospheric CO2 concentration at global scales. CO2 observations retrieved by an Atmospheric Infrared Sounder (AIRS) were inter-compared with the Global Atmosphere Watch Program (GAW) and HIAPER Pole-to-Pole Observations (HIPPOs), with reference to the measurements obtained using high-resolution ground-based Fourier Transform Spectrometers (FTS) in the Total Carbon Column Observing Network (TCCON) from near-surface level to the mid-to-high troposphere. After vertically integrating the AIRS-retrieved values with the column averaging kernels of TCCON measurements, the AIRS observations are spatio-temporally compared with HIPPO-integrated profiles in the mid-to-high troposphere. Five selected GAW stations are used for comparisons with TCCON sites near the surface of the Earth. The results of AIRS, TCCON (5–6 km), GAW and TCCON (1 km) CO2 measurements from 2007 to 2013 are compared, analyzed and discussed at their respective altitudes. The outcomes indicate that the difference of about 3.0 ppmv between AIRS and GAW or other highly accurate in situ surface measurements is mainly due to the different vertical altitudes, rather than the errors in the AIRS. The study reported here also explores the potential of AIRS satellite observations for analyzing the spatial distribution and seasonal variation of CO2 concentration at global scales.

Validation of Carbon Trace Gas Profile Retrievals from the NOAA-Unique Combined Atmospheric Processing System for the Cross-Track Infrared Sounder

This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2), from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), a NOAA enterprise algorithm that retrieves atmospheric profile environmental data records (EDRs) under global non-precipitating (clear to partly cloudy) conditions. Vertical information about atmospheric trace gases is obtained from the Cross-track Infrared Sounder (CrIS), an infrared Fourier transform spectrometer that measures high resolution Earth radiance spectra from NOAA operational low earth orbit (LEO) satellites, including the Suomi National Polar-orbiting Partnership (SNPP) and follow-on Joint Polar Satellite System (JPSS) series beginning with NOAA-20. The NUCAPS CO, CH4, and CO2 profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH4 and CO2 product accuracies are both found to be within ±1%, with precisions of ≈1.5% and ⪅0.5%, respectively, throughout the tropospheric column.

Integrating Computational Methods to Investigate the Macroecology of Microbiomes

Studies in microbiology have long been mostly restricted to small spatial scales. However, recent technological advances, such as new sequencing methodologies, have ushered an era of large-scale sequencing of environmental DNA data from multiple biomes worldwide. These global datasets can now be used to explore long standing questions of microbial ecology. New methodological approaches and concepts are being developed to study such large-scale patterns in microbial communities, resulting in new perspectives that represent a significant advances for both microbiology and macroecology. Here, we identify and review important conceptual, computational, and methodological challenges and opportunities in microbial macroecology. Specifically, we discuss the challenges of handling and analyzing large amounts of microbiome data to understand taxa distribution and co-occurrence patterns. We also discuss approaches for modeling microbial communities based on environmental data, including information on biological interactions to make full use of available Big Data. Finally, we summarize the methods presented in a general approach aimed to aid microbiologists in addressing fundamental questions in microbial macroecology, including classical propositions (such as “everything is everywhere, but the environment selects”) as well as applied ecological problems, such as those posed by human induced global environmental changes.

Detection of Methane Emission from a Local Source Using GOSAT Target Observations

Emissions of atmospheric methane (CH4), which greatly contributes to radiative forcing, have larger uncertainties than those for carbon dioxide (CO2). The Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) launched in 2009 has demonstrated global grid observations of the total column density of CO2 and CH4 from space, and thus reduced uncertainty in the global flux estimation. In this paper, we present a case study on local CH4 emission detection from a single-point source using an available series of GOSAT data. By modifying the grid observation pattern, the pointing mechanism of TANSO-FTS targets a natural gas leak point at Aliso Canyon in Southern California, where the clear-sky frequency is high. To enhance local emission estimates, we retrieved CO2 and CH4 partial column-averaged dry-air mole fractions of the lower troposphere (XCO2 (LT) and XCH4 (LT)) by simultaneous use of both sunlight reflected from Earth’s surface and thermal emissions from the atmosphere. The time-series data of Aliso Canyon showed a large enhancement that decreased with time after its initial blowout, compared with reference point data and filtered with wind direction simulated by the Weather Research and Forecasting (WRF) model.

The Global Spatiotemporal Distribution of the Mid-Tropospheric CO2 Concentration and Analysis of the Controlling Factors

The atmospheric infrared sounder (AIRS) provides a robust and accurate data source to investigate the variability of mid-tropospheric CO2 globally. In this paper, we use the AIRS CO2 product and other auxiliary data to survey the spatiotemporal distribution characteristics of mid-tropospheric CO2 and the controlling factors using linear regression, empirical orthogonal functions (EOFs), geostatistical analysis, and correlation analysis. The results show that areas with low mid-tropospheric CO2 concentrations (20°S–5°N) (384.2 ppm) are formed as a result of subsidence in the atmosphere, the presence of the Amazon rainforest, and the lack of high CO2 emission areas. The areas with high mid-tropospheric CO2 concentrations (30°N–70°N) (382.1 ppm) are formed due to high CO2 emissions. The global mid-tropospheric CO2 concentrations increased gradually (the annual average rate of increase in CO2 concentration is 2.11 ppm/a), with the highest concentration occurring in spring (384.0 ppm) and the lowest value in winter (382.5 ppm). The amplitude of the seasonal variation retrieved from AIRS (average: 1.38 ppm) is consistent with that of comprehensive observation network for trace gases (CONTRAIL), but smaller than the surface ground stations, which is related to altitude and coverage. These results contribute to a comprehensive understanding of the spatiotemporal distribution of mid-tropospheric CO2 and related mechanisms.

AIRS retrieved CO2 and its association with climatic parameters over India during 2004-2011

Atmospheric Infrared Sounder (AIRS) retrieved mid-tropospheric Carbon Dioxide (CO2) have been used to study the variability and its association with the climatic parameters over India during 2004 to 2011. The study also aims in understanding transport of CO2 from surface to mid-troposphere over India. The annual cycle of mid-tropospheric CO2 shows gradual increase in concentration from January till the month of May at the rate ~0.6 ppm/month. It decreases continuously in summer monsoon (JJAS) at the same rate during which strong westerlies persists over the region. A slight increase is seen during winter monsoon (DJF). Being a greenhouse gas, annual cycle of CO2 show good resemblance with annual cycle of surface air temperature with correlation coefficient (CC) of +0.8. Annual cycle of vertical velocity indicate inverse pattern compared to annual cycle of CO2. High values of mid-tropospheric CO2 correspond to upward wind, while low values of mid-tropospheric CO2 correspond to downward wind. In addition to vertical motion, zonal winds are also contributing towards the transport of CO2 from surface to mid-troposphere. Vegetation as it absorbs CO2 at surface level, show inverse annual cycle to that of annual cycle of CO2 (CC-0.64). Seasonal variation of rainfall-CO2 shows similarities with seasonal variation of NDVI-CO2. However, the use of long period data sets for CO2 at the surface and at the mid-troposphere will be an advantage to confirm these results.

Spectral modelling near the 1.6 μm window for satellite based estimation of CO2

Measurements of inter annual CO2 variability are important inputs for modelling global carbon cycle. Satellite observations play important role in quantification and modelling of CO2 fluxes in the atmosphere, where observed radiances in narrow spectral channels are used to estimate the trace gas concentration using spectroscopic principles. The 1.6 μm spectral window is important for CO2 detection and study of the two CO2 bands in this region is performed at different spectral resolutions. In order to select the optimum spectral resolution and wavelength positions, suitable for CO2 estimation from satellite platform, sensitivities of different spectral lines to changes in CO2 concentration are studied. Analysis is carried out using a line by line FASCOD radiative transfer model in tropical atmospheric and rural aerosol conditions. The CO2 concentration is varied from 200 to 1000 ppmv and spectral resolution is varied from 0.025 nm to 10 nm. It is observed that atmospheric transmittances reduce sharply with increase in CO2 concentration. With decrease in resolution initially the sensitivity steeply reduces but at resolutions lower than 0.15 nm the sensitivity remains nearly constant. The Continuum Interpolated Band Ratio method is used for inverse concentration retrieval. Based on the study it is evaluated that 0.2 nm is the optimum limit for resolution.

Remote sensing of particulate pollution from space: have we reached the promised land?

The recent literature on satellite remote sensing of air quality is reviewed. 2009 is the 50th anniversary of the first satellite atmospheric observations. For the first 40 of those years, atmospheric composition measurements, meteorology, and atmospheric structure and dynamics dominated the missions launched. Since 1995, 42 instruments relevant to air quality measurements have been put into orbit. Trace gases such as ozone, nitric oxide, nitrogen dioxide, water, oxygen/tetraoxygen, bromine oxide, sulfur dioxide, formaldehyde, glyoxal, chlorine dioxide, chlorine monoxide, and nitrate radical have been measured in the stratosphere and troposphere in column measurements. Aerosol optical depth (AOD) is a focus of this review and a significant body of literature exists that shows that ground-level fine particulate matter (PM2.5) can be estimated from columnar AOD. Precision of the measurement of AOD is +/-20% and the prediction of PM2.5 from AOD is order +/-30% in the most careful studies. The air quality needs that can use such predictions are examined. Satellite measurements are important to event detection, transport and model prediction, and emission estimation. It is suggested that ground-based measurements, models, and satellite measurements should be viewed as a system, each component of which is necessary to better understand air quality.