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.

Frequency stabilization of distributed-feedback laser diodes at 1572 nm for lidar measurements of atmospheric carbon dioxide

We demonstrate a wavelength-locked laser source that rapidly steps through six wavelengths distributed across a 1572.335 nm carbon dioxide (CO(2)) absorption line to allow precise measurements of atmospheric CO(2) absorption. A distributed-feedback laser diode (DFB-LD) was frequency-locked to the CO(2) line center by using a frequency modulation technique, limiting its peak-to-peak frequency drift to 0.3 MHz at 0.8 s averaging time over 72 hours. Four online DFB-LDs were then offset locked to this laser using phase-locked loops, retaining virtually the same absolute frequency stability. These online and two offline DFB-LDs were subsequently amplitude switched and combined. This produced a precise wavelength-stepped laser pulse train, to be amplified for CO(2) measurements.

Spaceborne estimate of atmospheric CO2 column by use of the differential absorption method: error analysis

For better knowledge of the carbon cycle, there is a need for spaceborne measurements of atmospheric CO2 concentration. Because the gradients are relatively small, the accuracy requirements are better than 1%. We analyze the feasibility of a CO2-weighted-column estimate, using the differential absorption technique, from high-resolution spectroscopic measurements in the 1.6- and 2-microm CO2 absorption bands. Several sources of uncertainty that can be neglected for other gases with less stringent accuracy requirements need to be assessed. We attempt a quantification of errors due to the radiometric noise, uncertainties in the temperature, humidity and surface pressure uncertainty, spectroscopic coefficients, and atmospheric scattering. Atmospheric scattering is the major source of error [5 parts per 10 (ppm) for a subvisual cirrus cloud with an assumed optical thickness of 0.03], and additional research is needed to properly assess the accuracy of correction methods. Spectroscopic data are currently a major source of uncertainty but can be improved with specific ground-based sunphotometry measurements. The other sources of error amount to several ppm, which is less than, but close to, the accuracy requirements. Fortunately, these errors are mostly random and will therefore be reduced by proper averaging.