Investigations of the spatial and temporal resolution of retrievals of atmospheric CO2 from the Atmospheric InfraRed Sounder (AIRS).
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SubjectsAIRS, Remote Sensing, Carbon Cycle Science
Physics, Atmospheric Science (0608)
Remote Sensing (0799)
As the dominant anthropogenic greenhouse gas, carbon dioxide (CO2), represents an important component of climate change (IPCC 2007). Owing to burning of fossil fuels and deforestation, atmospheric CO2 concentrations have increased over 110 parts-per-million by volume (ppmv) from 270 ppmv to 380 ppmv since the dawn of the Industrial Revolution. Understanding of the spatial distribution of the sources and sinks of atmospheric CO2 is necessary not only to predict the future atmospheric abundances but also their effect on future climate. Although designed for deriving high precision temperature and moisture profiles, NASA's Atmospheric InfraRed Sounder (AIRS) IR measurements include broad vertical sensitivity (between 3 and 10 km) to atmospheric CO2 variations. Coupled with AIRS' broad swath pattern and a technique referred to as ""cloud-clearing"" these measurements enable daily global spatial coverage. Nevertheless, AIRS' ability to determine the spatial distribution of carbon dioxide (CO2) is strongly dependent on its ability to separate the radiative effects of CO2 from temperature not to mention measurement uncertainties due to clouds and other geophysical variables such as moisture and ozone. This research presents a thorough investigation into the temporal and spatial scales that the AIRS can separate temperature (and other geophysical variables) from CO2. Through our detailed understanding of the way satellites view the Earth's atmosphere, we have developed an algorithm capable of retrieving global middle-to-upper tropospheric CO2 concentrations in all-weather conditions with total uncertainties ranging between 1 to 2 ppmv. From a radiative perspective, roughly equivalent to 30 mK to 60 mK, 1 to 2 ppmv, is an awesome feat for a space-borne sensor. Necessary for the remarkable performance of this algorithm, we developed methodologies capable of separating the radiative effect of CO2 variability from temperature, improved the fast rapid transmittance algorithm for AIRS, and derived algorithm diagnostics that provide the case-dependent skill of AIRS algorithms for temperature and all other constituents (e.g. H2O, O3, CO, CH4, and CO2) from theoretical considerations. As a result, the 1 to 2 ppmv uncertainties match extremely well with simulation; experiments performed before validation experiments had collected data and the retrieval algorithm was still in its infancy.