Detecting Climate Trends Using AIRS, IASI, and CrIS Brightness Temperature Spectra

The Joint Polar Satellite System (JPSS) Cross-track Infrared Sounder (CrIS) launched in 2011 aboard Suomi NPP, provides low radiometric uncertainty required to monitor small-scale atmospheric trends. Its high spectral resolution give the ability to analyze specific atmospheric layers. We utilize NASA's Atmospheric Infrared Sounder (AIRS), with a data record that extends from its 2002 launch to the present, to demonstrate the principle given that trends can take decades to reach statistical significance. The Infrared Atmospheric Sounding Interferometer (IASI) onboard Metop- (A launched in 2006, B in 2012) complement this data record. Future infrared sounders with similar capabilities will augment these measurements into the future.

We have created a global data set from these infrared measurements, using the nadir-most observations for each of the aforementioned instruments. We can filter the data based upon spatial, diurnal and seasonal properties to discern trends for a given spectral channel and, therefore, a specific atmospheric layer. Subtle differences between spectral sampling among the three instruments can lead significant differences in the resultant probability distribution functions for similar spectral channels. We take advantage of the higher (0.25 cm-1) IASI spectral resolution to subsample the IASI spectra onto AIRS and CrIS spectral grids to better compare AIRS/IASI and CrIS/IASI trends in the brightness temperature anomalies.

To better understand the dependence of trace gases on the measured brightness temperature spectral time-series, a companion study has utilized coincident vertical profiles of stratospheric carbon dioxide, water vapor and ozone concentration are used to infer a correlation with the CrIS brightness temperatures. The goal was to investigate the role of ozone heating and carbon dioxide cooling on the observed brightness temperature spectra. Results from that study will be presented alongside the climate trend analysis.