Determining Longwave Forcing and Feedback Using Infrared Spectra and GNSS Radio Occultation

Abstract

The authors investigate whether combining a data type derived from radio occultation (RO) with the infrared spectral data in an optimal detection method improves the quantification of longwave radiative forcing and feedback. Signals derived from a doubled-CO2 experiment in a theoretical study are used. When the uncertainties in both data types are conservatively estimated, jointly detecting the feedbacks of tropospheric temperature and water vapor, stratospheric temperature, and high-level cloud from the two data types should reduce the mean errors by more than 50%. This improvement is achieved because the RO measurement helps disentangle the radiance signals that are ambiguous in the infrared spectrum. The result signifies the complementary information content in infrared spectral and radio occultation data types, which can be effectively combined in optimal detection to accurately quantify the longwave radiative forcing and feedback. The results herein show that the radiative forcing of CO2 and the longwave radiative feedbacks of tropospheric temperature, tropospheric water vapor, and stratospheric temperature can be accurately quantified from the combined data types, with relative errors in their global mean values being less than 4%, 10%, 15%, and 20%, respectively.