Observations of the Infrared Outgoing Spectrum of the Earth from Space: The Effects of Temporal and Spatial Sampling

Abstract

A recent comparison between data taken by two different satellite instruments, the Interferometric Monitor of Greenhouse Gases (IMG) that flew in 1997 and the Infrared Interferometer Spectrometer (IRIS) that flew in 1970, showed evidence of a change in the clear-sky greenhouse radiative forcing due to the increase in greenhouse gas concentrations between those years. A possibly even more intriguing question is whether the data can be used to extract unambiguous information about the radiative feedback processes that accompany such a change of forcing, especially cloud feedback. This paper is an investigation of this question, with particular reference to the uncertainties introduced into the differences between IMG and IRIS spectra due to their different patterns of temporal and spatial sampling. This has been approached by modeling the sampling problem, using high-resolution proxy scenes of top-of-the-atmosphere 11-?m brightness temperature, TB11, taken from International Satellite Cloud Climatology Project (ISCCP) data, sampled according to the characteristics of IRIS and IMG, respectively. The results suggest that while the sampling pattern of the IRIS instrument is sufficiently well distributed and dense to generate monthly regional mean brightness temperatures that are within 1.5 K of the true all-sky values, the IMG sampling is too sparse and yields results that differ from the true case by up to 6.0 K. Under cloud-free conditions the agreement with the true field for both instruments improves to within a few tenths of a kelvin. Comparisons with the observed IMG?IRIS difference spectra show that these uncertainties due to sampling presently limit the conclusions that can be drawn about climatically significant feedback processes. However, further analysis using the sampling characteristics of the Advanced Infrared Sounder (AIRS) instrument suggests that as climate change progresses, spectral measurements may be able to pick out significant changes due to processes such as cloud feedback.