Trends in spectrally resolved outgoing longwave radiation from 10 years of satellite measurements

Abstract

In recent years, the interest has grown in satellite-derived hyperspectral radiance measurements for assessing the individual impact of climate drivers and their cascade of feedbacks on the outgoing longwave radiation (OLR). In this paper, we use 10 years (2008–2017) of reprocessed radiances from the infrared atmospheric sounding interferometer (IASI) to evaluate the linear trends in clear-sky spectrally resolved OLR (SOLR) in the range [645–2300] cm−1. Spatial inhomogeneities are observed in most of the analyzed spectral regions. These mostly reflected the natural variability of the atmospheric temperature and composition but long-term changes in greenhouse gases concentrations are also highlighted. In particular, the increase of atmospheric CO2 and CH4 led to significant negative trends in the SOLR of −0.05 to −0.3% per year in the spectral region corresponding to the ν2 and the ν3 CO2 and in the ν4 CH4 band. Most of the trends associated with the natural variability of the OLR can be related to the El Niño/Southern Oscillation activity and its teleconnections in the studied period. This is the case for the channels most affected by the temperature variations of the surface and the first layers of the atmosphere but also for the channels corresponding to the ν2 H2O and the ν3 O3 bands.

Greenhouse Gas Forcing and Climate Feedback Signatures Identified in Hyperspectral Infrared Satellite Observations

“…The OLR at 1,270-1,280 cm −1 associated with CH 4 and N 2 O also shows a steady decline, but not as large as the CO 2 wing's OLR trend. In a qualitative sense these results are consistent with past observational work (Harries et al, 2001;Whitburn et al, 2021), providing more confidence in the AIRS trends.…”

A Decomposition of the Atmospheric and Surface Contributions to the Outgoing Longwave Radiation

“…For example, OLR spectral measurements were found to afford a rigorous test of the simulations of global climate models (GCMs) and provide useful clues for identifying the causes of the model biases (Feldman et al., 2014; X. Huang et al., 2008; Y. Huang & Ramaswamy, 2008; Y. Huang, Ramaswamy, Huang, et al., 2007). The rich information in the OLR spectra also makes it possible to spectrally fingerprint the radiative forcings and feedbacks that drive and modulate the global climate change (Brindley & Bantges, 2016; Harries et al., 2008; Y. Huang, 2013; Y. Huang, Leroy, & Andersonet, 2010; Y. Huang, Leroy, Geroet, al., 2010; Y. Huang & Ramaswamy, 2009; Slingo & Webb, 1997; Whitburn et al., 2021). Hence, in addition to the total OLR , we are motivated to analyze spectrally decomposed OLR and how it results from the contributions of different atmospheric layers.…”

The IASI NH3 version 4 product: averaging kernels and improved consistency

“…This was first noted in Van Damme et al ( 2021), where a spurious trend was seen in the HRI NH 3 data over remote regions. It was attributed to the increase in global carbon dioxide (CO 2 ) concentrations, because of the presence of a weak CO 2 absorption band in the 920-990 cm −1 spectral region (Whitburn et al, 2021) where NH 3 has its strongest absorption. A linear correction on the HRI of the order of 0.03 yr −1 was introduced to compensate for this effect.…”