Task 1 – Atmosphere outgoing radiation derived from IASI data

Spectrally resolved OLR

Our understanding of the Earth’s climate system and our ability to model future climate changes relies on accurate measurements of the Earth’s Outgoing Longwave Radiation (OLR) (W m-2) at the top of the atmosphere (TOA) (i.e. the radiation emitted by the Earth-atmosphere system and leaving to space). While OLR has been monitored by dedicated broadband instruments for more than four decades, these only provide spectrally integrated OLR over a broad spectral range which makes it difficult to assess the individual impact of the different climate drivers (especially greenhouse gases) and to track down deficiencies in climate models. A better constraint can, in principle, be obtained from spectrally resolved OLR (W m-2 cm-1) (i.e. the integrand of broadband OLR) derived from hyperspectral infrared sounders by giving access to the spectral signature of particular climate feedbacks and processes.

Method

A first achievement was the development of a dedicated algorithm for the estimation of the spectrally resolved OLR from radiance measurements (W m-2 cm-1 sr-1) made by the IASI sounder on board the Metop satellites. The spectral OLR is derived at the spectral sampling of the instrument (0.25 cm-1) in the range 645-2300 cm-1. To the best of our knowledge, no other OLR product exists for IASI measurements. It is also the first time that OLR is derived at such spectral resolution, with any instrument. The method is described in details in a paper published in the Journal of Climate (Whitburn, et al. 2020) and is summarized in Figure 1.1. It relies, as most of other OLR retrieval algorithms, on the use of pre-calculated empirical angular distribution models (ADMs) that directly link the directional radiance measurement to the corresponding OLR. These ADMs were developed from synthetic spectra for a large selection of scene types (more than 140,000 in total) associated with different states of the atmosphere and the surface. In total, 10 years of IASI derived spectral OLR have been processed globally (2° by 2° grid) on a daily basis.

Figure 1.1. Conceptual flowchart of the OLR retrieval algorithm

Short-term trends in the spectral OLR

We next started to use the IASI data to analyze short-term trends in the spectral OLR. The key figure of the study is presented below (Figure 1.2) and shows the spectral trends calculated from the longitude averaged daily spectral OLR (sea measurements only) between 2008 and 2017 (second panel). A first analysis reveals immediately: 1) a positive trend in the window region band (except at high latitude) due to the increasing surface temperature and 2) a negative trend in the CO2 band forced by the increases in CO2 concentrations (stratospheric cooling). A deeper analysis is carried out, by looking in parallel at the IASI channel layer contribution (Figure 2, bottom panel) – representing basically the altitude from which most radiation observed at the TOA comes from –, which should allow to identify and explain the changes in OLR in the spectral bands influenced by the main drivers of climate change (e.g. CO2, CH4, H2O, N2O, etc.). The outcomes should be of great value for modelers to help them in better constraining their climate models.

Figure 1.2. Upper panel : Example of IASI spectrum (in Brightness Temperature [K]), Second: Spectral trends from longitude averaged daily OLR (sea measurements only)  between 2008 and 2017  (expressed in % W m-2 cm-1 yr-1 – normalized by the year 2008). Statistically non-significant trends (95% confidence) are hatched. Third: Trends averaged over all latitude bands. CO2 and CH4 Jacobians are also shown. Bottom panel : IASI channel layer contribution.

Interannual variations in the spectral OLR

The second study, on the interannual variations in the spectral OLR, investigates the correlation between the OLR and well-known climate factors. Some meaningful questions that will be investigated include: 1) what are the climate phenomena that influence the most the interannual variability of OLR in different spectral bands? 2) Are these phenomena independent or intercorrelated? We rely for this on Empirical Orthogonal Function (EOF) analysis of global spectral OLR. An example of global EOF spatial patterns of the first six OLR modes (skin temperature channels) calculated for the 2008-2017 period is shown in Figure 1.3. The first mode appears to be highly correlated with the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) and the El Niño Modoki Index (EMI) for the period considered.

Figure 1.3. Global EOF spatial patterns of the first six OLR modes (skin temperature channels only) calculated for the 2008-2017 period.

Bibliography

Whitburn, S., L. Clarisse, S. Bauduin, M. George, D. Hurtmans, S. Safieddine, P.F. Coheur, and C. Clerbaux. 2020. « Spectrally resolved fluxes from IASI data: Retrieval algorithm for clear-sky measurements. » Journal of Climate. doi:10.1175/jcli-d-19-0523.1.

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