Trends in spectrally resolved OLR from 10 years of IASI measurements

2021 ◽  
Author(s):  
Simon Whitburn ◽  
Lieven Clarisse ◽  
Andy Delcloo ◽  
Steven Dewitte ◽  
Marie Bouillon ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Data Retrieval ◽  
Radiation Budget ◽  
Retrieval Algorithm ◽  
Earth Atmosphere ◽  
Clear Sky ◽  
The Earth ◽  
Atmosphere System ◽  
Spectrally Resolved ◽  
The Impact

<p>The Earth's Outgoing Longwave Radiation (OLR) is a key component in the study of climate. As part of the Earth's radiation budget, it reflects how the Earth-atmosphere system compensates the incoming solar radiation at the top of the atmosphere. At equilibrium, the two quantities compensate each other on average. Any variation of the climate drivers (e.g. greenhouse gases) causes an energy imbalance which leads to a climate response (e.g. surface temperature increase), with the effect of bringing the radiation budget back to equilibrium. Considerable improvements in our understanding of the Earth-atmosphere system and of its long-term changes have been achieved in the last four decades through the exploitation of measurements from dedicated broadband instruments. However, such instruments only provide spectrally integrated OLR over a broad spectral range and are therefore not well suited for tracking separately the impact of the different parameters affecting the OLR.</p><p>Better constraints can, in principle, be obtained from spectrally resolved OLR (i.e. the integrand of broadband OLR, in units of W m<sup>-2</sup> cm<sup>-1</sup>) derived from infrared hyperspectral sounders. Recently, a dedicated algorithm was developed to derive clear-sky spectrally resolved OLR from the Infrared Atmospheric Sounding Interferometer (IASI) at the 0.25 cm<sup>-1</sup> native spectral sampling of the L1C spectra (Whitburn et al. 2020).  Here, we analyze the changes in 10 years (2008-2017) of the IASI-derived OLR and we relate them to known changes in greenhouse gases concentrations (CO<sub>2</sub>, CH<sub>4</sub>, H<sub>2</sub>O, …) and climate phenomena activity such as El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO).</p><p>Whitburn, S., Clarisse, L., Bauduin, S., George, M., Hurtmans, D., Safieddine, S., Coheur, P. F., and Clerbaux, C. (2020). <strong>Spectrally Resolved Fuxes from IASI Data: Retrieval algorithm for Clear-Sky Measurements</strong>. Journal of Climate. doi: 10.1175/jcli-d-19-0523.1</p>

Spectrally resolved OLR from IASI measurements

2020 ◽  
Author(s):  
Simon Whitburn ◽  
Lieven Clarisse ◽  
Sophie Bauduin ◽  
Steven Dewitte ◽  
Maya George ◽  
...  
Keyword(s):  
Spectral Range ◽  
Climate Models ◽  
Southern Oscillation ◽  
Radiation Budget ◽  
Retrieval Algorithm ◽  
Distribution Models ◽  
Reanalysis Dataset ◽  
The Earth ◽  
Spectrally Resolved ◽  
The Impact

<p>The Earth’s Outgoing Longwave Radiation (OLR) is a key component in the study of climate feedbacks and processes. As part of the Earth’s radiation budget, it reflects how the Earth-atmosphere system compensates the incoming solar radiation at the top of the atmosphere. It can be retrieved from the radiance intensities measured by satellite sounders and integrated over all the zenith angles of observation. Since satellite instruments generally acquire the radiance at a limited number of viewing angle directions and because the radiance field is not isotropic, the conversion is however not straightforward. This problem is usually overcome by the use of empirical angular distribution models (ADMs) developed for different scene types that directly link the directional radiance measurement to the corresponding OLR.</p><p>OLR estimates from dedicated broadband instruments are available since the mid-1970s; however, such instruments only provide an integrated OLR estimate over a broad spectral range. They are therefore not well suited for tracking separately the impact of the different parameters affecting the OLR (including greenhouse gases), making it difficult to track down deficiencies in climate models. Currently, several hyperspectral instruments in space acquire radiances in the thermal infrared spectral range, and in principle, these should allow to better constrain the OLR. However, as these instruments were not specifically designed to measure the OLR, there are several challenges to overcome. Here we propose a new retrieval algorithm for the estimation of the spectrally resolved OLR from measurements made by the IASI sounder on board the Metop satellites. It is based on a set of spectrally resolved ADMs developed from synthetic spectra for a large selection of scene types associated with different states of the atmosphere and the surface. Atmospheric and surface parameters are derived from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset and selected using a dissimilarity-based subset selection algorithm. These spectral ADMs are then used to convert the measured IASI radiances into spectral OLR.</p><p>We then evaluate how the IASI OLR compare with the CERES and the AIRS integrated and spectral OLR. We analyze the interannual variations in OLR over 10 years of IASI measurements for selected spectral channels using EOF analysis and we connect them with well-known climate phenomena such as El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO).</p>

The impact of cloud inhomogeneities on the Earth radiation budget: the 14 October 1989 I.C.E. convective cloud case study

1994 ◽  
Vol 12 (2/3) ◽  
pp. 240-253 ◽  
Author(s):  
F. Parol ◽  
J. C. Buriez ◽  
D. Crétel ◽  
Y. Fouquart
Keyword(s):  
Aspect Ratio ◽  
Water Content ◽  
Liquid Water ◽  
Convective Cloud ◽  
Liquid Water Content ◽  
Radiation Budget ◽  
The Earth ◽  
Earth Radiation Budget ◽  
The Impact ◽  
Earth Radiation

Abstract. Through their multiple interactions with radiation, clouds have an important impact on the climate. Nonetheless, the simulation of clouds in climate models is still coarse. The present evolution of modeling tends to a more realistic representation of the liquid water content; thus the problem of its subgrid scale distribution is crucial. For a convective cloud field observed during ICE 89, Landsat TM data (resolution: 30m) have been analyzed in order to quantify the respective influences of both the horizontal distribution of liquid water content and cloud shape on the Earth radiation budget. The cloud field was found to be rather well-represented by a stochastic distribution of hemi-ellipsoidal clouds whose horizontal aspect ratio is close to 2 and whose vertical aspect ratio decreases as the cloud cell area increases. For that particular cloud field, neglecting the influence of the cloud shape leads to an over-estimate of the outgoing longwave flux; in the shortwave, it leads to an over-estimate of the reflected flux for high solar elevations but strongly depends on cloud cell orientations for low elevations. On the other hand, neglecting the influence of cloud size distribution leads to systematic over-estimate of their impact on the shortwave radiation whereas the effect is close to zero in the thermal range. The overall effect of the heterogeneities is estimated to be of the order of 10 W m-2 for the conditions of that Landsat picture (solar zenith angle 65°, cloud cover 70%); it might reach 40 W m-2 for an overhead sun and overcast cloud conditions.

Spatiotemporal variations of the ultraviolet upward radiation of background formations in the Earth-atmosphere system

2000 ◽  
Vol 67 (3) ◽  
pp. 225
Author(s):  
V. I. Barysheva ◽  
A. V. Ivanov ◽  
A. A. Kamenev ◽  
V. D. Starichenkova
Keyword(s):  
Earth Atmosphere ◽  
Spatiotemporal Variations ◽  
The Earth ◽  
Atmosphere System

scholarly journals GFIT3: a full physics retrieval algorithm for remote sensing of greenhouse gases in the presence of aerosols

2021 ◽  
Vol 14 (10) ◽  
pp. 6483-6507
Author(s):  
Zhao-Cheng Zeng ◽  
Vijay Natraj ◽  
Feng Xu ◽  
Sihe Chen ◽  
Fang-Ying Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Los Angeles ◽  
Greenhouse Gases ◽  
High Spectral Resolution ◽  
Ratio Method ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Atmospheric Scattering ◽  
Aerosol Scattering ◽  
The Impact

Abstract. Remote sensing of greenhouse gases (GHGs) in cities, where high GHG emissions are typically associated with heavy aerosol loading, is challenging due to retrieval uncertainties caused by the imperfect characterization of scattering by aerosols. We investigate this problem by developing GFIT3, a full physics algorithm to retrieve GHGs (CO2 and CH4) by accounting for aerosol scattering effects in polluted urban atmospheres. In particular, the algorithm includes coarse- (including sea salt and dust) and fine- (including organic carbon, black carbon, and sulfate) mode aerosols in the radiative transfer model. The performance of GFIT3 is assessed using high-spectral-resolution observations over the Los Angeles (LA) megacity made by the California Laboratory for Atmospheric Remote Sensing Fourier transform spectrometer (CLARS-FTS). CLARS-FTS is located on Mt. Wilson, California, at 1.67 km a.s.l. overlooking the LA Basin, and it makes observations of reflected sunlight in the near-infrared spectral range. The first set of evaluations are performed by conducting retrieval experiments using synthetic spectra. We find that errors in the retrievals of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) due to uncertainties in the aerosol optical properties and atmospheric a priori profiles are less than 1 % on average. This indicates that atmospheric scattering does not induce a large bias in the retrievals when the aerosols are properly characterized. The methodology is then further evaluated by comparing GHG retrievals using GFIT3 with those obtained from the CLARS-GFIT algorithm (used for currently operational CLARS retrievals) that does not account for aerosol scattering. We find a significant correlation between retrieval bias and aerosol optical depth (AOD). A comparison of GFIT3 AOD retrievals with collocated ground-based observations from AErosol RObotic NETwork (AERONET) shows that the developed algorithm produces very accurate results, with biases in AOD estimates of about 0.02. Finally, we assess the uncertainty in the widely used tracer–tracer ratio method to obtain CH4 emissions based on CO2 emissions and find that using the CH4/CO2 ratio effectively cancels out biases due to aerosol scattering. Overall, this study of applying GFIT3 to CLARS-FTS observations improves our understanding of the impact of aerosol scattering on the remote sensing of GHGs in polluted urban atmospheric environments. GHG retrievals from CLARS-FTS are potentially complementary to existing ground-based and spaceborne observations to monitor anthropogenic GHG fluxes in megacities.

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