Effects of Atmospheric Absorption of Incoming Radiation on the Radiation Limit of the Troposphere: Reply

In response to a comment on their previous note about the Voigt line profile, here the authors clarify relevant statements and numeric algorithms in the original note

Interannual variations of tropical upper tropospheric humidity and tropical rainy‐region SST: Comparisons between models, reanalyses, and observations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95337/1/jgrd16515.pd

A strict test in climate modeling with spectrally resolved radiances: GCM simulation versus AIRS observations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95152/1/grl23741.pd

Sensitivity of modeled far‐IR radiation budgets in polar continents to treatments of snow surface and ice cloud radiative properties

While most general circulation models assume spectrally independent surface emissivity and nonscattering clouds in their longwave radiation treatment, spectral variation of the index of refraction of ice indicates that in the far IR, snow surface emissivity can vary considerably and ice clouds can cause nonnegligible scattering. These effects are more important for high‐elevation polar continents where the dry and cold atmosphere is not opaque in the far IR. We carry out sensitivity studies to show that in a winter month over the Antarctic Plateau including snow surface spectral emissivity and ice cloud scattering in radiative transfer calculation reduces net upward far‐IR flux at both top of atmosphere and surface. The magnitudes of such reductions in monthly mean all‐sky far‐IR flux range from 0.72 to 1.47 Wm −2 , with comparable contributions from the cloud scattering and the surface spectral emissivity. The reduction is also sensitive to sizes of both snow grains and cloud particles. Key Points Ice cloud and snow surface radiative properties vary considerably in the far IR Snow surface emissivity and cloud scattering affect far IR comparably Even for far‐IR radiation alone, the impact is nonnegligiblePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109339/1/Auxiliary_material_Aug27.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109339/2/grl52118.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109339/3/TableS01.pd

Spectrally Resolved Flux Derived from Collocated AIRS and CERES Observations and its Application in Model Validation

Spectrally resolved outgoing IR flux, the integrand of the outgoing longwave radiation (OLR), has its unique value in evaluating model simulations. Here we describe an algorithm of deriving such clear-sky outgoing spectral flux through the whole IR region from the collocated Atmospheric Infrared Sounder (AIRS) and the Clouds & the Earth's Radiant Energy System (CERES) measurements over the tropical oceans. Based on the scene types and corresponding angular distribution models (ADMs) used in the CERES Single Satellite Footprint (SSF) dataset, spectrally-dependent ADMs are developed and used to estimate the spectral flux at each AIRS channel. A multivariate linear prediction scheme is then used to estimate spectral fluxes at frequencies not covered by the AIRS instrument. The whole algorithm is validated using synthetic spectra as well as the CERES OLR measurements. Using the GFDL AM2 model simulation as a case study, the application of the derived clear-sky outgoing spectral flux in model evaluation is illustrated. By comparing the observed and simulated spectral flux in 2004, compensating errors in the simulated OLR from different absorption bands can be revealed, so does the errors from frequencies within a given absorption band. Discrepancies between the simulated and observed spatial distributions and seasonal evolutions of the spectral fluxes at different spectral ranges are further discussed. The methodology described in this study can be applied to other surface types as well as cloudy-sky observations and corresponding model evaluations

Spatial and spectral variability of the outgoing thermal IR spectra from AIRS: A case study of July 2003

Here we present a survey of the spatial variability in different climate zones seen from AIRS data using the spectral EOF analysis. Over the tropical and subtropical oceans, the first principal component (PC1) is mostly due to the thermal contrast between surface and thick cold cloud tops. The second principal component (PC2) is mainly due to the spatial variation of the lower tropospheric humidity (LTH) and the low clouds. The signature of dust aerosol over the Arabian Sea and the Atlantic off the coast of North Africa in the summertime can be clearly seen in the PC2. Both the PC1 and the PC2 capture the upper tropospheric water vapor variability due to the forced orthogonality of EOFs. The third principal component (PC3) is mainly due to the spatial variation of the lower stratospheric temperature. Over the midlatitude oceans, the PC1 is still due to the thermal contrast of emission temperature. During wintertime, the PC2 is mainly due to stratospheric temperature variations. In the summer, the PC2 over the southern hemisphere is still due to stratospheric temperature variations, but in the northern hemisphere it is mainly due to the variations of the LTH and the low clouds. An exploratory study using synthetic spectra based on a NCAR CAM2 simulation shows that the model could account for the essential features in the data as well as provide an explanation of the three leading PCs. Major disagreements exist in the location of the ITCZ, the dust aerosol, and the lower stratospheric temperature

Impact of Cloud Longwave Scattering on Radiative Fluxes Associated With the Madden‐Julian Oscillation in the Indian Ocean and Maritime Continent

Previous studies suggested that cloud longwave radiation contributes to the development and maintenance of the Madden‐Julian Oscillation (MJO) and model‐based convection is highly sensitive to the radiation scheme. However, currently used radiation schemes do not take cloud longwave scattering into account, resulting in an overestimation of the outgoing longwave radiation (OLR) and an underestimation of the downward longwave flux at the surface. We use combined active and passive satellite cloud property retrievals to quantify the one‐layer cloud OLR and heating rate (HR) biases introduced by neglecting cloud longwave scattering in the Indian Ocean and Maritime Continent in the context of MJO, with a focus on its phases 3, 5, and 6. The results show that the satellite‐detected one‐layer cloud area consists primarily of ice clouds, particularly during the boreal winter in the 4‐year study period. An increased ice cloud area fraction of one‐layer cloud groups is present up to 5 days before the onset of MJO events. If longwave scattering is neglected, the composite mean OLR overestimation over the one‐layer ice cloud area from 5 days before to 4 days after the MJO passage is approximately 3.5 to 5.0 W m−2. Neglecting longwave scattering also leads to a HR underestimation at cloud base and an overestimation at cloud top, making the base‐to‐top heating gradient less sharp at the cloud‐resolving scale.Key PointsDuration of one‐layer ice cloud coverage increases up to 5 days before the Madden‐Julian Oscillation (MJO) passageNeglecting longwave scattering leads to a 3.5 to 5.0 W m−2 overestimation of the outgoing longwave radiation (OLR)Neglecting longwave scattering leads to a less sharp heating gradient from cloud base to cloud topPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155944/1/jgrd56305_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155944/2/jgrd56305.pd