Spectrally resolved fluxes derived from collocated AIRS and CERES measurements...

Huang, X., N. Loeb, and W. Yang (2010), Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: 2. Cloudy sky and band‐by‐band cloud radiative forcing over the tropical oceans, J. Geophys. Res., 115, D21101, doi:10.1029/2010JD013932.

We first present an algorithm for deriving cloudy sky outgoing spectral flux through the entire longwave spectrum from the collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth’s Radiant Energy System (CERES) measurements over the tropical oceans. The algorithm is similar to the one described in part 1 of this series of studies: spectral angular dependent models are developed to estimate the spectral flux of each AIRS channel, and then a multivariate linear prediction scheme is used to estimate spectral fluxes at frequencies not covered by the AIRS instrument. The entire algorithm is validated against synthetic spectra as well as the CERES outgoing longwave radiation (OLR) measurements. Mean difference between the OLR estimated in this way and the collocated CERES OLR is 2.15 W m−2 with a standard deviation of 5.51 W m−2. The algorithm behaves consistently well for different combinations of cloud fractions and cloud‐surface temperature difference, indicating the robustness of the algorithm for various cloudy scenes. Then, using the Geophysical Fluid Dynamics Laboratory AM2 model as a case study, we illustrate the merit of band‐by‐band cloud radiative forcings (CRFs) derived from this algorithm in model evaluation. The AM2 tropical annual mean band‐by‐band CRFs generally agree with the observed counterparts, but some systematic biases in the window bands and over the marine‐stratus regions can be clearly identified. An idealized model is used to interpret the results and to explain why the fractional contribution of each band to the broadband CRF is worthy for studying: it is sensitive to cloud height but largely insensitive to the cloud fraction.

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Modeling Analysis and Prediction Program (MAP)