Representing 3-D cloud radiation effects in two-stream schemes: 2. Matrix formulation and broadband evaluationHogan, R. J. ORCID: https://orcid.org/0000-0002-3180-5157, Schafer, S. A. K., Klinger, C., Chiu, J. C. and Mayer, B. (2016) Representing 3-D cloud radiation effects in two-stream schemes: 2. Matrix formulation and broadband evaluation. Journal of Geophysical Research: Atmospheres, 121 (14). pp. 8583-8599. ISSN 2169-8996
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1002/2016JD024875 Abstract/SummaryEstimating the impact of radiation transport through cloud sides on the global energy budget is hampered by the lack of a fast radiation scheme suitable for use in global atmospheric models that can represent these effects in both the shortwave and longwave. This two-part paper describes the development of such a scheme, which we refer to as the Speedy Algorithm for Radiative Transfer through Cloud Sides (SPARTACUS). The principle of the method is to add extra terms to the two-stream equations to represent lateral transport between clear and cloudy regions, which vary in proportion to the length of cloud edge as a function of height. The present paper describes a robust and accurate method for solving the coupled system of equations in both the shortwave and longwave in terms of matrix exponentials. This solver has been coupled to a correlated-k model for gas absorption. We then confirm the accuracy of SPARTACUS by performing broadband comparisons with fully 3-D radiation calculations by the Monte Carlo model “MYSTIC” for a cumulus cloud field, examining particularly the percentage change in cloud radiative effect (CRE) when 3-D effects are introduced. In the shortwave, SPARTACUS correctly captures this change to CRE, which varies with solar zenith angle between −25% and +120%. In the longwave, SPARTACUS captures well the increase in radiative cooling of the cloud, although it is only able to correctly simulate the 30% increase in surface CRE (around 4 W m−2) if an approximate correction is made for cloud clustering.
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