Articles | Volume 16, issue 21
https://doi.org/10.5194/acp-16-13791-2016
https://doi.org/10.5194/acp-16-13791-2016
Research article
 | 
08 Nov 2016
Research article |  | 08 Nov 2016

The spectral signature of cloud spatial structure in shortwave irradiance

Shi Song, K. Sebastian Schmidt, Peter Pilewskie, Michael D. King, Andrew K. Heidinger, Andi Walther, Hironobu Iwabuchi, Gala Wind, and Odele M. Coddington

Abstract. In this paper, we used cloud imagery from a NASA field experiment in conjunction with three-dimensional radiative transfer calculations to show that cloud spatial structure manifests itself as a spectral signature in shortwave irradiance fields – specifically in transmittance and net horizontal photon transport in the visible and near-ultraviolet wavelength range. We found a robust correlation between the magnitude of net horizontal photon transport (H) and its spectral dependence (slope), which is scale-invariant and holds for the entire pixel population of a domain. This was surprising at first given the large degree of spatial inhomogeneity. We prove that the underlying physical mechanism for this phenomenon is molecular scattering in conjunction with cloud spatial structure. On this basis, we developed a simple parameterization through a single parameter ε, which quantifies the characteristic spectral signature of spatial inhomogeneities. In the case we studied, neglecting net horizontal photon transport leads to a local transmittance bias of ±12–19 %, even at the relatively coarse spatial resolution of 20 km. Since three-dimensional effects depend on the spatial context of a given pixel in a nontrivial way, the spectral dimension of this problem may emerge as the starting point for future bias corrections.

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Short summary
The radiative effects of spatially complex cloud fields are notoriously difficult to estimate and are afflicted with errors up to ±50 % of the incident solar radiation. We find that horizontal photon transport, the leading cause for these three-dimensional effects, manifests itself through a spectral fingerprint – a new observable that holds promise for reducing the errors associated with spatial complexity by moving the problem to the spectral dimension.
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