Partial validation of a lossy compression approach to computing radiative transfer in cloud system‐resolving models

Abstract

AbstractCloud system‐resolving models (CSRMs) routinely calculate radiative flux profiles via the Independent Column Approximation (ICA). The ICA applies 1D radiative transfer models (RTMs) to all columns in a CSRM's domain. For this study, the Partitioned Gauss–Legendre Quadrature (PGLQ) method replaced the ICA in a CSRM. The PGLQ applies RTMs to columns, identified with GLQ rules, and distributes their flux profiles to the other columns. The PGLQ approach is likened to a lossy compression algorithm that trades information for efficiency. While verification and validation of an audio compression algorithm rest, respectively, on file size reduction and sound quality according to listeners, for the PGLQ they rest on increasing while maintaining, according to experimenters, the integrity of CSRM simulations. A CSRM was run for 80 days in radiative‐convective equilibrium (1,024 × 1,024 columns and horizontal grid‐spacing of 0.25 km) for sea‐surface temperature SST = 295 and 300 K; the last 40 days were analysed. Simulations using the ICA represent the control; experiments used the PGLQ calling the RTMs fRT = 200, 5,000 and 50,000 fewer times than the control. For fRT = 50,000, several key variables spanning time/domain‐averaged cloud and radiation properties, a measure of cloud (convection) aggregation, and horizontal fluctuations of cloud and radiation fields, differ significantly from the control. In contrast, corresponding differences between the control and PGLQ with fRT ≤ 5,000, for both a given SST and differences between SSTs, are often minor, in all respects, and appear to be drawn from a single population. While these results partially validate the PGLQ method for fRT ≤ 5,000, they also indicate that overly large reductions in radiative information are detrimental.