RRTMGP: A fast and accurate radiation code for the next decade

Abstract:

Atmospheric radiative processes are key drivers of the Earth’s climate and must be accurately represented in global circulations models (GCMs) to allow faithful simulations of the planet’s past, present, and future. The radiation code RRTMG is widely utilized by global modeling centers for both climate and weather predictions, but it has become increasingly out-of-date. The code’s structure is not well suited for the current generation of computer architectures and its stored absorption coefficients are not consistent with the most recent spectroscopic information.

We are developing a new broadband radiation code for the current generation of computational architectures. This code, called RRTMGP, will be a completely restructured and modern version of RRTMG. The new code preserves the strengths of the existing RRTMG parameterization, especially the high accuracy of the k-distribution treatment of absorption by gases, but the entire code is being rewritten to provide highly efficient computation across a range of architectures. Our redesign includes refactoring the code into discrete kernels corresponding to fundamental computational elements (e.g. gas optics), optimizing the code for operating on multiple columns in parallel, simplifying the subroutine interface, revisiting the existing gas optics interpolation scheme to reduce branching, and adding flexibility with respect to run-time choices of streams, need for consideration of scattering, aerosol and cloud optics, etc.

The result of the proposed development will be a single, well-supported and well-validated code amenable to optimization across a wide range of platforms. Our main emphasis is on highly-parallel platforms including Graphical Processing Units (GPUs) and Many-Integrated-Core processors (MICs), which experience shows can accelerate broadband radiation calculations by as much as a factor of fifty. RRTMGP will provide highly efficient and accurate radiative fluxes calculations for coupled global predictions at essentially all spatial resolutions, therefore contributing to the lessening of a key bottleneck in highly complex and coupled atmospheric models, namely the large fraction of computational time currently required for the calculation of radiative fluxes and heating rates.