Radiation schemes are fundamental components of weather and climate models that need to be both efficient and accurate. In this work we refactor ecRad, a flexible radiation scheme developed at the European Centre for Medium-Range Weather Forecasts (ECMWF). The goal was to improve performance especially with ecCKD, a new gas optics scheme that requires only 32 spectral intervals in the longwave and shortwave to be accurate. This speeds up ecRad considerably, but also reduces performance due to short inner loops. We therefore carry out both higher-level code restructuring and kernel-level optimizations for the radiative transfer solvers TripleClouds and SPARTACUS. SPARTACUS computes cloud 3D radiative effects, which have so far been neglected in large-scale models. We exploit the lack of vertical loop dependencies in key computations by merging the spectral and vertical dimensions, improving vectorization and instruction-level parallelism. On the new AMD Rome-based ECMWF supercomputer, we obtain a 3-fold speedup for both solvers when using 32-term ecCKD models. Combining ecCKD with optimized code results in very fast yet accurate radiation computations: with TripleClouds we achieve 1.7 TFLOPs and a throughput of 621 columns/ms on a 128-core node. This is 11.5 times faster than ecRad in Integrated Forecasting System cycle 47r3, which uses a more noisy solver (McICA) and less accurate gas optics (RRTMG). SPARTACUS with ecCKD is now 2.4 times faster than CY47r3-ecRad, making cloud 3D radiative effects affordable to compute within large-scale models. Preliminary results show that SPARTACUS slightly improves forecasts of 2-metre temperature and low clouds in the tropics.

Numerical experiments have revealed the importance of horizontal transport of light in the presence of clouds (“3D effects’), with consequences for climate, weather and solar resource availability predictions. Yet, atmospheric models continue to use 1D radiative transfer schemes. In this paper, we provide observational evidence for 3D effects. Observed direct and diffuse surface fluxes are compared to outputs from the ecRad radiative transfer scheme run on retrieved cloud profiles. The direct-beam calculation takes careful account of the field-of-view of the pyrheliometer to ensure consistency between observed and modeled direct fluxes. Only the solver that accounts for 3D effects is able to reproduce the observed mean direct–diffuse partition of surface fluxes as a function of solar zenith angle and cloud cover. This validates the physically-based model for 3D radiative processes included in ecRad. These results are in favor of a shift of paradigm from 1D to 3D radiation in large-scale models.

Process-scale development, evaluation and calibration of physically-based parameterizations are key to improve weather and climate models. Cloud–radiation interactions are a central issue because of their major role in global energy balance and climate sensitivity. In a series of papers, we propose papers a strategy for process-based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of single-column versions of climate models with explicit simulations of boundary-layer clouds (LES). Parts I and II apply this framework to the calibration of boundary layer parameters targeting first boundary layer characteristics and then global radiation balance at the top of the atmosphere. This third part focuses on the calibration of cloud geometry parameters that appear in the parameterization of radiation. The solar component of a radiative transfer scheme (ecRad) is run in offline single-column mode on input cloud profiles synthesized from an ensemble of LES outputs. A recent version of ecRad that includes explicit representation of the effects of cloud geometry and horizontal transport is evaluated and calibrated by comparing radiative metrics to reference values provided by Monte Carlo 3D radiative transfer computations. Errors on TOA, surface and absorbed fluxes estimated by ecRad are computed for an ensemble of cumulus fields. The average root-mean-square error can be less than 5 Wm$^{-2}$ provided that 3D effects are represented and that cloud geometry parameters are well calibrated. A key result is that configurations using calibrated parameters yield better predictions than those using parameter values diagnosed in the LES fields.