Implementation of a machine-learned gas optics parameterization in the
ECMWF Integrated Forecasting System
Abstract
Radiation schemes are physically important but computationally expensive
components of weather and climate models. This has spurred efforts to
replace them with a cheap emulator based on neural networks (NN),
obtaining large speed-ups, but at the expense of accuracy, energy
conservation and generalization. An alternative approach which is slower
but more robust than full emulation is to use NNs to predict optical
properties, without abandoning the radiative transfer equations.
Recently, NNs were developed to replace the RRTMGP gas optics scheme,
and shown to be accurate while improving speed.However, the evaluations
were based solely on offline radiation computations. In this paper, we
describe the implementation and prognostic evaluation of RRTMGP-NN in
the Integrated Forecasting System (IFS) of the European Centre for
Medium-Range Weather Forecasts (ECMWF). The new gas optics scheme was
incorporated into ecRad, the modular ECMWF radiation scheme. Using a
hybrid loss function designed to reduce radiative forcing errors, and an
early stopping method based on monitoring fluxes and heating rates with
respect to a line-by-line benchmark, new NN models were trained on
RRTMGP k-distributions with reduced spectral resolutions. Offline
evaluation shows a very high level of accuracy for clear-sky fluxes and
heating rates; for instance the RMSE in shortwave surface downwelling
flux is 0.78 W m−2 for RRTMGP and 0.80 W m−2 for RRTMGP-NN in a
present-day scenario, while upwelling flux errors are actually smaller
for the NN. Because our approach does not affect the treatment of
clouds, no additional errors will be introduced for cloudy profiles.
RRTMGP-NN closely reproduces radiative forcings for 5 important
greenhouse gases across a wide range of concentrations such as 8x CO2.
To assess the impact of different gas optics schemes in the IFS, four
1-year coupled ocean-atmosphere simulations were performed for each
configuration. The results show that RRTMGP-NN and RRTMGP produce very
similar model climates, with the differences being smaller than those
between existing schemes, and statistically insignificant for zonal
means of single-level quantities such as surface temperature. The use of
RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas
optics being almost 3 times faster), and is also faster than the older
and less accurate RRTMG which is used in the current operational cycle
of the IFS