Process‐Based Climate Model Development Harnessing Machine Learning: III. The Representation of Cumulus Geometry and Their 3D Radiative Effects. (16th April 2021) - Villefranque, Najda; Blanco, Stéphane; Couvreux, Fleur; Fournier, Richard; Gautrais, Jacques; Hogan, Robin J.; Hourdin, Frédéric; Volodina, Victoria; Williamson, Daniel
Abstract: Process‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose 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 dynamics and clouds (Large‐Eddy Simulations [LES]). This paper focuses on the calibration of cloud geometry parameters (vertical overlap, horizontal heterogeneity, and cloud size) that appear in the parameterization of radiation. The solar component of a radiative transfer (RT) scheme that includes a parameterization for 3D radiative effects of clouds (SPARTACUS) is run in offline single‐column mode on an ensemble of input cloud profiles synthesized from LES outputs. The space of cloud geometry parameter values is efficiently explored by sampling a large number of parameter sets (configurations) from which radiative metrics are computed using fast surrogate models that emulate the SPARTACUS solver. The sampled configurations are evaluated by comparing these radiative metrics to reference values provided by a 3D RT Monte Carlo model. The best calibrated configurations yield better predictions of TOA and surface fluxes than the one that uses parameter values computed from the 3D cloud fields: The root‐mean‐square errors averaged over cumulus cloud fields and solar angles are reduced from ∼10 Wm −2 with LES‐derived parameters to ∼5 Wm −2 with adjusted parameters. However, the calibration of cloud geometry fails to reduce the errors on absorption, which remain around 2–4 Wm −2 . Plain Language Summary: A way to improve the accuracy of climate models is to improve the physical formulations that represent the effects of small‐scale processes on the evolution of atmospheric state. Processes that involve clouds and radiation are particularly important due to their key role on climate. Choosing values for the parameters inherent to these formulations is a difficult task. This series of papers presents a rigorous strategy for calibrating models. It is based on comparisons between high‐resolution models that accurately represent clouds and single‐column versions of a climate model, on the basis of process‐oriented metrics such as cloud height. A set of acceptable parameters is efficiently found using machine learning techniques. In this third part, the parameters that control the radiative effects of cloud geometry are calibrated. A recent radiation model that includes realistic representation of the radiative effect of cloud heterogeneity, cloud vertical structure and cloud size is evaluated and calibrated using references that are provided by a ray‐tracing algorithm that tracks virtual photons in virtual cloud fields produced by high‐resolution models (Large‐Eddy Simulations [LES]). Calibration improves the model with respect to using parameters diagnosed in the LES. Good agreement is found only when interception of sunlight by cloud sides is represented. Key Points: A novel calibration approach is applied to an offline radiation scheme to disentangle sources of uncertainty in cloud radiative effects The SPARTACUS solver is run on cloud profiles derived from Large‐Eddy Simulations (LES) cumulus fields and compared to Monte Carlo 3D radiative transfer computations Adjusting SPARTACUS cloud geometry parameters provides effective values that improve surface and TOA fluxes compared to LES‐derived values