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Process-based climate model development harnessing machine learning: III. The Representation of Cumulus Geometry and their 3D Radiative Effects
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  • Najda Villefranque,
  • Danny Williamson,
  • Fleur Couvreux,
  • Frédéric Hourdin,
  • Jacques Gautrais,
  • Richard Fournier,
  • Robin J Hogan,
  • Stéphane Blanco,
  • Victoria Volodina
Najda Villefranque
Centre National de Recherches Météorologiques

Corresponding Author:[email protected]

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Danny Williamson
University of Exeter
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Fleur Couvreux
Université Toulouse, CNRM, Meteo-France, CNRS
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Frédéric Hourdin
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Jacques Gautrais
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Richard Fournier
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Robin J Hogan
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Stéphane Blanco
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Victoria Volodina
Turing Institute
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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.