Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study

Zouzoua, Maurin; Bastin, Sophie; Lohou, Fabienne; Lothon, Marie; Chiriaco, Marjolaine; Jome, Mathilde; Mallet, Cécile; Barthes, Laurent; Canut, Guylaine

doi:https://doi.org/10.5194/gmd-18-3211-2025

Articles | Volume 18, issue 11

https://doi.org/10.5194/gmd-18-3211-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-3211-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 11

Methods for assessment of models

|

02 Jun 2025

Methods for assessment of models |

| 02 Jun 2025

Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study

Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut

Data sets

Meteorological, Soil Data and Surface Turbulent Fluxes – Meteopole Station J. Etienne https://doi.org/10.25326/44

Model code and software

Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study Maurin Zouzoua https://doi.org/10.5281/zenodo.11261853

Short summary

This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.