Article Dans Une Revue Geoscientific Model Development Année : 2025

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

Résumé

This study proposes using a data-driven statistical model to freeze errors due to differences in environmental forcing when evaluating surface turbulent heat fluxes from weather and climate numerical models with observations. It takes advantage of continuous acquisition over approximately 10 years of near-surface sensible and latent heat fluxes (H and LE respectively) together with ancillary parameters at the Météopole flux station, a supersite of the Aerosol, Clouds and Trace Gases Research Infrastructure in France (ACTRIS-FR), located in Toulouse. The statistical model consists of several multi-layer perceptrons (MLPs) with the same architecture. A total of 13 variables characterizing environmental forcing in the surface layer on an hourly timescale are used as input parameters to estimate the observed H and LE simultaneously. The MLPs are trained using 5-year observational data under a 5-fold cross-validation. The remaining data are used to test the estimates under unknown conditions. The performance of the statistical model ranges within the state-of-the-art surface parameterization schemes on hourly and seasonal timescales. It also has a good generalization ability, but it hardly estimates negative H and large LE. A case study is conducted with data from a regional climate simulation. The statistical model is used to evaluate the simulated fluxes in the simulated environment to better examine the flaws of their numerical formulation throughout the simulation. Comparison of simulated fluxes with observed and MLP-based fluxes shows different results. According to MLP-based fluxes in the simulated environment, the land surface scheme of this climate model tends to underestimate large sensible heat flux. Thus, it incorrectly partitions between surface heating and evaporation during the late summer. Our innovative method provides insight into different techniques for evaluating simulated near-surface turbulent heat fluxes when a long period of comprehensive observations is available. It can usefully support ongoing efforts to improve surface parameterization schemes.

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DOI

Cite 10.25326/44 Jeu de données Maurel, W. (2019). Meteorological, soil data and surface turbulent fluxes - Meteopole station [Data set]. AERIS. https://doi.org/10.25326/44

DOI

Cite 10.5281/zenodo.11261853 Article ZOUZOUA, M. (2024). Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study. Zenodo. https://doi.org/10.5281/ZENODO.11261853

Dates et versions

insu-05141829 , version 1 (03-07-2025)

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Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, et al.. Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study. Geoscientific Model Development, 2025, 18 (11), pp.3211-3239. ⟨10.5194/gmd-18-3211-2025⟩. ⟨insu-05141829⟩
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