Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
<p>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 1...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-06-01
|
| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/3211/2025/gmd-18-3211-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | <p>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 (<span class="inline-formula"><i>H</i></span> and <i>LE</i> 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 <span class="inline-formula"><i>H</i></span> and <i>LE</i> simultaneously. The MLPs are trained using 5-<span class="inline-formula">year</span> 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 <span class="inline-formula"><i>H</i></span> and large <i>LE</i>. 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.</p> |
|---|---|
| ISSN: | 1991-959X 1991-9603 |