the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Abstract. This study proposes the use of a data-driven statistical model to freeze the errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from weather and climate numerical models with the observations. It takes advantage of continuous acquisition over approximately ten years of near-surface sensible and latent heat fluxes (H and LE respectively) together with ancillary parameters over the supersite "Météopole" of the French national research infrastructure ACTRIS-FR, located in Toulouse. The statistical model consists of several multi-layer perceptrons (MLPs) with the same architecture. Thirteen variables characterizing the environmental forcing in the surface layer at an hourly time scale are used as input parameters to estimate H and LE simultaneously. The MLPs are trained using 5-year observational data under a 5-fold cross-validation. The remaining data is used to test the estimates on unknown conditions. A case study is performed with data from a regional climate simulation. The performance of the statistical model ranges within the state-of-the-art surface parametrization schemes on hourly and seasonal time scales. It has also a good generalization ability, but hardly estimates negative H and large LE. The statistical model is used to evaluate the simulated fluxes under 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 show 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 differently evaluating the simulated near-surface turbulent heat fluxes when a long period of comprehensive observations is available. It can usefully support ongoing efforts for improvements of surface parametrization schemes.
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Status: open (until 08 Jul 2024)
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CEC1: 'Comment on egusphere-2024-568 - No Compliance with GMD's policy', Juan Antonio Añel, 12 May 2024
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
You have not published in a permanent repository neither the code used in your work nor the input or output data you use, which does not comply with our policy. Therefore, you must reply to this comment as soon as possible with the requested information (both links and DOIs).
Also, you must include the modified 'Code and Data Availability' section, including the mentioned information, in any potentially reviewed version of your manuscript.
If you do not fix this problem, we will have to reject your manuscript for publication in our journal. I should note that, given this lack of compliance with our policy, your manuscript should not have been accepted in Discussions. Therefore, the current situation with your manuscript is irregular.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/egusphere-2024-568-CEC1 -
CC1: 'Reply on CEC1', Maurin Zouzoua, 21 May 2024
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Citation: https://doi.org/
10.5194/egusphere-2024-568-CC1 -
AC1: 'Reply on CEC1', Maurin Zouzoua, 24 May 2024
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Dear Juan,
Thank you for your helpful feedback. An example of a workflow to evaluate the modelled turbulent heat fluxes using our approach can be found at this link: https://doi.org/10.5281/zenodo.11261853. The latest update of Meteopole observational data is now in use. The manuscript will be updated accordingly.
Citation: https://doi.org/10.5194/egusphere-2024-568-AC1
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CC1: 'Reply on CEC1', Maurin Zouzoua, 21 May 2024
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