Using artificial neural network to better evaluate surface turbulent heat fluxes in weather and climate numerical models
Authors: Zouzoua maurin , Bastin Sophie , Chiriaco Marjolaine , Lothon Marie , Lohou Fabienne , Barthès Laurent , Mallet Cécile , Derrien Solène , Cheruy Frédérique , Bazile Eric , Polcher Jan , Roehrig Romain , Canut Guylaine 
Published: June 2022

&lt;p&gt;&lt;span&gt;The &lt;/span&gt;&lt;span&gt;s&lt;/span&gt;&lt;span&gt;urface turbulent fluxes, namely sensible and latent heat fluxes, are keys &lt;/span&gt;&lt;span&gt;factors&lt;/span&gt; &lt;span&gt;governing the boundary layer processes. &lt;/span&gt;&lt;span&gt;Therefore, &lt;/span&gt;&lt;span&gt;their &lt;/span&gt;&lt;span&gt;correct &lt;/span&gt;&lt;span&gt;representation&lt;/span&gt;&lt;span&gt; in numerical models is &lt;/span&gt;&lt;span&gt;crucial&lt;/span&gt;&lt;span&gt; for accurate weather forecasts and climate &lt;/span&gt;&lt;span&gt;projections&lt;/span&gt;&lt;span&gt;. However, the &lt;/span&gt;&lt;span&gt;formulation&lt;/span&gt;&lt;span&gt; of these fluxes in &lt;/span&gt;&lt;span&gt;such&lt;/span&gt;&lt;span&gt; models is the second source of uncertainty, &lt;/span&gt;&lt;span&gt;leading to incorrect surface-atmosphere interactions in the simulations&lt;/span&gt;&lt;span&gt;. Model evaluation is essential to draw development perspectives. Existing method&lt;/span&gt;&lt;span&gt;s&lt;/span&gt;&lt;span&gt; mostly consist of direct comparison between observed and modelled fluxes, blending other sources of errors such as incoherent grid-scale &lt;/span&gt;&lt;span&gt;representation (soil and vegetation types)&lt;/span&gt;&lt;span&gt; and inaccurate environmental forcing (radiative fluxes, temperature, &lt;/span&gt;&lt;span&gt;moisture&lt;/span&gt;&lt;span&gt; and wind speed). Thus, &lt;/span&gt;&lt;span&gt;q&lt;/span&gt;&lt;span&gt;uantifying errors solely due to fluxes formulation &lt;/span&gt;&lt;span&gt;is&lt;/span&gt;&lt;span&gt; still challenging. This study, within the framework of the French project MOSAI (Model and Observation for Surface-Atmosphere Interactions), aims at &lt;/span&gt;&lt;span&gt;proposing&lt;/span&gt;&lt;span&gt; a novel evaluation approach to &lt;/span&gt;&lt;span&gt;better&lt;/span&gt;&lt;span&gt; identify the weakness of numerical models &lt;/span&gt;&lt;span&gt;in &lt;/span&gt;&lt;span&gt;surface turbulent fluxes formulation. &lt;/span&gt;&lt;span&gt;T&lt;/span&gt;&lt;span&gt;he concept is to freeze the errors due to other sources &lt;/span&gt;&lt;span&gt;by&lt;/span&gt; &lt;span&gt;using&lt;/span&gt;&lt;span&gt; a &lt;/span&gt;&lt;span&gt;machine-learning&lt;/span&gt;&lt;span&gt; model, notably &lt;/span&gt;&lt;span&gt;a multi-layer perceptron, trained to estimate the fluxes from variables describing the conditions in the surface layer. Hourly data collected over several years at three operational instrumented sites of ACTRIS-France research infrastructure (SIRTA in Paris, M&amp;#233;t&amp;#233;o-pole in Toulouse and P2OA in Lannemezan), are used. Then, after adaptation to the outputs of numerical models involved in &lt;/span&gt;&lt;span&gt;the &lt;/span&gt;&lt;span&gt;MOSAI project (RegIPSL, LMDZ, AROME and ARPEGE), the trained-perception will be applied to assess their surface turbulent fluxes.&lt;/span&gt;&lt;/p&gt;