# Process-Level Evaluation of the Land-Atmosphere Interactions Within CNRM-CM6-1 Single-Column Model Configuration

Emilie Bernard, Romain Roehrig, Fleur Couvreux, Guylaine Canut, Christine

Delire, Fabienne Lohou, Marie Lothon, Bertrand Decharme

► To cite this version:

Emilie Bernard, Romain Roehrig, Fleur Couvreux, Guylaine Canut, Christine Delire, et al. Process-Level Evaluation of the Land-Atmosphere Interactions Within CNRM-CM6-1 Single-Column Model Configuration. Journal of Advances in Modeling Earth Systems, 2025, 17 (11), 10.1029/2025MS005090. http://hal-05370480

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RESEARCH ARTICLE

10.1029/2025MS005090

**Key Points:**

* Four single-column model cases in which land and atmosphere are coupled are developed, differing only by their homogeneous land cover type

* The ARPEGE and Meso-NH models coupled to the SURFEX platform capture fairly well the observed diurnal cycle of land-atmosphere interactions

* In the two models, the land-atmosphere coupling over the four land covers is most sensitive to the vegetation hydric stress parameterization

**Correspondence to:**

E. Bernard,
emilie.bernard@umr-cnrm.fr

**Citation:**

Bernard, E., Roehrig, R., Couvreux, F., Canut, G., Delire, C., Lohou, F., et al. (2025). Process-level evaluation of the land-atmosphere interactions within CNRM-CM6-1 single-column model configuration. *Journal of Advances in Modeling Earth Systems, 17*, e2025MS005090. https://doi.org/10.1029/2025MS005090

Received 27 MAR 2025

Accepted 29 SEP 2025

© 2025 The Author(s). Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# Process-Level Evaluation of the Land-Atmosphere Interactions Within CNRM-CM6-1 Single-Column Model Configuration

Emilie Bernard¹ Ⓓ, Romain Roehrig¹ Ⓓ, Fleur Couvreux¹ Ⓓ, Guylaine Canut¹, Christine Delire¹ Ⓓ, Fabienne Lohou² Ⓓ, Marie Lothon² Ⓓ, and Bertrand Decharme¹ Ⓓ

¹Météo-France, CNRS, Université de Toulouse, CNRM, Toulouse, France, ²Laboratoire d'Aérologie, Université de Toulouse, CNRS, Toulouse, France

**Abstract** Land-atmosphere coupling involves multiple processes occurring across different temporal and spatial scales. These complex processes are not yet fully represented in climate or numerical weather prediction models. To evaluate these coupled models and improve or develop new parameterizations, four different single-column model setups are proposed. These setups vary in the type of spatially homogeneous land cover: grassland, wheat field, corn field, and pine forest. They are based on the single-column version of the CNRM-CM6-1 model, which couples the ARPEGE-Climat atmospheric component with the SURFEX surface modeling platform. First, the simulation results are compared with observational data, focusing on the radiative balance, surface fluxes, soil temperature and moisture, as well as air temperature, specific humidity, and wind speed near the surface. The evaluation shows that the ARPEGE-SURFEX single-column model can accurately replicate both surface and atmospheric conditions. Next, the same modeling framework is applied to the Meso-NH-SURFEX large-eddy simulation model. These simulations provide a consistent reference for evaluating the boundary-layer in CNRM-CM6-1, particularly in terms of potential temperature and specific humidity throughout the day. Finally, a sensitivity study on the impact of surface parameters on land-atmosphere coupling reveals that the hydric stress parameterization, by modifying canopy conductance and transpiration, is the most influential driver of temperature and moisture in the mixed layer. This work highlights the advantages of using the ARPEGE-SURFEX single-column configuration as a reliable platform for experimentation and parameterization improvement, and local climate modeling. The study cases developed are made available for further evaluation of land-atmosphere coupling.

**Plain Language Summary** Exchanges of water and energy at the interface between the surface and the atmosphere are still poorly represented in climate or numerical weather prediction models. First, improving the representation of these exchanges at the land-atmosphere interface is essential. Second, it's important to evaluate the performance of current models by comparing their outputs with observations. To address these two goals, this study develops study cases using data from a measurement campaign conducted in southern France in June 2011. Four very different types of vegetation were selected on a clear-sky day: a grassland, a corn field, a wheat field, and a pine forest. Early summer is ideal for studying land-atmosphere exchanges, as vegetation is fully developed. The study provides detailed conditions for the simulations, including soil, surface, and atmospheric properties, as well as initial values such as soil temperature, humidity, and atmospheric temperature, humidity, wind, and pressure. These cases were tested on both low- and high-resolution models, showing promising results. The aim is for these cases to be used with different models to develop future improvements and validate performances.

## 1. Introduction

Land-atmosphere coupling is defined as the co-variability between land and atmospheric state variables mediated by fluxes of momentum, energy, water and other tracers at the land-atmosphere interface. It involves a wide range of processes (e.g., Betts, 2009; Ek & Mahrt, 1994), which control the weather and climate at the local scale, but also influence the larger scales of the climate system. Land-atmosphere coupling is thus critical for many human activities, for example, water and food resources, energy production, agriculture (Suni et al., 2015).

The fluxes at the land-atmosphere interface are tightly related to the land and atmospheric states but also to the land surface physiographical properties (e.g., topography, vegetation cover and type, soil texture, Nicholson, 1988;
Bonan, 1997; Bounoua et al., 2002; Foley et al., 2005; Sterling et al., 2013). For instance, surface albedo controls to a large extent the net radiative input of energy entering the soil-vegetation continuum, thus the sum of soil, sensible and latent heat fluxes, while their partition is mostly driven by soil moisture and the ability of the vegetation to evapotranspirate. In turn, these two fluxes influence the boundary-layer depth, the formation of clouds and possibly the triggering of precipitation (e.g., Betts et al., 1996; Eltahir, 1998; Findell & Eltahir, 2003a, 2003b; Williams et al., 2016). A large number of feedback mechanisms between the land and the atmosphere also have been identified (e.g., Ek & Holtslag, 2004; Gentine et al., 2013; Seo & Dirmeyer, 2022). They occur over a wide range of spatial and temporal scales (Jach et al., 2022), from the local scale over a few hours (e.g., within the diurnal cycle of near-surface temperature and humidity, clouds and convection, Bou-Zeid et al., 2020; Chaboureau et al., 2004; Schlemmer et al., 2012; Rieck et al., 2014; Fast et al., 2019) to the regional scale over a few days or weeks (e.g., low- and high-pressure systems, Zeng & Yuan, 2021; H. Chen et al., 2020) and the planetary scale over seasons or years (e.g., planetary wave, Zhao et al., 2001). For example, if soil moisture is sufficiently high in relation to air humidity, evapotranspiration settles, moistens the air which finally induces a decrease in evapotranspiration in a feedback loop (Ek & Mahrt, 1994). This affects the development of boundary-layer clouds which, as they form, could reduce the sensible heat flux by intercepting the net radiation input. These few examples of possible land-atmosphere feedback that illustrate the complex boundary-layer interactions. Moreover, these interactions are also influenced by external large-scale forcing (e.g., advection and pressure gradients).

Documentation and understanding of land-atmosphere coupling has advanced owing to dedicated field campaigns (e.g., F. Chen & Zhang, 2009; Guillod et al., 2014; Jahromi et al., 2022) and to the deployment of long-term instrumented sites measuring relevant variables (e.g., Atmospheric Radiation Measurement Southern Great Plains Site or Cabauw observational site—Sisterson et al., 2016; Ackerman & Stokes, 2003; Bosveld, 2020). However, such observations remain local in space and/or time and often do not cover all the variables needed to fully analyze the processes involved in the land-atmosphere coupling. Efforts to enhance observation data sets are to be continued (e.g., Findell et al., 2015; Wulfmeyer et al., 2023). Numerical models have thus complemented observations, notably helping interpret them, isolate the role of certain processes and understand specific interactions (Betts, 2009; Hay-Chapman & Dirmeyer, 2023; Lee & Hohenegger, 2024).

In 2019, the Working Group on Numerical Experimentation (WGNE) surveyed the weather and climate modeling communities. It was emphasized that the fluxes at the land-atmosphere interface was one of their two largest concerns for model errors and uncertainties (Reynolds et al., 2020). While weather and climate models represent each component of the Earth's climate system: the atmosphere, land, ocean and ice, this study focuses on the land-atmosphere part of climate simulations. A General Circulation Model (GCM) is used for the atmospheric part and a land surface model (LSM) is utilized for continental covers. The latter supplies lower-boundary conditions of energy, water, momentum and tracers to the atmospheric part. GCMs include a dynamical core to solve the geophysical fluid dynamics equations on the sphere and a set of process-based physical parameterizations to compute the effects of processes that are of subgrid scale or which are not incorporated in the dynamics and physics of fluids, onto the resolved model state variables. This physical component of GCMs provides an approximate representation of radiative transfer, water phase changes and microphysical processes, as well as of subgrid-scale motions that redistribute energy, water and momentum vertically, either through isotropic small-scale turbulence or owing to more coherent features such as convective updrafts or downdrafts (e.g., Roehrig et al., 2020). For the latest processes, models now often use an eddy-diffusivity mass-flux framework (J. Han et al., 2016; Hourdin et al., 2002; Sušelj et al., 2013). At the surface, fluxes rely on closed surface energy and water balances and are derived using the Monin-Obukhov Similarity Theory (MOST, Monin & Obukhov, 1954; Webb, 1970; Paulson, 1970). The fluxes are further modulated by the prescribed or dynamical characteristics of the vegetation and soil, through roughness, diffusion and evapotranspiration processes.

As a consequence, the coupling in weather and climate models involves a wide number of interactions between their various components. For example, errors in near-surface atmospheric water vapor may impact subsequent precipitation events (Wang et al., 2024) or temperature extreme (Lorenz et al., 2012). Near-surface atmospheric water vapor is significantly influenced by soil moisture and surface evapotranspiration (e.g., Dirmeyer et al., 2006), but also by the ability of the boundary-layer to export humidity away from the surface up to the free troposphere, especially when it is convective (e.g., Torres et al., 2019). In addition, G. Liu et al. (2013) highlight the potential role of errors in prescribed surface parameters (e.g., roughness length) in near-surface temperature and water vapor errors, while Kleidon and Heimann (1998) or Barlage et al. (2021) emphasize the potential role of missing or simplified representation of groundwater-related processes.
Disentangling model errors in the representation of the land-atmosphere coupling is a tough effort, especially given the increasing complexity of weather and climate models. While many evaluation analyzes are performed with the full three-dimensional complexity of models, the single-column model (SCM) approach is appealing, at least as a first step (e.g., Betts & Miller, 1986; Randall et al., 1996). It is computationally cheap, so fully relevant to rapidly implement and validate new model developments (Bogenschutz et al., 2020). By isolating a GCM atmospheric and land column, the SCM framework also removes several feedbacks, thus often simplifying the interpretation of model results and sensitivities. SCM cases are often developed based on field campaigns so as to ease detailed comparisons between model results and observations (e.g., Zhang et al., 2016). In the case of the atmosphere, SCM simulations are now routinely compared with fully consistent large-eddy simulations (LES) that explicitly resolved many processes of interests, especially within the boundary-layer (e.g., Couvreux et al., 2021). This SCM/LES framework has been instrumental in recent boundary-layer parameterization developments (e.g., Couvreux et al., 2021; Hourdin et al., 2020; Marcel et al., 2024; Pergaud et al., 2009; Rochetin et al., 2014).

While the SCM framework is widely used for the development and evaluation of physics components of at-
mosphere and land models, it is more rarely used with configurations in which both components are fully coupled.
For cases over land surfaces, atmospheric SCMs often use prescribed turbulent surface fluxes (e.g., Audouin
et al., 2021; Lenderink et al., 2004), while land surface are forced by observed near-surface atmospheric pa-
rameters (e.g., Best et al., 2015; Collier et al., 2018; Haughton et al., 2016; Wartenburger et al., 2018). In both
cases, the coupling is either highly constrained or totally removed, which is a clear limitation to finely evaluate
and improve its representation (Schulz et al., 2001). A few studies do emphasize the interest to use the SCM
framework with land-atmosphere coupled configurations (e.g., Ek & Holtslag, 2004), but only a few SCM
frameworks for land-atmosphere coupled configurations has been proposed to the community, namely the phases
3 and 4 of the Global Energy and Water cycle Exchanges (GEWEX) Atmospheric boundary-layer Study
(GABLS) initiative (Bazile et al., 2015; Bosveld, Baas, Steeneveld et al., 2014; Bosveld, Baas, van Meijgaard,
et al., 2014; Le Moigne et al., 2022), the latest of which targeted the coupling between land ice/snow and a stable
boundary-layer over Antarctica, and the Diurnal land/atmosphere Coupling Experiment (DICE—Best
et al., 2025) initiative. Based on the GABLS4 experiment, Couvreux et al. (2020) improved the simulation setup
with a prescribed surface temperature and then integrate partly land-atmosphere coupling with the computation of
surface fluxes. Our main objective here is to enrich the library of SCM cases relevant for land-atmosphere
coupling processes.

The SCM cases proposed in this article use a land surface that has spatially homogeneous properties. Our primary objective is to characterize the specific impact of different types of land surface on the atmosphere and its coupling with the surface. A future work will increase the complexity of the land surface as to include land surface heterogeneities in order to address the general models' difficulty to reproduce land-atmosphere coupling in this context (e.g., Findell et al., 2024; Tang et al., 2018).

The study case focuses on the land atmosphere coupling in ARPEGE-Climat (Roehrig et al., 2020) and SURFEX land surface model (Masson et al., 2013) in single column setup used for CNRM-CM6-1 CMIP comparison (Eyring et al., 2016). It relies on a comparison of this setup with observations of the boundary-layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign (Lothon et al., 2014).

The SCM cases described in details hereafter are aimed to be useful for models other than the ARPEGE-SURFEX model used here. Also, coherent large-eddy simulations are conducted with Meso-NH non hydrostatic model (Lac et al., 2018), both for assessing their ability to reproduce observation of the land-atmosphere interface and for use as a reference for evaluation of the ability of our ARPEGE-SURFEX SCM to represent boundary-layer processes and their sensitivity to surface properties. These large-eddy simulations can be used as a reference for other climate model simulations.

Section 2 describes the sites and the relevant observations used to develop the SCM cases and to evaluate the SCM behavior. Section 3 follows with the description of the land-atmosphere coupled SCM setup. Sections 4 and 5 assess the skills of the ARPEGE-SURFEX model to capture the evolution of the land, atmosphere and interface state variables, using observations and large-eddy simulations as references. Section 6 concludes and offers a few perspectives of the present work.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Map of the study area localizing weather stations (yellow), 60-m mast (red) and radiosoundings (pink). Map data: Google Satellite, Airbus/CNES/Landsat/Copernicus/Maxar Technologies, 2025.</figcaption></figure>

## 2. Site Description and Data

### 2.1. The BLLAST Field Campaign

The Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign was carried out from 14 June to 8 July 2011 near the Pyrenean Platform for Observation of the Atmosphere (P2OA) at Lannemezan (43.1°N–0.36°E) in the south of France (Lothon et al., 2014). The main focus of BLLAST was the turbulence decaying transition between the daytime convective and the nighttime more stable boundary-layers.

P2OA is located over a 200 km² plateau at an altitude of 588 m above sea level, about 40 km north of the Pyrenees foothills. The area has a continental climate due to its inland location. The entire observation period occurred over relatively constant weather associated with anticyclonic conditions and accompanied by weak winds (Lothon et al., 2014).

Multiple atmospheric variables were measured at different heights such as temperature, specific humidity and wind, by aircraft (up to 1.5 km), radiosoundings and tethered balloons (up to 20 km), towers (from 2 to 60 m) and ground-based remote sensing (10 m–16 km). Radiosoundings were launched during the intensive observation period days at 06:00, 12:00, 18:00, and 24:00 UTC and frequent radiosoundings sample the lower troposphere only, during afternoons. In addition to the campaign observations, a permanent 60 m mast on the P2OA site, provides year-round measurements at 2, 15, 30, 45, and 60 m.

Specifically, four different land covers (Figure 1) were instrumented to characterize the land-atmosphere coupling: a grassland, a wheat field, a corn field and a pine forest (see Table 1 for the performed measurements, relevant for the present work). On each of these sites, latent and sensible heat fluxes are measured through an eddy-covariance approach (e.g., Swinbank, 1951). Observations over the grassland, the wheat field, and the corn field were collected approximately at 3 m above the ground. For the pine forest, they were taken at a height of 32 m above the ground, which corresponds to 6 m above the tree canopy.

The instrumented sites sampled a variety of vegetation types, thereby providing the opportunity to analyze the land-atmosphere interactions over various surface types. This measurement campaign will be used, for this
<table><caption>**Table 1**
Observations Collected During the BLLAST Field Campaign on the Four Selected Sites, Namely the Grassland, the Wheat Field, the Corn Field and the Pine Forest</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2">Measurement type</th><th rowspan="2">Frequency</th><th colspan="3">Sampling heights (canopy height in m)</th></tr><tr><th>Grassland (0.20)</th><th>Corn field (0.60)</th><th>Wheat field (0.40)</th><th>Pine forest (26)</th></tr></thead><tbody><tr><td>Meridional and zonal wind</td><td>Sonic anemometer</td><td>20 Hz</td><td>3 m</td><td>5 m</td><td>3 m</td><td>32 m</td></tr><tr><td>Air temperature, air moisture</td><td>Eddy covariance</td><td>20 Hz</td><td>3 m</td><td>5 m</td><td>3 m</td><td>32 m</td></tr><tr><td>SW<sub>dn</sub>, SW<sub>up</sub>, LW<sub>dn</sub>, LW<sub>up</sub></td><td>Radiometry</td><td>0.01 Hz</td><td>3 m</td><td>5 m</td><td>3 m</td><td>—</td></tr><tr><td>Sensible and latent heat fluxes</td><td>Eddy covariance</td><td>20 Hz</td><td>3 m</td><td>3 m</td><td>3 m</td><td>32 m</td></tr><tr><td>Soil heat flux</td><td>Thermopile</td><td>0.01 Hz</td><td>3 m</td><td>3 m</td><td>3 m</td><td>—</td></tr><tr><td>Soil temperature</td><td>Thermo-resistance</td><td>0.01 Hz</td><td>-2 cm, -5 cm, -10 cm, -25 cm, -50 cm</td><td>—</td><td>-2 cm, -5 cm, -10 cm, -25 cm, -50 cm</td><td>—</td></tr><tr><td>Soil moisture</td><td>Dielectric method</td><td>0.01 Hz</td><td>—</td><td>-1 cm</td><td>—</td><td>—</td></tr></tbody></table>

reason, to evaluate the land-atmosphere coupling of ARPEGE-SURFEX. In addition, previous work of Darbieu et al. (2015) defined a study case on the golden day of the 20 June 2011 on each sampled land cover with prescribed fluxes. The field campaign provides for this day a rather comprehensive set of measurements. Also, the 20 June 2011 meteorological conditions simplify the analysis of the boundary-layer evolution (light winds and clear-sky conditions) and make it more easy to simulate by numerical models. Further important for the present work, 20 June 2011 occurs during the vegetation transition from spring to summer and thus provides contrasted surface conditions of large interest to evaluate the surface-atmosphere coupling in numerical models with the framework that we develop. In the following, we revisit the study case developed by Darbieu et al. (2015) by adding an interactive surface for the four selected land covers.

## 2.2. Correction of the Non-Closure in the Observed Surface Energy Balance

An objective of the present study is to compare measurements of surface turbulent fluxes to fluxes simulated by a land-atmosphere model. While the surface energy balance is in principle closed in such models, it remains challenging to close it by combining different sources of observations. The sum of latent and sensible heat fluxes sampled by eddy covariance is in general weaker than the total available energy (net radiation flux minus ground heat flux, Moore, 1976, 1986; Panin et al., 1998; Oncley et al., 2007) regardless of the type of surface cover or measurement configuration (Foken et al., 2011):

$$R_{\text{net}} - G = \text{LE} + \text{H} + \varepsilon \quad (1)$$

where $R_{\text{net}}$ is the net radiation at the surface (positive downwards), LE the latent heat flux (positive upwards), H the sensible heat flux (positive upwards), G the heat flux into the ground (positive downwards), and $\varepsilon$ is the non-closure residual (positive upwards). Note that the heat storage between the surface (or the canopy for high vegetation) and the depth at which the sensor is measuring the ground heat flux is included in the epsilon term.

Figure 2 illustrates the non-closure error for the three BLLAST land covers over which all required surface fluxes are available (solid lines). The residual can reach up to 200 W m⁻², and on average, it represents approximately 34% of the total available energy (TAE i.e. $R_{\text{net}} - G$) over the grassland, 21% over the wheat field and 27% over the corn field (dashed lines). This is consistent with previous estimates (i.e., ±30% over a forest site, mean of 20% for the FLUXNET sites, from 6% for evergreen broadleaf forests to 30% in mixed forests or crops: Verma et al., 1986; Wilson et al., 2002; Stoy et al., 2013;

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Surface energy balance non-closure error (in W m⁻², solid lines, left y-axis) and its fraction of the TAE (in %, dotted lines, right y-axis) for the three BLLAST instrumented sites for 20 June 2011, namely grassland (green), corn field (orange) and wheat field (pink).</figcaption></figure>
Mauder et al., 2020), and confirms that a correction of this energy balance non-closure is needed for a fair comparison between observations and model output, or at least that such uncertainties need to be properly accounted for. Note that we acknowledge that uncertainties in observed radiation may impact the energy budget imbalance. However, these uncertainties are generally smaller in magnitude compared to those in surface turbulent fluxes (Stoy et al., 2013). In this case study, we identified an uncertainty of 50 W m⁻² in the observed radiation between the three sites.

The understanding of this non-closure remains an active area of research (e.g., Dabberdt et al., 1993; Mauder et al., 2020). It more likely arises from the measurements of turbulent fluxes than those of radiation and ground heat fluxes as sampling issues lead to lower uncertainties (Twine et al., 2000). Instrument error in eddy-covariance sampling could explain approximately half of the missing energy flux (Frank et al., 2016). Additional sources of energy may also be missed such as the canopy heat storage for example, or the existence of additional terms in the surface energy balance equation that might represent an energy to work against the atmospheric pressure and the expansion of air resulting from heating (Durand, 2022). One hypothesis is in particular that mesoscale circulations are not well captured by the sampling time interval used in eddy-covariance computations (e.g., H. Liu et al., 2021; Lan et al., 2023).

Several correction methods have been developed to attribute a major part or the totality of the non-closure residual to the sensible heat flux (Charuchittipan et al., 2014; Ingwersen et al., 2011), the latent heat flux (Eder et al., 2015; Stannard et al., 1994; Wohlfahrt et al., 2010), or both using a Bowen-ratio-preserving approach (Barr et al., 1994; Blanken et al., 1997). This latest correction assumes that errors can be ascribed to the measured sensible and latent heat fluxes but that their ratio is well captured. Mauder et al. (2018) argue that it is likely the most appropriate correction method as latent and sensible heat fluxes are expected to be captured similarly by the eddy-covariance sensor. As a result, it is commonly used, also due to its simplicity and robustness (e.g., Pastorello et al., 2020). We thus choose to apply this Bowen-ratio-preserving correction on BLLAST measurements, using the following equations:

$$LE_{corr} = LE + \varepsilon \times \frac{LE}{LE + H} \qquad (2)$$

$$H_{corr} = H + \varepsilon \times \frac{H}{LE + H} \qquad (3)$$

Note that ground heat flux is not sampled on the pine forest site, consequently the flux correction can not be applied.

## 3. Land-Atmosphere SCM Simulation and LES Setup

As mentioned earlier, the simulated case is based on one of the golden days of the BLLAST field campaign, namely 20 June 2011. It corresponds to a clear-sky day during which a convective boundary-layer develops. This case was already studied in Darbieu et al. (2015) with a LES. Their LES setup is revisited here to make it slightly more idealized, and extended to include land-atmosphere interactions (their LES uses prescribed surface fluxes). The case setup covers the time period from 05:00 UTC to 18:00 UTC.

### 3.1. Atmosphere Initialization and Forcing

The profiles of zonal and meridional wind components, potential temperature ($\theta$) and relative humidity with respect to liquid water used to initialize the simulation are idealized from the 05:00 UTC radiosounding measurements and the 60-m mast measurements at 2, 15, 30, 45, and 60 m. Basically, a piecewise linear function is defined for each variable to capture its main vertical variations, especially within the boundary-layer (see Figure 3). More specifically, close to surface we directly use the 60-mast data, keeping a simple piecewise linear fit and having in mind that the vertical resolution of numerical models is usually rather coarse (e.g., first level at 5–10 m above the ground, first model layer thickness of 10–20 m). We also assume that the near-surface profile of these variables does not depend on the land cover. This may be rather crude given the range of measurements over the four sites (dots in Figures 3a–3d), but sensitivity tests reveal a weak impact of the near-surface atmospheric initial state (not shown). Besides, no obvious choice is available to consistently connect observed near-surface values at the four sites to the available atmospheric profiles above. Surface pressure is initialized to 950 hPa and kept constant during the
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Atmospheric profiles at 05:15 UTC, 20 June 2011 of (a) potential temperature (Celsius degrees), (b) relative humidity with respect to liquid water (%), (c) zonal and (d) meridian winds (m s⁻¹) measured by the radiosounding (black or gray lines), the P2OA 60-m mast (red lines) and the meteorological stations over the grassland, wheat field, corn field and forest BLLAST sites (dots), and idealized for the present study (blue lines). Panels (a)–(d) provide higher resolution in the lowest 140 m, while (e)–(h) provide the profiles from the surface to 20 km, with ERA5 data (dashed black lines). Note that for potential temperature and relative humidity the radiosounding data are plotted, while for the wind components, the noisy radiosounding data are smoothed using the Savitzky-Golay filter.</figcaption></figure>

simulations. The radiosounding stopped near 17.5 km. ERA5 data (Hersbach et al., 2020) is used above up to 50 km except for the relative humidity profiles which is set to 0% above 12 km.

The near-surface large-scale atmospheric forcing is weak (weak wind, weak pressure gradients, not shown) so that we adopt the simple approach of not applying any forcing to the atmospheric column (i.e., no advective tendencies, no geostrophic wind, no nudging). As shown later, this hypothesis still allows a rather good capture of the observations.

The radiative transfer parameterization is activated. The 2011 total solar irradiance of 1361 W m⁻² is taken from Matthes et al. (2017). Greenhouse gases are supposed well-mixed in the atmosphere and their concentrations are prescribed to the 2011 observed values (Meinshausen et al., 2017): 390.94 ppmv for CO₂, 1,813.07 ppmv for CH₄, 324.16 ppbv for N₂O, 779.12 pptv for CFC-11 and 528.53 pptv for CFC-12. The ozone profile is derived from the climatology of Fortuin and Langematz (1995) through interpolation in space and time at the specific location of Lannemezan and the date of 20 June 2011. For the sake of simplicity, a clean sky is assumed (i.e., no aerosols in the atmospheric column). Sensitivity tests were conducted to assess the validity of this assumption. These tests show an impact of including a typical aerosol climatology lower than 10 W m⁻² and 2 W m⁻² on the
<table><caption>**Table 2**
Physiographic Parameters for Each BLLAST Site Used in the Present Simulations</caption><thead><tr><th>BLLAST site</th><th>Grassland</th><th>Corn field</th><th>Wheat field</th><th>Pine forest</th></tr></thead><tbody><tr><td>Latitude</td><td>43.127813</td><td>43.091824</td><td>43.131564</td><td>43.109027</td></tr><tr><td>Longitude</td><td>0.366117</td><td>0.359047</td><td>0.358425</td><td>0.360318</td></tr><tr><td>Clay fraction<sup>a</sup></td><td>0.19</td><td>0.19</td><td>0.19</td><td>0.19</td></tr><tr><td>Sand fraction<sup>a</sup></td><td>0.37</td><td>0.37</td><td>0.37</td><td>0.37</td></tr><tr><th>SURFEX vegetation type</th><th>Grassland</th><th>C4 Crop</th><th>C3 Crop</th><th>Evergreen temperate needleleaf trees</th></tr><tr><td>Vegetation fraction<sup>b</sup> (α<sub>veg</sub>)</td><td>0.95</td><td>0.83</td><td>0.83</td><td>0.95</td></tr><tr><td>Bare soil albedos<sup>b,c</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>UV</td><td>0.104</td><td>0.077</td><td>0.104</td><td>0.081</td></tr><tr><td>Visible</td><td>0.104</td><td>0.077</td><td>0.104</td><td>0.081</td></tr><tr><td>Near-infrared</td><td>0.176</td><td>0.251</td><td>0.179</td><td>0.285</td></tr><tr><td>Vegetation albedos<sup>†</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>UV</td><td>0.063</td><td>0.1</td><td>0.003</td><td>0.026</td></tr><tr><td>Visible</td><td>0.063</td><td>0.1</td><td>0.003</td><td>0.026</td></tr><tr><td>Near-infrared</td><td>0.327</td><td>0.167</td><td>0.19</td><td>0.1546</td></tr><tr><td>Surface emissivity</td><td>0.969</td><td>0.959</td><td>0.965</td><td>0.969</td></tr><tr><td>Roughness lengths (in m)</td><td></td><td></td><td></td><td></td></tr><tr><td>momentum</td><td>0.079</td><td>0.06606</td><td>0.08583</td><td>1.43</td></tr><tr><td>heat/moisture</td><td>0.0079</td><td>0.006606</td><td>0.008583</td><td>0.143</td></tr><tr><td>Leaf Area Index<sup>b</sup> (LAI)</td><td>3.65</td><td>2.62</td><td>2.96</td><td>4.82</td></tr><tr><td>Minimal stomatal resistance<sup>b</sup> left (in sm<sup>-1</sup>)</td><td>40</td><td>120</td><td>40</td><td>150</td></tr><tr><td>Ground depth<sup>b</sup> (in m)</td><td>1.5</td><td>2</td><td>2</td><td>3</td></tr><tr><td>Root depth<sup>b</sup> (in m)</td><td>1</td><td>1.5</td><td>1.5</td><td>2</td></tr><tr><td>Tree height<sup>b</sup> (H<sub>t</sub>, in m)</td><td>—</td><td>—</td><td>—</td><td>11</td></tr></tbody></table>

downwelling shortwave and longwave radiation fluxes, respectively (see for details on the ARPEGE aerosol data set, and reference therein: Roehrig et al., 2020).

## 3.2. Surface Physiographic Properties

While existing atmospheric models are initialized and forced in a fairly consistent manner across numerical diversity, land-surface models exhibit a wider variety of choices for representing soil and vegetation properties between each of them. Nevertheless, we hope that the choices we make here, which are consistent with the SURFEX land-surface model (Decharme et al., 2019; Masson et al., 2013), can easily be adapted to other land-surface models. SURFEX uses the 1-km ECOCLIMAP-II global database (Faroux et al., 2013) to provide values for its required physiographic parameters. In each 1-km pixel, the land cover information is described by a land cover class, which encompasses fractions of 19 vegetation types, each of them having distinct physiographic characteristics. There are 573 such land cover classes. In addition, the database provides decadal climatologies of the Leaf Area Index (LAI), derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations (see for details: Faroux et al., 2013) and of the surface albedo in the Ultra-Violet (UV, 0.3–0.4 µm), visible (0.4–0.75 µm) and near-infrared (0.75–2.5 µm) spectral ranges, also based on MODIS observations (Carrer et al., 2014). For each of the four sites considered in the present work, the closest 1-km ECOCLIMAP-II pixel is considered, and within each land cover class, the SURFEX parameters are derived from the vegetation type the most consistent with the site characteristics. SURFEX parameters are summarized in Table 2. Only part of them

<footnote><sup>a</sup>These parameters are based on the Harmonized World Soil Database (HWSD, FAO et al., 2012) values, default soil texture database for SURFEX. <sup>b</sup>These parameters are required for SURFEX. The other ones are derived using SURFEX parameterizations (see Appendix A). <sup>c</sup>Vegetation albedos are adapted using the observations at each BLLAST site (see Appendix B).</footnote>
<table><caption>**Table 3**
Initial (05:00 UTC, 20 June 2011) Values of Soil Temperature and Soil Wetness Index (SWI) for Each BLLAST Site at Observation Depths</caption><thead><tr><th></th><th></th><th>Grassland</th><th>Corn field</th><th>Wheat field</th><th>Pine forest</th></tr></thead><tbody><tr><td rowspan="6">Temperature (K)</td><td>2 cm</td><td><strong>288.0</strong></td><td><strong>286.5</strong></td><td><strong>283.3</strong></td><td>283.3</td></tr><tr><td>5 cm</td><td><strong>289.5</strong></td><td>288.0</td><td><strong>285.4</strong></td><td>285.4</td></tr><tr><td>10 cm</td><td><strong>290.4</strong></td><td>288.9</td><td><strong>284.5</strong></td><td>284.5</td></tr><tr><td>25 cm</td><td><strong>291.2</strong></td><td>288.8</td><td><strong>286.5</strong></td><td>286.5</td></tr><tr><td>50 cm</td><td><strong>290.2</strong></td><td>288.7</td><td><strong>286.4</strong></td><td>286.4</td></tr><tr><td>100 cm</td><td>290.2</td><td>288.7</td><td>286.4</td><td>286.4</td></tr><tr><td rowspan="3">SWI</td><td>1 cm</td><td>0.4</td><td><strong>0.4</strong></td><td>0.4</td><td>0.4</td></tr><tr><td>50 cm</td><td>0.4</td><td>0.4</td><td>0.4</td><td>0.4</td></tr><tr><td>100 cm</td><td>0.6</td><td>0.6</td><td>0.6</td><td>0.6</td></tr></tbody></table>

Note. Observations are indicated with bold font, AROME analyzed data with italic font. Normal font indicates the choice made by the authors for initialization consistency across the sites (see text for details).

are required as input for the model. The other part are derived according to selected vegetation type parameterizations. Note that ECOCLIMAP-II surface albedos are close to observed values, but still slightly different. Thus, to keep the shortwave surface energy input consistent between simulations and observations, broadband surface albedos estimated from the radiation measurements are used to adjust the UV, visible and near-infrared albedos used in the simulations (Appendix B). Uncertainty remains regarding LAI values given that the BLLAST campaign did not measure it. Nevertheless, the reasonable model results that follow indicate that the ECOCLIMAP-II LAI values are not too unrealistic for our four sites.

### 3.3. Soil Initialization

The grassland and wheat field sites were instrumented for soil temperature measurements between 2 and 50 cm. The corn field site has soil temperature and moisture measurements at 2 and 1 cm, respectively. There was no measurement within the soil at the pine forest site. The available data are summarized in bold font in Table 3. These observations are complemented by the analyses of the AROME-France weather forecast model in operation at Meteo-France (Seity et al., 2011). The closest grid cell of the 06:00 UTC analyzes on 20 June 2011 is considered here (all sites are within the same grid cell).

For the corn field site, we assume similar variations with depth as for the grassland site, offset by -1.5°C given the soil temperature difference at 2 cm. In the pine forest site, the soil is expected to be colder than the grassland soil due to its higher capability to retain soil moisture. Therefore, we assume soil temperature to be the same as for the wheat field site. Below 50 cm, soil temperature is assumed to be constant, equal to the 50 cm value.

Soil moisture is only available at 1 cm in the corn field site (0.35 m³ m⁻³). Note that to the authors' knowledge, there was no irrigation in any of the instrumented field sites during the previous days. Below 1 cm, we use AROME-France data to provide information on soil moisture at different depths.

Soil moisture is expressed as a Soil Wetness Index (SWI), which can be easily transposed to different land-surface modeling context:

$$SWI = \frac{W_g - W_{wilt}}{W_{fc} - W_{wilt}} \quad (4)$$

where $W_g$ is soil moisture (in m³ m⁻³), $W_{wilt}$ that at the wilting point (minimum soil moisture before the wilting of the plant) and $W_{fc}$ that at field capacity (maximum soil moisture before runoff).

For the observations (only at the corn field), the soil moisture at wilting point (0.26 m³ m⁻³) and field capacity (0.48 m³ m⁻³) are computed using the soil moisture measurements for the month of June, which sampled both dry and wet periods. We then obtain a SWI value of 0.4 for the soil layer closest to the surface.

The AROME-France data is converted into SWI values using the model soil texture (based on the Harmonized World Soil Database, FAO et al., 2012), soil water content at field capacity is then 0.38 m³ m⁻³ and soil water content at wilting point is 0.23 m³ m⁻³. Soil water content at wilting point and field capacity derived from observations represent very local soil conditions at the specific location of the sensor in the corn field, whereas the values used in AROME-France are derived a soil database with a resolution of 2.5 km, which may explain the differences in these values between the two sources. We assume a similar SWI profile for the four simulated sites. The final initial soil temperature and moisture states are provided in Table 3. The mentioned soil depths correspond to observations levels. To match the soil depths in SURFEX, values are linearly interpolated.

## 4. Land-Atmosphere Coupling Within ARPEGE-SURFEX Over the Grassland Site

Single-column model simulations following the previously-described protocol are performed with the ARPEGE-SURFEX SCM configuration (hereafter referred to as ARPEGE), and with the Meso-NH-SURFEX large-eddy
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 4.** (a) ARPEGE (solid lines) and Meso-NH (dashed lines) errors at the grassland site for the downwelling (cyan) and upwelling (green) shortwave (SW), downwelling (red) and upwelling (purple) longwave (LW), and net (gold) surface radiative fluxes (in W m⁻²). Panels (b) and (c), same as (a) but for the corn and wheat field sites, respectively.</figcaption></figure>

model (hereafter referred to as Meso-NH). ARPEGE, Meso-NH and SURFEX models are briefly described in Appendix A. The present section details behavior of the simulation over the grassland cover while Section 5.1 provides a synthesis of the simulations over the other land covers.

## 4.1. Radiation Balance at the Land-Atmosphere Interface

The net radiation heat flux at the land-atmosphere interface is the primary driver of the surface turbulent heat fluxes. To ensure a relevant comparison between the two models and the observations, it is thus critical to have a good consistency between simulated and observed radiation fluxes. Figure 4 shows the models' errors with respect to BLLAST observations for each radiation component, and for the three covers for which observations are available. Note that the surface albedo is prescribed in SURFEX in a consistent way with observations (see Section 3.2 and Appendix B).

Over the grassland site (Figure 4a), errors in the longwave components are small, consistently with a good simulation by the models of near-surface atmospheric temperature and moisture conditions and surface skin temperature and emissivity. Downwelling shortwave radiation is overestimated by about 50 W m⁻² in the early morning hours (05:00 UTC to 12:00 UTC). The impact on the reflected surface shortwave flux error is weak because of the weak broadband surface albedo diurnal cycle in the observations (see Figure B1) and the prescription of a constant albedo in the models. While this error in the downwelling surface shortwave radiation flux may seem significant, it is rather weak compared to the raw flux (about 7%) and inconsistent across the three sites in Figure 4. The three sites are only a few kilometers away from each other, and 20 June 2011 was a clear-sky day. These differences presumably reflect uncertainties in the flux measurements, possibly due to calibration weaknesses of the radiometer instruments (CNR1, Kipp and Zonen, Class 2). This sensor has a high sensitivity to tilt that can lead to an error up to about ±25 W m⁻² in observed fluxes. The net radiative flux error mostly follows that of the downwelling shortwave. The differences between ARPEGE and Meso-NH are small, within about 10 W m⁻². While a specific attention was devoted to have a setup as consistent as possible between the two models (solar astronomy, ozone and aerosol treatment, radiative transfer scheme), a few differences remain. We see at least three sources of differences. While they use the same radiative transfer scheme (see Appendix A), its implementation differs slightly between the two models. Also, the top of the Meso-NH domain stops at 4 km and a standard temperature profile is used above for radiative computations, which is different to the one used in ARPEGE. Finally, the simulated atmospheric temperature and moisture profiles are slightly different between the two models (see the next sections).

Overall, the model errors and models' differences are small, within observation and modeling uncertainties. These uncertainties do not prevent a fair comparison between these three data sets, but need to be kept in mind for the following model evaluation.

## 4.2. Surface Sensible and Latent Heat Fluxes

The daily evolution of the latent, sensible and ground heat fluxes over the grassland site in Figure 5a exhibit a typical diurnal cycle under clear-sky and convective conditions: both sensible and latent heat fluxes increase in the morning, reach a maximum near midday, and then decrease close to zero near
<figure><img src="image_9.png" /><figcaption>**Figure 5.** (a) Latent (solid lines), sensible (dashed lines) and the opposite of ground (dotted lines) heat fluxes (in W m⁻²) at the grassland site for ARPEGE (blue), Meso-NH (red), corrected observations (black) and raw observations (gray). The red envelope indicates 95% of Meso-NH values with order 2.5% and 97.5% quantiles as minimum and maximum respectively. Panels (b)–(d), same as (a) but for the corn field, the wheat field and the pine forest sites, respectively.</figcaption></figure>

sunset. This daily evolution closely follows the net radiation heat flux at the surface, mostly driven by the solar radiation reaching the surface. For the sake of graphic visualization, the opposite of the ground heat flux is represented in this figure. This ground heat flux is of the order of 100 W m⁻², and thus modulates the sum of the surface turbulent heat fluxes compared to the net input of radiative heat. The distribution of the net input of energy between sensible and latent heat is quantified with the Bowen ratio. The Bowen ratio is driven by the water availability within the soil and the vegetation ability to evapotranspirate it. The daily-mean observed Bowen ratio is 0.24 for the grassland site.

Figure 5a illustrates the good performance of the ARPEGE and Meso-NH models in comparison to observations. Note that for the Meso-NH simulation, the spatial mean and standard deviation are displayed. Couvreux et al. (2016) showed also a good performance of operational models (AROME, ARPEGE, ECMWF) during the same BLLAST field campaign. On average (see the mean bias error between models and observations in Table 4), the latent heat flux is overestimated by about 22.7 W m⁻² in ARPEGE and 31.5 W m⁻² in Meso-NH, while the sensible heat flux is underestimated by about 3.9 W m⁻² and 12.4 W m⁻² for ARPEGE and Meso-NH, respectively. The ground heat flux is overestimated by about 1.42 W m⁻² in ARPEGE and underestimated by 1.18 W m⁻² in Meso-NH. The sum of the errors is within the uncertainties discussed in Section 4.1.

The mean observed Bowen ratio (0.24) is higher than the simulated Bowen ratios (0.20 and 0.18 for ARPEGE and Meso-NH, respectively), possibly related to uncertainties in the model surface description. ECOCLIMAP-II database provides a 1-km resolution values that might not be sufficient to characterize the local surface conditions samples by observations. Moreover, the LAI climatological annual cycle might not capture appropriately the 2011 LAI annual cycle (see Section 3.2).
<caption>**Table 4**</caption>

Skill Scores of ARPEGE and Meso-NH With Respect to the Corrected Observations for the Latent (LE), Sensible (H), and Ground (G) Heat Fluxes: Correlation (r), Mean Bias Error (MBE) and Normalized Root Mean Square Deviation (c-RMSE) for the Grassland, the Corn Field, the Wheat Field, and the Pine Forest Sites (Raw Observation for This Latter Site)

<table><thead><tr><th rowspan="2">Cover</th><th rowspan="2">Model</th><th rowspan="2">Mean Bowen ratio</th><th colspan="3">r</th><th colspan="3">MBE</th><th colspan="3">c-RMSE</th></tr><tr><th>LE</th><th>H</th><th>G</th><th>LE</th><th>H</th><th>G</th><th>LE</th><th>H</th><th>G</th></tr></thead><tbody><tr><td rowspan="3">Grassland</td><td>Obs. (corr.)</td><td>0.24</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ARPEGE</td><td>0.20</td><td>0.99</td><td>0.98</td><td>0.70</td><td>22.7</td><td>-3.9</td><td>1.42</td><td>20.0</td><td>11.3</td><td>22.5</td></tr><tr><td>Meso-NH</td><td>0.18</td><td>0.99</td><td>0.98</td><td>0.36</td><td>31.5</td><td>-12.4</td><td>-1.18</td><td>23.7</td><td>9.4</td><td>32.7</td></tr><tr><td rowspan="3">Corn field</td><td>Obs. (corr.)</td><td>0.40</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ARPEGE</td><td>0.32</td><td>0.90</td><td>0.82</td><td>0.63</td><td>8.70</td><td>-21.5</td><td>-3.1</td><td>66.5</td><td>47.9</td><td>37.2</td></tr><tr><td>Meso-NH</td><td>0.36</td><td>0.90</td><td>0.80</td><td>0.52</td><td>-8.2</td><td>-15.1</td><td>-5.8</td><td>63.8</td><td>48.3</td><td>41.9</td></tr><tr><td rowspan="3">Wheat field</td><td>Obs. (corr.)</td><td>0.76</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ARPEGE</td><td>0.62</td><td>0.78</td><td>0.66</td><td>0.89</td><td>-15.9</td><td>-35.4</td><td>53.8</td><td>62.5</td><td>73.2</td><td>24.3</td></tr><tr><td>Meso-NH</td><td>0.59</td><td>0.77</td><td>0.82</td><td>0.29</td><td>2.9</td><td>-48.0</td><td>39.4</td><td>81.3</td><td>52.0</td><td>58.0</td></tr><tr><td rowspan="3">Pine Forest</td><td>Obs. (raw)</td><td>1.04</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ARPEGE</td><td>0.61</td><td>0.98</td><td>0.94</td><td>-</td><td>60.3</td><td>-58.6</td><td>-</td><td>51.0</td><td>52.5</td><td>-</td></tr><tr><td>Meso-NH</td><td>0.40</td><td>0.99</td><td>0.86</td><td>-</td><td>113.7</td><td>-93.6</td><td>-</td><td>80.2</td><td>73.0</td><td>-</td></tr></tbody></table>

In both models, the latent heat flux increases slightly faster than in the observations. This is consistent with more shortwave radiation reaching the surface in the morning (Figure 4). Given that the ground heat flux increases more slowly in the observations than in the models, this may also indicate that the surface albedo is higher at low solar angle during the early morning than the value prescribed in the models. This dependency of surface albedo to solar angle will require further investigation in the future (Gueymard et al., 2019; Y. Han et al., 2024; Wang et al., 2005).

Table 4 further summarizes the models' performance for the latent, sensible and ground heat fluxes, in terms of correlation, mean bias error (MBE) and centered Root Mean Square Error (c-RMSE, the mean of the values is subtracted from the values before computing the RMSE between observations and modeled values). The c-RMSE is defined by:

$$c\text{-RMSE} = \sqrt{\frac{1}{N} \sum_{i=1}^{N} [(m_i - \bar{m}) - (\sigma - \bar{\sigma})]^2} \quad (5)$$

where, *m* is model value for the selected land cover and *σ* is observation.

Overall, the high scores highlight the relevance of both the Bowen-ratio correction method to the observed sensible and latent heat fluxes and the SCM/LES setup.

### 4.3. Soil Temperatures

Figure 6a highlights the daily evolution of soil temperature at various depths from 1 to 50 cm. The temperature diurnal cycle amplitude is larger close to the surface, approximately following the diurnal cycle of downwelling shortwave radiation at surface. When the depth increases, the diurnal cycle amplitude reduces and its peak shifts later in the afternoon or early evening, as a consequence of the soil inertia and its role in the soil response to surface heating. At 25 cm, the diurnal cycle is weak, with an amplitude less than 1 K, and at 50 cm, it has fully disappeared. In comparison, at 1 cm, the amplitude reaches about 12 K.

ARPEGE approximately captures this time evolution of soil temperatures over the grassland site and its dependence to depth (Figure 6a), highlighting the relevance of the chosen soil properties and moisture initialization. Nevertheless, the warming of the near-surface soil layers occurs at a faster rate, consistently with an higher ground heat flux in the morning (Figure 5). The soil temperatures simulated by ARPEGE are thus overestimated, and as a consequence of the diurnal cycle damping with increasing depth, this overestimation reduces with
<figure><img src="image_10.png" /><figcaption>**Figure 6.** (a) ARPEGE (dashed lines), Meso-NH (dotted lines), and observed (solid lines) soil temperature (in K) at five depths for the grassland site: 2 cm (pink), 5 cm (purple), 10 cm (light blue), 25 cm (green), 50 cm (yellow). Panels (b) same as (a) but for the corn field site at a single depth: 1 cm (darkred).</figcaption></figure>

increasing depth. Meso-NH results are similar to those of ARPEGE. These results demonstrate the rather good relevance of the SURFEX database soil properties and moisture initialization. A more detailed tuning of the soil properties would have been possible, but is beyond the scope of the present work.

4.4. Near-Surface Atmospheric State

The state of the near-surface atmosphere results from the balance between fluxes of momentum, energy and water vapor at the surface by turbulent processes and those at the top of the atmospheric surface layer or above due to turbulent and convective processes. At the grassland site, the 3-m temperature continuously increases during daytime, from 287.5 K at 05:00 UTC to a maximum of 297.5 K at 16:00 UTC (Figure 7a). The increase is sharper in the morning. The 3-m specific humidity also increases in the morning from 9 g kg⁻¹ to 11.5 g kg⁻¹ near 11:00 UTC (Figure 8a). It remains approximately constant afterward. This behavior is consistent with the increase of surface sensible and latent heat fluxes in the morning while later on the convective boundary-layer needs develop and become efficient at exporting heat and moisture upwards. It can be assumed that the warm air is transported by both entrainment and surface heat flux. This leads to an increase in temperature. For the specific humidity, there seems to be a compensating effect between the latent heat flux and the entrainment. This could explain the stagnation of the values in the afternoon.

The 3-m wind speed also exhibits a sharp increase from 0.3 m s⁻¹ at 07:00 UTC to 2 m s⁻¹ at 11:00 UTC (Figure 9a). This is consistent with the progressive deepening of the convective boundary-layer that can transport higher momentum (e.g., Figure C1) from above toward the surface. Note that this diurnal evolution is subject to large-scale forcing, supposed here to be weak, but also to more local circulations that can develop due to the landscape heterogeneities.

The model captures rather accurately the diurnal evolution of the 3-m air temperature, with a slightly slower increase during the morning time. Regarding the 3-m specific humidity, the model simulates one pike consistent with the observations in the morning, before deviating by about 1–2 g kg⁻¹ away from the observations during the afternoon. Finally, the model captures the 3-m wind speed increase, but with a much smoother transition and a slight underestimate by 0.5 m s⁻¹ in the afternoon. This underestimate is even larger in the Meso-NH LES. While this indicates significant and interesting discrepancies in the momentum mixing between the two models that will deserve further investigation in the future, this also presumably emphasizes a missing large-scale or meso-scale forcing of the wind in our setup.

First introduced by Betts (1992), the mixing diagram quantifies the exchanges of heat and moisture in the mixed layer during daytime under the influence of radiation and surface turbulent fluxes, based on the diurnal evolution of the near-surface temperature and humidity assuming that potential temperature and specific humidity are well-mixed in the boundary-layer. The mixing diagram thus describes the diurnal co-evolution of the near-surface air temperature and specific humidity (converted into energy variables in kJ kg⁻¹ using the dry air heat capacity at constant pressure Cₚ and the latent heat of vapourization Lᵥ, respectively). Santanello et al. (2009, 2013) adapt
<figure><img src="image_11.png" /><figcaption>Figure 7. (a) ARPEGE (blue line), Meso-NH (red line) and observed (black line) air temperature (in K) at 3 m at the grassland site. Panels (b)–(d) same as (a) but for the corn field, wheat field and pine forest sites, respectively at 5, 3, and 32 m (6 m above forest canopy).</figcaption></figure>

this representation by including thermodynamic diagnostics to specifically evaluate the land-atmosphere coupling. The vector directing coefficient represents the Bowen ratio (Santanello et al., 2009) at the surface ($\beta_{sfc} = (C_p * \Delta T_{sfc})/(L_v * \Delta q_{sfc})$) and the top of the boundary-layer $\beta_{ent} = (C_p * \Delta T_{ent})/(L_v * \Delta q_{ent})$).

The computation of the vectors rely on the following equations from Santanello et al. (2009):

$$C_p * \Delta T_{sfc} = \frac{\overline{H_{sfc}} * \Delta t}{\rho_m * h_{PBL}} \qquad (6)$$

$$L_v * \Delta q_{sfc} = \frac{\overline{LE_{sfc}} * \Delta t}{\rho_m * h_{PBL}} \qquad (7)$$

where $C_p$ is the specific heat of dry air and $L_v$ is the latent heat of vapourization of water, $\Delta T_{sfc}$ and $\Delta q_{sfc}$ are variations of temperature and specific humidity, respectively, at the surface between 05:00 UTC and 18:00 UTC, $\overline{H_{sfc}}$ and $\overline{LE_{sfc}}$ are the average sensible and latent heat fluxes between 05:00 UTC and 18:00 UTC, $\Delta t$ is the time in seconds between 05:00 UTC and 18:00 UTC, $\rho_m$ is the density in the mixed layer and $h_{PBL}$ is the average planetary boundary-layer height between 05:00 UTC and 18:00 UTC.

These equations allow the computation of the surface vector. Since the surface vector and the end point are known, it is possible to estimate the entrainment vector. Consequently, the heat (latent) entrainment flux can be computed from the $C_p * \Delta T_{ent}$ ($L_v * \Delta q_{ent}$) component. Ratios between their energy ($A_H$) or moisture ($A_{LE}$) components characterize the respective contributions of the exchanges at the surface and the top of the boundary-layer to the energy and water budgets of the boundary-layer (see e.g., Santanello et al., 2009).
<figure><img src="image_12.png" /><figcaption>**Figure 8.** (a) ARPEGE–SURFEX (blue line), Meso-NH–SURFEX (red line) and observed (black line) air specific humidity (in g kg⁻¹) at 3 m at the grassland site. Panels (b)–(d) same as (a) but for the corn field, wheat field and pine forest sites, respectively at 5, 3, and 32 m (6 m above forest canopy).</figcaption></figure>

The mixing diagram of the grassland site is presented in Figure 10a from 05:00 UTC to 18:00 UTC, on 20 June 2011 and the value of these metrics are given in Table 5. Note that to compute the vectors for observations, we assumed the same observed boundary-layer height for all land cover, derived from the BLLAST frequent radiosoundings. For ARPEGE and Meso-NH simulations, the boundary-layer height is computed with the potential temperature threshold method (see Canut et al., 2010, for boundary-layer height definition).

As highlighted in Santanello et al. (2009), the mixing diagram based on the observation at the grassland site (Figure 10a) is typical of wet soils. The low $\beta_{sfc}$ indicates a significant increase of latent heat flux in the mixed layer during the day as a result of evapotranspiration by the soil and the vegetation, while sensible heat increases comparatively much less. In contrast, at the top of the boundary-layer, the Bowen ratio of entrainment flux $\beta_{ent}$ is negative (entrainment heats and dries the boundary-layer) and large in absolute value ($|\beta_{ent}| > 1$) indicating a higher import of sensible heat than of latent heat. As a result, the boundary-layer growth is mostly driven by the entrainment of dry air at its top ($A_H > 1$), while its water budget is mostly constrained by surface fluxes ($A_{LE} < 1$).

The two models reproduce rather well this temperature-humidity diurnal co-evolution despite the higher modeled temperatures compared to observations after 12:00 UTC. It translates a rapid increase of temperature and specific humidity, diminishing in the end the relative humidity during the day to a slow decrease of humidity. Nevertheless both models present a larger increase in humidity at the start of the simulation (from 05:00 UTC to 09:00 UTC), relative humidity that remains between 70% and 60% after 09:00 UTC as sampled by observations. As for the vectors, we can notice that the directing coefficients of the arrows for the surface part are very close, with 0.18 and 0.16 respectively for ARPEGE and Meso-NH. Indeed, $\beta_{sfc}$ values are also very close between observations and models (Table 5). Moreover, it was also the case with the computed Bowen ratios from surface fluxes (Table 4), concluding that the different methodologies applied to compute these two Bowen ratios induce only slight differences. Lengths of the arrows are different between observation and models. Indeed, both models show greater
<figure><img src="image_13.png" /><figcaption>**Figure 9.** (a) ARPEGE (blue line), Meso-NH (red line) and observed (black line) wind speed (in m s⁻¹) at 3 m at the grassland site. Panels (b)–(d) same as (a) but for the corn field, wheat field and pine forest sites, respectively at 5, 3, and 32 m (6 m above forest canopy).</figcaption></figure>

moisture and heat input from the surface, as well as more drying and less heating from the entrainment zone compared to observations (Figure 10a). In fact, we observe a significantly higher $A_H$ in the observations than in the models, indicating a greater entrainment heat flux in the observations (Table 4) compared to the models. For both the observations and models, the entrainment heat flux ($A_H > 1$) indicates that the heat flux introduced by entrainment to the mixed layer is greater than the heat flux at the surface. Regarding humidity, there is significant drying due to entrainment, resulting in negative values ($A_{LE} < 0$). The moisture input from the surface dominates over the entrainment effect ($|A_{LE}| > 1$). Note that the mean planetary boundary-layer height for observation vectors is computed based on radiosoundings measurements that are not conducted specifically on the grassland site and can include the effects of local heterogeneities. The differences between ARPEGE and Meso-NH originate from a different representation of entrainment at the top of the boundary-layer, most likely in terms of its associated coherent structures (thermals, e.g., Hourdin et al., 2002).

4.5. Evolution of the Boundary-Layer

Near the surface, BLLAST observations provide reference data to evaluate ARPEGE and Meso-NH, probably not too much impacted by the surrounding heterogeneous landscape. To document the lower troposphere, frequent radiosoundings were performed during the BLLAST field campaign. They however integrate with height the effects of the heterogeneous landscape, and are possibly impacted by the large-scale advections that might be less negligible at upper levels. These differences with our idealized setup hamper our comparison between models' behavior and observation. Nevertheless, for the sake of completeness, we add data from the BLLAST frequent radiosoundings (Legain et al., 2013) in the figure discussed in the present section (Figure 11), though we will focus on the boundary-layer differences between ARPEGE and Meso-NH, considering here the Meso-NH LES as a more relevant reference (e.g., Guichard & Couvreux, 2017). Besides, as Meso-NH and ARPEGE also have
<figure><img src="image_14.png" /></figure>

**Figure 10.** (a) Mixing diagram showing the diurnal co-evolution of the 3-m air temperature and specific humidity from 05:00 UTC (dot) to 18:00 UTC (square) on 20 June 2011 simulated by ARPEGE (blue line) and Meso-NH (red line), and provided by the observations (black line) for the grassland. The surface (approximately horizontal) and entrainment (approximately vertical) vectors indicate the contribution of the surface and the entrainment fluxes to the energy and water budget of the boundary-layer (In observations, there is an additional contribution of the large-scale advection of energy and moisture). The light blue dotted lines indicate isoline of relative humidity (in %). The axes are expressed in kJ kg⁻¹ after multiplying temperature and specific humidity by the specific heat of dry air Cₚ and the latent heat of vaporization of water Lᵥ, respectively. Panels (b)–(d) same as (a) but for the corn field, the wheat field and the pine forest sites, respectively at 5, 3, and 32 m (6 m above forest canopy). For consistency among the different land cover, the observation diagnostics are computed with non-corrected surface fluxes.

similar surface turbulent fluxes, differences in the boundary-layer properties between the two models are ex-
pected to be related to the parameterization of turbulence and dry convection in ARPEGE.

The ARPEGE and Meso-NH profiles of potential temperature, specific humidity and wind speed are shown in Figures 11a–11c, respectively, at three times of the day, namely 09:00 UTC, 13:00 UTC and 17:00 UTC. Meso-NH simulates the diurnal development of a convective boundary-layer, reaching a depth of 500 m at 09:00 UTC, 1,100 m at 13:00 UTC and 1,400 m at 17:00 UTC (Figure 11a). The convective boundary-layer exhibits an unstable surface layer mostly driven by the sensible heat flux and a sharp inversion at its top. In-between, the atmosphere is mostly neutral, that is well-mixed in terms of potential temperature, specific humidity and momentum.

ARPEGE mostly succeeds in reproducing the boundary-layer properties and their evolution along the day (Figures 11a–11c). The potential temperature profile remains slightly unstable in the whole boundary-layer. This suggest a lack of activity of the convection scheme of the model (e.g., Hourdin et al., 2002), or although differences in the radiative heating could also have an impact. The possible lack of convective mixing in ARPEGE is also consistent with a simulated moisture profile that is not mixed enough (Figure 11b): ARPEGE overestimates the specific humidity in the lower boundary-layer by about 0.05 g kg⁻¹, while it underestimates it at the top of the boundary-layer. The wind speed is also not mixed enough in the boundary-layer, leading to a slight wind-speed overestimation closer to the surface.
<table><caption>**Table 5**
Metrics Derived From the Surface and Entrainment Vectors Used in Figure 10 for ARPEGE and Meso-NH Simulations, and the Observations for the Grassland, the Corn Field, the Wheat Field, and the Pine Forest Sites</caption><thead><tr><th>Cover</th><th>Model</th><th>A<sub>LE</sub></th><th>A<sub>H</sub></th><th>β<sub>sfc</sub></th><th>β<sub>ent</sub></th></tr></thead><tbody><tr><td rowspan="3">Grassland</td><td>Obs.</td><td>-0.60</td><td>4.54</td><td>0.21</td><td>-1.55</td></tr><tr><td>ARPEGE</td><td>-0.65</td><td>1.62</td><td>0.18</td><td>-0.46</td></tr><tr><td>Meso-NH</td><td>-0.51</td><td>2.25</td><td>0.16</td><td>-0.69</td></tr><tr><td rowspan="3">Corn field</td><td>Obs.</td><td>-0.47</td><td>2.15</td><td>0.39</td><td>-1.78</td></tr><tr><td>ARPEGE</td><td>-0.72</td><td>1.81</td><td>0.30</td><td>-0.76</td></tr><tr><td>Meso-NH</td><td>-0.67</td><td>2.33</td><td>0.33</td><td>-1.15</td></tr><tr><td rowspan="3">Wheat field</td><td>Obs.</td><td>-0.30</td><td>0.91</td><td>0.72</td><td>-2.16</td></tr><tr><td>ARPEGE</td><td>-1.10</td><td>1.82</td><td>0.63</td><td>-1.05</td></tr><tr><td>Meso-NH</td><td>-0.79</td><td>2.18</td><td>0.51</td><td>-1.41</td></tr><tr><td rowspan="3">Pine Forest</td><td>Obs.</td><td>-0.46</td><td>0.04</td><td>1.0</td><td>-0.09</td></tr><tr><td>ARPEGE</td><td>-0.89</td><td>1.49</td><td>0.54</td><td>-0.91</td></tr><tr><td>Meso-NH</td><td>-0.42</td><td>1.65</td><td>0.32</td><td>-1.27</td></tr></tbody></table>

Note. $\beta_{sfc}$ ($=(C_p * \Delta T_{sfc})/(L_v * \Delta q_{sfc})$) and $\beta_{ent}$ ($=(C_p * \Delta T_{ent})/(L_v * \Delta q_{ent})$) are the slope of the vectors, that correspond to Bowen ratios. $A_{LE}$ ($=LE_{ent}/LE_{sfc}$) and $A_{H}$ ($=H_{ent}/H_{sfc}$) are the entrainment ratios of moisture and heat. See text for further details.

# 5. Sensitivity of the Land-Atmosphere Coupling Representation to Land Cover Properties

This section assesses how the previous results found over the grassland site are sensitive to land cover (Subsection 5.1) and the origin of the differences between land-atmosphere coupling for each land cover (Subsection 5.2).

## 5.1. Sensitivity to Vegetation Type

Surface radiation fluxes are only available at the corn and wheat field sites. They are rather well captured by both the ARPEGE and Meso-NH models (Figures 4b and 4c), with similar deviations from the observations for individual flux components. The net fluxes are slightly overestimated by about 20 W m⁻² over the corn field and 50 W m⁻² after 09:00 UTC over the wheat field. As discussed in Section 4.1, we are within the range of uncertainties due to observations and the modeling setup.

Surface turbulent fluxes are sensitive to the soil conditions and land cover properties (Figures 5b–5d). Differences between the four sites can reach 200 W m⁻². A range of Bowen ratios from 0.20 to 1.04 is sampled (Table 4). As for the grassland site, ARPEGE and Meso-NH reproduce appropriately the diurnal evolution of the surface sensible, latent and ground heat fluxes over the corn field site. To some extent, this is also the case for ARPEGE over the wheat field site, except the occurrence of a sudden and localized bump followed by a sharp decrease in the latent heat flux near 09:00 UTC. This behavior is related to a threshold used within the SURFEX hydric stress parameterization that limits transpiration of C3 crops (see the SURFEX part of Appendix A for more

details on this parameterization). Dry convection in the boundary layer might activate faster on the wheat field, thereby severely drying out the surface layer. The vegetation initially responds by increasing its evapotranspiration but this is not sufficient to humidify the surface layer and counteract its drying by turbulence and convection. At a certain time, the plant's capacity is exceeded, leading to a significant reduction of evapotranspiration. After this sudden bump followed by a decrease, the latent heat flux comes back close to observed values. This wheat field site setup thus provides an interesting case to better evaluate and constrain the SURFEX hydric stress parameterization. This is however beyond the present work. Consistently with the use of the same land surface model, Meso-NH also displays this sudden bump followed by a sharp decrease but slightly earlier near 07:30 UTC. Overall, Meso-NH overestimates slightly the latent heat flux by 50 W m⁻² and underestimates the sensible heat by almost 100 W m⁻². Over the pine forest site, conclusions about the models' skills are more difficult to draw because of the lack of radiation flux measurements to correct the measured eddy-correlation turbulent fluxes. Rather large differences are found between ARPEGE and Meso-NH, and the observed Bowen ratio is not well captured by the two models (Table 4). This pine forest site thus provides an interesting setup to assess how high roughness lengths seems to develop significantly different boundary-layers within the two models (see also Figures 7d, 8d, and 9d).

Soil temperature was only measured at a 1-cm depth in the corn field site. The comparison with the models indicates a rather good capture of its diurnal cycle (Figure 6b), as for the grassland site (Figure 6a).

Observations of near-surface temperature (Figures 7a–7d) and specific humidity (Figures 8a–8d), completed by relative humidity (Figures D1a–D1d), exhibit a similar diurnal scenario typical of a wet soil. Warmer temperatures are reached over the corn field site, while they are colder above the pine forest canopy. The wheat field site is moister than the grassland and the corn field sites and the pine forest site is the drier site. The two models underestimate the near-surface temperature over the corn field site at the beginning of the day, but they catch up observations later in the afternoon. Over the two other sites, near-surface temperatures in the afternoon are overestimated by 5 K or more. Reproducing the evolution of the near-surface specific humidity is more challenging, especially over the wheat field where the hydric stress parameterization and the sharp drop it induces in the latent heat flux in the morning lead the models in another drier state. Large differences also emerge between ARPEGE and Meso-NH over the pine forest, consistently with the large differences in the surface latent heat fluxes (Figure 5d). These conclusions are further emphasized by the mixing diagrams on Figures 10a–10d. The
<figure><img src="image_15.png" /><figcaption>**Figure 11.** (a) ARPEGE (blue lines), Meso-NH (red lines) and observed (black lines) potential temperature (in K) profiles at 09:00 UTC (dotted lines), 13:00 UTC (dashed lines) and 17:00 UTC (solid lines) at the grassland site. Panels (b) and (c), same as (a) but for specific humidity (in g kg⁻¹) and wind speed (in m s⁻¹), respectively.</figcaption></figure>

consistency between models and observations is higher over the corn field site. Over the wheat field and the pine forest, the entrainment at the top of the boundary layer is significantly overestimated by the models. Note this may indicate a lack of temperature and moisture horizontal advections in the model setup. In both cases, this yields drier and warmer boundary layers in the models than in the observations.

The near-surface wind speed is rather similar over the grassland and corn field sites, with a sharp wind speed increase near 08:00 UTC followed by a rather constant wind-speed period (about 2 m s⁻¹). The two other sites display a slightly different scenario, with a wind-speed decrease in the early morning leading to a minimum near 08:00–09:00 UTC. Then the wind speed sharply increases up to about 2 m s⁻¹. The two models develop a diurnal wind-speed increase but at a rather weak rate. These model deficiencies may be attributed to the lack of a larger scale wind forcing in the models' setup. Note also that ARPEGE always overestimates the near-surface wind speed compared to Meso-NH, differences being the largest when the surface roughness length is the highest (the roughness length is less over the grassland than over the corn field, but also less than over the wheat field and the pine forest sites, see Table 2). This highlights significant differences in the representation of the boundary-layer mixing of momentum and its interaction with surface roughness, which will deserve further investigation in the future.

## 5.2. Impact of Surface Properties on Land-Atmosphere Coupling

The four setups, which only differs by their land surface boundary conditions (physiographic properties, vegetation transpiration, soil initialization), exhibit large differences in the land-atmosphere coupling and the way it influences the diurnal boundary-layer development (e.g., Figure 10). In this last section, we assess which part of these differences are the most influential to explain the model sensitivity to land surface conditions. Starting from a land cover type, we modified the hydric stress parameterization step by step (for the different vegetation types), the LAI, the surface radiative properties (surface emissivity and albedo), roughness lengths and soil initialization (ground depth and soil temperature profile), in order to comparatively study the other land cover types (Figure 12, see Table 2 for the parameter values of each land cover). Note that the order of the modifications does not impact our conclusions.

The main driver of the coupling and boundary-layer changes between the four land covers is the hydric stress parameterization (Figure 12), that is the way vegetation responds in the SURFEX model to a reduced water availability, some plants adopting a drought-avoiding (defensive) strategy, some others being drought-tolerant (offensive strategy) (see Calvet, 2000, for more details). As mentioned in the previous section, this leads to a drop in the wheat field transpiration, unseen in the observations (Figure 5c). Vegetation thus acts as a decisive player in modulating the Bowen ratio and thereby its impacts on the boundary-layer dynamics.

The second key factor influencing temperature and humidity changes in the mixed layer varies depending on the land cover. Soil initialization plays a significant role in the differences between the grassland site and the corn field site. For the wheat field site, the leaf area index is the second most important factor, compensating for the effects of the hydric stress parameterization. This is due to the fact that vegetation density influences the plant's transpiration ability, which in turn affects surface heat fluxes. In the case of the pine forest
<figure><img src="image_16.png" /></figure>

**Figure 12.** Diurnal co-evolution of the hourly (black dots) 3-m air temperature and specific humidity from 05:00 UTC (dot) to 18:00 UTC (square) on the 20 June 2011 simulated by the ARPEGE-SURFEX in response to parameterization incremental changes from the grassland to (a) the corn field, (b) the wheat field and (c) the pine forest sites. Starting from the grassland site, each color represents the incremental addition of the hydric stress parameterization of the pine forest (blue), the leaf-area index of the pine forest (purple), the surface radiative properties of the pine forest (green), the roughness length of the pine forest (orange) and the soil initialization (temperature and depths, red). The light blue dotted isolines indicate relative humidity percentages. The axes are expressed in kJ kg⁻¹ after multiplying specific humidity by the latent heat of vaporization of water and temperature by the specific heat of dry air.

<figure><img src="image_17.png" /></figure>

**Figure 13.** ARPEGE simulation (solid lines) and Meso-NH (dashed lines) at the grassland (green), the corn field (orange), the wheat field (pink) and the pine forest (brown) sites for the diurnal cycle (05:00–18:00 UTC) of the boundary-layer height (in m) evolution as a function of cumulated sensible heat flux (in MJ m⁻²).

site, hydric stress is the sole determining factor, with other parameters having only a minimal effect. Most likely because wind speeds were low on the selected day, roughness length had a minimal effect on the mixed layer state.

Consequently, an additional analysis in the context of higher winds would be valuable, as it could enhance the effect of the roughness of the vegetation on the mixing in the boundary-layer and maybe increase entrainment of dry and warm air. Sensitivity studies considering different seasons would also be interesting, as vegetation transpiration, LAI, and soil moisture can vary significantly throughout the year.

From a bulk clear-sky mixed-layer perspective, without any residual mixed layer from a previous day, we expect the boundary-layer depth to be mostly driven by the net input of energy from the surface, the entrainment component being proportional to it (Stull, 2012). ARPEGE and Meso-NH consistently exhibit this behavior: once the Bowen ratio is determined by the surface properties and the initial soil conditions, the sensible heat flux mostly responds to the net radiation at the surface and its accumulation since the early morning approximately drives the diurnal deepening of the boundary layer (Figure 13), at least within the range of surface roughness lengths of the grassland, corn field and wheat field sites. The pine forest case deviates substantially from the other three cases, possibly emphasizing that the
boundary layer properties are also driven by the dynamical coupling with the surface. Indeed, differences between ARPEGE and Meso-NH are exacerbated when a higher roughness lengths is prescribed (not shown).

As a result, ARPEGE and Meso-NH differences in their simulation of the boundary-layer depth, and thus the boundary-layer bulk properties, is at first order driven by different Bowen ratios and net radiation fluxes at the surface.

## 6. Conclusion

This study described the development of four study cases made to ease model developments and evaluate land-atmosphere coupling in numerical weather prediction and climate models. This work then highlights a methodology for creating coupled SCM homogeneous cases, based on observations from the BLLAST field campaign with default modeling setup relying on ARPEGE-SURFEX CNRM-CM6-1.

This work reiterates the importance of correcting heat fluxes measured by eddy covariance for the evaluation of closed surface energy balance models, which have not yet been systematically applied. Indeed, consistency between the model and corrected observations supports the correction method based on the conservation of the Bowen ratio. Correction of these fluxes over the pine forest remains challenging, primarily due to the difficulty in estimating the energy stored within the tree canopy.

Moreover, the present study has demonstrated the potential of the multi-land cover observations collected during the BLLAST field campaign. It has been shown to be a valuable tool for the creation and evaluation of modeled homogeneous cases, including a grassland, a corn field, a wheat field and a pine forest.

The comparison between the model and observations demonstrates that the representation of soil temperature, surface fluxes (latent, sensible and in the ground) and low atmosphere state (air temperature, specific humidity and wind speed) in the grassland and corn field cases is satisfactory. However, the results of this study suggest that the constraints imposed by the hydric stress parameterization of C3 crops (used for the wheat field case) have resulted in an underestimation of plant transpiration under the selected atmospheric conditions (convective day with clear sky and low wind). This problem has not yet been clearly identified in offline mode. This emphasizes then the necessity to test parameterization also in land-atmosphere coupling (online) setup, as feedbacks can have a significant impact on both the surface and the atmosphere.

The results of the comparison between SCM setups and LES demonstrate that ARPEGE coupled with SURFEX provides an accurate representation of the atmospheric conditions (potential temperature, specific humidity and wind speed profiles). However, the mixed layer at the end of the day exhibits, on average, a higher temperature and a lower specific humidity. It is possible that the ARPEGE convective scheme could be improved in order to achieve greater efficiency.

The coupling between the land and the atmosphere is primarily influenced by the sensible heat flux, as also evidenced by the findings of this study. The most influential surface parameterization on the ARPEGE-SURFEX land-atmosphere coupling is the hydric stress parameterization of SURFEX. This parameterization is highly dependent on the choice of vegetation type and rules the ability of the cover to evapotranspirate.

This study shows the good efficiency in land-atmosphere coupling of the ARPEGE-SURFEX coupled single column model in homogeneous study cases, making it a valuable tool for conducting simple and cost-effective simulations.

The objective of the aforementioned study cases is to be easily accessible and straightforwardly adapted to other land-atmosphere coupled models in order to help evaluate coupled modeled and provide a setup to rely on for the development of new parameterizations.

The creation of these cases underscores the necessity for a greater amount of observations for such setups development. The main focus is laid on observations of all components of the energy balance with the objective of verifying or rectifying its closure, particularly in the context of eddy-covariance sampling. The MOSAI project (Lohou et al., 2022) aims to evaluate and improve interactions between the surface and the atmosphere in numerical weather and climate prediction models using long-term observations and will attempt to fill this gap.

The study cases presented here represent a preliminary investigation into the potential of combining homogeneous covers to create well-defined heterogeneous environments. The aim is to extend and evaluate the approach
in different weather conditions (e.g., cloudy sky, rainfall events, windy conditions) and to a wider range of land cover types.

## Appendix A: Models Setups

The ARPEGE model is developed at CNRM for numerical weather prediction and climate research purposes.

In this study, we use the ARPEGE climate version 6.4 which is a slightly-updated version of the version 6.3 described in Roehrig et al. (2020) and which served as the atmospheric component of the CNRM-CM6-1 climate model in the context of the sixth phase of the Coupled Model Intercomparison Project (CMIP6, Eyring et al., 2016). The ARPEGE model is composed of a dynamical core and a full suite of physical parameterizations. Its corresponding single-column model version MUSC (for Modèle Unifié, Simple Colonne, Malardel, 2004) is used for this experiment. The ARPEGE model represents the atmosphere up to 82 km with a 91-level vertical grid. The first model level is near 10 m. The longwave radiation scheme is based on the rapid radiation transfer model (RRTM, Mlawer et al., 1997) while the shortwave radiation scheme is derived from Fouquart (1980) and J. Morcrette et al. (2008). The latest utilizes six spectral bands (0.185–0.25, 0.25–0.44, 0.44–0.69, 0.69–1.19, 1.19–2.38, and 2.38–4.00 µm). The turbulence parameterization of ARPEGE follows the approach of Cuxart et al. (2000) to represent the small-scale turbulent mixing of dry static energy, water vapor and momentum, with the addition of the condensation effect. The turbulent diffusion coefficients are proportional to the mixing length of Bougeault and Lacarrere (1989).

Meso-NH model (MESOscale Non-Hydrostatic, Lafore et al., 1998; Lac et al., 2018) is an open-source atmospheric limited area model developed jointly by the CNRM and the LAERO. This large-eddy scale model resolves turbulent eddies and convective-scale processes. For this study, version 5.6 is used. Horizontally, the domain is discretized in a 256 by 256 meshes of 50 m length which corresponds to a 12.8 km by 12.8 km domain. Vertically, the model represent the atmospheric boundary-layer up to 4,000 m. The vertical grid is divided into 95 levels. The radiation scheme used in Meso-NH is the same as in ARPEGE, namely RRTM (Mlawer et al., 1997) based on J.-J. Morcrette (2002). The turbulence scheme is implemented according to the work of Cuxart et al. (2000) and uses the Deardorff mixing length scheme (Deardorff, 1976).

SURFEX (for SURface EXternalisée, Masson et al., 2013) is a surface modeling platform comprising four types of surfaces: natural land surface, continental water (lakes and rivers), oceans and urbanized areas. This modeling platform can be run offline, that is with imposed atmospheric forcings or online, coupled to an atmospheric model as it is the case in this work. The exact same SURFEX configuration is coupled to the atmospheric model ARPEGE (Roehrig et al., 2020) and large-eddy scale model Meso-NH (Lac et al., 2018; Lafore et al., 1998).

For this study, only the natural land surface scheme (ISBA, Boone et al., 1999) is used. As part of ISBA scheme, the configuration ISBA-A-gs (Calvet et al., 1998) that improves vegetation stomatal aperture is activated. It impacts leaf stomatal and mesophyll conductances, LAI and the response of the plant to soil water stress in respect of the land covers and in response to climate, soil properties, and atmospheric carbon dioxide concentration. This hydric stress parameterization is specific to the land cover type (e.g., grassland, C3 crop, C4 crop, deciduous forest, evergreen forest, etc.). First the vegetation stress is classified as offensive or defensive based on the land cover type. Then, it modifies how vegetation responds to a limitation of water in the soil, and to strong vapor pressure deficit. Therefore, defining the land cover type in SURFEX plays a significant role in the evapotranspiration response of the vegetation with the same soil water content and air moisture deficit. Moreover, the soil diffusivity parameterization ISBA-DIF (Decharme et al., 2011) refine the evolution of soil moisture and temperature profiles within the soil.

The hydrodynamic properties of the soil are derived from the prescribed fractions of sand and clay using the pedotransfer functions from Clapp and Hornberger (1978). The emissivity (ε) for the mesh grid is computed the vegetation surface fraction αveg:

$$ \varepsilon = \alpha_{\text{veg}} \times 0.97 + (1 - \alpha_{\text{veg}}) \times 0.94 \quad (\text{A1}) $$

The roughness length depends on the imposed type of vegetation:

* For the grassland: z0 = 0.13 × LAI/6
* For the corn field: $z0 = 0.13 \times \min(2.5, \exp(\frac{L_{AI}-3.5}{1.3}))$

* For the wheat field: $z0 = 0.13 \times \min(1, \exp(\frac{L_{AI}-3.5}{1.3}))$

* For the pine forest: $z0 = 0.13 \times H_{tree}$.

The chosen golden day of the BLLAST field campaign is a clear-sky day. Because a very few short-lived thin clouds occurs in ARPEGE, the cloud scheme is inactivated for the sake of simplicity. Interactive radiation is activated in both models. In order to achieve an ARPEGE-SURFEX SCM setup as similar as possible to Meso-NH-SURFEX LES, the solar astronomy is adapted in ARPEGE to fit that used in Meso-NH. Radiation is called every timestep in ARPEGE (300 s) while in Meso-NH the dynamic time step is 3.5 s and radiation is also called every 300 s.

## Appendix B: Albedo Evaluation

The ECOCLIMAP-II database provides albedo values for the chosen land covers. We have chosen to evaluate the values provided for this database in the light of sampled observed radiation. The ECOCLIMAP-II albedo values (showed in Table 2) are divided into two categories: bare soil albedo and vegetation albedo, each category being dependent on a near-infrared albedo and a visible and ultraviolet albedo. A broadband albedo is then calculated from these values to ease comparison with the broadband albedo that can be calculated from observations. The equation used to calculate the broadband albedo from observed and simulated radiation is B1.

$$ \text{Albedo} = \frac{SW_{up}}{SW_{down}} \qquad (B1) $$

where $SW_{up}$ is upward shortwave radiation and $SW_{down}$ is upward shortwave radiation.

The values given by the ECOCLIMAP-II database are of the same order of magnitude as those found experimentally. The grassland albedo from ECOCLIMAP-II is close to observations. It is too low for the corn field and almost double for the wheat field.

For canopies allowing this modification (i.e., with shortwave radiation observations), the albedo values were then adjusted to achieve an albedo closer to that observed during the day (08:00 UTC-15:00 UTC). Albedo derived for the ECOCLIMAP-II database has then been adapted to be closer to observations. Figure B1 shows the albedo provided by the ECOCLIMAP-II database, computed from the observations and consequently modified.

<figure><img src="image_18.png" /><figcaption>Figure B1. Observed (solid lines), derived from ECOCLIMAP-II database (dotted lines) and adapted to observations (dashed lines) values of effective albedo for the grassland (green), the corn field (orange) and the wheat fields (purple) sites.</figcaption></figure>

## Appendix C: Atmospheric Vertical Profiles

The atmospheric profiles for the corn field, wheat field and pine forest sites are shown in Figures C1a-C1i. These figures complement the atmospheric state evaluation presented in Section 4.5.
<figure><img src="image_19.png" /><figcaption>**Figure C1.** (a) ARPEGE SCM simulation (blue line), Meso-NH LES (red line) and observed (black line) potential temperature (a, d, g) at 9:00 UTC (dotted line), 12:00 UTC (dashed line) and 15:00 UTC (solid line) for the corn field, the wheat field and the pine forest sites. (b, e, h) and (c, f, i), same as (a, d, g) but for specific humidity and wind speed, respectively.</figcaption></figure>

Both ARPEGE and Meso-NH simulate nearly identical boundary layer heights for the corn field and wheat field sites. However, for the pine forest, ARPEGE simulates a higher boundary layer height than Meso-NH. This difference is consistent with the higher sensible heat flux observed in ARPEGE compared to Meso-NH.

As shown in Figures 7a–7d, 8a–8d, and 9a–9d, the air temperature and wind speed in the mixed layer over the corn field are lower in the ARPEGE simulation than in Meso-NH, while specific humidity is higher in ARPEGE. At the beginning of the day, the properties of the mixed layer over the wheat field differ significantly between ARPEGE and Meso-NH. However, these differences tend to diminish throughout the day, as highlighted in Figure 10c. By the end of the day, the earlier observed discrepancies in surface turbulent fluxes tend to have minimal impact on the simulated mixed layer variables, notably temperature and humidity.

## Appendix D: Near-Surface Atmospheric State

In addition to Figures 7a–7d, 8a–8d, 9a–9d, and Figure D1 complements the near-surface atmospheric state evaluation by showing specific humidity, as discussed in Section 4.4. Simulated relative humidity closely matches observations for the grassland and the corn field. However, for the wheat field and the pine forest, both models underestimate specific humidity by about 30%. This underestimation of specific humidity coincides with an overestimation of air temperature, which is consistent with the observed relative humidity discrepancies. These differences between the models and observations are likely due to the previously mentioned modeling issues in SURFEX, particularly the parameterization of hydric stress and possibly errors in the pine forest albedo.
<figure><img src="image_20.png" /><figcaption>**Figure D1.** (a) ARPEGE SCM simulation (blue line), Meso-NH LES (red line) and observed (black line) air relative humidity (in %) at 3 m at the grass land site. Panels (b)–(d) same as (a) but for the corn field, wheat field and pine forest sites, respectively at 5, 3, and 6 m.</figcaption></figure>

**Conflict of Interest**

The authors declare no conflicts of interest relevant to this study.

**Data Availability Statement**

The complete description and initialization of the study cases mentioned in this work are available on this link: https://zenodo.org/records/15045485 (Roehrig & Bernard, 2025). Meso-NH and ARPEGE simulation files used in this study are available here: https://zenodo.org/records/17347624 (Bernard et al., 2025). Data analysis scripts can be found at: https://zenodo.org/records/zenodo.17427071 (Bernard & Roehrig, 2025). The observational data sets from the BLLAST field campaign are accessible on the BLLAST website at https://bllast.aeris-data.fr/catalogue/ (Lothon et al., 2014). For the grassland and wheat field sites specifically, references are available at https://doi.org/10.6096/bllastEdgesite (Boer, 2011); For the corn field site, data can be found at https://doi.org/10.6096/BLLAST. SurfaceEnergyBalanceCorn (Legain & Traullé, 2011); For the pine forest site, observations can be found at https://doi.org/10.6096/bllastForestsite (Pardyjak, 2011); For the radiosoundings, data can be found at https://doi.org/10.6096/bllastFrequentsoundingssite2 (Legain, 2011). For access to the ARPEGE-Climate version 6 code, please contact the authors. Meso-NH model V5.6 is freely available at this url: http://mesonh.aero.obs-mip.fr/mesonh56/Welcome. The SURFEX numerical platform, including ISBA natural land surface scheme, is also available online (https://www.umr-cnrm.fr/surfex/).
Acknowledgments

We are grateful to the three anonymous
reviewers who provided valuable insights
and relevant comments that improved the
manuscript. The present work benefited a
lot from the MOSAI project. We thank all
members of the project for their insight and
remarks during workshops and seminars.
MOSAI is funded by French Agence
Nationale de la Recherche (ANR, grant
number ANR-20-CE01-0018) and takes
advantage of long-term measurements
acquired in the framework of the Aerosol,
Clouds and Trace Gases Research
Infrastructure (ACTRIS; https://www.
actris.fr) and the Integrated Carbon
Observation System (ICOS; https://www.
icos-cp.eu/). The AERIS data center
(https://www.aeris-data.fr/) is in charge of
the data distribution and of the website of
MOSAI project (https://mosai.aeris-data.
fr). The present work benefited from
measurements acquired during the
BLLAST field campaign, which was made
possible owing to the contribution of the
ANR, the Institut National des Sciences de
l'Univers (INSU) from the Centre national
de la Recherche Scientifique (CNRS), the
LEFE-IMAGO program, Météo-France,
the Observatoire Midi-Pyrénées,
University of Toulouse, the European
Facility for Airborne Research (EUFAR)
projects BLLATE-1&2, the European
Cooperation in the field of Scientific and
Technical (COST) action ES0802 and the
Spanish MINECO projects CGL2009-
08609, CGL2012-37416-C04-03,
CGL2012-37416-C04-02 and CGL2011-
13477-E. The field experiment would not
have occurred without the significant
contribution of each of the participating
European and American research groups.
The BLLAST field experiment (grant
number ANR-12-JS06-0007) was hosted
by the instrumented site of Centre de
Recherches Atmosphériques, Laboratoire
d'Aérologie, Observatoire Midi-Pyrénées,
Lannemezan, France. Its 60-m tower is
partly supported by the POCTEFA/
FLUXPYR European program. BLLAST
data are managed by the SEDOO team,
from Observatoire Midi-Pyrénées. See
http://bllast.sedoo.fr for all contributions.
We also acknowledge support from the
DEPHY research group, funded by CNRS/
INSU and Météo-France, and from the
ANR - France 2030 as part of the PEPR
TRACCS program under grant number
ANR-22-EXTR-0008 (TRACCS-PC7
IMPRESSION-ESM project).

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