This study focuses on the field campaign SCARCE (Synchronized Characterization of Aerosol, Radon and Carbon dioxide Exchanges in drylands), carried out between 18 July 2023 and 15 January 2024 at Balsa Blanca (36.94° N, 2.03° W, 205 m a.s.l., GHG-Europe site code: ES-Agu, referred to as Aguamarga in some studies) in Southeastern Spain. The station is located within the Cabo de Gata-Níjar Natural Park, a Mediterranean semiarid grassland of significant ecological importance at 6 km from the western coast of the Mediterranean Sea. The climate of the region is classified as dry subtropical semiarid, or BSh Köppen classification (El-Kenawy et al., 2022), coincident with the most arid region of Spain with a mean annual precipitation of around 220 mm and temperature of around 18 °C. Additionally, precipitation occurs in the form of rain pulses followed by long dry periods, with the most significant being the dry season from June to September/October (López-Ballesteros et al., 2016). These precipitation pulses trigger significant CO₂ emissions mainly due to soil respiration caused by rapid reactivation of microbial and root activity in the ecosystem (López-Ballesteros et al., 2016). Winds exhibit two dominant patterns, northeast and southwest, aligned with the coastline and the local orography, corresponding to the easterly and westerly wind regimes of the Alborán Sea (Abril-Gago et al., 2025a).

The landscape is characterized by patches of vegetation and bare soil. The dominant vegetation species is Macrochloa tenacissima, strongly adapted to severe water stress, with other less common species including Chamaerops humilis, Rhamnus lycioides, Asparagus horridus, Olea europaea var. sylvestris, Pistacia lentiscus, and Rubia peregrina (Rey et al., 2011). The growing season occurs from late autumn to early spring, when vegetation is more active (Serrano-Ortiz et al., 2014) due to the availability of water resources (López-Ballesteros et al., 2016). This period is followed by a non-growing period, characterized by strong water stress, higher temperatures, and a lack of precipitation.

The station is located over quaternary conglomerates and Neogene-Quaternary sediments cemented by lime (caliche), in a flat alluvial fan of gentle slopes (2 % to 6 %; Rey et al., 2017). Typical soils correspond to Calcaric Lithic Leptosol soils, shallow (10 cm maximum) and alkaline (pH above 8), with petrocalcic horizons and saturated in carbonates. The texture of the soil is sandy loam, with sand (61 %), silt (23 %), and clay (16 %), with a bulk density of 1.25 g cm⁻³. Groundcover consists of gravel and rock bare soil (49 %) and vegetation (51 %; Rey et al., 2011).

The station centers on a flux tower equipped for eddy covariance measurements. The 3.5 m tower was equipped with an enclosed-path LI-7200RS (LI-COR, Lincoln, NE, USA) gas analyzer that measures CO₂ and H₂O densities and atmospheric pressure, synchronized with a Gill HS-100-3 (Gill Instruments Ltd, Lymington, UK) three-axis sonic anemometer, together with a HMP45C thermohygrometer (Campbell scientific, Logan, UT, USA; hereafter CSI). These data were recorded by a CR3000 (CSI) data logger at 10 Hz. Micrometeorological data were later processed with EddyPro (LI-COR) version 7.0.8 over 30 min intervals. Finally, quality flags provided by EddyPro (Mauder and Foken, 2006) were considered and only data with 0 or 1 flag values were labeled as valid and consequently included in the analysis. Additionally, an ARG100 (CSI) pluviometer provided 30 min precipitation data registered on a CR1000X (CSI) with a resolution of 0.2 mm per tip.

For the present analysis, atmospheric CO₂ concentration (CO2air in ppm), CO₂ flux (FCO2, in µmol m⁻² s⁻¹), atmospheric pressure (Pair, in kPa), relative humidity (RH^air, in %) and temperature (Tair, in °C), horizontal wind speed (Vh, in m s⁻¹) and direction (in degrees), and friction velocity (u∗, in m s⁻¹) information was taken from the flux tower upon processing and filtering whereas precipitation data (in mm) were directly obtained from the pluviometer.

A HALO Photonics StreamLine XR Doppler lidar was deployed at Balsa Blanca to characterize boundary-layer winds and turbulence during the SCARCE campaign, from 18 July 2023 to 15 January 2024. The system emitted laser radiation at 1565 nm with a pulse repetition rate of 10 kHz and detected the backscattered signal using a heterodyne detector (Pentikäinen et al., 2020). The Doppler shift introduced by atmospheric constituents was then retrieved from the signal. For this purpose, the standardized software package “HALO lidar toolbox” (Manninen, 2019) was used, producing multiple products including 3D wind fields and turbulence parameters. The system operated at a 1 s pulse rate, although processed products were generated with various temporal resolutions, with a default vertical resolution of 30 m. The system's effective full-overlap height was estimated at 90 m above ground level, defining the lowest reliable altitude (the altitude of 105 m was used as a reference, corresponding to the center of the 30 m interval starting at 90 m). The maximum detectable altitude varied depending on atmospheric turbidity, found within the atmospheric boundary layer (Ortiz-Amezcua et al., 2022b). Details of the deployment during SCARCE were provided by Abril-Gago et al. (2025a).

In the current study, two different products from the Doppler lidar were used: the wind shear (sh), representing the vertical gradient of horizontal wind velocity and direction, used as an indicator of mechanical atmospheric mixing (Ortiz-Amezcua et al., 2022a; Andújar-Maqueda et al., 2025); and the turbulent kinetic energy (TKE) dissipation rate (ϵ), quantifying the rate at which the TKE was dissipated into internal thermal energy (O'Connor et al., 2010). Atmospheric turbulence was assumed to be wind-shear-driven when sh exceeded 0.03 s⁻¹ (Manninen et al., 2018). Values of ϵ greater than 10⁻⁴ m² s⁻³ were indicative of intense turbulent activity (e.g., O'Connor et al., 2010), often associated with convection (Manninen et al., 2018), while lower values corresponded to atmospheric stability and stratification. Values of ϵ were filtered based on the signal-to-noise ratio (SNR). Vertical intervals with SNR lower than 0.006 (or equivalently -22.2 dB) were excluded from the analysis (Manninen et al., 2018). Finally, the original 3 min resolution of the sh and ϵ profiles was averaged into 30 min intervals, while the 30 m vertical resolution was aggregated into three broader layers, encompassing the ranges up to 195 m (sh^195 m, ϵ195m), up to 495 m (sh^495 m, ϵ495m) and up to 1005 m (sh^1005 m, ϵ1005m). Additionally, the particle transport velocities (vt), directly related to upward and downward particle transport, calculated by Abril-Gago et al. (2025a) for the same location and period were incorporated into the statistical analysis.

A CHM15k-Nimbus ceilometer (Lufft, Fellbach, Germany), part of the ICENET network (Cazorla et al., 2017), was deployed alongside the Doppler lidar. This instrument operates by emitting laser pulses at a wavelength of 1064 nm with repetition rates between 5 and 7 kHz, and detecting the atmospheric backscatter using an avalanche photodiode. The device records the range-corrected signal (RCS) at a 1 min interval and 15 m vertical resolution. While full overlap of the laser beam with the receiver field of view occurs around 1500 m above ground level, a significant portion of the signal (approximately 90 %) is captured within the 555–885 m range. The RCS data were processed using the STRATfinder algorithm (Kotthaus et al., 2020) to retrieve the Mixing Layer Height (MLH), the convective mixing layer that develops during daytime due to surface heating, and the Atmospheric Boundary Layer Height (ABLH), which represents the top of the atmospheric boundary layer including the mixing layer and residual layers of previous days, marking the transition to the free troposphere (Kotthaus et al., 2023). To mitigate the impact of incomplete overlap at low altitudes, the RCS data were corrected using the overlap function provided by the manufacturer, providing reliable ABLH and MLH retrievals down to approximately 210–255 m (Kotthaus et al., 2020). Further methodological details related to the derivation of these layers during the present campaign are available in Abril-Gago et al. (2025a).

The vadose zone of the station was monitored using a set of CO₂ sensors, Rn radioactivity detectors, and volumetric water content (θ), temperature, and pressure sensors installed in a similar configuration at three depths: 15, 50 and 150 cm. A similar setup was installed in a borehole approximately 18 m deep, which did not intersect the groundwater table. The borehole was sealed at the surface with a cap and equipped with a casing 15 cm in diameter. The first meter of the borehole casing closer to the surface was ungrooved, followed by a slotted section extending to the bottom, allowing interaction between the surrounding soil and the borehole interior. The additional borehole setup provides an alternative perspective on subsurface gas dynamics, as the instruments sample air circulating within the borehole, which is less affected by soil porosity than sensors installed directly within the soil profile. Three GMP252 (Vaisala Inc., Vantaa, Finland) CO₂ mole fraction probes were installed at the depths of 15, 50 and 150 cm (CO215cm, CO250cm and CO2150cm, respectively, in ppm), each paired with a CS655 (Campbell Scientific) water content reflectometer, providing temperature (T15cm, T50cm and T150cm, in °C) and θ (θ15cm, θ50cm and θ150cm, in m³ m⁻³) measurements. Additionally, the 50 cm depth included an AlphaE (Bertin Technologies, Montigny-le-Bretonneux, France) Rn radioactivity detector (Rn^50 cm, in Bq m⁻³) which also provided pressure (P50cm, in kPa) and humidity (RH^50 cm, in %) measurements. Finally, the same configuration was applied in the borehole, with a GMP252, a HygroVUE5 (Campbell Scientific) temperature and relative humidity sensor and an AlphaE sensor were located within the first meter, providing CO₂ concentration (CO2bh), temperature (Tbh), relative humidity (RH^bh), Rn activity (Rn^bh) and pressure (Pbh). Every sensor was connected to a CR1000X (CSI) data logger and data were averaged and recorded with a 30 min temporal resolution.

The raw 30 min series of every variable were detrended in order to remove the dominant diurnal cycle (24 h variations; Sánchez-Cañete et al., 2013). The resulting series reflected the underlying trends of the changes, which would highlight ventilation events not caused by daily patterns. A simple 24 h rolling average was applied to the series. Afterwards, the normality of the detrended series was refuted by Shapiro-Wilk and Anderson-Darling normality tests. Thus, non-parametric statistics had to be performed on the series. Additionally, an augmented Dickey-Fuller test confirmed the stationarity of the 30-min series, so no abrupt changes were present.

The study's statistical analysis focused on specific selected periods (ventilation events) and not on the whole available data series. Therefore, a precise identification of the events was first performed. Each event would exhibit a sudden increase in Rn activity (from now on understood as Rn concentration) and CO₂ concentration (enriching ventilation), followed by an equally fast decrease in the concentrations (diluting ventilation). However, some of these phenomena might be caused by precipitation, which triggers significant CO₂ emissions from the soil (Vargas et al., 2018) to the ecosystem (López-Ballesteros et al., 2016) in the experimental site. Thus, periods during and up to one day after precipitation were not considered in this study, thereby reducing potential biases associated with soil moisture variability and its effect on Rn emanation (Zhuo et al., 2006). Additionally, based on previous studies (i.e., Sánchez-Cañete et al., 2013; Moya et al., 2019) the concentration increase had to coincide with an atmospheric pressure decrease, and equivalently a concentration decrease coincided with a pressure increase. Atmospheric pressure was used here exclusively as a physical criterion for event detection, while the subsequent analysis focused on the role of boundary layer dynamics in modulating the events through different meteorological variables. Furthermore, the soil humidity has also been identified as an important factor for CO₂ ventilation, so a θ15cm below 0.07 m³ m⁻³ was required, ensuring that the soil humidity was sufficiently low (Moya et al., 2022), allowing airflow through soil pores. Additionally, high-turbulence conditions were imposed, with a daily average u∗ threshold of 0.2 m s⁻¹ at surface level, as established for different sites across the world by Moya et al. (2022). For this study, the high-turbulence condition was applied also to the Doppler lidar turbulence indicator, and the maximum ϵ value during the event should exceed 10⁻⁴ m² s⁻³ (O'Connor et al., 2010). The high-turbulence conditions were met easily, with daily average u∗ exceeding 0.2 m s⁻¹ for every potential event, and maximum ϵ values exceeding 10⁻² m² s⁻³ for the selected events. Furthermore, the whole event had to last more than one day.

The beginning of each event was defined as the onset of a sustained (minimum 8 h) and rapid increase in tracer concentrations, exceeding 90 Bq m⁻³ h⁻¹ for Rn (Rn^50 cm or Rn^bh) or 80 ppm h⁻¹ for CO₂ (CO2150cm), the latter being the most variable CO₂ series and most sensitive to the concentration changes. The end of the event corresponded to the completion of a similarly sustained and rapid decrease in concentrations during the diluting phase. Sensitivity tests using minimum event durations between 6 and 12 h showed no significant impact on the statistical results. Therefore, given the use of 30 min averaged data and events lasting longer than one day, small uncertainties in the exact start and end times are not expected to significantly affect the correlation analyses. The inter-event periods were not included in the analysis, since they are dominated by slow, steady CO₂ production driven by biogeochemical processes (recharge, Sánchez-Cañete et al., 2013) rather than by mechanical processes linked to atmospheric phenomena. In the end, a total of 10 events were identified, encompassing 661 intervals of 30 min resolution, entailing 7.6 % of the available database.

Following standardization of the series and identification of enriching and diluting ventilation periods, bivariate Spearman correlation coefficients, ρS, were computed to assess the strength and direction of monotonic relationships between the study variables (Y variables) and the potential drivers (X variables). The study variables included CO₂ and Rn concentrations (CO2air, CO215cm, CO250cm, CO2150cm, CO2bh, Rn^50 cm and Rn^bh), as well as CO₂ flux (FCO2). CO2air and FCO2 were included among the study variables to initially assess their relationship with the potential drivers, but later the analysis focused on the soil CO₂ and Rn concentrations. The potential drivers were surface atmospheric pressure (Pair) and the surface friction velocity (u∗) from the flux tower, the Doppler lidar's wind shear and turbulent kinetic energy dissipation rate at different height ranges (sh^195 m, sh^495 m, sh^1005 m, ϵ195m, ϵ495m and ϵ1005m) and the ceilometer's ABLH and MLH estimations. Rn concentration and CO₂ fluxes were also included among the X variables to confirm their known relationship with CO₂ concentrations, but they were not considered as potential drivers.

Once the importance of potential drivers was confirmed using ρS, different statistical procedures were conducted to investigate their role as real drivers of the soil CO₂ and Rn dynamics.

A partial correlation analysis was performed. The Spearman correlation coefficients between the potential drivers and the soil CO₂ and Rn concentrations were calculated, but separately controlling for each potential driver to be constant for the calculation. The way the ρS varies highlights the importance of each potential driver over soil CO₂ and Rn levels and among them. For example, let us take A as a potential driver which shows a strong Spearman correlation with CO₂ and Rn levels. If the ρS for A are invariant when the rest of drivers are forced to be constant, this would indicate that A is a real driver whose effect over the concentrations is independent of the other drivers. Similarly, if the ρS of other drivers were to reduce to zero when A is held constant, this would suggest that A could rule the other drivers. Equivalently, if the ρS of the other drivers were not to change when A is forced to be constant, this would indicate that A is not a real driver, but it is rather ruled by other real drivers. This partial correlation analysis was conducted taking as potential drivers: Pair, u∗, ϵ195m, ϵ495m, ϵ1005m, sh^195 m, sh^495 m, sh^1005 m, ABLH and MLH.

To justify the use of bivariate Spearman correlations, a multicollinearity assessment was performed by calculating the Variance Inflation Factors (VIFs) of the potential drivers, yielding values below the common threshold of 5. This result suggests that multicollinearity was not significant and that the partial correlation results reliably reflect the independent effects of the drivers. Notably, significant collinearity was observed only between ϵ and sh at different altitudes, as expected, whereas their collinearity with the remaining variables remained below the threshold.

Finally, temporal shifts of each detrended potential driver series were performed and the ρS were recalculated. Since drivers experience changes before the variables they govern, if correlations with the concentration improve by shifting the driver series a given interval, this suggests which drivers actuate the underlying mechanisms. Thus, time series of a subset of potential drivers were shifted by a time lag between 180 min before and 180 min after the origin, in 30 min intervals. For these 12 shifted series the ρS with the soil CO₂ and Rn concentrations were recalculated. If the ρS increases for a series shifted by any interval after the origin (delayed in time), this suggests that the driver changes precede the concentration changes, and possibly its changes were affecting the concentrations. On the other hand, if the ρS increases for a series shifted by any interval before the origin (earlier in time), then the potential driver is experiencing changes later than the concentrations, suggesting that this is not a primary driver of the concentration's changes.

Figure 1 displays an example of two consecutive ventilation events that occurred between 1 and 5 November 2023. The diluting ventilation period of a previous event is also visible on 30 October 2023.

Figure 1a shows the atmospheric and vadose-zone CO₂ concentration series at different depths. Shaded red areas indicate enriching ventilation periods, while blue areas indicate diluting ventilation periods. Note that the transition periods (no shading in Fig. 1) between enriching (red shaded) and diluting (blue shaded) ventilation periods, typically lasting around 12 h, were excluded from the statistical analysis, to ensure consistent tendencies among variables, since each concentration may respond to atmospheric changes at slightly different times. It can be observed that the deeper the sensor, the higher the measured concentration, especially during the maximum values recorded, with averages values ± standard deviation of 430 ± 4 ppm, 950 ± 180 ppm, 1800 ± 450 ppm, 1840 ± 760 ppm and 1100 ± 550 ppm for CO2air, CO215cm, CO250cm, CO2150cm, CO2bh, respectively.

Figure 1b exhibits how CO₂ and Rn concentrations behaved similarly throughout the process, confirming the usefulness of soil Rn as a proxy for soil CO₂ concentration under mechanically driven, non-biological influences. Unfortunately, the Rn sensor at 50 cm depth failed during this interval. During enriching ventilation periods, soil concentrations increased significantly, indicating that CO₂ and Rn from deeper layers were being transported upward, coinciding with a marked drop in Pair. In contrast, during diluting ventilation periods, concentrations decreased significantly, coinciding with a significant Pair increase, suggesting that atmospheric air (low in CO₂ and Rn) was infiltrating soil pores. On 5 November 2023, Pair began a steady increase, remaining high until 8 November. During this period, soil concentrations remained low and nearly constant, indicating that negligible recharge of CO₂ or Rn was possible during this period.

The borehole behaved differently, facilitating mixing from 0 to 18 m depth, although the CO2bh instruments were located closer to the surface. After the enriching phase, CO2bh approximated CO250cm, while after the dilution it approached CO215cm or even CO2air, indicating high permeability in the borehole that allowed both deep CO₂-rich air and atmospheric air to move freely. Rn^bh dropped to near-zero (atmospheric level) during diluting ventilation events, confirming that atmospheric air with low Rn concentrations was easily entering the borehole. In fact, the average Rn over this period was 750 ± 750 Bq m⁻³, with a variance coefficient (ratio between the standard deviation and the mean value) of 100 %, indicating that the Rn level varies between maxima values after enrichment and near zero concentrations after dilution.

Figure 1c shows u∗ from the sonic anemometer and the Doppler-lidar-derived wind shear at different atmospheric height ranges. Their behavior resembled the concentration series, with both turbulence and wind shear increasing during periods of decreasing Pair, and decreasing when Pair rose. Both u∗ and sh in the whole column remained low throughout the high-pressure period at the end of the series. Finally, Fig. 1d presents the Doppler lidar's ϵ. Two distinct behaviors were observed. ϵ195m appeared unrelated to the other variables, while ϵ495m and ϵ1005m were more clearly connected. Specially, ϵ495m and ϵ1005m exhibited significant increases (note the logarithmic scale on the vertical axis) during enriching ventilation periods, and significant decreases during diluting ventilation, similar to the trends observed in Fig. 1a, b and c. It is noteworthy that the timing of u∗, sh and ϵ maxima and minima generally coincided with those of the CO₂ and Rn concentrations, reinforcing the fact that these variables were synchronized by specific atmospheric drivers.

This section gathers the calculation of the ρS for the different studied variables. A total of 10 events, encompassing 661 intervals of 30 min resolution, were detected and analyzed. Initially, all the variables mentioned in Sect. 2 were considered. However, some were omitted from the final analysis shown here to avoid repetition for the sake of readability. Temperature (Tair, T15cm, T50cm, T150cm and Tbh), relative humidity (RH^air, RH^50 cm, and RH^bh) and soil water content (θ15cm, θ50cm and θ150cm) were correlated with the CO₂ and Rn concentrations. While some of these correlations were statistically significant, they are not presented here, as none of these state variables could be considered dynamic drivers of the ventilation events; rather, they seem to vary as a consequence of ventilation or because of common external forcing. Additionally, P50cm, and Pbh exhibited behavior similar to Pair and were also excluded from the correlation matrix figures.

Figure 2 displays the ρS matrix for the study variables (CO₂ and Rn concentrations and CO₂ flux) and the selected atmospheric variables, calculated with the selected 661 intervals of 30 min resolution. All ρS greater than 0.4 or less than -0.4 were statistically significant, with p-values below 0.05. Most ρS outside this range were also statistically significant. Figure 2a shows the ρS for the enriching ventilation periods, while Fig. 2b exhibits those for the diluting ventilation intervals. In both cases, soil CO₂ and Rn concentrations showed a strong positive correlation, with an average ρS of 0.92 ± 0.04, confirming that these variables were closely linked. Since Rn is an inert species, this strong correlation confirmed that Rn is an ideal proxy for identifying soil CO₂ ventilation events driven by atmospheric mechanisms (e.g., Kowalski et al., 2008; Sánchez-Cañete et al., 2011; Pla et al., 2023). In contrast, CO2air showed a lower but still significant correlation with soil CO₂ and Rn concentrations (0.67 ± 0.20), suggesting that, although related, their drivers did not actuate equally. On the other hand, FCO2 showed no correlation during enriching ventilation periods (-0.23 ± 0.11), but a significant negative correlation during diluting ventilation (-0.72 ± 0.04). This suggests that FCO2 was not influenced by enriching mechanisms, while dilution of subterranean CO₂ happens to the atmosphere, producing a significant emission of CO₂ to the air (e.g., Sánchez-Cañete et al., 2011, 2013, 2016). As preconditioned, Pair showed a strong negative correlation in both periods (-0.94 ± 0.04); however, the importance of Pair as a potential driver is already known (e.g., Sánchez-Cañete et al., 2013; Moya et al., 2019). Additionally, u∗ correlated significantly and positively, especially during diluting ventilation (0.92 ± 0.03), supporting previous reports of its role in controlling CO₂ and Rn dynamics in soils at nearby sites (e.g., Sánchez-Cañete et al., 2016). As observed in Fig. 1, Pair increases correspond to CO₂ and Rn concentration and u∗ decreases, and vice versa.

Figure 2 also shows the ρS between sh and ϵ, and the soil CO₂ and Rn concentrations. A consistent and significant positive correlation was observed for the wind shear and turbulence in the higher height ranges (0.85 ± 0.03, 0.87 ± 0.03, 0.84 ± 0.04, 0.83 ± 0.05, for sh^495 m, sh^1005 m, ϵ495m and ϵ1005m, respectively). However, low-altitude wind shear and turbulence (sh^195 m, ϵ195m) were only weakly correlated during enriching ventilation periods (0.64 ± 0.07 and 0.74 ± 0.06, respectively). sh^195 m showed a significant positive correlation during diluting ventilation (0.83 ± 0.04), while ϵ195m showed no correlation in that period (0.17 ± 0.15). Several considerations should be considered before interpreting these correlations. First, sh^195 m and ϵ195m were more affected by noise, due to the smaller vertical integration of the signal. Additionally, turbulence at these ranges was more sensitive to local heterogeneities and short-lived eddies, which may have different effects on the soil and atmosphere. In contrast, sh^495 m, sh^1005 m, ϵ495m and ϵ1005m represented averages over much deeper height ranges, typically covering the whole ABL in this location (Abril-Gago et al., 2025a), reducing noise and emphasizing large-scale eddies and synoptic-scale mechanisms. Therefore, boundary layer averaged wind shear and turbulence appeared to be better predictors of soil CO₂ and Rn dynamics, versus the Doppler-derived turbulence at lower altitudes. As observed in Fig. 1, increases in CO₂ and Rn concentrations were accompanied by increases in sh^195 m, sh^495 m, sh^1005 m, ϵ495m and ϵ1005m. Although not shown here, a strong correlation was observed between u∗, sh and ϵ, only for the higher-altitude Doppler lidar ranges.

Finally, the relevance of ceilometer-derived ABLH and MLH was also explored. Negative correlations were found with CO₂ and Rn concentrations, being the strongest for ABLH during diluting ventilation events (-0.77 ± 0.08). This indicates that a significant increase in ABLH coincided with periods of reduced CO₂ and Rn levels.

Wind direction was not included in the correlation due to its circular nature. Nevertheless, it was consistently observed in every event, both enriching and diluting, that the wind blew from E or NE, indicating a preferred direction for their occurrence. This direction aligned with one of the two prevailing wind directions observed at the station (Abril-Gago et al., 2025a). Enriching ventilation periods began in the evening, between 18 and 00 UTC, and lasted until midday of the following day. Diluting ventilation periods started in the evening and continued until the afternoon of the next day. This pattern contrasts with observations at Llano de los Juanes (36.93° N, 2.75° W, 1600 m), located about 65 km west of Balsa Blanca, where nighttime ventilation is absent (Sánchez-Cañete et al., 2011), possibly due to site-specific differences in the dominant ventilation driver: Pair at Balsa Blanca versus u∗ at Llano de los Juanes. Ventilation periods also showed marked meteorological differences. Enriching periods were characterized by lower surface turbulence and ABLH, with an average ± standard deviation values of u∗=0.31 ± 0.13 m s⁻¹ and ABLH = 490 ± 200 m. In contrast, diluting periods exhibited higher surface turbulence and ABLH, with u∗=0.46 ± 0.17 m s⁻¹ and ABLH = 700 ± 470 m. Other meteorological variables did not show significant differences between these periods.

Enriching ventilation periods were also associated with reduced vertical particle transport in the atmosphere, with average vt495m=0.99 ± 0.87 cm s⁻¹, similar to days with low convective mixing (Abril-Gago et al., 2025a). Diluting ventilation periods corresponded to enhanced vertical particle transport, with vt495m=2.0 ± 1.9 cm s⁻¹, comparable to days characterized by strong turbulence, whether from convective or mechanical mixing. This value was higher than those reported by Abril-Gago et al. (2025a) in Tables 1 and 2 for this location (0.90 cm s⁻¹ for the whole day and 1.56 cm s⁻¹ for the daytime period), suggesting that vertical transport, and consequently convective or mechanical mixing, during the identified diluting ventilation periods was exceptionally intense, even during nighttime. These results further highlight the connection between soil CO₂ and Rn dynamics and atmospheric particle exchanges, indicating that both were likely driven by similar atmospheric processes.

Several atmospheric mechanisms have been proposed as potential drivers of soil CO₂ and Rn concentrations. In this section, different statistical analyses were conducted to determine which of these variables function as real drivers, and which were merely correlated due to being influenced by common underlying processes. To that end, partial correlation coefficients were recomputed to evaluate how the ρS changed when a specific variable (i.e., potential driver) is held constant.

Figure 3 gathers the average ρS of the potential drivers with the soil CO₂ and Rn concentrations, when each potential driver was independently held constant. A group of potential drivers was easily discarded, namely ϵ195m, sh^195 m, ABLH and MLH, as holding them constant did not meaningfully affect the ρS of the remaining variables. This suggested that these were not real drivers of the soil CO₂ and Rn dynamics and they were consequently omitted from Fig. 3. The same applies to u∗, but only during enriching ventilation periods. During dilution ventilation events, average ρS were slightly reduced (in absolute terms) when ϵ495m, ϵ1005m, sh^495 m and sh^1005 m were individually held constant, implying that while these variables may influence the dynamics, they were unlikely to be the main drivers.

Among all potential drivers, Pair emerged as the most significant driver for both enriching and diluting ventilation periods. When held constant, it caused a notable decrease in the ρS of the other variables (0.20 ± 0.14), indicating its fundamental role as a determinant of ventilation. Moreover, its ρS changed the least when other variables were controlled (-0.75 ± 0.08), suggesting a strong independence. u∗ also played a significant role during diluting ventilation periods, while ϵ495m, ϵ1005m, sh^495 m and sh^1005 m appeared to be more relevant as an enriching ventilation mechanism, with ϵ495m and ϵ1005m showing the most consistent influence. The results were almost identical when calculating the average ρS separately for the correlations between the potential drivers and the soil CO₂ at different depths, and for those between the potential drivers and soil Rn concentrations at different depths.

The assessment of multicollinearity among the potential drivers was conducted, and the VIFs were all below the threshold of 5 (ranging from 1.9 to 4.2), indicating that each variable exhibited an acceptable level of independence. This suggests that the relationships identified between the drivers and the soil concentrations were not artificially influenced by redundant collinearity, and that the previous bivariate partial correlation results were reliable. As expected, ϵ495m, ϵ1005m, sh^495 m and sh^1005 m displayed a high degree of collinearity among themselves. This behavior is anticipated, as both ϵ and sh are derived from the same Doppler lidar signal, and measurements extending to higher altitudes also encapsulate information from lower height ranges.

A temporal shift of the series Pair, u∗, ϵ495m, ϵ1005m, sh^495 m and sh^1005 m was performed, and the ρS were recalculated to identify the primary driver of the soil CO₂ and Rn concentrations.

For the periods and potential drivers considered in this study, the shifted correlations revealed that Pair maximized its ρS (increasing slightly by 2.2 %) during enriching ventilation intervals when shifted 30 min earlier, indicating that changes in Pair occurred after changes in CO₂ and Rn concentrations. Conversely, during diluting ventilation events, the maximum ρS were observed with no temporal shift, suggesting that Pair changes occurred simultaneously with soil concentration changes. The opposite pattern was observed for u∗, with ρS maximizing (increasing slightly by 2.1 %) when shifted 30 min earlier for diluting ventilation events and maximizing during enriching ventilation periods with the unshifted series.

ϵ1005m, sh^495 m and sh^1005 m reached their maximum ρS with the original series during diluting ventilation, while ϵ495m maximized ρS (increasing significantly by 3.9 %) when the series was shifted 30 min later, indicating that changes in ϵ495m occurred before dilution and suggesting its importance as primary driver. During enriching ventilation periods, ϵ495m, ϵ1005m, sh^495 m and sh^1005 m showed maximum ρS (increasing by 1.2 %, 0.6 %, 1.9 % and 1.6 %, respectively) when shifted 30, 30, 60 and 30 min later, respectively, suggesting their roles as primary drivers.

To summarize the previous findings, the following conceptual model of the ventilation phenomenon is proposed.

At the start of an enriching ventilation period (Fig. 4a), boundary layer ϵ and sh start to increase anomalously (in the afternoon, even after daytime convection has ceased). Shortly thereafter, soil CO₂ and Rn concentrations begin to increase, accompanied by a drop in surface pressure and a rise in surface friction velocity. This state persists until noon of the following day, coinciding with a shallow ABLH. The combination of a poorly developed boundary layer, and elevated turbulence and wind shear suggests a situation of limited convection but enhanced mechanical mixing within the boundary layer. Consistently, CO₂ fluxes remain relatively low during this phase (0.05 ± 0.11 µmol m⁻² s⁻¹), approximately half of those observed under non-ventilation conditions, supporting the interpretation that CO₂ is being temporarily stored within the soil.

Then, in the afternoon, boundary-layer ϵ and sh begin to decrease notably (Fig. 4b), followed by a similar decrease in surface friction velocity and a rise in surface pressure, coinciding with the dilution of soil CO₂ and Rn. This period extends into the next afternoon and coincides with a higher ABLH and enhanced vertical particle transport within the boundary layer. The increase in surface pressure suggests the pumping of atmospheric air poor in CO₂ and Rn into the soil pores, compressing air within the soil and displacing CO₂-rich soil air to the atmosphere producing a significant CO₂ flux (0.30 ± 0.13 µmol m⁻² s⁻¹) up to three times that observed under non-ventilation conditions.