Observations of the CloudRoots-Amazon22 campaign were collected at the Amazon Tall Tower Observatory (ATTO) site between 8–21 August 2022, during the Amazonian dry season. The campaign added detailed cloud- and leaf-level observations to the continuous, high-quality tower flux and concentration measurements . For this study specifically, observations were collected from both the 325 m ATTO tall tower (-2.1448° N, -59.0008° E) and the shorter 81 m Instant tower (-2.1468° N, -59.0068° E), which are 670 m apart . We only made use of the observations during the nocturnal period, which we defined as the period between sunset (18:00 LT) and sunrise (06:00 LT), where LT, local time, is UTC-4. Additionally, we excluded the final two days of the campaign (20 and 21 August) because of multiple unavailable variables, including longwave radiative and CO2 flux observations.

Sensors on the towers are placed at 14 height levels between the two towers, continuously monitoring atmospheric variables, trace gases, and turbulent fluxes (Table 1). The turbulent fluxes were calculated over 30 min intervals and corrected for outliers, temporal trends, double rotation of the wind field, time lags, spectral losses, crosswind effects, and WPL (Webb–Pearman–Leuning) density fluctuations . More detailed instrument and sampling details specific to the CloudRoots-Amazon22 campaign can be found in , and for the permanent instrumentation in , , and .

The characteristics of the area surrounding ATTO are described in more detail by . We summarise here the most relevant information for this study. The ATTO site is located on top of a plateau (ca. 120 m above mean sea level) in the central Amazon. The plateau is characterised by small-scale topography, with maximum elevation changes of 100 m. The vegetation at the ATTO site can be characterised as a dense tropical forest with a LAI of approximately 5.3 m2m-2 . The leaf area density profile is bimodal, indicating the presence of an understory vegetation layer at approximately 4 m . The average tree height is 20.7±0.4 m, but the tallest trees reach 36–40 m. For the study, we defined the aerodynamic canopy top as 32 m following . For the interpretation of the vertical profiles, it is important to mention that the Instant tower is surrounded by dense vegetation, whereas the ATTO tall tower is situated in a more disturbed, open-forest environment. Because the within-canopy measurements were conducted at the Instant tower, they are representative of the dense forest canopy. We constructed the vertical profiles by combining measurements from both towers, an approach previously demonstrated to be valid by .

To estimate hNBL, we used five different criteria based on the vertical profiles of potential temperature (θ), CO2, horizontal wind speed (U), Richardson number (Ri) and TKE above the canopy top. The criteria are based on the formulations from and adjusted to fit the CloudRoots-Amazon22 observations. The five methods are illustrated in Fig. 1, highlighting the contrast between strongly stable (red) and weakly stable (blue) stratification regimes. Before estimating hNBL, we fitted a 2nd-order logarithmic function to the observed vertical profiles of θ, CO2 and U from the canopy top up to the highest measuring level. The 2nd-order logarithmic function was fitted using a least-squares polynomial. The fitted analytical logarithmic functions enable us to estimate hNBL not only at the present measuring heights, but also in between the measuring levels. For Ri and TKE, vertical profiles were linearly interpolated to reflect the observed non-logarithmic pattern in Ri and TKE. For future reference, we indicate the calculated hNBL as hcriteria for each of the criteria. In the following paragraphs, we further elaborate on each criterion.

In the NBL, θ increases with height above the canopy due to the cooling of the canopy top. However, this increase is not uniform, near the canopy, the vertical gradient is large, while toward the top of the NBL, the gradient weakens and gradually approaches zero. Therefore, we defined the NBL height as the lowest height at which the vertical gradient of θ falls below a prescribed low threshold (Fig. 1a). The threshold was defined as 3×10-3 m-1. To determine the height at which this threshold was reached, θ was normalised as θN=θ/Δθ, where Δθ is the difference between θ at 300 m (the top of the tower) and 32 m (the canopy top). This normalisation removes differences in temperature magnitude between nights and between the beginning and end of a given night. However, it does not alter the vertical temperature gradient used to estimate hNBL. It is important to emphasise that θN is used only to estimate hNBL, all other analyses are based on the observed θ.

For CO2, the NBL is characterised by a negative CO2 gradient with height, approaching 0 ppmm-1 near the top of the stable layer. Utilising a similar method as for θ, we normalised the CO2 gradient with height and chose a threshold. The threshold was defined as 2.5×10-3 m-1 (Fig. 1b).

The U criterion was subdivided into two parts, based on whether a nocturnal low-level jet (NLLJ) was observed above the NBL or not. In the measuring period of the CloudRoots-Amazon22 campaign, a weak NLLJ was observed during two nights (12–13 August and 17–18 August). When an NLLJ is present above the NBL, we defined hU as the height where U=Umax (red line in Fig. 1c), because the NLLJ wind maximum forms on top of the stable NBL, where atmospheric friction approaches zero. This method was already found to yield trustworthy results above the Amazon canopy by . When no NLLJ is present, we used the U gradient with height (Fig. 1c). Similar to θ and CO2, we normalised the U gradient and chose a threshold of 2.5×10-3 m-1.

The final two methods, based on Ri (calculated as the gradient Richardson number) and TKE, are only applied in the weakly stable regime, as sufficient turbulence above the canopy is required for the application of the criteria. We considered there is sufficient turbulence when Ri is below the critical Richardson number (Ric = 0.25) and TKE is greater than 0.10 m2s-2 at the canopy top. Both methods are based on the assumption that all turbulence has dissipated in the residual layer, where the atmosphere is neutral. Therefore, hRi is found when Ri≥Ric (Fig. 1d) and hTKE is determined at the first height where TKE= 0.2TKE0, where TKE0 is the observed TKE value at the canopy top. We chose to use a threshold of 20 % of the TKE values relative to the canopy top (instead of 5 %), due to predominantly stable conditions during the observation period (Fig. 1e).

The strongly stable periods were excluded for the following reasons. For Ri, because if Ri>Ric at the canopy top, the hRi will always be found at the canopy top, this might not be valid. For TKE, we need the distinction because if TKE<0.10 m2s-2 at the canopy top, TKE0 already approaches zero and does not show an 80 % decrease at higher elevations in the vertical profile.

To understand the importance of canopy-atmosphere interactions, we studied the temporal evolution of atmospheric stability, focusing on both the in-canopy atmosphere, the stable layer aloft and the interaction at the canopy-atmosphere interface. To compare the stability of the in-canopy and stable layer aloft, we introduce a bulk representation of Ri for both layers based on the work of . For the stable layer above canopy, the Richardson number (Riatm) is defined by Eq. (1), and for the in-canopy layer including the canopy roughness layer, the Richardson number (Rican) is determined by Eq. (2). In the equations, the subscripts of θ and U, indicate the measurement heights of θ and U, g is the gravitational acceleration (9.81 ms-2), Δz is the difference in height between the two heights indicated in the subscripts and θref is a reference temperature, taken as the potential temperature at the highest measuring level on ATTO tall tower (298 m). 1Riatm=gΔzθrefθ81-θ35(U81-U35)22Rican=gΔzθrefθ35-θ5(U35-U5)2

To assess and quantify the coupling and exchange between the in-canopy atmosphere and the stable layer aloft, we evaluated the governing equations of TKE, heat and CO2 for the selected strongly and weakly stable periods (Sect. 2.4.1).

To calculate the budgets using only our observations, we had to make the following two assumptions: (1) The atmosphere is horizontally homogeneous (∂ψ‾∂x=∂ψ‾∂y=0) and (2) subsidence (vertically advective term) is negligible (w‾=0). In addition, we aligned the coordinate system with the mean wind such that only the u component of the wind remained. We evaluated the governing equations using 30 min averaged data (indicated by the overbars). Vertical derivatives were calculated using one level above and below the desired height, except for the vertical divergence of the CO2 flux, for which, due to a lack of measurements at all levels, a first-order method was employed, which may affect the accuracy of the calculations. The residual (R), or non-closure of the budgets, represents the uncertainty due to neglected terms of the governing equation that could not be calculated with our observations, and the observational uncertainty. In the following, we explain the strict selection of strongly and weakly stable regimes and each governing equation individually.

We found values of hNBL ranging from 74 to 298 m across all criteria (Fig. 2), with hRi yielding the lowest estimate and hU the highest. The average of five criteria is between 142–164 m throughout the night (Fig. 2a – grey line). The large observed variation within our study period can be linked to alternating strongly and weakly stable periods. With the selected hours in Table 3, we can find that in our strictly defined strongly stable regimes we estimated hNBL to be 133 m (five-method mean S1, S2, S3, and S4) while it was 165 m (five-method mean W1, W2, W3 and W4) in the weakly stable regime.

The estimation of hθ (Fig. 2b) closely follows the mean, with an average value of 150±27.1 m. The variation in hθ can be attributed to the variability in the strength of the thermal stratification both intra- and inter-nightly. The stratification strength decreases when turbulent mixing occurs, which increases hθ; conversely, hθ remained low in the absence of turbulent mixing.

The hCO2 is estimated to be 145±42.2 m (Fig. 2c) which is slight below the average. Interestingly, the median of hCO2 remained around 140 m throughout the night, indicating that the observed variation was driven by a few exceptionally high hCO2 values. We hypothesise that these higher values were caused by bursts of intermittent turbulence, which transported CO2 from within the canopy aloft . This influx of CO2 disrupted the strong vertical gradient, resulting in higher calculated values of hCO2.

Estimates of hU (Fig. 2d) are found to be 156±46.5 m. The large variation in hU is attributed to variable wind conditions during the campaign (Appendix B). The variation was strongly influenced by nights with almost linear vertical wind profiles (high hU) and nights with an NLLJ (low hU). Linear wind profiles were observed on the nights between 13–17 August. In these nights, the observed hU were all ≥167 m. Weak NLLJ, with maximum wind speeds of 5.5 ms-1, were observed during the nights of 12–13 August and 17–18 August. On the nights when the NLLJ was observed, the low-level wind (between 35–100 m) was from a North-northeastern direction. This same wind direction at ATTO has been found to originate from an area with low surface roughness by , promoting stable stratification. Additionally, Northeastern winds have been associated with low elevated jet noses by . Both findings are consistent with our observations; during these nights, we observed low-level jet nose heights between 80–100 m, low wind speeds at the top of the tower (≤3.0 ms-1), and shallow NBLs (≤135 m).

For hRi and hTKE, it is important to stress that these values were only calculated during periods with sufficient turbulence (weakly stable periods). Additionally, the estimations are potentially influenced by the presence of elevated layers of enhanced turbulence, which may disrupt the methods. The limited number of calculations particularly influenced results for hTKE, as it could only be calculated in 20 % of the analysed hours. The estimation of hRi had an average of 134±29.5 m (Fig. 2e). We observed that hRi is often significantly lower than hθ and hCO2, during turbulent periods, raising questions as to whether this method accurately reflects the underlying dynamics. The hTKE is calculated to be 193±50.9 m (Fig. 5f), which is higher than average. The significant variation may be partly attributed to the limited number of estimations. Additionally, we observe that the TKE often did not reduce by 80 % within the vertical profile. It is possible that the frequent occurrence of very stable conditions complicated the application of this method during the CloudRoots-Amazon22 period, as previous studies at the ATTO site have yielded reliable results using similar turbulence-based methods .

The hNBL above the Amazon rainforest has been investigated in a limited number of studies. All these studies reported hNBL values ranging from 80 to 250 m . We compare our results a bit more carefully to the three studies that also used ATTO data. Two estimated hNBL utilising the vertical turbulence observations installed at ATTO and one using the NLLJ nose height . The studies report hNBL values ranging from 81 to 247 m, with specifically finding 120±28 m for the 2022 dry season. While this range is consistent with our results, slight differences persist. Such variations were anticipated, given that the different criteria are governed by distinct physical processes.

Overall, we consider the range, derived from all criteria together, to yield a stable representation of hNBL during variable turbulent regimes. With this finding, we also show that gradient methods give reliable estimations of hNBL. These gradient methods can be useful in NBL studies, particularly for locations where no vertical turbulence profiles are available.

The temporal evolution of the vertical atmospheric structure differs within and above the canopy (Fig. 3). At the canopy – atmosphere interface, between 25 (24) and 81 (79) m, we observe an increase (decrease) in the θ (CO2) gradient from 0.040 Km-1 (-0.09 ppmm-1) to 0.046 Km-1 (-0.51 ppmm-1) throughout the night. In contrast, below the canopy (between 5–25 m), the θ (CO2) gradient decreased (increased) from 0.067 Km-1 (-1.77 ppmm-1) to 0.015 Km-1 (-0.69 ppmm-1).

Above the canopy, the atmospheric stratification strengthens during the night, while the within-canopy layer mixes over time. After sunset (18:00–20:00 LT), below the canopy, a shallow stable daytime layer was present within the canopy. The shallow stable layer has been observed to form during daytime by . Throughout the night, the atmospheric stratification within the canopy layer weakens, enabling air mixing and thereby leading to better mixed CO2 profiles throughout the canopy layer.

We hypothesise that the mixing within the canopy layer originates from the cold layer forming in the dense canopy crown, roughly between 15–30 m . In this layer, the vegetation biomass cools down, creating a locally cold layer, which promotes sinking motions and enhances turbulence in the canopy layer. Enhanced turbulence within the canopy has also been reported in previous studies . found that a radiative imbalance of ≤-40 Wm-2 is needed to make this sinking motion possible, this threshold was met during most nights during CloudRoots-Amazon22 campaign (not shown). In addition, we can confirm the downward motion of cold air by turbulence observations. When we inspect the selected hours used in Sect. 3.3 (Table 3) as an example, we observed a positive heat flux (w′θ′‾) during S2, S3, S4, and W3 within the canopy layer, which can be associated to the downward movement of cold air (w′<0 and θ′<0) (Appendix C).

In addition to the observed temporal evolution, we must acknowledge the large variability between the nights. The largest variation is observed just after sunset (18:00–20:00 LT) and before sunrise (04:00–06:00 LT). After sunset, the large potential temperature variation, driven partially by temperature changes within two-hour windows (black error bars), can be attributed in part to strong longwave radiative cooling of the canopy at the onset of the night . In contrast, the large variation in the CO2 molar fraction at 4 m is mainly driven by inter-night variability (red error bars). This pronounced nocturnal CO2 variability may result from three mechanisms: (1) the strength of the stable stratification, which is influenced by radiative cooling and the extent of CO2 transport from the stable canopy layer into the atmosphere aloft , (2) the absolute temperature and moisture conditions, influencing the respiration rate and amount of CO2 stored in the stable daytime layer and (3) the preference of CO2 to build up in low topographical points , where low-level horizontal advection can cause large differences in CO2 molar fraction at the surface. Because of the non-homogeneous influence of the mechanisms over time, it remains hard to tell the dominant mechanism, this might well be variable between nights.

Just before sunrise, we observe large variation in CO2 molar fractions throughout the vertical profile. The large variation can be explained by enhanced mixing during sunrise. In a previous study by , they found that the transport of CO2 in the early morning differs between low and high wind speeds. The variable wind conditions during the CloudRoots-Amazon22 period (Appendix B), may explain the large contrast between nights.

We evaluated the governing equations of TKE (Sect. 3.3.1), heat (Sect. 3.3.2) and CO2 (Sect. 3.3.3) above the canopy, for the periods classified as strongly and weakly stable (Table 3). Vertical profiles for all six criteria differ distinctly between the two regimes (Fig. 5). The strongly stable vertical θ profile shows a larger temperature gradient between 25–50 m (2.08 K) than the weakly stable profile (1.29 K) (Fig. 5a). For CO2 (Fig. 5b), a similar pattern is observed: the CO2 gradient between 38–79 m is larger for the strongly stable regime (-21.8 ppm) than during the weakly stable regime (-5.5 ppm). These differences are attributed to a combination of the nocturnal temporal evolution of θ and CO2, as well as the influence of variable turbulent activity during the regimes.

Horizontal wind speed clearly differs between the strongly and weakly stable regimes (Fig. 5c). Already at 25 m, the wind speed and gradient with height in the weakly stable profile are enhanced compared to the strongly stable profile. At 50 m, the threshold of 2.5 ms-1 is reached in the weakly stable profile, but not for the strongly stable profile. Additionally, in the weakly stable regime, TKE (Fig. 5d) and u* (Fig. 5f) at 50 m exceed the thresholds (Table 2) and Ri<Ric (Fig. 5e), in contrast to the strongly stable regime. The observed differences vanish above 200 m for TKE, Ri, and u*, suggesting that the turbulent regime does not significantly influence the residual layer. The observed differences between the regimes are consistent with a previous study by .

Eddy covariance measurements in general, and thus also the data collected during the CloudRoots-Amazon22 campaign, are challenging to use during the night . With eddy covariance devices, the turbulent transport of momentum, heat and CO2 by eddies is measured. Within a strongly stable NBL, the limited vertical extent of small eddies, combined with low turbulence intensity and non-linear processes, introduces uncertainties in the data series. Additionally, 30 min averaging intervals may filter the inherent intermittency of turbulence , although such intermittency likely drives the variability observed within those periods. Investigating the underlying mechanisms and frequency of these intermittent events would require an analysis of shorter time intervals (five minutes or less). Therefore, leveraging high-frequency ATTO observations to quantify the influence of intermittent activity on CO2 and temperature variability is an essential next step.

These future analyses could lead to improved NBL parameterisation by incorporating the effects of intermittent bursts. Such intermittency arises from a diversity of processes, including surface-layer interactions as described by , , as well as elevated layers of turbulence associated with NLLJs, and other mesoscale motions . Additionally, to capture complex canopy-atmosphere interactions, the model parameterisation should account not only for intermittent, but also for the complex, stability-dependent coupling between the NBL and the within-canopy layer.

In this study, we did not explicitly study the effect of clouds on the NBL evolution. However, observed that cloudy conditions are more typical of the weakly stable regime. During the CloudRoots-Amazon22 observations, high clouds were, among other nights, observed during the first half of the night between 13–14 August (Appendix D). The presence of these clouds may have limited the development of a strongly stratified layer during the early part of the night, potentially contributing to the weakly stable atmospheric conditions observed during periods W1 and W2.

Additionally, daytime studies have found effects of clouds on trace gas transport from the canopy up into the atmosphere . The importance of the daytime clouds suggests that, also during the night, clouds could have a significant role in CO2 exchange. So far, the influence of nocturnal clouds on this transport remains poorly understood and has only been evaluated in a few studies. observed that during the night the CO2 molar fraction in the atmosphere decreases after a rainfall event, which was correlated with increased humidity, decreased temperature and radiation. found that the passage of nocturnal deep convective clouds can generate substantial upward ejections of CO2 from the within-canopy air. The magnitude of these ejections depends on the amount of CO2 stored within the canopy, which, as we show, is closely related to the prevailing nocturnal stability regime.

Our findings indicate the need to include an explicit representation of the tall canopy and improve representations to include the transition between weakly and strongly stable NBL, allowing for rapid intermittent periods . In addition, high-resolution measurements are vital to observe sinking motions of cold air into the canopy. Therefore, measurements of the upward and downward longwave radiation and temperature with high vertical resolution (±1 m) in the cooling layer (20–30 m) are advised. These measurements would also make it possible to validate the RTE + RRTMGP model output with observations, providing more confidence in our atmospheric longwave cooling rate estimate and the radiation model in general.

Finally, these observational studies could enable us to bridge the gap between one-layer and multilayer canopy models . The advancement to multi-layer canopy models is crucial due to increasing model resolution, but even so, to improve model parameterisations in large-scale models. The high-resolution vertical observations are of great importance for validating state-of-the-art, multi-layer canopy models using large-eddy simulation techniques . These models have already proven to yield important improvements in representing canopy-atmosphere interactions, atmospheric stability and topographical effects. Therefore, we think the multilayer canopy models are the next step in improving the complex nocturnal vegetation-atmosphere interactions.