On the basis of measurements over different surfaces, an inertial sublayer (ISL), where Monin‐Obukhov Similarity Theory applies, exists above z=3h, where h is canopy height. The roughness sublayer is within h<z<3h. Most studies of the surface layer above forests, however, are able to probe only a narrow region above h. Therefore, direct verification of an ISL above tall forests is difficult. In this study we conducted a systematic analysis of unstable turbulence characteristics at heights from 40 to 325 m, measured at an 80m, and the recently built 325‐m Amazon Tall Tower Observatory towers over the Amazon forest. Our analyses have revealed no indication of the existence of an ISL; instead, the roughness sublayer directly merges with the convective mixed layer above. Implications for estimates of momentum and scalar fluxes in numerical models and observational studies can be significant.

Isoprene fluxes vary seasonally with changes in environmental factors (e.g., solar radiation and temperature) and biological factors (e.g., leaf phenology). However, our understanding of the seasonal patterns of isoprene fluxes and the associated mechanistic controls is still limited, especially in Amazonian evergreen forests. In this paper, we aim to connect intensive, field-based measurements of canopy isoprene flux over a central Amazonian evergreen forest site with meteorological observations and with tower-mounted camera leaf phenology to improve our understanding of patterns and causes of isoprene flux seasonality. Our results demonstrate that the highest isoprene emissions are observed during the dry and dry-to-wet transition seasons, whereas the lowest emissions were found during the wet-to-dry transition season. Our results also indicate that light and temperature cannot totally explain isoprene flux seasonality. Instead, the camera-derived leaf area index (LAI) of recently mature leaf age class (e.g., leaf ages of 3–5 months) exhibits the highest correlation with observed isoprene flux seasonality (R² = 0.59, p < 0.05). Attempting to better represent leaf phenology in the Model of Emissions of Gases and Aerosols from Nature (MEGAN 2.1), we improved the leaf age algorithm by utilizing results from the camera-derived leaf phenology that provided LAI categorized into three different leaf ages. The model results show that the observations of age-dependent isoprene emission capacity, in conjunction with camera-derived leaf age demography, significantly improved simulations in terms of seasonal variations in isoprene fluxes (R² = 0.52, p < 0.05). This study highlights the importance of accounting for differences in isoprene emission capacity across canopy leaf age classes and identifying forest adaptive mechanisms that underlie seasonal variation in isoprene emissions in Amazonia.

We analyzed the vertical wind profile measured at six experimental tower sites in dense forest in the Amazon Basin and examined how well two simple models can reproduce these observations. In general, the vertical wind profile below the canopy is strongly affected by the forest structure. From the forest floor to 0.65h (where h = 35 m is the average height of the forest canopy for sites considered), the wind profile is approximately constant with height with speeds less than 1 ms⁻¹. Above 0.65 to 2.25h, the wind speed increases with height. Testing these data with the Yi and Souza models showed that each was able to reproduce satisfactorily the vertical wind profile for different experimental sites in the Amazon. Using the Souza Model, it was possible to use fewer input variables necessary to simulate the profile.

The analysis of gross primary production (GPP) is crucial to better understand CO₂ exchanges between terrestrial ecosystems and the atmosphere, while the quantification of water-use efficiency (WUE) allows for the estimation of the compensation between carbon gained and water lost by the ecosystem. Understanding these dynamics is essential to better comprehend the responses of environments to ongoing climatic changes. The objective of the present study was to analyze, through AMERIFLUX and LBA network measurements, the variability of GPP and WUE in four distinct tropical biomes in Brazil: Pantanal, Amazonia, Caatinga and Cerrado (savanna). Furthermore, data measured by eddy covariance systems were used to assess remotely sensed GPP products (MOD17). We found a distinct seasonality of meteorological variables and energy fluxes with different latent heat controls regarding available energy in each site. Remotely sensed GPP was satisfactorily related with observed data, despite weak correlations in interannual estimates and consistent overestimations and underestimations during certain months. WUE was strongly dependent on water availability, with values of 0.95 gC kg⁻¹ H₂O (5.79 gC kg⁻¹ H₂O) in the wetter (drier) sites. These values reveal new thresholds that had not been previously reported in the literature. Our findings have crucial implications for ecosystem management and the design of climate policies regarding the conservation of tropical biomes, since WUE is expected to change in the ongoing climate change scenario that indicates an increase in frequency and severity of dry periods.