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.

Using a high resolution model of Large Eddies Simulation (LES), named PALM from PArallel LES Model, a set of simulations were performed to understand how turbulence and convection behave in a pasture and forest sites in Amazonia during the dry and rainy seasons. Related to seasonality, dry period presented higher differences of values (40 W m⁻²) and patterns over the sites, while in the wet period have more similar characteristics (difference of −10 W m⁻²). The pasture site had more convection than the forest, with effective mixing and a deeper boundary layer (2600 m). The vertical decrease of sensible heat flux with altitude fed convection and also influenced the convective boundary layer (CBL) height. Regarding the components of turbulent kinetic energy equation, the thermal production was the most important component and the dissipation rate responded with higher growth, especially in cases of greatest mechanical production at the forest surface reaching values up to −20.0.

This study investigated the erosion of the nocturnal boundary layer (NBL) over the central Amazon using a high-resolution model of large-eddy simulation (LES) named PArallel Les Model (PALM) and observational data from Green Ocean Amazon (GoAmazon) project 2014/5. This data set was collected during four intense observation periods (IOPs) in the dry and rainy seasons in the years 2014 (considered a typical year) and 2015, during which an El Niño–Southern Oscillation (ENSO) event predominated and provoked an intense dry season. The outputs from the PALM simulations represented reasonably well the NBL erosion, and the results showed that it has different characteristics between the seasons. During the rainy season, the IOPs exhibited slow surface heating and less intense convection, which resulted in a longer erosion period, typically about 3 h after sunrise (that occurs at 06:00 local time). In contrast, dry IOPs showed more intensive surface warming with stronger convection, resulting in faster NBL erosion, about 2 h after sunrise. A conceptual model was derived to investigate the complete erosion during sunrise hours when there is a very shallow mixed layer formed close to the surface and a stable layer above. The kinematic heat flux for heating this layer during the erosion period showed that for the rainy season, the energy emitted from the surface and the entrainment was not enough to fully heat the NBL layer and erode it. Approximately 30% of additional energy was used in the system, which could come from the release of energy from biomass. The dry period of 2014 showed stronger heating, but it was also not enough, requiring approximately 6% of additional energy. However, for the 2015 dry period, which was under the influence of the ENSO event, it was shown that the released surface fluxes were sufficient to fully heat the layer. The erosion time of the NBL probably influenced the development of the convective boundary layer (CBL), wherein greater vertical development was observed in the dry season IOPs (~1500 m), while the rainy season IOPs had a shallower layer (~1200 m).

With few measurement sites and a great need to validate satellite data to characterize the exchange of energy and carbon fluxes in tropical forest areas, quantified by the Net Ecosystem Exchange (NEE) and associated with phenological measurements, there is an increasing need for studies aimed at characterizing the Amazonian environment in its biosphere–atmosphere interaction, considering the accelerated deforestation in recent years. Using data from a flux measurement tower in the Caxiuanã-PA forest (2005–2008), climatic data, CO₂ exchange estimated by eddy covariance, as well as Gross Primary Productivity (GPP) data and satellite vegetation indices (from MODIS), this work aimed to describe the site’s energy, climatic and carbon cycle flux patterns, correlating its gross primary productivity with satellite vegetation indices. The results found were: (1) marked seasonality of climatic variables and energy flows, with evapotranspiration and air temperature on the site following the annual march of solar radiation and precipitation; (2) energy fluxes in phase and dependent on available energy; (3) the site as a carbon sink (−569.7 ± 444.9 gC m⁻² year⁻¹), with intensity varying according to the site’s annual water availability; (4) low correlation between productivity data and vegetation indices, corroborating data in the literature on these variables in this type of ecosystem. The results show the importance of preserving this type of environment for the mitigation of global warming and the need to improve satellite estimates for this region. NDVI and EVI patterns follow radiative availability, as does LAI, but without direct capture related to GPP data, which correlates better with satellite data only in the months with the highest LAI. The results show the significant difference at a point measurement to a satellite interpolation, presenting how important preserving any type of environment is, even related to its size, for the global climate balance, and also the need to improve satellite estimates for smaller areas.

Climate variability directly influences agriculture, especially in a scenario of global change and transition to a sustainable bioeconomy. This study analyzed historical series (1994–2023) of productivity and harvested area of annual crops (corn, cassava, and beans) and perennial crops (pineapple, cocoa, annatto, avocado, and guava), in order to understand the relationship between rainfall, maximum temperature, and agricultural production in northern Brazil. To achieve this, the Augmented Dickey–Fuller (ADF) test was applied to verify the stationarity of the series, and principal component analysis (PCA) was used to identify correlation patterns between climate and production variables. The ADF test showed that annual precipitation is stationary, while maximum temperature is non-stationary, confirming a warming trend. Among the crops, only bean productivity was stationary, albeit at low levels, while corn, cassava, and cocoa showed non-stationary behavior, reflecting technological advances combined with climatic pressures. PCA indicated different responses: corn showed a positive association with temperature, but signs of recent stagnation, whereas cassava and beans depended more on precipitation, demonstrating vulnerability to drought. Among perennials, avocado and guava responded positively to increased temperature, while annatto and pineapple were more dependent on rainfall. Cocoa showed a balanced correlation with both variables. It can be concluded that climate impacts on agriculture are heterogeneous and require specific adaptive strategies. From a bioeconomy perspective, the importance of productive diversification, technological innovation, and public policies aimed at climate resilience and the sustainability of low-carbon value chains is highlighted.

This paper presents the observational, remote sensing, and model simulation used to analyze southern Brazil Antarctic ozone hole influence (SBAOHI) events that occurred between 2005 and 2014. To analyze it, we use total ozone column (TOC) data provided by a Brewer spectrophotometer (BS) and the OMI (Ozone Monitoring Instrument). In addition to the AURA/MLS (Microwave Limb Sounder) instrument, satellite ozone profiles were utilized with DYBAL (Dynamical Barrier Localization) code in the MIMOSA (Modélisation Isentrope du Transport Mésoéchelle de l’Ozone Stratosphérique par Advection) model Potential Vorticity (PV) fields. TOC has 7.0 ± 2.9 DU reductions average in 62 events. October has more events (30.7%). Polar tongue events are 19.3% in total, being more frequently observed in October (50% of cases), with medium intensity (58.2%), and in the stratosphere medium levels (55.0%). Already, polar filament events (80.7%) are more frequent in September (32.0%), with medium intensity (42.0%), and stratosphere medium levels (40.7%).