High‐quality atmospheric CO₂ measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO₂. In this study, we present the first 6 years (2014–2019) of continuous, high‐precision measurements of atmospheric CO₂ at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO₂ regional signal () that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter‐annual scales, we find differences in phase between and the local eddy covariance net ecosystem exchange (EC‐NEE), which is interpreted as an indicator of a decoupling between local and non‐local drivers of . In addition, we present how the 2015–2016 El Niño‐induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter‐annual variability of together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO₂ exchange. We use both non‐optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO₂ from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data‐driven non‐optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.

Isoprene emissions are a key component in biosphere–atmosphere interactions, and the most significant global source is the Amazon rainforest. However, intra- and interannual variations in biological and environmental factors that regulate isoprene emission from Amazonia are not well understood and, thereby, are poorly represented in models. Here, with datasets covering several years of measurements at the Amazon Tall Tower Observatory (ATTO) in central Amazonia, Brazil, we (1) quantified canopy profiles of isoprene mixing ratios across seasons of normal and anomalous years and related them to the main drivers of isoprene emission – solar radiation, temperature, and leaf phenology; (2) evaluated the effect of leaf age on the magnitude of the isoprene emission factor (E_s) from different tree species and scaled up to canopy with intra- and interannual leaf age distribution derived by a phenocam; and (3) adapted the leaf age algorithm from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) with observed changes in E_s across leaf ages. Our results showed that the variability in isoprene mixing ratios was higher between seasons (max during the dry-to-wet transition seasons) than between years, with values from the extreme 2015 El Niño year not significantly higher than in normal years. In addition, model runs considering in situ observations of canopy E_s and the modification on the leaf age algorithm with leaf-level observations of E_s presented considerable improvements in the simulated isoprene flux. This shows that MEGAN estimates of isoprene emission can be improved when biological processes are mechanistically incorporated into the model.

The atmospheric boundary layer height ($zi$) is a key parameter in the vertical transport of mass, energy, moisture, and chemical species between the surface and the free atmosphere. There is a lack of long-term and continuous observations of $zi$, however, particularly for remote regions, such as the Amazon forest. Reanalysis products, such as ERA5, can fill this gap by providing temporally and spatially resolved information on $zi$. In this work, we evaluate the ERA5 estimates of $zi$ ($zi$-ERA5) for two locations in the Amazon and corrected them by means of ceilometer, radiosondes, and SODAR measurements ($zi$-experimental). The experimental data were obtained at the remote Amazon Tall Tower Observatory (ATTO) with its pristine tropical forest cover and the T3 site downwind of the city of Manaus with a mixture of forest ($63\%$), pasture ($17\%$), and rivers ($20\%$). We focus on the rather typical year 2014 and the El Niño year 2015. The comparison of the experimental vs. ERA5 $zi$ data yielded the following results: (i) $zi$-ERA5 underestimates $zi$-experimental daytime at the T3 site for both years 2014 (30%, underestimate) and 2015 (15%, underestimate); (ii) $zi$-ERA5 overestimates $zi$-experimental daytime at ATTO site (12%, overestimate); (iii) during nighttime, no significant correlation between the $zi$-experimental and $zi$-ERA5 was observed. Based on these findings, we propose a correction for the daytime $zi$-ERA5, for both sites and for both years, which yields a better agreement between experimental and ERA5 data. These results and corrections are relevant for studies at ATTO and the T3 site and can likely also be applied at further locations in the Amazon.

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO₂) exchange over Europe at high resolution (0.1 × 0.2^∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2 ^∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2 ^∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO₂ fluxes are included in our product, based on Jena CarboScope ocean CO₂ fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO₂ mole fractions over Europe. We assess the skill of the CTE-HR CO₂ fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO₂ mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO₂ fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO₂, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO₂ (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO₂ observations as in CTE. We furthermore compare CO₂ concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO₂ concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO₂ mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO₂ modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO₂ data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022 a).

Methane (CH4) atmospheric mixing ratio measurements are analyzed for the period between June 2013 and November 2018 at the Amazon Tall Tower Observatory (ATTO). We describe the seasonal and diurnal patterns of nighttime events in which CH4 mixing ratios at the uppermost (79 m a.g.l.) inlet are significantly higher than the lowermost inlet (4 m a.g.l.) by 8 ppb or more. These nighttime events were found to be associated with a wind direction originating from the southeast and wind speeds between 2 and 5 m s−1. We found that these events happen under specific nighttime atmospheric conditions when compared to other nights, exhibiting less variable sensible heat flux, low net radiation and a strong thermal stratification above the canopy. Our analysis indicates that even at wind speeds of 5.8 m s−1 the turbulence intensity, given by the standard deviation of the vertical velocity, is suppressed to values lower than 0.3 m s−1. Given these findings, we suggest that these nighttime CH4 enhancements are advected from their source location by horizontal nonturbulent motions. The most likely source location is the Uatumã River, possibly influenced by dead stands of flooded forest trees that may be enhancing CH4 emissions from those areas. Finally, biomass burning and the Amazon River were discarded as potential CH4 sources.

The Vegetation Photosynthesis and Respiration Model (VPRM) is a well-established tool for estimating carbon exchange fluxes between the atmosphere and the biosphere. The gross primary production (GPP) and respiration (R_eco) of the ecosystem are modeled separately at high spatial and temporal resolution using the satellite-derived enhanced vegetation index (EVI) and land surface water index (LSWI), as well as meteorological variables for solar irradiance and surface temperature. The net ecosystem exchange (NEE) is calculated as the difference between the gross flux GPP and respiration. VPRM is widely used as a biospheric flux model in atmospheric transport modeling, most often on scales ranging from city to continent, but also in studies of biospheric carbon budgets and their changes with climate extremes. Historically, satellite-based surface reflectances from the 500 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) have been used to determine the EVI and LSWI. However, MODIS is reaching the end of its lifetime and will soon be decommissioned. Therefore, we present an updated version of VPRM, pyVPRM, which provides a software framework with a modular structure that can be used with various satellite products, land cover maps, meteorological data sources, and VPRM model parameterizations. Our tool naturally provides an interface to use satellite data from Sentinel-2, MODIS, and VIIRS, as well as global high-resolution land cover classification maps from the Copernicus Dynamic Land Cover Collection 3 and ESA WorldCover at 100 and 10 m resolution, respectively. Neither product is static; hence, dynamic changes of the land cover from year to year can be represented. Using Sentinel-2, ecosystem fluxes can be calculated at a resolution of up to 20 m, providing more accurate flux estimates in heterogeneous landscapes like croplands and making it possible to resolve small-scale vegetation patches common in urban areas. In contrast, VIIRS data are at the same resolution as MODIS and thus can provide continuity once MODIS is discontinued, requiring only minor adjustments to the VPRM data preprocessing. In addition, pyVPRM improves the data handling, for example, for snow-covered scenes. This paper presents the pyVPRM framework, discusses changes and improvements compared to previous VPRM implementations, and provides VPRM parameters for the European domain based on indices calculated from MODIS, Sentinel-2, and VIIRS using a new selection of eddy-covariance observations from 97 flux tower sites. Using pyVPRM and the new parameters, we observe significant improvements in the estimation of the European carbon budget. The results conform well with those from inversion studies.