While the Amazon rainforest area has a known effect on precipitation and global water vapour circulation, it is still poorly understood. This is in part due to the lack and inconsistency in atmospheric observations in the area. This dataset holds the high resolution (0.5 x 0.5 deg; 8 vertical levels) monthly means of 5 atmospheric variables (air temperature, pressure, water vapour pressure, vertical velocity and horizontal wind speed) over the Amazon Basin for the period 1972 to 2009. This data is public and in particular, version 1.0 is citable (DOI: 10.5285/2dfce039-cd71-43b3-bed4-98978e78f1bb).

This dataset holds the high resolution (0.5 x 0.5 deg; 8 vertical levels) monthly means of 5 atmospheric variables (air temperature, pressure, water vapour pressure, vertical velocity and horizontal wind speed) over the Amazon Basin for the period 1972 to 2009 (version 1.0). This data is public and citable (DOI: 10.5285/2dfce039-cd71-43b3-bed4-98978e78f1bb). It was constructed using the predictive capabilities of Time-Delayed Neural Networks (TDNN) method. Thirty years of monthly averages of current climate data (1971-2000) of the NCEP/NCAR reanalysis dataset were used to train the TDNNs, which were then validated on the next 10 years (2001-2010). Once validated, the downscaling model was fed with the higher resolution CRU TS3.1 data and SRTM-1km elevation data (thereby obtaining the higher resolution dataset).

This dataset contains greenhouse gas profile measurements from the Amazon Integrated Carbon Analysis (AMAZONICA) project. AMAZONICA was an UK-Brasil Consortium funded by NERC (Natural Environmental Reasearch Council, UK) which aimed to quantify the carbon balance of the Amazon Basin and its associated contribution to global atmospheric change, to apportion and understand the processes contributing to the net Basin-wide flux observed and, to allow improved assessments of the likely role of the Amazon Basin in contributing and/or alleviating future planetary change. Data were collected and collated by the AMAZONICA team in the UK and Brazil and were deposited at BADC before the end of the project (expected end 2012 - mid 2013).

The dataset contains information of Diameter at Breast Height (DBH) of 8,729 trees. These trees are distributed in 29 RAINFOR network forest plots across the Brazilian Amazon, comprising the states of Acre, Mato Grosso and Pará. All the plot censuses are located in terra-firme non-flooded lowland forests. The measurements were collected between 2017 and 2019. The Amazon Forest Inventory Network is a long-term, international collaboration to understand the dynamics of Amazon ecosystems. Since 2000 they have developed a framework for systematic monitoring of forests from the ground-up, centred on plots that track the fate of trees and species, and includes soil and plant biogeochemical records, as well as intensive monitoring of carbon cycle processes at some sites. RAINFOR works with partners across the nations of Amazonia to support and sustain forest monitoring and help develop new generations of Amazon ecologists. The work of RAINFOR is currently supported by funding agencies in Brazil, the UK, and the EU. Full details about this dataset can be found at https://doi.org/10.5285/63d4b774-4e03-4db2-95ad-dcca18f0d681

Data are presented showing for individual seedling, herbivory damage at the leaf level; galls, pathogens, trail herbivory presence/absence qualitative data; and leaf mortality. Data were collected in each leaf from a plot based fertilisation experiment. The experiment was carried out at the Biological Dynamics of Forest Fragments Project (BDFFP) approximately 100 km north of Manaus. Data were collected bimonthly from February 2019 to January 2020, by the dataset first author. Leaf loss in percentage was made using the choice for direct visual estimate. We also followed the recommendations proposed by the authors, sectoring the leaves with a millimetre grid, improving measurement accuracy. The presence of Galls, pathogens and trail herbivory presence/absence qualitative data were also collected in each leaf. The work was carried out as part of the Amazon Fertilization Experiment (AFEX), funded by the Natural Environment Research Council (NERC), Award reference NE/L007223/1, the Brazilian government (Researcher scholarship) and the Biological Dynamics of Forest Fragments Project (BDFFP - logistical support and camps maintanance). Full details about this dataset can be found at https://doi.org/10.5285/2b8029ff-ddf5-47b2-9231-5fa0cbb6cd41

This dataset contains radiocarbon dating of pieces of macrocharcoal (~ ≥ 1 mm) collected from soil in Guyana, Peru and Brazil in plots located in the Amazon forest. All the sites are terra-firme, non-seasonally flooded and are part of the RAINFOR network. In total, 60 pieces of macrocharcoal were dated. The Amazon Forest Inventory Network is a long-term, international collaboration to understand the dynamics of Amazon ecosystems. Since 2000 they have developed a framework for systematic monitoring of forests from the ground-up, centred on plots that track the fate of trees and species, and includes soil and plant biogeochemical records, as well as intensive monitoring of carbon cycle processes at some sites. RAINFOR works with partners across the nations of Amazonia to support and sustain forest monitoring and help develop new generations of Amazon ecologists. The work of RAINFOR is currently supported by funding agencies in Brazil, the UK, and the EU. Full details about this dataset can be found at https://doi.org/10.5285/b06a08bc-39e5-4401-87dd-9568fd5048fd

[This dataset is embargoed until May 1, 2025]. This dataset contains measurements of soil pyrogenic carbon, ratio of %PyC to %Bulk Carbon and organic carbon, which were collected in a soil fertility gradient in the Amazon Basin. All samples were taken in old-growth forests. In total, 49 forest plots were sampled and analysed for PyC soil concentration, representing 395 soil samples. Full details about this dataset can be found at https://doi.org/10.5285/6410a578-d21a-4285-8e9c-57efbe2b60d5

Data are presented showing seedling height, diameter at ground height (DGH), total number of leaves, number of leaves with herbivory damage and leaf mortality, from a plot based fertilisation experiment. The experiment was carried out at the Biological Dynamics of Forest Fragments Project (BDFFP) approximately 100 km north of Manaus. Data were collected bimonthlyfrom February 2019 to January 2020, by the dataset first author. Height measurements were made with a tape measure and DRH measurements were made with digital calipers. Leaf numbers, damage and mortality were made from visual observations. The data were collected to investigate the possible effects of different fertiliser applications on seedling height, totalnumber of leaves, number of leaves with herbivory damage and leaf mortality.The work was carried out as part of the Amazon Fertilization Experiment (AFEX), funded by the Natural Environment Research Council (NERC), Award reference NE/L007223/1, by the Brazilian government (Researcher scholarship) and the Biological Dynamics of Forest Fragments Project (BDFFP - logistical support and camps maintanance). Full details about this dataset can be found at https://doi.org/10.5285/2da56eb1-ff01-48de-ba2a-d3afceefc85f

Spatial data files holding gridded parameter maps of surface soil hydraulic parameters derived from a selection of pedotransfer functions. Modern land surface model simulations capture soil profile water movement through the use of soil hydraulics sub-models, but good hydraulic parameterisations are often lacking - especially in the tropics - and it is this lack that we fill here in the context of South America. Optimal hydraulic parameter values are given for the Brooks and Corey, Campbell, van Genuchten-Mualem and van Genuchten-Burdine soil hydraulic models, which are widely-used hydraulic sub-models in many land surface models (e.g. Joint UK Land Environment Simulator JULES). Full details about this dataset can be found at https://doi.org/10.5285/4078678b-768f-43ff-abba-b87712f648e9

The Quantifying the Amazon Isoprene Budget: Reconciling Top-down versus Bottom-up Emission Estimates project ran a unique high resolution model for the Amazon basin, able to simulate isoprene emissions and atmospheric chemistry. Model outputs are available through CEDA. This was a NERC funded project (NE/G013810/1).