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 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).

This data set consist of a single file which contains a set of optimised global surface fluxes of methane (CH4), produced through variational inverse methods using the TOMCAT chemical transport model, and the INVICAT inverse transport model. These surface fluxes are produced as monthly mean values on the (approximately) 5.6-degree horizontal model grid. The associated uncertainty for the flux from each grid cell is also included. The fluxes and uncertainties are global and cover the period Jan 2010 - Dec 2018. The emissions from fossil fuels are labelled FF_FLUX, whilst the uncertainties are labelled FF_ERROR. The emissions from natural, agricultural and biomass burning sources are labelled NAT_FLUX, whilst the uncertainties are labelled NAT_ERROR. These two sectors (fossil fuel and non-fossil fuel) are solved for separately in the inversion. Flux and uncertainty units are kg(CH4)/m2/s, and time units are days since January 1st 2010. These emissions show improved performance relative to independent observations when included in the TOMCAT model. Further details about the data can be found in Wilson et al. (2020) in the documentation section.