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).

This dataset contains monthly global carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution. A spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020 and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data, as in Sathyendranath et al. (2020). Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v5.0 dataset). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. Mixed Layer Depth (MLD) was obtained from the French Research Institute for Exploration of the Sea (Ifremer). In situ datasets P-I parameters were incorporated as described in Kulk et al. (2020). The phytoplankton carbon products were generated as part of the ESA Biological Pump and Carbon Exchange Processes (BICEP) project. Support from the Simons Foundation grant ‘Computational Biogeochemical Modeling of Marine Ecosystems’ (CBIOMES, number 549947) and from the National Centre of Earth Observation (NCEO) is acknowledged. Data are provided as netCDF files containing carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution. Additional variables that were used for the calculation of the phytoplankton carbon products are also provided, including chlorophyll-a (chl_a in mg m-3), photosynthetically activate radiation (par, in µmol photons m-2 d-1), mixed layer depth (mld in m) and the mean spectral nondimensional irradiance (mean_spectral_i_star). References: Sathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252 Kulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovač, Ž.; Lutz, V.A.; Marañón, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12,826. doi:10.3390/rs12050826

Profiles of greenhouse gases CO, CO2 and CH4 taken on board a small aircraft descending in a spiral from approximately 4,420m to about 300m a.s.l. (as close to the forest canopy as possible). Samples were taken by semi-automatically filling 12 (for the Tabatinga (TAB 69.7W, 6.0S), Alta Floresta (ALF 56.7W, 8.9S) and Rio Branco (RBA 67.9W, 9.3S) sites) and 17 (for the Santarem (SAN 65.0W, 2.9S) site) 0.7-litre flasks controlled from a microprocessor and contained in one suitcase. The profiles were taken frequently throughout the measurement campaign (2010-2012) between 12:00 and 13:00 local time - at which time, the boundary layer is close to being fully developed. Once a vertical profile had been sampled (one suitcase filled) it was analysed at the IPEN Atmospheric Chemistry Laboratory in Sao Paulo, using a replica of the NOAA/ ESRL trace gas analysis system.

Chemistry of the Antarctic Boundary Layer and the Interface with Snow (CHABLIS) is a Natural Environment Research Council (NERC) and Antarctic Funding Initiative (AFI) funded project, aimed at studying the chemistry of the Antarctic Boundary Layer in greater detail, and for a longer duration, than has previously been attempted. Field measurements were carried out at the British Antarctic Survey station, Halley, at the Clean Air Sector Laboratory (CASLab). Year-round measurements began in February 2004, and a summer campaign focussing on oxidants ran during January/February 2005, after which CHABLIS fieldwork ended. The dataset includes Aethalometer black carbon data at Halley Station. Access to this dataset is now public.

Data comprise measurements of plant biomass and community composition, soil microbial community composition, greenhouse gas emissions and soil carbon and nitrogen pools from a drought experiment superimposed on a the long-term Colt Park grassland restoration experiment in northern England. Rainfall was manipulated using rain-out shelters on experimental grassland plots where fertiliser application and seed addition have been managed to enhance plant species diversity. The scientific purpose was to test the hypothesis that management aimed at biodiversity restoration increases the resistance and recovery of carbon cycling to short-term summer drought. Full details about this dataset can be found at https://doi.org/10.5285/8a41b2a2-01d7-409e-adf5-fba3f3770f29

This web map service presents modelled estimates of soil pH, carbon concentration (g kg-1), nitrogen concentration (% dry weight soil) and invertebrate density (individuals m-2) at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The models are based on data from Countryside Survey sample locations across Great Britain and are representative of 0-8cm soil depth for invertebrates and 0-15 cm soil depth for other variables. The Countryside Survey looks at a range of physical, chemical and biological properties of the topsoil from a representative sample of habitats across the UK. Loss-on-ignition (LOI) was determined by combustion of 10g dry soil at 375 degrees Celsius for 16 hours; carbon concentration was estimated by multiplying LOI by a factor of 0.55. Soil N concentration was determined using a total elemental analyser. Soil pH was measured using 10g of field moist soil with 25ml de-ionised water giving a ratio of soil to water of 1:2.5 by weight. Soil invertebrates were extracted from cores using a dry Tullgren extraction method and enumerated by microscope

This dataset contains measured daily values of precipitation, air and soil temperature, soil water content, measured net ecosystem exchange (NEE) fluxes using eddy covariance, calculated gross primary production (GPP), terrestrial ecosystem respiration (TER) and net biome production (NBP) fluxes using an online tool (http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/upload.php), measured fluxes of methane and nitrous oxide using static chambers and measured fluxes of nitrous oxide using eddy covariance, measured fluxes of nitrogen oxides (NOx) using automatic chambers, measured nitrogen and carbon leaching, livestock density, nitrogen (N) and carbon (C) input from mineral and organic fertiliser and yield of a managed grassland (Easterbush, 03°02'W, 55°52' N, 190 m a.s.l ) in South East Scotland. Data were collected between January 2002 and December 2010. Furthermore the dataset contains one off soil carbon and nitrogen data collected in 2004 and 2010. The dataset also contains monthly dry N deposition data from a field nearby Easterbush (about 300 m distance) measured with a DELTA system from 2002-2010. The data were collected as part of the three European projects GREENGRASS (EC EVK2-CT2001-00105), the NitroEurope Integrated Project (contract 017841) and CarboEurope (Contract No. GOCE-CT-2003-505572). Full details about this dataset can be found at https://doi.org/10.5285/7e6e6955-a9d7-4f8a-961e-3fa3d56d0ead

Ecosystem functions (including aboveground carbon, topsoil carbon, topsoil nitrogen, decomposition rates, soil invertebrate feeding, tree regeneration & vegetation structure) in woodland creation sites and unforested and mature forest areas in the Scottish Highlands. Full details about this dataset can be found at https://doi.org/10.5285/b76223dd-28ae-4b0e-acd3-3b2104eaae0c

Location of peat cores and peat properties including moisture, bulk density, ash and organic matter content for short cores (50 cm) collected 10 month post-fire in high, medium and low severity areas within a drained and a near natural area in the footprint of a severe wildfire that impacted >6500 ha of blanket bog and wet heath in the Flow Country of Northern Scotland. Full details about this dataset can be found at https://doi.org/10.5285/31d3b90b-ca4d-41db-bf29-c9f7a426a0cc

The data represent a quantitative measure of aboveground (vegetation) biomass, organic carbon content and aboveground (vegetation) carbon from 144 vegetation samples collected across ten UK saltmarshes between 2019 and 2020. Sites were chosen to represent contrasting habitat types in the United Kingdom, in particular sediment types, vegetation, and sea level history. Full details about this dataset can be found at https://doi.org/10.5285/f71c9f3e-0ae1-4318-a3ea-1dd30b7af3be