This work is focused on results from a recent controlled sub-seabed in situ carbon dioxide (CO2) release experiment carried out during May–October 2012 in Ardmucknish Bay on the Scottish west coast. Three types of pCO2 sensors (fluorescence, NDIR and ISFET-based technologies) were used in combination with multiparameter instruments measuring oxygen, temperature, salinity and currents in the water column at the epicentre of release and further away. It was shown that distribution of seafloor CO2 emissions features high spatial and temporal heterogeneity. The highest pCO2 values (~1250 µatm) were detected at low tide around a bubble stream and within centimetres distance from the seafloor. Further up in the water column, 30-100 cm above the seabed, the gradients decreased, but continued to indicate elevated pCO2 at the epicentre of release throughout the injection campaign with the peak values between 400 and 740 µatm. High-frequency parallel measurements from two instruments placed within 1 m from each other, relocation of one of the instruments at the release site and 2D horizontal mapping of the release and control sites confirmed a localized impact from CO2 emissions. Observed effects on the water column were temporary and post-injection recovery took <7 days. A multivariate statistical approach was used to recognize the periods when the system was dominated by natural forcing with strong correlation between variation in pCO2 and O2, and when it was influenced by purposefully released CO2. Use of a hydrodynamic circulation model, calibrated with in situ data, was crucial to establishing background conditions in this complex and dynamic shallow water system. This is a publication in QICS Special Issue - International Journal of Greenhouse Gas Control, Dariia Atamanchuk et. al. Doi:10.1016/j.ijggc.2014.10.021.

Modelled annual average production loss (thousand tonnes per 1 degree by 1 degree grid cell) due to ground-level ozone pollution is presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum), for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for production loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop production losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is a step towards mitigation of the problem. The production loss calculations were done as part of the NERC funded SUNRISE project (NEC06476) and National Capability Project NC-Air quality impacts on food security, ecosystems and health (NEC05574). Full details about this dataset can be found at https://doi.org/10.5285/0aa7911a-ab5f-4b08-a225-28b1e8344d01

Modelled average percentage yield loss due to ground-level ozone pollution (per 1 degree by 1 degree grid cell) are presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum) for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for yield loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop yield losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is one of the first steps towards mitigation of the problem. The yield loss calculations were done as part of the NERC funded SUNRISE project and National Capability Project NC-Air quality impacts on food security, ecosystems and health. Full details about this dataset can be found at https://doi.org/10.5285/2a932995-f040-4724-ad21-3e92ae8a2540

A Yield Constraint Score (YCS; scale of 1-5) was developed for the effect of five key crop stresses (ozone, pests and diseases, soil nutrients, heat stress and aridity) on the production of the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum). Data are on a global scale at 1 deg by 1deg resolution, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. To derive the YCS for each crop stress, spatial data on a global scale were gathered. Modelled ozone data (2010-2012) were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Pests and diseases data (2002-2004) were downloaded from a Centre for Agriculture and Biosciences International (CABI) database providing estimates for pre-harvest crop losses due to weeds, animal, pathogens and viruses, compiled from the literature. Soil nutrient classifications (for 2009, derived using soil attributes from the Harmonized World Soil Database (HWSD)) were downloaded from the GAEZ data portal. A heat stress index was calculated using daily temperature data (1990-2014) to determine whether the temperature within a 30-day thermal-sensitive period exceeded crop tolerance thresholds. Global Aridity Index data (1950-2000) were downloaded from the Consultative Group for International Agricultural Research’s Consortium for Spatial Information (CGIAR-CSI). The Yield Constraint Score provides an indication of where each stress is predicted to be affecting crop yield globally and the magnitude of the effect. The YCS data were developed as part of the NERC funded SUNRISE project and the National Capability Project NC-Air quality impacts on food security, ecosystems and health. Full details about this dataset can be found at https://doi.org/10.5285/d347ed22-2b57-4dce-88e3-31a4d00d4358

[THIS DATASET HAS BEEN WITHDRAWN]. Modelled average percentage yield loss due to ground-level ozone pollution (per 1 degree by 1 degree grid cell) are presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum) for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for yield loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop yield losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is one of the first steps towards mitigation of the problem. The yield loss calculations were done as part of the NERC funded SUNRISE project (NEC06476). Full details about this dataset can be found at https://doi.org/10.5285/181a7dd5-0fd4-482a-afce-0fa6875b5fb3

Net ecosystem exchange and methane fluxes were measured from a hemi-boreal ombrotrophic fen in Southern Sweden. An automated chamber system, SkyLine2D, was used to measure the fluxes near-continuously from August 2017 to September 2019. Four ecotypes were identified: sphagnum (Sphagnum spp), eriophorum, heather and water, to assess how these different ecotypes would respond to drought. The 2018 drought allowed comparison of fluxes between drought and non-drought years (May to September), and their recovery the following year. Full details about this dataset can be found at https://doi.org/10.5285/d7bfc4ed-8ead-4d06-8e45-b592c1f48f3f

This dataset includes key photosynthesis and respiration data collected from three common garden sites along an elevation/temperature gradient in the Colombian Andes. Raw A-Ci data, the Vcmax (carboxylation of RuBP by the enzyme Rubisco) and Jmax (the regeneration of RuBP by the electron transport chain) values estimated from this data, and Rdark (leaf dark respiration) values collected using spot measurements, are all available, along with variables such as leaf temperature (°C), relative humidity (%) and pressure values (kPa) returned by the LI-6800 portable photosynthesis system. Full details about this dataset can be found at https://doi.org/10.5285/60dd0b8f-f0c3-4e30-841b-1c2067052974