[THIS DATASET HAS BEEN WITHDRAWN]. Standardised Precipitation Index (SPI) data for Integrated Hydrological Units (IHU) groups (Kral et al. [1]). SPI is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. [2]. SPI is calculated for different accumulation periods: 1, 3, 6, 12, 18, 24 months. Each of these is in turn calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. NOTE: the difference between this dataset with the previously published dataset 'Standardised Precipitation Index time series for IHU Groups (1961-2012)' [SPI_IHU_groups] (Tanguy et al., 2015 [3]), apart from the temporal extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Keller et al., 2015 [4], Tanguy et al., 2014 [5]) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). Within Historic Droughts project (grant number: NE/L01016X/1), the Met Office has digitised historic rainfall and temperature data to produce high quality historic rainfall and temperature grids, which motivated the change in the underlying data to calculate SPI. The methodology to calculate SPI is the same in the two datasets. [1] Kral, F., Fry, M., Dixon, H. (2015). Integrated Hydrological Units of the United Kingdom: Groups. NERC-Environmental Information Data Centre doi:10.5285/f1cd5e33-2633-4304-bbc2-b8d34711d902 [2] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. [3] Tanguy, M.; Kral., F.; Fry, M.; Svensson, C.; Hannaford, J. (2015). Standardised Precipitation Index time series for Integrated Hydrological Units Groups (1961-2012). NERC Environmental Information Data Centre. https://doi.org/10.5285/dfd59438-2170-4472-b810-bab33a83d09f [4] Keller, V. D. J., Tanguy, M., Prosdocimi, I., Terry, J. A., Hitt, O., Cole, S. J., Fry, M., Morris, D. G., and Dixon, H.: CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological use, Earth Syst. Sci. Data Discuss., 8, 83-112, doi:10.5194/essdd-8-83-2015, 2015. [5] Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D. G.; Keller, V. D. J. (2014). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2012) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e Full details about this dataset can be found at https://doi.org/10.5285/047d914f-2a65-4e9c-b191-09abf57423db

This datasets contains 323 observations of borehole breakouts across and drilling induced tensile fractures from borehole imaging used to re-characterise the UK stress field orientation in 2016. This was published in the Journal of Marine and Petroleum Geology and is openly available using doi:10.1016/j.marpetgeo.2016.02.012 The observations relate to 39 wells from Central and Northern England and are provided with links to screen grabs of the images for clarity. The basic well meta data is supplied along with a description of the dataset. The Images were generated in the IMAGE DISPLAY module of the Landmark RECALL software. and are supplied on an “as shown” basis. Descriptions of the tools and the techniques used are listed in the accompanying paper: KINGDON, A., FELLGETT, M. W. & WILLIAMS, J. D. O. 2016. Use of borehole imaging to improve understanding of the in-situ stress orientation of Central and Northern England and its implications for unconventional hydrocarbon resources. Marine and Petroleum Geology, 73, 1-20.

Data produced during three BGS ground gas surveys (August 2018, and May and October 2019) of up to 83 point measurements across four pre-determined locations within the UK Geoenergy Observatories (UKGEOS) Glasgow site, located to the south of the Cuningar Loop Woodland Park. The dataset includes measurements of CH4 and CO2 flux between the ground surface and lower atmosphere, along with concentrations of CO2, O2, H2, H2S and ‘residual balance’ in near surface ground gas measured at c.70 cm below ground level. Further details about the dataset can be found in the accompanying report. http://nora.nerc.ac.uk/id/eprint/528737/

[THIS DATASET HAS BEEN WITHDRAWN]. This dataset includes litterfall data from the experimental plots at the Climoor field site in the Clocaenog forest, NE Wales. Litterfall (natural senesced plant material) was collected in litterfall traps (12 x 7.5cm pots standing slightly proud of the soil/litter surface, emptied monthly). Litterfall was calculated by drying the contents of the traps and weighing the samples; values were calculated for each quadrat (total weight (g) only) and for each plot (total weight (g) and weight per metre squared (g/m2)). Data spans the periods Oct 1999 to Jan 2004 and July 2008 to June 2011. Full details about this dataset can be found at https://doi.org/10.5285/dd4dfc72-dafe-44a2-af2b-0118d949d7ad

[THIS APPLICATION HAS BEEN WITHDRAWN]. MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from specified environmental data. It also allows plotting of relationships between species' occurrence and individual covariates so the user can see what effect each environmental variable has on the specific species in question. The package is built under R 2.10.1 and depends on R packages 'leaps', 'earth', 'fields' and 'mgcv'. Full details about this application can be found at https://doi.org/10.5285/c4d0393e-ff0a-47da-84e0-09ca9182e6cb

Cloud properties derived from the merged series of AVHRR instruments on the NOAA-15 to NOAA-18 satellites by the ESA Cloud CCI project. The L3S dataset consists of data combined (averaged) from into a global space-time grid, with a spatial resolution of 0.5 degrees lat/lon and a temporal resolution of 1 month. This dataset is version 1.0 data from Phase 1 of the CCI project.

Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1. This dataset contains measurements of atmospheric electricity and electric potential gradient made using a Cambell Scientific CS110 electric-field mill at Bristol.

This dataset contains along-track sea level anomalies derived from satellite altimetry. Altimeter along-track sea level measurements from the RA2 instrument on ENVISAT and the Altika instrument on SARAL satellite missions have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia). The product benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections. The main objective of this product is to provide accurate altimeter Sea Level Anomalies (SLA) time series as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar to the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies. The Envisat and SARAL/AltiKa missions have the same ground track but the temporal gap between both missions prevents from computing reliable trends during the total period between both missions. This dataset has been produced by the Climate Change Initiative Coastal Sea Level team, within the extension phase of the European Sapce Agency (ESA) Climate Change Initiative.

The future 25km regional pan-Africa (P25: Present) data were produced using the Met Office's Unified Model, the IMPALA (Improving Model Processes for African cLimAte) project ran a ten year timeslice simulation that is representative of end the 21st century (2095-2105) using a 30-year averaged sea surface temperature (SST) anomaly (2085-2115 relative to 1975-2005). Parameters include (but not limited to); near-surface air temperature, outgoing longwave radiation, surface latent heat flux and surface sensible heat flux. The NERC funded IMPALA project is within the Future Climate for Africa (FCFA) programme. Here a subset of variables of the data produced is provided in NetCDF format for community reuse, variables are available at a range of temporal frequencies.

These data were produced to contribute towards the EU ENSEMBLES project. The agzkb model run was a historic anthropogenic experiment. The boundary conditions of this experiment were as follows: Time-varying from 1859 to 2000, well-mixed greenhouse gases (GHGs), ozone emissions datasets, sulphate and carbonaceous aerosols, land surface/vegetation, constant volcanic stratospheric aerosols & Solar irradiance. These data are provided in the Met Office PP format, but tools are available to extract subsets in NetCDF and other formats.