This data set contains UK-wide maps of ten different among-model ensemble approaches for two services: above ground Carbon stock and water supply. The data for Carbon comes as fourteen TIF maps for above ground carbon storage at a 1-km2 resolution with associated world files: ten approaches, with a double option for two of those, together with maps of variation among models and among ensembles. For water, the data comes as one shapefile with polygons per watershed, each polygon containing these fourteen estimates. For all maps, 600dpi jpg depictions are added to the supporting information. Directory location independent layer files are included to aid scaling and providing the colour palettes. Ensemble output maps were calculated with different approaches following the supporting documentation and associated publication. Uncertainty estimates for these services are included as variation among contributing model outputs and among the employed ensemble approaches. The work was completed under the ‘EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models’ project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme. Full details about this dataset can be found at https://doi.org/10.5285/a9ae773d-b742-4d42-ae42-2b594bae5d38

This dataset contains the gridded estimates per 1 km2 for mean and median ensemble outputs from 4-6 individual ecosystem service models for Sub-Saharan Africa, for above ground Carbon stock, firewood use, charcoal use and grazing use. Water use and supply are identically supplied as polygons. Individual model outputs are taken from previously published research. Making ensembles results in a smoothing effect whereby the individual model uncertainties are cancelled out and a signal of interest is more likely to emerge. Included ecosystem service models were: InVEST, Co$ting Nature, WaterWorld, Monetary value benefits transfer, LPJ-GUESS and Scholes models. Ensemble outputs have been normalised, therefore these ensembles project relative levels of service across the full area and can be used, for example, for optimisation or assignment of most important or sensitive areas. The work was completed under the ‘EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models’ project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme. Full details about this dataset can be found at https://doi.org/10.5285/11689000-f791-4fdb-8e12-08a7d87ad75f