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  • This dataset consists of monthly spatial patterns of meteorological change for 22 Global Circulation Models (GCMs). The patterns are a set of regression coefficients, each representing the change per degree of mean global warming over land, for the corresponding meteorological variable. The meteorological variables analysed for each GCM include: surface temperature change per degree global warming (K/K); surface relative humidity change per degree global warming (%/K); wind change per degree global warming (m/s/K); longwave change per degree global warming (W/m2/K); shortwave change per degree global warming (W/m2/K); precipitation change per degree global warming (mm/day/K) and pressure change per degree global warming (hPa/K). The 22 GCMs emulated are: BCCR-BCM2.0, CGCM3.1(T47), CNRM-CM3, CSIRO-Mk3.0, CSIRO-Mk3.5, GFDL-CM2.0, GFDL-CM2.1, GISS-EH, GISS-ER, FGOALS-g1.0, INGV-SXG, INM-CM3.0, IPSL-CM4, MIROC3.2(hires), MIROC3.2(medres), ECHO-G, ECHAM5/MPI-OM, MRI-CGCM2.3.2, CCSM3, PCM, UKMO-HadCM3, UKMO-HadGEM1. The supporting information document associated with this metadata includes parameters for an energy balance model (IMOGEN EBM) that calculates the amount of global warming. Each GCM output has been re-mapped on to UKMO-HadCM3 grid, with resolution of 3.75 deg longitude and 2.5 deg latitude; this produces a surface spatial resolution of about 417 km E-W x 278 km N-S, reducing to 295 km E-W x 278 km N-S at 45 degrees North and South. This corresponds to 1631 land points, each of which has a row in the provided data files. The data presented here is calibration of IMOGEN EBM parameters and patterns against 22 GCMs in the CMIP3 GCM ensemble. Full details about this dataset can be found at https://doi.org/10.5285/4e8f3459-4101-4c85-a598-80b3c739f580

  • This dataset contains Dissolved oxygen concentrations from 2 different locations in Durleigh Reservoir in Somerset, England. Two miniDOT oxygen loggers were fitted with miniWIPER’s and deployed at 2 locations in Durleigh on 30 May 2018. Both sensors were collected on 5 October 2018 and the raw data files are presented in this dataset. Full details about this dataset can be found at https://doi.org/10.5285/26b35c45-c174-4930-b82c-bcd0d23c39e1

  • This dataset consists of monthly spatial patterns of meteorological change for 34 Global Circulation Models (GCMs). The patterns are a set of regression coefficients, each representing the change per degree of mean global warming over land, for the corresponding meteorological variable. The meteorological variables analysed for each GCM include: surface temperature change per degree global warming (K K-1); surface relative humidity change per degree global warming (percentage of K-1); wind change per degree global warming (m s-1 K-1); longwave change per degree global warming (W m-2 K 1); shortwave change per degree global warming (W m-2 K-1); precipitation change per degree global warming (mm day-1 K-1) and pressure change per degree global warming (hPa K-1). The supporting information document associated with this metadata includes parameters for an energy balance model (IMOGEN EBM) that calculates the amount of global warming. Each GCM output has been re-mapped on to UKMO-HadCM3 grid, with resolution of 3.75° longitude and 2.5° latitude; this produces a surface spatial resolution of about 417 km E-W x 278 km N-S, reducing to 295 km E-W x 278 km N-S at 45° North and South. This corresponds to 1631 land points, each of which has a row in the provided data files. The data presented here is calibration of IMOGEN EBM parameters and patterns against 34 GCMs in the Coupled Model Intercomparison Project phase 5 (CMIP5) GCM ensemble. Further information about the dataset and its derivation can be found in Comyn-Platt, E. et al. (in press). Carbon budgets for 1.5 and 2°C targets lowered by natural wetland and permafrost feedbacks. Nature Geoscience. http://doi.org/10.1038/s41561-018-0174-9 Full details about this dataset can be found at https://doi.org/10.5285/343885af-0f5e-4062-88e1-a9e612f77779

  • The dataset contains genotypes for samples used to validate a 50K single nucleotide polymorphism (SNP, DNA mutation) Axiom array for Scots pine (Pinus sylvestris) and closely related members of the Pinus mugo complex. Full details about this dataset can be found at https://doi.org/10.5285/7ee55609-d6b1-4693-8b36-2bf84fef76c2