A new monthly long term average (climatology) of Leaf Area Index (LAI) has been developed for use as ancillary data with the Joint UK Land Environment Simulator (JULES) Land Surface Model and the UK Met Office Unified Model. It is derived from an improved version of long time series of LAI from the original Global LAnd Surface Satellite (GLASS) products (http://www.glass.umd.edu/LAI/MODIS/0.05D/). The GLASS data consists of a time series of LAI from Moderate Resolution Imaging Spectroradiometer (MODIS) surface-reflectance data for the period 2000-2014. The MODIS data was provided in a spatial resolution of 1km in a sinusoidal projection and is interpolated into 0.5deg on a geographic latitude/longitude projection in this dataset. The total LAI from MODIS is segregated into five different Plant Functional Types (PFTs) using the fractional coverage of each PFT from the Climate Change Initiative (CCI) Land Cover data. For this reason this new LAI climatology should be used in combination with the CCI PFT data, which is also provided here. Two variables are provided with the dataset containing LAI, each covering the same spatial and time extent. The PFT data provided with this dataset covers a time span of only one year, 2010. - Leaf Area Index (LAI) - LAI is an important parameter in land-surface models, influencing the surface roughness, transpiration rate and the soil water content and temperature. Numerous outputs of vegetation models such as net primary productivity (NPP), evapotranspiration (ET), light absorption by plants (FAPAR), nutrient dynamics etc., are influenced by LAI where it is a key variable in energy and water balance calculations. - Vegetation Canopy Height (H) - H plays an important role in the interface between the atmosphere and land surface and it impacts weather and climate at local to global scales by modulating aerodynamic conductance and vegetation dynamics. Therefore, H is fundamentally needed for the calculation of turbulent exchanges of energy and mass between the atmosphere and the terrestrial ecosystem. One variable is provided with the dataset containing CCI PFTs: - Fractional coverage of 5 PFTS or vegetation classes and 4 land use classes – The 5 PFTs are Broad Leaf, Needle Leaf, C3 Grass, C4 Grass and Shrub. The 4 land use classes are Urban area, Inland Water, Bare Soil and Snow/Ice. Full details about this dataset can be found at https://doi.org/10.5285/6d07d60a-4cb9-44e4-be39-89ea40365236

These data contain 408 instances of annual model output from JULES/IMOGEN simulations, covering the period between 1850-2100. Each simulation (which corresponds to one netcdf file) provides annual average of carbon stocks of the land, atmosphere and ocean store required to calculate the anthropogenic fossil fuel emissions as the residual of the yearly changes. Also included are the global warming variables, fractional land-cover, natural wetland extent and methane (CH4) flux and the soil temperature and moisture content for additional analysis. The spatial coverage is global with spatial resolution of the data is 2.5 degrees latitude, 3.75 degrees longitude. This dataset is the model output that was used in Comyn-Platt et al (2018) [ Comyn-Platt, E. et al. (2018). Carbon budgets for 1.5 and 2C targets lowered by natural wetland and permafrost feedbacks. Nature Geoscience. https://doi.org/10.1038/s41561-018-0174-9] Full details about this dataset can be found at https://doi.org/10.5285/1cebd79c-02e7-475a-a1da-1f26a963d41e

This dataset contains measurements of plant biomass and leaf-level functional traits from sugarcane plants of four different genotypes that were grown under different ozone (O3) conditions in Open Top Chambers for approximately 90 days. It also contains the calculated phytotoxic ozone dose for each of the four genotypes, the O3 concentration measurements and the environmental conditions (air temperature, relative humidity, and photosynthetically active radiation). The four genotypes tested were: Saccharum officinarum L. cv. Badila, Saccharum spontaneum cv. Mandalay, Q240, and CTC4. Full details about this dataset can be found at https://doi.org/10.5285/68adb7d4-6138-4d70-b469-2471349b331a

This dataset contains gridded model outputs of the predicted risk to C4 sugarcane production across south central Brazil for 2010-2014. The outputs are given as production in kg m-2 yr-1, percentage of control production (%) and production losses in kg yr-1 and Tg yr-1. The spatial resolution is 1.25 x 1.875 degrees. Three different levels of ozone susceptibility (low, moderate or high) and two distinct threshold values of phytotoxic ozone dose (0 and 2 nmol m-2 s-1) were considered. Full details about this dataset can be found at https://doi.org/10.5285/1513d8ed-67a9-40fc-a8e5-bd7864d0d422

[THIS DATASET HAS BEEN WITHDRAWN]. This dataset contains daily and sub-daily hydrometeorological and soil moisture observations from COSMOS-UK (cosmic-ray soil moisture) monitoring network from October 2013 to the end of 2022. These data are from 51 sites across the UK recording a range of hydrometeorological and soil variables. Each site in the network records the following hydrometeorological and soil data at 30-minute resolution: Radiation (short wave, long wave, and net), precipitation, atmospheric pressure, air temperature, wind speed and direction, humidity, soil heat flux, and soil temperature and volumetric water content (VWC), measured by point sensors at various depths. Each site hosts a cosmic-ray sensing probe; a novel sensor technology which counts fast neutrons in the surrounding atmosphere. In combination with the recorded hydrometeorological data, neutron counts are used to derive VWC over a field scale (COSMOS VWC), at two temporal resolutions (hourly and daily). The presence of snow leads to erroneously high measurements of COSMOS VWC due to all the extra water in the surrounding area. Included in the daily data are indications of snow days, on which, the COSMOS VWC are adjusted, and the snow water equivalent (SWE) is given. The potential evapotranspiration (PE), derived from recorded hydrometeorological and soil are also included at daily resolution. Two levels of quality control are carried out, firstly data is run through a series of automated checks, such as range tests and spike tests, and then all data is manually inspected each week where any other faults are picked up, including sensor faults or connection issues. Quality control flags are provided for all recorded (30 minute) data, indicating the reason for any missing data. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/5060cc27-0b5b-471b-86eb-71f96da0c80f

This dataset contains daily and sub-daily hydrometeorological and soil moisture observations from COSMOS-UK (cosmic-ray soil moisture) monitoring network from October 2013 to the end of 2023. These data are from 51 sites across the UK recording a range of hydrometeorological and soil variables. Each site in the network records the following hydrometeorological and soil data at 30-minute resolution: Radiation (short wave, long wave, and net), precipitation, atmospheric pressure, air temperature, wind speed and direction, humidity, soil heat flux, and soil temperature and volumetric water content (VWC), measured by point sensors at various depths. Each site hosts a cosmic-ray sensing probe; a novel sensor technology which counts fast neutrons in the surrounding atmosphere. In combination with the recorded hydrometeorological data, neutron counts are used to derive VWC over a field scale (COSMOS VWC), provide at daily resolution. The presence of snow leads to erroneously high measurements of COSMOS VWC due to all the extra water in the surrounding area. Included in the daily data are indications of snow days, on which, the COSMOS VWC are adjusted, and the snow water equivalent (SWE) is given. The potential evapotranspiration (PE), derived from recorded hydrometeorological and soil are also included at daily resolution. Two levels of quality control are carried out, firstly data is run through a series of automated checks, such as range tests and spike tests, and then all data is manually inspected each week where any other faults are picked up, including sensor faults or connection issues. Quality control flags are provided for all recorded (30 minute) data, indicating the reason for any missing data. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/399ed9b1-bf59-4d85-9832-ee4d29f49bfb