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Model inputs, model code and aggregated model outputs (seasonal, annual) for every grassland field simulated (2017-2018) in "Myrgiotis et al. The carbon budget of the managed grasslands of Great Britain - informed by earth observations, Biogeosciences, 2022". Input files include (1) climate data (sitecode_M.npy) and (2) satellite based observations of Leaf Area Index (sitecode_O.pkl). Model code written in fortran. Model outputs (i.e. carbon pools, fluxes and balance for 2017 and 2018) aggregated annually (annual_outputs.csv) and monthly (annual_outputs.csv).

The dataset comprises full list of species of macrozoobenthos collected from the Pechora Sea (SE Barents Sea). Grab samples were collected from 10 stations in the Pechora Bay from aboard RV Kartesh in 2020-2021. Macrobenthic invertebrates were identified with the maximum level of certainty through optical microscopy using regional taxonomic keys. All taxonomic names were standardised using the World Register of Marine Species (WoRMS). All specimens have been counted and weighted (wet biomass) on Ohaus Adventurer scales with reported accuracy to 0.01 g. Bivalve molluscs and gastropods were weighed in shells. Biomass (g. m-2) and abundance (ind m-2) are used to characterise macrozoobenthos. The sampling and identification work was carried out in collaboration with specialists from Lomonosov Moscow State University Marine Research Center and P.P. Shirshov Institute of Oceanology.

Examples of bicontinuous interfacially jammed emulsion gels ("bijels") are presented with a designed gradient in the channel size along the sample. These samples are created by quenching binary fluids which have a gradient in particle concentration along the sample, since the channel size is determined by the local particle concentration. A gradient in local particle concentration is achieved using a two-stage loading process, with different particle volume fractions in each stage. Confocal microscopy and image analysis were used to quantitatively measure the channel size of the bijels. Bijels with a gradient in channel size of up to 2.8%/mm have been created. Such tailored soft materials could act as templates for energy materials optimised for both high ionic transport rates (high power) and high interfacial area (high energy density), potentially making them useful in novel energy applications. See README.txt file in dataset for further information. Also see arXiv pre-print for further information.

This dataset contains measures of fitness traits from Eschscholzia californica progeny which were experimentally supplemented with selfed or outcrossed pollen to determine the effects of self-fertilisation on a plant which has a low propensity to self. A glasshouse experiment was conducted using 40 plants. On each plant two flowers were emasculated and the first supplemented with outcrossed pollen and the second with self-pollen. From each supplemented plant, a seed was sowed from the outcrossed fruit and from the selfed fruit. The following fitness traits were recorded; the germination rate, the duration from germination to reproductive maturity (time of first flower), together with the height (cm) and biomass (number of flowers and buds) at reproductive maturity. The dataset was part of a larger experiment looking at the effect of floral resources on the pollination services to isolated plants. We performed a glasshouse experiment using 40 artificially crossed plants. On each plant, we emasculated two flowers and supplemented the first with outcrossed pollen and the second with self-pollen. This involved methodically wiping two dehiscing anthers from a donor plant or the focal plant onto the receptive stigma with dissecting tweezers, before covering it in fine muslin. From each supplemented plant, we sowed a seed from the outcrossed fruit and from the selfed fruit (given that selfed fruits predominantly only produced one seed) into 1L pots. These were then stored under glasshouse conditions before the fitness traits were measured.

CPM_data -- seasonal maximum rainfall, seasonal mean and time of max for CPM simulations within about 200 km of Edinburgh. Sub directories contain data for each ensemble and for each time period. gev_fits_roll -- GEV fits to the CPM data images -- various images used nimrod_data -- samples of Nimrod radar data for July 4th 2021 (1km data) and summer 2021 & 2020 (5km data) radar_precip -- processed Nimrod data -- monthly max, mean and time of max for 1km 15 minute data (and some other data) ratios_roll -- computed distributions, intensity ratios and probability ratios. Not too hard to generate but saves time when plotting covariates -- covariate data from CPM data. orog_land-cpm_BI_2.2km.nc -- orographic data for the CPM. Contains height on rotated long/lat grid. * u2018_clc2018_v2020_20u1_ratster100m -- Corine Land Cover. Obtained from https://land.copernicus.eu/pan-european/corine-land-cover/clc-2000/view * uk_strm -- DEM regridded to OS GB grid at resoln of circa 100m. Obtained from https://doi.org/10.7488/ds/1928 * bdline_essh_gb -- boundary line data from the OS. Available from https://osdatahub.os.uk/downloads/open/BoundaryLine * GB_OS_boundaries -- created from bdline_essh_gb data using create_counties.py * GEBCO_topog_bathy_scotland.nc -- BEBCO topographic & bathy data. Extracted from 10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f * -- files/directories not present as user should get those themselves. Data for paper entitled "The impact of an extreme cloud burst on Edinburgh Castle" by Simon F. B. Tett, YoungHwa Cha, Kate Donovan, Gina-Maria Geffers and Ed Hawkins. This includes seasonal maximum rainfall from the UKCP18 CPM simulations for a roughly 200x200 km region centred on Edinburgh, and 15-minute radar maxima. Also includes parameters from GEV fits.

Tundra soils are one of the world’s largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. The landscape-scale controls of litter decomposition are poorly understood in tundra ecosystems, which hinders our understanding of the global carbon cycle. We examined the extent to which the thermal sum of surface air temperature, soil moisture and permafrost thaw depth influenced litter mass loss and decomposition rates (k), and at which spatial thresholds an environmental variable becomes a reliable predictor of decomposition, using the Tea Bag Index protocol across a heterogeneous tundra landscape on Qikiqtaruk - Herschel Island, Yukon, Canada. We found greater green tea litter mass loss and faster decomposition rates (k) in wetter areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness and higher surface thermal sums. We also found higher decomposition rates (k) on north-facing relative to south-facing aspects at microsites that were wetter rather than warmer. Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at local scales (< 50 m2) better than thermal sum. Surprisingly, there was no strong control of elevation or slope on litter decomposition. Our results reveal that there is considerable scale dependency in the environmental controls of tundra litter decomposition, with moisture playing a greater role than the thermal sum at < 50 m2 scales. Our findings highlight the importance and complexity of microenvironmental controls on litter decomposition in estimates of carbon cycling in a rapidly warming tundra biome. We used the ‘brms’ package (Bürkner, 2017) and weakly informative priors (half Student-t priors with 3 degrees of freedom) for all models, with two chains of 8000 iterations each and a warmup value of 2000. We conducted all analyses in R version 3.6.3. The code and data used for this study can be found at the following repository: https://github.com/ShrubHub/MicroTeaHub/ Large data: https://zenodo.org/record/6411321

Twenty soil cores were collected from a field site in Lincolnshire in March 2011, three weeks after planting and Nitrogen fertiliser addition. Soil cores of 150-180 millimetre (mm) depth, containing approximately 1.6 kilogram soil (dry weight) were extracted in Polyvinyl chloride (PVC) pipes (height 215 mm depth 102 mm) and stored at 4 degrees centigrade for 30 days. A four-treatment factorial experiment was designed using soils un-amended or amended with biochar and un-wetted or wetted with deionised water (5 replicates per treatment). Soil in all the cores was mixed to 7 centimetre (cm) depth. To half of the cores, biochar (less than 2 mm) was mixed into the soil at a rate of 3 percent soil dry weight (approximately 22 tons per hectare (t ha-1)). After allowing for any potential Carbon dioxide (CO2) flush from newly-mixed soil to equilibrate for seven days, the cores were placed at 16 degrees centigrade in the dark. Un-wetted soil cores were maintained at 23 percent Gravimetric moisture content (GMC), whilst the GMC of 'wetted' soil cores was increased to 28 percent GMC at the time zero (t0) of four wetting events on day 17, 46, 67 and 116. These water addition rates were based on mean and maximum monthly soil GMC measured in the field between 2009-2010. Data from an investigation of the effects of biochar application to soil on greenhouse gas emissions using soil from a bioenergy crop (Miscanthus X. giganteus). Data include physical (bulk density) and chemical analyses of the soil (total carbon (C) and nitrogen (N), extractable ammonium and nitrate), and greenhouse gas (GHG) emissions (carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O)) during incubations. Data were collected during two incubation experiments investigating the effects of temperature, soil moisture and soil aeration on biochar induced suppression of GHG emissions. Biochar is a carbon rich substances which is being advocated as a climate mitigation tool to increase carbon sequestration and reduce nitrous oxide emissions.

Survey similarity between the different surveys assessed via a non-parametric test for paired nominal data (McNemar test). The McNemar test was performed in SPSS (IBM, version 24) to pairwise compare moss, eukaryotic algae/cyanobacteria, and lichen cover between the 1961, 2004, and 2018 datasets, independent from surface type and surface type specific (3 survey years x 3 functional groups of vegetation x 5 surface types = a total of 45 individual pairings). Some pairings could not be tested (invalid) because some functional vegetation groups did not exist on certain surface types (e. g. moss on LLAC surfaces), or because the sample size was too low. Total sample size for each test varied because only plots that were free of snow were compared. Where the McNemar test output variables indicated suitable results, the kappa test statistic was extracted as a similarity score for the pairing Supplementary data tables and figures to: The longest baseline record of vegetation dynamics in Antarctica reveals acute sensitivity to water availability

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).