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Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.C4MIP.MOHC.UKESM1-0-LL' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

This spreadsheet contains nine tabs to present the data used in the article 'Microclimate-driven trends in spring-emergence phenology in a temperate reptile (Vipera berus): Evidence for a potential ‘climate trap’?' (Turner & Maclean, 2022; Ecology and Evolution). The first tab, labelled 'Metadata_README', contains metadata for the dataset, including identification and affliliations of the authors, a description of the tabs in the spreadsheet, and descriptions of data labels used in the spreadsheet tabs. The second tab, labelled 'adder_sightings', comprises of records of Vipera berus (adder) sightings in Cornwall, United Kingdom, sourced from the Environmental Records Centre for Cornwall and the Isles of Scilly (www.erccis.org.uk), the Record Pool (www.recordpool.org.uk) and the Cornish Biodiversity Network (www.cornishbiodiversitynetwork.org). Due to data sensitivities and issues associated with the General Data Protection Regulation, information pertaining to the locations and dates of adder sightings in some instances in the dataset have only be provided at reduced spatial and temporal resolutions. A unique identification code for locations has been attributed to records. For full-resolution access, contact the data custodian and corresponding author. The raw datasets of adder sightings were filtered prior to inclusion in the analysis in Turner and Maclean. See the main text for all filtering procedures and microclimate modelling. The remaining tabs contain data relating to each adder sighting location in 'adder_sightings' for each year 1983 - 2017 computed from microclimate models using the microclima R package (Maclean et al., 2019). The third tab, labelled 'total_spring_frost', contains annual rates of spring ground frost. The fourth, fifth and sixt tabs, labelled 'Cue1(i)', "Cue1(ii)', and 'Cue1(iii)', each contain predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using the 5th, 2.5th and 10th percentile thresholds, respectively, of an accumulated (degree-hours) temperature cue for adder emergence. The seventh tab, labelled 'Cue2', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a sharp rise in accumulated (degree-hours) temperature cue for adder emergence. The eighth tab, labelled 'Cue3', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a below-ground temperature gradient collapse cue for adder emergence. Lastly, the ninth tab, labelled 'Cue4', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a critical air temperature (10°C) cue for adder emergence. The main text presents the analysis of adder emergence and spring ground frost data from the 'Cue1(i)'. Analysis of data from 'Cue1(ii)', 'Cue1(iii)', 'Cue2', 'Cue3', and 'Cue4' are presented in the Supplementary Information for Turner and Maclean. Climate change will increase the exposure of organisms to higher temperatures, but can also drive phenological shifts that alter their susceptibility to conditions at the onset of breeding cycles. Organisms rely on climatic cues to time annual life-cycle events, but the extent to which climate change has altered cue reliability remains unclear. Here, we examine the risk of a ‘climate trap’ – a climatically-driven desynchronisation of the cues that determine life-cycle events and fitness later in the season in a temperate reptile, the European adder (Vipera berus). During the winter, adders hibernate underground, buffered against sub-zero temperatures, and re-emerge in the spring to reproduce. We derived annual spring-emergence trends between 1983 and 2017 from historical observations in Cornwall, United Kingdom, and related these trends to the microclimatic conditions that adders experienced. Using a mechanistic microclimate model, estimates of below- and near-ground temperatures were used to derive accumulated degree-hour and absolute temperature thresholds that predicted annual spring-emergence timing. Trends in annual emergence timing and subsequent exposure to ground frost were then quantified. We found that adders have advanced their phenology towards earlier emergence. Earlier emergence was associated with increased exposure to ground frost and, contradicting the expected effects of macroclimate warming, increased post-emergence exposure to ground frost at some locations. The susceptibility of adders to this ‘climate trap’ was related to the rate at which frost risk diminishes relative to advancement in phenology, which depends on the seasonality of climate. We emphasise the need to consider exposure to changing microclimatic conditions when forecasting biological impacts of climate change.

1. Aphids are abundant in natural and managed vegetation, supporting a diverse community of organisms and causing damage to agricultural crops. Due to a changing climate, periods of drought are anticipated to increase, and the potential consequences of this for aphid-plant interactions are unclear. 2. Using a meta-analysis and synthesis approach, we aimed to advance understanding of how increased drought incidence will affect this ecologically and economically important insect group, and to characterise any potential underlying mechanisms. We used qualitative and quantitative synthesis techniques to determine whether drought stress has a negative, positive, or null effect on aphid fitness and examined these effects in relation to 1) aphid biology, 2) geographical region, 3) host plant biology. 3. Across all studies, aphid fitness is typically reduced under drought. Subgroup analysis detected no difference in relation to aphid biology, geographical region, or the aphid-plant combination, indicating the negative effect of drought on aphids is potentially universal. Furthermore, drought stress had a negative impact on plant vigour and increased plant concentrations of defensive chemicals, suggesting the observed response of aphids is associated with reduced plant vigour and increased chemical defence in drought-stressed plants. 4. We propose a conceptual model to predict drought effects on aphid fitness in relation to plant vigour and defence to stimulate further research. Please check the ReadMe for an explanation of the values included in the dataset. Please note that n/a values are included in the Global_Dataset tab for plant meta-analysis data (_Plant_Vigour, _Plant_Defence, and _Plant_Nutrition), these indicate studies that did not report these parameters. Data was collected and curated using standard systematic literature synthesis approaches. The effect size (Hedges' g) reported in the dataset was calculated from extracted means and standard deviations.

The Characterisation of Feedstocks project provides an understanding of UK produced 2nd generation energy biomass properties, how these vary and what causes this variability. In this project, several types of UK-grown biomass, produced under varying conditions, were sampled. The biomass sampled included Miscanthus, Short Rotation Forestry (SRF) and Short Rotation Coppice (SRC) Willow. The samples were tested to an agreed schedule in an accredited laboratory. The results were analysed against the planting, growing, harvesting and storage conditions (i.e.The provenance) to understand what impacts different production and storage methods have on the biomass properties.The main outcome of this project is a better understanding of the key characteristics of UK biomass feedstocks (focusing on second generation) relevant in downstream energy conversion applications, and howthese characteristics vary by provenance. This Excel Workbook presents the data arising from all experiments carried out under the Characterisation of Feedstocks Project.The data set includes:The sites sampled, the conditions at the time of sampling, provenance data, soil laboratory results and biomass laboratory results.The feedstock studies carried out to yield these data are shown in the “Workbook Details” sheet – this sheet also briefly describes the quality assurance process for the data. As a result of the breadth and depth of data provided in these tables, the ETI anticipates that these data will be useful to a range users includingAcademics whose research focuses on bioenergy crop production or use of bioenergy crops in pre-treatment or conversion technologies;Modellers seeking biomass physical property dataBioenergy project developers seeking to understand their design envelopes;Existing commercial biomass users seeking to understand process performance;Biomass buyers assessing risks and defining fuel specifications.

JULES is a community model available for use after registering on the JULES repository (https://code.metoffice.gov.uk/trac/jules). The model version used here was r9448, which is a branch of vn4.8 with updates to represent harvesting of bioenergy crops and for running IMOGEN in inverse mode (using specified temperature pathways instead of specified concentration pathways). To use JULES-IMOGEN, patterns from 34 GCMs are needed, which are available from https://doi.org/10.5285/343885af-0f5e-4062-88e1-a9e612f77779. JULES requires a series of input files (initial conditions, model grid, CO2 concentration, land-use per grid cell, soil parameters, etc.). These are provided in the “ancillary_files” directory of the dataset. JULES can be run with a system called Rose, which stores model settings. We also include an excel spreadsheet with the suites used for the Harper et al. (2018) paper. The suites are available from https://code.metoffice.gov.uk/trac/roses-u (registration required). The original IMAGE land use data is available from https://data.knmi.nl/datasets?q=PBL. We used v17 land fractions from SSP2-SPA0-RCP1.9 and SSP2-SPA2-RCP2.6. These were regridded from half degree to the N48 resolution using ESMF Regrid patch interpolation method from NCAR Command Language (http://ncl.ucar.edu/Document/Functions/ESMF/ESMF_regrid.shtml). The actual fractions used in the simulations are in both the input and output files. For further details, see Harper et al. (https://doi.org/10.1038/s41467-018-05340-z). The experimental design is the same as in Comyn-Platt et al. (https://doi.org/10.1038/s41561-018-0174-9), which also has a linked dataset for the historical period. This dataset includes six sets of model output from JULES/IMOGEN simulations. Each set includes output from JULES (the Joint UK Land Environment Simulator) run with 34 climate change patterns from 2000-2099. The outputs provide carbon stocks and variables related to the surface energy budget to understand the implications of land-based climate mitigation.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.MOHC.UKESM1-0-LL.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

This dataset contains terrestrial fluxes of nitrous oxide (N2O), methane (CH4) and ecosystem respiration (carbon dioxide (CO2)) calculated from static chamber measurements in riparian buffers of oil palm plantations on mineral soil, in Riau, Sumatra, Indonesia. Measurements were made monthly, from January 2019 until September 2021, with a break from April 2019 to October 2019 to allow for felling and replanting, and another break from January 2021 to June 2021 due to Covid-19 restrictions. To help to reduce the environmental impact of oil palm plantations, riparian buffers are now required by regulations in many Southeast Asian countries. The experiments were conducted to investigate the impact of greenhouse gas emissions from the riparian buffers. Research was funded through NERC grant NE/R000131/1 Sustainable Use of Natural Resources to Improve Human Health and Support Economic Development (SUNRISE) Greenhouse gas concentrations were measured using static chambers, enclosed for 45 minutes. Multiple regressions (including linear and hierarchical multiple regression) were fitted to calculate the best fit flux, using the RCflux R package, written by Dr Peter Levy (UKCEH).

This dataset reports results on seedling growth and survival for two hyphal exclusion experiments in a subtropical forest. The data include survival status, height, total biomass and the biomass of component plant parts, percentage root colonisation by mycorrhizas, for tree seedlings of ten common species including five ectomycorrhizal (ECM) and five arbuscular mycorrhizal (AM) species, which were transplanted in the in-growth cores with windows covering different sizes of nylon meshes (35 vs. 0.5 µm). The dataset provides raw data on growth and survival metrics for each seedling, plus identifying codes for the dominant sites where the experiments were conducted, as well as experimental block, mesh treatment, botanical names for the tree species, and mycorrhizal type. The data were entered into Excel spreadsheets and exported as comma separated value files (csv). Study area - the Heishiding Nature Reserve (111°53’E, 23°27’N, 150-927 m a.s.l.) in Guangdong Province of south China. Mesh-walled cores were assembled from 16 cm diameter × 30 cm deep PVC piping, perforated with six 8-cm-diameter windows which were regularly distributed along the side with three of them at the depth of 4-12 cm and the other three at 16-24 cm. The cores were lined with 35 µm or 0.5 µm nylon mesh (Plastok Associates Ltd, Birkenhead, UK) to cover the bottom and the windows, which was attached using transparent superglue (Pattex(R), Henkel Adhesives Ltd., Shantou, China). Nylon mesh with a pore size of 35 µm excludes roots of neighboring plants, but allows mycorrhizal hyphae access to the transplanted seedlings; while cores with a 0.5 µm mesh exclude both fine roots and hyphae, with only free-living soil microorganisms passing through. We then covered the sides and the bottoms of all cores with 2 mm nylon mesh, to prevent soil fauna damaging the smaller size meshes.

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.