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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.

Natural potentials for future cropland expansion The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions. We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops’ specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel. In addition the natural biophysical conditions, we consider today’s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure. In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops. Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the ‘natural expansion potential index’ (Iexp) that expresses the natural potential for an area to be converted into cropland as follows: Iexp = S * Aav The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (Aav) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future. Further information Detailled information are available in the following publication: Delzeit, R., F. Zabel, C. Meyer and T. Václavík (2017). Addressing future trade-offs between biodiversity and cropland expansion to improve food security. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1 Contact Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department für Geographie, LMU München (www.geografie.uni-muenchen.de) This research was carried out within the framework of the GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services) Project, which has been supported by the German Ministry of Education and Research (BMBF) program on sustainable land management (grant number: 01LL0901E).

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.

Project: Convective and Orographically-induced Precipitation Study - Weather forecast models have not been successful in improving the Quantitative Precipitation Forecast during the last 16 years. One reason for this stagnation is the lack of comprehensive, high-quality data sets usable for model validation as well as for data assimilation, thus leading to improved initial fields in numerical models. Theoretical analyses have identified the requirements measured data have to meet in order to close the gaps in process understanding. In field campaigns, it has been shown that the newest generation of remote sensing systems has the potential to yield data sets of the required quality. It is therefore time to combine the most powerful remote sensing instruments with proven ground-based and airborne measurement techniques in an Intensive Observations Period (IOP). Its goal is to serve as a backbone for the Priority Program SPP 1167 by producing the demanded data sets of unachieved accuracy and resolution. This requires a sophisticated scientific preparation and a careful coordination between the efforts of the institutions involved. For the first time, the pre-convective environment, the formation of clouds and the onset and development of precipitation as well as its intensity will be observed in four dimensions simultaneously in a region of sufficient size. This shall be achieved by combining the IOP with international programs and by collaboration between leading scientists in Europe, US, and other countries. Thus, the IOP, which we call Convective and Orographically-induced Precipitation Study (COPS), is a unique opportunity for an international field campaign featuring the newest generation of measurement systems such as scanning radar and lidar and leading to outstanding advances in atmospheric sciences. Please be aware of the common COPS/GOP/D-PHASE data policy, which you please find at http://cops.wdc-climate.de/ Summary: The energy balance stations run by University of Bayreuth continuously measured radiation and soil parameters over different land types with a sampling frequency of 1 Hz averaged to 1 min values within the data logger. After a check for plausibility the 1 min values have been averaged to 30 min intervals, which are provided in this data set. The instrumentation was different on each location. The following was measured depending on the station: - soil heat flux - soil temperature - volumetric soil water content - longwave radiation components - shortwave radiation components - tipping bucket rain gauge measurements The ground heat flux including the heat storage in the upper soil layer was determined from the measured soil heat flux, soil temperatures and volumetric soil water contents according to the 'simple measurement' (SM) method according to Liebethal and Foken (2007).

The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Yield gaps, thus considering the spatial variation in environment and the production system. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c2bbd5ed-fd4c-4a3f-b0b1-113a5d4f3ddf}. The yield gaps plotted in the map were calculated as the average values of 7 years (the year 2004 -2010). The unit is Megagram per hectare (Mg ha-1) which is equivalent to tons ha-1. The climate data at the national scale was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies(https://data.giss.nasa.gov/impacts/agmipcf/agmerra/), AgMERRA.The dataset is stored at 0.25°×0.25° horizontal resolution (~25km). Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Maize yields (Mg ha-1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004 - 2010) at administrative zone level have been collected from the Central Statistical Agency, Ethiopia.

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.