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Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

TECHNICAL INFO No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign. This publication may include a number of files that contain the same measurements (duplicate files, "dupes") as multiple raw data backups were performed on the data. The duplicate data can be differentiated by the information found in the timestamp column and file header. ACKNOWLEDGEMENTS Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding COPYRIGHT NOTICE Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved. CREATOR NOTICE This work is owned by the Principal Investigators (PIs) of the Urbisphere project. ATTRIBUTION NOTICE The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). DISCLAIMER NOTICE The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected. MATERIAL NOTICE The notices cover data in databases, text and images contained in the work. MATERIAL URI Urbisphere project Original logger data files from radiometer intercomparison measurements at SIRTA Atmospheric Observatory in Palaiseau in the Greater Paris region.

The dataset consists of the code and data used for the preprint "Climate change contribution to the 2023 autumn temperature records in Vienna". It contains two objects: The station data of mean monthly temperature for Vienna Hohe-Warte from 1750 to 2023 (vienna_hohe-warte.csv), which also can be downloaded here: http://www.zamg.ac.at/histalp/dataset/station/csv.php. The code for modeling and producing the figures of the preprint (autumn_temperature.R).

Files for the manuscript “Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Soils and Fast Cycling of a Boreal Forest Ecosystem” 1. “Raw_Data” folder contains the files in .xlsx: - Lab_Atmospheric_Samples: D14C results from ambient air at the sampled heights. - Lab_Soil_Respiration: D14C results with date and integration time for the FFSR sampling campaign. - Lab_Solid_Samples: D14C and TOC results for soil, vegetation, roots, fungi and incubation samples.

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.DAMIP.MPI-M.MPI-ESM1-2-LR' 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 MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

The response of the single-celled ciliates to increased temperature during global warming is critical for the structure and functioning of freshwater food webs. I conducted a meta-analysis of the literature from field studies and experimental evidence to assess the parameters characterising the thermal response of freshwater ciliates. The shape of the thermal performance curve predicts the ciliates’ survival at supraoptimal temperatures (i.e., the width of the thermal safety margin, TSM). The ciliates’ typical TSM is ~5°C. One-third of the freshwater ciliates dwelling permanently or occasionally in the pelagial cannot survive at temperatures exceeding 30°C. Likewise, cold-stenothermic species, which represent a significant fraction of euplanktonic ciliates, cannot survive by evolutionary adaptation to rapidly warming environments. The statistical analysis revealed that the ciliates’ thermal performance is affected by their planktonic lifestyle (euplanktonic versus tychoplanktonic), ability to form cysts, and nutritional ecology. Bactivorous ciliates have the widest temperature niche, and algivorous ciliates have the narrowest temperature niche. Phenotypic plasticity and genetic variation, favouring the selection of pre-adapted species in a new environment, are widespread among freshwater ciliates. However, the lack of evidence for the temperature optima and imprecisely defined tolerance limits of most species hamper the present analysis. The extent of acclimation and adaptation requires further research with more ciliate species than the few chosen thus far. Recent eco-evolutionary experimental work and modelling approaches demonstrated that the ciliates’ thermal responses follow general trends predicted by the metabolic theory of ecology and mechanistic functions inherent in enzyme kinetics. The present analysis identified current knowledge gaps and avenues for future research that may serve as a model study for other biota. Thermal adaptation may conflict with adaptation to other stressors (predators, food availability, pH), making general predictions on the future role of freshwater ciliates in a warmer environment difficult, if not impossible, at the moment. # Data from: Thermal response of freshwater ciliates: can they survive at elevated lake temperatures? [https://doi.org/10.5061/dryad.jdfn2z3jr](https://doi.org/10.5061/dryad.jdfn2z3jr) The dataset results from a meta-analysis to assess the parameters characterising the thermal response of freshwater ciliates (i.e., minimum and maximum temperature tolerated, temperature niche breadth). Cyst formation, the nutritional type, and the planktonic lifestyle were considered as factors affecting the ciliates’ thermal performance. ## Description of the data and file structure The main dataset reporting ciliate species and synonyms, taxonomic affiliation, minimum and maximum temperature and the temperature range tolerated, cysts formation, mixotrophic nutrition, food type, and planktonic lifestyle are reported in the 'Dataset_v4.xlsx' file. This is the main document. Taxonomic affiliation (i.e., order) following Adl et al. (2019, reference [65]J, the GBIF Backbone Taxonomy, and Lynn (2008; reference [66]). Details on the references - i.e., authors, publication year, title, journal/book, volume, and page/article numbers used to compile this dataset and some comments can be found in 'References.xlsx'. Empty cells mean that information is unavailable. References A-E are the main sources of the dataset, i.e., comprehensive review articles published by W. Foissner and colleagues in the 1990s. References 1-64 are case studies, published mainly after 1999. References 65 and 66 refer to the taxonomic affiliation of the ciliate species. More details about each column of the main document can be found in the 'Units_table.xlsx' file. ## Sharing/Access information Data was derived from the following sources: * ISI Web of Science (All Data Bases) * Google Scholar ## Code/Software R statistical software (v 4.0.5, R Core Team 2021) with the packages lme4, lmtest, multcomp, AICcmodavg. WebPlotDigitizer (Version 4.6) for data extraction from figures ## Version changes **06-aug-2024**: Taxonomic affiliation (order) corrected according to GBIF. Genus *Tintinnidium* is now in the order Oligotrichida. I scrutinised the detailed literature compilations by Foissner and colleagues published in the 1990s; these references are listed as primary sources A-E in the Dataset, see References.xlsx and README.txt) to obtain an overview of the thermal performance, resting cyst formation, and nutritional ecology of planktonic freshwater ciliates. I then searched the ISI Web of Science (All Data Bases) for updates and cross-references of Foissner’s works and further temperature records from (mainly) field studies. Search terms (in all fields) for the latter were ciliate* AND temperature NOT marine NOT ocean NOT soil NOT parasit* (1,339 hits). I followed the PRISMA guidelines in combination with EndNote 20 to filter out the records eligible for screening and analysis. Temperature data for assessing the minimum (Tmin) and maximum temperature (Tmax) of occurrence were eventually extracted from 68 publications. However, because Foissner’s works present extensive reviews, the actual number of publications used for the analysis is much higher. The final dataset obtained from field studies comprised 206 ciliate species. Next, I searched the ISI Web of Science for experimental results, using ciliate* AND temperature AND growth rate* NOT marine as search terms (218 records). Removing results from unsuitable research areas (mainly from medical research) reduced the records to 71 publications, which were screened. The combination of ciliate* AND numerical response NOT marine yielded 40 studies, ciliate* AND thermal performance 21 hits. I checked the selected articles for citations and cross-references using Google Scholar to identify any publications that might have slipped my attention. Eventually, I picked experimental results from 18 studies. If the literature data were only shown in figures, I extracted the data from the plots with WebPlotDigitizer (Version 4.6). I analysed the dataset with the R Statistical Software using the packages lme4, lmerTest, stats, multcomp, AICcmodavg and car.

The PV-Live dataset comprises data from a network of 40 solar irradiance measurement stations across the German state of Baden-Württemberg. All stations measure global horizontal irradiance and temperature with a pyranometer, and global tilted irradiance in east, south, and west direction with tilt angles of 25° with three photovoltaic reference cells in minute resolution. A quality control scheme has been developed specifically for this dataset and is applied to the measurements before publication. The minute resolution irradiance and temperature measurements are published with the derived quality flags. A description of the dataset and the quality control scheme is given in Lorenz et al. (2022) and Lorenz et al. (2020). The dataset contains data from September 2020 onwards. It will be continuously extended by adding data of the previous month on a monthly basis. QUALITY FLAGS Two types of flags are provided with the data. Each sensor is assigned a general quality flag, which is based on a combination of different tests. In addition, a shading flag is given, which is not sensor specific, i.e. it applies to all irradiance sensors simultaneously. The flags can have the levels 'passed, all tests complete', 'passed, not all tests complete', 'failed tests, likely erroneous' or 'failed tests, most likely erroneous'. If the flag level of the general quality flags is 'failed tests, most likely erroneous' the corresponding measurement value is set to NaN. DATA FORMAT The data is published as monthly .zip archives. Each archive contains the following files: 1. Tab separated data files (tng000XX_YYYY-MM.tsv) for each station, containing measurements and quality flags of one month 2. Tab separated station location metadata (metadata_stations_YYYY-MM.tsv) 3. Metadata of the dataset describing variable names and quality flag levels (metadata_measurements.json) 4. General comments on data availability and quality for the month (comments_quality_control_YYYY-MM.txt) 5. Log file of changes (change_log_YYYY-MM.txt) Station location metadata is given on a monthly basis because stations can be relocated. Therefore, we recommend to use the metadata valid for its corresponding month. VERSION UPDATES Version 7: - Recalculation of height information for all stations - One station has been moved to a new, nearby location on 16th September 2021 ACKNOWLEDGEMENTS The data have been collected and processed by Fraunhofer ISE in the framework of PV-Live, a project in cooperation with TransnetBW. We thank our station partners for cooperation in installing and maintaining our measurement stations: EnBW Solar, Badenova, Pohlen Solar, Oekogeno Solar7, Ecovision, Hochschule Ulm, Hofgemeinschaft Heggelbach, Soltechnics-solution and the Stadtwerke Karlsruhe, Grünstadt, Buchen, Crailsheim, Schwäbisch Hall, Pforzheim, Konstanz, Waldshut-Tiengen, Schwäbisch Gmünd, Ravensburg, Eberbach, Baden-Baden.

The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.