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This data set describes the population dynamics of Wilson's Storm Petrels (Oceanites oceanicus) at King George Island (Isla 25 de Mayo, Antarctica) over a forty year period (1978 – 2020). It includes all available data on Wilson's Storm Petrels from two colonies: around the Argentinian Base Carlini (62°14′S, 58°40′W; CA, formerly called Base Jubany) and the Henryk Arctowski Polish Antarctic Station (62°09′S, 58°27′W; HA). Data on population productivity (number of nests, eggs, chicks and fledglings) was collected by regular visits to the colonies and searching for nest burrows, or monitoring of the egg or chick if found. Data on adult abundance and estimated age categories (i.e., presence of foot spots; Quillfeldt et al. (2000, doi:10.1007/s003000000167) were collected at CA by using the same size mistnet every study year in the same location within the breeding colony. Chicks were measured regularly (varying intervals depending on the study) at both CA and HA. Chick tarsus was measured using callipers (vernier or digital depending on the study year) to the nearest 0.1 mm, chick wing length was measured using wing rulers to the nearest 1 mm, and chick body mass was measured using mechanical or digital scales depending on the study year to the nearest 0.1 g. Chick growth rates were calculated based on the linear growth period following Ausems et al. (2020, doi:10.1016/j.scitotenv.2020.138768). Chick food loads (g) were recorded at CA and determined based on changes in chick body mass on consecutive days (Gladbach et al. (2009, doi:10.1007/s00300-009-0628-z); Kuepper et al. (2018, doi:10.1016/j.cbpa.2018.06.018). This study was further supported by the Erasmus+ programm and thee German Academic Exchange Service (DAAD)

Vegetation surveys were carried out in four different study areas in the Sakha Republic, Russia: in the mountainous region of the Verkhoyansk Range within the Oymyakonsky and Tomponsky District (Event EN21-201 - EN21-219), and in three lowland regions of Central Yakutia within the Churapchinsky, Tattinsky and the Megino-Kangalassky District (Event EN21220 - EN21264). The study area is located within the boreal forest biome that is underlain by permafrost soils. The aim was to record the projective ground vegetation in different boreal forest types studied during the RU-Land_2021_Yakutia summer field campaign in August and September 2021.Ground vegetation was surveyed for different vegetation types within a circular forest plot of 15m radius. Depending on the heterogeneity of the forest plot, multiple vegetation types (VA, VB, or VC) were chosen for the survey. The assignment of a vegetation type is always unique to a site. Their cover on the circular forest plot was recorded in percent.In total, 84 vegetation types at 58 forest plots were assessed. All data were collected by scientists form the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) Germany, the University of Potsdam Germany, and the North-Easter Federal University of Yakutsk (NEFU) Russia.

# Can niche plasticity mediate species persistence under ocean acidification? [https://doi.org/10.5061/dryad.x0k6djhtq](https://doi.org/10.5061/dryad.x0k6djhtq) This dataset originates from a study investigating the impact of ocean acidification on a temperate rocky reef fish assemblage using natural CO2 vents as analogues. The dataset covers various niche dimensions, including trophic, habitat, and behavioural niches. The study focused on how fish niches are modified in response to ocean acidification, assessing changes in breadth, shift, and overlap with other species between the acidified site and the control site. ## Description of the data and file structure #### Raw\_single\_niche\_data The “*Raw_single_niche_data*” dataset consists of seven spreadsheets, each sharing two essential columns: 'group' and 'community'. These columns are crucial for subsequent analysis using the SIBER framework. **group** = species * Common = common triplefin, *Forsterygion lapillum* * Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* * Blue_eyed = blue-eyed triplefin, *Notoclinops segmentatus* * Blenny = crested blenny, *Parablennius laticlavius* **community** = treatment * C = control * V = CO2 vents **Description of the seven spreadsheets:** 1. **Isotopes -** the dataset includes ratios of 13C/12C and 15N/14N expressed in the conventional δ notation as parts per thousand deviation from international standards. Stable isotopes were derived from a total of 251 fishes collected across three years of sampling. iso1= δ13C iso2= δ15N 2. **Stomach volumetric** - The dataset includes estimated volumetric measures of stomach contents, where the volume contribution of each prey category relative to the total stomach content (100%) was visually estimated. Data were collected between 2018 and 2019. The stomach content was analysed with this method for common triplefin, Yaldwyn's triplefin, blue eyed triplefin and crested blenny. There are 19 prey categories. 3. **Stomach count** - All prey items were counted in 10 prey categories: copepods, ostracods, polychaetes, amphipods, gastropods, bivalves, tanaids, mites, isopods , and others. Digested items that were not identifiable were excluded from the analysis. The stomach content was analysed with this method for common triplefin, Yaldwyn's triplefin and blue eyed triplefin. 4. **Stomach biomass -** The dataset includes calculated biomass derived from the mass of prey subsamples within each category, multiplied by their count. 5. **Habitat** - The microhabitat occupied and habitat orientation (horizontal, angled and vertical) was recorded using free roaming visual surveys on SCUBA (February 2018). *Microhabitat types:* t. = turf algae <10 cm in height ca. = erect calcareous algae cca. = crustose coralline algae b. = bare rocky substratum sp. = encrusting fleshy green algae cobble. = cobbles (~0.5–2 cm in diameter) *Type of surface orientation:* hor = horizontal angle = angled vert = vertical 6. **Behaviour** - Behavioural variables quantified from underwater footage and expressed as rates per minute. The behaviours are: swimming, jumping, feeding, attacking and fleeing from an attack. 7. **Aquarium**: Data from an aquarium experiment involving *Forsterygion lapillum and Notoclinops yaldwyni*, showing the proportion of time spent in available habitat types to assess habitat preference in controlled conditions. Time in each habitat type and spent in activity was derived from video recordings of 10 minutes and expressed as a proportion of total observation time. Common = common triplefin, *Forsterygion lapillum* Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* Common.c = common triplefin in presence of Yaldwyn’s triplefin Yaldwyn.c = Yaldwyn’s triplefin in presence of common triplefin turf.horizontal = time spent on horizontal turf substratum bare.horizontal = time spent on horizontal bare substratum turf.vertical = time spent on vertical turf substratum bottom = time spent on the bottom of the tank swimming = time spent swimming aquarium.wall = time spent on the walls of the tank switches = numbers of changes between habitats #### Unified\_overlap\_dataset The *“Unified_overlap_dataset”* consists of ten spreadsheets, each sharing “id”, “year”, “location” and “species “column (with few exceptions detailed). These first columns need to be factors for analysis using the Unified overlap framework. We used the R scripts provided in the original study ([Geange et al, 2011](https://doi.org/10.1111/j.2041-210X.2010.00070.x)), as detailed in the manuscript. Data for control and vents are in separate data sheets, with C = control and V = vent. **Id**: sample number **Year:** year the data were collected **Location:** North (n) or South (s), site location **Species**: fish species * Common = common triplefin, *Forsterygion lapillum* * Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* * Blue_eyed = blue-eyed triplefin, *Notoclinops segmentatus* * Blenny = crested blenny, *Parablennius laticlavius* We used the same data as per previous section. **Isotopes C and Isotopes V:** * iso1= δ13C * iso2= δ15N **Diet V and Diet C:** For **stomach content**: we used only volumetric stomach content data as inclusive of all species of interest. It is not raw data, but we used the reduced dimension obtained from nonmetric multidimensional scaling (nMDS), thus the 2 columns resulting from this analysis are vol1 and vol2. Raw data are in the datasheet **Stomach volumetric** in the “*Raw_single_niche_data*” dataset. **Habitat association C and Habitat association V** / **Habitat - C and Habitat - V** For **Habitat association**, the columns are id, species, habitat and position. The habitat association for each species is categorical based on habitat occupied and position (e.g., turf - vertical). Information for Crested blenny were extracted from the behavioural video recordings (with each video being a replicate). The dataset is then linked to **Habitat cover** in both control (C) and vent (V) sites to determine the choice of the habitat based on habitat availability. Therefore, the habitat cover only presents the percentage cover of each habitat type at control and vent. *Habitat:* turf = turf algae <10 cm in height ca = erect calcareous algae cca = crustose coralline algae barren = bare rocky substratum sp = encrusting fleshy green algae cobble = cobbles (~0.5–2 cm in diameter) sand = sand *Position:* hor = horizontal angle = angled vert = vertical **Behaviour C and Behaviour V**: Behavioural variables quantified from underwater footage and expressed as rates per minute. The behaviours are: swimming, jumping, feeding, attacking and fleeing from an attack. Reference: Geange, S. W., Pledger, S., Burns, K. C., & Shima, J. S. (2011). A unified analysis of niche overlap incorporating data of different types. *Methods in Ecology and Evolution*, 2(2), 175-184. [https://doi.org/10.1111/j.2041-210X.2010.00070.x](https://doi.org/10.1111/j.2041-210X.2010.00070.x) We used a small hand net and a mixture of ethanol and clove oil to collect the four species of interest (Forsterygion lapillum, Notoclinops yaldwyni, Notoclinops segmentatus and Parablennius laticlavius) at both control and vent sites over four years. For stable isotope analysis, white muscle tissue was extracted from each fish and oven-dried at 60 °C. The dried tissue was subsequently ground using a ball mill. Powdered muscle tissue from each fish was individually weighed into tin capsules and analysed for stable δ 15N and δ13C isotopes. Samples were combusted in an elemental analyser (EuroVector, EuroEA) coupled to a mass spectrometer (Nu Instruments Horizon) at the University of Adelaide. We then analysed the isotopic niche in SIBER. For stomach content analysis the entire gut was extracted from each fish. Using a stereomicroscope, for count and biomass, all prey items in the stomach were counted first. For each prey category, well-preserved individuals were photographed and their mass was calculated based on length and width. The average mass per individual for each category was then multiplied by the count to determine total prey biomass. For the volumetric method, the volume contribution of each prey category relative to the total stomach content was visually estimated (algae were accounted for). Digested items that were not identifiable were excluded from the analysis. Each stomach content dataset was reduced to two dimensions with non-metric multidimensional scaling (nMDS) to be then analysed in SIBER. To assess habitat choice, visual surveys were conducted on SCUBA, to record the microhabitat type and orientation occupied by Forsterygion lapillum, Notoclinops yaldwyni and Notoclinops segmentatus. The resulting dataset comprised a total of 17 distinct combinations of habitat types and surface orientations. The dataset was simplified to two dimensions using correspondence analysis (CA) for subsequent SIBER analysis. Fish behaviour was assessed using GoPro cameras both in situ and during controlled aquarium experiments. In the field, recordings lasted 30 minutes across 4 days, with analysis conducted using VLC. Initial acclimation and periodic intervals (10 minutes every 5 minutes) were excluded from analysis. In controlled aquarium settings, individuals of Forsterygion lapillum and Notoclinops yaldwyni were observed both in isolation and paired. Their habitat preference, surface orientation, and activity levels were recorded for 10 minutes to assess behaviour independent of external influences. Both datasets were dimensionally reduced for analysis in SIBER: non-metric multidimensional scaling (nMDS) was applied to the in situ behavioral data, while principal component analysis (PCA) was used for the aquarium experiments. Unified analysis of niche overlap We quantified the local realised niche space for each fish species at control and vent along the four niche classes, adapting the data as follows: isotopes (continuous data): raw data. stomach content (continuous data): reduced dimension from the volumetric measure of the previous step. habitat association (elective score): habitat and orientation preference linked to Manly’s Alpha association matrix. behaviour (continuous data): raw data. Global change stressors can modify ecological niches of species, and hence alter ecological interactions within communities and food webs. Yet, some species might take advantage of a fast-changing environment, and allow species with high niche plasticity to thrive under climate change. We used natural CO2 vents to test the effects of ocean acidification on niche modifications of a temperate rocky reef fish assemblage. We quantified three ecological niche traits (overlap, shift, and breadth) across three key niche dimensions (trophic, habitat, and behavioural). Only one species increased its niche width along multiple niche dimensions (trophic and behavioural), shifted its niche in the remaining (habitat), and was the only species to experience a highly increased density (i.e. doubling) at vents. The other three species that showed slightly increased or declining densities at vents only displayed a niche width increase in one (habitat niche) out of seven niche metrics considered. This niche modification was likely in response to habitat simplification (transition to a system dominated by turf algae) under ocean acidification. We further show that at the vents, the less abundant fishes have a negligible competitive impact on the most abundant and common species. Hence, this species appears to expand its niche space overlapping with other species, consequently leading to lower abundances of the latter under elevated CO2. We conclude that niche plasticity across multiple dimensions could be a potential adaptation in fishes to benefit from a changing environment in a high-CO2 world. 

Total oxygen uptake rates were assessed by conducting sediment core incubations. After MUC retrieval and sediment core preparation on deck, three cores were taken to a dark, temperature controlled laboratory on board Polarstern that was refrigerated to 2 °C-4 °C. Incubation procedure generally followed the approach described by Link et al. (2013, https://doi.org/10.5194/bg-10-5911-2013).

# Long-term changes in taxonomic and functional composition of European marine fish communities The GitHub linked repository is here: [European_demersal_fish_assemblages (](https://github.com/auroreRECE/European_demersal_fish_assemblages)DOI [10.5281/zenodo.11190119](https://zenodo.org/doi/10.5281/zenodo.11190119)) ## Overview This project is dedicated to studying the influence of environmental conditions and fishing on the functional and taxonomic structure of a demersal fish community in Europe. This GitHub repository provides the code of the Receveur et al. (2024) publication in Ecography. ## Data files description ### df\_MFA.csv This file contains the coordinates resulting from the Multiple Factor Analysis (MFA): * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Dim.1 : the coordinate of each trawl on the first MFA dimension ; * Dim.2 : the coordinate of each trawl on the second MFA dimension ; * Dim.3 : the coordinate of each trawl on the third MFA dimension ; ### df\_PCA.csv This file contains the coordinates * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Dim.1 : the coordinate of each trawl on the first PCA dimension ; * Dim.2 : the coordinate of each trawl on the second PCA dimension ; * Dim.3 : the coordinate of each trawl on the third PCA dimension ; ### df\_env.csv This file contains the following environmental parameters: * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Year : the Year of each trawl ; * Quarter : the Quarter of each trawl ; * Ecoregion : the Ecoregion where each trawl has been done; * Survey : the name of the Survey ; * x_my_spatial_id : the longitude of the ICES rectangle where the trawl has been done ; * y_my_spatial_id : the latitude of the ICES rectangle where the trawl has been done ; * my_spatial_id : an ID for the ICES rectangle where the trawl has been done ; * depth : the bottom depth (meters) ; * depth_span : the bottom depth variability (maximum depth of the ICES cell - minimum depth) (meters) ; * chloro_mea: the mean chlorophyll-a concentration (mg/m³) ; * mlotst_mea : the mean mixed layer depth (meters) ; * oxy_bottom_mea : the mean bottom dissolved oxygen (umol/l) ; * oxy_surf_mea : the mean surface dissolved oxygen (umol/l) ; * temp_bottom_mea : the mean bottom temperature (°C) ; * temp_surf_mea : the mean surface temperature (°C) ; * curr_surf_mea : the mean surface current strength (m/s) ; * curr_bottom_mea : the mean bottom current strength (m/s) ; * sal_surf_mea : the mean surface salinity (PSU) ; * chloro_std : the standard deviation of chlorophyll-a concentration (mg/m³) ; * mlotst_std : the standard deviation of mixed layer depth (meters) ; * oxy_bottom_std : the standard deviation of bottom dissolved oxygen (umol/l) ; * oxy_surf_std : the standard deviation of surface dissolved oxygen (umol/l) ; * temp_bottom_std : the standard deviation of bottom temperature (°C) ; * temp_surf_std : the standard deviation of surface temperature (°C) ; * curr_surf_std : the standard deviation of surface current strength (m/s) ; * curr_bottom_std : the standard deviation of bottom current strength (m/s) ; * sal_surf_std : the standard deviation of surface salinity (PSU). ## Raw Data sources ### Biological data Trawls content is publicly available for the North East Atlantic (DATRAS database). Mediterranean data (MEDITS database) are available upon request to Maritime Affairs and Fisheries (MARE DATACOLLECTIONFRAMEWORK). The project uses the following surveys: | Survey Code | Survey name | Area | Period | References | | :---------- | :----------------------------------------------------- | :------------------------------------- | :-------: | :--------: | | BITS | Baltic International Trawl Survey | Baltic Sea | 1994-2019 | 4 | | BTS | Beam Trawl Survey | Celtic Sea; English Channel; North Sea | 1997-2019 | 7 | | BTS-VIII | Beam Trawl Survey – Bay of Biscay | Bay of Biscay | 2011-2019 | 7 | | DWS | Deepwater Survey | Irish Sea | 2006-2007 | 8 | | DYFS | Inshore Beam Trawl Survey | Southern North Sea | 2002-2019 | 7 | | EVHOE | French Southern Atlantic Bottom trawl Survey | Bay of Biscay and Celtic Sea | 2003-2019 | 1 | | FR-CGFS | French Channel ground Survey | English Channel | 1997-2019 | 2 | | IE-IAMS | Irish Anglerfish and megrim Survey | Scottish rockall and Irish Sea | 2016-2019 | 2 | | IE-IGFS | Irish Groundfish | Ireland Shelf Sea | 2003-2019 | 2 | | MEDITS | International bottom trawl survey in the Mediterranean | Mediterranean Sea | 1994-2018 | 9 | | NIGFS | Northern Ireland Groundfish Survey | Irish Sea | 2009-2019 | 2 | | NS-IBTS | North Sea International Bottom Trawl Survey | North Sea | 1997-2019 | 2 | | PT-IBTS | Portuguese International Bottom Trawl Survey | Portugal Shelf Sea | 2003-2017 | 2 | | ROCKALL | Scottish Rockall Survey (until 2010) | Rockall plateau | 2003-2009 | 2 | | SCOROC | Scottish Rockall Survey (from 2011) | Scottish plateau | 2011-2019 | 2 | | SCOWCGFS | Scottish West Coast Groundfish Survey | Scottish west coast | 2011-2019 | 2 | | SNS | Sole Net Survey | Southern North Sea | 2002-2019 | 7 | | SP-ARSA | Spanish Gulf of Cadiz Bottom Trawl Survey | Spain | 2003-2019 | 6 | | SP-NORTH | Spanish North Bottom Trawl Survey | North of Spain | 2003-2019 | 2 | | SP-PORC | Spanish Porcupine Bottom Trawl Survey | Irish Sea | 2003-2019 | 5 | | SWC-IBTS | Scottish West Coast International Bottom Trawl Survey | Scotland Shelf Sea | 1999-2010 | 2 | ### Trait data The complete traits data table is available upon request. It is a combination of the publicly available PANGAEA database, Fishbase information, and inference based on the FISHLIFE project. ### Environmental variables The data used are all publicly available on the Copernicus website. ### Fishing data The data used are all publicly available on the Global Fishing Watch website. ## Recommended Citation Please use the following citation: Receveur, A., Leprieur F., Ellingsen K., Keith D., Kleisner K., McLean M., Mérigot B., Mills K., Mouillot D., Rufino M., Trindade-Santos I., Van Hoey G., Albouy C., Auber A. Data for “Long-term changes in taxonomic and functional composition of European marine fish communities.” Dryad Digital Repository. (2024). doi.org/10.5061/dryad.x69p8czsj ## Acknowledgments This research is a product of the MAESTRO group funded by the synthesis center CESAB of the French Foundation for Research on Biodiversity (FRB). We thank France Filière Pêche (FFP) who founded the MAESTRO project. We also warmly thank all those who have contributed in any way to the scientific surveys and data collection/provision (European Institutions and scientists implicated in DATRAS-BTS, MEDITS, and DCF). ## References 1. ICES. The EVHOE survey (France). ICES Documents. (1997). Available at: https://archimer.ifremer.fr/doc/00036/14707/12013.pdf 2. ICES. Manual of the IBTS North Eastern Atlantic Surveys. Series of ICES Survey Protocols SISP 15 (2017). doi:10.17895/ices.pub.3519 3. ICES. Manual for the International Bottom Trawl Surveys Revision VIII. Series of ICES Survey Protocols SISP 10 - IBTS IX. (2015). 4. https://ices-library.figshare.com/articles/report/SISP_7_-*Manual_for_the_Baltic_International_Trawl_Surveys_BITS*/19050986 5. https://gis.ices.dk/geonetwork/srv/api/records/ce94a257-c8b3-44f7-9fd0-6bd7449ce073 6. http://ices.dk/sites/pub/CM%20Doccuments/2002/D/D0302A.pdf 7. https://ices-library.figshare.com/articles/report/SISP_14_-*Manual_for_the_Offshore_Beam_Trawl_Surveys_WGBEAM*/19051328 8. https://gis.ices.dk/geonetwork/srv/api/records/936b4fb7-9baa-4dbc-abd0-b1b7bda16406 9. https://archimer.ifremer.fr/doc/00117/22783/20585.pdf Evidence of large-scale biodiversity degradation in marine ecosystems has been reported worldwide, yet most research has focused on few species of interest or on limited spatiotemporal scales. Here we assessed the spatial and temporal changes in the taxonomic and functional composition of fish communities in European seas over the last 25 years (1994-2019). We then explored how these community changes were linked to environmental gradients and fishing pressure. We show that the spatial variation in fish species composition is more than two times higher than the temporal variation, with a marked spatial continuum in taxonomic composition and a more homogenous pattern in functional composition. The regions warming the fastest are experiencing an increasing dominance and total abundance of r-strategy fish species (lower age of maturity). Conversely, regions warming more slowly show an increasing dominance and total abundance of K-strategy species (high trophic level and late reproduction). Among the considered environmental variables, sea surface temperature, surface salinity, and chlorophyll-a most consistently influenced communities’ spatial patterns, while bottom temperature and oxygen had the most consistent influence on temporal patterns. Changes in communities’ functional composition were more closely related to environmental conditions than taxonomic changes. Our study demonstrates the importance of integrating community-level species traits across multi-decadal scales and across a large region to better capture and understand ecosystem-wide responses and provides a different lens on community dynamics that could be used to support sustainable fisheries management.

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).

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.ScenarioMIP.CSIRO-ARCCSS.ACCESS-CM2.ssp245' 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 Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, 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), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.