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A version of EXIOBASE multi-regional SUTs V3.3 for 2011 and that has been: Expanded in labels for all categories (including synonyms, country regions and names in the multiindexes to facilitate slicing). Expanded by including characterization tables (originally developed under DESIRE FP7) The datasets are pickled and are meant to be used with pycirk a modelling software to simulate EEIO structural change due to technological and policy interventions. https://cmlplatform.github.io/pycirk/ This repository has been updated to contain the pxp ITA mrEEIO tables that are created by pycirk: mrIO_V3.3.pkl contains the regular pxp ITA mrEEIO of EXIOBASE for the year 2011 mrIO_V3.3.sm.pkl contains the pxp ITA mrEEIO of EXIOBASE for the year which have been modified to show the secondary materials industry which is typically missing from the EXIOBASE mrEEIO tables in pxp format

Here the processed experimental data of the accepted paper given below is documented and made available: Berger, F., Neuhaus, L., Onnen, D., Hölling, M., Schepers, G., and Kühn, M.: Experimental analysis of the dynamic inflow effect due to coherent gusts, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-2, accepted, 2022.

# Local reflects global: Life-stage dependent changes in the phenology of coastal habitat use by North Sea herring [https://doi.org/10.5061/dryad.1c59zw43g](https://doi.org/10.5061/dryad.1c59zw43g) This dataset contains the raw data and R-scripts used in the manuscript **Local reflects global: Life-stage dependent changes in the phenology of coastal habitat use by North Sea herring**. ## Description of the data and file structure The dataset is composed of a raw **Data ** folder and an **R-code ** folder that can be used to run the analyses presented in the manuscript. ## Data folder The **Data ** folder, contains four .csv files : * *Daily_Jetty_Data.csv - containing the local Wadden Sea water temperature time series* * *NS_temp_series.csv - containing the combined regional North Sea water temperature time series* * *The NS_temp folder contains .csv files with the separate years of the North Sea water temperature time series, and these* *are brought together separately in the Herring_1982_2021_separate_spring_and_fall_model_code.R* * *WH_DENHDR_1982_1999.csv & WH_DENHDR_2000_2021.csv - contains the tidal data for the Marsdiep where the fyke is located*. Next to this, the **Data ** folder contains the subfolder **vanstdagen_fuiknr1 ** containing multiple fish catch data files: * *length_code_info.csv - Description of each length_code number in the datafile vanst_haring_fuiknr1.csv* * *vanst_haring_fuiknr1.csv - The raw catch data of herring in the fyke* * *vanstdagen_fuiknr1.csv -* *The amount of time (hours) the fyke was opened/operated at each catch day*. **Note: In the data files any missing column values have been filled with an "NA" value, this means no data were available for this specific variable at this specific row.** **Note: Column and variable descriptions for each separate dataset is provided in METADATA.xlsx file in the Data folder** ## R-code folder The **R code ** folder contains three separate R scripts: * *GCB_Herring_1982_2021_code.R - is the main script used to analyse the overall change in herring catch trends over time* * *Herring_1982_2021_separate_spring_and_fall_model_code. R - is a supplementary script that can be used to separately assess the spring and autumn trends* * *Model_by_is_season_structure_code.R - is a supplementary script where the seasonal term in the additive model is formulated alternatively (using the by='season') and the implications for model outcome and model fit can be assessed.* Climate warming is affecting the suitability and utilisation of coastal habitats by marine fishes around the world. Phenological changes are an important indicator of population responses to climate-induced changes but remain difficult to detect in marine fish populations. The design of large-scale monitoring surveys does not allow fine-grained temporal inference of population responses, while the responses of ecologically and economically important species groups such as small pelagic fish are particularly sensitive to temporal resolution. Here, we use the longest, highest-resolution time series of species composition and abundance of marine fishes in northern Europe to detect possible phenological shifts in the small pelagic North Sea herring. We detect a clear forward temporal shift in the phenology of nearshore habitat use by small juvenile North Sea herring. This forward shift can best be explained by changes in water temperatures in the North Sea. We find that reducing the temporal resolution of our data to reflect the resolution typical of larger surveys makes it difficult to detect phenological shifts and drastically reduces the effect sizes of environmental covariates such as seawater temperature. Our study therefore shows how local, long-term, high-resolution time series of fish catches are essential to understand the general phenological responses of marine fishes to climate warming and to define ecological indicators of system-level changes. This data is part of the long-term NIOZ kom-fyke monitoring program (https://www.nioz.nl/en/expertise/wadden-delta-research-centre/expertise-wadden/fish/kom-fyke-monitoring)

# Reproductive data Fagus sylvatica: Widespread masting breakdown in beech [https://doi.org/10.5061/dryad.qz612jmps](https://doi.org/10.5061/dryad.qz612jmps) This dataset, used in the Global Change Biology article "Widespread breakdown in masting in European beech due to rising summer temperatures", contains 50 time series of population-level annual reproductive data by European beech (*Fagus sylvatica*, L) across Europe. The dataset builds on the open-access dataset [MASTREE+](https://doi.org/10.1111/gcb.16130), and expands it for European beech. ## Description of the data The dataset column names follow that of MASTREE+. A description of MASTREE+ column names (Modified from Table 1 in the [MASTREE+ article)](https://doi.org/10.1111/gcb.16130): | *Columns* | *Description* | *Contains NA?* | | :-------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------- | | Alpha\_Number | Unique code associated with each original source of data, that is, the publication, report or thesis containing extracted data, or the previously unpublished data set included in MASTREE+. | No | | Segment | Temporal segment of a time-series containing gaps (note that years with no observations are not recorded). Individual timeseries can consist of multiple segments. | No | | Site\_number | Code to differentiate multiple sites from the same original source (Alpha\_Number/Study\_ID). | No | | Variable\_number | Code to differentiate multiple measures of reproductive output from the same species-site combination (e.g. where seeds and cones were recorded separately). | No | | Year | Year of observation. | No | | Species | Species identifier, standardised to The Plant List nomenclature. ‘spp.’ is used to indicate a record identified to the genus level only. ‘MIXED’ indicates a non-species-specific community-level estimate of annual reproductive effort. | No | | Species\_code | Six-character species identifier. | No | | Mono\_Poly | Monocarpic (semelparous) or Polycarpic (iteroparous) species. | No | | Value | The measured value of annual reproductive output. | No | | VarType | Continuous or ordinal data. Continuous time-series are recorded on a continuous scale. Ordinal series are recorded on an ordered categorical scale. All ordinal series are rescaled to start at 1 (lowest reproductive effort) and to contain only integer values. | No | | Max\_value | The unit of measurement, where VarType is continuous (otherwise: NA). | No | | Unit | The maximum value in a time-series. | No | | Variable | Categorical classification of the measured variable. Options limited to: cone, flower, fruit, seed, pollen, total reproduction organs. | No | | Collection\_method | Classification of the method used to measure reproductive effort. Options are limited to: cone count, cone scar count, flower count, fruit count, fruit scar sound, seed count, seed trap, pollen count, lake sediment pollen count, harvest record, visual crop assessment, other quantification, dendrochronological reconstruction. | No | | Latitude | Latitude of the record, in decimal degrees. | No | | Longitude | Longitude of the record, in decimal degrees. | No | | Coordinate\_flag | A flag to indicate the precision of the latitude and longitude. A = coordinates provided in the original source B = coordinates estimated by the compiler based on a map or other location information provided in the original source C = coordinates estimated by the compiler as the approximate centre point of the smallest clearly defined geographical unit provided in the original source (e.g. county, state, island), and potentially of low precision. | No | | Site | A site name or description, based on information in the original source. | No | | Country | The country where the observation was recorded. | No | | Elevation | The elevation of the sample site in metres above sea level, where provided in the original source (otherwise: NA). | Yes | | Spatial\_unit | Categorical classification of spatial scale represented by the record, estimated by the compiler based on information provided in the original source. stand = <100 ha, patch = 100–10,000 ha, region = 10,000–1,000,000 ha, super-region = >1,000,000 ha. | No | | No\_indivs | Either the number of monitored individual plants, or the number of litter traps. NA indicates no information in the original source, and 9999 indicates that while the number of monitored individuals was not specified, the source indicated to the compiler that the sample size was likely ≥10 individuals or litter traps. | No | | Start | The first year of observations for the complete time-series, including all segments. | No | | End | The final year of observations for the complete time-series, including all segments. | No | | Length | The number of years of observations. Note that may not be equal to the number of years between the Start and End of the time-series, due to gaps in the time-series. | No | | Reference | Identification for the original source of the data. | No | | Record\_type | Categorisation of the original source. Peer-reviewed = extracted from peer reviewed literature Grey = extracted from grey literature Unpublished = unpublished data. | No | | ID\_enterer | Identification of the original compiler of the data. AHP, Andrew Hacket-Pain; ES, Eliane Schermer; JVM, Jose Moris; XTT, Tingting Xue; TC, Thomas Caignard; DV, Davide Vecchio; DA, Davide Ascoli; IP, Ian Pearse; JL, Jalene LaMontagne; JVD, Joep van Dormolen. | No | | Date\_entry | Date of data entry into MASTREE+ in the format yyyy-mm-dd. | No | | Note on data location | Notes on the location of the data within the original source, such as page or figure number. If not provided, NA. | Yes | | Comments | Additional comments. If not provided, NA. | Yes | | Study\_ID | Unique code associated with each source of data. M\_ = series extracted from published literature; A\_ = series incorporated from Ascoli et al. (2020), Ascoli, Maringer, et al. (2017) and Ascoli, Vacchiano, et al. (2017); PLK\_ = series incorporated from Pearse et al. (2017); D\_ = unpublished data sets. NA is attributed if no study ID has been previously associated with this time-series in MASTREE+ v.1. | Yes | Note that the new beech reproductive data has been assigned an arbitrary Alpha_Number for the purpose of this study. Future MASTREE+ updates which incorporate this new data may alter the time series ID columns (e.g. Alpha_Number, Site_number, Variable_number). MASTREE+ updates can be found on [GITHUB](https://github.com/JJFoest/MASTREEplus). Climate change effects on tree reproduction are poorly understood even though the resilience of populations relies on sufficient regeneration to balance increasing rates of mortality. Forest-forming tree species often mast, i.e. reproduce through synchronised year-to-year variation in seed production, which improves pollination and reduces seed predation. Recent observations in European beech show, however, that current climate change can dampen interannual variation and synchrony of seed production, and that this masting breakdown drastically reduces the viability of seed crops. Importantly, it is unclear under which conditions masting breakdown occurs, and how widespread breakdown is in this pan-European species. Here, we analysed 50 long-term datasets of population-level seed production, sampled across the distribution of European beech, and identified increasing summer temperatures as the general driver of masting breakdown. Specifically, increases in site-specific mean maximum temperatures during June and July were observed across most of the species range, while the interannual variability of population-level seed production (CVp) decreased. The declines in CVp were greatest where temperatures increased most rapidly. Additionally, the occurrence of crop failures and low-seed years has decreased during the last four decades, signalling altered starvation effects of masting on seed predators. Notably, CVp did not vary among sites according to site mean summer temperature. Instead, masting breakdown occurs in response to warming local temperatures (i.e. increasing relative temperatures), such that the risk is not restricted to populations growing in warm average conditions. As lowered CVp can reduce viable seed production despite the overall increase in seed count, our results warn that a covert mechanism is underway that may hinder the regeneration potential of European beech under climate change, with great potential to alter forest functioning and community dynamics.

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.DWD.MPI-ESM1-2-HR' 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-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 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 Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

# Data and code for: Modelling the carbon balance in bryophytes and lichens: presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects ## Description of the data and file structure ### **File list** · Nikolic_et_al_2023_CO2_curve_data_Lange_2002.csv · Nikolic_et_al_2023_Light_curve_data_Lange_2004.csv · Nikolic_et_al_2023_Tempetarure_curve_data_Lange_2004.csv · Nikolic_et_al_2023_Tempetarure_dark_respiration_curve_data_Lange_2004.csv · Nikolic_et_al_2023_Water_curve_data_Lange_2004.csv · Nikolic_et_al_2023_Water_dark_respiration_curves_data_Lange_2002.csv · Nikolic_et_al_2023_Microclimatic_input_data_17-Sept-01-Oct-1993_Lange_2004.csv · Nikolic_et_al_2023_Parameters_for_the_model_P_aurata_and_L_muralis.csv · Nikolic_et_al_2023_Microclimatic_input_data_17-24-Sep-93.csv · Nikolic_et_al_2023\_ Microclimatic_input_data_24-Sep-1-Oct-93.csv · Nikolic_et_al_2023_Getting_parameters_from_response_curves.R · Nikolic_et_al_2023_PoiCarb_model.R ### **File descriptions** **Nikolic_et_al_2023_CO2_curve_data_Lange_2002.csv** Data of measured responses of CO2-exchange rates to different CO2 levels. Gas-exchange measurements were made on the lichen *Protoparmeliopsis muralis (Lange, 2002).* We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: CO2abs – CO2 concentration in ppm A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Light_curve_data_Lange_2004.csv** Data of measured responses of CO2-exchange rates (net photosynthesis and dark respiration) to different light (PAR) levels. Gas-exchange measurements were made on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004). We did not have access to original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: PAR - Photosynthetic Active Radiation expressed in µmol m-2 s-1 A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Tempetarure_curve_data_Lange_2004.csv** Data of measured responses of CO2-exchange rates (net photosynthesis and dark respiration) to different temperature levels. Gas-exchange measurements were made on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004). We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: Tcuv - Temperature in Celsius degrees measured A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Tempetarure_dark_respiration_curve_data_Lange_2004.csv** Data of measured responses of CO2-exchange rates (dark respiration) to different temperature levels. Gas-exchange measurements were made on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004). We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: Tcuv - Temperature in Celsius degrees measured A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Water_curve_data_Lange_2004.csv** Data of measured responses of CO2-exchange rates to changes in lichen water content. Gas-exchange measurements were made on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004). We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: WC - Relative Water content expressed in % of the dry mass A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Water_dark_respiration_curves_data_Lange_2002.csv** Data of measured responses of CO2-exchange rates (dark respiration) to changes in lichen water content. Gas-exchange measurements were made on the lichen *Protoparmeliopsis muralis (Lange, 2002).* We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: WC - Relative Water content expressed in % of the dry mass A – The instantaneous gas-exchange rate in µmol m-2 s-1 **Nikolic_et_al_2023_Microclimatic_input_data_17-Sept-01-Oct-1993_Lange_2004.csv** Microclimatic data together with gas-exchange measurements data which we used for model validation and also to run the climate change experiments examples. There are data for 15 days of in situ gas-exchange measurements on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004) together with the following climatic factors: air temperature, PAR, and lichen water content, determined at the same time as the CO2-exchange measurements. We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: Datum – date of each record in the form: 17-Sep-93 time – date and time of each record PAR - Photosynthetic Active Radiation expressed in µmol m-2 s-1 T - Temperature in Celsius degrees measured WC - Relative Water content expressed in % of the dry mass CO2 - CO2 levels expressed in ppm Ameasured – Measured gas-exchange rate in nmolg-1s-1 dWC – Difference in water content between two measurements (this we used to determine coefficient k, would not be needed if you have the water loss curve measured on different VPDs) coef_k – drying speed coefficient start – contains the date and time for the beginning of the daylight for each day, the rest of the column is filled with NAs (NA stands for not available, this is how the missing values are represented in R). This column is added to the original data to be able to plot the periods of daylight and night in different colors end – contains the date and time for the end of the daylight for each day, the rest of the column is filled with NAs (NA stands for not available, this is how the missing values are represented in R). This column is added to the original data to be able to plot the periods of daylight and night in different colors day_night - contains the string value either day, night or NA (NA stands for not available, this is how the missing values are represented in R), this column is added to the original data to be able to plot the periods of daylight and night in different colors **Nikolic_et_al_2023_Parameters_for_the_model_P_aurata_and_L_muralis.csv** Table with parameters we used for validation. To use the PoiCarb 1.0 model, you will need a table like this with parameters for your species. You can obtain the same table by running the **Nikolic_et_al_2023_Getting_parameters_from_response_curves.R** Explanation for each column in the file: LC_par_a, LC_par_b, LC_par_c are the columns containing parameters from the light-response curve; WC_par_a, WC_par_b, WC_par_c are the columns containing parameters from the water-response curve; WC_Rd_par_a, WC_Rd_par_b, WC_Rd_par_c are the columns containing parameters from the dark respiration water-response curve; CO2_par_a, CO2_par_b, CO2_par_c are the columns containing parameters from the CO2-response curve; T_par_a, T_par_b, T_par_c are the columns containing parameters from the temperature-response curve; T_Rd_par_a, T_Rd_par_b are the columns containing parameters from the dark respiration temperature-response curve. **Nikolic_et_al_2023_Microclimatic_input_data_17-24-Sep-93.csv** **Nikolic_et_al_2023\_ Microclimatic_input_data_24-Sep-1-Oct-93.csv** These two files contain microclimatic data, the same columns and data as in Nikolic_et_al_2023_Microclimatic_input_data_17-Sept-01-Oct-1993_Lange_2004.csv, just separated into two different files, it was better for plotting. **Nikolic_et_al_2023_Getting_parameters_from_response_curves.R** R script to be used to get the parameters from the environmental gas exchange response curves and drying speed curves. **Nikolic_et_al_2023_PoiCarb_model.R** PoiCarb model R script. The script is commented, in case something is not clear enough or you have questions write to the author (). ## Sharing/Access information Data was derived from the following sources: * Lange, O. L. 2002. Photosynthetic productivity of the epilithic lichen *Lecanora muralis*: Long-term field monitoring of CO2 exchange and its physiological interpretation. I. Dependence of photosynthesis on water content, light, temperature, and CO2 concentration from laboratory measurements. *Flora *197: 233–249. * Lange, O. L., B. Büdel, H. Zellner, G. Zotz, and A. Meyer. 1994. Field measurements of water relations and CO2 exchange of the tropical, cyanobacterial basidiolichen *Dictyonema glabratum* in a Panamanian rainforest*. *Botanica Acta* 107: 279–290. ## Code/Software There are two R scripts that can be downloaded together with the data. Nikolic_et_al_2023_Getting_parameters_from_response_curves.R and Nikolic_et_al_2023_PoiCarb_model.R. Both scripts are commented (have explanations and notes how to use them). Premise Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO2 exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO2 exchange rates of these poikilohydric organisms change through time as a function of weather conditions. Methods PoiCarb simulates diel fluctuations of CO2 exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO2-exchange responses to light, temperature, atmospheric CO2 concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO2 exchange to such courses from field measurements on the tropical lichen Crocodia aurata. To demonstrate the model's usefulness, we simulated potential climate-change effects. Results Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration. Conclusions Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation. Usage Notes We here present the data and code used in this paper. The list of data files together with their detailed explanations can be found in the README.PDF

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.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.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 MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.