Overview The Human Vital Signs Dataset is a comprehensive collection of key physiological parameters recorded from patients. This dataset is designed to support research in medical diagnostics, patient monitoring, and predictive analytics. It includes both original attributes and derived features to provide a holistic view of patient health.

Attributes Patient ID

Description: A unique identifier assigned to each patient. Type: Integer Example: 1, 2, 3, ... Heart Rate

Description: The number of heartbeats per minute. Type: Integer Range: 60-100 bpm (for this dataset) Example: 72, 85, 90 Respiratory Rate

Description: The number of breaths taken per minute. Type: Integer Range: 12-20 breaths per minute (for this dataset) Example: 16, 18, 15 Timestamp

Description: The exact time at which the vital signs were recorded. Type: Datetime Format: YYYY-MM-DD HH:MM Example: 2023-07-19 10:15:30 Body Temperature

Description: The body temperature measured in degrees Celsius. Type: Float Range: 36.0-37.5°C (for this dataset) Example: 36.7, 37.0, 36.5 Oxygen Saturation

Description: The percentage of oxygen-bound hemoglobin in the blood. Type: Float Range: 95-100% (for this dataset) Example: 98.5, 97.2, 99.1 Systolic Blood Pressure

Description: The pressure in the arteries when the heart beats (systolic pressure). Type: Integer Range: 110-140 mmHg (for this dataset) Example: 120, 130, 115 Diastolic Blood Pressure

Description: The pressure in the arteries when the heart rests between beats (diastolic pressure). Type: Integer Range: 70-90 mmHg (for this dataset) Example: 80, 75, 85 Age

Description: The age of the patient. Type: Integer Range: 18-90 years (for this dataset) Example: 25, 45, 60 Gender

Description: The gender of the patient. Type: Categorical Categories: Male, Female Example: Male, Female Weight (kg)

Description: The weight of the patient in kilograms. Type: Float Range: 50-100 kg (for this dataset) Example: 70.5, 80.3, 65.2 Height (m)

Description: The height of the patient in meters. Type: Float Range: 1.5-2.0 m (for this dataset) Example: 1.75, 1.68, 1.82 Derived Features Derived_HRV (Heart Rate Variability)

Description: A measure of the variation in time between heartbeats. Type: Float Formula: 𝐻 𝑅

𝑉

Standard Deviation of Heart Rate over a Period Mean Heart Rate over the Same Period HRV= Mean Heart Rate over the Same Period Standard Deviation of Heart Rate over a Period ​

Example: 0.10, 0.12, 0.08 Derived_Pulse_Pressure (Pulse Pressure)

Description: The difference between systolic and diastolic blood pressure. Type: Integer Formula: 𝑃

𝑃

Systolic Blood Pressure − Diastolic Blood Pressure PP=Systolic Blood Pressure−Diastolic Blood Pressure Example: 40, 45, 30 Derived_BMI (Body Mass Index)

Description: A measure of body fat based on weight and height. Type: Float Formula: 𝐵 𝑀

𝐼

Weight (kg) ( Height (m) ) 2 BMI= (Height (m)) 2

Weight (kg) ​

Example: 22.8, 25.4, 20.3 Derived_MAP (Mean Arterial Pressure)

Description: An average blood pressure in an individual during a single cardiac cycle. Type: Float Formula: 𝑀 𝐴

𝑃

Diastolic Blood Pressure + 1 3 ( Systolic Blood Pressure − Diastolic Blood Pressure ) MAP=Diastolic Blood Pressure+ 3 1 ​ (Systolic Blood Pressure−Diastolic Blood Pressure) Example: 93.3, 100.0, 88.7 Target Feature Risk Category Description: Classification of patients into "High Risk" or "Low Risk" based on their vital signs. Type: Categorical Categories: High Risk, Low Risk Criteria: High Risk: Any of the following conditions Heart Rate: > 90 bpm or < 60 bpm Respiratory Rate: > 20 breaths per minute or < 12 breaths per minute Body Temperature: > 37.5°C or < 36.0°C Oxygen Saturation: < 95% Systolic Blood Pressure: > 140 mmHg or < 110 mmHg Diastolic Blood Pressure: > 90 mmHg or < 70 mmHg BMI: > 30 or < 18.5 Low Risk: None of the above conditions Example: High Risk, Low Risk This dataset, with a total of 200,000 samples, provides a robust foundation for various machine learning and statistical analysis tasks aimed at understanding and predicting patient health outcomes based on vital signs. The inclusion of both original attributes and derived features enhances the richness and utility of the dataset.

This data set provides a soil map with estimates of soil carbon (C) in g C/m2 for 20-cm layers from the surface to one meter depth for the conterminous United States.STATSGO v.1 (State Soil Geographic Database, Soil Survey Staff, 1994) data were used to estimate by 20-cm intervals to a 1-m depth the mean soil carbon for each of the STATSGO-delineated soil map units. These map units are the polygons represented in the provided Shapefile data product.

Important note: If you get a message that .zip archive is corrupt, please try updating WinRAR or right-click the folder and select Extract All on Windows or use unzip command on Linux terminal. If the issue persists, email: kamath.harsh@utexas.edu

Abstract

We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

Data

We are also supplying a vector file to represent the data coverage, and this file will receive updates as data for new city is added. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). These formats are compatible with the SUEWS and SOLWEIG models for the simulation of urban energy balance and thermal comfort. The vector files employ the Universal Transverse Mercator (UTM) projection. Both the vector and raster files are compatible with GIS platforms like QGIS and ArcGIS and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the BEP-BEM urban model in the urban WRF system in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These files can be directly incorporated into the WRF pre-processing system (WPS). The UT-GLOBUS UCPs are determined using a moving kernel with a size of 1 km2 and spacing of 300 meters in both the X and Y directions

Data coverage

The 'Coverage_xxxx.gpkg' files provide that geographical extents of cities that are included in our dataset.

How to find your city in the UT-GLOBUS dataset

Open the 'coverage' geopackage (.gpkg) files in QGIS or ArcGIS. Click on the city polygons and get the 'Label'/City name. Find a folder with the same 'Label'/City name. All the data for the periticular city will be in the folder.

How to run BEP-BEM model in WRF using UT-GLOBUS urban canopy parameters

The dataset in this record is the output of the CCSM4 (Community Climate System Model version 4). Please see the collection reference below for outputs from the other models described here.These data are the outputs of three general circulation climate models (GCMs), CCSM4, MRI-CGCM3, and IPSL-CM5A-LR for the period 1950-2100. Runs of each GCM were carried out as part of the fifth phase of the Coupled Model Intercomparison Project. Future runs were forced with the RCP 8.5 emissions scenario. They were downscaled to a one km spatial resolution using a quantile matching approach. The three GCMs were chosen because they were shown to recreate climate well in Alaska during the last few decades and because they span the range of potential conditions during the 21st century as projected by all climate models included in the IPCC AR5. Variables include daily minimum and maximum Temperature (°C), daily sum of precipitation (mm), daily sum of shortwave radiation (Mj m-2), and mean VPD (kPa). This dataset includes the following files. The gridded netCDFs are provided as compressed .tar.gz files. Extensive metadata is embedded within each netCDF.CCSM4-hist-prcp.tar.gz : Daily precipitation (mm) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-prcp.tar.gz : Daily precipitation (mm) for 2006-2100 from the CCSM4 GCM.CCSM4-hist-srad.tar.gz : Daily shortwave radiation (mJ m-2) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-srad.tar.gz : Daily shortwave solar radiation (mJ m-2) for 2006-2100 from the CCSM4 GCM.CCSM4-hist-tmax.tar.gz : Daily maximum temperature (deg C) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-tmax.tar.gz : Daily maximum temperature (deg C)for 2006-2100 from the CCSM4 GCM.CCSM4-hist-tmin.tar.gz : Daily minimum temperature (deg C) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-tmin.tar.gz : Daily minimum temperature (deg C)for 2006-2100 from the CCSM4 GCM.CCSM4-hist-vap.tar.gz : Daily vapor pressure (kPa) for 1950-2005 from the CCSM4 GCM.

CCSM4-future-vap.tar.gz : Daily vapor pressure (kPa) for 2006-2100 from the CCSM4 GCM.

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

This dataset consists of measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer in woodland savanna using eddy covariance techniques.

The site is woodland savanna with an overstory co-dominated by tree species E. tetrodonta, C. latifolia, Terminalia grandiflora, Sorghum sp. and Heteropogon triticeus. Average canopy height measures 16.4 m.
Elevation of the site is close to 110m and mean annual precipitation at a nearby Bureau of Meteorology site is 1170mm. Maximum temperatures range from 37.5°C (in October) to 31.2°C (in June), while minimum temperatures range from 12.6°C (in July) to 23.8°C (in January). Maximum temperatures range seasonally by 6.3°C and minimum temperatures by 11.2°C.

The instrument mast is 23 meters tall. Heat, water vapour and carbon dioxide measurements are taken using the open-path eddy flux technique. Temperature, humidity, wind speed, wind direction, rainfall, incoming and reflected shortwave radiation and net radiation are measured above the canopy.
Ancillary measurements taken at the site include LAI, leaf-scale physiological properties (gas exchange, leaf isotope ratios, N and chlorophyll concentrations), vegetation optical properties and soil physical properties. Airborne based remote sensing (Lidar and hyperspectral measurements) was carried out across the transect in September 2008.

This data is also available at http://data.ozflux.org.au .

Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here.

1. Datasets and model used

The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T_MRT) and the UTCI (Lindberg et al., 2008). T_MRT is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS.

2. Data availability

Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy

Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.

3. Data format

The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.

4. Companion dataset

Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]

References

1. Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.
2. Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. International journal of biometeorology, 52, 697-713.
3. Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. Environmental modelling & software, 99, 70-87.
4. Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. International journal of biometeorology, 56, 421-428.
5. Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.
6. Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. Urban Climate, 51, 101656.

Projections of extreme event metrics and threshold exceedances are produced by analyzing the Climate Model Intercomparison Program Phase 6 Localized Constructed Analogs (CMIP6-LOCA2) data set. The primary daily temperature and precipitation data are summarized to 36 annual metrics and 4 monthly metrics. This data set includes output from 27 GCMs for the period 1950-2100 under ssp245, ssp370, and ssp585 scenarios for the Contiguous United States with partial coverage in Mexico and Canada. To support climate research within and outside the Department of Interior these data are distributed in a variety of formats: individual model grids for all years, gridded climatologies (1961-1990, 1971-2000, 1981-2010, 1991-2020, and Global Warming Levels +1.5 °C, +2.0 °C, +3.0 °C), and time series spatially averaged to United States county and watershed boundaries (HUC8 from the Watershed Boundary Dataset). Ensemble averages are provided for the Weighted Multi-Model Mean (WMMM) and Multi-Model M ...

This data set provides selected ground temperature time series of long-term established boreholes in permafrost regions. Data include mean annual ground temperature (MAGT), borehole ID's, depths of measurements in a borehole, coordinates (lat, long, elevation), and principle investigators of the boreholes. Data comprise averaged values per year in °C from 1978 to 2016 CE at depths ranging from 3.6 to 24.4 m (mean 16.2 m) below surface. The entire data set provides n=483 temperature values and was used, including methods description, in the overview figure in Biskaborn et al. 2019 (Fig. 1). […]

Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Data for Box TS4 from Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Box TS4, Figure 1 shows global mean sea level change on different time scales and under different scenarios.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144, doi:10.1017/9781009157896.002.

When citing the SSP-based sea-level projections, please also include the following citation: Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Global Mean Sea-Level Rise Projections. Version 20210809. https://doi.org/10.5281/zenodo.5914710.

Figure subpanels

The figure has three panels. Panel a shows global mean sea level (GMSL) change from 1900 to 2150, observed (1900–2018) and projected under the Shared Socioeconomic Pathway (SSP) scenarios (2000–2150). Panel b shows GMSL change on 100-, 2,000-, and 10,000-year time scales as a function of global surface temperature. Panel c shows timing of exceedance of different GMSL thresholds under different SSPs.

Final data is only available for panel c.

List of data provided

This dataset contains:

Global mean sea level change time-series from 1901-2150 for: - Observed global mean sea level change (1901-2018). - Projected global mean sea level change (2005-2150).

Data provided in relation to figure

Data provided in relation to Box TS4, Figure 1:

SSP-based global mean sea level projections are archived as

Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., Nowicki, S., Palmer, M. D., Smith, C., Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Krinner, G., Mix, A., … Pearson, B. (2021). IPCC AR6 Sea Level Projections (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5914710

Panel c:

• FigTS4-1c-milestone_ssp119_data.nc
• FigTS4-1c-milestone_ssp126_data.nc
• FigTS4-1c-milestone_ssp126_data.nc
• FigTS4-1c-milestone_ssp126_data.nc
• FigTS4-1c-milestone_ssp126_data.nc

See sections 9.6.3.2 and 9.6.3.3 for detailed information on the SSP-based global mean sea level projections and their production.

Notes on reproducing the figure from the provided data -------... For full abstract see: https://catalogue.ceda.ac.uk/uuid/923b94820acd42a1888eaae24de328f8.

This data set provides estimates of daily mean two meter air temperature from the European Center for Medium Range Weather Forecasts (ECMWF) ERA-40 Reanalysis (ECMWF 2002). Data cover the period 1 January 1979 to 31 August 2002. Data were obtained from the National Center for Atmospheric Research (NCAR) Data Support Section, dataset ds117.0 (). Two meter temperatures are archived as 6 hour mean values. Daily means were computed from these values. Data were regridded from the native N80 (roughly 1 degree resolution) reduced Gaussian grid to the Equal-Area Scalable Earth (EASE) 25km grid using a bilinear interpolation scheme. This scheme is described in ECMWF [2003]. The ERA-40 system assimilates available surface temperature observations. Fields should therefore be superior to those from the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) re-analysis, in which there is no assimilation of surface temperature data. The data is presented in 24 sub-datasets of different spatial and temporal aggregations.

See README file

Input Data for Figure 12.7 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 12.7 shows projected changes in selected climatic impact-driver indices for Australasia.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.

Figure subpanels

The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).

List of data provided

This dataset contains: - spatial field over Australasia of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5 - Shoreline position change over Australasia (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020) - regional averages in Australasia of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for: - CMIP6 historical, ssp126 and ssp585 - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5 - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C - regional averages in Australasia of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)

NAU, CAU, EAU, SAU and NZ are domains used in the model.

Data provided in relation to figure

Data provided in relation to Figure 12.7:

Panel a: - Q100_map_panel_a_AUS_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2015 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the AUS CORDEX domain

Panel b: - CoastalRecession_Australasia_RCP85_2100.json: pointwise values (color points on the map) for Australasia of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)

Panel c: - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with: - ${ensemble}: CMIP5, CMIP6 or CORDEX-core - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85 - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2080-2099 (long term) - ${CORDEX_domain}: the CORDEX domain - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with: - ${ensemble}: CMIP5, CMIP6 or CORDEX-core - ${GWL}: the Global Warming Level: 1.5, 2 and 4 - ${CORDEX_domain}: the CORDEX domain

Panel d: - globalErosionProjections_by_AR6_region_${scenario}_${horizon).json: regional averages of shoreline position changes ... For full abstract see: https://catalogue.ceda.ac.uk/uuid/537b22f0230448fdb9a4ec806ed54d84.

This data set was used in Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2023-15, in review, 2023 to allow the spinup for the ice shee model PISM. 'temp_time_seris,oisotopestimes ,sealeveltimes,sealevel_time_series' were used. Originally this data set was provided by the University of Montana ("Jesse Johnson, Brian Hand, Tim Bocek - University of Montana" )

but was offline during publication. We therefore upload it here, so it can be assessd. Original descripion of the sea rise master data set was:

There are currently three different versions of the Greenland Present Day Data Set available: * Greenland Obsolescent Data Set (Greenland_5km_dev1.0.nc) * Greenland Standard Data Set ((Greenland_5km_v1.1.nc) * Greenland Developmental Data Set (Greenland_5km_dev1.2.nc) Each of these data sets contains the fields listed below. Data Fields: * Longitude * Latitude * Bed Topography (Bamber 2001) and bathymetry (Jakobsson et al. 2008). Data courtesy of Ed Bueler. See note 1 below. * Ice Thickness (Bamber 2001). See note 1 below. * Surface Elevaton (Bamber 2001). See note 1 below. * Mean Annual Near-surface (2 meter) Air Temperature. See note 2 below. * Mean Annual Precipitation. See note 2 below. * Basal Heat Flux (Shapiro and Ritzwoller 2004). Data courtesy of Ed Bueler. * Interferometrically Measured Surface Velocity (Joughin, Smith, Howat, and Scambos, in preparation). * Surface Balance Velocity - Created at the University of Montana by Jesse Johnson in July 2009. * Time Rate of Change of Ice Sheet Surface Height (Bea Csathol, Toni. Schenk, C.J. van der Veen, William B Krabill, Presented at the AGU 2009 Fall Meeting). * Land Cover (Bea Csathol, Toni. Schenk, C.J. van der Veen, William B Krabill, Presented at the AGU 2009 Fall Meeting). * Oxygen Isotopes Record and associated Temperature Time Series from the Greenland Ice Core Project (GRIP). Notes: 1. The bed topography in each of the three available data sets have been modified from that given by Bamber to incorporate the Center for Remote Sensing of Ice Sheets (CReSIS) data in the Jakobshavn region. In the "Obsolescent" and "Standard" Data Sets local spatial averages of the CReSIS data were calculated for each 5km grid point. Values on the border of the region for which there is CReSIS data were assigned an average of the new values and the original values to decrease artificial gradients outside. The "Developmental" Data Set uses an algorithm developed by Ute Herzfeld which preserves the continuity and depth of the trough below the glacier. The changes to the bed topography in the Jakobshavn region also affect the ice thickness and upper surface fields. (The upper surface is only affected at a few grid points where the Cresis data places the topography above the original upper ice surface.) 2. The climate data differs between the "Obsolescent" Data Set and the later "Standard" and "Developmental" Data Sets. The "Obsolescent" Data Set uses the temperature parameterization of Fausto et al (2009) and a juxtaposition of precipitation data provided by Evan Burgess (Burgess et al 2009) for regions where there is permanent ice with data provided by Bea Csatho (van der Veen, Bromwich, Csatho, and Kim 2001) for regions where there is not permanent ice. The later ("Standard" and "Developmental") Data Sets use climate data provided by Janneke Ettema (Ettema et al 2009). This data includes Runoff, Surface Mass Balance, and Surface Temperature fields in addition to the Two-meter Temperature and Precipitaion fields. Janneke Ettema (personal correspondence) provides the following comment: "I would recommend to use the surface temperature as boundary condition for ice dynamic model instead of the 2 meter temperature. They might differ significantly, especially for Greenland where Ts is limited to 0C and T2m could rise over the melting point. Furthermore, T2m is a result of interpolating the temperature at the lowest atmospheric model layer and the surface temperature using a certain lapse rate. The surface temperature is a direct result from the energy balance computed at the ice sheet surface."

Abstract: MODIS Collection 6.1 8-day gap-filled Gross Primary Production (GPP) and Net Photosynthesis data on the MODIS sinusoidal grid are taken from the netCDF files produced at ICDC, for which the bit-encoded quality information given in the HDF-files was already decoded, and re-gridded to build a global map of grid-cell mean GPP and net photosynthesis and their variances on a global equirectangular climate modeling grid (CMG). Only those GPP or net photosynthesis values are used where i) the cloud flag indicates either clear sky or assumed clear sky, where the MODLAND quality is good and where the confidence flag suggests best quality or good quality data. The confidence flag is provided as a grid-cell mean rounded value with fractions of the five original flags being provided for convenience. Cloud conditions are included in form of the primary cloud flag and the fraction this primary cloud flag occupies among the valid 500 m sinusoidal grid grid cells. Two separate layers of the number of valid grid cells of the 500 m sinusoidal grid are given, one is for the geophysical data and one is for the flags.

TableOfContents: grid cell mean Gross_Primary_Production (GPP); grid cell mean Net Photosynthesis; GPP standard deviation over grid cell; Net Photosynthesis standard deviation over grid cell; number of used GPP or net photosynthesis values per grid cell; number of used confidence and quality flag values per grid cell; grid cell mean confidence flag; fraction of confidence flag 0 in grid cell; fraction of confidence flag 1 in grid cell; fraction of confidence flag 2 in grid cell; fraction of confidence flag 3 in grid cell; fraction of confidence flag 4 in grid cell; primary cloud flag; primary cloud flag fraction

Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2023-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA

Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005

Units: Units for all variables (see TableOfContents): kg C m-2; kg C m-2; kg C m-2; kg C m-2; 1; 1; 1; percent; percent; percent; percent; percent; 1; percent

geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land

Size: (files are packed into one zip-archive per year)

• 2001-2021 and 2023: 46 files per year, each approximately 12975000 bytes
• 2000: 40 files of same size
• Data of the year 2022 are not published because data provided by LPDAAC seemed to be not reliable

Format: netCDF

DataSources:

Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A2HGF.061 (last accessed: 2024-05-07), see also https://lpdaac.usgs.gov/products/mod17a2hgfv061/ (last accessed: 2024-05-07)

Data on sinusoidal grid tiles in netCDF format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-05-10) or https://doi.org/10.25592/uhhfdm.14463 (last accessed: 2024-06-25).

Contact: stefan.kern (at) uni-hamburg.de

Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-05-10)

Meteorological data collected by the automatic weather stations at the Troll research station and Troll airfield in Dronning Maud Land, Antarctica. There usually is one datapoint per minute from each station.

Data

Raw data from the stations is stored in near real-time as 'json' in a REST API. In addition a yearly dump is made available as a gzip compressed 'ndjson' file (these can be found under the files tab located directly below the dataset title).

Yearly dumps are labeled as follows:

• 2023-Airfield/Station-01/02.ndjson.gz:
  • 2023: year of the dump.
  • Airfield/Station: location of the station.
  • 01/02: '01' indicates the legacy stations and '02' indicates the current stations.
  • ndjson: data format (newline delimited json).
  • gz: compression format (gzip)

Data is collected in a best effort manner which means that every now and then gaps have occurred. Where possible we've attempted to backfill these gaps with the same data stored in a different meteorological database. It has to be noted that the backfill data contains less parameters than the regular records and has a lower temporal resolution.

NOTE! As indicated this is RAW data and no quality assurance has been performed! Make sure to perform sanity checks before further usage of this data.

Check below for an explanation of the parameters used by the different stations.

Current Troll research station (station_id = kldata/nationalnr=99990/type=504)

The current sensor station at Troll research station was set up in October 2017 at coordinates approximately -72.011861N, 2.541167E.

Pressure (P*) is measured by a Vaisala PTB330 barometer about 1.5 meters above ground.

Wind variables (D*, F*, KLF*) are measured by two anemometers, 2 meters (*0_2) and 10 meters (*0_0) above ground. Until February 2020 these were model Gill WindObserver 75. While using those, data often were dropped when wind gusts reached about 50 m/s. From March 2020, these were replaced with RM Young 05106-45 sensors, solving the problem of dropped data at high wind speeds.

Temperature (T*) is measured with three sensors; T*0_0 by a PT100 1/10 DIN in an unventilated shield, 2 meters above ground and T*1_0 and T*0_10 by a Vaisala HMP155A in a ventilated radiation shield, 2 and 10 meters above ground, respectively. The two Vaisala HMP155A also measure humidity (UU*)

Battery voltage and time ("measured") is monitored by a Campbell CR1000 datalogger.

• AA0_0 : Barograph curve indicating pressure variations during the last 3 hours. See below for details.
• BAT0_0 : Battery voltage in V.
• DD0_x : Mean wind direction during the last 10 minutes in degrees.
• DX0_x : Maximum wind direction 10 minute gliding mean during the last hour in degrees.
• FF0_x : Wind speed 10-minute vector average in m/s.
• FG0_x : Wind speed maximum 3 second gust during the last hour in m/s.
• FX0_x : Wind speed maximum 10 minutes mean during the last hour in m/s.
• KLFG0_x : Time when FG0_x was observed in [h]hmm.
• KLFX0_x : Time when FX0_x was observed in [h]hmm.
• PH0_0 : Sea level pressure, adjusted using the ICAO standard atmosphere in hPa.
• PO0_0 : Atmospheric pressure at station elevation in hPa.
• PON0_0 : Minimum PO0_0 in the last hour in hPa.
• POX0_0 : Maximum PO0_0 in the last hour in hPa.
• PP0_0 : Absolute value of the change in pressure from the observation 3 hours ago in hPa.
• PR0_0 : Sea level pressure, adjusted using the observed temperature in hPa.
• STATVENT0_x : Status of the ventilator in the radiation shields for the HMP155A: 0 ok; 1 fan not running.
• TAx_x : Air temperature mean during the last minute in °C.
• TANx_x : Minimum temperature during the last hour in °C.
• TAXx_x : Maximum temperature during the last hour in °C.
• UU0_x : Relative humidity in percent.
• id : UUID for this measurement and this station and date.
• measured : Observation date and time in UTC in ISO 8601 format.
• station_id : Station identification.

Current Troll airfield (station_id = kldata/nationalnr=92100/type=504)

The current sensor station at Troll airfield was set up in September 2018 at coordinates approximately -71.95685N, 2.46662E. In March 2019, the airfield sensor station got an upgrade so it delivered much more data.

Pressure (Px) is measured by a Vaisala PTB330 barometer about 1.5 meters above ground.

Wind variables (Dx, Fx, KLFx) are measured by a RM Young 5108-45 Alpine HD. This sensor was mounted 6 meters above ice surface until February 2020. Since March 2020, the sensor has been mounted 10 meters above the ice surface.

Temperature (Tx) is measured by a PT100 1/10 DIN in unventilated radiation shield 2 meters above ground.

Humidity (UU0_0) is measured by a Vaisala HMP155A in unventilated radiation shield 2 meters above ground.

Ice temperature (TIx) is measured by a Beaded Stream thermistor string in the ice below the station.

Battery voltage and time ("measured") is monitored by a Campbell CR1000 datalogger.

• AA0_0 : Barograph curve indicating pressure variations during the last 3 hours. See below for details.
• BAT0_0 : Battery voltage in V.
• DD0_6 : Mean wind direction during the last 10 minutes in degrees.
• DX0_6 : Maximum wind direction 10 minute gliding mean during the last hour in degrees.
• FF0_6 : Wind speed 10-minute vector average in m/s.
• FG0_6 : Wind speed maximum 3 second gust during the last hour in m/s.
• FX0_6 : Wind speed maximum 10 minutes mean during the last hour in m/s.
• KLFG0_6 : Time when FG0_6 was observed in [h]hmm.
• KLFX0_6 : Time when FX0_6 was observed in [h]hmm.
• PH0_0 : Sea level pressure, adjusted using the ICAO standard atmosphere in hPa.
• PO0_0 : Atmospheric pressure at station elevation in hPa.
• PON0_0 : Minimum PO0_0 in the last hour in hPa.
• POX0_0 : Maximum PO0_0 in the last hour in hPa.
• PP0_0 : Absolute value of the change in pressure from the observation 3 hours ago in hPa.
• PR0_0 : Sea level pressure, adjusted using the observed temperature in hPa.
• TA0_0 : Air temperature mean during the last minute in °C.
• TAN0_0 : Minimum temperature during the last hour in °C.
• TAX0_0 : Maximum temperature during the last hour in °C.
• TI0_x : Ice temperature at x cm below surface in °C.
• UU0_0 : Relative humidity in percent.
• id : UUID for this measurement and this station and date.
• measured : Observation date and time in UTC in ISO 8601 format.
• station_id : Station identification.

Legacy Troll research station (station_id = legacy_S01)

The first station was set up in September 2005 close to the research station at approximately -72.0115N, 2.5337E. It was decommissioned in January 2019. In the beginning, Vaisala WT501 sensors were used for wind measurements, but the one at 2 meters height stopped working in February 2007 and the one at 10 meters height stopped in December 2007. Both were replaced with Gill WindObserver instruments in April 2009. No wind data is available between those dates. The instrument changes affect the variables DD0_x, DG0_x, FF0_x, and FG0_x.

Pressure (Px) was measured by a Vaisala PTB220 barometer about 1.5 meters above ground.

Wind variables (Dx, Fx, KLFx) were first measured by Vaisala WT501 instruments and later (see above) by Gill WindObserver instruments. The sensors were 2 meters (Dx0_2) and 10 meters (Dx0_0) above ground.

Temperature (Tx) was measured by a PT100 1/10 DIN in unventilated radiation shield. The sensors were 2 meters (Tx0_0) and 10 meters (Tx0_10) above ground.

Humidity (UU0_0) was likely measured by a Vaisala HMP155A in unventilated radiation shield. The sensors were 2 meters (UU0_0) and 10 meters (UU0_10) above ground.

Temperature below ground (TGx) was measured by a thermistor string in the ice below the station.

Time ("measured") was monitored by a Campbell CR1000 datalogger.

The radiation data should be used with extreme caution; there are multiple large sources of error. The LW values were measured with Kipp & Zonen CG4 sensors and the SW values with Kipp & Zonen CM21 sensors. All four sensors were mounted in K&Z CV2 heating and ventilation units. Unfortunately, the temperatures of the sensors were not recorded and the LW values are not corrected for the instrument’s temperature. The best a user can do is assume the instrument had the same temperature as the air and perform a rough correction that way. This adds to the uncertainty in these data, which is large anyway because the sensors were not maintained or calibrated after installation. By 2017, when they were taken down, the glass domes on the CM21 sensors were severely sandblasted on the upwind (north-east) side. They could also be affected by morning shading from the nearby garage that was built north-east of the site in 2012.

• AA0_0 : Barograph curve indicating pressure variations during the last 3 hours. See below for details.
• DD0_x : Mean wind direction during the last 10 minutes in degrees.
• DG0_x : Wind direction in degrees.
• FF0_x : Wind speed 10-minute vector average in m/s.
• FG0_x : Wind speed maximum 3 second gust during the last minute in m/s.
• LWin : Incoming longwave (infrared) radiation in W/m². See warning above!
• LWout : Outgoing longwave (infrared) radiation in W/m². See warning above!
• PH0_0 : Sea level pressure, adjusted using the ICAO standard atmosphere in hPa.
• PO0_0 : Atmospheric pressure at station elevation in hPa.

- PP0_0 : Absolute value of the change in pressure from the observation 3 hours ago in hPa.

Input Data for Figure 12.5 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 12.5 shows projected changes in selected climatic impact-driver indices for Africa.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.

Figure subpanels

The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).

List of data provided

This dataset contains: - spatial field over Africa of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5 - Shoreline position change over Africa (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020) - regional averages in Africa of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for: - CMIP6 historical, ssp126 and ssp585 - CMIP5 and CORDEX historical, RCP2.6 and RCP8.5 - for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods - and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C - regional averages in Africa of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)

SAH, ARP, WAF, CAF, NEAF, SEAF, WSAF, ESAF, MDG, NEU, WCE and MED are domains used in the model.

Data provided in relation to figure

Data provided in relation to Figure 12.5:

Panel a: - Q100_map_panel_a_AFR_less_MED_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2015 (recent past) for CORDEX RCP8.5; the data is from the AFR CORDEX domain, without the MED AR6 region - Q100_map_panel_a_MED_for_AFR_from_EUR_divdra.nc: same as previous file but for the MED AR6 region, taken from the EUR CORDEX domain

Panel b: - CoastalRecession_AFRICA_RCP85_2100.json: pointwise values (color points on the map) for Africa of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)

Panel c: - txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with: - ${ensemble}: CMIP5, CMIP6 or CORDEX-core - ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85 - ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2080-2099 (long term) - ${CORDEX_domain}: the CORDEX domain - txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with: - ${ensemble}: CMIP5, CMIP6 or CORDEX-core - ${GWL}: the Global Warming Level: 1.5, 2 and 4 - ${CORDEX_domain}: the C... For full abstract see: https://catalogue.ceda.ac.uk/uuid/91c218d3a80f4c43ac665d0bdf0ed5e7.

The Entremont area is 300 square km wide and located in the western part of the Swiss Alps between the Mont-Blanc Massif and the Valaisian Alps, slightly north of the main crest of the alpine range. Altitude varies between 4313 m above see level (asl) at the top of the Grand-Combin and 714 m asl for the outlet of the valleys system. The geological composition is essentially dominated by metamorphic rocks (gneiss, shale, quartzite), however some sedimentary layers (limestone, dolomite) are inserted. The mean annual air temperature is about 0 C at 2300 m asl and annual precipitation among ranges between 1000 and 1500 mm at 2000 m asl with a maximum value of about 2000 mm on the alpine crest (Grand-St-Bernard pass). Equilibrium line of glaciers is over 3000 m asl in the north-facing slopes, the timberline is at about 2100 m asl and permafrost creeping features exist between 2000 and 3000 m asl, what results in a great variety of mountain and morphological landscapes.Aerial photographs analyses and field morphological observations have been performed during summer 1995 in order to inventory and map (1 to 25000) 321 well-developed to embryonic rock glaciers and protalus lobes (coexisting features have been separated) of total area 10.4 square. km. Rock glacier activity classification results from interpretation of both geomorphological and environmental signs. Some geophysics (BTS measurements and DC resistivity soundings) have been performed between 1995 and 1997 on a few rock glaciers in order to confirm the initial interpretation. Data described in this file on the CAPS Version 1.0 CD-ROM, June 1998, are--1. Location (X, Y)-- Swiss coordinates near the center of the feature.2. Front elevation (Z)-- Elevation (m a.s.l.) at the foot of the front (accuracy: 10 m).3. Length-- Maximal distance (m) between front and roots (accuracy: 10 - 20%).4. Width-- Mean width (m) of the feature (accuracy: 10%). 5. Orientation-- Global exposition of the rock glacier.6. Activity-- Interpretation by field indicators observation, only in a few cases by geophysics.7. Surface (ha)-- Calculated by 1 to 25,000 numerical mapping (accuracy: 5 - 10%).8. Origin-- Talus or moraine (debris) derived rock glacier ?Other information such as lithology, spring temperature, vegetation cover, inclination, roots elevation are partly available, but not described in the present data set.

Input Data for Figure 12.8 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 12.8 shows projected changes in selected climatic impact-driver indices for Central and South America.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson- Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi:10.1017/9781009157896.014.

Figure subpanels

The figure has four panels, with general data provided in the central directory and specific data in 3 folders (Q100_CMIP5, Q100_CMIP6, Q1000_CORDEX-core).

List of data provided

This dataset contains: - spatial field over South-America and Central-America of mean change in 1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX models for 2041-2060 relative to 1995-2014 for RCP8.5

• Shoreline position change over South-America (pointwise) along sandy coasts by the year 2100 relative to 2010 (meters; negative values indicate shoreline retreat) from the CMIP5 based data set presented by Vousdoukas et al. (2020)

• regional averages in South-America and Central-America of Q100 (median value and the 10th-90th percentile range of model ensemble values across each model ensemble) over land areas for the WGI reference AR6 regions (defined in Chapter 1) for:

  • CMIP6 historical, ssp126 and ssp585

  • CMIP5 and CORDEX historical, RCP2.6 and RCP8.5

  • for the ‘recent past’ (1995-2014), mid-term (2041-2060) and long-term (2081-2100) time periods

  • and for three global warming levels (defined relative to the preindustrial period 1850-1900): 1.5°C, 2°C and 4°C

• regional averages in South-America and Central-America of CMIP5 based projections (mean change estimates and bars the 5th-95th percentile range of associated uncertainty) of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 and RCP4.5 from Vousdoukas et al. (2020)

NWS, NSA, SAM, NES, SWS, SES, SSA, CAR and SCA are domains used in the model.

Data provided in relation to figure

Data provided in relation to Figure 12.8:

Panel a:

• Q100_map_panel_a_SAM_divdra.nc: Field (colors plotted on the map) of changes of 1-in-100yr river discharge per unit catchment area between 2041-2060 (mid-term) and 1995-2015 (recent past) for CORDEX RCP8.5; the file contains the data for the regions from the SAM CORDEX domain

• Q100_map_panel_a_CAM_for_SAM_divdra.nc: same as previous file for the regions from the CAM CORDEX domain

Panel b:

• CoastalRecession_SOUTH-AMERICA_RCP85_2100.json: pointwise values (color points on the map) for South-America and Central-America of shoreline position mean changes between 2100 (long-term) and 2010 (recent past) from the CMIP5 based data set presented by Vousdoukas et al. (2020)

Panel c:

• txt files containing the median and 5th/95th percentiles of each ensemble of the 1-in-100yr river discharge per unit catchment area (Q100) regional averages of time slices: Q100_${ensemble}/Q100_${scenario}_${period}.nc_${CORDEX_domain}.txt, with:

  • ${ensemble}: CMIP5, CMIP6 or CORDEX-core
  • ${scenario}: the name of the scenario : ssp126, ssp585, rcp26, rcp85
  • ${period}: the explicit period used to compute the temporal average: 1995-2014 (recent past), 2041-2060 (mid-term) and 2081-2099 (long term)
  • ${CORDEX_domain}: the CORDEX domain

• txt files containing the Q100 regional averages of global warming levels: Q100_${ensemble}/${GWL}_${CORDEX_domain}.txt, with:

  • ${ensemble}: CMIP5, CMIP6 or CORDE... For full abstract see: https://catalogue.ceda.ac.uk/uuid/0b5c980aa58447508eccdda79554b2b7.