Global One-Eighth Degree Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01
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Global One-Eighth Degree Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01
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sardinia_rhs_HadGEM2-ES_rcp4p5_2005-2099_monthly.dat
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Scenario emissions and temperature data for PROVIDE project
Data for tier 1 and tier 2 PROVIDE scenarios. Tier 1 scenarios are mostly from integrated assessment models. Tier 2 scenarios are much more numerous and are kept in a separately zipped folder for temperatures and csv file for emissions data. The temperature folders contains the full set of FaIR runs for scenarios entirely defined by emissions. Summaries are much smaller files containing quantile info for each scenario, including the scenarios defined by combinations of emissions and temperature trends. 10 Tier 1 scenarios until 2100 15 Tier 1 scenarios defined until 2300, all of which are variations of the original 10 Many Tier 2 scenarios, aiming to completely tile reasonable emissions space parameterised with 4 variables Several objectives of the PROVIDE project depend on a set of scenarios that can be modelled through either a ‘classical’ forward-looking approach or by a novel approach that ‘reverses the impact chain’. These scenarios are also key elements for the integration of PROVIDE findings in the outward-looking stakeholder Dashboard of the project. Here we describe the set of scenarios that has been developed and will be used within PROVIDE. In total, PROVIDE explores three complementary approaches: 10 distinct tier 1 scenarios extending until 2100, mostly based on the existing literature, used for short-term assessments of impacts 15 distinct tier 1 scenarios extending until 2300, based on different extensions of the 10 literature scenarios, used for assessing longer-run impacts and the geophysical impact of significant temperature overshoot ~1350 distinct tier 2 scenarios, exploring several dimensions of emissions space systematically, such as CO2 net zero date and relative methane intensity. This is used to explore which scenarios are compatible with given climate outcomes. These scenarios can be used to reverse the traditional impact chain, going from acceptable climate risks to descriptions of acceptable emissions.
Zenodo2024-05-21 更新00
IPCC DDC: CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp245
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.CNRM-CERFACS.CNRM-CM6-1-HR.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
DataCite Commons2025-03-07 更新00
Table S1.
Annual root mean squared error (RMSE) and mean absolute error (MAE) between weather station data and different domain designs for the period 1981–2010 for maximum two-meter temperature. Bold numbers indicate the lowest values for MAE and RMSE for the high-resolution 5 km domain. Table S2. Annual root mean squared error (RMSE), mean absolute error (MAE), and temporal correlation between weather station data and different domain designs for the period 1981–2010 for precipitation. Bold numbers indicate the lowest values for MAE and RMSE for the high-resolution 5 km domain. Fig S1. Comparison of different WRF designs with weather station data. The panels A-C show the monthly maximum temperature in degrees Celsius and panels D-F show the monthly precipitation sum in millimeters for the years 1981–2010. A and D: Gangaw, B and E: Shwebo, and C and F: Mandalay. The orange colors indicate the domain covering the Dry Zone (Fig 1A), the pink colors indicate the domains of the Dry Zone with outskirts (Fig 1B) and the teal colors indicate the domain covering the Bay of Bengal (Fig 1C). The bright colors indicate the coarser domain of 25 km resolution and the dark color represent the high resolution 5 km domain. The grey boxes indicate the weather station data, whereas the grey shading shows the full range of the weather station data for each months for better comparison with the other datasets. The boxes represent the 25th to 75th percentile, while the whiskers extend to the value that is no more than 1.5 times the interquartile range away from the box. Fig S2. Comparison of Bay of Bengal WRF design with weather station, MSWEP and IMERG data. The grey boxes indicate the weather station data, whereas the grey shading shows the full range of the weather station data for each months for better comparison with the other datasets. The green boxes indicates the 5 km domain of the Bay of Bengal domain design, the red boxes indicate MSWEP data and the orange boxes represent IMERG data. The boxes represent the 25th to 75th percentile, while the whiskers extend to the value that is no more than 1.5 times the interquartile range away from the box. Fig S3. Increase in monthly 2-metre average temperature in Myanmar for different scenarios, time periods and months. The panels A and D show the maximum 2-metre temperature in May and April, respectively, for the period 1981–2010. Panels B-C and E-F show the difference between the future climate and the present climate. B: the difference with respect to SSP2-4.5 mid century for May, C: the difference with respect to SSP2-4.5 end of century for May, E: the difference with respect to SSP5-8.5 mid century for May, and F: the difference with respect to SSP5-8.5 end of century for April. The open-source Myanmar State and Regional Boundaries MIMU v9.4 dataset provided all the state and region boundaries and Natural Earth data provides the countries’ outline. Fig S4. Increase in monthly 2-metre maximum temperature in Myanmar for different scenarios and periods for June. The panels A and D show the maximum 2-metre temperature in June for the period 1981–2010.Panels B-C and E-F show the difference between the future climate and the present climate. B: the difference with respect to SSP2-4.5 mid century, C: the difference with respect to SSP2-4.5 end of century, E: the difference with respect to SSP5-8.5 mid century, and F: the difference with respect to SSP5-8.5 end of century. The open-source Myanmar State and Regional Boundaries MIMU v9.4 dataset provided all the state and region boundaries and the Natural Earth data provides the countries’ outline. Fig S5. Projected change in monthly precipitation at different stations. The boxplots show the distribution of the 30 values at each month (x-axis) at different stations (compare Fig 1) for the present climate simulation (grey box), the mid- and end of century simulation for the SSP2-4.5 (light and dark blue boxes, respectively) and the mid- and end of century simulation for the SSP5-8.5 (orange and red boxes, respectively). The boxes represent the 25th to 75th percentile,while the whiskers extend to the value that is no more than 1.5 times the interquartile range away from the box. (ZIP)
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Country-Level Population and Downscaled Projections Based on the SRES A1, B1, and A2 Scenarios, 1990-2100
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Characteristics of dry and wet periods based on SPEI under the A2 scenario.
Characteristics of dry and wet periods based on SPEI under the A2 scenario.
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IPCC-AR6_CMIP6 Regional Subset North America uas
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 a subset of the CMIP6 input data assessed by the IPCC AR6 WGI authors for region North America (170°W - 45°W, 5°N - 85°N). Included is the monthly mean eastward wind in 10m height (uas) data of the experiments historical, ssp126, ssp245, ssp370, and ssp585 for models providing more than 5 out of 12 core variables. Further details are provided in the additional information "IPCC AR6 Data for Regions".
DataCite Commons2025-03-05 更新00
Gridded socio-economic capitals for the SSPs
Overview This repository contains global gridded projections of 6 socioeconomic capitals for the Shared Socioeconomic Pathways. A central goal of this data is to enable improved modelling of the human dimensions of the SSPs, particularly in a coupled socio-environmental context. Key: Health and education are the relevant indices from the human development index; The Gini Coefficient is a measure of relative inequality; MA = Market access (after Verburg et al., 2011); WAP = Working age population; TES = total energy supply. Meta data Data are presented at 0.25 degree resolution. They are provided in 10-yearly intervals from 2020. 1km2 projections of working age population, total energy supply and the Gini coefficient are also made available via the Open Science Foundation: OSF | Global_SSP_Database1_2020-2100_1km; OSF | Global_SSP_Database2_2020-2100_1km; OSF | Global_SSP_Database3_2020-2100_1km; OSF | Global_SSP_Database4_2020–2100_1km Citation and contact information A paper describing these data is currently in review. If you wish to cite this rather than the data repository, please use: Perkins, O., Saxena, A., Millington, J.D.A., Brown, C., Seo, B. & Rounsevell, M. (in review). Global gridded data for the Shared Socio-economic Pathways. Socio-environmental systems modelling (in review). Questions, comments and corrections to: oliver.perkins@kcl.ac.uk & ankita.saxena@kit.edu
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