cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/dffe.ccis.01102024
title: Future Drought index: Ensemble 10th,50th and 90th percentiles of Annual standardised precipitation index: A drought measure specified as a precipitation deficit (SPI) for 2.0 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.15493/dffe.ccis.24102024
title: Future Extreme heat index: Ensemble 10th,50th and 90th percentiles of Annual heatwave frequency: the number of heatwave days (hwf) for 1.5, 2.0 and 3.0 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.15493/dffe.ccis.09102024
title: Future Drought index: Ensemble 10th,50th and 90th percentiles of Annual maximum length of dry spell: maximum number of consecutive days with RR < 1mm (cdd) for 3.0 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.15493/dffe.ccis.26102024
title: Future Extreme heat index: Ensemble 10th,50th and 90th percentiles of Annual heatwave magnitude: the mean temperature of all heatwaves (HWMId) for 1.5 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.15493/dffe.ccis.03102024
title: Future Extreme heat index: Ensemble 10th,50th and 90th percentiles of Annual maximum of daily man temperature (txx) for 2.0 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.15493/dffe.ccis.23102024
title: Future Extreme heat index: Ensemble 10th,50th and 90th percentiles of Annual heatwave frequency: the number of heatwave days (hwf) for 3.0 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.14469/hpc/11525
title: NMR Data for (Aqua)(ethanol) bis(4-(ethoxycarbonyl)-1-phenyl-1H-pyrazol-5-olate)calcium (20), 1H, 13C, HMBC

description: Primary NMR data for compound 20. (Aqua)(ethanol) bis(4-(ethoxycarbonyl)-1-phenyl-1H-pyrazol-5-olate)calcium . Please download the .mnpub files to obtain a single-use license file for use with MestreNova.
DOI: 10.15493/dffe.ccis.02102024
title: Future Extreme heat index: Ensemble 10th,50th and 90th percentiles of Annual maximum of daily man temperature (txx) for 1.5 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.15493/dffe.ccis.18102024
title: Future Extreme heat index: Ensemble 10th,50th and 90th percentiles of Annual heatwave duration: the length of the longest heatwave (hwd) for 2.0 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.15493/dffe.ccis.06102024
title: Future Consecutive dry days index: Ensemble 10th,50th and 90th percentiles of Annual maximum length of dry spell: maximum number of consecutive days with RR < 1mm (cdd) for baseline period of 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.
DOI: 10.15493/dffe.ccis.29102024
title: Future Extreme heat index: Ensemble 10th,50th and 90th percentiles of Annual heatwave magnitude: the mean temperature of all heatwaves (HWMId) for 1.5, 2.0 and 3.0 degree global temperature scenario relative to the period 1961-1991

description: National Climate Change Information System
description: The climate extremes projections over the Free State Province were conducted using the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets as part of the Assessment Report (AR) 6 of the Intergovernmental Panel on Climate Change (IPCC). The focus was on climate extreme events including heat, drought, heatwaves, and flooding. Calculations of climate extreme indices that speak to the identified extreme events were conducted using 10 CMIP6 climate models from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) community. The ISIMIP models were statistically downscaled using the CMIP6 climate model scenario 4, Shared Socioeconomic Pathways 3-70 (SSP3-7.0), also known as the regional rivalry scenario, in the context of the Paris Agreement temperature goals. Under this scenario, a resurgence of nationalism and regional conflicts overshadow global issues. Governments increasingly prioritize national and regional security, leading to reduced investments in education and technological development. Inequality grows, and some regions experience severe environmental damage. These extreme indices were selected from the 27 core indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) widely used for analysing extreme events in a changing climate. The calculations were conducted for the baseline period (1961-1990). The level of model uncertainty was represented by ensemble percentiles (10th, 50th and 90th percentiles) from the 10 models in which agreement between the models on the projected spatial pattern of the monthly maximum value of daily maximum temperature (Txx) for the baseline period is illustrated.
description: All datasets were downloaded from ISIMIP as netcdf and processed using Python to extreme climate indices of interest.