Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

This dataset provides comprehensive hydrometeorological data from South Korea, sourced through the WAMIS Open API. It includes hourly, daily, and monthly records of precipitation, water levels, meteorological conditions, river flow rates, and suspended sediment loads. The data is collected from various stations across South Korea and is regularly updated to support environmental monitoring, research, and water resource management. Users can access real-time and historical data, making this dataset valuable for climate studies, hydrological modeling, and infrastructure planning.

This catalog includes the following data resources:

Hourly Precipitation Data: Precipitation levels recorded every hour over the last 3 days.

Daily Precipitation Data: Daily precipitation measurements covering the last 3 months.

Monthly Precipitation Data: Monthly precipitation data spanning the last 3 years.

Hourly Water Level Data: Water level data recorded hourly for various rivers, updated every 3 hours.

Daily Water Level Data: Daily water level records from the last 3 months for multiple stations.

Hourly Meteorological Data: Hourly meteorological data including temperature, humidity, wind speed, and solar radiation.

Daily Meteorological Data: Daily meteorological summaries, ideal for longer-term climate analysis.

Daily River Flow Rate Data: Daily records of river flow rates for the current year.

Suspended Sediment Load Data: Information on sediment load concentrations and flow rates over the last 3 years.

Do people care about future generations? Moral philosophers say we should, but it is unclear whether laypeople agree. In particular, humanity’s inadequate efforts to mitigate climate change could be due to public indifference or heavy discounting of future generations’ well-being. Using surveys and survey experiments in four countries—Sweden, Spain, South Korea, and China—we found that most people say they care about future generations, and would be willing to reduce their standard of living so that people can enjoy better lives in the future. However, not everyone who says they care supports two public actions that could be taken for the benefit of future generations: policies to reduce either global warming or national debt. We find evidence that much of people’s apparent lack of concern for future generations is actually due to distrust of major social institutions, and associated doubts about the effectiveness of future-oriented policies.

This dataset contains dekadal rainfall indicators, computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) version 2 and the CHIRPS-GEFS short term rainfall forecasts, aggregated by subnational administrative units.

Included indicators are (for each dekad):

• 10 day rainfall mm
• rainfall 1-month rolling aggregation mm
• rainfall 3-month rolling aggregation mm
• rainfall long term average mm
• rainfall 1-month rolling aggregation long term average mm
• rainfall 3-month rolling aggregation long term average mm
• rainfall anomaly %
• rainfall 1-month anomaly %
• rainfall 3-month anomaly %

The administrative units used for aggregation are based on WFP data and contain a Pcode reference attributed to each unit. The number of input pixels used to create the aggregates, is provided in the n_pixels column. Finally, the type column indicates if the value is based on a forecast, a preliminary or a final product.

Forecasts are issued on the 6th, 16th, and 26th of each month for the upcoming 10-day period (dekad), then updated with improved versions on the 1st, 11th, and 21st. Preliminary observations replace the previous dekad’s forecast on the 3rd, 13th, and 23rd, and are later replaced by final observations—published mid-month (13th or 23rd)—covering all three dekads of the prior month. Please find a summary below:

Publication Day: Forecast type, Covers (Dekad)

• 1st: Updated forecast, 1–10 of the same month
• 6th: Initial forecast, 11–20 of the same month
• 11th: Updated forecast, 1–10 of the same month
• 16th: Initial forecast, 21–end of the same month
• 21st: Updated forecast, 11–20 of the same month
• 26th: Initial forecast, 1–10 of the following month

For more on CHIRPS-GEFS forecasts, see: https://www.chc.ucsb.edu/data/chirps-gefs

For further details, please see the methodology section.

For extreme temperature, we used climate extreme indices provided by CLIVAR (Climate and Ocean-Variability, Predictability, and Change) ETCCDI (Expert Team on Climate Change Detection and Indices). ETCCDI has provided 27 climate extreme indices not only with global reanalysis datasets but with CMIP5 simulations. The indices data are available on-line and the results with CMIP5 simulations were summarized by Sillmann et al. [2013]. For our analysis, we downloaded a monthly minimum of daily minimum surface air temperature (TNn) and a monthly maximum of daily maximum temperature (TXx). Among the CMIP5, 27 model results available on their website, we used 23 model results containing both of the TNn and TXx for all of the historical, RCP 4.5 and 8.5 experiments.

Since our focus is on boreal-winter extreme temperature, we selected the lowest TNn and highest TXx among the three months of December-January-February every year from 1861 to 2005 for the historical simulation and from 2006 to 2099 for the RCP 4.5 and RCP 8.5 scenario. Before the spatial averaging over the analysis domain (34°N-43°N in latitude and 124°E-131°E in longitude including the Korean Peninsula), we had remapped all of the simulation data onto a 1.5° x 1.5° horizontal resolution.

The time of unprecedented climate (TUC) for extreme temperature is defined in this study as the beginning year when the extreme temperature projected for the future climate scenarios exceed a critical value in all subsequent years during the RCP scenario runs.

In this study, the critical value for extreme temperatures is specified as a 50-year return level which is rather arbitrary but refers to a rough estimate for the social lifetime of a man. One may find the return level empirically from historical data, but this study estimates it using a Generalized Extreme Value distribution function as suggested by Kharin et al. [2007]. Based on the CMIP5 historical simulation data using R, we obtained three parameters determining a GEV distribution for each model, respectively for TNn and TXx. The GEV distribution for each model and variable has been verified using a Q-Q (quantile-quantile) plot if it adequately describes the CMIP5 historical data. All of the models showed the Q-Q plot within the 95% confidence range (Figure 1a for GFDL-ESM2G TXx for an instance). Then, we estimated the return level from the distribution and TUC from the RCP scenario runs for the wintertime TNn and TXx averaged over Korea.

Lookup table and evaluation simulation data for publication "Transclim (v1.0): a Chemistry-Climate Response Model for Assessing the Effect of Mitigation Strategies for Road Traffic on Ozone" by V. Rieger and V. Grewe in GMD, 2022 (https://doi.org/10.5194/gmd-15-5883-2022)

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.4713 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0751 and 0.0318 (in million kms), corressponding to 15.9291% and 6.7372% respectively of the total road length in the dataset region. 0.3645 million km or 77.3337% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0012 million km of information (corressponding to 0.3169% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Do people care about future generations? Moral philosophers say we should, but it is unclear whether laypeople agree. In particular, humanity’s inadequate efforts to mitigate climate change could be due to public indifference or heavy discounting of future generations’ well-being. Using surveys and survey experiments in four countries—Sweden, Spain, South Korea, and China—we found that most people say they care about future generations, and would be willing to reduce their standard of living so that people can enjoy better lives in the future. However, not everyone who says they care supports two public actions that could be taken for the benefit of future generations: policies to reduce either global warming or national debt. We find evidence that much of people’s apparent lack of concern for future generations is actually due to distrust of major social institutions, and associated doubts about the effectiveness of future-oriented policies. Bryr människor sig om framtida generationer? Moralfilosofer säger att vi borde göra det, men det är oklart om lekmän håller med. I synnerhet kan mänsklighetens otillräckliga ansträngningar för att mildra klimatförändringen bero på allmän likgiltighet eller kraftig diskontering av kommande generationers välbefinnande. Med hjälp av undersökningar och undersökningsexperiment i fyra länder - Sverige, Spanien, Sydkorea och Kina - fann vi att de flesta säger att de bryr sig om framtida generationer och skulle vara villiga att sänka sin levnadsstandard så att människor kan få bättre liv i framtiden. Men inte alla som säger att de bryr sig stöder två offentliga åtgärder som kan vidtas till förmån för framtida generationer: politik för att minska antingen global uppvärmning eller statsskuld. Vi hittar bevis för att mycket av människors uppenbara brist på oro för framtida generationer faktiskt beror på misstro mot stora sociala institutioner och därmed förbundna tvivel om effektiviteten i framtidsinriktad politik. Vi genomförde vår studie i fyra länder med olika nivåer av politiskt förtroende: Sverige, Spanien, Sydkorea och Kina. Baserat på tidigare omröstningar och studier är institutionellt förtroende högt i Sverige och Kina och lågt i Spanien och Sydkorea. Vi valde också dessa fyra länder eftersom de spänner över två kulturellt distinkta världsregioner, och befolkningen i de fyra länderna är också kända för att ha olika nivåer av optimism om framtiden. Undersökningarna gjordes av det internationella företaget Ipsos MORI, med online-paneler för vuxna. Uppnådda N var: Sverige 1084 (spänner över åldersintervallet 16-65); Spanien 1298 (16-65); Sydkorea 1176 (18-54); och Kina 1165 (18-50). Eftersom de är online-paneler är proverna inte helt representativa för de nationella befolkningarna. I synnerhet det kinesiska urvalet innehåller oproportionerligt yngre, mer urbana och mer utbildade respondenter. We conducted our study in four countries with different levels of political trust: Sweden, Spain, South Korea, and China. Based on prior polls and studies, levels of institutional trust are high in Sweden and China, and low in Spain and South Korea. We also chose these four countries because they span two culturally distinct world regions, and the populations of the four countries are also known to possess different levels of optimism about the future. The surveys were fielded by the international firm Ipsos MORI, using online panels of adults. Achieved N’s were: Sweden 1084 (spanning the age range 16-65); Spain 1298 (16-65); South Korea 1176 (18-54); and China 1165 (18-50). Being online panels, the samples are not perfectly representative of the national populations. The Chinese sample, in particular, includes disproportionately younger, more urban, and more educated respondents. Mixed probability and non-probability Blandat sannolikhets- och icke-sannolikhetsurval

Ant species occurrence records from East AsiaAnt species checklists representing local species pools across 159 islands and areas in East Asia were compiled, including Japan, South Korea, North Korea, China and Russia (Far East). The records represent nominal valid species and are based on 1093 publications from 1874-2014. This .txt file consists of two columns, listing species and area names. The preceding letters before the area names separated by an underscore abbreviate countries or island archipelagoes where the areas belong to (C China, DT Daito Islands, IZ Izu Islands, J Japan, JM Japanese main islands, NK North Korea, OG Ogasawara Islands, R Russia, RC Ryukyu Islands - central, RFE Russia Far East, RN Ryukyu Islands - north, RS Ryukyu Islands south, SK South Korea, SKM South Korea mainland, SN Senkaku Islands).Dryad-data.txt

Summary of carbon sequestration opportunities in South Korea for recognised blue carbon habitats.

South Korea Mean Drought Index data was reported at 0.109 NA in 2021. This records a decrease from the previous number of 1.303 NA for 2020. South Korea Mean Drought Index data is updated yearly, averaging -0.000 NA from Dec 1960 (Median) to 2021, with 62 observations. The data reached an all-time high of 2.537 NA in 2003 and a record low of -2.011 NA in 1994. South Korea Mean Drought Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Korea – Table KR.World Bank.WDI: Environmental: Climate Risk. The SPEI fulfills the requirements of a drought index since its multi-scalar character enables it to be used by different scientific disciplines to detect, monitor, and analyze droughts. Like the sc-PDSI and the SPI, the SPEI can measure drought severity according to its intensity and duration, and can identify the onset and end of drought episodes. The SPEI allows comparison of drought severity through time and space, since it can be calculated over a wide range of climates, as can the SPI.;Global SPEI database (SPEIbase). https://spei.csic.es/database.html;;

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NIMS-KMA.KACE-1-0-G.ssp370' 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 KACE1.0-GLOMAP climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: MOM4p1 (tripolar primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE-HadGEM3-GSI8 (tripolar primarily 1deg; 360 x 200 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

These data include all datasets published for 'CMIP6.CMIP.NIMS-KMA.UKESM1-0-LL.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

These data include all datasets published for 'CMIP6.ScenarioMIP.NIMS-KMA.KACE-1-0-G.ssp126' 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 KACE1.0-GLOMAP climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: MOM4p1 (tripolar primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE-HadGEM3-GSI8 (tripolar primarily 1deg; 360 x 200 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.NIMS-KMA.KACE-1-0-G.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The KACE1.0-GLOMAP climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: MOM4p1 (tripolar primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE-HadGEM3-GSI8 (tripolar primarily 1deg; 360 x 200 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. These data include all datasets published for 'CMIP6.CMIP.NIMS-KMA.UKESM1-0-LL.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.

The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Project: 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. - Project website: https://pcmdi.llnl.gov/CMIP6.

These data include all datasets published for 'CMIP6.CMIP.NIMS-KMA.UKESM1-0-LL.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.NIMS-KMA.UKESM1-0-LL.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

These data include all datasets published for 'CMIP6.CMIP.NIMS-KMA.KACE-1-0-G.1pctCO2' 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 KACE1.0-GLOMAP climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: MOM4p1 (tripolar primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE-HadGEM3-GSI8 (tripolar primarily 1deg; 360 x 200 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

North Korea Mean Drought Index data was reported at 0.225 NA in 2021. This records a decrease from the previous number of 1.307 NA for 2020. North Korea Mean Drought Index data is updated yearly, averaging -0.110 NA from Dec 1960 (Median) to 2021, with 62 observations. The data reached an all-time high of 2.125 NA in 1990 and a record low of -1.798 NA in 2014. North Korea Mean Drought Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s North Korea – Table KP.World Bank.WDI: Environmental: Climate Risk. The SPEI fulfills the requirements of a drought index since its multi-scalar character enables it to be used by different scientific disciplines to detect, monitor, and analyze droughts. Like the sc-PDSI and the SPI, the SPEI can measure drought severity according to its intensity and duration, and can identify the onset and end of drought episodes. The SPEI allows comparison of drought severity through time and space, since it can be calculated over a wide range of climates, as can the SPI.;Global SPEI database (SPEIbase). https://spei.csic.es/database.html;;