External datasets catalogue

NOAAGlobalTempv6 (version 6.0.0)

JRA-3Q (Japanese Reanalysis for Three Quarters of a Century)

JMA is currently conducting the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q), which covers the period from September 1947 onward to extend the current period of data coverage and improve the quality of long-term reanalysis. The project involves a sophisticated data assimilation system (based on the operational set-up as of December 2018) incorporating development results from the operational NWP system and sea surface temperature analysis achieved since JRA-55 (based on the operational set-up as of December 2009). New datasets of past observations are also assimilated, including rescued historical observations and reprocessed satellite data supplied by meteorological and satellite centers worldwide. Many of the deficiencies of JRA-55 are alleviated in JRA-3Q, providing a high-quality homogeneous reanalysis dataset that covers the previous 75 years.

Daily mean temperature, E-OBS gridded dataset version 29.0e update

Production, value added and exports in the environmental goods and services sector (env_ac_egss2) — 27 May 2024

ETC questionnaire (not direct link to download available) — 08 May 2024

Production, value added and exports in the environmental goods and services sector [ENV_AC_EGSS2__custom_10778178] — 30 Apr 2024

Vulnerability deciles (direct link to dataset not available) — 30 Apr 2024

The vulnerability index is composed of six socioeconomic variables: GDP per capita, proportion of adults with higher education, proportion of elderly, proportion of artitifical surfaces, proportion of elderly living alone, overweight prevalence. Each variable was normalised - spatially and temperally - using a linear min-max rescaling. The variables received equal weight before summation. The vulnerability index was computed under current (2015) socioeconomic conditions (the baseline) and under four Shared Socioeconomic Pathways (SSPs) for the year 2050. Population and GDP projections were retrieved from the Joint Research Centre at a 0.1° spatial resolution. Projections of artificial surfaces were produced in the IMPRESSIONS project for a 10' lat-lon spatial grid using a regional urban growth model parametrised with assumptions of age group-specific residential preferences under the four European SSPs. Age-specific population projections at NUTS-2 level were retreieved from the IMPRESSIONS project and further downscaled ona 0.1° spatial grid. Education projections were retrieved from quantification of the global SSPs at national scale and further downscaled to NUTS-2 level and finally disaggregated to a 0.1° spatial grid assuming a homogeneous proportion of people with higher education within each NUTS-2 region. Projections of overweight prevalence and of the proportion of elderly living alone are based on expert-based modelling approach. More information on the methodology can be found in https://doi.org/10.1016/j.gloplacha.2018.09.013.

Number of summer heat wave days (HWs) (direct link to dataset not available) — 30 Apr 2024

Heat waves are defined as periods with at least 6 consecutive days where daily maximum temperature exceeds a given threshold during the summer period (June-July-August), set as the 90th percentile of daily maximum temperature for the baseline period (1986-2005). Heat wave days were computed for seven high-resolution (0.11°) regional climate model simulations from EURO-CORDEX and then averaged over the 2-year summer periods by calculating the multi-model-median values. The variability across the different model simulations is displayed through the interquartile range which is shown in the legend. 

Number of summer heat wave days (HWs) (direct link to dataset not available) — 30 Apr 2024

Water Exploitation Index (WEI) — 29 Apr 2024

High Fire Danger Days — 24 Apr 2024

The data was assembled on behalf of the Copernicus Climate Change Service (C3S) based on climate projections hosted on the C3S Climate Data Store (CDS). The observed index data are from a dataset based on the ERA5 reanalysis and updated in near real time. It is produced by the Copernicus Emergency Management Service for the Global ECMWF Fire Forecasting model (GEFF) and the European Forest Fire Information System (EFFIS). The simulated index data are from a set of 6 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. These simulations have a daily temporal resolution, a spatial resolution of 0.1° x 0.1° and cover scenarios RCP4.5 and RCP8.5. More technical specifications can be found in the datasets documentation  found on this website: https://climate-adapt.eea.europa.eu/en/metadata/indicators/high-fire-danger-days The data was assembled on behalf of the Copernicus Climate Change Service (C3S) based on climate projections hosted on the C3S Climate Data Store (CDS). The observed index data are from a dataset based on the ERA5 reanalysis and updated in near real time. It is produced by the Copernicus Emergency Management Service for the Global ECMWF Fire Forecasting model (GEFF) and the European Forest Fire Information System (EFFIS). The simulated index data are from a set of 6 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. These simulations have a daily temporal resolution, a spatial resolution of 0.1° x 0.1° and cover scenarios RCP4.5 and RCP8.5. More technical specifications can be found in the datasets documentation  found on this website: https://climate-adapt.eea.europa.eu/en/metadata/indicators/high-fire-danger-days 

Employment in the environmental goods and services sector [env_ac_egss1__custom_10715540] — 19 Apr 2024

Employment by A*10 industry breakdowns [NAMA_10_A10_E__custom_10717044] — 19 Apr 2024

CAP Result indicators dashboard — 18 Apr 2024

Indicators of antimicrobial use in animals — 18 Apr 2024

EUCRA land regions (direct link to dataset not available) — 15 Apr 2024

Data compiled by ETC

Ratio of percentage area at flood risk between regions in Q5 (top 20%) and Q1 (bottom 20%) of proportion of people over 65, Jun. 2022 — 09 Apr 2024

This vector dataset presents, at EU-27 member state level for 2020, the ratio of the average (mean) percentage of area potentially exposed to flooding between administrative units containing population with the highest percentage of people over 65 years old (top 20% or 5th quintile) and the administrative units containing population with the lowest proportion of people over 65 years old (bottom 20% or 1st quintile) in a given country. The data sources behind the dataset are the 'EEA Percentage of population over 65 years (2020), Nov. 2021' and the 'EEA potential flood-prone area extent, Jan. 2020'

Ratio of percentage area at flood risk between regions in Q5 (top 20%) and Q1 (bottom 20%) of unemployment rate, Jun. 2022 — 09 Apr 2024

This vector dataset presents, at EU27 member state level for 2020, the ratio of the average (mean) percentage of area potentially exposed to flooding between administrative units containing population with the highest unemployment rate (top 20% or 5th quintile) and the administrative units containing population with the lowest unemployment rate (bottom 20% or 1st quintile) in a given country. The data sources behind the dataset are the 'Percentage of unemployed people in working age population (2020), Nov. 2021' and the 'EEA potential flood-prone area extent, Jan. 2020'

Industrial Reporting under the Industrial Emissions Directive 2010/75/EU and European Pollutant Release and Transfer Register Regulation (EC) No 166/2006 - ver. 10.0 Dec 2023 (Tabular data)

Extreme Precipitation Days — 04 Apr 2024