Note: a new time-series dataset from ERA5 has been published — this one won't be updated/maintained anymore

Country averages of meteorological variables generated using the R routines available in the package panas based on the Copernicus Climate Change ERA5 reanalyses. The time-series are at hourly resolution and the included variables are:

• 2-meter temperature (t2m),
• snow depth (snow_depth),
• mean sea-level pressure (mslp),
• runoff,
• surface solar radiation (ssrd),
• surface solar radiation with clear-sky (ssrdc),
• temperature at 850hPa (t850),
• total precipitation (total_prec),
• zonal (west-east direction) wind speed at 10m (u10) and 100m (u100),
• meridional (north-sud) wind speed at 10m (v10) and 100m (v100),
• dew point temperature (dew)

The original gridded data has been averaged considered the national borders of the following countries (European 2-letter country codes are used, i.e. ISO 3166 alpha-2 codes with the exception of GB->UK and GR->EL): AL, AT, BA, BE, BG, BY, CH, CY, CZ, DE, DK, DZ, EE, EL, ES, FI, FR, HR, HU, IE, IS, IT, LT, LU, LV, MD, ME, MK, NL, NO, PL, PT, RO, RS, SE, SI, SK, UA, UK.

The unit measures here used are listed in the official page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/era5-hourly-data-on-single-levels-from-2000-to-2017?tab=overview

The script used to generate the files is available on github here

Large-sample datasets are essential in hydrological science to support modelling studies and global assessments. This dataset is an extension to Caravan, a global community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world (Kratzert et al. 2023).

The extension includes a subset of those hydrological discharge data and station-based watersheds from the Global Runoff Data Centre (GRDC), which are covered by an open data policy (Attribution 4.0 International; CC BY 4.0). In total, the dataset covers stations from 5356 catchments and 25 countries worldwide with a time series record from 1950 – 2023.

GRDC is an international data centre operating under the auspices of the World Meteorological Organization (WMO) at the German Federal Institute of Hydrology (BfG). Established in 1988, it holds the most substantive collection of quality assured river discharge data worldwide. Primary providers of river discharge data and associated metadata are the National Hydrological and Hydro-Meteorological Services of WMO Member States.

Reference:

Kratzert, F., Nearing, G., Addor, N. et al. Caravan - A global community dataset for large-sample hydrology. Sci Data 10, 61 (2023). https://doi.org/10.1038/s41597-023-01975-w

Update:

With version 0.2 a bug has been fixed that affected the time series of four bands of all GRDC gauges in the GRDC extension. The affected bands were total_precipitation, surface_net_solar_radiation, surface_net_thermal_radiation and potential_evaporation, i.e. all features that are accumulated over the day, as per definition of ERA5-Land.For details look at https://github.com/kratzert/Caravan/issues/26.

Version 0.3: Data description file added.Version 0.4: Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) in the meteorological forcing data and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND").Version 0.5: License overview of the respective countries has been added.Dataset description has been modified and improved.

Dataset structure:

The dataset is provided in the following two file formats:1. caravan-grdc-extension-csv.zip: provides the time series data as comma-separated text files (CSV) (downloadable as 8.8 GB zip archive)2. caravan-grdc-extension-nc.zip: provides the time series data in the Network Common Data Form (NetCDF) (downloadable as 7.6 GB zip archive)

The data in the versions 0.1-0.3 are identical. Version 0.4 added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND.

Further details of the structure of the dataset are described in the data description file.

If you use this dataset please cite the accompanying paper (Lea et al., 2024)

Maps of key (bio-)climatic variables derived from all currently available ERA5-Land reanalysis data (Muñoz Sabater et al., 2019). These have been calculated for:

1. Annual timescales from 1951-2022 (this dataset); and

2. All possible World Meteorological Organisation (WMO) 30 year climate baseline periods, including: 1951 to 1980; 1961 to 1990; 1971 to 2000; 1981 to 2010; and 1991 to 2020 (see link).

Annual timescale data are calculated using monthly statistics using calendar months that account for leap years. WMO baseline maps are calculated by taking the mean of all annual timescale ERALClim maps that fall within the time periods stated above (inclusive). Image bands are named to map onto equivalent BioClim variables (Fick and Hijmans, 2017).

Global data are provided here in GeoTIFF format as multiband images (where each band represents a different year/variable depending on the data downloaded) at a spatial scale of 0.1 degrees within a WGS84 grid (EPSG:4326). If users require data from point locations and/or subset regions for a specific time range or for a custom range of variables, these can be easily accessed using the Google Earth Engine Climate Tool (GEEClimT; Lea et al.). Access to this tool requires a Google Earth Engine account, and is free to use for academic research and education purposes. If you use any data extracted using this tool, please cite Lea et al., 2024.

Descriptions of each band within the dataset are listed below:

bio1 - Mean 2 m air temperature derived from hourly data (units: degrees C).

bio2 - Annual mean of monthly mean diurnal 2 m air temperature ranges (units: degrees C).

bio3 - Isothermality (100 * bio2 / bio7) (no units).

bio4 - Standard deviation of monthly mean 2 m air temperatures (units: degrees C).

bio5 - Mean of maximum 2 m air temperature for the warmest month (units: degrees C).

bio6 - Mean of minimum 2 m air temperature for the coldest month (units: degrees C).

bio7 - Annual range of 2 m air temperature (bio5 - bio6) (units: degrees C).

bio8 - Mean 2 m air temperature of wettest 3 month period (units: degrees C).

bio9 - Mean 2 m air temperature of driest 3 month period (units: degrees C).

bio10 - Mean 2 m air temperature of warmest 3 month period (units: degrees C).

bio11 - Mean 2 m air temperature of coldest 3 month period (units: degrees C).

bio12 - Total annual precipitation (units: mm).

bio13 - Total precipitation of wettest month (units: mm).

bio14 - Total precipitation of driest month (units: mm).

bio15 - Precipitation Seasonality (Coefficient of Variation, based on monthly total precipitation data) (no units).

bio16 - Total precipitation in wettest 3 month period (units: mm).

bio17 - Total precipitation in driest 3 month period (units: mm).

bio18 - Total precipitation in warmest 3 month period (units: mm).

bio19 - Total precipitation in coldest 3 month period (units: mm).

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur. It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power. Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles and cutouts to be downloaded and extracted as noted in the documentation. The provided cutouts are spatiotemporal subsets of the European weather data from the ECMWF ERA5 reanalysis dataset and the CMSAF SARAH-2 solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the atlite tool (https://atlite.readthedocs.io/). ECMWF ERA5 Source: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview Terms of Use: https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf CMSAF SARAH-2 Pfeifroth, Uwe; Kothe, Steffen; Müller, Richard; Trentmann, Jörg; Hollmann, Rainer; Fuchs, Petra; Werscheck, Martin (2017): Surface Radiation Data Set - Heliosat (SARAH) - Edition 2, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V002, https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002. Terms of Use: https://www.eumetsat.int/cs/idcplg?IdcService=GET_FILE&dDocName=pdf_leg_data_policy&allowInterrupt=1&noSaveAs=1&RevisionSelectionMethod=LatestReleased

The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.

It has been created in the context of the SeasFire project, which deals with "Earth System Deep Learning for Seasonal Fire Forecasting" and is funded by the European Space Agency (ESA) in the context of ESA Future EO-1 Science for Society Call.

It contains 21 years of data (2001-2021) in an 8-days time resolution and 0.25 degrees grid resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.

Datacube properties

Feature

Value

Spatial Coverage

Global

Temporal Coverage

2001 to 2021

Spatial Resolution

0.25 deg x 0.25 deg

Temporal Resolution

8 days

Number of Variables

54

Tutorial Link

https://github.com/SeasFire/seasfire-datacube

    Full name
    DataArray name
    Unit
    Contact *




    Dataset: ERA5 Meteo Reanalysis Data





    Mean sea level pressure
    mslp
    Pa
    NOA


    Total precipitation
    tp
    m
    MPI


    Relative humidity
    rel_hum
    %
    MPI


    Vapor Pressure Deficit
    vpd
    hPa
    MPI


    Sea Surface Temperature
    sst
    K
    MPI


    Skin temperature
    skt
    K
    MPI


    Wind speed at 10 meters
    ws10
    m*s-2
    MPI


    Temperature at 2 meters - Mean
    t2m_mean
    K
    MPI


    Temperature at 2 meters - Min
    t2m_min
    K
    MPI


    Temperature at 2 meters - Max
    t2m_max
    K
    MPI


    Surface net solar radiation
    ssr
    MJ m-2
    MPI


    Surface solar radiation downwards
    ssrd
    MJ m-2
    MPI


    Volumetric soil water level 1
    swvl1
    m3/m3
    MPI







          Volumetric soil water level 2




    swvl2
    m3/m3
    MPI


    Volumetric soil water level 3
    swvl3
    m3/m3
    MPI


    Volumetric soil water level 4
    swvl4
    m3/m3
    MPI


    Land-Sea mask
    lsm
    0-1
    NOA


    Dataset: Copernicus

CEMS

    Drought Code Maximum
    drought_code_max
    unitless
    NOA


    Drought Code Average
    drought_code_mean
    unitless
    NOA


    Fire Weather Index Maximum
    fwi_max
    unitless
    NOA


    Fire Weather Index Average
    fwi_mean
    unitless
    NOA


    Dataset: CAMS: Global Fire Assimilation System (GFAS)





    Carbon dioxide emissions from wildfires
    cams_co2fire
    kg/m²
    NOA


    Fire radiative power
    cams_frpfire
    W/m²
    NOA


    Dataset: FireCCI - European Space Agency’s Climate Change Initiative





    Burned Areas from Fire Climate Change Initiative (FCCI)
    fcci_ba
    ha
    NOA


    Valid mask of FCCI burned areas
    fcci_ba_valid_mask
    0-1
    NOA



    Fraction of burnable area
    fcci_fraction_of_burnable_area
    %
    NOA


    Number of patches
    fcci_number_of_patches
    N
    NOA


    Fraction of observed area
    fcci_fraction_of_observed_area
    %
    NOA


    Dataset: Nasa MODIS MOD11C1, MOD13C1, MCD15A2





    Land Surface temperature at day
    lst_day
    K
    MPI


    Leaf Area Index
    lai
    m²/m²
    MPI


    Normalized Difference Vegetation Index
    ndvi
    unitless
    MPI


    Dataset: Nasa SEDAC Gridded Population of the World (GPW), v4





    Population density
    pop_dens
    persons per square kilometers
    NOA


    Dataset: Global Fire Emissions Database (GFED)





    Burned Areas from GFED (large fires only)
    gfed_ba
    hectares (ha)
    MPI


    Valid mask of GFED burned areas
    gfed_ba_valid_mask
    0-1
    NOA


    GFED basis regions
    gfed_region
    N
    NOA


    Dataset: Global Wildfire Information System (GWIS)





    Burned Areas from GWIS
    gwis_ba
    ha
    NOA


    Valid mask of GWIS burned areas
    gwis_ba_valid_mask
    0-1
    NOA


    Dataset: NOAA Climate Indices





    Arctic Oscillation Index
    oci_ao
    unitless
    NOA


    Western Pacific Index
    oci_wp
    unitless
    NOA


    Pacific North American Index
    oci_pna
    unitless
    NOA


    North Atlantic Oscillation
    oci_nao
    unitless
    NOA


    Southern Oscillation Index
    oci_soi
    unitless
    NOA


    Global Mean Land/Ocean Temperature
    oci_gmsst
    unitless
    NOA


    Pacific Decadal Oscillation
    oci_pdo
    unitless
    NOA


    Eastern Asia/Western Russia
    oci_ea
    unitless
    NOA


    East Pacific/North Pacific Oscillation
    oci_epo
    unitless
    NOA


    Nino 3.4 Anomaly
    oci_nino_34_anom
    unitless
    NOA


    Bivariate ENSO Timeseries
    oci_censo
    unitless
    NOA


    Dataset: ESA CCI





    Land Cover Class 0 - No data
    lccs_class_0
    %
    NOA


    Land Cover Class 1 - Agriculture
    lccs_class_1
    %
    NOA


    Land Cover Class 2 - Forest
    lccs_class_2
    %
    NOA


    Land Cover Class 3 - Grassland
    lccs_class_3
    %
    NOA


    Land Cover Class 4 - Wetlands
    lccs_class_4
    %
    NOA


    Land Cover Class 5 - Settlement
    lccs_class_5
    %
    NOA


    Land Cover Class 6 - Shrubland
    lccs_class_6
    %
    NOA


    Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice
    lccs_class_7
    %
    NOA


    Land Cover Class 8 - Water Bodies
    lccs_class_8
    %
    NOA


    Dataset: Biomes





    Dataset: Calculated





    Grid Area in square meters
    area
    m²
    NOA

*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante noa.gr) has led the efforts to collect and process them.

The dataset includes model output from the ACCESS-AM2 model corresponding to the MARCUS (Measurements of Aerosols, Radiation and Clouds over the Southern Oceans) 2017-2018 voyages and the CAMMPCAN (Chemical and Mesoscale Mechanisms of Polar Cell Aerosol Nucleation) 2018-2019 voyages. The MARCUS voyages included a limited number of CAMMPCAN instruments while the CAMMPCAN voyages included the full suite of instruments.

The model version used was ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) run with CMIP6 AMIP configuration, nudged with ERA5 reanalysis and full chemistry switched on. The model was configured with a horizontal resolution of 1.25◦ latitude and 1.875◦ longitude and 85 vertical levels. ACCESS-AM2 uses the UK Met Office’s Unified Model Global Atmosphere (UM10.6 GA7.1) as the atmosphere module, the Community Atmosphere Biosphere Land Exchange model version 2.5 (CABLE2.5) as the land-surface module and the Global Model of Aerosol Processes (GLOMAP-mode) as the aerosol module. More information on the ACCESS-AM2 model can be found at https://doi.org/10.1071/ES19033.

The data is at daily means spanning 29-10-2017 to 26-03-2018 (149 days) for MARCUS, and 25-10-2018 to 24-03-2019 (151 days) for CAMMPCAN.

Files included in this upload include:

aa1718_cg893_track.nc: aerosol, chemistry, and meteorology model data for the MARCUS voyages

cg893_daily_mean_MARCUS_size_distributions.nc: calculated aerosol size distribution model data for the MARCUS voyages

aa1819_cg893_track.nc: aerosol, chemistry, and meteorology model data for the CAMMPCAN voyages

cg893_daily_mean_CC_size_distributions.nc: calculated aerosol size distribution model data for the CAMMPCAN voyages

File names refer to Aurora Australis, voyage years (either 2017-2018 or 2018-2019), followed by the model run and data type.

An overview of the variable field names, variable long names, height profile availability and units available in the dataset is provided in VariablesOverview.xlsx.

A Jupyter Notebook is also included and contains scripts that can be used to create figures for preliminary analysis using the model data.

Additional information:

MARCUS details: https://asr.science.energy.gov/meetings/stm/presentations/2017/473.pdf

CAMMPCAN details: https://findanexpert.unimelb.edu.au/project/102792-cammpcan-%E2%80%93-chemical-and-mesoscale-mechanisms-of-polar-cell-aerosol-nucleation

MARCUS Observations: https://doi.org/10.26179/5e54ab5e5d56f

CAMMPCAN Observations: https://doi.org/10.26179/5e546f452145d

The GitHub repository containing the code used to produce these datasets can be found here: https://github.com/llamprey/aurora_voyages

Versions:

1.0.0: Initial Version.

1.1.0: Fixed bug where CN and CCN fields were incorrectly calculated.

1.2.0: Updated aerosol size distribution files

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1974.

Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.

For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

Those files were created using the KrigR package.

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2016.

Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.

For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

Those files were created using the KrigR package.

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1972.

Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.

For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

Those files were created using the KrigR package.

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2018.

Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.

For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

Those files were created using the KrigR package.