ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products. ERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5. ERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover (LC) maps, four quality flags are produced to document the reliability of the classification and change detection. In order to ensure continuity, these land cover maps are consistent with the series of global annual LC maps from the 1990s to 2015 produced by the European Space Agency (ESA) Climate Change Initiative (CCI), which are also available on the ESA CCI LC viewer. To produce this dataset, the entire Medium Resolution Imaging Spectrometer (MERIS) Full and Reduced Resolution archive from 2003 to 2012 was first classified into a unique 10-year baseline LC map. This is then back- and up-dated using change detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V), Sentinel-3 OLCI (S3 OLCI) and Sentinel-3 SLSTR (S3 SLSTR) time series from 2013. Beyond the climate-modelling communities, this dataset’s long-term consistency, yearly updates, and high thematic detail on a global scale have made it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research.

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

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

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per month. Unit: mm month-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

Metadata Contact: ECMWF - European Centre for Medium-Range Weather Forecasts

Resource Contact: ECMWF Support Portal

Data lineage:

Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

Resource constraints:

License Permission This License is free of charge, worldwide, non-exclusive, royalty free and perpetual. Access to Copernicus Products is given for any purpose in so far as it is lawful, whereas use may include, but is not limited to: reproduction; distribution; communication to the public; adaptation, modification and combination with other data and information; or any combination of the foregoing. Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: • 'Generated using Copernicus Climate Change Service information [Year]' and/or • 'Generated using Copernicus Atmosphere Monitoring Service information [Year]'

More information on Copernicus License in PDF version at https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Online resources:

ERA5 weather data (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview ) which are used in the context of https://github.com/openkfw/GermanRenewableEnergy . The data comprises hourly as well as monthly resulations with regards to different parameters of interest like temperature, pressure, components of wind etc. for the time intervall 2000 up to 2023. Unzipped, the hourly data will use about 10 GB of disk space.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

Relative humidity at 09h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

Metadata Contact: ECMWF - European Centre for Medium-Range Weather Forecasts

Resource Contact: ECMWF Support Portal

Data lineage:

Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

Resource constraints:

License Permission

This License is free of charge, worldwide, non-exclusive, royalty free and perpetual. Access to Copernicus Products is given for any purpose in so far as it is lawful, whereas use may include, but is not limited to: reproduction; distribution; communication to the public; adaptation, modification and combination with other data and information; or any combination of the foregoing.

Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice:

• Generated using Copernicus Climate Change Service information [Year]

and/or

• Generated using Copernicus Atmosphere Monitoring Service information [Year]

More information on Copernicus License in PDF version at: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Online resources:

Data download from original source

This catalogue entry provides the gridded climate data (monthly/annual timeseries) used for the Copernicus Climate Change Service Atlas (C3S Atlas). The gridded datasets consist of in-situ and satellite observation-based datasets, reanalyses (CERRA, ERA5, ERA5-Land, and ORAS5) and global (CMIP5 and CMIP6) and regional (CORDEX) climate projections for the variables and indices included in the C3S Atlas. This dataset complements the Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas (IPCC Atlas dataset hereafter), including new datasets, variables and indices. The variables and indices describe various types of climatic impact characteristics: heat and cold, wet and dry, snow and ice, wind and radiation, ocean, circulation and drought characteristics of the climate system. All data sources included in this entry are available in the Climate Data Store (CDS, see “Related data” in the sidebar). Contrary to the frozen IPCC Atlas dataset, this entry will update adding new data on a regular basis. This dataset includes gridded information with monthly/annual temporal resolution for observations/reanalyses of the recent past and climate projections for the 35 variables and indices computed from daily/monthly data across the different datasets. The climate projections are based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios. The datasets are harmonised using regular latitude-longitude grids. Bias correction is available for threshold-based indices. Two methods are available, depending on the variable; linear scaling and the ISIMIP method. This dataset allows the reproduction, expansion and customisation of the climate change products provided interactively by the Copernicus Interactive Climate Atlas. This is an interactive web application displaying global/regional maps of observed trends and climate changes for future periods across scenarios or for global warming levels, and regionally aggregated time series, seasonal cycle plots and climate stripes.

This entry covers pressure-level data at the original time resolution (once every 12 hours). Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

ERA5-Land monthly averaged data January 2019

Dataset has been retrieved on the Copernicus Climate data Store (https://cds.climate.copernicus.eu/#!/home) and is meant to be used for teaching purposes only. This dataset is used in the Galaxy training on "Visualize Climate data with Panoply in Galaxy".

See https://training.galaxyproject.org/ (topic: climate) for more information.

Product type: Monthly averaged reanalysis

Variable:

10m u-component of wind, 10m v-component of wind, 2m temperature, Leaf area index, high vegetation, Leaf area index, low vegetation, Snow cover, Snow depth

Year:

2019

Month:

January

Time:

00:00

Format:

NetCDF (experimental)

Reference evapotranspiration per day with a spatial resolution of 0.1 degree. Unit: mm day-1. The dataset contains daily values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56.

The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S).

The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-10-30

Contact points:

Metadata Contact: AQUASTAT

Resource Contact: AQUASTAT

Data lineage:

Copernicus Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

Resource constraints:

• The dataset contains modified Copernicus Climate Change Service information [1979-to date];

• Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

More information on Copernicus License in PDF version at https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Online resources:

Download Reference Evapotranspiration - AgERA5 derived (Daily - ~10km)

The datasets for SeasonBench-EA Benchmark, which contains the monthly reanalysis data (1940-2024) and ensemble forecasts for CMCC. All data are downloaded from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/) and reorganized for benchmark construction. Please ensure compliance with the CDS Licence when using or redistributing the data.

This catalogue entry provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). CMIP6 data underpins the Intergovernmental Panel on Climate Change 6th Assessment Report. The use of these data is mostly aimed at:

addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past.

The term "experiments" refers to the three main categories of CMIP6 simulations:

Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2014. Climate projection experiments following the combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The SSP scenarios provide different pathways of the future climate forcing. The period covered is typically 2015-2100.

This catalogue entry provides both two- and three-dimensional data, along with an option to apply spatial and/or temporal subsetting to data requests. This is a new feature of the global climate projection dataset, which relies on compute processes run simultaneously in the ESGF nodes, where the data are originally located. The data are produced by the participating institutes of the CMIP6 project.

Past and future weather extremes across Europe

This repository contains the annual exceedance index data for past and future weather extremes across Europe on NUTS1 scale. The code and an accompanying paper analyzing the impact of this weather extremes on the European agricultural sector on subnational scale will be published during 2023. We use a percentile-based approach to assess the annual exceedance index of the four weather extremes heat waves, cold waves, fire-risk and droughts for the past (1981–2020) and future (2006–2100) [Zhang et al., 2005]. For the past, we used daily weather records on a grid level (around 11 km at the equator) from the ERA5-Land reanalysis dataset, and for future projections, we use modelled daily weather records from EURO-CORDEX [Christensen et al., 2020, Muñoz, 2019]. For past and future fire-risk we use precalculated fire weathernindex data from ERA5 and EURO-CORDEX, respectively [Giannakopoulos et al., 2020]. We used the model average of the following driving GCMs and RCMs for future projections: ICHECs Earth System Model (EC-Earth), MPI-Ms Earth System Model (MPI-ESM-LR), SMHIs Regional Climate Model (RCA4). The baseline period for the historical scenario is 1981–2010, and for future projections 1981–2005. Daily thresholds for heat waves, cold waves, and flash droughts are estimated from the 90th percentile of the daily minimum and maximum temperature, 10th percentile of the daily minimum and maximum temperature, and 30th percentile of the soil volumetric water content (0–28cm), respectively [**Sutanto** et al., 2020]. We use a five days centre data window for all three extreme events to estimate the thresholds from the previously listed baseline periods. The annual exceedance index for heat waves is calculated as the sum of days, at least for three consecutive days; the daily temperature values exceed the thresholds for June, July, and August. For cold waves, the annual exceedance index is the sum of days, at least for three consecutive days; the daily temperature values are below the thresholds for January, February, October, November, and December. In-base, exceedance is calculated using bootstrapping (1000x repetitions) for both extreme events. Heat and cold wave exceedance indices are rescaled to NUTS1 regions using a maximum resampling. We use sequent peak analysis to detect annual flash droughts, remove minor droughts, and pool interdependent droughts for the season from June to October [**Biggs** et al., 2004]. The annual exceedance index of droughts is rescaled to NUTS1 regions by using a mean resampling. Parameters for fire-risk are listed in the table below while.

Parameters of the analysis of the percentile-based extreme.

Type	Variable	Percentile	Window	Min duration	Rescaling	Months	Bootstrapping
Heat wave	tmin and tmax	90	5	3	max	6, 7, 8	yes
Cold wave	tmin and tmax	10	5	3	max	1, 2, 10, 11, 12	yes
Flash drought	swvl 0-28cm	30	5	5	mean	6, 7, 8, 9, 10	no
Fire risk	FWI	90	5	1	mean	3, 4, 5, 6, 7, 8, 9	yes

Xuebin Zhang, Gabriele Hegerl, Francis W. Zwiers, and Jesse Kenyon. Avoiding inhomogeneity in percentile-based indices of temperature extremes. Journal of Climate, 18 (11):1641–1651, 2005. ISSN 08948755. doi: 10.1175/JCLI3366.1.

Samuel Jonson Sutanto, Claudia Vitolo, Claudia Di Napoli, Mirko D’Andrea, and Henny A.J. Van Lanen. Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale. Environment International, 134 (March 2019):105276, jan 2020. ISSN 01604120. doi: 10.1016/j.envint.2019.105276.

J. Sabater Muñoz. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2019.

O. B. Christensen, W. J. Gutowski, G. Nikulin, and S. Legutke. CORDEX Archive Design, 2020. URL https://is-enes-data.github.io/cordex_archive_specifications.pdf

Barry J. F. Biggs, Bente Clausen, Siegfried Demuth, Miriam Fendeková, Lars Gottschalk, Alan Gustard, Hege Hisdal, Matthew G. R. Holmes, Ian G. Jowett, Ladislav Kašpárek, Artur Kasprzyk, Elzbieta Kupczyk, Henny A.J. Van Lanen, Henrik Madsen, Terry J. Marsh, Bjarne Moeslund, Oldřich Novický, Elisabeth Peters, Wojciech Pokojski, Erik P. Querner, Gwyn Rees, Lars Roald, Kerstin Stahl, Lena M. Tallaksen, and Andrew R. Young. Hydrological Drought: Processes and Estimation Methods for Stream- flow and Groundwater. Elsevier, 1 edition, 2004. ISBN 0444517677.

Giannakopoulos, C., Karali, A., Cauchy, A. (2020): Fire danger indicators for Europe from 1970 to 2098 derived from climate projections, version 1.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.ca755de7

Funding
Tobias Seydewitz acknowledges funding from the German Federal Ministry of Education and Research for the [BIOCLIMAPATHS](https://www.pik-potsdam.de/en/output/projects/all/647) project (grant agreement No 01LS1906A) under the Axis-ERANET call. The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Surface temperature: Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to: - aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the spatial resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA Data available is the daily average, minimum and maximum of surface temperature. Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

This entry covers pressure-level data aggregated on a monthly time resolution. Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

The Copernicus Climate Change Service (C3S) seasonal forecast service is based on data from several state-of-the-art seasonal prediction systems.

Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. Unit: m s-1. The Wind Speed variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

Metadata Contact: ECMWF - European Centre for Medium-Range Weather Forecasts

Resource Contact: ECMWF Support Portal

Data lineage:

Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

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Data download from original source