Background & Summary

Generally, energy system analyses in research and for system optimization have been based on one year simulations, where the weather input from selected years, representing specific climatological criteria, have been used. Due do the increasing impacts of anthropogenic climate change1 and the growing importance of renewable energy, an adequate representation of weather related components in energy system analyses and modelling is needed and even required by recent European regulations2. Up to now adequate meteorological data sets are only available for local application as for example in Murcia3 or Brazil4, but not for several countries or the European Union.

Within the Austrian research project “SECURES”5, a research consortia with experts in energy modelling, meteorology and regional climate modelling, developed a new meteorological data set (SECURES-Met)6, that allows a transient electricity modelling for multiple historical years and even climate change projections for the whole 21st century. This European data set is quite novel as it comprises the majority of needs arising from energy modelling, without losing the necessary accuracy to represent the spatial complexity of meteorological variables. The data set has a temporal resolution of one hour and covers the continental EU electricity market (see Fig. 1), includes all relevant meteorological variables or derived indicators (see Table 2), accounts for meteorological spatial variability up to a resolution of 1 × 1 km, and data size is small enough for use in standard electricity modelling software.

Fig. 1
figure 1

SECURES domain (green area), and the different aggregation levels used for Europe and Austria.

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To achieve all these characteristics, variable optimized aggregation algorithms have been developed and the final data sets for electricity modelling are aggregated on NUTS-07 and NUTS-2 level for Europe, and for Austria additionally an aggregation on NUTS-3 level is available. Fig. 1 shows the domain of the SECURES-Met data set (green coloured area). The NUTS-0 level (states) are outlined in red and NUTS2 level (provinces) are outlined in grey. The NUTS-3 regions for Austria are shown in the Austrian map (top right). To account for the offshore wind power production, “Economic Exclusive Zones”8 are used (blue lines and shaded area). The aggregation of the variables and indicators to the NUTS- levels reduces the size of the data set dramatically, the data can simply be stored in ASCII format (csv files) and no compression is required.

SECURES-Met includes the air temperature at 2m-height. This allows the modelling of heating and cooling demand, the efficiency of thermal power plants (e.g., nuclear), and other temperature related processes. To model photovoltaic energy production, solar radiation flux on horizontal surface (global radiation) and the direct normal irradiance (BNI), which gives the radiation flux perpendicular to the incoming solar radiation, is available. As indicator for wind power production the “Potential Wind Power” is provided. This indicator is based on the ten-metre-wind speed, but is converted to the wind speed in 150 m above the ground to account for the height of wind power plants and multiplied with the power curve of selected off- and onshore turbines. The hydro-power indicator is based on run-off-river data and is combined with the installed potential production capacity of hydropower plants (run-off-river and reservoir) based on9. The hydropower indicator is only available on daily basis while all others indicators are available on hourly basis.

Methods

The SECURES-Met dataset is based on a variety of data sets reaching from meteorological time series from reanalyses models (ERA5, ERA5-Land, COSMO-REA6) and climate change projections from regional climate models (EURO-CORDEX10), satellite observations (Helio-Clim), hydrological river runoff, to static data sets as the digital elevation model, NUTS regions, and population density. In Table 1 a brief description of all used data sets is shown.

Table 1 List of base datasets used for producing SECURES-Met.
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The meteorological variables are based on historical observations taken from the ERA5-Land reanalysis data set. This data set contains all necessary meteorological variables on land on hourly basis, with a spatial resolution of 0.1° (~11 km). For offshore areas (Exclusive Economic Zone, EEZ) the ERA5 data has been rescaled to the ERA5-Land grid using a patch regridding method from ESMF11. This was also done for the climate scenarios to have all meteorological information on the same grid and same projection.

The selected climate change projections reflect the possible range of human behaviour in terms of greenhouse gas emissions within the 21st century. During a stakeholder process within the SECURES project, it was decided, to have one low emission scenario that fulfils the two-degree criteria from the UNFCCC Paris agreement from 2015. The second projection should be a high emission scenario usually described as “business as usual”. The selection of the models is based on the ensemble analyses of the newest climate model generation CMIP6, where the emission scenario SSP1-2.6 fulfils the two-degree target and the scenario SSP3-7.0 is taken as business as usual. The selected EURO-CORDEX models were forced by the RCP 4.5 for the low emission scenario and by RCP 8.5 for the high emission scenario.

The selection of possible regional climate projections was limited to the models, for which hydrological river runoff data for whole Europe on daily basis were available. The E-HYPE database provides such information. For the low emission scenario we selected the model “ICHEC-EC.EARTH-rcp45-rlilp1_KNMI-RACMO22E” and for business as usual “ICHEC-EC.EARTH-rcp85-rlilp1_KNMI-RACMO22E”. In a later section, an evaluation how well these models fulfil the representation of the ensemble mean is shown (see Fig. 4). All meteorological variables used from these two models were bias corrected with the ERA5-Land data with a quantile delta mapping approach12.

As the climate change projections only provide daily data, a temporal disaggregation was required. For that purpose, the surface wind and global radiation were disaggregated using a statistical approach. Historical hourly ERA5 and ERA5-Land (1991–2020) data were used to build an average day-curve for every day of the year. This curve was further smoothed by applying a seven-day rolling mean for every hour individually. To ensure continuous data at day changes, the mean values of two consecutive days were averaged during the hours near day changes. Temperature was disaggregated by modelling altering minimum and maximum temperatures of consecutive days with a cosine function, following13. For that purpose, the declination was calculated with the methods of Bourges14 and Spencer15. The minimum temperature was assumed to occur at sunrise rounded to the full hour, the maximal temperature two hours after noon, also rounded to the full hour.

A core task for the data generation was to minimize the data set size but conserving as much as possible of the necessary high spatial resolution information. So for every indicator, an optimized disaggregation to gain high spatial resolution information and an aggregation to reduce data size was developed.

For temperature and radiation data, those variables are specifically relevant in locations with high population density and therefore high electricity demands. Where people live, it is more likely for solar panels to be set up on roofs. For temperatures in mountainous area, a high spatial resolution is crucial, as temperature has a strong elevation dependency and the population density is higher at the low elevated valleys. Therefore, the two-metre-temperature and the global radiation were calculated as

  • areal mean, where every grid cell was weighted an equal amount,

  • and population density weighted mean, where the LsPop population density data was used on a one-kilometre basis during aggregation.

Radiation was provided in W/m² as two parameters. First, as the global radiation (GLO) directly available from ERA5-Land and the EURO-CORDEX models. Second, the direct normal irradiance (BNI), which is the radiation on a surface normal to the direction of the sun, was calculated. This parameter is required to calculate the incident radiation on an inclined surface, for example a solar panel. The HelioClim dataset was used to estimate BNI from global radiation for every NUTS-2 region for each hour of the day. The estimation was done using quantile mapping on an eight-day basis. GLO values below 10 W/m2 were not converted to BNI, but GLO was taken directly. This approach was chosen due to lacking data quality for low radiation. BNI exceeding the solar constant of 1361 W/m² were set to that value. HelioClim data have limitations at latitudes higher than 60 degree. At latitudes above 60 °C (mainly Scandinavia), GLO is a more robust radiation indicator.

As temperature is highly dependent on altitude, a lapse rate of –6.5 °C/km was applied for the disaggregation from the ERA5-Land grid to the 1 km grid of LsPop for every individual hourly value. Temperature was provided in °Celsius. Fig. 2a shows an example for the high spatial resolution of the LsPop data set and Fig. 6a a comparison of the spatial mean and population weighting aggregation in the period 1981–2010 as an example for the differences. Population weighted temperature tends to be warmer because valleys, where people live, are weighted higher in mountainous regions and thus the temperature is more accurate than a simple spatial mean.

Fig. 2
figure 2

Population density for the City of Vienna, Austria as example for the LsPop dataset (a), locations of the hydropower plants from JRC database (b), wind mask for land (red) and offshore (blue). The darker the red colour the higher is the fraction within an ERA5-Land grid cell, that is suitable for wind power plants. White indicates, that no wind power plants are allowed (c). Relative power curves as a function of wind speed at 150 m above ground used for land (blue) and offshore (orange) wind power plants.

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For the wind power indicator several steps have been necessary. As the turbine height of wind power plants is approximately 150 m above ground level and the ERA5(-Land) and EURO-CORDEX variables provide wind speed at 10 m height, wind speeds had to be converted to the higher altitudes. This transfer is not linear and depends on roughness, vertical stability and topography. Therefore a statistical approach was chosen. The COSMO-REA6 dataset provides wind speed at 150 m and the period of 1995–2019. This data was regridded to the ERA5-Land resolution. Afterwards, the empirical cumulative distribution function was calculated for each grid point and the 10 m surface wind percentiles of the models were mapped to the percentiles of the COSMO-REA6 to get the corresponding wind speed at 150 m. Then the relative power curves for land and offshore wind turbines, visible in Fig. 2d were applied to the wind speed. As representative turbines N163-4.9516 was chosen for land and V164-800017 for offshore. Afterwards, a map provided by COSMO-REA618, indicating suitable spots for wind power plants across Europe was aggregated to the ERA5-Land grid by building the arithmetic mean, yielding the fraction of suitable area per grid box. This mask was then used as weights for the power curves to aggregate the wind power to the NUTS regions, and EEZ and is also visible in Fig. 2c. The power output is therefore normalized to 1. Efficiency loss was not considered but can be applied by users of the data set using their own assumptions.

The hydropower indicator is based on the E-HYPE scenarios. The utilized variable is the daily mean river discharge in m³/s. As river discharge in Europe has no distinct diurnal cycle, disregarding very small highly glaciated catchments in summer, river discharge is not subject to change on hourly basis, daily resolution is sufficient and no temporal disaggregation was required.

The JRC Hydro Power Plant Database was used for information and characteristics about the hydropower plants over Europe. Missing data of average annual generation was estimated using the representative full load hour (FLH) for hydropower from TYNDP 2020 scenarios from ENTSO-E9 by considering distributed generation scenario and climate of the year 1984 in the specific countries.

Individual power plants were first divided into runoff-river and reservoir plants and then attributed to a specific E-HYPE sub-basin. The daily mean power was then assumed to be proportional to the daily mean runoff, up to a maximum capacity for run-of-river power plants. The scaling factor between daily mean discharge and daily mean power was calculated from the mean annual energy production of each individual plant in an iterative process. Afterwards, the results in MW were normalized to 1 by dividing through the annual mean power.

As every data set, also SECURES-Met6 has limitations, stemming from the data sets used or the methods deployed to produce the final product. Some information concerning skills and limitation of the data sets used, can be found in the references listed in Table 1. An evaluation of ERA5 and ERA5-Land is given in19. Concerning wind data, an evaluation of ERA 5 Land and COSMO-REA5 is given in20.Base quality information concerning E-hype can be found in21 and an evaluation of EURO-CORDEX regional climate models with the focus on precipitation is given in22. The population density data used for aggregation is from the year 2008 and a more recent data set (e.g. GPWv423) would be more accurate, but the effect on the aggregation should be minimal. The used constant temperature lapse rate is also a simplification, that especially leads to an overestimation of the lapse rate during winter and night-time temperatures due to inversions, but it is still an improvement compared to the coarse resolution data in complex terrain.

Data Records

SECURES-Met6 is available in a tabular csv format for the historical period (1981–2020, hydro power only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 1951–2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the spatial aggregation level

NUTS0 (country-wide),

NUTS2 (province-wide),

NUTS3 (province-Austria only),

and EEZ (Exclusive Economic Zones, offshore only).

The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As population weighted temperature and radiation represent values in geographical complex areas in a more representative way for solar power, it is highly recommended to use population weighted files. Spatial mean should be used for reference only.

Data can be downloaded as zip files. Each zip (except Meta.zip) is organized in the following directory structure: AGGREGATION-LEVEL/DATAFILE.csv

AGGREGATION-LEVEL is one of the following:

NUTS0_Europe aggregated on NUTS0 level (countries) for Europe

NUTS2_Europe aggregated on NUTS2 level (provinces) for Europe

NUTS3_Austria aggregated on NUTS3 level (districts) for Austria

NUTS0_offshore aggregated on NUTS0 level (countries) for Europe, offshore, wind power only

EEZ_offshore aggregated on EEZ level (economic exclusive zones) for Europe, offshore, wind power only

DATAFILE is composed like: VARIABLE_AGGREGATION-LEVEL_REGION_AGGREGATION-METHOD_MODEL_FREQUENCY_TIMEPERIOD

VARIABLE is one of:

HYD-RES daily mean power from reservoir plants, unit: MW (sum aggregation), 1 (normalized aggregation)

HYD-ROR daily mean power from ROR plants, unit: MW (sum aggregation), 1 (normalized aggregation)

GLO hourly mean global radiation, unit: W*m**-2

BNI hourly direct normal irradiation, unit: W*m**-2

T2M hourly air temperature 2 m above ground, unit: °C

WP hourly potential wind power production, normalized between 0 and 1, unit: 1

REGION is one of: offshore, Europe and Austria

In Fig. 3 an organigram of the structure of the SECURES-Met data is shown. All data are open source and available at ZENODO in the same structure.

Fig. 3
figure 3

Organigram of the file structure and the information given by the file name of the SECURES-Met data set.

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In Table 2 all final products of SECURES-Met are described shortly.

Table 2 List of variables and definitions within the SECURES-Met data set.
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All these products are available for historical period from 1981 till 2020 and for two climate change projections, one low emission scenario fulfilling the 2 degree target24, and a high emission scenario that follow the CMIP 625 recommendation for “business as usual” scenario SSP3-7.026, for the whole 21st century.

All data is available at Zenodo6

Technical Validation

The two selected regional climate projections represent the average development of climate scenarios fulfilling the two-degree target (low emission scenario) and the business as usual scenario (high emission scenario). In Fig. 4 the development of the annual mean temperature of the two selected models (dashed blue and red lines) during the whole 21st century and the European domain is compared with the ensemble (29 models) of global climate models of the newest generation (CMIP6) for the low emission scenario SSP1-2.6 (blue) and the high emission scenario SSP3.7.0 (red). The bold lines represent the ensemble medians and the shaded areas the range between the 10th and 90th percentile. Both selected models lie within the 10th and 90th percentile of the respective ensemble during the 21st century and for the most time of the century, the difference between the selected model and the ensemble median of the ensemble is in the order of few 0.1 °C and thus much smaller than the climate change signal. The two selected models are therefore good representatives for the ensemble means of the selected emission scenarios, especially till the middle and at the end of the 21st century.

Fig. 4
figure 4

Ensemble median (line) and 10th to 90th percentile range (coloured area) of the annual mean temperature anomaly compared to current climate (1991–2020) of the SECURES-Met Europe domain, for the CMIP6 ensemble forced by the SSP1-2.6 (blue) and SSP3-7.0 (red) emission scenario. The number in the parenthesis indicates the numbers of models within the ensemble. The dashed lines gives the same indicator for the two selected EURO-CORDEX models.

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The quality of the temporal disaggregation of daily meteorological data to hourly data is shown in Fig. 5. For this comparison, the ERA5-Land hourly values are aggregated to daily values, as they are provided by regional climate projections, subsequently disaggregated with the selected statistical method and then compared with the original hourly data. The results show a good reproduction of the diurnal cycle for all three variables in summer cases as well as winter cases.

Fig. 5
figure 5

Comparison of hourly values for all necessary meteorological variables from ERA5-Land (blue) and the temporal disaggregated values estimated from daily data (orange) for a grid cell near Vienna, Austria. The left side represents a summer case and the right side a winter case.

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In Fig. 6 comparison of the climatological mean temperature for the period 1981–2010 for the whole SECURES-Met domain is shown, based on simple arithmetic averaging for the NUTS2 region (a) and population weighted average (b). In flat areas (e.g. northern Germany or Poland), insignificant differences occur but in mountainous areas (e.g. Austria or Norway), the population weighted mean temperature is significantly warmer. This represents the real heating and cooling demand of the people living in mountainous regions much better than a simple arithmetic mean.

Fig. 6
figure 6

Comparison of temperature aggregation methods. Spatial mean (a) and population weighted (b) annual mean temperature for the period 1981–2010 [°C]. In mountainous areas, the population weighted temperature is higher, as the majority of people live in the low elevated valleys.

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For radiation, also the population weighted mean is better representing the real situation, as photovoltaic panels frequently are mounted on roofs. In Fig. 7 the climatological mean of the variable GLO is shown. The variable BMI is similar, both variables reflect the north-south gradient and BNI is systematic higher than GLO, but the range is modified by different cloudiness in Europe.

Fig. 7
figure 7

Radiation climatology for the period 1981–2010 for SECURES-Met land domain, based on ERA5-Land for global radiation. Aggregated population weighted on NUTS 2 regions.

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In Fig. 8 the normalized wind power climatology is shown for NUTS-2 on land and for EEZ on the ocean. The wind power potential is not only depending on the local wind speed but also on the area suitable for wind power plants. This is clearly seen in Norway. Here the offshore locations have high wind power potential and the land area, due to the mountainous character, only low.

Fig. 8
figure 8

Normalized wind power climatology for the period 1981–2010 for SECURES-Met land domain, based on ERA5-Land (a) and for SECURES-Met offshore regions, based on ERA5 (b). Aggregated on NUTS-2 regions on land and on EEZ regions.

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To validate the reliability of the hydropower generation, the seasonal cycle for the run-off-river plants (Fig. 9a) and the reservoir plants (b) are shown for historical production (blue line) in Austria. Within the current climate the run-off-river plants have two maxima, one in April/May and one in November, whereas the spring maxima is associated with the peak of snow melt in the mountains. This maximum vanishes in the business as usual scenario till the end of the 21st century and only one maximum can be seen during winter. The reservoir plant production has a clear maximum during the snow and glacier melt period from May to July in the current climate. Till the end of the century (red line) this maximum weakens significantly and occurs earlier in the year and a pronounced reduction occurs during summer. These findings correspond well with common results of hydrological scenarios for Europe in the 21st century27.

Fig. 9
figure 9

Annual course of energy production for run-of-river (a) and reservoir (b) hydropower plants for the RCP 8.5 scenario, normalized to 1 with the mean energy production of the period (1971–2005) in Austria.

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User Notes

This dataset provides the basis for direct use or further processing in energy system modelling. The generated time series for wind (onshore and offshore) and hydropower generation (run-of-river and reservoir) can be directly used in energy system modelling. The hydro power data are only given on daily base, as run–of-river hydro power generation has no distinct diurnal cycle and reservoir hydro power generation is strongly controlled by demand. As the unit for this indicator is power (unit Megawatt), the daily values can be used as hourly values if needed. This implies that the hydro power generation has no diurnal cycle. In most energy system models daily or even weekly or monthly representation of hydro power operation is used28.

Temperature and radiation data can be used to model solar energy production and temperature-dependent electricity demand and supply components like heating and cooling demand, efficiency losses of batteries or photovoltaic panels, and thermal losses of thermal power plants. This can be used to assess security of supply in the context of climate-related extreme events in electricity systems or adequacy problems in future energy systems.

An exemplary application of the dataset can be found in further publications in the course of the project SECURES (see https://www.secures.at/publications).