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The final selection of six climate models and four scenarios is summarised in Table 2.1.

Table 2.1: List of global climate models used to generate the climate indicators, showing the contributing institutions, spatial and temporal resolutions, available future scenarios, and key model configurations.

[1] Lovato, T. and Peano, D. (2020). CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365  http://esgf-node.llnl.gov/search/cmip6/?mip_era=CMIP6&activity_id=ScenarioMIP&institution_id=CMCC&source_id=CMCC-CM2-SR5

[2] EC-Earth Consortium (EC-Earth) (2023); Döscher, R., et al. (2022). IPCC DDC: EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP. WDCC at DKRZ. https://doi.org/10.26050/WDCC/AR6.C6CMEEE3

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A spatial mask is then applied to all variables to restrict the domain to the area defined by the Global Wind Atlas ² version 3 (GWA3). This domain includes all land areas and extends up to 300 km offshore, while excluding polar regions. The mask is further extended to include the grid points required to cover the ENTSO-E Pan-European Climate Database (PECD) ³ . This extension explains the rectangular shape visible over Europe in the final domain mask. The resulting spatial coverage is shown in Figure 2.1.

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Wind speed at 100 m (WS100) for each climate model and scenario is then derived from the corresponding 10 m wind speed (WS10) using the wind power law:

v_{2} = v_{1} \left( \frac{h_2}{h_1} \right)^{\alpha}

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• v₁​ is the wind speed at height h₁ (10 m)

• v₂​ is the wind speed at height h₂​ (100 m)

• α is the dimensionless wind shear exponent.

Further details on the wind profile scaling methodology and on the computation of the α coefficient are provided on the page Power Law for Wind Profile Scaling.

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• Spatial Aggregation Procedure

• Temporal Aggregation Procedure 

Overview of Climate Indicators

The dataset provides a suite of climate indicators, delivered in both gridded NetCDF format and regionally aggregated CSV format. These indicators serve as the foundation for the derivation of energy indicators and support a broad range of climate-related analyses and applications.

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Table 2.2: Gridded and aggregated climate indicators provided in the historical data stream.

Figure 2.2: Example of time-series of annual aggregations for selected for selected climate indicators at country level (ADM0). Climate indicators include: 2m temperature (TA, annual mean), total daily precipitation  total daily precipitation (TP, annual sum), global  global horizontal irradiance (GHI, annual mean), and 10m  and 10m wind speed (WS10, annual sum). Each plot shows data for a specific country: United States (US) for TA, Italy (IT) for TP, India (IN) for GHI, and South Africa (ZA) for WS10. Historical values are based on the the ERA5 reanalysis (blue solid line; time period: 1995-2025). For climate projections, depending on the climate indicator, each plot displays either multiple scenarios for a single model or multiple models for a single scenario. The line colours are specified in the legend of each plot. When a single model or scenario is selected, its name is indicated in the plot title.             

 Quality control

A quality control (QC) procedure is applied to all climate indicators described in the previous section to ensure data integrity, physical consistency, and methodological robustness. The QC combines automated checks on the gridded outputs with targeted diagnostic analyses of the bias-adjustment methods.

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Some examples for 2 m temperature demonstrate how the moving-window CDF-based approach captures non-stationarity in future projections by progressively adjusting high-end extremes across overlapping time windows (Figure 2.3). Similar diagnostics are shown for total daily precipitation (Figure 2.4) and 10 m wind speed (Figure 2.5).

For Global Horizontal Irradiance (GHI), which is corrected using a delta adjustment, Probability Density Functions (PDFs) are analysed instead. These diagnostics confirm that the correction appropriately shifts the distributions in line with the ERA5 climatology (Figure 2.6).

Image ModifiedImage ModifiedImage ModifiedImage ModifiedImage Modified

Figure 2.3: Example of assessment of the CDFt bias-adjustment method applied to the 2 m temperature of the CMIP6 BCCS climate projection model for scenario SSP2-4.5 at a grid-point over Australia (24.75°S, 138.5°E), shown by the purple marker on the map (plot b). The plots (a, c-f) show the four Cumulative Distribution Functions of: (1) the source (historical, orange line), (2) target (ERA5, blue line), (3) raw projection (projection, green line), and (4) bias-adjusted projection data (red line). All CDFs refer to a February time step at 06:00 UTC across different bias-adjustment (BA) output windows: (a) third BA output window (2035-2044); (c, e) fourth and fifth BA output windows (2045-2054 and 2055-2064, respectively); and (d, f) zoomed-in views of the highest values from plots (c) and (e).

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Figure 2.4: Example of assessment of the CDFt bias-adjustment method (CDF curves on the left plot) to the total daily precipitation of the CMIP6 AWCM climate projection model (future scenario: SSP3-7.0) at a grid-point located over the Amazon Basin region (8.25°S, 56°W; purple marker on the map on the right) for time-steps occurring in October over the third bias-adjustment output window (2055-2074).

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Figure 2.5: Example of assessment of the CDFt bias-adjustment method to the 10m wind speed of the CMIP6 MEHR climate projection model (future scenario: SSP5-8.5) at a grid-point located over Patagonia (49.75°S, 73°W; purple marker on the map on the right) for time-steps occurring in April at 18:00 UTC over the fourth bias-adjustment output window (2075-2094).

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Figure 2.6: Example of assessment of the Delta adjustment method to bias adjust Global Horizontal Irradiance (GHI). The left panel shows the Probability Density Functions (PDFs) for the CMIP6 CMR5 climate projection model (scenario SSP1-2.6): (1) raw original projection (green line and histogram), and (2) bias-adjusted projection (red line and histogram). The blue and orange vertical solid lines represent the climatology of ERA5 and CMIP6 historical data, respectively, over the period 1995–2014. All data refer to a grid-point located over India (25.5°N, 80.75°E; orange marker on the map on the right) for time-steps occurring in May at 12:00 UTC over the last (fourth) bias-adjustment output window (2075-2100).

Extreme precipitation over sensitive regions

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Several mitigation strategies were tested, including clipping bias-adjusted precipitation in dry regions using thresholds derived from the target climatology. While some local improvements were achieved, residual inconsistencies remained across grid points and projection periods. As these approaches were not considered sufficiently robust, no additional correction beyond removing the most extreme outliers (> 2 m day⁻¹) is applied. Users are therefore advised to interpret precipitation projections with caution in sensitive regions and to consult uncorrected climate projection data when investigating local extremes.

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Figure 2.7: Grid-points where at least one time-step exhibits a total precipitation value exceeding 2 m (orange markers). The size of the marker depends on the number of occurrences (i.e. time steps) of this exceedance at that location considering the CMIP6 AWCM, BCCS and ECE3 climate projection models and the SSP5-8.5 scenario over the whole projection period, 2015-2100.

High-end values of wind speed and irradiance

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For GHI, following delta adjustment, some locations show values exceeding 2500 W m⁻². This intentionally high threshold is used to test the robustness of the correction over a wide range of conditions. Since these irradiance values stem from large but valid correction factors, they are also retained (Figure 2.9).

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Figure 2.8: Example of assessment of the CDFt bias-adjustment method at a location where the 10m wind speed exceeds 100 m s^-1, which is the upper limit of the reasonable values for wind speed (black solid vertical line on the left plot). All curves refer to the CMIP6 MRM2 climate projection model (climate scenario: SSP2-4.5) at a grid-point located over eastern Russia (53.75°N, 134.75°E) for time-steps occurring in January at 16:00 UTC over the second bias-adjustment window (2035-2054).

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Figure 2.9: Example of assessment of the Delta adjustment method at a location where Global Horizontal Irradiance (GHI) exceeds 2500 W m⁻², the upper limit of reasonable values for GHI (black dashed vertical line, left panel). The left panel shows the Probability Density Functions (PDFs) for the CMIP6 CMR5 climate projection model (scenario SSP1-2.6): (1) raw original projection (green line and histogram), and (2) bias-adjusted projection (red line and histogram). The blue and orange vertical solid lines represent the climatology of ERA5 and CMIP6 historical data, respectively, over the period 1995–2014. All data refer to a grid-point located over Peru (13.75°S, 71.25°W; orange marker on the map, right panel) for time steps occurring in November at 17:00 UTC during the first bias-adjustment window (2015–2034).

Consistency between historical and projection wind speed at 100 m

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Section2_6_5 Section2_6_5

An additional QC step evaluates the consistency between historical and projection data for 100 m wind speed by comparing country-level averages over the overlapping period 2015–2024. As described in the Global climate and energy indicators from 1950 to present derived from reanalysis: Product User Guide (PUG) (Section 2.4, Wind Profile Scaling for High Heights), the historical stream provides two alternative version of the ERA5 100 m wind  100 m wind speed: 1) the bias-adjusted WS100, which is obtained by bias-adjusting the native ERA5 100 m wind speed (WS100) using the Global the Global Wind Atlas version 2 (GWA2), and 2) the the Alpha-based WS100, which is computed using the power law, based on the bias-adjusted historical ERA5 10 m wind speed adjusted historical ERA5 10 m wind speed (WS10). The Alpha-based WS100 has been produced to improve consistency between historical and projection dataconsistency between historical and projection data, particularly in climate projections for which the native 100 m wind speed is not available. To assess the offset in the transition from the historical to the projection stream, both versions of the ERA5 WS100 (bias-adjusted and Alpha-based) were compared to the climate projections.

Using aggregated files at the country-level (ADM0), the offset was evaluated by comparing ERA5 and CMIP6-projection averages over the overlapping period 2015–2024. For each CMIP6 model, the four SSP averages were first used to define a model-specific envelope and evaluate whether the ERA5 value falls inside or outside this range. When ERA5 fall outside the envelope, the offset was computed as the difference between the ERA5 average and the CMIP6 multi-scenario average over the same period. This offset was then compared to a predefined threshold of 0.2 m s^-1, chosen to exceed typical model noise while remaining physically meaningful. Results show that offsets are generally small and that the alternative formulation improves alignment with projections, reducing both the number of affected countries and the magnitude of the largest discrepancies (Table 2.2).    

Table 2.2: Evaluation of the offset between ERA5 (historical stream) and CMIP6 (projection stream) data for 100 m wind speed (WS100) over the period 2015–2024. For each CMIP6 model, the offset has been assessed at the country-level aggregation (ADM0) for the two versions of ERA5 WS100 (bias-adjusted and Alpha-based). The table reports: the number and percentage of countries showing an offset larger than the predefined threshold (0.2 m s^-1), together with the country code (ISO 3166-1 alpha2) and value (in m/s) of the largest absolute offset.

Computation of the Energy Indicators

The energy indicators provided in this dataset are derived from the climate indicators described in the previous sections, using dedicated models that convert meteorological variables into estimates of electricity generation or demand. These models are designed to reflect the physical and operational characteristics of different energy technologies. The energy indicators include:

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The availability of two versions of ERA5 100 m wind speed (bias-adjusted and Alpha-based WS100) results in two corresponding versions of ERA wind power indicators. In Section 2.6.5, the offset between historical and projection WS100 has been assessed. Using the same methodology, the offset has been evaluated for the onshore wind power indicator at 135 m hub height, which requires the largest wind-speed extrapolation using the power law. Table 2.3 summarizes the offset evaluation for WON at 135 m across the six CMIP6 climate projections.

Table 2.3: Evaluation of the offset between ERA5 (historical stream) and CMIP6 (projection stream) data for WON at 135 m wind speed (WS100) over the period 2015–2024. For each CMIP6 model, the offset has been assessed at the country-level aggregation (ADM0) for the two versions of ERA5 WON at 135 mproduced with either the bias-adjusted or the Alpha-based WS100. The table reports: the number and percentage of countries showing an offset larger than the predefined threshold (0.01 Mw/Mw), together with the country code (ISO 3166-1 alpha2) and value (in Mw/Mw) of the largest absolute offset.

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Figure 3.1: Example of time series of annual

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means for on-shore wind power (WON) at three different turbine heights (84 m, 100 m, and 135 m), aggregated at country level (ADM0) over South Africa (ZA). Historical values cover the time period 1995-2025 and provide two alternative version of ERA5 reanalysis for turbine heights at 100 m and 135 m: using bias-adjusted WS100 (ERA5-BA, blue solid line) or Alpha-based WS100 (ERA5-Alpha, black solid line) to extrapolate wind speed at the turbine height. For turbine heights at 84 m, wind speed at 10 m (WS10) is internally extrapolated to the hub height using the power law. Climate projection values are based on the CMIP6 MEHR climate projection model, showing four future scenarios (period: 2015-2100). Line colours are specified in each plot legend. Note that the y-axis range differs between the three plots.

Solar Photovoltaic Energy Indicator

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Section3_2_2 Section3_2_2

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See Solar Photovoltaic Energy Conversion Model for more details.

Figure 3.2 below shows an example of time series of annual means for solar PV capacity factor (SPV).

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Figure 3.2: Example of time series of annual means for solar PV capacity factor (SPV), aggregated at country level (ADM0) over Spain (ES). Historical values are based on the ERA5 reanalysis (blue solid line; time period: 1995-2025), while climate projections are based on the CMIP6 CMR5 climate projection model, showing the four future scenarios (period: 2015-2100). Line colours are specified in each plot legend.

Hydropower Energy Indicators

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Section2_10_4 Section2_10_4

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See Hydropower Conversion Models, Section 1 "Random Forest Regression Model (European Domain)" for more details.

Figure 3.3 below shows examples of time series of annual sums for the three hydropower indicators.

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Figure 3.3: Example of time series of annual sums for hydro power (HP) for three indicators (generation from reservoirs, HRG; inflows to reservoirs, HRI; and generation from run-of-river and pondage, HRO), aggregated at country level (ADM0) over France (FR). Historical values are based on the ERA5 reanalysis (blue solid line; time period: 1995-2025), while climate projections are based on the CMIP6 AWCM climate projection model, showing the four future scenarios (period: 2016-2100). For HP, the time series starts in 2016 since the Random Forest model does not have data of temperature and precipitation for the previous six months to compute HP. Line colours are specified in each plot legend. 

Installed Capacity Weighted Precipitation (IWP) Proxy (Global Domain)

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Aggregated IWP time series as CSV files at ADM0 level at monthly, seasonal and annual temporal resolution.

See Hydropower Conversion Models, Section 2 "Installed Capacity Weighted Precipitation (IWP) Proxy (Global Domain)" for more details.

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Figure 3.4: Global Energy Monitor HP plants operating (blue) and in construction (red). The bigger the size of each plant (dot), the bigger the Installed Capacity.

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Figure3_4 Figure3_4

Electricity and Energy Demand Indicators

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See Electricity and Energy Demand Models, Section 1 "Electricity Demand Model (European Domain)", for more details.

Figure 3.5 below shows an example of time series of annual means for the EDM.

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Figure 3.5: Example of time series of annual means for the Electricity Demand Model (EDM) indicator, aggregated at country level (ADM0) over Belgium (BE). Historical values are based on the ERA5 reanalysis (blue solid line; time period: 1995-2025), while climate projections are based on the CMIP6 AWCM climate projection model, showing the four future scenarios (period: 2016-2100). Line colours are specified in the plot legend.

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Figure3_5 Figure3_5

Energy Demand Proxy (Global Domain)

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Figure 3.6 below shows examples of time series of annual means for the CDD, HDD and EDD.

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Figure3_6 Figure3_6

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Figure 3.6: Example of time series of annual sumss for Cooling Degree Days (CDD), Heating Degree Days (HDD) and Energy Degree Days (EDD), aggregated at country level (ADM0) over Australia (AU). Historical values are based on the ERA5 reanalysis (blue solid line; time period: 1995-2025), while climate projections are based on the CMIP6 AWCM climate projection model, showing the four future scenarios (period: 2015-2100). Line colours are specified in each plot legend.

Gridded and Aggregated Energy Indicators

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Section3_3 Section3_3

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Table 3.1: Gridded and aggregated energy indicators in the projections data stream.

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Table3_1 Table3_1

Energy Indicator		Units	Period	Source	Domain / spatial resolution	Data Type	Temporal resolution of the Gridded Data (NetCDF)	Temporal resolution of the Aggregated Data (CSV)
Wind power	Wind power onshore (WON): 3 technologies	CF [MW/MW]	2015 - 2100	Bias-adjusted CMIP6 WS10, and WS100 computed via power law from WS10. Turbine data from thewindpower.net	Global / 0.25° x 0.25°	Gridded (NetCDF) and Aggregated at ADM0 and ADM1 levels (CSV)	Hourly	Hourly, daily, monthly, seasonal, annual
	Wind power offshore (WOF): 2 technologies	CF [MW/MW]	2015 - 2100	WS100 computed via power law from WS10. Turbine data from thewindpower.net	Global / 0.25° x 0.25°	Gridded (NetCDF)	Hourly	-
Solar generation	SPV	CF [MW/MW]	2015 - 2100	Bias-adjusted CMIP6 TA and GHI, tilt and azimuth configurations from PV plants	Global / 0.25° x 0.25°	Gridded (NetCDF) and Aggregated at ADM0 and ADM1 levels (CSV)	Hourly	Hourly, daily, monthly, seasonal, annual
Hydropower	Generation from reservoirs (HRG), Generation from run-of-river and pondage (HRO), Inflows to reservoirs (HRI)	MWh	2015 - 2100	Bias-adjusted CMIP6 TP and TA, data from ENTSO-E Transparency Platform	European countries	ADM0 (CSV)	-	Weekly, monthly, seasonal, annual
	Installed capacity weighted precipitation (IWP)	mm/n-months	2015 - 2100	Bias-adjusted CMIP6 TP, hydropower plants data from Global Energy Monitor	Global	ADM0 (CSV)	-	Monthly, seasonal, annual
Electricity demand	EDM	MWh	2015 - 2100	ENTSO-E load, bias-adjusted CMIP6 TA, WS10 and GHI	European countries	ADM0 (CSV)	-	Daily, monthly, seasonal, annual
Energy demand	Heating Degree Days (HDD), Cooling Degree Days (CDD), Energy Degree Days (EDD)	°C	2015 - 2100	Bias-adjusted CMIP6 TA	Global	Aggregated at ADM0 level (CSV)	-	Monthly, seasonal, annual

Footnotes Display

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Known issues

Extreme Daily Precipitation Values in Bias-Adjusted Projections

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This paragraph aims to explain the filename convention used for the dataset described in this Product User Guide. Table 5.1 details the structure and possible fields of the filenames. Specifically, the last column indicates the corresponding section of the CDS catalogue where users can personalise their choice. If "Not applicable" is indicated, it means that the user cannot modify this field, and the data are downloaded with fixed characteristics that are not customizable. Table 5.2 details the structure and filenames of the ancillary NetCDF files (described in Table 5.3) that are available in the CDS under the widget "Weights and masks".

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Table5_1 Table5_1

Table 5.1: Filename convention used in the dataset described in this Product User Guide.

Position in the filename	Possible substrings for each position in the filename	Description	Option in the CDS download form
0	P (Projections)	Temporal period covered	Not applicable
1	CMI6 (CMIP6 models)	Data source	Not applicable
2	CMCC (Centro Euro-Mediterraneo Cambiamenti Climatici) ECE3 (European Community Earth) MPI- (Max Planck Institute) BCC- (Beijing Climate Centre) AWI- (Alfred Wegener Institute) MRI- (Meteorological Research Institute)	Climate producing center	Not applicable
3	CMR5 (CMCC-CM2-SR5 r1i1p1f1) ECE3 (EC-Earth3 r1i1p1f1) MEHR (MPI-ESM1-2-HR r1i1p1f1) BCCS (BCC-CSM2-MR r1i1p1f1) AWCM (AWI-CM-1-1-MR r1i1p1f1) MRM2 (MRI-ESM2-0 r1i1p1f1)	Climate model	Climate model
4	WS- (10m wind speed and 100m wind speed extrapolated), TA- (2m temperature), GHI (Surface solar radiation downwards), TP- (Total precipitation)	Climate variable	Variable (Climate)
	CDD (Cooling degree days), EDD (Energy degree days), EDM (Electricity demand), HDD (Heating degree days), HRG (Hydropower reservoirs generation), HRI (Hydropower reservoirs inflow), HRO (Hydropower run-of-river generation), IWP (Installed-capacity-weighted total precipitation), SPV (Solar photovoltaic generation capacity factor), WON (Wind power onshore capacity factor), WOF (Wind power offshore capacity factor)	Energy variable	Variable (Energy)
5	NA--- (EDM, HRG, HRI, HRO, IWP, SPV), 0000m (TP, GHI), 0002m (TA, CDD, EDD, HDD), 0010m (WS10), 0084m (WON), 0100m (WS100, WON), 0105m (WOF), 0135m (WON), 0150m (WOF)	Level (meters above sea level) Variable(s) provided at that vertical level	Not applicable
6	Glob (Global domain), Euro (European domain)	Spatial domain	Region (Not applicable)
7	025d (0.25°), ADM0 (ADMIN0), ADM1 (ADMIN1)	Spatial resolution	Spatial resolution
8

SYYYYMMDDhhmm (starting year, month, day, hour, minute)

YYYYMM*

Date

Start date

	Year Month
9

EYYYYMMDDhhmm (ending year, month, day, hour, minute)

10

End date

Year

Month

ACC (accumulated), INS (Instantaneous), CFR (Capacity factor), NRG (Energy)

Type of data

Not applicable

11

10

MAP (gridded data), TIM (time series)

Data structure/typology

Not applicable

12

11

01h (1-hour), 01d (1-day), 07d (7-day), 01m (1-month), 03m (3-month), 01y (1-year)

Temporal resolution

Temporal resolution

13

12

NA-

Lead time

Not applicable

14

13

noc (no correction), mbc (mean bias correction), cdf (cumulative distribution function)

Bias adjustment method

Not applicable

15

14

NA-, org (data at the finest Temporal resolution available, with no Temporal aggregation applied), avg (data averaged over the selected Temporal resolution), sum (data cumulated over the selected Temporal resolution)

Temporal aggregation

Not applicable

16

15

NA

Ensamble number

Not applicable

17

16

SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5

IPCC emission scenario

Emission scenario

18

17

NA--- WP010 (CDS label IC8HH105E: Vestas V164/8000, offshore, hub height = 105 m, installed capacity = 8 MW) WP011 (CDS label IC2.5HH100E: GE Energy 2.5-103, onshore, hub height = 100 m, installed capacity = 2.5 MW) WP002 (CDS label IC3.3HH84: Gamesa G132/3300, onshore, hub height = 84 m, installed capacity = 3.3 MW) WP013 (CDS label IC15HH150E: IEA 15MW_240_RWT, offshore, hub height = 150 m, installed capacity = 15 MW) WP014 (CDS label IC6HH135E: NREL Bespoke_6MW_170, onshore, hub height = 135 m, installed capacity = 6 MW)

Technology

Technological specification

19

18

NA--- StRnF (Statistical model/Random Forests) StGAM (Statistical model/GAM) PhM01 (Physical Model/method1 SPV) PhM02 (Physical Model/method2 WP) PhM03 (Physical Model/method3 proxy EDD) PhM04 (Physical Model/method4 proxy IWP)

Energy conversion model

Not applicable

20

19

v1.00

File version

Version

21

20

.nc (NetCDF) .csv (comma-separated values)

File formats

Not applicable

Examples of filenames: 

* Due to the requirements of the CDS catalogue entry, the fields “Start date” and “End date” are returned after retrieval as a single field in the format YYYYmm, where the year and month correspond to the end date of the original file.

Examples of filenames: 

• P_CMI6_P_CMI6_ECEC_ECE3_TP-_0000m_Glob_025d_S205001010000_E205001310000202501_ACC_MAP_01d_NA-_cdf_org_NA_SP245_NA—_NA—_v1.00.nc
  This file contains projection data (P) from CMIP6 models (CMI6). The climate producing centre is EC-Earth Consortium (ECEC), and the climate model used is EC-Earth3 r1i1p1f1 (ECE3). The variable is total precipitation (TP-) at 0 m height (0000m). The coverage is the global domain (Glob) with a 0.25° spatial resolution (025d). Data span from 01/01/2050 at 00:00 UTC (S205001010000) to 31/01/2050 at 23:00 UTC (E205001312300correspond to January 2050 (205001). The data are accumulated (ACC), provided as gridded data (MAP), with an hourly temporal resolution (01h). The lead time is not available (NA-). Bias adjustment has been performed using the cumulative distribution function method (cdf), and the data are provided at their original hourly resolution without additional temporal aggregation (org). The ensemble number is not available (NA). The emission scenario is SSP2-4.5 (SP245). The technology specification and energy conversion model are not applicable (NA---_NA---). The file version is (v1.00) and the file format is NetCDF (.nc).
• P_CMI6_BCC-_BCCS_WS-_0010m_Glob_ADM0_S210012010000_E210012312100210012_INS_TIM_01m_NA-_cdf_avg_NA_SP245_NA---_NA—_v1.00.csv
  This file contains projection data (P) from CMIP6 models (CMI6). The climate producing centre is Beijing Climate Centre (BCC-), and the climate model used is BCC-CSM2-MR r1i1p1f1 (BCCS). The variable is wind speed (WS-) at 10 m height (0010m). The coverage is the global domain (Glob), aggregated at the ADM0 (country) level. Data span from 01/12/2100 at 00:00 UTC (S210012010000) to 31/12/2100 at 21:00 UTC (E210012312100correspond to December 2100 (210012). The data are instantaneous (INS), provided as time series (TIM), with a monthly temporal resolution (01m). The lead time is not available (NA-). Bias adjustment has been applied using the cumulative distribution function method (cdf), and the values are averaged over the selected temporal resolution (avg). The ensemble number is not available (NA). The emission scenario is SSP2-4.5 (SP245). The technology specification and energy conversion model are not applicable (NA---_NA---). The file version is (v1.00) and the file format is CSV (.csv).
• P_CMI6_BCC-_BCCS_WS-_0100m_Glob_025d_S210012010000_E210012312100210012_INS_MAP_01h_NA-_noc_org_NA_SP245_NA---_NA—_v1.00.nc
  This file contains projection data (P) from CMIP6 models (CMI6). The climate producing centre is Beijing Climate Centre (BCC-), and the climate model used is BCC-CSM2-MR r1i1p1f1 (BCCS). The variable is wind speed (WS-) at 100 m height (0100m). The coverage is the global domain (Glob) with a 0.25° spatial resolution (025d). Data span from 01/12/2100 at 00:00 UTC (S210012010000) to 31/12/2100 at 21:00 UTC (E210012312100 Data correspond to December 2100 (210012). The data are instantaneous (INS), provided as gridded data (MAP), with an hourly temporal resolution (01h). The lead time is not available (NA-). No bias adjustment has been applied (noc) since the data are obtained through wind power law extrapolation from ERA5 bias-adjusted 10m wind speed, and the data are provided at their original hourly resolution without additional temporal aggregation (org). The ensemble number is not available (NA). The emission scenario is SSP2-4.5 (SP245). The technology specification and energy conversion model are not applicable (NA---_NA---). The file version is (v1.00) and the file format is NetCDF (.nc).

• P_CMI6_AWI-_AWCM_WON_0135m_Glob_025d_S210012010000_E210012312100210012_CFR_MAP_01h_NA-_noc_org_NA_SP126_WP014_PhM02_v1.00.nc
  This file contains projection data (P) from CMIP6 models (CMI6). The climate producing centre is Alfred Wegener Institute (AWI-), and the climate model used is AWI-CM-1-1-MR r1i1p1f1 (AWCM). The variable is onshore wind power capacity factor (WON) at 135 m hub height (0135m). The coverage is the global domain (Glob) with a 0.25° spatial resolution (025d). Data span from 01/12/2100 at 00:00 UTC (S210012010000) to 31/12/2100 at 21:00 UTC (E210012312100correspond to December 2100 (210012). The data are expressed as capacity factor (CFR), provided as gridded data (MAP), with an hourly temporal resolution (01h). The lead time is not available (NA-). No bias correction has been applied (noc), and the data are provided at their original hourly resolution without additional temporal aggregation (org). The ensemble number is not available (NA). The emission scenario is SSP1-2.6 (SP126). The technology specification is (WP014) (NREL Bespoke_6MW_170, onshore, hub height 135 m, installed capacity 6 MW). The energy conversion model used is Physical Model method 2 (PhM02). The file version is (v1.00) and the file format is NetCDF (.nc).

• P_CMI6_AWI-_AWCM_WON_0084m_Glob_025d_S210012010000_E210012312100210012_CFR_MAP_01h_NA-_noc_org_NA_SP126_WP002_PhM02_v1.00.nc
  This file contains projection data (P) from CMIP6 models (CMI6). The climate producing centre is Alfred Wegener Institute (AWI-), and the climate model used is AWI-CM-1-1-MR r1i1p1f1 (AWCM). The variable is onshore wind power capacity factor (WON) at 84 m hub height (0084m). The coverage is the global domain (Glob) with a 0.25° spatial resolution (025d). Data span from 01/12/2100 at 00:00 UTC (S210012010000) to 31/12/2100 at 21:00 UTC (E210012312100 Data correspond to December 2100 (210012). The data are expressed as capacity factor (CFR), provided as gridded data (MAP), with an hourly temporal resolution (01h). The lead time is not available (NA-). No bias correction has been applied (noc), and the data are provided at their original hourly resolution without additional temporal aggregation (org). The ensemble number is not available (NA). The emission scenario is SSP1-2.6 (SP126). The technology specification is (WP002) (Gamesa G132/3300, onshore, hub height 84 m, installed capacity 3.3 MW). The energy conversion model used is Physical Model method 2 (PhM02). The file version is (v1.00) and the file format is NetCDF (.nc).

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Table5_2 Table5_2

Table 5.2: Filename convention for ancillary data used in the historical data stream and that are available in the CDS under the widget "Weights and masks".

Position in the filename	Possible substrings for each position in the filename	Description	Option in the CDS download form
0	ANCI (Ancillary)	Category	Not applicable
1	ADM0-mask (Country aggregation mask), ADM0B-mask (Country aggregation mask for energy demand), ADM1-mask (Sub-national aggregation mask), ALPH-coeff (Wind power-law coefficients (alpha)), DOMM-mask (Dataset domain mask), IWP-optlag (Hydropower optimal precipitation lag), LATW-coeff (Latitude weighting coefficients), NUT2-mask (NUTS-2 aggregation mask for European countries), POPW-coeff (Population weighting coefficients), SPVL-mask (Solar PV land mask), SPVM-mask (Solar PV exclusion mask), WPLM-mask (Wind power land mask), WPM-mask (Wind power exclusion mask), WPSM-mask (Offshore wind sea mask), WS100E5-mean (Mean wind speed at 100m from ERA5), WS100G2-mean (Mean wind speed at 100m from GWA2), WS10E5-mean (Mean wind speed at 10m from ERA5), WS10G2-mean (Mean wind speed at 10m from GWA2)	Variable	Variable (Weights and masks)
2	C3S2LOT1	Name of the Contract	Not applicable
3	025d	Spatial Resolution	Not applicable
4	v1.00	File version	Not applicable
5	.nc (NetCDF)	File formats	Not applicable

Example of filename for the ancillary data: ANCI_LATW-coeff_C3S2LOT1_025d_v1.00.nc.

This file contains ancillary data (ANCI) used to adjust the gridded data with the proper latitudinal weights (LATW-coeff) during the spatial aggregation procedure. The file has been created within the framework of the C3S2 Lot1 contract (C3S2LOT1).The spatial resolution is 0.25° (025d); the file version is v1.00, and the file format is NetCDF(.nc).

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Table5_3 Table5_3

Table 5.3: Description of the ancillary data and their characteristics. These files are available for download in the CDS under the widget "Weights and masks".

Filename	Description	Internal Variable	Corresponding name in the widget "Weights and masks"
ANCI_ADM0-mask_C3S2LOT1_025d_v1.00.nc	Country-level mask used for spatial aggregation of model outputs	mask	Country aggregation mask
ANCI_ADM0B-mask_C3S2LOT1_025d_v1.00.nc	Country-level mask used specifically for HDD/CDD/EDD aggregation	mask	Country aggregation mask (energy demand)
ANCI_ADM1-mask_C3S2LOT1_025d_v1.00.nc	Sub-national (ADM1) mask for spatial aggregation	mask	Sub-national aggregation mask
ANCI_ALPH-coeff_C3S2LOT1_025d_v1.00.nc	Power law exponent (α) used to extrapolate wind to hub height	alpha	Wind power-law coefficients (alpha)
ANCI_DOMM-mask_C3S2LOT1_025d_v1.00.nc	Domain mask used to limit geographic extent and save disk space	mask	Dataset domain mask
ANCI_IWP-optlag_C3S2LOT1_ADM0_v1.00.csv	Optimal lag (n-month) for precipitation accumulation per country	—	Hydropower optimal precipitation lag
ANCI_LATW-coeff_C3S2LOT1_025d_v1.00.nc	Cosine of latitude for each grid cell (used as spatial weight)	lat_weights	Latitude weighting coefficients
ANCI_NUT2-mask_C3S2LOT1_025d_v1.00.nc	Sub-national (NUTS2 Eurostat regions) mask used for TP spatial aggregation over Europe (specifically needed for subsequent IWP calculations)	mask	Sub-national aggregation mask (European countries)
ANCI_POPW-coeff_C3S2LOT1_025d_v1.00.nc	Gridded population used for weighting in CDD/HDD/EDD	PopCount	Population weighting coefficients
ANCI_SPVL-mask_C3S2LOT1_025d_v1.00.nc	Sea exclusion mask used to limit SPV model domain	PVmask	Solar PV land mask
ANCI_SPVM-mask_C3S2LOT1_025d_v1.00.nc	Combined exclusion layers for SPV modelling	PVmask	Solar PV exclusion mask
ANCI_WPLM-mask_C3S2LOT1_025d_v1.00.nc	Land mask used to restrict offshore wind modelling	mask	Wind power land mask
ANCI_WPM-mask_C3S2LOT1_025d_v1.00.nc	Combined exclusion layers for wind power modelling	m_rest	Wind power regions mask
ANCI_WPSM-mask_C3S2LOT1_025d_v1.00.nc	Sea mask used to restrict onshore wind power model domain	mask	Offshore wind sea mask
ANCI_WS10E5-mean_C3S2LOT1_025d_v1.00.nc	ERA5-based climatology of 10 m wind speed	ws10	Mean wind speed at 10 m (ERA5)
ANCI_WS10G2-mean_C3S2LOT1_025d_v1.00.nc	GWA2-based climatology of 10 m wind speed	ws10	Mean wind speed at 10 m (GWA2)
ANCI_WS100E5-mean_C3S2LOT1_025d_v1.00.nc	ERA5-based climatology of 100 m wind speed	ws100	Mean wind speed at 100 m (ERA5)
ANCI_WS100G2-mean_C3S2LOT1_025d_v1.00.nc	GWA2-based climatology of 100 m wind speed	ws100	Mean wind speed at 100 m (GWA2)

Metadata

The header of the time series CSV files contains the following metadata descriptors. An example of a Cooling Degree Days variable is presented below.

...

IEA (2023), Weather for Energy Tracker, IEA, Paris https://www.iea.org/data-and-statistics/data-tools/weather-for-energy-tracker

IPCC (2021). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001 

Jourdier, B.: Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France, Adv. Sci. Res., 17, 63–77, https://doi.org/10.5194/asr-17-63-2020, 2020.

Klucher, T. M., “Evaluation of models to predict insolation on tilted surfaces”, Solar Energy, vol. 23, pp. 111–114, 1979. (https://doi.org/10.1016/0038-092X(79)90110-5)

Koivisto M., K. Plakas, E. R. Hurtado Ellmann, N. Davis, P. Sørensen, “Application of microscale wind and detailed wind power plant data in large-scale wind generation simulations”, Electric Power Systems Research, vol. 190, 106638, January 2021(https://doi.org/10.1016/j.epsr.2020.106638)

Lledó, Ll.; Torralba, V.; Soret, A.; Ramon, J.; Doblas-Reyes, F.J. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100, 2019.

...

Martin, N., & Ruiz, J. M., “Calculation of the PV modules angular losses under field conditions by means of an analytical model”, Solar Energy Materials and Solar Cells, vol. 70, pp. 25–38, 2001. (https://doi.org/10.1016/S0927-0248(00)00408-6)

Martin, N., & Ruiz, J. M., Corrigendum to “Calculation of the PV modules angular losses under field conditions by means of an analytical model”, Solar Energy Materials and Solar Cells, vol. 110, pp. 154, 2013.

...

Mortensen N. G., “Wind resource assessment using the WAsP software”, WindEurope DTU report, 2018 (https://backend.orbit.dtu.dk/ws/portalfiles/portal/164389714/Wind_resource_assessment_using_the_WAsP_software_DTU_Wind_Energy_E_0174_.pdf).

Murcia Leon J. P., M. J. Koivisto, P. Sørensen, P. Magnant, “Power Fluctuations In High Installation Density Offshore Wind Fleets”, Wind Energy Science, vol. 6, pp. 461–476, 2021. (https://doi.org/10.5194/wes-6-461-2021).

Murcia J. P., M. J. Koivisto, G. Luzia, B. T. Olsen, A. N. Hahmann, P. E. Sørensen, M. Als, “Validation of European-scale simulated wind speed and wind generation time series”, Applied Energy, vol. 305, 117794, January 2022 (https://doi.org/10.1016/j.apenergy.2021.117794).

...

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, pp.2825-2830.

Pierrot. A., Goude, Y. Short-term electricity load forecasting with generalized additive models. Proceedings of ISAP power. 2011 Sep 2011.

pvlib,  python v0.9.3 documentation, Klucher irradiance transposition function. Retrieved November 30, 2022, from https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irradiance.klucher.html, n.d.

pvlib,  python v0.9.3 documentation, ground diffuse irradiance function. Retrieved November 30, 2022, from https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irradiance.klucher.html, n.d.

PyPi sg2 package entry. Retrieved November 30, 2022, from https://pypi.org/project/sg2/, n.d.

Ross, R.G., “Interface design considerations for terrestrial solar cell modules,” in Photovoltaic Specialists Conference Record, pp. 801-806, United States of America, 1976.

...

Skartveit, A., & Olseth, J. A., “A model for the diffuse fraction of hourly global radiation”, Solar Energy, vol. 38, pp. 271-274, 1987. (https://doi.org/10.1016/0038-092X(87)90049-1)

Swisher P., J. P. Murcia Leon, J. Gea-Bermúdez, M. Koivisto, H. Madsen, M. Münster, “Competitiveness of a low specific power, low cut-out wind speed wind turbine in North and Central Europe towards 2050”, Applied Energy, vol. 306, part B, 118043, January 2022 (https://doi.org/10.1016/j.apenergy.2021.118043).

Temps, R. C., & Coulson, K. L., “Solar radiation incident upon slopes of different orientations”, Solar Energy, vol. 19, pp. 179–184 (https://doi.org/10.1016/0038-092X(77)90056-1).

Wood, N.; Goude, Y.; Shaw, S. Generalized Additive Models for Large Data Sets, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 64, Issue 1, January 2015, Pages 139–155, https://doi.org/10.1111/rssc.12068

...

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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt, the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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