Marco Gaetani, Elisabetta Vignati, Fabio

Monforti, Thomas Huld, Alessandro Dosio,

Frank Raes

Forename(s) Surname(s)

Climate modelling and renewable energy resource assessment

2 0 1 5

European Commission

Joint Research Centre

Institute for Environment and Sustainability

Institute for Energy and Transport

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E-mail: Elisabetta.vignati@ec.europa.eu

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Abstract

The objective of this work is to explore the ability of climate models in assessing the future energy potential from

renewable sources. Specifically, this study focuses on the two renewable sources which are more directly related to

meteorological variables, namely photovoltaic solar and wind energy. By using a global climate model for the

global scale and a set of regional climate models with finer resolution for Europe, the response of photovoltaic

and wind energy up to the mid-21st century (2030-2050) is analysed, and different scenarios of green-house

gases and anthropogenic aerosols emissions are compared.

Science and Policy Report

Climate modelling and renewable energy resource assessment

Marco Gaetani (1)

Elisabetta Vignati (2)

Fabio Monforti Ferrario (3)

Thomas Huld (3)

Alessandro Dosio (1)

Frank Raes (1)

(1) Institute for Environment and Sustainability, Climate Risk Management Unit, H.7

(2) Institute for Environment and Sustainability, Air and Climate Unit, H.2

(3) Institute for Energy and Transport, Renewable Energy Unit, F.7

Table of Contents

Executive Summary .............................................................................................................................. 7

1. Climate models and numerical experiments .................................................................................. 8

1.1. The ECHAM5-HAM global aerosol-climate model .................................................................... 8

1.2. ENSEMBLES regional climate models ....................................................................................... 9

2. Photovoltaic energy assessment ..................................................................................................... 13

2.1. Background ................................................................................................................................ 13

2.2. Photovoltaic performance model ............................................................................................... 13

2.3. PVE in Europe and Africa in ECHAM5-HAM simulations ...................................................... 14

2.4. PVE in Europe in ENSEMBLES simulations ............................................................................ 21

2.5. Summary .................................................................................................................................... 28

3. Wind power assessment .................................................................................................................. 32

3.1. Background ................................................................................................................................ 32

3.2. Wind speed distribution and power ........................................................................................... 32

3.3. WE in ECHAM5-HAM simulations .......................................................................................... 34

3.4. WE in Europe in ENSEMBLES simulations ............................................................................. 42

3.5. Summary .................................................................................................................................... 49

4. Summary, conclusions and perspectives ....................................................................................... 52

Acknowledgments ............................................................................................................................... 54

Executive Summary

The nexus between renewable energies and climate has been often investigated in the

perspective of the impact on global climate deriving from an increased penetration of

renewable sources in the world energy mix, and the associated reduction in carbon dioxide

emissions. The nexus has a second direction too, as climate change is also expected to act on

the meteorological variables ultimately governing the availability and geographical location

of several renewable resources. Scientific literature on this topic is relatively scarce, and the

Intergovernmental Panel on Climate Change itself has pointed out that “Climate change will

have impacts on the size and geographic distribution of the technical potential for renewable

energy sources, but research into the magnitude of these possible effects is nascent”.

The objective of this work is to explore the ability of climate models in assessing the future

energy potential from renewable sources. Specifically, this study focuses on the two

renewable sources which are more directly related to meteorological variables, namely

photovoltaic solar and wind energy. By using a global climate model for the global scale and

a set of regional climate models with finer resolution for Europe, the response of photovoltaic

and wind energy up to the mid-21st century (2030 to 2050) is analysed, and different

scenarios of green-house gases and anthropogenic aerosols emissions are compared.

Results show a sizeable impact of possible strong reduction in anthropogenic aerosols

emissions on the future climate change, this reduction inducing a severe global warming (2.5

K global average) and significant modifications to large scale atmospheric circulation,

especially in the Northern Hemisphere. Photovoltaic energy productivity appears strongly

related to modifications in cloudiness resulting from changes in large scale atmospheric

circulation. Specifically, a reduction is observed in eastern Europe and northern Africa, while

an increase is observed in western Europe and eastern Mediterranean. A significant response

in wind energy availability is also observed, with modifications in wind distribution resulting

in significant changes in wind power density. However, the observed changes occur mainly

off-shore, while over land changes are marginal. Moreover, productive and non-productive

regions at the present time tend not to change their features in the future.

The presented results indicate that climate modelling is a valuable tool for investigating the

future changes in photovoltaic and wind energy availability. Indeed, energy productivity

shows sensitivity to the simulation of different future emission scenarios, and a coherent

relationship with the projected modifications in climate dynamics. Therefore, this study

encourages a broader use of climate models in the assessment of renewable energies future

availability, and the improvement of that features of climate simulations specific to

renewable energies applications.

This report is organized as follow: in Chapter 1, the climate modelling tools are introduced,

and the different simulation set-ups are described; in Chapter 2, the photovoltaic performance

model used to derive the energy produced by photovoltaic systems is described, and the

future availability of photovoltaic energy is assessed for Europe and Africa; in Chapter 3, the

wind-related parameters associated to wind energy production are discussed, and the future

wind energy availability is assessed for Europe and at the global scale; in Chapter 4, the

results are summarized, conclusions are drawn, and future perspectives are discussed.

1. Climate models and numerical experiments

In this Chapter, the climate models that were employed for assessing the evolution from

present time to mid-21st

century (i.e., to the period between 2030 and 2050) of the relevant

meteorological variables are presented together with the emission scenarios for the

anthropogenic green-house gases (GHG) and aerosols assumed.

1.1. The ECHAM5-HAM global aerosol-climate model

The ECHAM5-HAM modelling system is based on the atmospheric general circulation

model ECHAM5 (Roeckner et al. 2003) coupled with a mixed layer ocean (Roeckner et al.

1995) model and extended by the microphysical aerosol model HAM (Stier et al. 2005) and a

cloud scheme providing a prognostic treatment of cloud droplet and ice crystal number

concentration (Lohmann et al. 2007).

ECHAM5 solves the prognostic variables (vorticity, divergence, surface pressure,

temperature, water vapour, cloud liquid water and cloud ice) on a T63 horizontal grid (i.e.,

about 1.8° latitude-longitude on a Gaussian Grid) and 31 vertical levels from the surface up

to 10 hPa. Fractional cloud cover is predicted from relative humidity according to Sundquist

et al. (1989). The shortwave radiation scheme includes 6 bands in the visible and ultraviolet

(Cagnazzo et al. 2007).

The microphysical aerosol module HAM predicts the evolution of an ensemble of interacting

aerosol modes and is composed of the microphysical core M7 (Vignati et al. 2004), an

emission module, a sulphur oxidation chemistry scheme using prescribed oxidant

concentrations for OH, NO2, O3 and H2O2 (Feichter et al. 1996), a deposition module and a

module defining the aerosol radiative properties. The HAM module treats the composition,

size distribution and mixing state of aerosols as prognostic variables. The current set-up

includes the major global aerosol compounds: sulphate, black carbon, particulate organic

mass, sea salt and mineral dust. The aerosol optical properties are explicitly simulated within

the framework of the Mie theory and provided as input for the radiation scheme in ECHAM5.

Emission scenarios

The climate simulations are forced with emission scenarios developed for the 4th Assessment

Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) (Solomon et al.

2007). GHG concentrations are taken from the B2 scenario described in the Special Report

on Emissions Scenarios (SRES) (Nakicenovic and Swart 2000): the SRES B2 storyline

describes a world with intermediate population and economic growth, in which the emphasis

is on local solutions to economic, social and environmental sustainability.

The anthropogenic emissions of carbonaceous aerosols, namely black carbon (BC) and

organic carbon (OC), and of sulphur dioxide (SO2), the main precursor of sulphate aerosols,

are extracted from an aerosol emission inventory developed by the International Institute for

Applied System Analysis. Two possible future developments are considered: current

legislation (CLE) and maximum feasible reduction (MFR) (Cofala et al. 2007). CLE assumes

the full compliance of the presently decided control legislations for future developments,

while MFR assumes the full implementation of the most advanced available technologies.

These scenarios are built using the projection of human activity level (industrial production,

fuel consumption, livestock numbers, crop farming, waste treatment and disposal) based on

current national perspectives on the economic and energy development up to the year 2030,

in regions where data are available. Elsewhere, the economic and energy future trends

estimated in the SRES B2 scenario (Riahi and Roehl 2005) are considered. Biomass burning

emissions, both of anthropogenic and natural origin, are assumed as for 2000. Finally,

changes in land use are not taken into account.

Experimental set-up

The climate simulations analysed in this study have been performed by Kloster et al. (2008),

in the framework of the EUCAARI project (Kulmala et al. 2011). The near future changes in

climate and energy productivity are assessed by analysing the differences between the year

2030 and the present-day (year 2000) conditions reproduced in climate equilibrium

simulations. A 100-yr control simulation is performed with present day (year 2000) GHG

concentrations, aerosol and aerosol precursor emissions. Three 60-yr sensitivity experiments

are performed for the year 2030, using GHG concentrations from the SRES B2 scenario and

three different combinations of aerosols emissions scenarios: (1) in the 2030GHG

experiment, aerosols emissions are kept at the 2000 level; (2) in the 2030CLEMFR

experiment, MFR is assumed in Europe and CLE elsewhere; (3) in the 2030MFR experiment,

MFR is assumed worldwide. The 2030GHG experiment, in which only GHG concentrations

change while the emissions of aerosols and aerosols precursors remain at the year 2000 level,

is performed to disentangle the effects of changes in GHG concentrations. The experimental

set-ups, along with the 2030-2000 differences in the global averages of the anthropogenic

aerosols emissions, are summarized in Table 1.1.

Table 1.1: ECHAM5-HAM experimental set-ups and 2030-2000 percentage differences in

global emissions of anthropogenic aerosols (Kloster et al. 2008, Kulmala et al. 2011,

Sillmann et al. 2013).

Experiment GHG Aerosols emissions 2030-2000 emissions change

BC SO2 OC

2000 2000 2000

2030GHG 2030 2000

2030CLEMFR 2030 2030 CLE worldwide

and MFR in Europe

-13 % 1 % -8 %

2030MFR 2030 2030 MFR -27 % -42 % -12 %

1.2. ENSEMBLES regional climate models

The ENSEMBLES project, funded under the European Commission’s Sixth Framework

Program from 2004 to 2009, aimed to provide probabilistic estimates of climatic risk through

climate models. The project developed an ensemble climate forecast system to construct

integrated scenarios of future climate change across a range of time (seasonal, decadal and

longer) and spatial scales (global, regional and local) (Van der Linden and Mitchell 2009).

The ENSEMBLES 21st century simulations have been set up following the recommendations

made by the IPCC for the AR4 (Meehl et al. 2007), to assess different sources of uncertainty

in climate change projections. Aiming to isolate the model errors, an ensemble of different

climate models, a so called multimodel ensemble, is used to sample uncertainties in model

formulation and isolate model errors (Palmer et al. 2005). Moreover, three different emission

scenarios, namely SRES A2, A1B and B1 (Nakicenovic and Swart 2000), following different

storylines for the economic and cultural development of the world, are produced to sample

possible developments of GHG emissions in the future. A set of high resolution climate

change projections has been performed by state-of-the-art global and regional climate models

(GCM and RCM, respectively). RCM are used for the dynamical downscale (Giorgi 1990) of

the GCM outputs to a finer resolution over Europe.

RCM climate simulations use the A1B scenario for GHG concentration, and cover the period

between 1950 and 2050 (some of them reach 2100), on a common domain over Europe (from

South Mediterranean coast to Cape North), at 25 km horizontal resolution. The A1B scenario

assumes a world of very rapid economic growth, with a global population peak in mid-

century. In this study the climate evolution from 1961 to 2050, simulated by 12 RCM, is

analysed. Details on RCM are summarized in Table 1.2.

Table 1.2: Institutions participating to the ENSEMBLES project, regional climate models

and driving global models.

Institution RCM Driving GCM

Community Climate Change Consortium for Ireland (C4I) RCA3 HadCM3

Meteo France, Centre National de Recherches Meteorologiques

(CNRM) RM5.1 ARPEGE

Danish Meteorological Institute (DMI) HIRHAM5

ARPEGE

ECHAM5

BCM

Swiss Federal Institute of Technology Zurich (ETHZ) CLM HadCM3

Royal Netherlands Meteorological Institute (KNMI) RACMO2 ECHAM5

Met-Office, Hadley Centre for Climate Prediction and Research

(METO-HC) HadRM3 HadCM3

Max-Planck Institut für Meteorologie (MPI-M) REMO ECHAM5

Swedish Meteorological and Hydrological Institute (SMHI) RCA

BCM

ECHAM5

HadCM3

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2. Photovoltaic energy assessment

2.1. Background

Among renewable energy (RE) resources, electricity generated by solar photovoltaic modules

is showing a fast growth, with an expected capacity of 135 GW to be installed by the end of

2013 worldwide (Jäger-Waldau 2013). With such expectations and consequent investments

being mobilized by the photovoltaic sector, it makes sense to analyse how and to what extent

the current photovoltaic potential could be affected in the next decades by the expected

changes in the climate patterns, in terms of both energy output and infrastructure

vulnerability (Patt et al. 2013). However, despite the growing interest, only few studies have

investigated directly the impact of climate change on photovoltaic energy production

(Schaeffer et al. 2012). Some studies, based on climate models projections, have estimated by

the end of 21st century no or slight increase of the PV potential in Europe (Pasicko et al.

2012, Wachsmuth et al. 2013, Dowling 2013), few percent increase in China, and few percent

decrease in western USA and Saudi Arabia (Crook et al. 2011).

The photovoltaic energy (PVE) productivity is strongly related to solar radiation and, to a

smaller extent, to the temperature at the surface of the photovoltaic modules. Solar radiation

and surface temperature are in turn affected by the optical properties of the atmosphere, and

in particular, by its aerosols content. Indeed, aerosols interact directly with the solar radiation

through scattering and absorption (Angstroem 1962), and lead to temperature changes with

consequent impact on cloud droplets (Hansen et al. 1997). Moreover, aerosols affect the

cloud properties, enhancing cloud albedo by means of an increase in the number of cloud

droplets (Twomey 1977), and prolonging cloud lifetime through the formation of smaller

droplets which lower the precipitation probability (Albrecht 1989).

The near term climate change and the role of aerosols are particularly relevant for the

assessment of future PVE resources, which exploitation is based on technologies with 20 to

30 years lifetime (Skoczek et al. 2009, Jordan and Kurtz 2013), and strongly affected by the

radiative properties of the atmosphere. As an example, the global abatement of the

anthropogenic aerosols emissions is expected to produce a radiative forcing up to around 3

W/m2 at the top of the atmosphere over European Union (Kloster et al. 2008), and this

forcing may triple at the surface (Ramanathan and Carmichael 2008), which equals around 6

to 9 % of the yearly electricity generated in the European Union countries by a typical

photovoltaic system (Suri et al. 2007).

In this Chapter, the near future (i.e. in the period between 2030 and 2050) availability of PVE

in Europe and Africa is assessed, with a focus on the sensitivity of PVE resources to different

concentrations of anthropogenic aerosols. PVE is estimated through a model for photovoltaic

performance which uses as input the solar radiation and air temperature data from state-of-

the-art climate models.

2.2. Photovoltaic performance model

The photovoltaic performance model used in this study is the PV-GIS model, that integrates

climate variables in a model for inclined-plane irradiation and photovoltaic system output

(Suri et al. 2007, Suri and Hofierka 2004, Suri et al. 2005, Huld et al. 2012). The photovoltaic

modules are assumed to be mounted in a fixed position, facing equator, and at the local

optimum angle for maximum yearly energy yield (Huld et al. 2012). The effect of module

temperature and irradiance on photovoltaic module efficiency is accounted for by using the

model presented in Huld et al. (2010, 2011), which includes the effects of shallow-angle

reflectance (Martin and Ruiz 2001), while other losses in the system (e.g., inverter losses,

resistive losses in cables) are assumed to remain constant. The effect of snow and dust cover

are not included in the calculation. For the present calculation, the modules are assumed to

use crystalline silicon photovoltaic cells, the most prevalent photovoltaic module type

nowadays. PVE productivity is computed for a typical day in the month, by using the

monthly averages of global and diffuse horizontal irradiation, and daytime temperatures at

the surface of the modules (Gaetani et al. 2014).

2.3. PVE in Europe and Africa in ECHAM5-HAM simulations

In this Section, the near future PVE productivity in Europe and Africa is studied by analysing

the ECHAM5-HAM climate simulations. The climate variables relevant to the estimation of

PVE productivity, namely 2-metre air temperature (T2), surface solar radiation (SSR), and

total cloud cover (TCC), are analysed; along with the response of PVE productivity to

climate change. The climate and PVE response to different emissions scenarios is studied by

comparing by comparing changes expected for 2030 to the 2000 baseline, the so-called

‘2030-2000 differences'. The climate simulations are analysed after the model reaches of an

equilibrium state, and to this aim, only the last 60 and 30 years are used in the control and

sensitivity experiments to compute annual averages. The near future PVE assessment is

limited to Europe and Africa because of the coverage of the MSG irradiation data (Schmetz

et al. 2002) needed to compute the diffuse irradiation.

Near future climate change

The 2030-2000 difference in T2 for the three aerosols emissions scenarios is displayed in

Figure 2.1. A global significant warming is observed in the three experiments, along with a

pronounced north-south inter-hemispheric gradient. The intensity of the global warming is

directly related to the aerosols emissions reduction, with an average warming of 1.3 K in the

2030GHG simulation, 1.5 K in the 2030CLEMFR simulation, and 2.5 K in the 2030MFR

simulation. Also the inter-hemispheric gradient depends on the emissions scenarios, with the

Northern Hemisphere being 0.7 K warmer than the Southern in the 2030GHG experiment,

0.9 K in 2030CLEMFR and 2.0 K in 2030MFR. The increase in global warming and inter-

hemispheric thermal gradient is mainly related to the action of sulphate aerosols on the

atmospheric reflectivity of solar radiation, i.e., a reduction in the sulphate aerosols burden

results in an atmospheric warming (Kloster et al. 2010).

Figure 2.1: T2 annual mean [K], 2030-2000 differences in ECHAM5-HAM simulations: (a)

2030GHG-2000, (b) 2030CLEMFR-2000 and (c) 2030MFR-2000. Shadings indicate 95%

significant differences, measured by a Student’s t-test.

SSR (Figure 2.2) shows significant positive changes in the Tropics, at mid and high latitudes,

and negative changes in the sub-Tropics in all the 2030 simulations. The extension and

intensity of the simulated changes increase as the aerosols emissions decrease, confirming

that the climate change signal related to GHG increase is augmented by the reduction of

anthropogenic aerosols emissions. This aspect is widely discussed by Kloster et al. (2008,

2010), who highlighted that a reduction in aerosols emissions, without any intervention on

GHG, improves air quality, with a positive impact on human health, but may produce, on the

other hand, a strong increase in the global radiative forcing.

Figure 2.2: Same as Figure 2.1 but for SSR [W/m2]. Shadings indicate 95% significant

differences, measured by a Student’s t-test, while in white areas statistically significant

differences have not been found.

SSR variability is primarily driven by cloudiness (Chiacchio and Vitolo 2012, Bartok 2010),

and this is evident when computing the 2030-2000 TCC differences in the three scenarios.

The latitudinal distribution of TCC differences displayed in Figure 2.3 reflects the SSR

patterns in Figure 2.2, with reduced (increased) cloudiness over northern sub-Tropics and

mid-latitudes, and southern Tropics (northern Tropics) corresponding to increased (reduced)

SSR in the same regions.

Figure 2.3: Same as Figure 2.1 but for TCC, which is defined as the fraction of the grid-box

covered by clouds.

The simulated changes in cloudiness are related to modifications in the large scale

atmospheric circulation, which are in turn affected by the future increase in global

temperature and inter-hemispheric thermal gradient (Gaetani et al. 2014). In boreal summer,

an intensification of the monsoonal regime and associated cloudiness over northern Africa

and southern Asia is observed, along with a strengthening of the Hadley meridional

circulation which produces downward vertical motions, and associated subsidence and clear

sky conditions at subtropical latitudes (Figure 2.3) (Gaetani et al. 2014). In boreal winter, the

land-sea thermal gradient in the Northern Hemisphere contrasts the high pressure belt over

the American and Eurasian continents with lows over the Atlantic and Pacific oceans,

resulting in a modification of the mid-latitude atmospheric circulation and associated storm-

track. Specifically, a high-low pressure dipole is forced in the Euro-Atlantic sector, and it

orientates the westerly flow toward the Scandinavian peninsula, with a consequent excess of

cloudiness over the North Atlantic storm-track (Figure 2.3) (Gaetani et al. 2014).

Near future PVE assessment

The year 2000 annual PVE production estimated for Europe and Africa by using the

ECHAM5-HAM data into the photovoltaic performance model is presented in Figure 2.4.

The annual mean is directly related to the solar radiation pattern, showing maxima over the

desert areas and minima over the Equatorial belt and mid-latitudes, which are characterized

by higher cloud cover.

Figure 2.4: PVE production [kWh/m2]: ECHAM5-HAM year 2000 annual mean.

The response of PVE productivity in Europe and Africa to the different future emissions

scenarios is presented in Figure 2.5. Significant changes related to the increase of GHG

concentrations are observed in the 2030GHG experiment (Figure 2.5a). The productivity is

reduced in northern Africa (around 3%), and nastern Europe (around 6%), while an increase

in the productivity benefits southern Tropical Atlantic, and sub-Tropical North Atlantic

(around 3%). A similar pattern is observed when the 2030CLEMFR experiment is analysed

(Figure 2.5b). Larger differences are observed over northern Africa (up to 4%), sub-Tropical

North Atlantic, and western Europe (up to 5%), while the reduction over eastern Europe is

not significant. The largest change is observed when the MFR scenario is considered, with

expected changes up to 10% (Figure 2.5c). Such a strong abatement of aerosols emissions

worldwide produces a significant positive response in PVE productivity in southern Tropical

Atlantic (up to 6%), eastern Mediterranean (up to 3%), and western Europe (up to 10%). On

the other hand, a sizable reduction is observed in northern African continent, with a peak

around 6% in the Equatorial belt, and in eastern Europe (up to 7%).

Figure 2.5: Same as Figure 2.1 but for PVE production in %.

By comparing Figure 2.5 to Figures 2.1 and 2.2, the strong relationship between PVE and

solar radiation is evident, while the negative effect of the air temperature appears weak.

Therefore, the changes in the future PVE productivity may be connected to the modifications,

discussed above, in the large scale circulation affecting cloudiness. Thus, the increase in the

solar radiation and associated PVE productivity in western Europe and the Mediterranean

may be related to the subsidence and consequent reduced cloudiness produced by the

strengthening of the Hadley circulation in boreal summer, while the concomitant reduction in

eastern Europe is related to the cloudiness associated with the storm-track in boreal winter. A

similar relationship between atmospheric circulation over North Atlantic, and SSR in the

Euro-Atlantic sector has been already documented by Chiacchio and Wild (2010), and Pozo-

Vazquez et al. (2004). On the other hand, the solar radiation and PVE decrease in northern

Africa is linked to the augmented cloudiness associated with the reinforced monsoonal

activity in boreal summer.

2.4. PVE in Europe in ENSEMBLES simulations

In this Section, the PVE productivity in the mid-21st century in Europe is studied by

analysing the ENSEMBLES RCM simulations. The climate and PVE evolution from 1961 to

2050 is studied by computing the ensemble mean of the simulations and analysing trends and

differences among different decades.

Mid-21st century climate evolution

In Figure 2.6, the linear trend from 1961 to 2050 for surface temperature (Tsurf) annual means

is presented. A significant warming is observed all over Europe (around 2 K in 2050), with

higher values over Scandinavia and Russia. Moreover, an acceleration of the warming is also

observed from 2001, with around 1.5 K increase taking place in the 2001 to 2050 period.

Significant trends in SSR annual means are also observed (Figure 2.7), which are in line with

the ECHAM5-HAM results. Indeed, a decrease in SSR is expected in northern Europe in

mid-21st century, while an increase is projected in the Mediterranean region. As discussed in

Section 2.3, changes in SSR are related to modifications in the large scale atmospheric

circulation and associated cloud cover, which are in turn a consequence of the global

warming: the warmer is future climate, the stronger are modifications. This aspect is also

observed in SSR from regional experiments, with steeper trends from 2001 than in the 20th

century

.

Figure 2.6: ENSEMBLES RCM simulations: Tsurf trends [K/10year]: a) 1961 to 2050, b)

1961 to 2000, and c) 2001 to 2050. Shadings indicate 95% significant trends, measured by a

Mann-Kendall test.

Figure 2.7: same as Figure 2.6, but for SSR.

Mid-21st

century PVE assessment

The PVE evolution from 1961 to 2050 (Figure 2.8) reflects the SSR patterns, as already

observed in global simulations. Significant trends are observed in northern Europe (losses

around 70 kWh/m2 in 2050 in Scandinavia) and in the Mediterranean region (gains around 30

kWh/m2 in 2050 in the Iberian Peninsula). Significant negative trends are observed in

Scandinavia and Baltic countries in the 20th century and over the whole northern Europe in

the 21st century, while the PVE increment is significant only in the period 1961 to 2050. The

percentage differences in PVE productivity are estimated by computing the differences

between present situation (1991 to 2010), near future (2011 to 2030) and mid-21st century

(2031 to 2050). No significant differences are observed in the near future, while significant

differences are observed in the mid-21st century (Figure 2.9): around 5 % losses in northern

Europe, and around 1 % gains in Portugal and Galicia.

The difference with ECHAM5-HAM in the amplitude of the future projected changes in PVE

productivity (compare Figure 2.9 to 2.5) can be explained through the differences in the

experimental set-ups: in the ENSEMBLES simulations, the anthropogenic aerosols emissions

are assumed to follow the SRES storyline; while in the ECHAM5-HAM simulations, a

dramatic abatement of anthropogenic aerosols emissions is assumed, which results in a

stronger global warming, and consequent sizeable modifications in the large scale

atmospheric circulation (see Section 2.3). Indeed, PVE productivity estimated through

ENSEMBLES models is comparable to the productivity estimated by ECHAM5-HAM when

anthropogenic aerosols emissions are not modified regarding the SRES storyline (compare

Figure 2.9 to 2.5).

Figure 2.8: same as Figure 2.6, but for PVE [(kWh/m2)/10year].

Figure 2.9: ENSEMBLES RCM simulations: PVE differences [%]: a) (2011 to 2030) minus

(1991 to 2010), and b) (2031 to 2050) minus (1991 to 2010). Shadings indicate 95%

significant differences, measured by a Student’s t-test.

2.5. Summary

In this Chapter, mid-21st

century productivity of PVE in Europe and Africa is assessed by

integrating climate variables simulated by climate models into a model for the performance

of photovoltaic systems. PVE productivity shows sensitivity to the simulation of different

future scenarios, and a coherent relationship with the projected future modifications in the

climate dynamics. Results from ECHAM5-HAM GCM simulations indicate a relationship

between the projected global warming and the PVE productivity. Specifically, the increase in

surface temperature and north-south inter-hemispheric thermal gradient shifts northward and

intensifies the Hadley meridional circulation, producing augmented cloudiness and reduced

solar radiation over northern Africa, and clear sky and sunny conditions over western Europe

and the Mediterranean. Moreover, the land-sea thermal contrast in the Euro-Atlantic sector

affects the North Atlantic storm-track favouring storminess and reducing solar radiation over

northern and eastern Europe. Consequently, a significant reduction in PVE productivity is

observed in eastern Europe, and northern Africa (up to 7 %), while an increase is observed in

western Europe, and eastern Mediterranean (up to 10 %). The analysis of ENSEMBLES

RCM simulations shows coherence with the global pattern, and confirms the importance of

aerosols emissions and air quality policy options for the assessment of future climate change

and PVE productivity.

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photovoltaic and concentrated solar power energy output. Energy Environ. Sci. 4, 3101-3109

Dowling P. (2013): The impact of climate change on the European energy system. Energy

Policy 60, 406-417

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Hansen J., M. Sato, R. Ruedy (1997): Radiative forcing and climate response. J. Geophys.

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Huld T., G. Friesen, A. Skoczek, R. Kenny, T. Sample, M. Field, E. Dunlop (2011): A

power-rating model for crystalline silicon PV modules. Solar Energy Materials and Solar

Cells 95, 3359-3369

Huld T., R. Gottschalg, H.G. Beyer, M. Topic (2010): Mapping the performance of PV

modules, effects of module type and data averaging. Solar Energy 84, 324-338

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Jordan D.C. and S.R. Kurtz (2013): Photovoltaic Degradation Rates - an Analytical Review.

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3. Wind power assessment

3.1. Background

The use of wind energy (WE) in electricity generation is nowadays widely spread, and the

total wind power capacity installed worldwide has grown from 121 GW in 2008 to 371 GW

in 2014 (Global Wind Energy Council 2015 adjusted with JRC data). In Europe, wind

industry is growing rapidly, and the installed capacity has increased from 13 GW in 2000 to

129 GW in 2014, 14 % of its electricity generation capacity, and is capable of producing,

approximately 284 TWh of electricity or roughly 10 % of the EU electricity consumption.

(EWEA 2015). In this context, the high economic and technological potential of WE requires

a better understanding of the resource variability and its expected evolution in time.

In order to exploit results produced by the models described in Chapter 1, near-surfaces

winds (10m) are used as indicators of potential Wind Energy (WE) production under the

hypothesis of absolute values and time variability of hub-height wind speed being correlated

with the corresponding near-surface values. While abundant literature exists on the limits of

such a correlation on short time scales and in the presence of sudden turbulent phenomena,

especially at higher heights, this assumption approach has been considered acceptable when

investigating long-time averages and focussing on differences more than on absolute values,

as it is the case in the present study (see e.g., Tobin et al, 2015).

Near-surface winds, are strongly connected to the atmospheric synoptic scale variability and

seasonality, and to climate variability on different timescales (Garcia-Bustamante et al. 2013,

Pryor and Barthelmie 2010). The interest in WE development in connection with climate

change is also increasing, firstly because the role of WE for climate change mitigation, but

also because climate change is expected to alter the spatial distribution and quality of the

wind resource, affecting wind turbine design and operation. Consequently, climate change

may impact both the economic feasibility of exploiting wind resources, and the reliability of

electricity production once the capacity is installed (Pryor and Barthelmie 2010). Future

projections of WE resources at regional scale, derived from GCM simulations and GCM

dynamical and statistical downscaling, indicate slight modifications in wind speed mean

values and distribution (Pryor et al. 2005, Pryor and Barthelmie 2011, 2013). Specifically, in

Europe an increase is projected over northern Europe and a negative trend over southern

Europe (Reyers et al. 2014). However, substantial variations are observed in model-to-model

skill in reproducing the wind distribution and spatial variability (Pryor et al. 2012).

Furthermore, mid-latitude wind variability is largely driven by transient synoptic scale

circulation systems and is thus strongly linked to modes of internal climate variability (Pryor

and Ledolter 2010), which is imperfectly reproduced by climate models (Stevenson 2012).

In this Chapter, the near future (i.e., 2030 to 2050) availability of WE is assessed in Europe

and at the global scale, with a focus on the sensitivity of WE resources to different

concentrations of anthropogenic aerosols. WE availability is studied by analysing the 10-

metre wind speed data from state-of-the-art climate models.

3.2. Wind speed distribution and power

In this section, a model for wind speed distribution is presented, along with the main features

of wind speed fields which are analysed in this study, and a direct measure for available WE

is introduced.

Wind speed distribution

The distribution of wind speed, w, is assumed to be described by the Weibull probability

density function (Hennessey 1977),

f(w) = k/c * (w/c)(k-1)

* exp[-(w/c)k],

which is characterized by the scale parameter, c, related to the mean, and the dimensionless

shape parameter, k, related to the variability, which provides an approximation of the flatness

of the distribution (high values of the shape parameter are associated to narrow distributions).

The Weibull distribution is related to other probability distributions, e.g. it interpolates

between the exponential distribution (k = 1) and the Rayleigh distribution (k = 2). Mean and

variance of wind speed can be estimated through the parameters c and k:

E[w] = c * (1+1/k),

Var[w] = c2 * [(1+2/k) - ((1+1/k))

2].

Wind Power Density

WE availability is assessed by computing the wind power density (WPD) (Garcia-

Bustamante et al. 2009),

WPD = ½ * * w3, with = 1.225 - (1.194*10

-4*z),

where is the air density, and z the elevation. WPD is computed considering the operational

range of wind turbines in the 2 to 4 MW class (e.g. Vestas V-90), which is 4 ÷25 m/s at the

turbine hub-height, and 3÷19 m/s at 10-metre height according to Pryor et al. 20051.

The suitability of a certain area for wind power exploitation is usually also described by

means of the associated wind power class (WPC) ranking, based on WPD values. Class 3 or

greater, are suitable for most wind turbine applications, whereas Class 2 is marginal and

Class 1 is generally not suitable (Elliott et al. 1986). The ranges of WPD and the associated

WPC are shown in Table 3.1.

Table 3.1: Wind Power Classes definition (NREL, 2012)

Class WPD [W/m2] Resource Potential

1 <100 Not suitable

2 100÷150 Marginal

3 150÷200 Fair

4 200÷250 Good

5 250÷300 Excellent

6 300÷400 Outstanding

7 >400 Superb

1 It is worth noticing that such a range is expected to be extended in next years at least to 4÷30 m/s following

foreseeable technology improvements with the effect of a general potential increase.

Turbine Operational time.

Another parameter used in this study is the Turbine Operation time, defined again by (Pryor

et al. 2005) as the number of hours in a year for which 10m wind speed falls in the range 3-19

m/s i.e., the 10m equivalent wind speed for which a wind turbine of the class defined in the

previous paragraph is expected to be producing energy if available for it.

3.3. WE in ECHAM5-HAM simulations

In this Section, the near future WE productivity at the Global scale is studied through the

ECHAM5-HAM climate simulations. The modifications in wind speed distribution and WE

productivity induced by climate change are analysed. The response to different emissions

scenarios is studied by comparing 2030-2000 differences.

Near future wind change

The year 2000 annual mean of the 10-metre wind speed and the 2030-2000 difference for the

three aerosols emissions scenarios are displayed in Figure 3.1. The annual mean is

characterized by higher speed over the ocean than over land, higher speed at mid-latitudes

than in the Tropics, higher speed over the southern oceans than in the northern ones, and the

lowest values in mountain areas. The 2030-2000 differences are characterized by an

amplification of the significant differences as the aerosols emissions decrease (Kloster et al.

2009). The main features are: (1) decrease in the northern Tropics and sub-Tropics, with local

inverse anomalies in the Indian Ocean and the western Pacific, (2) decrease in the southern

sub-Tropics, (3) increase in the Arctic, in the southern sub-Tropics and mid-latitudes.

Figure 3.1: 10-metre wind speed [m/s], (a) year 2000 annual mean, and 2030-2000

differences in the three ECHAM5-HAM aerosols emissions simulations. Shadings indicate

95% significant differences, measured by a Kolmogorov-Smirnoff test.

The year 2000 annual average of the Weibull distribution scale parameter and the 2030-2000

difference for the three aerosols emissions scenarios are displayed in Figure 3.2. The scale

parameter is related to the mean value of the distribution, therefore annual mean and 2030-

2000 differences reflect the patterns presented in Figure 3.1.

Figure 3.2: Same as Figure 3.1, but for Weibull scale parameter [m/s].

The year 2000 annual average of the Weibull distribution shape parameter and the 2030-2000

difference for the three aerosols emissions scenarios are displayed in Figure 3.3. The shape

parameter is related to the variability of the distribution, with high values associated to

reduced variability. The year 2000 annual mean shows high variability over Tropical oceans,

with a minimum located around the mean position of the intertropical convergence zone

(ITCZ). The response of the wind variability varies in the three different scenarios. The 2030

GHG concentration does not affect the variability of the wind, while significant differences

are observed in 2030 when the aerosols emissions are reduced. Specifically, the

2030CLEMFR simulation shows a general increase in variability, with higher values over

Africa and South America; while in the 2030MFR experiment, significant positive (negative)

differences are observed over Indian Ocean, and southern (northern) Tropical Atlantic and

Pacific.

Figure 3.3: Same as Figure 3.1, but for Weibull shape parameter.

The changes in the wind distribution are studied through the analysis of the extremes, namely

10th and 90th percentile (Figures 3.4 and 3.5). The 2030GHG-2000 differences show sparse

significant changes of the 10th percentile, while the 90th percentile is reduced in the northern

oceans and Indian Ocean, consistently with the observed changes in the scale and shape

parameters (Figures 3.2 and 3.3). The 2030CLEMFR simulation shows diffuse positive

(negative) changes in the 10th (90th) percentile, reflecting the reduced variability indicated

by the shape parameter (Figure 3.3). The 2030MFR simulation show similar patterns for 10th

and 90th percentile modifications, with diffuse positive (negative) values in the southern

(northern) hemisphere, indicating a shift of the distribution towards higher mean (Figure 3.2).

On the other hand, opposite sign differences are locally observed in the Indian and Pacific

oceans, reflecting the strong modifications in the wind speed variability (Figure 3.3).

Figure 3.4: Same as Figure 3.1, but for 10th percentile of 10-metre wind speed [m/s].

Figure 3.5: Same as Figure 3.1, but for 90th percentile of 10-metre wind speed [m/s].

Near future WE assessment

The WE availability is assessed by computing the WPD (Garcia-Bustamante et al. 2009) and

the Operational Time as defined in paragraph 3.2. Figure 3.6 (top left panel) shows the global

operational time. Over the oceans, the operational time is close to 100% of the year, with

minimum along the Equator. Over the continents, the operational time shows very low values

below 1000h in the Equatorial belt and over mountain areas, while "flat" areas in the

temperate belt (such as northern Europe or central US) the operational time can reach up to

80÷90% of the actual time. The 2030-2000 differences are strongly affected by changes in

the wind probability distribution, specifically by changes in the extremes. The 2030 GHG

concentration weakly affects the operational time, while significant differences are observed

in 2030, when the aerosols emissions are reduced. The 2030CLEMFR simulation shows an

increase at mid-latitudes, South America, northern and southern Africa, Western Australia,

associated with positive changes in the 10th percentile (Figure 3.4), and a decrease in

southern Asia and over mountain areas in America, associated with negative changes in the

90th percentile (Figure 3.5). In the 2030MFR experiment, the operational time results

augmented in the southern Tropics, in association with positive changes in the 10th percentile

(Figure 3.4), and reduced in the northern Tropics, Europe and southern Asia, in association

with negative changes in the 90th percentile (Figure 3.5).

Figure 3.6: Same as Figure 3.1, but for wind turbine operational working time [h].

The WPD (Figure 3.7) partially reflects the main features of the wind mean (Figures 3.1 and

3.2), showing high values at mid-latitudes over the oceans, lower values in the Tropical

oceans, and very low values over the continents. This behaviour is reproduced in the 2030

simulations as well, with weak and sparse significant changes associated to the GHG

concentrations, a general reduction in the CLEMFR scenario, and a more complex pattern in

the MFR scenario. A strong abatement of the aerosols emissions produces a negative change

in the northern Tropics and sub-Tropics, with local inverse anomalies in the Indian Ocean

and the western Pacific, a negative change in the southern sub-Tropics, and a positive change

in the Arctic, in the southern sub-Tropics and mid-latitudes.

Figure 3.7: Same as Figure 3.1, but for WPD [W/m

2].

The WPC changes in the three scenarios are presented in Figures 3.8, 3.9 and 3.10. In the

2030GHG simulation, few sparse positive changes are observed, and negative changes with

poor spatial coherence are observed in the Tropical belt and at high latitudes (Figure 3.8).

Interestingly, the observed changes are almost everywhere within productive (WPC 1 and 2)

or not-productive (WPC 3 or more) ranges, except in few isolated locations (Figure 3.8).

The 2030CLEMFR experiment (Figure 3.9) shows results similar to those observed in the

2030GHG run, while the 2030MFR scenario produces more robust modifications (Figure

3.10). The WPC negative (positive) changes increase in extension and coherence in the

northern Tropics (Arctic Ocean), whereas no substantial exchanges between productive

(WPC 1 and 2) and non-productive (WPC 3 or more) ranges (Figure 3.10) took place.

Figure 3.8: 2030GHG-2000 differences in WPC, and exchanges between productive (WPC 1

and 2) and non-productive (WPC 3 or more) ranges.

Figure 3.9: Same as Figure 3.8, but for 2030CLEMFR-2000 differences.

Figure 3.10: Same as Figure 3.8, but for 2030MFR-2000 differences.

3.4. WE in Europe in ENSEMBLES simulations

In this Section, the WE productivity in the mid-21st century in Europe is studied by analysing

the ENSEMBLES RCM simulations. The evolution of wind resources and WE from 1961 to

2050 is studied by computing the ensemble mean of the simulations and analysing trends and

differences among different decades.

Mid-21st century evolution of the wind speed distribution

The 1961÷2050 evolution of the Weibull scale parameter over Europe is displayed in Figure

3.11. Significant trends are observed, especially offshore: Mediterranean Sea and North Sea

experience a negative trend, while in the Baltic Sea a positive tendency is expected.

Analysing the historical (1961 to 2000) and present-to-future (2001 to 2050) time ranges, it is

shown that the observed trends develop from 2001 onwards. This is in agreement with the

expected future changes in the North Atlantic atmospheric circulation, which are projected to

affect Europe with increasing atmospheric stability over the Mediterranean and enhanced

storminess over northern Europe (Pausata et al. 2014). The shape of wind distribution over

Europe is weakly affected by climate change, with sparse significant modifications observed

across Europe in both historical and present-to-future time ranges (Figure 3.12).

Figure 3.11: Weibull scale parameter trends [(m/s)/10year] in ENSEMBLES RCM

simulations: a) 1961 to 2050, b) 1961 to 2000, and c) 2001 to 2050. Shadings indicate 95%

significant trends, measured by a Mann-Kendall test.

Figure 3.12: Same as Figure 3.11, but for Weibull shape parameter.

Mid-21st century WE assessment

The WPD trends (Figure 3.13) reflect trends observed in the Weibull scale parameter. A

decreasing trend in WPD is simulated in the 90 years period from 1961 to 2050 offshore in

the Mediterranean Sea (with the WPD decrease reaching up to 30 W/m2) and in the area of

the Atlantic ocean just south of Iceland (with WPD decreasing by around 80 W/m2 in the

area), while an increase in WPD is expected in the Baltic Sea (where the WPD increases

could reach up to 40 W/m2) and the British Isles (with a smaller but significant increase by

up to 10 W/m2 in the area), with an acceleration of the changes in the present-to-future (2001

to 2050) time range.

Figure 3.13: Same as Figure 3.11, but for WPD [(W/m

2)/10year].

A deeper investigation of the future modifications in WPD reveals that differences with the

present time are significant at the mid-21st century (Figure 3.14). The comparison of near

future (2011 to 2030) and mid-21st century (2031 to 2050) decadal means with the present

time (1991 to 2010) shows that, in the near future, differences are small and the significant

locations are sparse and limited in extension, while sizable and robust changes are observed

in the mid-21st century.

However, the analysis of WE productivity through WPC reveals that the spatial coverage

over land of productive WPC (2 to 7) is practically unaffected by climate change, with a very

small shift from class 2 to 3 (changes limited to around 0.5% of the European land surface,

not discriminating between exploitable and not exploitable areas, see Table 3.2).

Figure 3.14: ENSEMBLES RCM simulations: WPD differences [W/m2]: a) (2011÷2030)

minus (1991÷2010), and b) (2031÷2050) minus (1991÷2010). Shadings indicate 95%

significant differences, measured by a Kolmogorov-Smirnoff test.

Table 3.2: Land coverage of productive WPC, percentage changes in decades. Estimations

take into account all the land surface, not discriminating between exploitable or not

exploitable areas.

Class 1991 to 2010 2011 to 2030 2031 to 2050

2 15.46 15.16 14.94

3 1.26 1.34 1.42

4 0.31 0.29 0.32

5 0.11 0.11 0.10

6 0.01 0.02 0.02

7 0.00 0.00 0.00

3.5. Summary

In this Chapter, mid-21st century productivity of WE is assessed by using climate models.

The ECHAM5-HAM aerosol-climate model is used to simulate changes at the Global scale,

while the changes in Europe are assessed at a finer resolution through the analysis of the

ENSEMBLES RCM. The numerical experiments simulate future climate under different

scenarios for future GHG and aerosols emissions, allowing to evaluate the sensitivity of the

future WE availability to projected climate change. Results from ECHAM5-HAM GCM

simulations indicate a significant response in WE availability in 2030, with the amplitude of

the signal directly related to the abatement of the anthropogenic aerosols. The changes are

generally negative, with some exceptions in the MFR scenario over southern oceans and

Asian monsoon region. The detected WE modifications appear to be related with changes in

both the mean and the shape of the wind distribution. However, from an operational point of

view, future changes do not impact significantly the geographical distribution of the WE

productivity, i.e., year 2000 productive and non-productive regions tend not to change their

own status. The ENSEMBLES climate simulations for Europe show modifications in the WE

availability consistent with the projected climate change (increase in northern Europe and

decrease in the Mediterranean), although the WE productivity is slightly affected in the next

decades. The low impact on WE productivity of future modifications in atmospheric

circulation can be explained with the prominent importance of surface and topographic

features for wind variability at very low heights (around 100 m). In this sight, on one hand,

the presented results demonstrate that climate modelling is a valuable tool for investigating

the future changes in WE availability, which shows sensitivity to the simulations of different

future scenarios, but on the other hand, the robustness of these results is limited by the coarse

temporal and spatial resolution of the models analysed.

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4. Summary, conclusions and perspectives

In this work, the mid-21st century (2030 to 2050) availability of photovoltaic and wind energy

in Europe and at the global scale is assessed by using climate projections simulated by global

and regional climate models. The climate simulations assume different scenarios for future

emissions of anthropogenic green-house gases and aerosols, aiming to evaluate the sensitivity

of future photovoltaic and wind energy availability to different projected climate changes.

Results from GCM simulations indicate a sizable impact of strong reduction in anthropogenic

aerosols emissions on the future climate change, projecting a severe global warming (2.5 K)

and significant modifications to large scale atmospheric circulation, especially in the

Northern Hemisphere, and results from RCM simulations over Europe confirm the global

picture. However, in RCM simulations no strong reduction in anthropogenic aerosols

emissions is considered, and future changes are less prominent. The importance of the air

quality policy options in the future climate change, and specifically for the future PVE

productivity is also highlighted.

PVE productivity appears strongly related to modifications in cloudiness resulting from

changes in large scale atmospheric circulation Specifically, a reduction in PVE productivity

is observed in eastern Europe, and northern Africa (up to 7%), while an increase is observed

in western Europe, and eastern Mediterranean (up to 10%).

A significant response in WE availability is observed, with modifications in wind distribution

resulting in significant changes in wind power density (WPD). However, the observed WPD

changes occur mainly off-shore, while over land changes are marginal. Moreover, productive

and non-productive regions at the present time tend not to change their characteristics in the

future.

The presented results demonstrate that climate modelling is a valuable tool for investigating

the future changes in PVE and WE productivity and complementarity. Indeed, energy

productivity shows sensitivity to the simulation of different future scenarios, and a coherent

relationship with the projected future modifications in the climate dynamics. Therefore, this

study encourages a broader use of climate models in the assessment of renewable energies

future availability, and the improvement of that features of climate simulations specific to

renewable energies applications. Specifically:

- Photovoltaic performance models would benefit from the inclusion of diffuse

irradiation component explicitly computed in climate simulations;

- Finer spatial and temporal resolutions (10 or less km and hourly time steps,

respectively) would be desirable in wind and hydro power applications, in which

accuracy in reconstructing topography and probability distribution of the

meteorological variables is crucial;

- Assessment of future biomass energy potentials would be also possible by using

Earth-System models simulations of future biomass evolution.

Acknowledgments

This work has been carried out in the framework of the Exploratory Research Project

“Climate modelling and renewable energy resource assessment” funded by European

Commission Joint Research Centre.

Authors wish to thank L. Pozzoli and S. Kloster for running the numerical experiments used

in this study and R. Lacal Arantegui for constructive comments.

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