Post on 26-Jun-2018
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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Elisabetta Vignati
Address: Joint Research Centre, Via E. Fermi 2749, I-21027 Ispra (VA), Italy
E-mail: Elisabetta.vignati@ec.europa.eu
Tel.: +39 0332 78 9414
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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 2.9: 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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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 3.10: 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.
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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