Climate change impacts on large-scale electricity system design decisions for the 21st Century
Efforts to reduce climate change, but also falling prices and significant technology developments currently drive an increased weather-dependent electricity production from renewable electricity sources. In light of the changing climate, it is highly relevant to investigate the extent of weather changes that directly impacts the best system design decisions for these weather-dependent technologies. Here, we use three IPCC representative CO concentrations pathways for the period 2006–2100 with six high-resolution climate experiments for the European domain. Climate elements are used to calculate bias adjusted 3-hourly time series of wind and solar generation, and temperature corrected demand time series for 30 European countries using state-of-the-art methodology. Weather-driven electricity system analysis methodology is then applied to compare four key metrics of highly renewable electricity systems. We find that climate change does not have a discernible impact on the key metrics of the combined electricity system dynamics, and conclude that the effect on important system design parameters can likely be ignored.
Globally, electricity production from wind and solar sources are increasing significantly. The increase is primarily driven by lower costs, and by political efforts to limit climate change. Climate change, however, may significantly change the weather that drives these sources of renewable energy. We find that the impact of climate change on a future highly renewable European electricity system is small compared to current differences from one weather year to another and compared to differences between designs, e.g., with different levels of international power transmission lines or different mix of wind and solar generators. As a consequence, sound electricity system design decisions and academic studies based on historical weather data will not be strongly affected by climate change.
The global climate is currently undergoing vast changes, primarily due to anthropogenic emissions of heat-trapping gases into the atmosphere . As a consequence, the global average surface temperature has increased since the pre-industrial ages by approximately 0.2 C per decade. A similar rate of temperature increase is projected to occur during the first half of the 21st century , and at present, the changing climate is showing global impacts on natural and human systems. In this work, we focus on the impacts of a changing climate on the dynamics of a future, highly renewable, large-scale electricity system for Europe.
Research on the impacts of climate change on large scale electricity systems is in its infancy and few studies have been performed in this field . For an aggregated European energy system, P. Downling  finds outweighing demand side impacts to the supply side on the basis of the POLES energy model. Due to global heating, the cooling demand increases while the heating demand decreases . More specifically, a study conducted by Pilli-Sihvola et al.  shows a decreasing need for heating in Central and Northern Europe based on the SRES A2, A1B and B1 emission scenarios. Southern Europe experiences a large increase in cooling demand that in turn overcomes the decrease in heating demand . This finding is in correspondence with a study on combined Germany and Austria for the last quarter of the 21st century . The spatial distribution of the heating and cooling needs are further reflected in increasing needs for electricity in Southern Europe and decreasing needs in Northern Europe . Individual country studies on Norway , Finland , Slovenia , Austria  and Switzerland  agree upon the latter findings. Spinoni et al.  shows that if population weighting is applied to the temperature profiles, the degree-day trends do not show remarkable differences during the 21st century for the RCP4.5 and RCP8.5 emission scenarios.
By using the SRES A1B emission scenario, Tobin et al.  and Barstad et al.  find negligible changes in the wind power potential over the Baltic Sea and the surroundings. Similar results are found by Tobin et al.  for Europe by using the RCP4.5 and RCP8.5 emissions scenarios. McColl et al.  arrive at similar conclusions for the UK, Seljom et al.  for a study-case on Norway based on 10 climate experiments and Koch et al.  for Germany by using the RCP2.6 and RCP8.5 emission scenarios for the period 2031–2060. Moreover, Schlott et al.  discover increasing wind correlation lengths along with increasing wind power generation in the same regions of Europe by using the RCP8.5 emission scenario. Tobin et al.  and Bloom et al.  project a decreasing wind power potential over the Mediterranean area by using the SRES A1B and A2 emission scenarios. Similar findings along with decreasing correlation lengths are shown by Schlott et al. . For an aggregated EU27, Dowling  found a slight increase in the wind power production until mid-century based on the SRES A1B and E1 emission scenarios. For a wind dominated European energy system under the impact of the RCP8.5 emission scenario, increasing needs for conventional power and storage are found in most of the central, north and north-west areas  with a more homogeneous wind power generation  at the end of the 21. century. Jerez et al.  project a decreasing solar power generation in the Scandinavian area and slight increases over Southern Europe by using the RCP4.5 and RCP8.5 emission scenarios. This is in correspondence with increased means of solar irradiation along with increased solar correlation length in large parts of Southern Europe . The opposite behaviour is observed for the Northern parts of Europe . For an aggregated EU27, an increasing amount of solar PV generation is found . Based on 44 electricity scenarios for Europe, Berrill et al.  find that large penetrations of wind and solar power emit the least within the ensemble of scenarios and as a consequence contribute the most to climate change mitigations.
We add to the literature by investigating the key infrastructure metrics of a fully connected, highly renewable European electricity system by using the latest generation of climate projections provided by the Intergovernal Panel on Climate Change, IPCC . The climate data used in this study originates from six combinations of four RCMs (Regional Climate Models), downscaling three CMIP (Coupled Model Inter-comparison Project) Phase 5  GCMs (Global Climate Models) under the IPCC representative concentration pathway scenarios, RCPs, see supplementary material. In order to represent a broad range of climate outcomes, three scenarios, RCP2.6 , RCP4.5  and RCP8.5 , have been implemented into the global climate models.
The climate scenarios are heavily influenced by the policy of the 2015 Paris agreement, high emission cost policies, and no climate policies, respectively. In particular, if the goals of the Paris agreement are to become real, there is an urgent need for short and long-term action in reducing the emissions, as explained in detail in the Emission Gap Report 2017 . The underlying assumptions of the pathways result in emissions as shown in Figure 1a. RCP2.6 and RCP4.5 project a decrease in the emissions during the 21st century, while RCP8.5 projects an increase which stagnates at the end of the century. These projections are reflected in the European average temperature for, e.g., the regional climate model HIRHAM5 , downscaling the global climate model ICHEC-EC-EARTH , from now on denoted as HIRHAM5-EC-EARTH, shown in Figure 1b. Based on the emission scenarios, 3-hourly data on climate elements with a spatial resolution has been fostered on behalf of the EURO-CORDEX project [33, 34]. Figures 2 b, c and d exemplify the 2080–2100 average near-surface temperature over Europe for the future emissions scenarios based on the climate model HIRHAM5-EC-EARTH. Panel a represents the absolute temperature of the historical period 1986–2006. The most extreme temperature increases reach values of more than .
In the present study, the main source of error originates from the performance of the GCMs that provides the boundary conditions for the RCMs used to calculate the time series of wind and solar power generation as well as electricity demand time series from which the final results are derived. Various studies have engaged in tracking the evolution of CMIP5 GCMs and their predecessor CMIP3 GCMs by comparison to observed data, satellite data or reanalysis. In the following, we summarize the quality of the climate elements that are most important for the present study.
Iqbal et al.  investigates the North Atlantic eddy-driven jet stream in 11 CMIP5 GCMs by evaluating the jet latitudes and wind speeds in a historical run from 1980-2004. Compared to reanalysis, typical biases between and are observed in the latitude mean seasonal cycle anomaly for a majority of the GCMs. The corresponding seasonal amplitude variations reach up to for a few GCMs, which is an over or underestimation of about compared to reanalysis. But compared to identical CMIP3 GCMs, it is an improvement . In addition, Iqbal et al.  shows a slight poleward shift in the jet stream under the IPCC RCP4.5 and RCP8.5 induced global warming during the end of the century. This is further confirmed for 26 CMIP5 GCMs under the IPCC RCP8.5 emission scenario, which shows a poleward jet shift of approximately in the Southern Hemisphere and in the North Atlantic and North Pacific . In all GCMs, the mean seasonal anomaly bias for the jet stream wind speeds is overestimated for the winter months while the opposite is the case for summer months. A majority of the GCMs are overestimating the corresponding amplitudes . The jet wind speed biases are smaller in the CMIP3 GCMs compared to the CMIP5 GCMs .
The GCM cloud simulations, which most directly affect the solar energy yield calculated in our study, remain the major source of inaccuracy in climate predictions. Lauer et al.  evaluates the total amount of cloud cover, amongst other climate elements, in 27 CMIP5 GCMs by comparing to satellite data covering the years 1986-2007. It is shown that the linear correlation coefficient of the GCM wise 20 year annual average of the total cloud amount ranges from 0.11 to 0.83 with a root mean square error of 10% to 23%. For CMIP5 experiments, the IPCCs Fifth Assessment Report AR5  presents a difference of to in regional changes of cloud fractions for the years 2081–2100 under the RCP4.5 emission scenario. For the RCP8.5 emissions scenario, a difference of to is presented.
The global radiation budget biases that are most strongly influenced by clouds are the incomming radiative shortwave flux at the surface along with the outgoing flux at top of the atmosphere and the reflected flux. For a number of CMIP5 GCMs, Li et al.  finds regional biases ranging from to more than for the incomming flux compared to EBAF-Surface flux radiation product for a time period 2000-2010. The multimodel global area average bias is reduced by compared to CMIP3 GCMs. Similar findings are evident for the regional outgoing flux as well as reflected flux with an improvement of the multimodel global area average bias with a factor of 2 compared to CMIP3 GCM. With these consideration in mind, it is evident that CMIP5 GCMs have improved since phase 3, although room for further improvement remains. In this work we are compensating for static biases in the solar energy yield as explained in the methods section.
In the present study, surface air temperatures are used to adjust the electrical demand for changes in heating and cooling needs. The GCM ability to accurately replicate this field is receiving increasing attention. A recent study by Cattiaux et al.  on the European domain shows negatively biased winter temperatures in the North compared to ground observations . Positively biased summer temperatures are observed in the East and Central Europe for 33 CMIP5 GCMs. For the total European domain, the GCM ensemble mean bias is approximately during winter months. The corresponding values for summer months are . Similar trends are found for the Northern Eurasia where the winter and summer periods show the largest biases . Small improvements have been made since CMIP3 GCMs .
The GCM errors are taken into account by evaluating this study for a set of three different models. In the results section we compare the main results for the ensemble of GCMs. Relatively small deviations are observed which leads to the conclusion that the current GCMs are highly suitable for this study.
This section describes the method of the electricity system modelling, data conversion and validation, and the electricity system key metrics. For a detailed descriptions of these quantities one is referred to the supplementary information (SI) 2.1, 2.2 and 2.3
Weather-driven electricity system modelling was introduced for the first time by Heide et al. . It is a type of top-down analysis that is particularly useful for isolating and understanding the impact of weather dynamics on an electricity system with a large penetration of variable sources, e.g, wind and solar. But unlike most technology-rich bottom-up models, it is not well-suited for determining, e.g., the fuel mix of conventional dispatchable production units. In this paper, we implement a simplified large-scale European electricity system, of which the supply side consists solely of wind and solar power generation as well as a generic dispatchable power source, e.g., dammed hydro power or gas turbines. The demand side consists of national electricity demand. This type of electricity system aggregates the country-wise wind and solar power generation profiles into system-wise profiles. The same applies for the electricity consumption profiles. The effect of power transmission in this system was introduced by Rodriguez et al. [46, 47].
The effect of climate change enters the modelling via the weather-dependent wind and solar generation time series as well as the temperature dependent part of the electricity demand. State-of-art methodology has been used to convert raw wind and solar climate data into 3-hourly country-wise wind and solar capacity factor time series. These have been validated for the historical period 1986–2006 against already bias adjusted capacity factors provided by Renewables.ninja [48, 49] by minimizing the relative entropy (see SI 3.1 and 3.2). Hourly country-wise electricity consumption profiles have been provided by the European Network of Transmission System Operators for Electricity, ENTSO-e . These have been corrected for effects of heating and cooling by using the degree day method (see SI 4). Additional detailed descriptions of these methods are found in the supplementary material.
In the work presented here, wind and solar generator capacities have been scaled such that the EU-wide average generation over the historical period matches the average demand for electricity. This choice has previously been shown to describe the dynamics of a highly renewable electricity system well . Due to the state of fluctuating weather events and electricity consumption behaviour, the generation and load profiles differ in most time steps. This difference is referred to as the generation-load mismatch, which is defined formally in the methods section. For high renewable penetrations, the generation-load mismatch means that surplus of the variable renewable electricity, VRE, will sometimes be available. This surplus may either be transported to other locations, used by new flexible consumption, stored for later use or simply curtailed. At other times, VRE generation is lacking and an ancillary system must balance the difference between supply and demand, e.g., by a combination of conventional power plants, dispatchable renewable electricity such as dammed hydropower, and electricity storage systems. Variations in instantaneous generation-load mismatch across the continent drive the transmission of renewable surplus to regions with a deficit. Furthermore, the maximum negative values of the mismatch describe the need for dispatchable power capacity, and variations in the mismatch describe the need for on-demand flexible reserves.
Key Metrics. The above considerations give rise to four key metrics that describe a highly renewable electricity system. These are all described formally in the methods section. The first key metric is the average dispatchable electricity delivered by the dispatchable power generators for a fixed wind and solar penetration. It is a good indicator of the utility value of the VRES, i.e. the amount of VRE that can directly cover the demand. This metric was first introduced by Heide et al. .
Generally, the useful electricity can be increased by means of geographical dispersion, e.g., by combining national systems to smooth out variations in both demand  and renewable generation . We measure this effect with the second metric which is defined as the absolute difference between balancing electricity required with and without unlimited transmission in a European power grid. We call the metric, the absolute benefit of transmission in contrast to the relative benefit of transmission used by Becker et al. . In this paper we do not detail the required transmission capacities, but note that many other studies find that it is cost efficient to build enough to harvest most of this benefit [54, 55].
The two first measures are concerned with the need for dispatchable electricity, which is closely related to, e.g., CO2 emissions. The third and fourth key metrics indicate what is required to stabilize an electricity system based on VRES and maintain security of supply. The third metric is measured by the maximum dispatchable capacity required during low VRE and high demand periods. A number of studies show that VRES only provides very limited firm capacity as in Rodriguez et. al. . However, this is not a good key metric as it does not capture the correlation between VRES and demand. The fourth and final key metric is the 3-hourly short-term variability of the balancing time series. This measure was shown to be a good proxy for the need for on-demand reserve capacity . The variability is largely caused by meso-scale turbulence patterns that have been shown to describe the spatio-temporal characteristics of wind and solar power generation well .
We note that the effect of many types of extreme weather conditions are implicitly captured by the key metrics that are calculated from the 3-hourly generation-load mismatch. For instance, an extended period of very hot weather in the RCP8.5 scenario would increase the use of dispatchable electricity and capacity as electricity demand for cooling would increase and the performance of solar panels would decrease compared to the historical scenario. Likewise, strong storms would cause wind turbines in areas with wind speeds exceeding, typically, 25 m/s to cut out abruptly, causing an increased short-term variability as well as increased needs for dispatchable electricity and capacity.
Initially, we present results only based only on the climate model HIRHAM5-EC-EARTH. Towards the end, we present the robustness of the results by comparing similar findings from all six climate models. We stress, however, that in the analysis, all six climate models have been treated identically.
Overall, the different climate scenarios show a minor impact on the key metrics of a highly renewable European electricity system. This is illustrated in Figure 3, where the key metrics are shown as a function of the wind-solar mix for the end of the century period 2080–2100, where all RCPs are most developed. We observe that the difference between average values for different climate scenarios is smaller than the corresponding annual variation (one sigma) in nearly all cases. In other words, annual variations within a given climate change scenario are larger than the difference between scenarios. A paired t-test reveals that, in most cases, the distributions of annual scores cannot be assumed to come from different underlying distributions, indicating that the null-hypothesis that climate change has no impact on the key metrics cannot be rejected (95% confidence). To some extend this finding is related to the choice of generator capacities relative to demand because a decrease in demand is balanced by an independent decrease in consumption. This is evident from Table 1, where it can be seen that both annual demand and wind and solar generation are decreasing slightly in the future scenarios. At a 95% significant level, the annual demand and annual solar capacity factors can both be considered different for the different climate change scenarios. The wind capacity factors, on the other hand, cannot be considered different. All test scores are listed in the supporting material.
The difference between the climate change scenarios can also be compared to the difference between systems with different wind-solar mix within a scenario. This comparison reveals to what extend it is more important to choose the right wind-solar mix independently of RCPs, or if it is most important to design the electricity system for the right RCP. For the dispatchable electricity key metric shown in Figure 3a, we find the largest difference between climate scenarios for a wind-solar mix of 1.0. The RCP8.5 scenario has an average value of 0.165 while the corresponding number for the historical period is 0.137. However, within each of the two scenarios, the difference between a wind dominated and a solar dominated mix is significantly larger. This means that in a planning context it is more important to consider the wind-solar mix than the degree of climate change. Similar arguments can be made for the other key metrics, i.e. the benefit of transmission, the dispatchable capacity and the short-term variability.
A solar-dominated electricity system requires dispatchable electricity that is slightly higher than half of the electric consumption. This is explained by the day/night pattern in which little dispatchable electricity is needed during day time and full dispatchable electricity needs are required for the night time. On the other hand, the production of wind power during both day and night time reduces the need for dispatchable electricity to less than 20% of the annual demand. These findings are in correspondence with earlier work . The benefit of transmission is highly dependent on the spatial correlation lengths of the wind speeds and solar irradiance . The high correlation lengths of the solar irradiance due to the systematic variability of the sun’s position in the sky give rise to low transmission benefits. Due to the production of PV power over large areas, minor needs for transmission are present. On the other hand, the smaller wind speed correlation length of approximately , leads to areas with high wind electricity production and areas with low wind power production. Under these circumstances, the need for transmission increases. The short-term variability for solar domination shows higher values in contrast to wind domination. This behaviour is due to stronger intra-day temporal variations in the solar irradiance compared to temporal variations in the wind speeds. It is further reflected in the dispatchable capacity.
A number of studies focus on the end of the century period alone. However, we also explore the evolution of the key metrics towards the end of the century. In the following we focus on the dispatchable electricity for a wind-solar mix of 0.8, shown in Figure 4. Similar results can be found for the other key metrics in the supporting material. For this case, we do not find a gradual transition from the historical reference period towards the end of the century. Instead, the trajectories of the RCPs change over time such that, e.g., the 20-year average values of each RCP cross the other trajectories multiple times. This means that the general conclusions about the relative importance of climate change on the electricity system, drawn above for the end of the century period, also hold for the transition, and it is not possible to regard specific numerical differences between RCPs as the product of a gradual evolution towards a stable climatic end-point. Rather the 20–year averages are not stable to an extent where they can be regarded as different between RCPs. A longer time window could be used instead. However, since the climate is changing during the course of the century, see Figure 1, long periods do not represent a stable climatic situation.
Above, we have discussed results based on the HIRHAM5-EC-EARTH climate model. Now we turn to the five other combinations of regional and global climate models included in the study in order to establish to what extent the results are in agreement across the models. In general, we do not find identical numerical results for all models despite the fact that all time series have been bias adjusted on the same historical period against the same reference. However, the overall findings remain, independently of the models. Figure 5 shows a comparison of the four electricity system key metrics for the different models in the historical period as well as in the end of the century period for the RCPs. In most cases, the 20-year average values fall between the first and the third quartile of the annual variation, which means that the difference between individual RCMs is smaller than the annual difference within a given RCM. For the variability, this is not the case. The main reason for this is that the wind and solar time series are bias adjusted such that the distribution of instantaneous values matches the reference. However, the variability depends on the correlation between consecutive hours. This difference is not directly subject to correction, which means that models are likely to behave more differently on this parameter.
The different climate change scenarios show a minor impact on the key metrics of a highly renewable European electricity system. On the supply side, both wind and solar power generation are affected in a way that generally reduces performance slightly as their average electricity output decreases and their variability increases with an increasing effect of climate change. However, temperature changes affect the demand for electricity stronger. In most cases, this has an opposing effect as winter demands, primarily in Northern Europe, are reduced due to the increased temperatures. As an example, increasing dispatchable electricity needs in the pathway with the most extreme climatic changes, RCP8.5, due to lower yields from VRES turn into decreasing needs due to lower electricity demands, and could most certainly be either reduced or increased further by impacts from sources not related to climatic conditions such as electricity efficiency measures, population change or increased electrification of, e.g., heating and transportation sectors . The findings above are in agreement with a study on Southern Africa which also shows negligible effects of climate change on the wind and solar industry .
In designing a future highly renewable electricity system that is robust against climate change, it is important to focus on reaching a mixture of wind and solar power generation that minimizes the need for dispatchable electricity as the climate, during the next century, does not bring prominent changes to the electricity system infrastructure key metrics.
An important consequence of these findings is that for most purposes, it is not required to take into account the effect of climate change on the VRES wind and solar when designing future highly renewable electricity systems. It is advisable, however, to consider changes in the electricity demand due to climatic changes as these may indirectly affect the best generator mix, e.g., by decreasing the winter demand and increasing the summer demand. Should a stronger sector coupling, in particular between the heating, cooling and electricity sectors, become a reality, these climatic effects would be increased.
The authors wish to express their gratitude to O. B. Christensen and F. Boberg from the Danish Meteorological Institute for their contribution to understanding the climate outcomes and for supplying out data from the regional climate model HIRHAM5. G. Nikulin from the Swedish Meteorological and Hydrological Institute is thanked for supplying out data from the regional climate model RCA4. E. van Meijgaard from the Royal Netherlands Meteorological Institute is thanked for supplying out data from the regional climate model RACMO22E. P. Lenzen from the German Climate Computing Center is thanked for supplying out data from the regional climate model CCLM4. Professor M. Greiner is thanked for fruitful discussions. A sincere thank to Mette Stig Hansen for her diligent proof reading of this paper. Thanks to Heidi Søndergaard for giving graphical inputs to the figures. Finally, thanks to the Aarhus University Research Foundation (AUFF) for providing financial support.
Correspondence and requests for materials should be addressed to G. B. Andresen (email: firstname.lastname@example.org) or S. Kozarcanin (email: email@example.com)
- 1 Gunnar Myhre, Drew Shindell, François-Marie Bréon, William Collins, Jan Fuglestvedt, Jianping Huang, Dorothy Koch, Jean-Francois Lamarque, David Lee, Blanca Mendoza, et al. Anthropogenic and natural radiative forcing. Climate change, 423:658–740, 2013.
- 2 James Hansen, Makiko Sato, Reto Ruedy, Ken Lo, David W Lea, and Martin Medina-Elizade. Global temperature change. Proceedings of the National Academy of Sciences, 103(39):14288–14293, 2006.
- 3 Juan-Carlos Ciscar and Paul Dowling. Integrated assessment of climate impacts and adaptation in the Energy sector. Energy Economics, 46:531–538, 2014.
- 4 Paul Dowling. The impact of climate change on the European energy system. Energy Policy, 60:406–417, 2013.
- 5 Karoliina Pilli-Sihvola, Piia Aatola, Markku Ollikainen, and Heikki Tuomenvirta. Climate change and electricity consumption – Witnessing increasing or decreasing use and costs? Energy Policy, 38(5):2409–2419, 2010.
- 6 G Totschnig, R Hirner, A Müller, L Kranzl, M Hummel, H-P Nachtnebel, P Stanzel, I Schicker, and H Formayer. Climate change impact and resilience in the electricity sector: The example of Austria and Germany. Energy Policy, 103:238–248, 2017.
- 7 Leonie Wenz, Anders Levermann, and Maximilian Auffhammer. North–south polarization of European electricity consumption under future warming. Proceedings of the National Academy of Sciences, page 201704339, 2017.
- 8 Pernille Seljom, Eva Rosenberg, Audun Fidje, Jan Erik Haugen, Michaela Meir, John Rekstad, and Thore Jarlset. Modelling the effects of climate change on the energy system – a case study of Norway. Energy Policy, 39(11):7310–7321, 2011.
- 9 Kirsti Jylhä, Juha Jokisalo, Kimmo Ruosteenoja, Karoliina Pilli-Sihvola, Targo Kalamees, Teija Seitola, Hanna M Mäkelä, Reijo Hyvönen, Mikko Laapas, and Achim Drebs. Energy demand for the heating and cooling of residential houses in Finland in a changing climate. Energy and Buildings, 99:104–116, 2015.
- 10 Mojca Dolinar, Boris Vidrih, Lučka Kajfež-Bogataj, and Sašo Medved. Predicted changes in energy demands for heating and cooling due to climate change. Physics and Chemistry of the Earth, Parts A/B/C, 35(1):100–106, 2010.
- 11 Tania Berger, Christoph Amann, Herbert Formayer, Azra Korjenic, Bernhard Pospischal, Christoph Neururer, and Roman Smutny. Impacts of climate change upon cooling and heating energy demand of office buildings in Vienna, Austria. Energy and buildings, 80:517–530, 2014.
- 12 M Christenson, H Manz, and D Gyalistras. Climate warming impact on degree-days and building energy demand in Switzerland. Energy Conversion and Management, 47(6):671–686, 2006.
- 13 Jonathan Spinoni, Jürgen V Vogt, Paulo Barbosa, Alessandro Dosio, Niall McCormick, Andrea Bigano, and Hans-Martin Füssel. Changes of heating and cooling degree-days in Europe from 1981 to 2100. International Journal of Climatology.
- 14 Isabelle Tobin, Robert Vautard, Irena Balog, François-Marie Bréon, Sonia Jerez, Paolo Michele Ruti, Françoise Thais, Mathieu Vrac, and Pascal Yiou. Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections. Climatic Change, 128(1-2):99–112, 2015.
- 15 Idar Barstad, Asgeir Sorteberg, and Michel dos-Santos Mesquita. Present and future offshore wind power potential in northern Europe based on downscaled global climate runs with adjusted SST and sea ice cover. Renewable Energy, 44:398–405, 2012.
- 16 Isabelle Tobin, Sonia Jerez, Robert Vautard, Françoise Thais, Erik Van Meijgaard, Andreas Prein, Michel Déqué, Sven Kotlarski, Cathrine Fox Maule, Grigory Nikulin, et al. Climate change impacts on the power generation potential of a European mid-century wind farms scenario. Environmental Research Letters, 11(3):034013, 2016.
- 17 Lynsey McColl, Erika J Palin, Hazel E Thornton, David MH Sexton, Richard Betts, and Ken Mylne. Assessing the potential impact of climate change on the UKâs electricity network. Climatic Change, 115(3-4):821–835, 2012.
- 18 Hagen Koch, Stefan Vögele, Fred F Hattermann, and Shaochun Huang. The impact of climate change and variability on the generation of electrical power. Meteorologische Zeitschrift, 24:173–188, 2015.
- 19 Markus Schlott, Kies Alexander, Tom Brown, David Schlachtberger, Jonas HÃ¶rsch, Stefan Schramm, and Martin Greiner. Impact of climate change on a future highly renewable european power system. 16th Wind Integration Workshop (Berlin / Germany)., 2017.
- 20 A Bloom, V Kotroni, and K Lagouvardos. Climate change impact of wind energy availability in the Eastern Mediterranean using the regional climate model PRECIS. Natural Hazards and Earth System Sciences, 8(6):1249–1257, 2008.
- 21 Juliane Weber, Jan Wohland, Mark Reyers, Julia Moemken, Charlotte Hoppe, Joaquim G Pinto, and Dirk Witthaut. Impact of climate change on backup energy and storage needs in wind-dominated power systems in Europe. arXiv preprint arXiv:1711.05569, 2017.
- 22 Jan Wohland, Mark Reyers, Juliane Weber, and Dirk Witthaut. More homogeneous wind conditions under strong climate change decrease the potential for inter-state balancing of electricity in Europe. Earth System Dynamics, 8(4):1047, 2017.
- 23 Sonia Jerez, Isabelle Tobin, Robert Vautard, Juan Pedro Montávez, Jose María López-Romero, Françoise Thais, Blanka Bartok, Ole Bøssing Christensen, Augustin Colette, Michel Déqué, et al. The impact of climate change on photovoltaic power generation in Europe. Nature communications, 6:10014, 2015.
- 24 Peter Berrill, Anders Arvesen, Yvonne Scholz, Hans Christian Gils, and Edgar G Hertwich. Environmental impacts of high penetration renewable energy scenarios for Europe. Environmental Research Letters, 11(1):014012, 2016.
- 25 Richard H Moss, Jae A Edmonds, Kathy A Hibbard, Martin R Manning, Steven K Rose, Detlef P Van Vuuren, Timothy R Carter, Seita Emori, Mikiko Kainuma, Tom Kram, et al. The next generation of scenarios for climate change research and assessment. Nature, 463(7282):747, 2010.
- 26 Karl E Taylor, Ronald J Stouffer, and Gerald A Meehl. An overview of cmip5 and the experiment design. Bulletin of the American Meteorological Society, 93(4):485–498, 2012.
- 27 Detlef P Vuuren, Elke Stehfest, Michel GJ Elzen, Tom Kram, Jasper Vliet, Sebastiaan Deetman, Morna Isaac, Kees Klein Goldewijk, Andries Hof, Angelica Mendoza Beltran, et al. RCP2. 6: exploring the possibility to keep global mean temperature increase below 2 C. Climatic Change, 109(1-2):95–116, 2011.
- 28 Allison M Thomson, Katherine V Calvin, Steven J Smith, G Page Kyle, April Volke, Pralit Patel, Sabrina Delgado-Arias, Ben Bond-Lamberty, Marshall A Wise, Leon E Clarke, et al. RCP4. 5: a pathway for stabilization of radiative forcing by 2100. Climatic change, 109(1-2):77, 2011.
- 29 Keywan Riahi, Shilpa Rao, Volker Krey, Cheolhung Cho, Vadim Chirkov, Guenther Fischer, Georg Kindermann, Nebojsa Nakicenovic, and Peter Rafaj. RCP 8.5âA scenario of comparatively high greenhouse gas emissions. Climatic Change, 109(1-2):33, 2011.
- 30 UNEP. The Emissions Gap Report 2017. 2017.
- 31 Ole Bøssing Christensen, Martin Drews, Jens Hesselbjerg Christensen, Klaus Dethloff, Klaus Ketelsen, Ines Hebestadt, and Anette Rinke. The HIRHAM Regional Climate Model. Version 5 (beta). 2007.
- 32 W Hazeleger, X Wang, C Severijns, S Ştefănescu, R Bintanja, A Sterl, Klaus Wyser, T Semmler, S Yang, B Van den Hurk, et al. EC-Earth V2.2: description and validation of a new seamless earth system prediction model. Climate dynamics, 39(11):2611–2629, 2012.
- 33 Sven Kotlarski, Klaus Keuler, Ole Bossing Christensen, Augustin Colette, Michel Déqué, Andreas Gobiet, Klaus Goergen, Daniela Jacob, Daniel Lüthi, Erik van Meijgaard, et al. Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geoscientific Model Development, 7(4):1297–1333, 2014.
- 34 AF Prein, Andreas Gobiet, Heimo Truhetz, Klaus Keuler, Klaus Goergen, Claas Teichmann, C Fox Maule, E Van Meijgaard, M Déqué, Grigory Nikulin, et al. Precipitation in the EURO-CORDEX 0.11 and 0.44 simulations: high resolution, high benefits? Climate dynamics, 46(1-2):383, 2016.
- 35 Waheed Iqbal, Wai-Nang Leung, and Abdel Hannachi. Analysis of the variability of the north atlantic eddy-driven jet stream in cmip5. Climate Dynamics, pages 1–13, 2017.
- 36 Abdel Hannachi, Elizabeth A Barnes, and Tim Woollings. Behaviour of the winter north atlantic eddy-driven jet stream in the cmip3 integrations. Climate dynamics, 41(3-4):995–1007, 2013.
- 37 Elizabeth A Barnes and Lorenzo Polvani. Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the cmip5 models. Journal of Climate, 26(18):7117–7135, 2013.
- 38 Axel Lauer and Kevin Hamilton. Simulating clouds with global climate models: A comparison of cmip5 results with cmip3 and satellite data. Journal of Climate, 26(11):3823–3845, 2013.
- 39 Matthew Collins, Reto Knutti, Julie Arblaster, J-L Dufresne, Thierry Fichefet, Pierre Friedlingstein, Xuejie Gao, WJ Gutowski, Tim Johns, Gerhard Krinner, et al. Long-term climate change: projections, commitments and irreversibility. 2013.
- 40 J-LF Li, DE Waliser, G Stephens, Seungwon Lee, T L’Ecuyer, Seiji Kato, Norman Loeb, and Hsi-Yen Ma. Characterizing and understanding radiation budget biases in cmip3/cmip5 gcms, contemporary gcm, and reanalysis. Journal of Geophysical Research: Atmospheres, 118(15):8166–8184, 2013.
- 41 Julien Cattiaux, Hervé Douville, and Yannick Peings. European temperatures in cmip5: origins of present-day biases and future uncertainties. Climate dynamics, 41(11-12):2889–2907, 2013.
- 42 Else JM Van Den Besselaar, Albert MG Klein Tank, Gerard Van Der Schrier, Mariama S Abass, Omar Baddour, Aryan FV Van Engelen, Andrea Freire, Peer Hechler, Bayu Imbang Laksono, Rudmer Jilderda, et al. International climate assessment & dataset: climate services across borders. Bulletin of the American Meteorological Society, 96(1):16–21, 2015.
- 43 Chiyuan Miao, Qingyun Duan, Qiaohong Sun, Yong Huang, Dongxian Kong, Tiantian Yang, Aizhong Ye, Zhenhua Di, and Wei Gong. Assessment of cmip5 climate models and projected temperature changes over northern eurasia. Environmental Research Letters, 9(5):055007, 2014.
- 44 Gerard A Meehl, Thomas F Stocker, William D Collins, Pierre Friedlingstein, T Gaye, Jonathan M Gregory, Akio Kitoh, Reto Knutti, James M Murphy, Akira Noda, et al. Global climate projections. 2007.
- 45 Dominik Heide, Lueder Von Bremen, Martin Greiner, Clemens Hoffmann, Markus Speckmann, and Stefan Bofinger. Seasonal optimal mix of wind and solar power in a future, highly renewable Europe. Renewable Energy, 35(11):2483–2489, 2010.
- 46 Rolando A Rodriguez, Magnus Dahl, Sarah Becker, and Martin Greiner. Localized vs. synchronized exports across a highly renewable pan-European transmission network. Energy, Sustainability and Society, 5(1):21, 2015.
- 47 Rolando A Rodriguez, Sarah Becker, Gorm B Andresen, Dominik Heide, and Martin Greiner. Transmission needs across a fully renewable European power system. Renewable Energy, 63:467–476, 2014.
- 48 Iain Staffell and Stefan Pfenninger. Using bias-corrected reanalysis to simulate current and future wind power output. Energy, 114:1224–1239, 2016.
- 49 Stefan Pfenninger and Iain Staffell. Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy, 114:1251–1265, 2016.
- 50 ENTSO-E. ENTSO-E at a glance.
- 51 Dominik Heide, Martin Greiner, Lueder Von Bremen, and Clemens Hoffmann. Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation. Renewable Energy, 36(9):2515–2523, 2011.
- 52 Bethany A Corcoran, Nick Jenkins, and Mark Z Jacobson. Effects of aggregating electric load in the United States. Energy policy, 46:399–416, 2012.
- 53 Sarah Becker, Bethany A Frew, Gorm B Andresen, Timo Zeyer, Stefan Schramm, Martin Greiner, and Mark Z Jacobson. Features of a fully renewable US electricity system: Optimized mixes of wind and solar PV and transmission grid extensions. Energy, 72:443–458, 2014.
- 54 Sarah Becker, Rolando A Rodriguez, Gorm B Andresen, Stefan Schramm, and Martin Greiner. Transmission grid extensions during the build-up of a fully renewable pan-European electricity supply. Energy, 64:404–418, 2014.
- 55 Katrin Schaber, Florian Steinke, and Thomas Hamacher. Transmission grid extensions for the integration of variable renewable energies in Europe: Who benefits where? Energy Policy, 43:123–135, 2012.
- 56 Hannele Holttinen, Juha Kiviluoma, Ana Estanqueiro, Tobias Aigner, Yih-Huei Wan, and Michael Milligan. Variability of load and net load in case of large scale distributed wind power. In 10th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Farms, August 2010., 2010.
- 57 Jon Olauson, Hans Bergström, and Mikael Bergkvist. Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data. Renewable Energy, 96:784–791, 2016.
- 58 Joakim Widén, Nicole Carpman, Valeria Castellucci, David Lingfors, Jon Olauson, Flore Remouit, Mikael Bergkvist, Mårten Grabbe, and Rafael Waters. Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources. Renewable and Sustainable Energy Reviews, 44:356–375, 2015.
- 59 Mark Z Jacobson, Mark A Delucchi, Guillaume Bazouin, Zack AF Bauer, Christa C Heavey, Emma Fisher, Sean B Morris, Diniana JY Piekutowski, Taylor A Vencill, and Tim W Yeskoo. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for the 50 United States. Energy & Environmental Science, 8(7):2093–2117, 2015.
- 60 Charles Fant, C Adam Schlosser, and Kenneth Strzepek. The impact of climate change on wind and solar resources in southern Africa. Applied Energy, 161:556–564, 2016.