CLIM4ENERGY Deliverable D6.5.0 Focus paper on climate ...clim4energy.climate.copernicus.eu/sites/default/files/C3S441_Lot2... · ECMWF COPERNICUS REPORT ... C3S_441_Lot2_CEA_201706_D6.5.0_Focus

ECMWF COPERNICUS REPORT

Copernicus Climate Change Service

CLIM4ENERGY Deliverable D6.5.0_Focus paper on climate change in the energy sector and progress report on fact sheets and user guidance

Issued by: HZG-GERICS

Date: 30/06/2017

Ref: C3S_441_Lot2_CEA_201706_D6.5.0_Focus Paper and Progress Report_v1

Official reference number service contract: 2016/C3S_441_Lot2_CEA/SC1

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


C3S_441_Lot2_CEA_2016SC1_D6.5.0_Focus paper and progress report_v1 Page 3 of 37

Contributors

GERICS (HZG) Elisabeth Viktor Claas Teichmann CEA Robert Vautard Météo-France Gaëlle Collin FMI Andrea Vajda Met Office Caroline Acton Nicolas Fournier EDF Laurent Dubus EDPR Daniel Cabezon Vattenfall Mikael Sundby RTE Virginie Dordonnat Metsäteho Oy Heikki Pajuoja





Table of Contents

1. Focus paper on Climate Change in the Energy sector: Benefitting from climate change information in the energy sector 6

1.1 Executive summary 6

1.2 Introduction: The importance of climate information for the energy sector 7

1.3 The CLIM4ENERGY project 10

1.4 Climate change impacts: Examples for a selection of specific energy topics 12 1.4.1 Wind Power Generation 12 1.4.2 Hydropower 13 1.4.3 Offshore Oil and Gas 14 1.4.4 Bioenergy production conditions (timber) 15 1.4.5 Electricity Demand – Generation Balance 16 1.4.6 Impact of Freezing Rain on the Energy Infrastructure 18

1.5 Conclusion: Science and industry working together 19

1.6 References 21

2. Progress report on Fact sheets and User guidance: from concept to implementation – first steps 28

2.1 Conceptual elements 28

2.2 Experimental Implementation 31

2.3 Challenges 35

2.4 Outlook 35



Introduction This report contains the final version of the Focus Paper on climate change and the energy sector, and the Progress report on fact sheets and user guidance. The Fact Sheets report shines light on the conceptual elements, the currently ongoing experimental implementation phase as well as on challenges met along the way and waiting ahead. The final Fact Sheets will be delivered at the end of the project in March 2018.

1. Focus paper on Climate Change in the Energy sector: Benefitting from climate change information in the energy sector

1.1 Executive summary The energy sector is not just concerned with climate change mitigation to reduce greenhouse gas emissions, but it is also facing climate change impacts causing the need to adapt to current and future conditions. The long-term trend of many energy-relevant climate variables is changing, the variability patterns are altered and the intensity and frequency of extreme events is shifting. Hence, the energy sector needs to rely on climate information to contribute to the management of supply security. As part of the Clim4Energy project, this document provides a synthesis of how climate information is used by different energy producers, grid operators and traders, how sensitive they are regarding climate and weather and how they benefit from climate services. Six specific and diverse topics are presented: wind power generation, hydropower, offshore oil and gas, bioenergy production conditions (timber), electricity demand – generation balance and the influences of freezing rain on the energy infrastructure.

While each topic requires specific information on the effects of climate change, all areas can benefit from climate information in the following ways:

- Due to the generally very extensive lifetime of an energy production facility the

planning process for a new plant requires a long-term perspective. For this reason, it

is crucial to consult information about the expected change of relevant climate

variables over this period of time and to integrate the findings in the decision-making.

In doing so, the expected profitability of this facility can be quantified more

realistically, which is relevant both for the operator and the investor.

- Seasonal forecasts can provide information on a shorter, more immediate time scale.

They allow characterizing the coming season for certain climate variables regarding

the probability of deviations from the long-term mean based on the past. This

complements the strategic management in energy companies for the next few months

regarding generation as well as expected demand.



Shortcomings in the applicability of climate information, such as performance and uncertainty, are discussed as well. The research community is consistently improving in these areas, at the same time, mechanisms have to be explored and put in place that enable climate-proof decision-making despite these challenges.

1.2 Introduction: The importance of climate information for the energy sector The energy sector is heavily involved in mitigation actions to reduce greenhouse gas emissions. It is responsible for more than 70% of global emissions, accounting for transportation and use for industry, with the subsector electricity as the main contributor (over 30%, see Figure 1). As stated in the 5th Assessment Report by the International Panel of Climate Change (IPCC), low carbon energy production must grow very fast in the coming decades in order to keep global warming below 2°C. More than 50% of the total energy supply and 80% of the electricity supply have to be based on low carbon technologies by 2050 (Mulugetta et al., 2014). The resulting transformation towards new ways of energy generation such as renewables, however, is influencing the sector’s vulnerability to climate change and its consequences, because low carbon energy production often depends on natural phenomena, such as wind, clouds, rain, sea currents, etc. The energy sector needs to be able to anticipate resources regarding seasonal variability as well as trends over decades and is therefore dependent on climate information.

Figure 1: Global contributions to greenhouse gas emissions per sector in 2013 (in MtCO2 or Mt equivalent CO2, abbr. MtCO2e). The sector emitting by far the most (energy) is further split up into subsectors. Graph based on data by Climate Analysis Indicators Tool (CAIT) Climate Data Explorer. 2017. Washington, DC: World Resources Institute. Available online at: http://cait.wri.org.

Climate, sometimes understood as the "average weather,” is defined as the measurement of the mean and variability of relevant quantities of certain variables (such as temperature, precipitation or wind) over a period of time (WMO, 2017). Climate change modulates weather impacts. And while weather is not predictable beyond a few days, estimating the likelihood of deviations from the mean and variability experienced in the past can provide valid input for planning purposes. Future climate projections, for instance, can indicate the long-term evolution of available resources over the next century. Forecasts for the forthcoming season

http://cait.wri.org/



may in some cases predict a meaningful probability of higher or lower than normal resources (Clark et al., 2017). This can help to identify situations with opposing fluctuations in energy supply and demand leading to load imbalance, and extreme situations which can interfere with operation and maintenance schedules.

For each of these aspects specific climate information can provide support to minimize risk, maximize efficiency and guarantee supply security. The required information can be categorized into three different time frames:

short-term information, covering climate information affecting days or a few weeks at

a time,

medium-term information focusing on impacts regarding seasonal variability, and

long-term information, providing climate change trends until the end of the century.

Climate change information is gathered through various channels. Observations collected in the field and remotely are combined with global climate models (GCMs) representing the complex interconnections of the different components of the Earth system (atmosphere, ocean, land masses, ice masses). The validation of climate model results is mainly based on observations: The models are run for a period in the past (“hindcast”) and the output is compared to observations. A correct representation of the past contributes greatly to the credibility of a model and to the reliability of its projections of the future. Regional climate models, which downscale GCM simulations, can be applied and tested similarly under varying climatic conditions to provide a closer look at specific parts of the world. Climate models are developed by several research institutions that apply different numerical algorithms and parameterisations. A comparison reveals potential dependencies due to similar methodologies or input data (Collins, 2017) and informs about each model’s strengths and weaknesses. Viewing the results across as many models as possible (“model ensemble”) is currently considered to provide the most well-rounded and state-of-the-art output range. Box 1 offers further details on sources of uncertainty in climate model results.

Originally, climate models were built to focus on the long-term earth system evolution under increasing greenhouse gas emissions, looking ahead to the middle or the end of the century. More recently, the research community has been able to also show more immediate changes in climate conditions, i.e. for the next winter or summer season. For these so-called seasonal predictions, different atmospheric processes are more dominant and decisive than for the assessment of long-term trends. The performance (“skill”) of seasonal forecasts depends a lot on the region and the season of interest. It can either be determined by analysing the hindcast, similar to the method applied to climate models, or by creating a forecast and validating it in retrospective. Conventional methods of planning for the next season rely on past observations and assume that the coming months will behave the same as in the years before. With climate change happening as rapidly as projected for the future, this method will not suffice to serve as a solid basis for planning. Hence, seasonal predictions are a sought-after source of information. However, only once the skill is further improved and the forecast consistently outperforms conventional methods it will be integrated in day-to-day operations (Clark et al., 2017).



Box 1: Uncertainties in climate model results.

Uncertainties in climate model results originate from three different sources (Pfeifer et al., 2015): (1) Model response: Climate models, just as any other model, are simplified

representations of reality. As a consequence, some mechanisms are represented

well and some are more difficult to represent, introducing a certain range of biases

in the results.

(2) Natural variability: The non-linearity of the climate system allows for different

outcomes even if the external forcing conditions are the same, but initial conditions

slightly differ.

(3) Scenario uncertainty: It is not possible to reliably predict the future development of

greenhouse gas emissions and the storage capacity of the oceans and the biosphere.

The emissions, for instance, depend on non-climatic influences such as policy

changes, regulation, innovation and technology and the willingness of society to

support and push for a reduction. As a result, a few different scenarios

(“representative concentration pathway”, RCP) are usually applied producing a

range of different possible realizations of the future. What does the industry think?

“One needs to take into account the climate uncertainty on the one hand, and the final need to take a deterministic, no-regret decision. At the end, you can’t decide to build a distribution of plants, but only one, which will be able to face different risks and hazards, among which climate is only one. So we’re working on uncertainty and uncertainty cascades estimation methods, to help the engineering units taking the best possible decisions. That being said, we try to provide them with the best possible estimation of uncertainty, by initially using the full range of climate projections, but then reducing the number of projections to a reasonable number, because impact models or plant design tools can’t handle tens of simulations.”

– Laurent Dubus, Expert Researcher, EDF



1.3 The CLIM4ENERGY project The European Union (EU) aspires to become a central source of authoritative and quality-assured climate information for Europe and worldwide. With the Copernicus Climate Change Service (“C3S”, climate.copernicus.eu) the EU aims at combining observations and model results to present a consistent, comprehensive and credible description of the past, current and future status of the climate. By providing information on the drivers and the impacts of climate change through key indicators, mitigation and adaptation can be supported and strengthened. C3S is still in development and will contribute economic value through three channels: (1) inform about risk and hazards to enforce protection on policy level, (2) improve planning of adaptation and mitigation measures and (3) promote new services related to climate change for the benefit of society. C3S is operated by the European Centre for Medium-Range Weather Forecast (ECMWF) in the UK on behalf of the EU. Clim4Energy is one of seven projects located in the Sectoral Information System (SIS) of C3S.

The project Clim4Energy demonstrates the value chain from climate variables to actionable information. By the end of the project, in March 2018, it will deliver 10 pan-European indicators relevant to the electricity segment of the energy sector informing about climate trends and variability (see Table 1). The indicators will be consistent across different energy sub-sectors, appropriately documented, and will include an estimation of uncertainties and a demonstration of use in the form of case studies. Clim4Energy is bringing together the complementary expertise of several climate research and service centres and energy practitioners, acting as co-designers. It is organized by thematic clusters, each led by one of the project partners.

As part of the project this focus paper summarizes the benefits and current ways of application of climate change information within the energy sector based on the latest scientific literature and conversations with partners from the industry. It will serve as a reference both for C3S, supporting the development of their service, and for those interested in the broader context and the relevance of climate change information in the energy sector regarding the six aforementioned topics.

More information on Clim4Energy and on accessing the data holding the indicators can be found at http://clim4energy.climate.copernicus.eu/.

https://climate.copernicus.eu/

http://clim4energy.climate.copernicus.eu/



Lead

Topic Indicator Climate research and service centres

Co-designers from the industry

Demonstration

CEA

– A

lter

nat

ive

ener

gies

an

d A

tom

ic E

ner

gy C

om

mis

sio

n, F

ran

ce

wind energy - wind power capacity factor BSC – Barcelona Supercomputing Center

EDPR – Energias de Portugal / Renewables

CN

RS

– N

atio

nal

Cen

ter

for

Scie

nti

fic

Res

earc

h, F

ran

ce

GER

ICS

– C

limat

e Se

rvic

e C

ente

r G

erm

any

/ H

ZG H

elm

ho

ltz-

Zen

tru

m G

ees

thac

ht

EDF

– E

lect

rici

té d

e Fr

ance

hydropower - inflow anomalies - anomalies in annual maximum snow

storage

SMHI – Swedish Meteorological and Hydrological Institute

Vattenfall Statkraft Montel

offshore oil and gas - change in sea level - significant wave height

Met Office UK Shell, Total

bioenergy (timber) - soil frost depth and snow depth - soil moisture

FMI – Finnish Meteorological Institute

Metsäteho Oy

demand – generation balance

- demand: energy consumption derived from daily temperature

- generation: wind power capacity factor (wind speed based) and solar capacity factor (based on solar irradiation and temperature)

Météo-France RTE – Réseau de Transport d’Electricité

freezing rain impact on energy infrastructure

- freezing rain indicator based on humidity and temperature

FMI – Finnish Meteorological Institute

Fingrid

Table 1: List of indicators being developed in the Clim4Energy project including the respective research centres and co-designers involved.



1.4 Climate change impacts: Examples for a selection of specific energy topics

1.4.1 Wind Power Generation

In Europe, the potential for wind power generation is not expected to change significantly until the middle of the century. Northern Europe will likely become slightly windier whereas southern Europe is likely to experience a small reduction in wind power potential (Pryor and Barthelmie, 2010; Reyers, Moemken and Pinto, 2016; Tobin et al., 2016). Unforeseen or man-made changes in land-use can influence this, however. It is assumed that an increase in vegetation can reduce wind speeds close to the surface and favour a “wind stilling” (McVicar and Roderick, 2010; Vautard et al., 2010). For offshore wind parks, regional changes are expected: the Mediterranean and the North Sea will likely see a negative trend whereas the wind power potential in the Baltic Sea will increase (Gaetani et al., 2015). Uncertainties in climate model results complicate local assessments of the future energy production potential of specific sites. The models can, however, provide good indications about possible regional scenarios (Richert and Matzarakis, 2014; Gonçalves-Ageitos et al., 2015; Tobin et al., 2015, 2016).

The seasonal cycle of wind energy potential is likely to intensify in most parts of Europe, i.e. the potential is expected to increase in winter, when wind speeds are already rather high, and decrease in summer. The area of central Europe does not show a clear trend (Reyers, Moemken and Pinto, 2016).

On shorter time scales, seasonal wind forecasts have the potential to provide probability information about expected wind intensities in the next season which can support upcoming business decisions (Soret et al., 2015). There is a clear value for the energy sector in skilful seasonal forecasts, however, at this stage the forecasts outperforming conventional methods are localized and not always robust (Doblas-Reyes et al., 2013; Clark et al., 2017). Additionally, the range of possible outcomes (uncertainty range) is rather broad and the prediction system has to be calibrated using observations (Torralba et al., 2017) before being valuable to assess the impact on wind power generation of the upcoming months.

Extremely high wind speeds during a storm can lead to a forced shutdown of wind turbines to prevent failure. The frequency of such interruptions might change due to climate change, albeit no significant trend in either direction could be detected so far (Chinchilla, Arnaltes and Burgos, 2006).

Due to the intermittent nature of wind power, storage capability and capacity will become more important as the contribution of wind power to the energy mix is increasing. Converting wind power to hydropower could be part of the solution (Schaeffer et al., 2012).



1.4.2 Hydropower Hydropower production plants have a lifetime of several decades, therefore climate change has to be considered already during the planning phase, since boundary conditions will change over the long run-time (Schaefli, 2015). Climate models can provide insights into large-scale trends of precipitation patterns which influence the inflow and water level of reservoirs. However, the resolution of most regional climate models is too coarse to explicitly resolve some of the processes involved in precipitation and run-off, thus they rely on more abstract simplifications, so-called parametrizations. This introduces uncertainties regarding the exact location and the intensity of changes. Results of climate models can therefore only give indications on future trends, e.g. that the production potential in southern Europe will most probably decrease (Aronica and Bonaccorso, 2013; López-Moreno et al., 2014). More recently, the development of very high resolving models has begun, explicitly representing convection, but additional research is necessary to fully exploit their potential (Leutwyler et al., 2017). Furthermore, certain impact models, such as multi-basin models that encompass routing schemes, can predict expected tendencies for change in long-term means and seasonality. After a validation process in areas with solid series of measurement these models can be particularly helpful for regions with no or only a few gauges (Donnelly, Andersson and Arheimer, 2016). Further research is needed to understand how impact models are affected by the existing uncertainties and how meaningful action can still be taken (Donnelly, Yang and Dahné, 2014). Since market prices and management practices also have a considerable influence on hydropower generation, a trans-disciplinary approach should be applied to optimize the interplay of all relevant factors (Gaudard et al., 2014).

Seasonal forecasts for precipitation can improve hydroelectric power management by providing information about whether the weather will be wetter or drier than usual. In order to be effective, the forecasts need to outperform conventional methods applied so far, such as using the historic climate as a proxy for the future (García-Morales and Dubus, 2007). Regular decisions that have to be made to manage a water reservoir can benefit from medium-term climate change information, such as changed timing of spring floods or reduced inflow after a dry summer, to adjust water levels and release schedules (Haguma et al., 2014).

Managers of the day-to-day activities at hydropower plants have to be aware of upcoming extreme floods or droughts to be able to put safety measures in place, for instance concerning dam failure or to protect water quality. It is challenging, however, to estimate future

What does the industry think?

“[…] once validated against measurements, they [seasonal forecasts] would be used as a complementary tool to estimate expected capacity factors of wind power plants and consequently the expected revenues. This could affect the trading strategy in the future.”

– Leading renewable energy company



probabilities of extreme precipitation events. Firstly, extreme precipitation is a local, short-lived phenomenon whereas climate projections perform best in larger regions and using average values over longer time periods. Secondly, the degree of change in greenhouse gas emissions, the main driver of climate change, is unforeseeable (Schaefli, 2015). Hence, climate projections usually focus on delivering general insights on the environmental conditions favourable for extreme precipitation within the emission scenarios provided by the IPCC (van Vuuren et al., 2011).

1.4.3 Offshore Oil and Gas Until 2100, the sea level will rise considerably around the globe. This is based on a variety of mechanisms playing together, with most of them being related to climate: changes in ocean mass due to ice loss from ice sheets and glaciers, thermal expansion, alterations in ocean circulation driven by climate change and changing freshwater fluxes, and the intensity of storm surges (Howard et al., 2014). Even though storm surge intensity is not the most influential factor on the list (Lowe et al., 2009; Cannaby et al., 2015) it can have dramatic consequences. For instance, the 50-year return period storm surge level (i.e. the storm surge level with a 2% chance to occur in any one year) is projected to increase by 58-106cm along the North western coastline of Europe (Howard et al., 2014). As a result, offshore oil rigs need to be retro-fitted to prepare for higher extreme water levels.

The intensity of the next winter season depends, amongst other things, on large scale air circulation patterns. The North Atlantic Oscillation (NAO) describes one aspect of these circulation patterns for Europe. Lately it was shown that seasonal forecasts for the expected NAO phase of the coming winter are correlated with disrupting incidents in the offshore energy sector. So, in areas where the seasonal forecast is skilful, i.e. outperforming the trend based solely on historic observations, it can be used to plan for maintenance and operational decisions (Palin et al., 2016). There are also first signs that forecasts for the winter season up to one year ahead can provide indications on the expected climatic conditions. This could simplify the planning of maintenance and construction work as well as expectations on revenue going forward (Dunstone et al., 2016). Projected trends in seasonal mean and extreme wave action are ambiguous still, but indicate little change for the North Sea (Lowe et al., 2009). Maintenance and construction work in Artic waters will likely become more cumbersome due to a higher frequency of freezing and thawing (Paskal, 2009).


“[…] we are interested [in climate information] since the inflow as described by hydrological data records effects both optimization of our hydro plants and the long term price forecast which are important both for planning and for investments.”

– Mikael Sundby, Senior Hydrologist, Vattenfall AB, Planning and Optimisation Nordic



For the oil and gas sector, extreme weather events bear a considerable potential for so-called “natech” accidents, i.e. accidents with significant safety and environmental risks leading to reputational damage plus high insurance costs. Offshore oil/gas platforms are vulnerable to a number of different extremes, such as storms, waves or heat. Oil rigs can break free or pipelines can get damaged, ships transporting oil from the platforms to refineries onshore can be impaired, risking contamination (Cruz and Krausmann, 2013). Extreme heat can weaken existing infrastructure (e.g. pipe systems) above water (Paskal, 2009). For the climate research community, the impact of climate change on the frequency and intensity of such extreme events in Europe, however, is not clear at this point (IPCC, 2012).

Managing climate change induced risks effectively entails a thorough risk assessment, cost/benefit analyses and appropriate protection measures such as upgrade, redundancies and contingency plans (Cruz and Krausmann, 2013; IPIECA, 2013). However, due to the complexity of modelling future climate projections on storminess and wave heights, only broad ranges of the expected change can be given. Quantifying the benefit of potential adaptation measures is therefore a difficult task which complicates the implementation of any preventive steps beyond the current safety protocol (Side et al., 2013). A common approach, applying the same tools and datasets throughout the energy sector, could help disentangle the interdependencies within the sector regarding climate change and lead to effective risk management (Byers and Amezaga, 2015). Further, the insurance industry can support adaptation efforts by putting a price tag on risk and incentivizing protection measures (Cruz and Krausmann, 2013).

1.4.4 Bioenergy production conditions (timber) The timber industry in Northern Europe is threatened by climate change from two different directions: On the one hand, a warmer and more humid climate will reduce the depth and duration of soil freezing during winter. As a consequence, trees lose anchorage and become more vulnerable to wind throw. With winter being the stormy season in Scandinavia, damages


“The industry does not rely on model data alone. If the model is unreliable, the design criteria are based on what they’ve measured. In the development of the project, the critical path from year 0 up to until year 5 in a project, with the survey taking at least a year – they have to provide some estimate for the design before that. This is why they work with Exploration & Production teams at the time of exploratory drilling in order to be able to understand better what is happening at the site by collecting data. […] When you have to design a new structure for the next 20-30 years they tackle uncertainty by stating that they make use of ranges. They would give 3 or 4 values to show the uncertainty in the data and support a risked based approach to decision making.”

– Leading oil and gas company



can be significant (Peltola, Kellomäki and Väisänen, 1999; Gregow et al., 2011). On the other hand, the decreased frost depth will also reduce the carrying capacity of the soil. Wood is harvested with heavy machinery, either during summer, when the soil is very dry, or during winter, once the soil is frozen. A softer soil will impair harvest conditions and therefore reduce the amount of bioenergy produced with timber (Kellomäki et al., 2010). At the same time the potential for growing biomass in regions with moderate climate might increase due to the warming and a higher level of CO2 in the atmosphere, leading to a longer growing season (Mima and Criqui, 2015).

Due to a warmer climate, it is likely that trees in Northern Europe will have to tolerate a higher wet snow load during winter. Additionally, climate conditions will become more favourable for rime accumulation. These two factors will increase the probability of forest damage in the cold season (Kämäräinen et al., 2016). For the warm season climate models estimate a doubling or tripling of the average number of forest fires in Northern Europe by the end of this century. This is due to a reduction in the soil moisture content caused by less precipitation and higher evaporation activity. Even though the modelled result shows a large range, it is clear that the risk of damaged raw material is enhanced (Mäkelä et al., 2014; Lehtonen et al., 2016).

1.4.5 Electricity Demand – Generation Balance To secure energy supply, understanding the expected long-term changes on the energy generation potential has to be complemented with studies on the anticipated development of energy consumption. Generally, temperature is correlated very well with electricity demand. The season which correlates best is dependent on the region, but often found during the summer months (De Felice, 2015; Thornton, Hoskins and Scaife, 2016). For some countries, such as France, the correlation is also very good in winter (personal communication, Météo-France). To prepare for the variability in energy demand, energy companies use typical meteorological years (TMY) to anticipate heating and cooling needs. Currently, the TMY approach is underestimating the amount of cooling necessary in summer (Grudzinska and Jakusik, 2015). Since extreme summer heat will very likely become more frequent in the future and winters will become warmer on average (IPCC, 2013), it is necessary to integrate future climate change considerations into the concept of TMY. The resulting effect on the yearly energy consumption due to hotter summers and warmer winters is unclear at the moment.


“[Climate information] will influence our decision making directly. For example where we will and can operate in different forests, how long time can we spend there. How to reduce environmental impacts of harvesting operations.”

– Heikki Pajuoja, Managing Director, Metsäteho Oy



Some models predict an increase (Taseska, Markovska and Callaway, 2012), whereas others foresee a decrease (Mima and Criqui, 2015) of energy consumption.

A future Europe with the majority of energy produced through renewables has to take advantage of the seasonality of renewable energy productivity. Currently, it is a much-discussed topic if exploiting the variability of certain energy sources in combination with a highly flexible grid is feasible and desirable, or if advancing innovation in the area of storage technology is more promising. According to Heide et al., 2010, the optimal mix of wind in winter and solar in summer for the scenario of a highly renewable Europe can reduce storage needs considerably. To turn this into reality and to reduce the volatility of renewable energy intake overall, large-scale connected grids need to be put in place. At the same time storable energy, such as hydropower, plays an important role in balancing renewable energy sources and storing energy generated in excess of the amount the grid can take up (Gaudard et al., 2016).

The coincidence of a high demand – low generation situation (e.g. a wind lull during a cold spell) can lead to electricity shortages (Clim4Energy, 2017). The most strenuous circumstances are likely to occur during heat waves, because (a) air conditioning and cooling can create extensive peak loads on the grid (Mima and Criqui, 2015) and (b) thermal power plants lose efficiency and have less cooling water at their disposal (Mulugetta et al., 2014). Cold spells can lead to equally precarious situations when demand spikes because of more intensive heating than usual and power generation is interrupted or slowed down due to the low temperatures. Using the correlation of temperature and energy demand, return periods for extreme events of different intensity can be calculated (Thornton, Hoskins and Scaife, 2016). Still, more research needs to be done regarding the impact of extreme weather events on energy demand (Mideksa and Kallbekken, 2010).


“RTE guarantees the permanent balance between electricity generation and demand. Every year, the next winter/summer is studied regarding this balance and if a failure/congestion risk is anticipated, then RTE warns all actors (including regulators) of the sector of a blackout risk.

In a longer term, RTE publishes adequacy studies using Météo-France current climate scenarios. These studies are meant to give a public insight about the probability of a blackout in the future due to the lack of generation means to face demand or the lack of network infrastructures.”

– Virginie Dordonnat, data scientist, RTE (Réseau de Transport d’Electricité, France)



1.4.6 Impact of Freezing Rain on the Energy Infrastructure Freezing rain and the resulting heavy load of ice accumulating on overhead transmission lines can cause a collapse of the electricity supply for large regions. The indicator developed in the context of Clim4Energy concentrates on mapping the regions affected by severe freezing rain and on analysing the changes in severity and frequency of freezing rain events in the predicted future climate. The effect of climate change on freezing rain is difficult to assess, because the occurrence of this phenomenon depends on several effects working against each other. Firstly, a general warming will shift the areas prone to freezing rain pole ward and shorten the time period with favourable conditions per year from five months to the coldest three months in winter (Cheng et al., 2012; Klima and Morgan, 2015). Secondly, climate change will most likely enhance precipitation in areas prone to freezing rain which will increase the frequency or intensity of such events. Simultaneously, with a warmed atmosphere, the air will be able to hold more water vapour and influence the occurrence probability and severity as well. And finally, even if future trends can be derived from models on the continental scale, local effects, e.g. due to mountains or valleys, can easily outweigh the trends (Klima and Morgan, 2015). Coherent long-term event histories provide insights into the unique characteristics that have to interact to create freezing rain and can support model improvements (Changnon, 2003; Changnon and Karl, 2003; Sanders et al., 2013; Markosek, 2015; Kämäräinen et al., 2016). So far, North America has been used as study area much more often than regions in Europe or elsewhere, possibly because the conditions for freezing rain are favourable in large areas of North America and the power grid is often above ground and therefore more vulnerable to freezing rain.

Once, the areas prone to freezing rain are identified, ice accretion models can provide the next step by quantifying the ice accumulation on transmission lines. This helps estimating the probability for failure for given weather situations and can supply repair activities with useful information about the location and extent of damage (Jones, 1996; Broström, 2007; McManus, Piltz and Sperry, 2008). Furthermore, these models provide support in determining design criteria and thresholds for transmission lines to sustain freezing rain. Yet, some uncertainty in the modelling remains concerning the exact amount of accumulated ice. Especially in mountainous areas, where measurements are scarce, such models can provide some valuable information (Jones, Ramsay and Lott, 2004; Hosek et al., 2011; Fikke, 2016).

There are plenty of measures to reduce damage caused by freezing rain, for instance trimming or removing trees threatening transmission lines (Jones and Mulherin, 1998), transferring transmission lines underground (Call, 2010; Armenakis and Nirupama, 2014) and creating redundancies and more independence within the electricity networks (Zhou et al., 2011; Institute for Catastrophic Loss Reduction, 2013). Beyond prevention measures, it is sensible to think about socioeconomic and ecological consequences and ripple effects that might prevent contingency plans from working, such as a disruption of the communication system. Shifting from a forecast-focused practice to a user-oriented plan will strengthen a successful response to such an extreme event (Zhou et al., 2011).



1.5 Conclusion: Science and industry working together One of the main goals of the energy sector, specifically electricity, is to secure supply at all times. Extreme weather events and climate change can threaten this security, since most ways of electricity production depend on certain climatic conditions in order to function properly. As laid out in the previous sections, knowledge and understanding about upcoming changes in the climate can absorb much of the potential negative consequences and help with the appropriate protection and adaptation measures (see Figure 2).

For this purpose, both seasonal forecasts and longer-term climate projections are useful as they can deduce expected future deviations from current and past observed climate for the more imminent effect on the coming season as well as for more distant, but equally important, trends until the middle or the end of this century. Based on these findings, utility and energy companies remain in control and are enabled to react fast and to adapt proactively.

Incorporating climate information in decision-making processes of power companies holds challenges. The integration in existing frameworks and internal models requires flexibility on both sides. The climate information needs to be tailored to the company structures and at the same time the mechanisms applied in-house might need to budge slightly from conventional concepts to accommodate this new piece of information including its uncertainties in the process chain. Concepts for translating climate model data into actionable items are therefore critical. The C3S project Clim4Energy is offering examples for solutions with climate change indicators specifically tailored to certain topics of interest in the energy sector.

With the gradual move towards renewable energies future-oriented approaches to optimize energy production and distribution are crucial for adapting to climate change. Collaborations across different energy subsectors could lead to a more flexible energy mix optimizing storage capacity and overcoming short-term supply bottlenecks (Heide et al., 2010; Gaudard et al., 2016). This, however, requires a coordination of available datasets for essential climate variables as exemplified in Clim4Energy. Furthermore, policy makers need to understand the development of the climate to create a legal framework that supports sustainable future resources and infrastructure, and associated necessary investments. The energy strategy of businesses and governments across Europe need to align in order to reach the most efficient energy mix (Monforti, Gaetani and Vignati, 2016).


“The motivation [to use climate information] is preparedness for the changing climate (design of transmission lines and substations, risk assessment, critical infrastructure protection).”

– Pekka Niemi, Manager, Corporate Security, Fingrid Plc



Overall, the understanding and handling of climate model results need to experience a boost by improving the knowledge transfer between scientists and users. A close collaboration between climate services and the energy sector as equal partners can support actions to maintain supply security under a changing climate.

Figure 2: From identifying areas of vulnerability to a sustainable energy supply guarantee through developing effective adaptation measures (Compiled based on literature referenced in this document). Climate induced vulnerability reveals itself in three main aspects: the long-term trend of many climate variables is changing, the variability patterns are altered and the intensity and frequency of extreme events is shifting. Through knowledge exchange between the energy industry and climate research and a supporting framework of regulations and laws together with competent risk management concepts can be developed to advance adaptation with the goal to secure sustainable energy supply.


“Taking climate information into account is essential, in particular about long term climate, because energy sector’s investments are huge, and re-dimensioning an existing asset is in general much more expensive than planning it correctly during its initial construction phase. So this is a technical & financial benefit.”

– Laurent Dubus, Expert Researcher, EDF



1.6 References Armenakis, C. and Nirupama, N. (2014) ‘Urban impacts of ice storms: Toronto December 2013’, Natural Hazards, 74(2), pp. 1291–1298. doi: 10.1007/s11069-014-1211-7.

Aronica, G. T. and Bonaccorso, B. (2013) ‘Climate change effects on hydropower potential in the Alcantara River basin in Sicily (Italy)’, Earth Interactions, 17(19), pp. 1–22. doi: 10.1175/2012EI000508.1.

Broström, E. (2007) Ice Storm Modelling in Transmission System Reliability Calculations. Licentiate Thesis. School of Electrical Engineering, Royal Institute of Technology, Stockholm, Sweden.

Byers, E. and Amezaga, J. M. (2015) ‘Infrastructure Report Card. A Climate Change Report Card for Infrastructure. Working Technical Paper. Nuclear , Coal , Oil and Gas Energy.’, School of Civil Engineering & Geosciences, Newcastle University, Newcastle upon Tyne, UK.

Call, D. A. (2010) ‘Changes in Ice Storm Impacts over Time: 1886–2000’, Weather, Climate, and Society, 2(1), pp. 23–35. doi: 10.1175/2009WCAS1013.1.

Cannaby, H., Palmer, M. D., Howard, T., Bricheno, L., Calvert, D., Krijnen, J., R.Wood, Tinker, J., Bunney, C., Harle, J., Saulter, A., O’Neill, C., Bellingham, C. and Lowe, J. (2015) ‘Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore’, Ocean Science, 12, pp. 2955–3001. doi: 10.5194/osd-12-2955-2015.

Changnon, S. A. (2003) ‘Characteristics of Ice Storms in the United States’, Journal of Applied Meteorology, 42, pp. 630–639. doi: 10.1175/1520-0450(2003)042<0630:COISIT>2.0.CO;2.

Changnon, S. A. and Karl, T. R. (2003) ‘Temporal and Spatial Variations of Freezing Rain in the Contiguous United States: 1948–2000’, Journal of Applied Meteorology, 42, pp. 1302–1315. doi: 10.1175/1520-0450(2003)042<1302:TASVOF>2.0.CO;2.

Cheng, C. S., Auld, H., Li, Q. and Li, G. (2012) ‘Possible impacts of climate change on extreme weather events at local scale in south-central Canada’, Climatic Change, 112(3–4), pp. 963–979. doi: 10.1007/s10584-011-0252-0.

Chinchilla, M., Arnaltes, S. and Burgos, J. C. (2006) ‘Control of Permanent-Magnet Generators Applied to Variable-Speed Wind-Energy Systems Connected to the Grid’, IEEE Transactions on Energy Conversion, 21(1), pp. 130–135. doi: 10.1109/TEC.2005.853735.

Clark, R. T., Bett, P. E., Thornton, H. E. and Scaife, A. A. (2017) ‘Skilful seasonal predictions for the European energy industry’, Environmental Research Letters, 12(2), pp. 1–9. doi: 10.1088/1748-9326/aa57ab.

Clim4Energy (2017) CASE STUDY : A winter power peak during the cold spell in February 2012. Available at: http://clim4energy.climate.copernicus.eu/sites/default/files/WP3_case_study_report.pdf.



Collins, M. (2017) ‘Still weighting to break the model democracy’, Geophysical Research Letters, 44, pp. 3328–3329. doi: 10.1002/2017GL073370.

Cruz, A. M. and Krausmann, E. (2013) ‘Vulnerability of the oil and gas sector to climate change and extreme weather events’, Climatic Change, 121(1), pp. 41–53. doi: 10.1007/s10584-013-0891-4.

Doblas-Reyes, F. J., García-Serrano, J., Lienert, F., Biescas, A. P. and Rodrigues, L. R. L. (2013) ‘Seasonal climate predictability and forecasting: Status and prospects’, Wiley Interdisciplinary Reviews: Climate Change, 4(4), pp. 245–268. doi: 10.1002/wcc.217.

Donnelly, C., Andersson, J. C. M. and Arheimer, B. (2016) ‘Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe’, Hydrological Sciences Journal, 61(2), pp. 255–273. doi: 10.1080/02626667.2015.1027710.

Donnelly, C., Yang, W. and Dahné, J. (2014) ‘River discharge to the Baltic Sea in a future climate’, Climatic Change, 122(1–2), pp. 157–170. doi: 10.1007/s10584-013-0941-y.

Dunstone, N., Smith, D., Scaife, A., Hermanson, L., Eade, R., Robinson, N., Andrews, M. and Knight, J. (2016) ‘Skilful predictions of the winter North Atlantic Oscillation one year ahead’, Nature Geoscience, 9, pp. 809–815. doi: 10.1038/ngeo2824.

De Felice, M. (2015) ‘Seasonal climate forecasts for medium-term electricity demand forecasting Seasonal Climate Forecasts for medium-term Electricity’, Applied Energy, 137, pp. 435–444. doi: 10.1016/j.apenergy.2014.10.030.

Fikke, S. M. (2016) ‘Ice Sets the Rules’, T & D World Magazine.

Gaetani, M., Vignati, E., Monforti, F., Huld, T., Dosio, A. and Raes, F. (2015) ‘Climate modelling and renewable energy resource assessment’, JRC Science and Policy Report.

García-Morales, M. B. and Dubus, L. (2007) ‘Forecasting precipitation for hydroelectric power management: how to exploit GCM’s seasonal ensemble forecasts’, International Journal of Climatology, 27, pp. 1691–1705. doi: 10.1002/joc.1608.

Gaudard, L., Gabbi, J., Bauder, A. and Romerio, F. (2016) ‘Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices’, Water Resources Management, 30(4), pp. 1325–1343. doi: 10.1007/s11269-015-1216-3.

Gaudard, L., Romerio, F., Dalla Valle, F., Gorret, R., Maran, S., Ravazzani, G., Stoffel, M. and Volonterio, M. (2014) ‘Climate change impacts on hydropower in the Swiss and Italian Alps’, Science of the Total Environment, 493, pp. 1211–1221. doi: 10.1016/j.scitotenv.2013.10.012.

Gonçalves-Ageitos, M., Barrera-Escoda, A., Baldasano, J. M. and Cunillera, J. (2015) ‘Modelling wind resources in climate change scenarios in complex terrains’, Renewable Energy, 76, pp. 670–678. doi: 10.1016/j.renene.2014.11.066.



Gregow, H., Peltola, H., Laapas, M., Saku, S. and Venäläinen, A. (2011) ‘Combined Occurrence of Wind , Snow Loading and Soil Frost with Implications for Risks to Forestry in Finland under the Current and Changing Climatic Conditions’, Silva Fennica, 45(1), pp. 35–54. Available at: http://www.metla.fi/silvafennica/full/sf45/sf451035.pdf.

Grudzinska, M. and Jakusik, E. (2015) ‘The efficiency of a typical meteorological year and actual climatic data in the analysis of energy demand in buildings’, Journal of Building Services Engineering Research & Technology, 0(0), pp. 1–12. doi: 10.1177/0143624415573454.

Haguma, D., Leconte, R., Côté, P., Krau, S. and Brissette, F. (2014) ‘Optimal Hydropower Generation Under Climate Change Conditions for a Northern Water Resources System’, Water Resources Management, 28(13), pp. 4631–4644. doi: 10.1007/s11269-014-0763-3.

Heide, D., von Bremen, L., Greiner, M., Hoffmann, C., Speckmann, M. and Bofinger, S. (2010) ‘Seasonal optimal mix of wind and solar power in a future, highly renewable Europe’, Renewable Energy, 35(11), pp. 2483–2489. doi: 10.1016/j.renene.2010.03.012.

Hosek, J., Musilek, P., Lozowski, E. and Pytlak, P. (2011) ‘Forecasting severe ice storms using numerical weather prediction: The March 2010 Newfoundland event’, Natural Hazards and Earth System Science, 11(2), pp. 587–595. doi: 10.5194/nhess-11-587-2011.

Howard, T., Pardaens, A. K., Bamber, J. L., Ridley, J., Spada, G., L. Hurkmans, R. T. W., Lowe, J. A. and Vaughan, D. (2014) ‘Sources of 21st century regional sea-level rise along the coast of northwest Europe’, Ocean Science, 10(3), pp. 473–483. doi: 10.5194/os-10-473-2014.

Institute for Catastrophic Loss Reduction (2013) 15 years later : Ice storm revisited. Available at: http://www.iclr.org/icestorm98engineering.html (Accessed: 5 April 2016).

IPCC (2012) ‘Summary for Policymakers’, in Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M., and Midgley, P. M. (eds) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–19.

IPCC (2013) ‘Summary for Policymakers’, in Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M. (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. doi: 10.1017/CBO9781107415324.004.

IPIECA (2013) Addressing adaptation in the oil and gas industry. Available at: http://www.ipieca.org/news/addressing-adaptation-in-the-oil-and-gas-industry/.

Jones, K. F. (1996) ‘Ice accretion in freezing rain’, CRREL Report 96-2.

Jones, K. F. and Mulherin, N. (1998) ‘An evaluation of the severity of the January 1998 ice



storm in northern New England’, Report for FEMA region 1.

Jones, K. F., Ramsay, A. C. and Lott, J. N. (2004) ‘Icing Severity in the December 2002 Freezing-Rain Storm from ASOS Data’, Monthly Weather Review, 132, pp. 1630–1644. doi: 10.1175/MWR2748.s2.

Kämäräinen, M., Hyvärinen, O., Jylhä, K., Vajda, A., Neiglick, S., Nuottokari, J. and Gregow, H. (2016) ‘A method to estimate freezing rain climatology from ERA-Interim reanalysis over Europe’, Nat. Hazards Earth Syst. Sci. Discuss. doi: 10.5194/nhess-2016-225.

Kellomäki, S., Maajärvi, M., Strandman, H., Kilpeläinen, A. and Peltola, H. (2010) ‘Change Effects on Snow Cover , Soil Moisture and Soil Frost in the Boreal Conditions over Finland’, Silva Fennica, 44(2), pp. 213–233. Available at: http://www.metla.fi/silvafennica/full/sf44/sf442213.pdf.

Klima, K. and Morgan, M. G. (2015) ‘Ice storm frequencies in a warmer climate’, Climatic Change, 133(2), pp. 209–222. doi: 10.1007/s10584-015-1460-9.

Lehtonen, I., Venälaïnen, A., Kämäraïnen, M., Peltola, H. and Gregow, H. (2016) ‘Risk of large-scale fires in boreal forests of Finland under changing climate’, Natural Hazards and Earth System Sciences, 16(1), pp. 239–253. doi: 10.5194/nhess-16-239-2016.

Leutwyler, D., Lüthi, D., Ban, N., Fuhrer, O. and Schär, C. (2017) ‘Evaluation of the Convection-Resolving Climate Modeling Approach on Continental Scales’, Journal of Geophysical Research: Atmospheres, 122, pp. 1–22. doi: 10.1002/2016JD026013.

López-Moreno, J. I., Zabalza, J., Vicente-Serrano, S. M., Revuelto, J., Gilaberte, M., Azorin-Molina, C., Morán-Tejeda, E., García-Ruiz, J. M. and Tague, C. (2014) ‘Impact of climate and land use change on water availability and reservoir management: Scenarios in the Upper Aragón River, Spanish Pyrenees’, Science of The Total Environment, 493, pp. 1222–1231. doi: 10.1016/j.scitotenv.2013.09.031.

Lowe, J. A., Howard, T. P., Pardaens, A., Tinker, J., Holt, J., Wakelin, S., Milne, G., Leake, J., Wolf, J., Horsburgh, K., Reeder, T., Jenkins, G., Ridley, J., Dye, S. and Bradley, S. (2009) UK Climate Projections science report: Marine and coastal projections. Met Office Hadley Centre, Exeter, UK.

Mäkelä, H. M., Venäläinen, A., Jylhä, K., Lehtonen, I. and Gregow, H. (2014) ‘Probabilistic projections of climatological forest fire danger in Finland’, Climate Research, 60, pp. 73–85. doi: 10.3354/cr01223.

Markosek, J. (2015) ‘Severe freezing rain in Slovenia’, The European Forecaster, Newsletter, pp. 38–42. doi: 10.1111/j.1468-1331.2010.03280.x.

McManus, G., Piltz, S. and Sperry, S. (2008) ‘Development and Testing of an Ice Accumulation Algorithm’, Extended abstract for 17th Conference on Applied Climatology.



McVicar, T. R. and Roderick, M. L. (2010) ‘Atmospheric science: Winds of change’, Nature Geoscience, 3(11), pp. 747–748. doi: 10.1038/ngeo1002.

Mideksa, T. K. and Kallbekken, S. (2010) ‘The impact of climate change on the electricity market: A review’, Energy Policy, 38(7), pp. 3579–3585. doi: 10.1016/j.enpol.2010.02.035.

Mima, S. and Criqui, P. (2015) ‘The Costs of Climate Change for the European Energy System , an Assessment with the POLES Model’, Environmental Modeling & Assessment, 20(4), pp. 303–319. doi: 10.1007/s10666-015-9449-3.

Monforti, F., Gaetani, M. and Vignati, E. (2016) ‘How synchronous is wind energy production among European countries?’, Renewable and Sustainable Energy Reviews, 59, pp. 1622–1638. doi: 10.1016/j.rser.2015.12.318.

Mulugetta, Y., Hertwich, E., Riahi, K., Gibon, T. and Neuhoff, K. (2014) ‘Chapter 7: Energy Systems’, in IPCC Fifth Assessment Report, Working Group 3 : Mitigation of Climate Change, pp. 511–598. doi: 10.1017/CBO9781107415416.

Palin, E. J., Scaife, A. A., Wallace, E., Pope, E. C. D., Arribas, A. and Brookshaw, A. (2016) ‘Skillful Seasonal Forecasts of Winter Disruption to the U . K . Transport System’, Journal of Applied Meteorology and Climatology, pp. 325–344. doi: 10.1175/JAMC-D-15-0102.1.

Paskal, C. (2009) ‘Briefing Paper. The Vulnerability of Energy Infrastructure to Environmental Change’, A joint publication of Catham House and Global EESE.

Peltola, H., Kellomäki, S. and Väisänen, H. (1999) ‘Model Computations of the Impact of Climatic Change on the Windthrow Risk of Trees’, Climatic Change, 41, pp. 17–36. doi: 10.1023/A:1005399822319.

Pfeifer, S., Bülow, K., Gobiet, A., Hänsler, A., Mudelsee, M., Otto, J., Rechid, D., Teichmann, C. and Jacob, D. (2015) ‘Robustness of Ensemble Climate Projections Analyzed with Climate Signal Maps: Seasonal and Extreme Precipitation for Germany’, Atmosphere, 6, pp. 677–698. doi: 10.3390/atmos6050677.

Pryor, S. C. and Barthelmie, R. J. (2010) ‘Climate change impacts on wind energy: A review’, Renewable and Sustainable Energy Reviews, 14(1), pp. 430–437. doi: 10.1016/j.rser.2009.07.028.

Reyers, M., Moemken, J. and Pinto, J. G. (2016) ‘Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble’, International Journal of Climatology, 36(2), pp. 783–796. doi: 10.1002/joc.4382.

Richert, C. N. and Matzarakis, A. (2014) ‘The climatic wind energy potential – present and future : GIS-analysis in the region of Freiburg im Breisgau based on observed data and Regional Climate Models’, Central European Journal of Geosciences, 6(2), pp. 243–255. doi: 10.2478/s13533-012-0174-z.



Sanders, K., Gravelle, C., Gagan, J. and Graves, C. (2013) ‘Characteristics of major ice storms in the central United States’, Journal of Operational Meteorology, 1(10), pp. 100–113. doi: 10.15191/nwajom.2013.0110.

Schaeffer, R., Szklo, A. S., Pereira de Lucena, A. F., Moreira Cesar Borba, B. S., Pupo Nogueira, L. P., Fleming, F. P., Troccoli, A., Harrison, M. and Boulahya, M. S. (2012) ‘Energy sector vulnerability to climate change: A review’, Energy, 38(1), pp. 1–12. doi: 10.1016/j.energy.2011.11.056.

Schaefli, B. (2015) ‘Projecting hydropower production under future climates: a guide for decision-makers and modelers to interpret and design climate change impact assessments’, Wiley Interdisciplinary Reviews: Water, 2, pp. 271–289. doi: 10.1002/wat2.1083.

Side, J., Harris, R., Woolf, D., Bell, M. and Brooks, A. (2013) ‘Impacts of climate change on built structures (offshore)’, MCCIP Science Review, (2), pp. 295–301. doi: 10.15414/raae.2013.16.02.24-39.

Soret, A., Fernandez, V. T., Davis, M., Doblas-Reyes, F. and Nube Gonzalez-Reviriego (2015) ‘Climate predictions for site selection : a new generation of risk management tools’, Presentation at Resource Assessment 2015, 2-3 June 2015. Helsinki.

Taseska, V., Markovska, N. and Callaway, J. M. (2012) ‘Evaluation of climate change impacts on energy demand’, Energy, 48(1), pp. 88–95. doi: 10.1016/j.energy.2012.06.053.

Thornton, H. E., Hoskins, B. J. and Scaife, A. A. (2016) ‘The role of temperature in the variability and extremes of electricity and gas demand in Great Britain’, Environmental Research Letters, 11, pp. 1–13. doi: 10.1088/1748-9326/11/11/114015.

Tobin, I., Jerez, S., Vautard, R., Thais, F., van Meijgaard, E., Prein, A., Déqué, M., Kotlarski, S., Maule, C. F., Nikulin, G., Noël, T. and Teichmann, C. (2016) ‘Climate change impacts on the power generation potential of a European mid-century wind farms scenario’, Environmental Research Letters, 11. doi: 10.1088/1748-9326/11/3/034013.

Tobin, I., Vautard, R., Balog, I., Bréon, F.-M., Jerez, S., Ruti, P. M., Thais, F., Vrac, M. and Yiou, P. (2015) ‘Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections’, Climatic Change, 128(1–2), pp. 99–112. doi: 10.1007/s10584-014-1291-0.

Torralba, V., Doblas-Reyes, F. J., MacLeod, D., Christel, I., Davis, M., Torralba, V., Doblas-Reyes, F. J., MacLeod, D., Christel, I. and Davis, M. (2017) ‘Seasonal climate prediction: a new source of information for the management of wind energy resources’, Journal of Applied Meteorology and Climatology, in press. doi: 10.1175/JAMC-D-16-0204.1.

Vautard, R., Cattiaux, J., Yiou, P., Thépaut, J.-N. and Ciais, P. (2010) ‘Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness’, Nature Geoscience, 3(11), pp. 756–761. doi: 10.1038/ngeo979.



van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J. F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J. and Rose, S. K. (2011) ‘The representative concentration pathways: An overview’, Climatic Change, 109(1), pp. 5–31. doi: 10.1007/s10584-011-0148-z.

WMO (2017) WMO FAQs. Available at: http://www.wmo.int/pages/prog/wcp/ccl/faqs.php (Accessed: 10 May 2017).

Zhou, B., Gu, L., Ding, Y., Shao, L., Wu, Z., Yang, X., Li, C., Li, Z., Wang, X., Cao, Y., Zeng, B., Yu, M., Wang, M., Wang, S., Sun, H., Duan, A., An, Y., Wang, X. and Kong, W. (2011) ‘The Great 2008 Chinese Ice Storm: Its socioeconomic-ecological impact and sustainability lessons learned’, Bulletin of the American Meteorological Society, 92(1), pp. 47–60. doi: 10.1175/2010BAMS2857.1.



2. Progress report on Fact sheets and User guidance: from concept to implementation – first steps

2.1 Conceptual elements In the first few months of the project, a concept for a “GERICS Climate Impact Fact Sheet to be used in the frame of the Clim4Energy project” (SD 6.5.1) was adapted to the Clim4Energy project in collaboration with the work package leaders and the co-designers. It contains a description of the purpose of the Fact Sheets, examples of other fact-based information material at GERICS and the structure and an implementation plan (see Table 1). Table 1: Table of contents of SD 6.5.1, explaining the concept and the planned structure of the GERICS Climate Impact Fact Sheet to be used in the frame of the Clim4Energy project. The structure of the Fact Sheet is highlighted in green.

Outline 1 General introduction of the GERICS Fact-Sheet concept 2 Examples of already developed fact-based information material in the context of climate change 2.1 The GERICS Climate Fact Sheets and the GERICS Site Specific Climate Fact Sheets 2.2 GERICS Sectoral-Country-Fact-Sheets 2.3 IMPACT2C – WebAtlas information page 3 GERICS Climate-Impact-Fact-Sheet used in Clim4Energy 3.1 Structure 3.1.1 Introduction

3.1.2 Key messages 3.1.3 Practical implication 3.1.4 Methods 3.1.5 Specific case study 3.1.6 European context 3.1.7 Further reading 3.2 List of fact sheet topics 3.3 Harmonization 4 Implementation plan 4.1 Visualization 4.2 Layout

In June 2016, a workshop was held at GERICS to develop common standards and the visualization of the climate indicators that are being developed in Clim4Energy. This is a crucial topic for the Fact Sheets, since each topic will be presented in one Fact Sheet with the same overall structure and underlying data. In doing so, comparability across the different Fact Sheets is given and the recurring structure will simplify understanding. Co-designers have mentioned that interdependencies between various energy sources are important to them. Since climate variability and change can have different effects on each individual energy source, it is of great interest to the energy sector to be able to compare different effects and reactions. This can also serve as foundation for the optimization of the energy mix applied by a utility company. One major outcome of the workshop was a harmonization matrix, summarizing the agreed common standards (see Table 2). This serves as basis for any input concerning the climate indicators developed in clim4energy for the Fact Sheets.



Table 2: Harmonization Table, also available in the wiki with the possibility to adjust in case it is necessary (https://wiki.gerics.de/clim4energy/HarmonizingDataAndMethods).

What / WP WP1 WP2 WP3 WP4 WP5 Possible?

Reference time period

1981-2010 1981-2010 1981-2010

1981-2010 1971-2000 (for models, for freezing rain)

1981-2010 (t.b.c.)

YES (but for WP4 for freezing rain)

Scenario for last years of reference period

RCP8.5 RCP8.5 RCP8.5 RCP8.5 RCP8.5 YES

Current climate period

2001-2030 2001-2030 2001-2030

2001-2030 (except for I4 Freezing rain)

2001-2030

t.b.c. (testing some variables for significant differences) (Robert)

Future time period

2016-2045 2036-2065

2016-2045 2036-2065

2016-2045 2036-2065

2016-2045 2036-2065 (for I4 Freezing rain 2021-2050)

2016-2045 2036-2065

YES (but for WP4 for freezing rain)

Scenarios RCP4.5, RCP8.5

RCP4.5, RCP8.5

RCP4.5, RCP8.5

RCP4.5, RCP8.5

RCP4.5, RCP8.5

YES (see proposal)

Seasons of interest

Winter season

t.b.d.

Seasonal forecast

t.b.d.

https://wiki.gerics.de/clim4energy/HarmonizingDataAndMethods



Grids (rotated, lat-lon, global regular, ...)

EURO-CORDEX (for climate projections)



EURO-CORDEX (for climate projections) (for I4 Freezing rain EUR44)


YES (but different resolution for I4 Freezing rain)

Spatial aggregation (e.g., country, catchment, square, ..)

t.b.d.

Uncertainty and Variability of the results (e.g., percentiles, median, mean)1

20-, 80- centile, median, based on 10 simulations (or more, in second version)



20-, 80- centile, median, based on 5-6 simulations (or more, in second version)


t.b.c. (but for WP4, which is based on 5-6 simulations)

Uncertainty (seasonal forecast)

Suggestion: below normal, above normal and unknown

Data basis

higher resolution data is needed for freezing rain Sea level rise (CMIP5 data)

1 Simulations will be provided by WP6.



Bias adjustment

t.b.d.

Indicators turbine technique for CF

Methodology, weather regimes

User: Methodology from WP3

Providor: Methodology from WP3

t.b.d.

Methodology, CF

Provider user t.b.d.

2.2 Experimental Implementation The concept paper describes each planned section of the Fact Sheet in detail, specifying the content, the purpose, and the format. Additionally, the person responsible for delivering the content is set there. In December 2016, WP1 (wind power generation) was able to provide us with first results for their indicator. For each section of the Fact Sheet, they delivered paragraphs of text and graphs. After some discussion and a few changes, a preliminary version of a Fact Sheet was created (see Figure 3). Figure 3 also depicts modifications made in collaboration with BSC to the original version, specific challenges that came about when working on each section and, if applicable, next steps and ideas for improving and finalizing the product. Note that the layout of this preliminary version is not final.



Figure 3: Screenshot of the preliminary version of a Fact Sheet for the topic "Wind Power Generation", developed in cooperation with BSC (4 pages). The comments in the boxes explain modifications made to the original text, specific challenges per section and point out ideas and possible next steps towards the final product.

Introduction modifications: shortened, really focusing on answering the following questions:

(1) What is the main purpose of the Fact Sheet? (2) Who is the main target group (3) Which indicator/method is applied

challenges: defining the assumed pre-existing knowledge of the target users, identifying redundant information

possible next steps: collaborate with co-designers to understand the level of pre-existing knowledge that can be expected of the reader

Key Messages modifications: substantiated value of presented forecast through adding an example

challenges: identifying redundant information, be as concrete as possible

possible next steps: ideal would be an infographic to summarize the key messages of the Fact Sheet

Practical Implications modifications: shortened, relocated parts of the information to a section better suitable for the content (methods), rephrased content in a more positive light emphasizing benefits while still staying aware of the limitations

challenges: collecting realistic and beneficial practical implications, be as concrete as possible



Method modifications: improved structure of the explanation of the derivation of the indicator (cooking recipe style), introduced graphs from case studies in order to simplify interpretation later on, focused on only two graphs, that are recurring throughout the Fact Sheet

challenges: staying brief but complete when explaining both the derivation of the indicator and the quality assessment, avoiding technical terms or explaining them well in non-scientific jargon (analogies can help)

possible next steps: the need for a “how to” guidance accompanying the Fact Sheet series has to be assessed

Case Studies modifications: removed and explained abbreviations

challenges: keeping the emphasis on the benefit of applying the presented climate information in this specific case study, showing a limited amount of graphs that can be intuitively understood (and that have been explained in the Methods section), keeping the graphs lean and simple



The concept and this preliminary draft were presented during the Copernicus Symposium on Climate Services for the Energy Sector on Feb 22/23, 2017 in Barcelona. The aim of this meeting was on the one hand to demonstrate the current status of the climate indicators as well as to present the outreach and communication tools and products and on the other hand to collect feedback from the participants. The Fact Sheets were shown as a means to support and optimize communication. Since businesses active in the energy sector, administrative organizations and research institutions were equally represented in the group of participants, it was possible to gather well-rounded and diverse feedback: It was mentioned that the first impression of any climate service product is very important for a user to decide if further time and effort will be spent applying the product. Additionally, ways must be found how to sustain and keep up the integration of climate information in decision-making processes, beyond a pilot study or a research project. Another aspect discussed the types of users of C3S. The users will be very diverse, hence the Fact Sheets must fit to a broad audience with various levels of background knowledge. Large energy corporations with their own R&D departments should gain information from them just as well as small organizations, administration offices and policy makers. Credibility, quality and relevancy of the presented information have to be clearly communicated and readily available. Also, easy access to the climate service product is crucial for frequent application. The Fact Sheets, for instance, could be placed prominently within the energy section of C3S as

European Context modifications: added preliminary content

possible next steps: base information on Focus Paper, could potentially be presented as a conceptual European map used for all Fact Sheets of this series

Further Reading modifications: standardized format of references



an introductory, brief overview of the topic. This could underline the relevancy of climate information for the energy sector and guide the user towards the provided indicators. Finally, most potential users liked the idea of providing a Fact Sheet along with the climate indicator data and are eager to see the final version.

2.3 Challenges Some climate indicators need input that is only available with certain specifications. At times, the requirement to base the information shown on the Fact Sheets on common standards is therefore tough to achieve. There will be a few exceptions regarding the underlying data (as shown in Table 2). We are aiming for one common format and layout as well, but since the six topics (wind energy, hydropower, bioenergy, oil & gas, generation – demand balance, freezing rain as threat to the energy sector’s infrastructure) are quite diverse, we might have to deviate for one or the other Fact Sheet to accommodate the information in the most useful way. WP 6 includes a task to develop a visualization tool that enables interactive and harmonized imagery. This will simplify a coherent layout for specific figures. The central challenge during the adaptation of the Fact Sheets to the Clim4Energy requirements, however, is the translation of the scientific content to non-scientific jargon and the restriction to only the relevant bits and pieces of information. This is the overall goal and purpose of the Fact Sheet, but at the same time difficult to achieve. We will draw from our in-house experience here at GERICS, as well as the feedback from the project partners, in particular our co-designers within clim4energy, to improve and sharpen the content.

2.4 Outlook Since the Focus Paper has priority until summer 2017, we will concentrate on collecting inputs from all project partners and on revising the preliminary version of the Wind Power Generation Fact Sheet in the coming months. In the second half of 2017 we will dedicate our project-related working time to completing the Fact Sheets. Please note, that we can only finalize the Fact Sheets with the finished visualization tool, i.e. in early 2018.




climate.copernicus.eu copernicus.eu ecmwf.int

ECMWF - Shinfield Park, Reading RG2 9AX, UK

Contact: [email protected]

Documents

CLIM4ENERGY Deliverable D6.5.0 Focus paper on climate ...clim4energy.climate.copernicus.eu/sites/default/files/C3S441_Lot2... · ECMWF COPERNICUS REPORT ... C3S_441_Lot2_CEA_201706_D6.5.0_Focus