Climate Services Volume 16
Response of southern African vegetation to climate change at 1.5 and 2.0° global warming above the pre-industrial level
Shakirudeen Lawal, Christopher Lennard, Bruce Hewitson
Climate change is expected to severely impact global vegetation. However, there is a poor understanding on the severity of its impact on southern African vegetation biomes under specific warming levels. The dearth of knowledge thus makes it challenging for policymakers to make informed decisions on mitigation and adaptation responses concerning biome management and associated ecosystem services. This study investigates this issue by estimating the impacts of climate change at 1.5 and 2 °C of global warming on the NDVI and several vegetation parameters. In addition, the paper evaluates how future climate change-exacerbated droughts could affect vegetation greenness.
The research used observed and simulated climate and vegetation datasets to assess future impacts of climate change on vegetation. We evaluate simulated NDVI from the CESM global model using the observational GIMMS NDVI dataset to determine how well the model represents regional NDVI. We also estimated the relationship between the NDVI and vegetation parameters over the region. Furthermore, a sensitivity study was performed to demonstrate the influence of fire on vegetation across the different southern African biomes.
Climate change at 1.5 and 2 °C warming could make it more urgent to successfully implement conservation, afforestation and reforestation programs in southern Africa. This is because of expanding aridification due to projected decline in semi-desert, dry savanna and Mediterranean biomes in the region. This will result in decline in the regional regulation of climate by vegetation, particularly as the projected increases in drought frequency and intensity over the region will impact vegetation and result in a generally warmer regional climate. Consequently there is an increasing risk of heat waves, which can damage soil properties, affect nutrient uptake by vegetation and cause to harm to vegetation health. This degradation will result in loss of revenue, food source and important biodiversity. The degradation also results in a reduction in the quality of soil water because vegetation act as reservoir for storage and purification of water. This reduced water availability is likely to impact food availability for animals and local populations.
Furthermore, there is likely to be an expansion of invasive species leading to biogeographical modifications and biodiversity loss; changes in wildfire seasonality, frequency and intensity which can affect the germination of seeds, growth of young plant; and ecosystem services such as water treatment and purification may be affected in the regions. Across different biomes, climate change could affect drought, potential evapotranspiration and NDVI in varying magnitudes and the response of vegetation to these impacts will vary thus requiring tailored management strategies for each biome.
In terms of practical implication, we suggest an urgent development and implementation of long-term mitigation measures as well as multi-sectoral engagement in order to reduce the impacts of warming on these biomes. We also recommend that there should be detailed analysis of regional ecosystem services and the extent to which they will be impacted by climate change.
A process-based statistical seasonal prediction of May–July rainfall anomalies over Texas and the Southern Great Plains of the United States
D. Nelun Fernando, Sudip Chakraborty, Rong Fu, Robert E. Mace
We have developed a hybrid dynamical-statistical rainfall forecast tool to enhance the reliability of the summer drought early warning over Texas and Southern Great Plains region of the United States. May and June are the wettest months across much of Texas and southern Great Plains region. July is the start of the rainfall season for the western part of the state. Failure of the May–July rains is an indicator that Texas is in the throes of a summer drought. Such a drought could worsen in August, generally the driest and hottest month of the year over much of the state. Improving seasonal forecasts of May–July rainfall over Texas thus serves as an early warning of the likelihood of summer drought over the state. Unfortunately, dynamical climate models have virtually no skill in predicting rainfall in this season over Texas and Southern Great Plains (Hao et al., 2018, Infanti and Kirtman, 2014, Livneh and Hoerling, 2016, Mo and Lyon, 2015, Slater et al., 2016). The dynamical-statistical prediction model reported in this study was developed at the request of the Texas Water Development Board. Its predictions have shown to be of direct utility for summer drought early warning over Texas.
The hybrid dynamical-statistical rainfall forecast tool was designed, developed, and tested in collaboration with the Texas Water Development Board, which is the state agency responsible for collecting and disseminating water data, compiling the state water plan based on sixteen regional water plans, and providing low-cost financing for water, wastewater, and flood mitigation projects. The model is based on our previous research, also undertaken in consultation with the Texas Water Development Board, on the predictability of drought over Texas.
The impetus for developing such a tool came on the heels of the 2011 drought over the state, which was the worst one-year drought on record. In response to the 2011 drought over Texas, the Texas Administrative Code § 358.3 (1) on Guidance Principles for the State Water Plan Development (http://txrules.elaws.us/rule/title31_chapter358_sec.358.3) was modified to state that: “The state water plan shall provide for the preparation for and response to drought conditions”. These rule changes require all regional water plans to have a chapter dedicated to drought response information, activities, and recommendations. With these rule modifications, it became a requirement, in 2012, that all regional water planning groups to include a chapter on drought management with the aim of implementing short-term water demand reductions in the face of impending or existing drought conditions. Each water user group in a water planning region is required to develop drought contingency plans and drought action triggers for their respective water supply sources. Water user groups need to consult existing information on impending or current drought conditions before deciding on whether to implement drought contingency triggers, which set in place voluntary or mandatory water use restrictions. Tools such as the May–July seasonal rainfall forecast provide water user groups with information on impending drought conditions. Such information has been used to aid their decisions on whether to plan for short-term water supply reductions.
Given the improvement in prediction skills demonstrated by the hybrid dynamical-statistical forecast, the Texas Water Development Board has been issuing county-level probabilistic rainfall forecasts for the May–July season, based on the hybrid forecast system, since 2016 via https://waterdatafortexas.org/drought/rainfall-forecasts. Archives of probabilistic forecasts and hindcasts of May–July rainfall, obtained using the statistical forecast model, are also available at this website.
In summary, we identified through user consultation, the key season in which having a skillful rainfall forecast would improve decision-making in the water management sector. We worked with an interdisciplinary team that included climate scientists, hydrologists, water managers, river authorities and developers of the scientific applications to provide the rainfall forecast via an interface that decision makers in the water sector across Texas consult for drought information. Through this interface, we have presented the forecast as county-level and Hydrological Unit Code (HUC) level 8 categorical probabilistic rainfall forecasts, and as quantitative forecasts with associated probabilities of exceedance curves so that users, such as reservoir operators, could select the type of forecast information of most relevance to their decision need. We have also provided detailed guidance on what types of information the forecast conveys and a link through which users could submit questions (https://waterdatafortexas.org/drought/rainfall-forecast-info) on the tool. The web interface is constantly being improved with feedback from users such as river authorities in the state. For example, we included the provision of the rainfall forecast by the U.S. Geological Survey’s Hydrological Unit Code (HUC) 8 level watershed regions within Texas based on feedback from the Brazos River Authority of Texas. The steps we have taken in designing, testing, sharing, communicating, and improving the rainfall forecast tool conform to the key steps needed for the development of a climate service prototype that is tailored to fit user requirements (Christel et al. 2018).
Recommendation domains to scale out climate change adaptation in cocoa production in Ghana
Christian Bunn, Peter Läderach, Amos Quaye, Sander Muilerman, Martin N.A. Noponen, Mark Lundy
Cocoa provides a livelihood to millions of people in West Africa. It is oftentimes the main cash source for smallholder families that struggle daily to make a better life for their families and themselves. Adapting these farmers to climate change is at the same time trivial and highly complex: trivial, because most good agricultural practices, correctly applied, can provide resilience against shocks and sustainability for long term changes. However, developing individualized solutions for all cocoa households currently appears unfeasible and smallholders commonly don’t have the means to develop forward looking solutions themselves. As a result, the risk of maladaptation or inefficient use of resources is high. The complexity is thus in the need to translate the crop- and site specificity of climatic changes into actionable and understandable decision support. Scalable climate smart technologies exist, but robust processes for large scale interventions to support farmers need to be developed.
In this study we developed recommendation domains to adapt cocoa production in Ghana to climate change. The recommendation domains are based on a degree of impact concept that supports stakeholders to design and roll out locally adequate adaptation measures. The domains are easier to interpret for stakeholders than previous impact assessments, but are equally methodologically robust. To develop the domains we combined a data driven method with a participatory validation process. The resulting maps were in line with state of the art climate impact assessments but additionally relate to local perceptions of cocoa ecological zones and production systems. As a result, the domains are framed within local knowledge and are more acceptable and accessible than conventional work.
We differentiated zones by the degree of necessary adaptation effort. At low change adaptation can be incremental, using relatively minor interventions such as integrated pest management, adjustment of shade levels and more generally, good agricultural practices. A higher degree of change requires a systemic adaptation using more shade trees, potentially drip irrigation, or a change of cocoa variety. In some zones, the climate of the future was found to be unsuitable at current cocoa locations and a transformation to other crops may be considered or production systems with a high degree of functional shade and income diversification. Additionally, we point out zones where global climate models don’t exhibit the necessary degree of agreement to support specific technological packages and emphasis should be put on increasing the resilience of producers.
Our recommendation domains can be used by public, private and non-governmental actors to roll out climate adaptation interventions at scale. Training activities can be differentiated by the likely degree of impact to provide assistance that responds to local needs. For example, farmers can be made aware of diversification options where needed, or encouraged to intensify production where adequate. Other stakeholders may use our work to focus deforestation prevention where most needed. Certification agencies can adjust compliance requirements across geographies in a feasible manner. Therefore our approach and concept should be considered for other regions and crops as a valuable step towards mainstreaming climate smart agriculture.
The issue of properly ordering climate indices calculation and bias correction before identifying spatial analogs for agricultural applications
Patrick Grenier, Annabelle Firlej, Anne Blondlot, Travis Logan, Marie-Pier Ricard
Many users of climate change information appreciate spatial analogs because they provide a direct representation of a plausible future climate and of its consequences for a given target location. For example, a policymaker told that increases of ~4 °C in temperature and of ~12% in precipitation during this century would lead summers of Montreal, Quebec, to look like today’s summers of Chester, Pennsylvania, could have one more way of testing impact-related hypotheses for such a climate scenario. Evidently, such analogical reasoning has limitations and should complement rather than replace other types of reasoning, such as process-based modelling chains, which also have their own limitations. Spatial analogs are relatively popular in the agriculture sector; this study is part of a project aimed at improving pest management and fruit production in southern Quebec, Canada.
Practical implications of this study concern primarily climate services centres providing users with spatial analogs and having to cope with technical aspects users are not necessarily aware of. More specifically, the study addresses the order of operations of two steps (indices calculation and bias correction) often performed before a plausible future climate for a target location is compared with those of candidate analogs. Of course, this ordering issue exists independently of the subsequent identification of spatial analogs, but investigating it in this particular context allows for an interesting perspective, including the self-analog test presented below.
This paper tests the three most obvious approaches regarding order of operations: performing bias correction (BC) on annual indices after these have been calculated from daily time series (the “indices – BCi” or “BC-after” approach); performing BC on daily time series before calculating the annual indices (the “BCd – indices” or “BC-before” approach); and performing BC both before and after indices calculation (the “BCd – indices – BCi” or “double-BC” approach). With BC-after, there is a risk of calculating meaningless annual indices if, for example, the index formulation contains a temperature threshold to be reached and the simulation has a severe cold bias. With BC-before, there is a risk that bias correction of the daily time series does not imply the complete bias correction of the derived annual indices. Results illustrate how these risks materialize in some cases, and show that double-BC is safer.
The recommendation to perform double bias correction is based on the use of one specific variant of quantile mapping, but it might also apply to other bias correction techniques. Moreover, results support two relatively general recommendations. First, any approach adopted to generate annual indices used for subsequent analog search should pass the self-analog test. This test consists in verifying whether searching for same-period spatial analogs (i.e., using identical time periods for both the target and the candidate analogs) identifies the target itself as its own best analog. In this study, this test highlights a conceptual deficiency of the BC-before approach. The second general recommendation is to give weight to a priori considerations (not just to a posteriori results) when bias correcting, as two approaches, one with conceptual deficiencies and a second appropriate approach, may lead to similar multi-simulation spatial analog patterns partly by compensation of pattern differences from one simulation to another.
Finally, although this study is rather technical, links between the concepts of spatial analogs, bias correction and climate change uncertainty generate reflections that can improve users’ interpretation of information such as spatial analog maps.
A systematic approach to assess climate information products applied to agriculture and food security in Guatemala and Colombia
Claudia Bouroncle, Anna Müller, Diana Giraldo, David Rios,Pablo Imbacha, Estuardo Girón, Fernando Portillo, Alejandra Boni, Jacob van Etten, Julian Ramirez-Villegas
Climate variability associated with climate change affects agricultural production and rural livelihoods throughout the world. Climate services (CS) address this challenge by making new information products available to policymakers, agricultural technicians, and farmers in order to enhance their adaptive capacity. Most of the literature on assessment of CS focuses on (a) their climate forecasting or crop modeling capability or (b) their socio-economic value. This study focuses on the use and usability of information products and the relationships among them. To explore these aspects, we developed an approach for systematic assessment of information products and applied it in two different contexts (droughts and food security in the Dry Corridor of Guatemala, and climate and agriculture in Colombia), thereby demonstrating the broad applicability of the method. The approach is suitable for operationalization by governmental, international cooperation, and private organizations. Regular application to monitor and assess sets of information products developed for specific thematic and geographic areas could contribute to their enhancement, in terms of information content and usability.
The approach comprises four steps. Step 1 determines the geographic and thematic scope of the analysis and identifies the information products to be included in the study. Step 2 explores the relationships between these products, in terms of information flows among networks of products, using standard statistical analysis. Step 3 assesses the use and usability of individual products from the perspective of users and gathers qualitative information on users’ perceptions of the characteristics of an ‘ideal’ information product. Step 4 provides feedback to the technicians and scientists involved in creating the products, summarizing the conclusions of the study in the form of recommendations for potential improvements to products and the network of products.
In practice, application of the approach would help guide future investments in CS and provide valuable information to producers of CS to orient their future development, for example, by filling information gaps and enhancing their usability. The practical implications derived from the application of the approach in the two case study context can be summarized as follows:
Knowing the position of the products in a CS network is crucial to identify measures that could be taken to enhance their effectiveness.
- In Guatemala and Colombia, Regional Climate Outlook Forums occupy a central position in CS networks and play a key role as primary sources of seasonal climate outlooks for use by the agriculture and food security sectors.
- In Guatemala, the Regional Outlook on Food and Nutritional Security occupies a less central position in the network that might be expected. This may make it more difficult for agricultural and food security stakeholders to access the climate forecasts that they need to inform decision making. This limitation could be addressed by building the capacity of stakeholders to use climate forecasts for food security management, and by strengthening linkages among stakeholders in the climate, agriculture, and food security sectors.
Knowledge of which products are currently used by the different groups of decision-makers can pinpoint gaps in the information available.
- In Guatemala, national-level decision-makers tend to use products that integrate national-scale climate, agriculture and food security forecasts. However, these products lack spatial and temporal detail required for application at local scales. Local decision-makers use shorter-term national-scale climate products. These results indicate a requirement for co-produced integrative local-scale products, and these are currently being developed.
Decision-makers’ perceptions of products currently available provide information on their evolving needs and inputs for the design of technical and procedural improvements. These could include:
- Optimizing timing, frequency, and delivery mechanism/channels.
- Enabling two-way (user-provider) communication and providing downloadable source information (identified as a priority in both countries).
- Using plain language, logical structure, and visual aids (priority in Guatemala).
- Integrating of climatic, agronomic, and food security information (priority in Guatemala).
- Using and comparing of trusted sources, and providing information on methods, results, and uncertainty levels to improve credibility (priority in Colombia).
- Incorporating local feedback to improve legitimacy (both countries).
- Providing information at an appropriate scale (both countries).
- Providing recommendations linked explicitly to the timeframes of decision-making processes, for example relating to sowing or harvesting (both countries).
Providing feedback on the assessment process to stakeholders is essential to validate its results.
- In both Guatemala and Colombia, CS providers stated that the synthesis of user perspectives helped them to understand how their products are being used. Information on user perspectives helped justify existing plans for improvements to their products as well as identifying further improvements that could be made.
CS is a dynamic field and new actors and products are continually emerging, reflecting the increasing importance of climate information for agriculture and food security under climate change. Thus, ideally, the systematic assessment of the usability of climate information products should be integrated into an M&E scheme that is implemented regularly to capture changes and developments.
A climate projection dataset tailored for the European energy sector
Blanka Bartók, Isabelle Tobin, Robert Vautard, Mathieu Vrac, Xia Jin, Guillaume Levavasseur, Sébastien Denvil, Laurent Dubus, Sylvie Parey, Paul-Antoine Michelangeli, Alberto Troccoli, Yves-Marie Saint-Drenan
The energy sector is sensitive to weather and climate in various ways (e.g. heating and cooling demand, extreme weather event-related damages on energy infrastructures, cooling water needs for thermo-electric power generation, renewable energy generation, etc.). This represents a challenge for energy generation-supply balance at all time scales. Climate information is then necessary for the energy sector to adapt efficiently to variability and changes in climate.
However, the use of climate projections in the energy sector has remained limited for several reasons: the wide variety of available climate datasets characterized with heterogeneity in terms of model ensembles and emission scenarios; unsuitability of temporal and spatial resolution of climate models for impact modelling; model biases; lack of guidance for users; no user-friendly platforms to data access; special data formats (e.g. NetCDF files) requiring certain software to be handled; among others.
In order to bridge this gap four energy-relevant variables (2 m temperature, 10 m wind speed, precipitation and surface solar radiation) from 11 EURO-CORDEX regional climate models (RCP4.6 and RCP 8.5) have been bias-adjusted at high spatial and temporal resolution to provide energy practitioners and decision-makers with a facilitated access and use of energy-relevant high-quality climate information for operations and planning. The new dataset is freely available via the Earth System Grid Federation (ESGF) nodes (https://esgf.llnl.gov/nodes.html).
Such high-resolution multi-model climate dataset represents a large amount of data, which can pose an obstacle for the climate information uptake by some users (data storage issues, computing time-consuming impact models, etc.). A sub-sampling methodology has been developed, which aims at favouring skilled models while preserving as much as possible the original spread in climate sensitivity and climate future scenarios with regard to variables of interest. This latter aspect is important for the energy sector in order to anticipate a wide range of plausible futures.
This dataset has been already used to derive energy-oriented indicators: wind power potential, solar power potential, inflow changes (the flow of water into reservoirs) for hydropower, power demand, power generation-supply balance indicator, frozen soil indicator.
Wind power- and photovoltaic capacity factor, which helps in the planning process of the location of a new wind/solar park project, could be an input for long-term trend analyses and efficiency calculations to evaluate the profitability of a specific wind park.
Inflow anomaly indicator enables effective preparation for the change in future fluctuations of total and seasonal inflow. Changes in inflow affect electricity prices and the optimisation of the operation of the hydropower plants. E.g. higher inflow in northern Europe most likely increases the potential for hydropower production in a region where hydropower production is already high. However, climate predictions also indicate higher winter temperatures and thus less need for energy for heating. In combination, these two changes should allow to use more hydropower generation, and limit water spillage in case the reservoirs capacity is not large enough to store the water in excess.
The impact of freezing rain on energy infrastructure can also be investigated which gives better emergency planning in regions identified as more exposed to the risk of the occurrence of freezing rain events. A detailed assessment with statistics on the duration of events, the prevailing wind conditions and trend analyses can support decision-making processes regarding potential adaptation measures.
On the climatological time scale, the indicator for bioenergy production conditions is the length of the season suited for forest harvesting operations. Forest harvester manufacturers can design and develop a new generation of harvesters for future conditions. Similarly, the information is useful for forestry factory investment decisions for future raw material costs, which are affected by costs of harvesting and logistics.
The energy demand indicator (estimated with heating degree-days weighted by population) aims to help the energy sector to anticipate the production needs and therefore the risk of imbalance between a strong demand and a poor renewable energy potential. For some countries, e.g. France, the electricity consumption is highly correlated with this indicator, therefore in this case a linear model can explain a lot regarding the electricity consumption variation.
However, there are some limitations to the use of the dataset: issues related to adequacy between model outputs and energy needs (e.g. wind speed at 10 m vs 100 m), availability of high-frequency outputs, adequacy between model spatial resolution and energy needs, bias-adjustment limitations (remaining biases over some areas, unavailability of observation at high resolution, dependency of projected trends on the adjustment method), manageable data volume, limited ensemble size and overweighted models.
Advancing climate services for the European renewable energy sector through capacity building and user engagement
C.M. Goodess, A. Troccoli, C. Acton, J.A. Añel, P.E. Bett, D.J. Brayshaw, M. De Felice, S.R. Dorling, L. Dubus, L. Penny, B. Percy, T. Ranchin, C. Thomas, M. Trolliet, L. Wald
The challenges associated with building a strong market for climate services, even for sectors such as energy with evident and increasing sensitivity and vulnerability to weather and climate variability and change, include generally low awareness and capacity and thus low uptake by potential users, as well as lack of appropriately tailored information. These challenges are being addressed by activities such as the European Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/) with its Sectoral Information System (SIS) contracts. The focus of this paper is European Climatic Energy Mixes (ECEM, https://climate.copernicus.eu/european-climate-energy-mixes), a C3S SIS which has successfully developed a proof-of-concept climate service for the energy sector.
User needs and requirements are highly dependent on the decision-making context and are therefore diverse, even across a ‘single’ sector. Thus, rather than attempting to deliver ‘everything’ for ‘everyone’, ECEM deliberately targeted the particularly climate/weather sensitive issues of electricity demand and renewable energy (wind, solar and hydropower) generation, supporting exploration of the demand-supply balance and its sensitivity to climate drivers. Having clearly identified the main subject focus, six categories of users and potential users were specified: (i) transmission and distribution system operators, (ii) energy generation companies, (iii) national and regional departments/agencies, (iv) international associations and coordination activities, (v) policy and operational research organisations, and (vi) commercial climate service providers for the energy sector. The latter category turned out to be a more important group than anticipated at the start of ECEM, illustrating that not all users are just ‘end users’ and that commercial activities are an essential component of the market adding value to the climate services chain. Having a clearly defined target audience made it easier to manage expectations and to delimit the scope of the service. This was particularly important within the constraints of the proof-of-concept contract, which aimed to develop a pre-operational, rather than a fully operational, service.
The final evaluation and feedback on the ECEM products and outcomes was generally positive, indicating that ECEM has helped to close the usability gap and delivered data and information that is credible, legitimate and salient. The ECEM team attributes this success to having had a good idea of the main target and structuring all user engagement activities to address this target. From the outset, the team of six institutions (including universities, a national weather service and an energy company) had a clear vision of what they wanted to achieve and a plan of the main direction of travel. This was based on their understanding of the potential new opportunities which climate services offer the energy industry and previous experience in producing data sets likely to be relevant for this community. The team did not, however, attempt to impose their vision on external stakeholders but instead devised an interactive and evolving process of stakeholder engagement. This meant that the team was flexible and dynamic and able to respond and adapt to users’ expectations and use cases. Whether or not the team fully achieved its stated ambition of ‘co-production’ can be debated – as can the meaning of such terms in practice. Nonetheless, it is concluded that having a good process of stakeholder engagement led to both good outputs and good outcomes.
The proof-of-concept climate service developed by ECEM encompasses three inter-related elements. The first and most obvious ‘product’ is the C3S ECEM Demonstrator (http://ecem.wemcouncil.org/). This interface allows users to visualise, explore and download climate and energy data sets on three different timescales (historical, seasonal forecasting and climate projections) aggregated to the country or, in some cases, sub-country, level. The second element is high-quality climate and energy data which can be used to inform an end-user problem with or without further tailoring by, for example, a commercial service or consultancy, or by in-house experts in an energy company. Data consistency and quality were highlighted as essential requirements of a climate service in the stakeholder evaluation and feedback. These data are embedded within the Demonstrator and can be downloaded in text format which was identified as the ‘first choice’ for much of the target audience. However, ECEM also identified a group of more technical users who are primarily interested in the data alone. For this group, a facility was provided to directly download by FTP large volumes of underlying gridded data in NetCDF format. The third element is ‘know-how’ about how to use climate information as actionable information. This was underpinned by a range of documentation and guidance embedded within the Demonstrator, including Key Messages and Event Case Studies, as well as a programme of webinars and workshops/symposia.
All three elements (the Demonstrator, data and ‘know how’) were assessed as important by ECEM stakeholders, although different individuals or organisations naturally had different priorities in terms of what was most important for them. Similarly, some Demonstrator users were more interested in climate than energy data, or in say the historic period rather than seasonal forecasting or climate projections. Thus, the Demonstrator was designed with multiple entry points – also recognising that decision making is not a linear process. At the same time, one of the greatest strengths of the ECEM team was its capacity to produce consistent and high-quality data for all three timescales, for both energy and climate, and then to integrate all into a single interface to enable seamless and transparent decision making across a business. The Demonstrator can therefore also be viewed as a ‘shop window’, which may attract a user in search of a particular product – but who may then explore other products on offer.
A key element in ECEM has been the compilation of a robust energy database, consisting of energy demand as well as generation from wind, solar and hydro power, together with information on installed generation capacity, for all EU countries. Such a database has recently been made available by the European Network of Transmission System Operators for Electricity (ENTSO-E) under an EU regulation, but these data present some issues and inconsistencies, which made their use difficult in ECEM. The point here is not to criticize the ENTSO-E data, rather to emphasize that significant progress needs to be made on energy data completeness and homogenization in order to develop operational, fit for purpose climate services for the energy sector.
In practice, it was not so easy to treat seasonal forecasting information, which requires displaying more frequently updated data, in the same way as the historical or projection information. Issues relating to the generally limited and specific skill over Europe, probabilistic formats and user expectations and use for operational decision making also emerged. During the final symposium, for example, many users were most interested in seasonal forecasts because they had less existing awareness and understanding of these elements and saw the Demonstrator as vital to understand what they are and how to potentially use them. Thus the expectations of these users were rather different to those for whom ‘Data is King’.
Through a carefully planned process of iterative engagement with stakeholders, ECEM has raised the capacity of both providers and users to deliver and use climate services for the European renewable energy sector. The emerging community of practice is now focused on spin-off activities encompassing development of an operational service (C3S Energy – https://climate.copernicus.eu/operational-service-energy-sector) as well as commercial products and services, and research, for example on the improved tailoring and assessment of the added value of seasonal forecasts