Table 2 – Building blocks for energy sector climate services, by Pillar and Area of Focus UIP CSIS Obs/Mon RMP & Resource Assessment Provide information about relevant repositories of data and products for resource and climate risk estimation Provide understanding of dataset quality for resource and climate risk estimation Provide support for the proper installation, operation and maintenance of meteorological instrumentation Provide estimation of resource and climate uncertainty Provide an appreciation of, and guidance on, climate variability and change Discuss options for improvement of resource and climate risk assessment Historical datasets of relevant meteorological data (in situ, and and related metadata Tailored data-sets to specific energy sectors (e.g. heating and cooling degree days, wind gusts, water temperature and river flows) Climate projections of relevant data Uncertainty estimates of resource and risk estimations Development of tailored climate values for energy systems codes, standards, best practices and guidelines In situ, and meteorological data for assessment of resources and risks Model-based historical meteorological data Climate change projections and their limitations Data policy; guidance and possible formulation of updated guidelines Ancillary datasets such as electric grid, distance to coast, elevations, populated centres, etc. Improvement of observation instrumentation Improvement of satellite retrieval and conversion algorithms Study on sensitivity of resource to atmospheric constituents (e.g. aerosols) Characterization of uncertainty of different data sources Methods to combine various data sources Approaches to bridge time scales and predictions from weather forecasts to seasonal predictions to climate change projections Approaches to localize or downscale climate change projections for specific decisions Approaches to assess and combine uncertainty information in individual data sources Impact assessments Identify relevant meteorological and climate phenomena for specific infrastructure (e.g. hail, snow and wind loads on PV Historical datasets and climate analyses of extreme events High-grade in situ data Observations and monitoring of relevant climate-related Characterization of extreme events and probabilities, return periods, probabilities of occurrence, exceedance thresholds resolution derived derived
panels, extreme rainfall and drought impacts on systems) Identify relevant environmental impacts, e.g. air quality and human health, wind turbines and wildlife Provide sound statistics on the impacts of weather and climate on energy systems and discuss assumptions Provide statistics on the impacts of energy systems on the environment and discuss assumptions Discuss climate support for national and international standards, codes, guidelines Update standards, codes, etc. taking into account tailored, up-to-date climate information Engage with civil society by providing assessments of scientifically validated energy system impacts Elicit stakeholders' input on suggested additional Detailed site-specific modelling Historical datasets and analyses of extreme events Projections of potential relevant meteorological/climate trends and changes Return periods, probabilities of occurrence, exceedance thresholds of relevant extreme events Support for national and international codes, standards, etc.. Climate change guidance for long term decisions and assets Relevant data for decision-support on established relationships between energy systems and air quality, gas emissions, wildlife and other environmental factors Prediction of air quality and gas dispersion in the variables for identification and mitigation of environmental impacts, e.g. on human health and safety and wildlife Air quality and gas emission database (e.g. carbon-based gases from shale gas extractions) Database on impact of above effects on health (link to Health Exemplar) and other externalities (e.g. water quality) Database on weather/climate risks to hydro-electricity facilities, solar panel risks to buildings, energy transport risks to communities, etc. Investigation of specific physical phenomena (e.g. ice accretion, ice plus wind loading on transmission lines) Downscaling of climate change projections; linkages between seasonal predictions and climate change projections Development of new parameterizations for numerical models in order to integrate energy features (e.g., wind turbines, albedo of solar plants) Research into climate-related environmental impacts associated with specific energy installations and technologies, e.g. particulate and gas emissions and their relationship to meteorological conditions Links between more efficient energy systems and GHG mitigation strategies, climate change impacts Identification of “win-win” energy systems supporting GHG mitigation, climate adaptation, development and poverty alleviation resolution
assessments of energy system impacts neighbourhood of relevant energy systems Integration of climate and weather information for near net zero energy communities and energy systems, GHG reduction and in support of sustainable energy use Development of new modelling experiments for geo-engineering evaluations (e.g., bio-char, albedo and solar plants) Site Selection & Financing Provide understanding of dataset quality for resource and risk estimations Provide an appreciation of, and guidance on, climate variability and change Elicit suggestions for improvement of data collection and statistical assessments High-quality historical datasets Uncertainty estimation of the resource and system risks Statistical properties, including extreme event probabilities, of resource and its risks Guidance on climate change trends and projections for future energy yield and risks Very high grade in situ data, both in terms of quality of instrumentation and temporal Detailed site-specific modelling (e.g. wind gust estimation, extreme low and high stream-flows) Enhanced ways to extend in situ data to encompass inter-annual variability of resource and its system risks Enhanced modelling of flow and orographic dependent features Enhanced quantification of weather and climate extremes Enhanced statistical estimation of probability distribution functions of relevant variables Approaches to bridge near term forecasting to decadal projections Operations & Maintenance Interpretation and accuracy of site-specific forecasts Issuance of relevant warnings at various lead times, with appropriate commentary and personal briefings Relative attributes of statistically- and Site specific short-range to seasonal forecasts Early warning systems based on statistical and physical modelling and at various lead times Analysis and forecasts of probabilities of extreme Site-specific ground station data Infrastructure specific meteorological data Database and analyses of historical meteorologically-driven problem (forensic) Forecasting tools to improve site-specific and sector-specific information Characterization of extreme events, return periods, probabilities of occurrence, exceedance thresholds
forecasts and seasonal predictions Elicit expert technical knowledge Support targeted training events from short-range to seasonal time scales Planning for operations and maintenance under future climate trends and projections events for operations and maintenance Linkages between short term, seasonal and longer term climate needs for operations and maintenance (e.g. planning for variability, ranges, trends) Improved communication methodologies to effectively convey warnings at various lead times Energy Integration (Market trading & Insurance; Energy efficiency) Interpretation of trends in demand and relevant meteorological/climate variables Seek energy market operators’ opinions on role of meteorology and climate in demand modelling Collect experiences in demand modelling and compile database, analyses (e.g. forensic analyses) Analysis of correlation between climatic indexes and conventional power plant efficiency and safety Increased interactions between energy traders/insurers and for the exchange of practices towards the development of improved Historical data of demand-related meteorological/climate variables Analysis and forecasts of probabilities of extreme events from to sub-seasonal to decadal range (e.g. plan energy infrastructure to meet future trends in demand) Short-term to seasonal scale meteorological forecasts of sites and/or regions, including synoptic assessments Assessment of historical performance of to seasonal forecasts Probabilistic of forecasts Historical datasets of meteorological/climate variables relevant for demand, insurance and energy efficiency Historical datasets of energy demand Model-based data to extend observation records Ancillary datasets such as shading, orientations of building and energy system response to weather variables Historical energy trade data Site-specific ground station data for triggering of weather index insurance policies Modelling of interaction between meteorological/climate variables and energy demand Forecasting tools to improve meteorologically-driven demand (including trends and projections longer into the future) Investigation of relationship between meteorological variables and energy efficiency of buildings or other energy systems (includes longer term seasonal and decadal trends) Improvement of skill of to, especially, seasonal forecasts targeted at regions where energy systems operate Tools to improve use of probabilistic information Approaches to bridge time scales for decisions from weather forecasts to seasonal predictions processing
tools (this is an area where meteorologists can learn a lot as industry uses advanced tools; at the same time commercial sensitivities make the exchange less straightforward) Climate trends and projections for the future Data policy for consistent use of observations for insurance payouts to climate change trends and projections Guidance on interpretation of climate change projections and their limitations and uncertainties CAPACITY DEVELOPMENT AND SUPPORT UIP – User Interface Platform; CSIS – Climate Services Information System; Obs/Mon – Observations and Monitoring; RMP – Research Modelling and Prediction