info:eu-repo/semantics/publishedVersion

[eng] In order to mitigate the climate change effects, the world is undergoing an energy transition from polluting sources towards renewable energies. This transition is turning the electricity system more dependent on atmospheric conditions and more prone to suffer the effects of climate variability. The atmospheric circulation is changing in certain aspects due to increasing concentrations of greenhouse gases in the atmosphere, but it also varies from year to year due to natural variability processes occurring in the Earth system at timescales of weeks, months and years. The atmosphere interacts with other components of the Earth System such as the ocean, the cryosphere or the continental surface, that evolve more slowly than the atmosphere and drive the low-frequency variability. The natural climate oscillations that occur at those timescales impact wind speed and wind power generation. Therefore a better knowledge of how the wind resource varies at sub-seasonal, seasonal and decadal time scales is key to understand the risks that the electricity system is facing. Anticipating this variability would also be helpful to many stakeholders in the energy sector to take precautionary actions. Forecasts at sub-seasonal, seasonal and decadal timescales are starting to be possible recently thanks to advances in climate modelling capabilities. Because climate variability is partly driven by coupled physical processes occurring in the Earth, numerical models that represent the interaction between different components of the Earth system can be employed to produce forecasts at these scales. The science of climate prediction deals with the challenge of producing predictions beyond meteorological timescales (i.e. weeks, months and years ahead) although not reaching the centennial timescales, which are studied with scenario-based climate projections. Climate predictions employ the current state of the atmosphere, the ocean, the cryosphere, and the land surface to produce numerical integrations of each component and the forcings and interactions between them to model the evolution of the Earth system as a whole. However, the usage of climate predictions in the wind power sector (or more generally in any specific decision-making context) poses a series of difficulties due to many complex aspects of this type of predictions. The efforts devoted in many initiatives to bring the needs of the users to the center of the discussion have given rise to the field of climate services. In order to assist decision-making, it is not only desirable to have the best predictions available but also to tailor them to the specific needs of each user. To achieve this goal, a dialogue with stakeholders needs to be established, and a trans- disciplinary approach needs to be set up to take advantage of the developments in many research fields regarding knowledge transfer and communication. The work presented in this dissertation advances the knowledge required to produce and successfully apply climate predictions to decision-making in the wind power sector and deals with the three aforementioned challenges: a) understanding the impact of climate oscillations at sub-seasonal and seasonal timescales on wind resource; b) developing methods to produce forecasts of wind speed and wind power generation at this scales; and c) facilitating the uptake of those predictions by means of a climate-services-based approach.[cat] Per tal de mitigar els efectes del canvi climàtic, tots els països del món estan duent a terme una transició energètica de fonts contaminants cap a energies renovables. Aquesta transició està incrementant la sensibilitat del sistema elèctric a les condicions atmosfèriques i fent-lo més vulnerable als efectes de la variabilitat climàtica. A escales de setmanes, mesos i anys, l'atmosfera interacciona amb altres components del sistema Terra com l'oceà, la criosfera o la superfície continental, que evolucionen més lentament que l'atmosfera, condicionant-ne la seva variabilitat a baixa freqüència. Al seu torn, les oscil·lacions que tenen lloc a aquestes escales temporals impacten el vent i la generació d'energia eòlica. Per tant, un millor coneixement de com varia el recurs eòlic a escales sub-estacionals, estacionals i decadals permetrà anticipar els riscs a què el sistema elèctric està sotmès. En segon lloc, anticipar aquesta variabilitat climàtica seria de gran utilitat a diversos actors del sistema energètic. L'ús de models climàtics que representen les interaccions entre les diferents components del sistema Terra permet abordar el repte de produir pronòstics més enllà de l'escala meteorològica (és a dir, a setmanes, mesos i anys vista). Malgrat tot, l'ús de les prediccions climàtiques en el sector de l'energia eòlica presenta una sèrie de dificultats degut a les complexitats d'aquest tipus de previsions. Per tal d'assistir la presa de decisions, no només és necessari disposar de les millors prediccions possibles sinó que cal també ajustar-les a les necessitats específiques de cada ús. Aquest objectiu només es pot assolir amb un diàleg constant i transdisciplinari entre els científics i les parts interessades que integri els avenços en diferents àmbits respecte la transferència de coneixement i la comunicació. Aquesta tesi avança el coneixement necessari per tal de produir i aplicar prediccions climàtiques a la presa de decisions per part de la indústria eòlica, abordant tres reptes: a) avaluar l'impacte d'oscil·lacions climàtiques sub-estacionals i estacional en el recurs eòlic; b) desenvolupar mètodes per produir prediccions de vent o de generació eòlica a aquestes escales; i c) facilitar l'adopció d'aquestes previsions mitjançant una aproximació basada en els serveis climàtics

Climate variability predictions for the wind energy industry: a climate services perspective

In order to mitigate the climate change effects, the world is undergoing an energy transition from polluting sources towards renewable energies. This transition is turning the electricity system more dependent on atmospheric conditions and more prone to suffer the effects of climate variability. The atmospheric circulation is changing in certain aspects due to increasing concentrations of greenhouse gases in the atmosphere, but it also varies from year to year due to natural variability processes occurring in the Earth system at timescales of weeks, months and years. The atmosphere interacts with other components of the Earth System such as the ocean, the cryosphere or the continental surface, that evolve more slowly than the atmosphere and drive the low-frequency variability. The natural climate oscillations that occur at those timescales impact wind speed and wind power generation. Therefore a better knowledge of how the wind resource varies at sub-seasonal, seasonal and decadal time scales is key to understand the risks that the electricity system is facing. Anticipating this variability would also be helpful to many stakeholders in the energy sector to take precautionary actions. Forecasts at sub-seasonal, seasonal and decadal timescales are starting to be possible recently thanks to advances in climate modelling capabilities. Because climate variability is partly driven by coupled physical processes occurring in the Earth, numerical models that represent the interaction between different components of the Earth system can be employed to produce forecasts at these scales. The science of climate prediction deals with the challenge of producing predictions beyond meteorological timescales (i.e. weeks, months and years ahead) although not reaching the centennial timescales, which are studied with scenario-based climate projections. Climate predictions employ the current state of the atmosphere, the ocean, the cryosphere, and the land surface to produce numerical integrations of each component and the forcings and interactions between them to model the evolution of the Earth system as a whole. However, the usage of climate predictions in the wind power sector (or more generally in any specific decision-making context) poses a series of difficulties due to many complex aspects of this type of predictions. The efforts devoted in many initiatives to bring the needs of the users to the center of the discussion have given rise to the field of climate services. In order to assist decision-making, it is not only desirable to have the best predictions available but also to tailor them to the specific needs of each user. To achieve this goal, a dialogue with stakeholders needs to be established, and a trans- disciplinary approach needs to be set up to take advantage of the developments in many research fields regarding knowledge transfer and communication. The work presented in this dissertation advances the knowledge required to produce and successfully apply climate predictions to decision-making in the wind power sector and deals with the three aforementioned challenges: a) understanding the impact of climate oscillations at sub-seasonal and seasonal timescales on wind resource; b) developing methods to produce forecasts of wind speed and wind power generation at this scales; and c) facilitating the uptake of those predictions by means of a climate-services-based approach.Per tal de mitigar els efectes del canvi climàtic, tots els països del món estan duent a terme una transició energètica de fonts contaminants cap a energies renovables. Aquesta transició està incrementant la sensibilitat del sistema elèctric a les condicions atmosfèriques i fent-lo més vulnerable als efectes de la variabilitat climàtica. A escales de setmanes, mesos i anys, l'atmosfera interacciona amb altres components del sistema Terra com l'oceà, la criosfera o la superfície continental, que evolucionen més lentament que l'atmosfera, condicionant-ne la seva variabilitat a baixa freqüència. Al seu torn, les oscil·lacions que tenen lloc a aquestes escales temporals impacten el vent i la generació d'energia eòlica. Per tant, un millor coneixement de com varia el recurs eòlic a escales sub-estacionals, estacionals i decadals permetrà anticipar els riscs a què el sistema elèctric està sotmès. En segon lloc, anticipar aquesta variabilitat climàtica seria de gran utilitat a diversos actors del sistema energètic. L'ús de models climàtics que representen les interaccions entre les diferents components del sistema Terra permet abordar el repte de produir pronòstics més enllà de l'escala meteorològica (és a dir, a setmanes, mesos i anys vista). Malgrat tot, l'ús de les prediccions climàtiques en el sector de l'energia eòlica presenta una sèrie de dificultats degut a les complexitats d'aquest tipus de previsions. Per tal d'assistir la presa de decisions, no només és necessari disposar de les millors prediccions possibles sinó que cal també ajustar-les a les necessitats específiques de cada ús. Aquest objectiu només es pot assolir amb un diàleg constant i transdisciplinari entre els científics i les parts interessades que integri els avenços en diferents àmbits respecte la transferència de coneixement i la comunicació. Aquesta tesi avança el coneixement necessari per tal de produir i aplicar prediccions climàtiques a la presa de decisions per part de la indústria eòlica, abordant tres reptes: a) avaluar l'impacte d'oscil·lacions climàtiques sub-estacionals i estacional en el recurs eòlic; b) desenvolupar mètodes per produir prediccions de vent o de generació eòlica a aquestes escales; i c) facilitar l'adopció d'aquestes previsions mitjançant una aproximació basada en els serveis climàtics

Seasonal predictions of energy-relevant climate variables through Euro-Atlantic Teleconnections

The goal of this analysis is the better understanding of how the large-scale atmospheric patterns affect the renewable resources over Europe and to investigate to what extent the dynamical predictions of the large-scale variability might be used to formulate empirical prediction of local climate conditions (relevant for the energy sector). The increasing integration of renewable energy into the power mix is making the electricity supply more vulnerable to climate variability, therefore increasing the need for skillful weather and climate predictions. Forecasting seasonal variations of energy relevant climate variables can help the transition to renewable energy and the entire energy industry to make better informed decision-making. At seasonal timescale climate variability can be described by recurring and persistent, large-scale patterns of atmospheric pressure and circulation anomalies that interest vast geographical areas. The main patterns of the North Atlantic region (Euro Atlantic Teleconnections, EATCs) drive variations in the surface climate over Europe. We analyze reanalysis dataset ERA5 and the multi-system seasonal forecast service provided by Copernicus Climate Change Service (C3S). We found that the observed EATC indices are strongly correlated with surface variables. However, the observed relationship between EATC patterns and surface impacts is not accurately reproduced by seasonal prediction systems. This opens the door to employ hybrid dynamical-statistical methods. The idea consists in combining the dynamical seasonal predictions of EATC indices with the observed relationship between EATCs and surface variables. We reconstructed the surface anomalies for multiple seasonal prediction systems and benchmarked these hybrid forecasts with the direct variable forecasts from the systems and also with the climatology. The analysis suggests that hybrid methodology can bring several improvements to the predictions of energy relevant Essential Climate Variables.This work was supported by the European Union’s Horizon 2020 research and innovation programme [Grant Numbers. No 776787, H2020 S2S4E] and by the National Italian project PAR 2019–2021 1.8 ‘Energia dal Mare’.Peer ReviewedPostprint (published version

How Skillful are the Multiannual Forecasts of Atlantic Hurricane Activity?

The recent emergence of near-term climate prediction, wherein climate models are initialized with the contemporaneous state of the Earth system and integrated up to 10 years into the future, has prompted the development of three different multiannual forecasting techniques of North Atlantic hurricane frequency. Descriptions of these three different approaches, as well as their respective skill, are available in the peer-reviewed literature, but because these various studies are sufficiently different in their details (e.g., period covered, metric used to compute the skill, measure of hurricane activity), it is nearly impossible to compare them. Using the latest decadal reforecasts currently available, we present a direct comparison of these three multiannual forecasting techniques with a combination of simple statistical models, with the hope of offering a perspective on the current state-of-the-art research in this field and the skill level currently reached by these forecasts. Using both deterministic and probabilistic approaches, we show that these forecast systems have a significant level of skill and can improve on simple alternatives, such as climatological and persistence forecasts.The first author would like to thank Isadora Jimenez for providing the necessary material for Fig. 2. The first author would like to acknowledge the financial support from the Ministerio de Economía, Industria y Competitividad (MINECO; Project CGL2014- 55764-R), the Risk Prediction Initiative at BIOS (Grant RPI2.0-2013-CARON), and the EU [Seventh Framework Programme (FP7); Grant Agreement GA603521]. We additionally acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. LPC's contract is cofinanced by the MINECO under the Juan de la Cierva Incorporacion postdoctoral fellowship number IJCI-2015-23367. Finally, we thank the National Hurricane Center for making the HURDAT2 data available. All climate model data are available at https://esgf-index1.ceda.ac.uk/projects/esgf-ceda/.Peer ReviewedPostprint (published version

On the reliability of global seasonal forecasts: sensitivity to ensemble size, hindcast length and region definition

One of the key quality aspects in a probabilistic prediction is its reliability. However, this property is difficult to estimate in the case of seasonal forecasts due to the limited size of most of the hindcasts that are available nowadays. To shed light on this issue, this work presents a detailed analysis of how the ensemble size, the hindcast length and the number of points pooled together within a particular region affect the resulting reliability estimates. To do so, we build on 42 land reference regions recently defined for the IPCC-AR6 and assess the reliability of global seasonal forecasts of temperature and precipitation from the European Center for Medium Weather Forecasts SEAS5 prediction system, which is compared against its predecessor, System4. Our results indicate that whereas longer hindcasts and larger ensembles lead to increased reliability estimates, the number of points that are pooled together within a homogeneous climate region is much less relevant.This research has been partially supported by the AfriCultuReS (“Enhancing Food Security in African Agricultural Systems with the Support of Remote Sensing”) and FOCUS-Africa projects, which received funding from the European Union's Horizon 2020 Research and Innovation Framework Programme under grant agreements No. 77465 and 869575, respectively.Peer ReviewedPostprint (published version

CSDownscale: an R package for statistical downscaling

Downscaling is any procedure to infer highresolution information from low-resolution variables. Many of these techniques have been defined and applied to climate predictions, which suffer from important biases due to the coarse global grids in which they are delivered. To help solve this undesirable issue, the R package resulting from this work provides a set of statistical downscaling methods for climate predictions, ready to be applied to refine the output of climate predictions

Development of a wind energy climate service based on seasonal climate prediction

Climate predictions tailored to the wind energy sector represent an innovation to better understand the future variability of wind energy resources. In this work an illustration of the downstream impact of the forecasts as a source of climate information, the post-processed seasonal predictions of wind speed and temperature will be used as input in a transfer model that translates climate information into capacity factor. This transfer model is based on multivariate regression that assumes a linear relationship between wind speed and temperature with the capacity factor

Co-design of sectoral climate services based on seasonal prediction information in the Mediterranean

We present in this contribution the varied experiences gathered in the co-design of a sectoral climate services collection, developed in the framework of the MEDSCOPE project, which have in common the application of seasonal predictions for the Mediterranean geographical and climatic region. Although the region is affected by low seasonal predictability limiting the skill of seasonal forecasting systems, which historically has hindered the development of downstream services, the project was originally conceived to exploit windows of opportunity with enhanced skill for developing and evaluating climate services in various sectors with high societal impact in the region: renewable energy, hydrology, and agriculture and forestry. The project also served as the scientific branch of the WMO-led Mediterranean Climate Outlook Forum (MedCOF) that had as objective -among others- partnership strengthening on climate services between providers and users within the Mediterranean region. The diversity of the MEDSCOPE experiences in co-designing shows the wide range of involvement and engagement of users in this process across the Mediterranean region, which benefits from the existing solid and organized MedCOF community of climate services providers and users. A common issue among the services described here -and also among other prototypes developed in the project- was related with the communication of forecasts uncertainty and skill for efficiently informing decision-making in practice. All MEDSCOPE project prototypes make use of an internally developed software package containing process-based methods for synthesising seasonal forecast data, as well as basic and advanced tools for obtaining tailored products. Another challenge assumed by the project refers to the demonstration of the economic, social, and environmental value of predictions provided by these MEDSCOPE prototypes.The work described in this paper has received funding from the MEDSCOPE project co-funded by the European Commission as part of ERA4CS, an ERA-NET initiated by JPI Climate, grant agreement 690462.Peer Reviewed"Article signat per 16 autors/es: Eroteida Sánchez-García, Ernesto Rodríguez-Camino, Valentina Bacciu, Marta Chiarle, José Costa-Saura, Maria Nieves Garrido, Llorenç Lledó, Beatriz Navascués, Roberta Paranunzio, Silvia Terzag, Giulio Bongiovanni, Valentina Mereu, Guido Nigrelli, Monia Santini, Albert Soret, Jostvon Hardenberg"Postprint (published version

L'ornamentació lliure en les sonates metòdiques de Telemann

Durant la primera meitat del segle XVIII, l'ornamentació de la melodia en els moviments lents sovint no s'anotava a la partitura, i es deixava en mans de l'intèrpret la seva improvisació. És, per tant, una de les decisions interpretatives que el músic actual ha de prendre a l'hora d'abordar aquest repertori. Una de les fonts més útils per estudiar aquesta pràctica històrica són les sonates metòdiques de Telemann. El primer moviment lent de cadascuna de les dotze sonates té una melodia simple i una d'ornamentada. En aquest treball s'estudiaran els ornaments que Telemann usà i es classificaran per tal de confeccionar una taula d'ornaments. Aquesta taula serà útil per decorar altres obres de Telemann i d'autors contemporanis que escriguessin en el mateix estil.Durante la primera mitad del siglo XVIII, la ornamentación de la melodía en los movimientos lentos a menudo no se escribía en las partituras, y se dejaba en manos del intérprete su improvisación. Se trata, pues, de una de las decisiones interpretativas que el músico actual debe tomar al abordar este repertorio. Una de las fuentes mas útiles para estudiar esta práctica histórica son las sonatas metódicas de Telemann. El primer movimiento lento de cada una de las doce sonatas tiene una melodía simple y una ornamentada. En este trabajo se estudiarán los ornamentos que Telemann empleó y se clasificarán para elaborar una tabla de ornamentos. Esta tabla será de utilidad para decorar otras obras de Telemann y de autores contemporáneos que escribieran en el mismo estilo.During the first half of the eighteenth century it was common not to write down ornamentations in slow movements. It's improvisation was left at performer's will. This is one of the interpretative challenges that modern performers face today when playing such repertoire. One of the most useful sources for studying this historical practice are Telemann's methodical sonatas. The first slow movement in each of this twelve sonates contains a simple melody and an ornamented one. In this work, Telemann ornaments will be studied and classified in order to elaborate an ornament's table. This table will be useful to embellish other works by Telemann and contemporary composers writing in the same style

Challenges in the selection of atmospheric circulation patterns for the wind energy sector

Abstract Atmospheric circulation patterns that prevail for several consecutive days over a specific region can have consequences for the wind energy sector as they may lead to a reduction of the wind power generation, impacting market prices or repayments of investments. The main goal of this study is to develop a user-oriented classification of atmospheric circulation patterns in the Euro-Atlantic region that helps to mitigate the impact of the atmospheric variability on the wind industry at seasonal timescales. Particularly, the seasonal forecasts of these frequencies of occurrence can be also beneficial to reduce the risk of the climate variability in wind energy activities. K-means clustering has been applied on the sea level pressure from the ERA5 reanalysis to produce a classification with three, four, five and six clusters per season. The spatial similarity between the different ERA5 classifications has revealed that four clusters are a good option for all the seasons except for summer when the atmospheric circulation can be described with only three clusters. However, the use of these classifications to reconstruct wind speed and temperature, key climate variables for the wind energy sector, has shown that four clusters per season are a good choice. The skill of five seasonal forecast systems in simulating the year-to-year variations in the frequency of occurrence of the atmospheric patterns is more dependent on the inherent skill of the sea level pressure than on the number of clusters employed. This result suggests that more work is needed to improve the performance of the seasonal forecast systems in the Euro-Atlantic domain to extract skilful forecast information from the circulation classification. Finally, this analysis illustrates that from a user perspective it is essential to consider the application when selecting a classification and to take into account different forecast systems.This research has been funded by the S2S4E (GA 776787) Horizon 2020 project, the Ministerio de Ciencia, Innovación y Universidades as part of the CLINSA project (CGL2017‐85791‐R) and the Juan de la Cierva – Incorporación Grant (IJCI‐2016‐29776). The analyses and plots of this work have been performed with the s2dverification (Manubens et al., 2018), CSTools (https://CRAN.R-project.org/package=CSTools) and startR (https://CRAN.R-project.org/package=startR) R‐language‐based software packages. Finally, we would like to thank Pierre‐Antoine Bretonnière, Margarida Samsó, Nicolau Manubens and Núria Pérez‐Zanón for their technical support at different stages of this project. We also acknowledge the two anonymous reviewers for their useful comments.Peer ReviewedPostprint (published version