Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service.The authors acknowledge funding support from the RESILIENCE (CGL2013-41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Special thanks to Nube Gonzalez-Reviriego and Albert Soret for helpful comments and discussion.\ud We also acknowledge the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2) and the New European Wind Atlas (NEWA) project funded from ERA-NET Plus, topic FP7-ENERGY.2013.10.1.2. We acknowledge the s2dverification and SpecsVerification R-based packages. Finally we would like to thank Pierre-Antoine Bretonnière, Oriol Mula and Nicolau\ud Manubens for their technical support at different stages of this project.Peer ReviewedPostprint (author's final draft
The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge-value’ gap, where a lack of evidence and awareness of the potential socio-economic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development – demonstrating both skill and utility across sectors – this dialogue can be used to help promote and accelerate the awareness, value and co-generation of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting timescale.
In areas with regular fishing coastal fleets seabirds may benefit from the predictability of discards from fishing vessels, but it is not clear to what extent birds rely on this predictable resource and whether foraging is synchronized with the diel availability of discards. In this paper we investigate if a typical scavenger species, the yellow‐legged gull Larus michahellis, takes advantage of the temporal and spatial predictability of fish discards in the western Mediterranean Sea. The activity and distribution of the trawling fleet in this area is regulated and very predictable in time and space. We gathered aerial survey data across a relatively large area close to the coast to study the spatial distribution and density of L. michahellis, and modelled the density distribution of the species in relation to several oceanographic, ecological and temporal variables, using two different modelling approaches: MARS (multivariate adaptative regression splines) and GLM (generalized linear models). Our models suggest that the spatial density of trawlers at sea and the time of the day are the best explanatory variables of gull distribution, and that gulls concentrate in areas with vessels mainly during fish discarding time, supporting the hypothesis that gulls optimize time foraging to take advantage of fishery waste predictability. Additional surveys from the main gull roosting sites inshore support this hypothesis, as gulls start leaving to the sea just before fishing is completed and vessels begin discarding fish scraps when back to the harbour. This study represents one of the few examples of applying MARS to density distribution modelling, although its application to marine ecosystems should be conducted with caution because of large areas with real absence data. GLMs have shown to be more adaptable to such kind of data. Our data confirm the importance of fishery waste for L. michahellis, not only as a food resource but also as a major driver of their activity and distribution patterns. The ability of seabirds to predict accurately when a food resource will be available implies that modelling their distribution at sea needs to include such variables, both in spatial and temporal dimensions.
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